Remote Sensing

22 pages, 27296 KiB

Open AccessEditor’s ChoiceArticle

Assessment of Tree Detection Methods in Multispectral Aerial Images

by Dagoberto Pulido, Joaquín Salas, Matthias Rös, Klaus Puettmann and Sertac Karaman

Remote Sens. 2020, 12(15), 2379; https://doi.org/10.3390/rs12152379 - 24 Jul 2020

Cited by 16 | Viewed by 5389

Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. [...] Read more.

Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. To advance towards the goal of efficiently assessing the carbon content of forest, we evaluate methods to detect trees from high-resolution images taken from unoccupied aerial systems (UAS). In the process, we introduce the Digital Elevated Vegetation Model (DEVM), a representation that combines multispectral images, digital surface models, and digital terrain models. We show that the DEVM facilitates the development of refined synthetic data to detect individual trees using deep learning-based approaches. We carried out experiments in two tree fields located in different countries. Simultaneously, we perform comparisons among an array of classical and deep learning-based methods highlighting the precision and reliability of the DEVM. Full article

(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)

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24 pages, 5019 KiB

Open AccessEditor’s ChoiceArticle

Estimating River Sediment Discharge in the Upper Mississippi River Using Landsat Imagery

by Jonathan A. Flores, Joan Q. Wu, Claudio O. Stöckle, Robert P. Ewing and **ao Yang

Remote Sens. 2020, 12(15), 2370; https://doi.org/10.3390/rs12152370 - 23 Jul 2020

Cited by 5 | Viewed by 4679

Abstract

With the decline of operational river gauges monitoring sediments, a viable means of quantifying sediment transport is needed. In this study, we address this issue by applying relationships between hydraulic geometry of river channels, water discharge, water-leaving surface reflectance (SR), and suspended sediment [...] Read more.

With the decline of operational river gauges monitoring sediments, a viable means of quantifying sediment transport is needed. In this study, we address this issue by applying relationships between hydraulic geometry of river channels, water discharge, water-leaving surface reflectance (SR), and suspended sediment concentration (SSC) to quantify sediment discharge with the aid of space-based observations. We examined 5490 Landsat scenes to estimate water discharge, SSC, and sediment discharge for the period from 1984 to 2017 at nine gauging sites along the Upper Mississippi River. We used recent advances in remote sensing of fluvial systems, such as automated river width extraction, Bayesian discharge inference with at-many-stations hydraulic geometry (AMHG), and SSC-SR regression models. With 621 Landsat scenes available from all the gauging sites, the results showed that the water discharge and SSC retrieval from Landsat imagery can yield reasonable sediment discharge estimates along the Upper Mississippi River. An overall relative bias of −25.4, mean absolute error (MAE) of 6.24 × 10⁴ tonne/day, relative root mean square error (RRMSE) of 1.21, and Nash–Sutcliffe Efficiency (NSE) of 0.49 were obtained for the sediment discharge estimation. Based on these statistical metrics, we identified three of the nine gauging sites (St. Louis, MO; Chester, IL; and Thebes, IL), which were in the downstream portion of the river, to be the best locations for estimating water and sediment discharge using Landsat imagery. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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47 pages, 1418 KiB

Open AccessEditor’s ChoiceArticle

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

by Badr El Mahrad, Alice Newton, John D. Icely, Ilias Kacimi, Samuel Abalansa and Maria Snoussi

Remote Sens. 2020, 12(14), 2313; https://doi.org/10.3390/rs12142313 - 18 Jul 2020

Cited by 71 | Viewed by 15241

Abstract

Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute [...] Read more.

Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for develo** further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed. Full article

(This article belongs to the Special Issue Earth Observation and Sustainable Development in Marine and Freshwater Systems)

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36 pages, 2187 KiB

Open AccessEditor’s ChoiceReview

Sentinel-2 Data for Land Cover/Use Map**: A Review

by Darius Phiri, Matamyo Simwanda, Serajis Salekin, Vincent R. Nyirenda, Yuji Murayama and Manjula Ranagalage

Remote Sens. 2020, 12(14), 2291; https://doi.org/10.3390/rs12142291 - 16 Jul 2020

Cited by 354 | Viewed by 35532

Abstract

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface [...] Read more.

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in develo** countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring. Full article

(This article belongs to the Section Urban Remote Sensing)

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19 pages, 4918 KiB

Open AccessEditor’s ChoiceArticle

Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements

by Shupeng Wang, Ronald J. van der A, Piet Stammes, Weihe Wang, Peng Zhang, Naimeng Lu, **ngying Zhang, Yanmeng Bi, ** Wang and Li Fang

Remote Sens. 2020, 12(14), 2204; https://doi.org/10.3390/rs12142204 - 10 Jul 2020

Cited by 17 | Viewed by 3889

Abstract

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO₂) from the TanSat observations using the ACOS (Atmospheric CO₂ Observations from Space) algorithm. The XCO₂ product has been validated with [...] Read more.

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO₂) from the TanSat observations using the ACOS (Atmospheric CO₂ Observations from Space) algorithm. The XCO₂ product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for 2 years of TanSat data from 2017 to 2018. Based on the correlation of the XCO₂ error over land with goodness of fit in three spectral bands at 0.76, 1.61 and 2.06 μm, we applied an a posteriori bias correction to TanSat retrievals. For overpass averaged results, XCO₂ retrievals show a standard deviation (SD) of ~2.45 ppm and a positive bias of ~0.27 ppm compared to collocated TCCON sites. The validation also shows a relatively higher positive bias and variance against TCCON over high-latitude regions. Three cases to evaluate TanSat target mode retrievals are investigated, including one field campaign at Dunhuang with measurements by a greenhouse gas analyzer deployed on an unmanned aerial vehicle and two cases with measurements by a ground-based Fourier-transform spectrometer in Bei**g. The results show the retrievals of all footprints, except footprint-6, have relatively low bias (within ~2 ppm). In addition, the orbital XCO₂ distributions over Australia and Northeast China between TanSat and the second Orbiting Carbon Observatory (OCO-2) on 20 April 2017 are compared. It shows that the mean XCO₂ from TanSat is slightly lower than that of OCO-2 with an average difference of ~0.85 ppm. A reasonable agreement in XCO₂ distribution is found over Australia and Northeast China between TanSat and OCO-2. Full article

(This article belongs to the Special Issue Remote Sensing of Air Pollutants and Carbon Emissions in Megacities)

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17 pages, 4778 KiB

Open AccessEditor’s ChoiceArticle

Development of the Chinese Space-Based Radiometric Benchmark Mission LIBRA

by Peng Zhang, Naimeng Lu, Chuanrong Li, Lei Ding, **aobing Zheng, Xuejun Zhang, **uqing Hu, **n Ye, Lingling Ma, Na Xu, Lin Chen and Johannes Schmetz

Remote Sens. 2020, 12(14), 2179; https://doi.org/10.3390/rs12142179 - 8 Jul 2020

Cited by 25 | Viewed by 4361

Abstract

Climate observations and their applications require measurements with high stability and low uncertainty in order to detect and assess climate variability and trends. The difficulty with space-based observations is that it is generally not possible to trace them to standard calibration references when [...] Read more.

Climate observations and their applications require measurements with high stability and low uncertainty in order to detect and assess climate variability and trends. The difficulty with space-based observations is that it is generally not possible to trace them to standard calibration references when in orbit. In order to overcome this problem, it has been proposed to deploy space-based radiometric reference systems which intercalibrate measurements from multiple satellite platforms. Such reference systems have been strongly recommended by international expert teams. This paper describes the Chinese Space-based Radiometric Benchmark (CSRB) project which has been under development since 2014. The goal of CSRB is to launch a reference-type satellite named LIBRA in around 2025. We present the roadmap for CSRB as well as requirements and specifications for LIBRA. Key technologies of the system include miniature phase-change cells providing fixed-temperature points, a cryogenic absolute radiometer, and a spontaneous parametric down-conversion detector. LIBRA will offer measurements with SI traceability for the outgoing radiation from the Earth and the incoming radiation from the Sun with high spectral resolution. The system will be realized with four payloads, i.e., the Infrared Spectrometer (IRS), the Earth-Moon Imaging Spectrometer (EMIS), the Total Solar Irradiance (TSI), and the Solar spectral Irradiance Traceable to Quantum benchmark (SITQ). An on-orbit mode for radiometric calibration traceability and a balloon-based demonstration system for LIBRA are introduced as well in the last part of this paper. As a complementary project to the Climate Absolute Radiance and Refractivity Observatory (CLARREO) and the Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS), LIBRA is expected to join the Earth observation satellite constellation and intends to contribute to space-based climate studies via publicly available data. Full article

(This article belongs to the Special Issue The Needs and Path Toward an SI-Traceable Space-based Climate Observing System)

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24 pages, 12842 KiB

Open AccessEditor’s ChoiceArticle

Gas Emission Craters and Mound-Predecessors in the North of West Siberia, Similarities and Differences

by Alexander Kizyakov, Marina Leibman, Mikhail Zimin, Anton Sonyushkin, Yury Dvornikov, Artem Khomutov, Damien Dhont, Eric Cauquil, Vladimir Pushkarev and Yulia Stanilovskaya

Remote Sens. 2020, 12(14), 2182; https://doi.org/10.3390/rs12142182 - 8 Jul 2020

Cited by 17 | Viewed by 7381

Abstract

Detailed analysis of five gas emission craters (GEC) found in the north of West Siberia is presented. Remote sensing data used in the study is verified by field surveys. Previous studies show that all of the GECs were preceded by mounds 2 to [...] Read more.

Detailed analysis of five gas emission craters (GEC) found in the north of West Siberia is presented. Remote sensing data used in the study is verified by field surveys. Previous studies show that all of the GECs were preceded by mounds 2 to 6 m high and 20 to 55 m in diameter. GECs initially were 20–25 m in diameter, which increased in the first years of their existence. GECs are found in various environmental (shrublands or moss-grass tundra) and geomorphic (river valley, terrace, slopes) conditions. The objective of the paper is to identify common and differing geomorphologic and environmental characteristics of all the five GEC, and their mound-predecessors. The study is based on a compilation of DSMs before and after the GEC formation using very high-resolution satellite imagery stereo pairs compared to ArcticDEM project data. Diversity of terrain and environmental settings along with rather a narrow range of GEC and mound-predecessor morphometric parameters allows concluding that the mechanism of GEC formation is most likely similar for all the GEC and is controlled rather by internal geologic and cryolithologic structure than by any surface properties. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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18 pages, 4749 KiB

Open AccessEditor’s ChoiceArticle

EANet: Edge-Aware Network for the Extraction of Buildings from Aerial Images

by Guang Yang, Qian Zhang and Guixu Zhang

Remote Sens. 2020, 12(13), 2161; https://doi.org/10.3390/rs12132161 - 6 Jul 2020

Cited by 59 | Viewed by 5992

Abstract

Deep learning methods have been used to extract buildings from remote sensing images and have achieved state-of-the-art performance. Most previous work has emphasized the multi-scale fusion of features or the enhancement of more receptive fields to achieve global features rather than focusing on [...] Read more.

Deep learning methods have been used to extract buildings from remote sensing images and have achieved state-of-the-art performance. Most previous work has emphasized the multi-scale fusion of features or the enhancement of more receptive fields to achieve global features rather than focusing on low-level details such as the edges. In this work, we propose a novel end-to-end edge-aware network, the EANet, and an edge-aware loss for getting accurate buildings from aerial images. Specifically, the architecture is composed of image segmentation networks and edge perception networks that, respectively, take charge of building prediction and edge investigation. The International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam segmentation benchmark and the Wuhan University (WHU) building benchmark were used to evaluate our approach, which, respectively, was found to achieve 90.19% and 93.33% intersection-over-union and top performance without using additional datasets, data augmentation, and post-processing. The EANet is effective in extracting buildings from aerial images, which shows that the quality of image segmentation can be improved by focusing on edge details. Full article

(This article belongs to the Special Issue Urban Land Use Map** and Analysis in the Big Data Era)

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18 pages, 17303 KiB

Open AccessEditor’s ChoiceArticle

Identification of Short-Rotation Eucalyptus Plantation at Large Scale Using Multi-Satellite Imageries and Cloud Computing Platform

by ** Deng, Shanxin Guo, Luyi Sun and **song Chen

Remote Sens. 2020, 12(13), 2153; https://doi.org/10.3390/rs12132153 - 5 Jul 2020

Cited by 22 | Viewed by 4747

Abstract

A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high [...] Read more.

A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high spatial resolution (30 m) at large scales, with the use of open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI), as well as a free cloud computing platform, Google Earth Engine (GEE). The method is composed of three main steps. First, a time series of Enhanced Vegetation Index (EVI) is constructed from Landsat data for each pixel, and a statistical hypothesis testing is followed to determine whether the pixel belongs to a tree plantation or not based on the idea that tree crops should be harvested in a specific period. Then, a broadleaf/needleleaf classification is applied to distinguish eucalyptus from coniferous trees such as pine and fir using the red-edge bands of Sentinel-2 data. Refinements based on superpixel are performed at last to remove the salt-and-pepper effects resulted from per-pixel detection. The proposed method allows gaps in the time series that are very common in tropical and subtropical regions by employing time series segmentation and statistical hypothesis testing, and could capture forest disturbances such as conversion of natural forest or agricultural lands to eucalyptus plantations emerged in recent years by using a short observing time. The experiment in Guangxi province of China demonstrated that the method had an overall accuracy of 87.97%, with producer’s accuracy of 63.85% and user’s accuracy of 66.89% for eucalyptus plantations. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry)

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20 pages, 21626 KiB

Open AccessEditor’s ChoiceArticle

Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS

by Mongkol Raksapatcharawong, Watcharee Veerakachen, Koki Homma, Masayasu Maki and Kazuo Oki

Remote Sens. 2020, 12(13), 2099; https://doi.org/10.3390/rs12132099 - 30 Jun 2020

Cited by 13 | Viewed by 4658

Abstract

Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a [...] Read more.

Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change. Full article

(This article belongs to the Special Issue Recent Advances of Remote Sensing in Monitoring Agro-Meteorological Disasters )

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16 pages, 7439 KiB

Open AccessEditor’s ChoiceArticle

Sea Level Variability in the Red Sea: A Persistent East–West Pattern

by Cheriyeri P. Abdulla and Abdullah M. Al-Subhi

Remote Sens. 2020, 12(13), 2090; https://doi.org/10.3390/rs12132090 - 30 Jun 2020

Cited by 8 | Viewed by 2636

Abstract

Based on 26 years of satellite altimetry, this study reveals the presence of a persistent east–west pattern in the sea level of the Red Sea, which is visible throughout the years when considering the east–west difference in sea level. This eastern–western (EW) difference [...] Read more.

Based on 26 years of satellite altimetry, this study reveals the presence of a persistent east–west pattern in the sea level of the Red Sea, which is visible throughout the years when considering the east–west difference in sea level. This eastern–western (EW) difference is positive during winter when a higher sea level is observed at the eastern coast of the Red Sea and the opposite occurs during summer. May and October are transition months that show a mixed pattern in the sea level difference. The EW difference in the southern Red Sea has a slightly higher range compared to that of the northern region during summer, by an average of 0.2 cm. Wavelet analysis shows a significant annual cycle along with other signals of lower magnitude for both the northern and southern Red Sea. Removing the annual cycle reveals two energy peaks with periodicities of <12 months and 3–7 years, representing the intraseasonal and El Nino—Southern Oscillation (ENSO) signals, respectively. Empirical Orthogonal Function (EOF) analysis shows that EOF1 corresponds to 98% of total variability, EOF2 to 1.3%, and EOF3 to 0.4%. The remote response of ENSO is evident in the variability in the atmospheric bridge, while that of the Indian Ocean Dipole (IOD) and North Atlantic Oscillation (NAO) is weak. Three physical mechanisms are responsible for the occurrence of this EW difference phenomenon, namely wind, buoyancy, and the polarity of eddies. Full article

(This article belongs to the Section Biogeosciences Remote Sensing)

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26 pages, 20028 KiB

Open AccessEditor’s ChoiceArticle

Analysis and Assessment of BDS-2 and BDS-3 Broadcast Ephemeris: Accuracy, the Datum of Broadcast Clocks and Its Impact on Single Point Positioning

by Guoqiang Jiao, Shuli Song, Yangyang Liu, Ke Su, Na Cheng and Shengli Wang

Remote Sens. 2020, 12(13), 2081; https://doi.org/10.3390/rs12132081 - 29 Jun 2020

Cited by 21 | Viewed by 3276

Abstract

For the global ordinary users, the broadcast ephemeris plays important roles in positioning, navigation and timing (PNT) services. With the construction of a new generation of the BeiDou navigation satellite system (BDS), the development of BDS has entered the era of globalization. It [...] Read more.

For the global ordinary users, the broadcast ephemeris plays important roles in positioning, navigation and timing (PNT) services. With the construction of a new generation of the BeiDou navigation satellite system (BDS), the development of BDS has entered the era of globalization. It is meaningful for global users to analyze and assess the BDS-2 and BDS-3 broadcast ephemeris. Therefore, the satellite orbits and clock offsets calculated by broadcast ephemeris are compared with the precise orbit and clock offset products provided by three analysis centers (i.e., Helmholtz Centre Potsdam German Research Center for Geosciences (GFZ), Wuhan University (WHU) and Shanghai Astronomical Observatory (SHA)), and the corresponding signal-in-space range error (SISRE) and the orbit-only SISRE are analyzed to assess the accuracy of BDS broadcast ephemeris. Due to the upgrade of BDS-3 satellite hardware technology and inter-satellite links payload and the development of satellite orbit determination algorithm, the accuracy of broadcast orbit and clock offsets has been greatly improved. The root mean square (RMS) of BDS-3 broadcast orbit errors is improved by 86.30%, 89.47% and 76.86%, and the standard deviation (STD) is improved by 79.41%, 77.00% and 76.78% compared with BDS-2 in the radial, along-track and cross-track directions. The corresponding RMS and STD of all BDS-3 satellite clock offsets are improved by 40.34% and 52.49% than that of BDS-2, respectively. Meanwhile, the mean RMS and STD are 1.78 m and 0.40 m for BDS-2 SISRE, 1.72 m and 0.34 m for BDS-2 orbit-only SISRE, 0.50 m and 0.14 m for BDS-3 SISRE, and 0.17 m and 0.04 m for BDS-3 orbit-only SISRE. It is noteworthy that the average broadcast-minus-precise (BMP) clock values of BDS-2 and BDS-3 are inconsistent, which can indirectly prove that the datum of broadcast clock offsets for BDS-2 and BDS-3 are inconsistent. The inconsistency of the datum of satellite clock offsets and receiver hardware delay bias between BDS-2 and BDS-3 will result in the inter-system bias (ISB) on the receiver segment. For JAVAD TRE_3 receivers, the ISB is relatively small and thus can be ignored. However, for the TRIMBLE ALLOY, SEPT POLARX5, CETC-54-GMR-4016, CETC-54-GMR-4011, GNSS-GGR and UB4B0-13478 receivers, estimating ISB can improve the positioning accuracy of single point positioning (SPP) by 20.15%, 19.81% and 12.76% in north, east and up directions, respectively. Full article

(This article belongs to the Special Issue Real-time GNSS Precise Positioning Service and its Augmentation Technology)

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23 pages, 6685 KiB

Open AccessEditor’s ChoiceArticle

From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States

by Anthony Mucia, Bertrand Bonan, Yongjun Zheng, Clément Albergel and Jean-Christophe Calvet

Remote Sens. 2020, 12(12), 2020; https://doi.org/10.3390/rs12122020 - 24 Jun 2020

Cited by 8 | Viewed by 3251

Abstract

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil [...] Read more.

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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6 pages, 2659 KiB

Open AccessEditor’s ChoiceLetter

Surface Temperature of the Planet Earth from Satellite Data over the Period 2003–2019

by José Antonio Sobrino, Susana García-Monteiro and Yves Julien

Remote Sens. 2020, 12(12), 2036; https://doi.org/10.3390/rs12122036 - 24 Jun 2020

Cited by 11 | Viewed by 3612

Abstract

This is an update of Sobrino et al.’s paper, published in January 2020, which extends the calculation of the Earth’s surface temperature to the period 2003–2019 and uses the new version 2019.0 for the sea surface temperature product MODIS, which is available from [...] Read more.

This is an update of Sobrino et al.’s paper, published in January 2020, which extends the calculation of the Earth’s surface temperature to the period 2003–2019 and uses the new version 2019.0 for the sea surface temperature product MODIS, which is available from 15 January 2020 and replaces version 2014.0. The land surface temperature was estimated from the MCD11C1 product for the same period. The results corroborate the temperature anomalies retrieved from climate models and improve the comparison with global annual air temperatures estimated by the NOAA’s National Climatic Data Center (NOAA-NCDC), with a correlation coefficient of 0.96. In addition, a trend of 0.021 ± 0.001 °C/year increase was found for the Earth’s surface temperature in this 17-year period. Full article

(This article belongs to the Section Remote Sensing Communications)

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25 pages, 6592 KiB

Open AccessEditor’s ChoiceArticle

An Estimation of Top-Down NO_x Emissions from OMI Sensor Over East Asia

by Kyung M. Han, Hyun S. Kim and Chul H. Song

Remote Sens. 2020, 12(12), 2004; https://doi.org/10.3390/rs12122004 - 22 Jun 2020

Cited by 4 | Viewed by 3444

Abstract

This study focuses on the estimation of top-down NO_x emissions over East Asia, integrating information on the levels of NO₂ and NO, wind vector, and geolocation from Ozone Monitoring Instrument (OMI) observations and Weather Research and Forecasting (WRF)-Community Multiscale Air Quality [...] Read more.

This study focuses on the estimation of top-down NO_x emissions over East Asia, integrating information on the levels of NO₂ and NO, wind vector, and geolocation from Ozone Monitoring Instrument (OMI) observations and Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model simulations. An algorithm was developed based on mass conservation to estimate the 30 km × 30 km resolved top-down NO_x emissions over East Asia. In particular, the algorithm developed in this study considered two main atmospheric factors—(i) NO_x transport from/to adjacent cells and (ii) calculations of the lifetimes of column NO_x (τ). In the sensitivity test, the analysis showed the improvements in the top-down NO_x estimation via filtering the data (τ ≤ 2 h). The best top-down NO_x emissions were inferred after the sixth iterations. Those emissions were 11.76 Tg N yr⁻¹ over China, 0.13 Tg N yr⁻¹ over North Korea, 0.46 Tg N yr⁻¹ over South Korea, and 0.68 Tg N yr⁻¹ over Japan. These values are 34%, 62%, 60%, and 47% larger than the current bottom-up NO_x emissions over these countries, respectively. A comparison between the CMAQ-estimated and OMI-retrieved NO₂ columns was made to confirm the accuracy of the newly estimated NO_x emission. The comparison confirmed that the estimated top-down NO_x emissions showed better agreements with observations (R² = 0.88 for January and 0.81 for July). Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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22 pages, 8535 KiB

Open AccessEditor’s ChoiceArticle

Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data

by Zhijiang Zhang, Lin Zhao and Aiwen Lin

Remote Sens. 2020, 12(12), 1927; https://doi.org/10.3390/rs12121927 - 14 Jun 2020

Cited by 9 | Viewed by 3632

Abstract

Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used [...] Read more.

Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used data in evaluating GPP. The launch of the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite provides the FAPAR and the OLCI Terrestrial Chlorophyll Index (OTCI) products with higher temporal resolution and smoother spatial distribution than MODIS, having the potential to monitor terrain GPP. OTCI is one of the red-edge indices and is particularly sensitive to canopy chlorophyll content related to GPP. The purpose of the study is to evaluate the performance of OLCI FAPAR and OTCI for the estimation of GPP across seven biomes in 2017–2018. To this end, OLCI FAPAR and OTCI products in combination with insitu meteorological data were first integrated into the MODIS GPP algorithm and in three OTCI-driven models to simulate GPP. The modeled GPP (GPP_OLCI-FAPAR and GPP_OTCI) were then compared with flux tower GPP (GPP_EC) for each site. Furthermore, the GPP_OLCI-FAPAR and GPP derived from the MODIS FAPAR (GPP_MODIS-FAPAR) were compared. Results showed that the performance of GPP_OLCI-FAPAR was varied in different sites, with the highest R² of 0.76 and lowest R² of 0.45. The OTCI-driven models that include APAR data exhibited a significant relationship with GPP_EC for all sites, and models using only OTCI provided the most varied performance, with the relationship between GPP_OTCI and GPP_EC from strong to nonsignificant. Moreover, GPP_OLCI-FAPAR (R² = 0.55) performed better than GPP_MODIS-FAPAR (R² = 0.44) across all biomes. These results demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and they also provide the basis for their combination with the soon-to-launch Fluorescence Explorer satellite and their integration with the Sentinel-3 land surface temperature product into light use models for GPP monitoring at regional and global scales. Full article

(This article belongs to the Special Issue Remote Sensing of Atmosphere and Underlying Surface Using OLCI and SLSTR on Board Sentinel-3: Calibration, Algorithms, Geophysical Products and Validation)

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18 pages, 331 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories

by Ronald E. McRoberts, Erik Næsset, Christophe Sannier, Stephen V. Stehman and Erkki O. Tomppo

Remote Sens. 2020, 12(11), 1891; https://doi.org/10.3390/rs12111891 - 11 Jun 2020

Cited by 11 | Viewed by 3709

Abstract

For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as [...] Read more.

For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)

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25 pages, 1764 KiB

Open AccessEditor’s ChoiceArticle

Adaptive Modeling of the Global Ionosphere Vertical Total Electron Content

by Eren Erdogan, Michael Schmidt, Andreas Goss, Barbara Görres and Florian Seitz

Remote Sens. 2020, 12(11), 1822; https://doi.org/10.3390/rs12111822 - 4 Jun 2020

Cited by 16 | Viewed by 3488

Abstract

The Kalman filter (KF) is widely applied in (ultra) rapid and (near) real-time ionosphere modeling to meet the demand on ionosphere products required in many applications extending from navigation and positioning to monitoring space weather events and naturals disasters. The requirement of a [...] Read more.

The Kalman filter (KF) is widely applied in (ultra) rapid and (near) real-time ionosphere modeling to meet the demand on ionosphere products required in many applications extending from navigation and positioning to monitoring space weather events and naturals disasters. The requirement of a prior definition of the stochastic models attached to the measurements and the dynamic models of the KF is a drawback associated with its standard implementation since model uncertainties can exhibit temporal variations or the time span of a given test data set would not be large enough. Adaptive methods can mitigate these problems by tuning the stochastic model parameters during the filter run-time. Accordingly, one of the primary objectives of our study is to apply an adaptive KF based on variance component estimation to compute the global Vertical Total Electron Content (VTEC) of the ionosphere by assimilating different ionospheric GNSS measurements. Secondly, the derived VTEC representation is based on a series expansion in terms of compactly supported B-spline functions. We highlight the morphological similarity of the spatial distributions and the magnitudes between VTEC values and the corresponding estimated B-spline coefficients. This similarity allows for deducing physical interpretations from the coefficients. In this context, an empirical adaptive model to account for the dynamic model uncertainties, representing the temporal variations of VTEC errors, is developed in this work according to the structure of B-spline coefficients. For the validation, the differential slant total electron content (dSTEC) analysis and a comparison with Jason-2/3 altimetry data are performed. Assessments show that the quality of the VTEC products derived by the presented algorithm is in good agreement, or even more accurate, with the products provided by IGS ionosphere analysis centers within the selected periods in 2015 and 2017. Furthermore, we show that the presented approach can be applied to different ionosphere conditions ranging from very high to low solar activity without concerning time-variable model uncertainties, including measurement error and process noise of the KF because the associated covariance matrices are computed in a self-adaptive manner during run-time. Full article

(This article belongs to the Special Issue Observing and Modelling Ionosphere and Thermosphere Using In Situ and Remote Sensing Techniques)

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19 pages, 8070 KiB

Open AccessEditor’s ChoiceArticle

Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil

by Marcus V. N. d’Oliveira, Eben N. Broadbent, Luis C. Oliveira, Danilo R. A. Almeida, Daniel A. Papa, Manuel E. Ferreira, Angelica M. Almeyda Zambrano, Carlos A. Silva, Felipe S. Avino, Gabriel A. Prata, Ricardo A. Mello, Evandro O. Figueiredo, Lúcio A. de Castro Jorge, Leomar Junior, Rafael W. Albuquerque, Pedro H. S. Brancalion, Ben Wilkinson and Marcelo Oliveira-da-Costa

Remote Sens. 2020, 12(11), 1754; https://doi.org/10.3390/rs12111754 - 29 May 2020

Cited by 29 | Viewed by 6823

Abstract

Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural [...] Read more.

Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 ± 1.8 vs. 381.2 ± 58 pts/m²). Differences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 ± 0.09 vs. 0.42 ± 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior. Full article

(This article belongs to the Special Issue Drones for Ecology and Conservation)

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43 pages, 10907 KiB

Open AccessEditor’s ChoiceReview

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends

by Thorsten Hoeser and Claudia Kuenzer

Remote Sens. 2020, 12(10), 1667; https://doi.org/10.3390/rs12101667 - 22 May 2020

Cited by 241 | Viewed by 47489

Abstract

Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and [...] Read more.

Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly develo** field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO. Full article

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21 pages, 9693 KiB

Open AccessEditor’s ChoiceArticle

Enhancing Methods for Under-Canopy Unmanned Aircraft System Based Photogrammetry in Complex Forests for Tree Diameter Measurement

by Sean Krisanski, Mohammad Sadegh Taskhiri and Paul Turner

Remote Sens. 2020, 12(10), 1652; https://doi.org/10.3390/rs12101652 - 21 May 2020

Cited by 38 | Viewed by 6346

Abstract

The application of Unmanned Aircraft Systems (UAS) beneath the forest canopy provides a potentially valuable alternative to ground-based measurement techniques in areas of dense canopy cover and undergrowth. This research presents results from a study of a consumer-grade UAS flown under the forest [...] Read more.

The application of Unmanned Aircraft Systems (UAS) beneath the forest canopy provides a potentially valuable alternative to ground-based measurement techniques in areas of dense canopy cover and undergrowth. This research presents results from a study of a consumer-grade UAS flown under the forest canopy in challenging forest and terrain conditions. This UAS was deployed to assess under-canopy UAS photogrammetry as an alternative to field measurements for obtaining stem diameters as well as ultra-high-resolution (~400,000 points/m²) 3D models of forest study sites. There were 378 tape-based diameter measurements collected from 99 stems in a native, unmanaged eucalyptus pulchella forest with mixed understory conditions and steep terrain. These measurements were used as a baseline to evaluate the accuracy of diameter measurements from under-canopy UAS-based photogrammetric point clouds. The diameter measurement accuracy was evaluated without the influence of a digital terrain model using an innovative tape-based method. A practical and detailed methodology is presented for the creation of these point clouds. Lastly, a metric called the Circumferential Completeness Index (CCI) was defined to address the absence of a clearly defined measure of point coverage when measuring stem diameters from forest point clouds. The measurement of the mean CCI is suggested for use in future studies to enable a consistent comparison of the coverage of forest point clouds using different sensors, point densities, trajectories, and methodologies. It was found that root-mean-squared-errors of diameter measurements were 0.011 m in Site 1 and 0.021 m in the more challenging Site 2. The point clouds in this study had a mean validated CCI of 0.78 for Site 1 and 0.7 for Site 2, with a mean unvalidated CCI of 0.86 for Site 1 and 0.89 for Site 2. The results in this study demonstrate that under-canopy UAS photogrammetry shows promise in becoming a practical alternative to traditional field measurements, however, these results are currently reliant upon the operator’s knowledge of photogrammetry and his/her ability to fly manually in object-rich environments. Future work should pursue solutions to autonomous operation, more complete point clouds, and a method for providing scale to point clouds when global navigation satellite systems are unavailable. Full article

(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)

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19 pages, 5722 KiB

Open AccessEditor’s ChoiceArticle

60 Years of Glacier Elevation and Mass Changes in the Maipo River Basin, Central Andes of Chile

by David Farías-Barahona, Álvaro Ayala, Claudio Bravo, Sebastián Vivero, Thorsten Seehaus, Saurabh Vijay, Marius Schaefer, Franco Buglio, Gino Casassa and Matthias H. Braun

Remote Sens. 2020, 12(10), 1658; https://doi.org/10.3390/rs12101658 - 21 May 2020

Cited by 21 | Viewed by 6180

Abstract

Glaciers in the central Andes of Chile are fundamental freshwater sources for ecosystems and communities. Overall, glaciers in this region have shown continuous recession and down-wasting, but long-term glacier mass balance studies providing precise estimates of these changes are scarce. Here, we present [...] Read more.

Glaciers in the central Andes of Chile are fundamental freshwater sources for ecosystems and communities. Overall, glaciers in this region have shown continuous recession and down-wasting, but long-term glacier mass balance studies providing precise estimates of these changes are scarce. Here, we present the first long-term (1955–2013/2015), region-specific glacier elevation and mass change estimates for the Maipo River Basin, from which the densely populated metropolitan region of Chile obtains most of its freshwater supply. We calculated glacier elevation and mass changes using historical topographic maps, Shuttle Radar Topography Mission (SRTM), TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X), and airborne Light Detection and Ranging (LiDAR) digital elevation models. The results indicated a mean regional glacier mass balance of −0.12 ± 0.06 m w.e.a⁻¹, with a total mass loss of 2.43 ± 0.26 Gt for the Maipo River Basin between 1955–2013. The most negative glacier mass balance was the Olivares sub-basin, with a mean value of −0.29 ± 0.07 m w.e.a⁻¹. We observed spatially heterogeneous glacier elevation and mass changes between 1955 and 2000, and more negative values between 2000 and 2013, with an acceleration in ice thinning rates starting in 2010, which coincides with the severe drought. Our results provide key information to improve glaciological and hydrological projections in a region where water resources are under pressure. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing III)

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20 pages, 8316 KiB

Open AccessEditor’s ChoiceArticle

LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest

by Alexandra Stefanidou, Ioannis Z. Gitas, Lauri Korhonen, Dimitris Stavrakoudis and Nikos Georgopoulos

Remote Sens. 2020, 12(10), 1565; https://doi.org/10.3390/rs12101565 - 14 May 2020

Cited by 20 | Viewed by 5008

Abstract

Accurate canopy base height (CBH) information is essential for forest and fire managers since it constitutes a key indicator of seedling growth, wood quality and forest health as well as a necessary input in fire behavior prediction systems such as FARSITE, FlamMap and [...] Read more.

Accurate canopy base height (CBH) information is essential for forest and fire managers since it constitutes a key indicator of seedling growth, wood quality and forest health as well as a necessary input in fire behavior prediction systems such as FARSITE, FlamMap and BEHAVE. The present study focused on the potential of airborne LiDAR data analysis to estimate plot-level CBH in a dense uneven-aged structured forest on complex terrain. A comparative study of two widely employed methods was performed, namely the voxel-based approach and regression analysis, which revealed a clear outperformance of the latter. More specifically, the voxel-based CBH estimates were found to lack correlation with the reference data (

R^{2} = 0.15

,

r R M S E = 42.36 %

) while most CBH values were overestimated resulting in an

r b i a s

of

- 17.52 %

. On the contrary, cross-validation of the developed regression model showcased an

R^{2}

,

r R M S E

and

r b i a s

of

0.61

,

18.19 %

and

- 0.09 %

respectively. Overall analysis of the results proved the voxel-based approach incapable of accurately estimating plot-level CBH due to vegetation and topographic heterogeneity of the forest environment, which however didn’t affect the regression analysis performance. Full article

(This article belongs to the Section Forest Remote Sensing)

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14 pages, 4648 KiB

Open AccessEditor’s ChoiceLetter

U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images—Case Study in the Joanópolis City, Brazil

by Fabien H. Wagner, Ricardo Dalagnol, Yuliya Tarabalka, Tassiana Y. F. Segantine, Rogério Thomé and Mayumi C. M. Hirye

Remote Sens. 2020, 12(10), 1544; https://doi.org/10.3390/rs12101544 - 12 May 2020

Cited by 45 | Viewed by 11880

Abstract

Currently, there exists a growing demand for individual building map** in regions of rapid urban growth in less-developed countries. Most existing methods can segment buildings but cannot discriminate adjacent buildings. Here, we present a new convolutional neural network architecture (CNN) called U-net-id that [...] Read more.

Currently, there exists a growing demand for individual building map** in regions of rapid urban growth in less-developed countries. Most existing methods can segment buildings but cannot discriminate adjacent buildings. Here, we present a new convolutional neural network architecture (CNN) called U-net-id that performs building instance segmentation. The proposed network is trained with WorldView-3 satellite RGB images (0.3 m) and three different labeled masks. The first is the building mask; the second is the border mask, which is the border of the building segment with 4 pixels added outside and 3 pixels inside; and the third is the inner segment mask, which is the segment of the building diminished by 2 pixels. The architecture consists of three parallel paths, one for each mask, all starting with a U-net model. To accurately capture the overlap between the masks, all activation layers of the U-nets are copied and concatenated on each path and sent to two additional convolutional layers before the output activation layers. The method was tested with a dataset of 7563 manually delineated individual buildings of the city of Joanópolis-SP, Brazil. On this dataset, the semantic segmentation showed an overall accuracy of 97.67% and an F1-Score of 0.937 and the building individual instance segmentation showed good performance with a mean intersection over union (IoU) of 0.582 (median IoU = 0.694). Full article

(This article belongs to the Special Issue Data Mining and Machine Learning in Urban Applications)

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20 pages, 2125 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Map** Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning

by Pablo Pérez Chaves, Gabriela Zuquim, Kalle Ruokolainen, Jasper Van doninck, Risto Kalliola, Elvira Gómez Rivero and Hanna Tuomisto

Remote Sens. 2020, 12(9), 1523; https://doi.org/10.3390/rs12091523 - 10 May 2020

Cited by 7 | Viewed by 8634

Abstract

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian [...] Read more.

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian national forest inventory (INFFS) is expected to change the situation. Here, we analyzed genus-level variation, summarized through non-metric multidimensional scaling (NMDS), in a set of 157 INFFS inventory plots in lowland to low mountain rain forests (<2000 m above sea level) using Landsat satellite imagery and climatic, edaphic, and elevation data as predictor variables. Genus-level floristic patterns have earlier been found to be indicative of species-level patterns. In correlation tests, the floristic variation of tree genera was most strongly related to Landsat variables and secondly to climatic variables. We used random forest regression, under varying criteria of feature selection and cross-validation, to predict the floristic composition on the basis of Landsat and environmental data. The best model explained >60% of the variation along NMDS axes 1 and 2 and 40% of the variation along NMDS axis 3. We used this model to predict the three NMDS dimensions at a 450-m resolution over all of the Peruvian Amazonia and classified the pixels into 10 floristic classes using k-means classification. An indicator analysis identified statistically significant indicator genera for 8 out of the 10 classes. The results are congruent with earlier studies, suggesting that the approach is robust and can be applied to other tropical regions, which is useful for reducing research gaps and for identifying suitable areas for conservation. Full article

(This article belongs to the Special Issue Remote Sensing for Biodiversity Map** and Monitoring)

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22 pages, 3392 KiB

Open AccessEditor’s ChoiceReview

Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

by Abolfazl Abdollahi, Biswajeet Pradhan, Nagesh Shukla, Subrata Chakraborty and Abdullah Alamri

Remote Sens. 2020, 12(9), 1444; https://doi.org/10.3390/rs12091444 - 2 May 2020

Cited by 191 | Viewed by 13718

Abstract

One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of [...] Read more.

One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively. Full article

(This article belongs to the Special Issue Traffic Assessment and Monitoring with Remote Sensing and Geospatial Modelling)

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19 pages, 10695 KiB

Open AccessEditor’s ChoiceArticle

Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification

by Victoria M. Scholl, Megan E. Cattau, Maxwell B. Joseph and Jennifer K. Balch

Remote Sens. 2020, 12(9), 1414; https://doi.org/10.3390/rs12091414 - 30 Apr 2020

Cited by 30 | Viewed by 7894

Abstract

Accurately map** tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements [...] Read more.

Accurately map** tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition map** using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters. Full article

(This article belongs to the Special Issue She Maps)

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14 pages, 1563 KiB

Open AccessEditor’s ChoiceArticle

Compatibility of Aerial and Terrestrial LiDAR for Quantifying Forest Structural Diversity

by Elizabeth A. LaRue, Franklin W. Wagner, Songlin Fei, Jeff W. Atkins, Robert T. Fahey, Christopher M. Gough and Brady S. Hardiman

Remote Sens. 2020, 12(9), 1407; https://doi.org/10.3390/rs12091407 - 29 Apr 2020

Cited by 48 | Viewed by 7633

Abstract

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem [...] Read more.

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that described heterogeneity of the outermost layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicated that aerial LiDAR could be of use in quantifying broad-scale variation in structural diversity across macroscales. Full article

(This article belongs to the Special Issue Lidar Remote Sensing of Forest Structure, Biomass and Dynamics)

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24 pages, 3122 KiB

Open AccessEditor’s ChoiceArticle

High Quality Zenith Tropospheric Delay Estimation Using a Low-Cost Dual-Frequency Receiver and Relative Antenna Calibration

by Andreas Krietemeyer, Hans van der Marel, Nick van de Giesen and Marie-Claire ten Veldhuis

Remote Sens. 2020, 12(9), 1393; https://doi.org/10.3390/rs12091393 - 28 Apr 2020

Cited by 33 | Viewed by 5729

Abstract

The recent release of consumer-grade dual-frequency receivers sparked scientific interest into use of these cost-efficient devices for high precision positioning and tropospheric delay estimations. Previous analyses with low-cost single-frequency receivers showed promising results for the estimation of Zenith Tropospheric Delays (ZTDs). However, their [...] Read more.

The recent release of consumer-grade dual-frequency receivers sparked scientific interest into use of these cost-efficient devices for high precision positioning and tropospheric delay estimations. Previous analyses with low-cost single-frequency receivers showed promising results for the estimation of Zenith Tropospheric Delays (ZTDs). However, their application is limited by the need to account for the ionospheric delay. In this paper we investigate the potential of a low-cost dual-frequency receiver (U-blox ZED-F9P) in combination with a range of different quality antennas. We show that the receiver itself is very well capable of achieving high-quality ZTD estimations. The limiting factor is the quality of the receiving antenna. To improve the applicability of mass-market antennas, a relative antenna calibration is performed, and new absolute Antenna Exchange Format (ANTEX) entries are created using a geodetic antenna as base. The performance of ZTD estimation with the tested antennas is evaluated, with and without antenna Phase Center Variation (PCV) corrections, using Precise Point Positioning (PPP). Without applying PCVs for the low-cost antennas, the Root Mean Square Errors (RMSE) of the estimated ZTDs are between 15 mm and 24 mm. Using the newly generated PCVs, the RMSE is reduced significantly to about 4 mm, a level that is excellent for meteorological applications. The standard U-blox ANN-MB-00 patch antenna, with a circular ground plane, after correcting the phase pattern yields comparable results (0.47 mm bias and 4.02 mm RMSE) to those from geodetic quality antennas, providing an all-round low-cost solution. The relative antenna calibration method presented in this paper opens the way for wide-spread application of low-cost receiver and antennas. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part II)

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20 pages, 8235 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Near Real-Time Monitoring of the Christmas 2018 Etna Eruption Using SEVIRI and Products Validation

by Stefano Corradini, Lorenzo Guerrieri, Dario Stelitano, Giuseppe Salerno, Simona Scollo, Luca Merucci, Michele Prestifilippo, Massimo Musacchio, Malvina Silvestri, Valerio Lombardo and Tommaso Caltabiano

Remote Sens. 2020, 12(8), 1336; https://doi.org/10.3390/rs12081336 - 23 Apr 2020

Cited by 31 | Viewed by 3930

Abstract

On the morning of 24 December 2018, an eruptive event occurred at Etna, which was followed the next day by a strong sequence of shallow earthquakes. The eruptive episode lasted until 30 December, ranging from moderate strombolian to lava fountain activity coupled with [...] Read more.

On the morning of 24 December 2018, an eruptive event occurred at Etna, which was followed the next day by a strong sequence of shallow earthquakes. The eruptive episode lasted until 30 December, ranging from moderate strombolian to lava fountain activity coupled with vigorous ash/gas emissions and a lava flow effusion toward the eastern volcano flank of Valle del Bove. In this work, the data collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments on board the Meteosat Second Generation (MSG) geostationary satellite are used to characterize the Etna activity by estimating the proximal and distal eruption parameters in near real time. The inversion of data indicates the onset of eruption on 24 December at 11:15 UTC, a maximum Time Average Discharge Rate (TADR) of 8.3 m³/s, a cumulative lava volume emitted of 0.5 Mm³, and a Volcanic Plume Top Height (VPTH) that reached a maximum altitude of 8 km above sea level (asl). The volcanic cloud ash and SO₂ result totally collocated, with an ash amount generally lower than SO₂ except on 24 December during the climax phase. A total amount of about 100 and 35 kt of SO₂ and ash respectively was emitted during the entire eruptive period, while the SO₂ fluxes reached peaks of more than 600 kg/s, with a mean value of about 185 kg/s. The SEVIRI VPTH, ash/SO₂ masses, and flux time series have been compared with the results obtained from the ground-based visible (VIS) cameras and FLux Automatic MEasurements (FLAME) networks, and the satellite images collected by the MODerate resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua- polar satellites. The analysis indicates good agreement between SEVIRI, VIS camera, and MODIS retrievals with VPTH, ash, and SO₂ estimations all within measurement errors. The SEVIRI and FLAME SO₂ flux retrievals show significant discrepancies due to the presence of volcanic ash and a gap of data on the FLAME network. The results obtained in this study show the ability of geostationary satellite systems to characterize eruptive events from the source to the atmosphere in near real time during the day and night, thus offering a powerful tool to mitigate volcanic risk on both local population and airspace and to give insight on volcanic processes. Full article

(This article belongs to the Special Issue Convective and Volcanic Clouds (CVC))

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23 pages, 3230 KiB

Open AccessEditor’s ChoiceArticle

Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

by Xuanlong Ma, Alfredo Huete, Ngoc Nguyen Tran, Jian Bi, Sicong Gao and Yelu Zeng

Remote Sens. 2020, 12(8), 1339; https://doi.org/10.3390/rs12081339 - 23 Apr 2020

Cited by 30 | Viewed by 8611

Abstract

Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we [...] Read more.

Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time. Full article

(This article belongs to the Special Issue Earth Monitoring from A New Generation of Geostationary Satellites)

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24 pages, 2097 KiB

Open AccessEditor’s ChoiceArticle

Relation of Photochemical Reflectance Indices Based on Different Wavelengths to the Parameters of Light Reactions in Photosystems I and II in Pea Plants

by Ekaterina Sukhova and Vladimir Sukhov

Remote Sens. 2020, 12(8), 1312; https://doi.org/10.3390/rs12081312 - 21 Apr 2020

Cited by 23 | Viewed by 3774

Abstract

Measurement and analysis of the numerous reflectance indices of plants is an effective approach for the remote sensing of plant physiological processes in agriculture and ecological monitoring. A photochemical reflectance index (PRI) plays an important role in this kind of remote sensing because [...] Read more.

Measurement and analysis of the numerous reflectance indices of plants is an effective approach for the remote sensing of plant physiological processes in agriculture and ecological monitoring. A photochemical reflectance index (PRI) plays an important role in this kind of remote sensing because it can be related to early changes in photosynthetic processes under the action of stressors (excess light, changes in temperature, drought, etc.). In particular, we previously showed that light-induced changes in PRIs could be strongly related to the energy-dependent component of the non-photochemical quenching in photosystem II. The aim of the present work was to undertake comparative analysis of the efficiency of using light-induced changes in PRIs (ΔPRIs) based on different wavelengths for the estimation of the parameters of photosynthetic light reactions (including the parameters of photosystem I). Pea plants were used in the investigation; the photosynthetic parameters were measured using the pulse-amplitude-modulated (PAM) fluorometer Dual-PAM-100 and the intensities of the reflected light were measured using the spectrometer S100. The ΔPRIs were calculated as ΔPRI(band,570), where the band was 531 nm for the typical PRI and 515, 525, 535, 545, or 555 nm for modified PRIs; 570 nm was the reference wavelength for all PRIs. There were several important results: (1) ∆PRI(525,570), ∆PRI(531,570), ∆PRI(535,570), and ∆PRI(545,570) could be used for estimation of most of the photosynthetic parameters under light only or under dark only conditions. (2) The combination of dark and light conditions decreased the efficiency of ∆PRIs for the estimation of the photosynthetic parameters; ∆PRI(535,570) and ∆PRI(545,570) had maximal efficiency under these conditions. (3) ∆PRI(515,570) and ∆PRI(525,570) mainly included the slow-relaxing component of PRI; in contrast, ∆PRI(531,570), ∆PRI(535,570), ∆PRI(545,570), and ∆PRI(555,570) mainly included the fast-relaxing component of PRI. These components were probably caused by different mechanisms. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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20 pages, 15631 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy

by Salvatore Falanga Bolognesi, Edoardo Pasolli, Oscar Rosario Belfiore, Carlo De Michele and Guido D’Urso

Remote Sens. 2020, 12(8), 1275; https://doi.org/10.3390/rs12081275 - 17 Apr 2020

Cited by 16 | Viewed by 6843

Abstract

Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project [...] Read more.

Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products. Full article

(This article belongs to the Section Environmental Remote Sensing)

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25 pages, 1451 KiB

Open AccessEditor’s ChoiceReview

An Overview of Platforms for Big Earth Observation Data Management and Analysis

by Vitor C. F. Gomes, Gilberto R. Queiroz and Karine R. Ferreira

Remote Sens. 2020, 12(8), 1253; https://doi.org/10.3390/rs12081253 - 16 Apr 2020

Cited by 170 | Viewed by 16874

Abstract

In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. [...] Read more.

In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. To meet these demands, novel technologies have been proposed and developed, based on cloud computing and distributed systems, such as array database systems, MapReduce systems and web services to access and process big Earth observation data. Currently, these technologies have been integrated into cutting edge platforms in order to support a new generation of SDI for big Earth observation data. This paper presents an overview of seven platforms for big Earth observation data management and analysis—Google Earth Engine (GEE), Sentinel Hub, Open Data Cube (ODC), System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL), openEO, JEODPP, and pipsCloud. We also provide a comparison of these platforms according to criteria that represent capabilities of the EO community interest. Full article

(This article belongs to the Special Issue Spatial Data Infrastructures for Big Geospatial Sensing Data)

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34 pages, 22244 KiB

Open AccessEditor’s ChoiceArticle

The Status of Earth Observation Techniques in Monitoring High Mountain Environments at the Example of Pasterze Glacier, Austria: Data, Methods, Accuracies, Processes, and Scales

by Michael Avian, Christian Bauer, Matthias Schlögl, Barbara Widhalm, Karl-Heinz Gutjahr, Michael Paster, Christoph Hauer, Melina Frießenbichler, Anton Neureiter, Gernot Weyss, Peter Flödl, Gernot Seier and Wolfgang Sulzer

Remote Sens. 2020, 12(8), 1251; https://doi.org/10.3390/rs12081251 - 15 Apr 2020

Cited by 13 | Viewed by 5972

Abstract

Earth observation offers a variety of techniques for monitoring and characterizing geomorphic processes in high mountain environments. Terrestrial laserscanning and unmanned aerial vehicles provide very high resolution data with high accuracy. Automatic cameras have become a valuable source of information—mostly in a qualitative [...] Read more.

Earth observation offers a variety of techniques for monitoring and characterizing geomorphic processes in high mountain environments. Terrestrial laserscanning and unmanned aerial vehicles provide very high resolution data with high accuracy. Automatic cameras have become a valuable source of information—mostly in a qualitative manner—in recent years. The availability of satellite data with very high revisiting time has gained momentum through the European Space Agency’s Sentinel missions, offering new application potential for Earth observation. This paper reviews the status of recent techniques such as terrestrial laserscanning, remote sensed imagery, and synthetic aperture radar in monitoring high mountain environments with a particular focus on the impact of new platforms such as Sentinel-1 and -2 as well as unmanned aerial vehicles. The study area comprises the high mountain glacial environment at the Pasterze Glacier, Austria. The area is characterized by a highly dynamic geomorphological evolution and by being subject to intensive scientific research as well as long-term monitoring. We primarily evaluate landform classification and process characterization for: (i) the proglacial lake; (ii) icebergs; (iii) the glacier river; (iv) valley-bottom processes; (v) slope processes; and (vi) rock wall processes. We focus on assessing the potential of every single method both in spatial and temporal resolution in characterizing different geomorphic processes. Examples of the individual techniques are evaluated qualitatively and quantitatively in the context of: (i) morphometric analysis; (ii) applicability in high alpine regions; and (iii) comparability of the methods among themselves. The final frame of this article includes considerations on scale dependent process detectability and characterization potentials of these Earth observation methods, along with strengths and limitations in applying these methods in high alpine regions. Full article

(This article belongs to the Special Issue Imaging Floods and Glacier Geohazards with Remote Sensing)

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20 pages, 5178 KiB

Open AccessEditor’s ChoiceArticle

Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

by Marko Mäkynen, Jari Haapala, Giuseppe Aulicino, Beena Balan-Saro**i, Magdalena Balmaseda, Alexandru Gegiuc, Fanny Girard-Ardhuin, Stefan Hendricks, Georg Heygster, Larysa Istomina, Lars Kaleschke, Juha Karvonen, Thomas Krumpen, Mikko Lensu, Michael Mayer, Flavio Parmiggiani, Robert Ricker, Eero Rinne, Amelie Schmitt, Markku Similä, Steffen Tietsche, Rasmus Tonboe, Peter Wadhams, Mai Winstrup and Hao Zuo Show full author list Hide full author list

Remote Sens. 2020, 12(7), 1214; https://doi.org/10.3390/rs12071214 - 10 Apr 2020

Cited by 16 | Viewed by 6980

Abstract

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from [...] Read more.

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services. Full article

(This article belongs to the Special Issue Polar Sea Ice: Detection, Monitoring and Modeling)

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17 pages, 6852 KiB

Open AccessEditor’s ChoiceArticle

by Disong Fu, Zijue Song, **aoling Zhang, Yunfei Wu, Minzheng Duan, Weiwei Pu, Zhiqiang Ma, Weijun Quan, Huaigang Zhou, Huizheng Che and **angao **a

Remote Sens. 2020, 12(7), 1193; https://doi.org/10.3390/rs12071193 - 8 Apr 2020

Cited by 11 | Viewed by 3065

Abstract

Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply [...] Read more.

Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM_2.5 and AOD at a rural station (RU) and an urban station (UR) in Bei**g. PM_2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM_2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM_2.5 in summer and minimum AOD, but maximum PM_2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM_2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM_2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM_2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM_2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM_2.5 from AOD. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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18 pages, 7241 KiB

Open AccessEditor’s ChoiceArticle

Regional Dependence of Atmospheric Responses to Oceanic Eddies in the North Pacific Ocean

by **lin Ji, **g Ma, Changming Dong, John C. H. Chiang and Dake Chen

Remote Sens. 2020, 12(7), 1161; https://doi.org/10.3390/rs12071161 - 4 Apr 2020

Cited by 10 | Viewed by 3435

Abstract

Based on sea surface height anomaly (SSHA) from satellite altimeter and microwave radiometer datasets, this study investigates atmospheric responses to oceanic eddies in four subdomains of the North Pacific Ocean with strongest eddy activity: Kuroshio Extension (KE), Subtropical Front (SF), California Coastal Current [...] Read more.

Based on sea surface height anomaly (SSHA) from satellite altimeter and microwave radiometer datasets, this study investigates atmospheric responses to oceanic eddies in four subdomains of the North Pacific Ocean with strongest eddy activity: Kuroshio Extension (KE), Subtropical Front (SF), California Coastal Current (CC) and Aleutian Islands (AI). Analyses show that anticyclonic eddies cause sea surface temperature, surface wind speed and precipitation rate to increase in all four subdomains, and vice versa. Through a further examination of the regional dependence of atmospheric responses to oceanic eddies, it is found that the strongest and the weakest surface wind speed responses (in winter and summer) are observed in the KE and AI region, respectively. For precipitation rate, seasonal variation of the atmospheric responses to oceanic eddies is strongest in winter and weakest in summer in the KE, CC and AI regions, but stronger in summer in the SF area. The reasons for such regional dependence and seasonality are the differences in the strength of SST anomalies, the vertical kinetic energy flux and atmospheric instability in the four subdomains. Full article

(This article belongs to the Section Ocean Remote Sensing)

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28 pages, 2087 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Remote Sensing of River Discharge: A Review and a Framing for the Discipline

by Colin J Gleason and Michael T Durand

Remote Sens. 2020, 12(7), 1107; https://doi.org/10.3390/rs12071107 - 31 Mar 2020

Cited by 87 | Viewed by 12015

Abstract

Remote sensing of river discharge (RSQ) is a burgeoning field rife with innovation. This innovation has resulted in a highly non-cohesive subfield of hydrology advancing at a rapid pace, and as a result misconceptions, mis-citations, and confusion are apparent among authors, readers, editors, [...] Read more.

Remote sensing of river discharge (RSQ) is a burgeoning field rife with innovation. This innovation has resulted in a highly non-cohesive subfield of hydrology advancing at a rapid pace, and as a result misconceptions, mis-citations, and confusion are apparent among authors, readers, editors, and reviewers. While the intellectually diverse subfield of RSQ practitioners can parse this confusion, the broader hydrology community views RSQ as a monolith and such confusion can be damaging. RSQ has not been comprehensively summarized over the past decade, and we believe that a summary of the recent literature has a potential to provide clarity to practitioners and general hydrologists alike. Therefore, we here summarize a broad swath of the literature, and find after our reading that the most appropriate way to summarize this literature is first by application area (into methods appropriate for gauged, semi-gauged, regionally gauged, politically ungauged, and totally ungauged basins) and next by methodology. We do not find categorizing by sensor useful, and everything from un-crewed aerial vehicles (UAVs) to satellites are considered here. Perhaps the most cogent theme to emerge from our reading is the need for context. All RSQ is employed in the service of furthering hydrologic understanding, and we argue that nearly all RSQ is useful in this pursuit provided it is properly contextualized. We argue that if authors place each new work into the correct application context, much confusion can be avoided, and we suggest a framework for such context here. Specifically, we define which RSQ techniques are and are not appropriate for ungauged basins, and further define what it means to be ‘ungauged’ in the context of RSQ. We also include political and economic realities of RSQ, as the objective of the field is sometimes to provide data purposefully cloistered by specific political decisions. This framing can enable RSQ to respond to hydrology at large with confidence and cohesion even in the face of methodological and application diversity evident within the literature. Finally, we embrace the intellectual diversity of RSQ and suggest the field is best served by a continuation of methodological proliferation rather than by a move toward orthodoxy and standardization. Full article

(This article belongs to the Special Issue Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge)

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20 pages, 7725 KiB

Open AccessEditor’s ChoiceArticle

Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

by Somayeh Nezami, Ehsan Khoramshahi, Olli Nevalainen, Ilkka Pölönen and Eija Honkavaara

Remote Sens. 2020, 12(7), 1070; https://doi.org/10.3390/rs12071070 - 26 Mar 2020

Cited by 124 | Viewed by 12522

Abstract

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep [...] Read more.

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications. Full article

(This article belongs to the Special Issue Thematic Information Extraction and Application in Forests)

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39 pages, 12479 KiB

Open AccessEditor’s ChoiceReview

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

by Arthur Elmes, Hamed Alemohammad, Ryan Avery, Kelly Caylor, J. Ronald Eastman, Lewis Fishgold, Mark A. Friedl, Meha Jain, Divyani Kohli, Juan Carlos Laso Bayas, Dalton Lunga, Jessica L. McCarty, Robert Gilmore Pontius, Andrew B. Reinmann, John Rogan, Lei Song, Hristiana Stoynova, Su Ye, Zhuang-Fang Yi and Lyndon Estes

Remote Sens. 2020, 12(6), 1034; https://doi.org/10.3390/rs12061034 - 23 Mar 2020

Cited by 54 | Viewed by 12329

Abstract

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training [...] Read more.

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure map**, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience. Full article

(This article belongs to the Section Environmental Remote Sensing)

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25 pages, 1435 KiB

Open AccessEditor’s ChoiceArticle

On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data

by Ana F. Militino, Mehdi Moradi and M. Dolores Ugarte

Remote Sens. 2020, 12(6), 1008; https://doi.org/10.3390/rs12061008 - 21 Mar 2020

Cited by 53 | Viewed by 8084

Abstract

Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann–Kendall and Cox–Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal [...] Read more.

Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann–Kendall and Cox–Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E.divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann–Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann–Kendall test is generally the preferable choice. Although Mann–Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018. Full article

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40 pages, 2089 KiB

Open AccessEditor’s ChoiceReview

Applications of Unmanned Aerial Vehicles in Cryosphere: Latest Advances and Prospects

by Clare Gaffey and Anshuman Bhardwaj

Remote Sens. 2020, 12(6), 948; https://doi.org/10.3390/rs12060948 - 15 Mar 2020

Cited by 95 | Viewed by 11905

Abstract

Owing to usual logistic hardships related to field-based cryospheric research, remote sensing has played a significant role in understanding the frozen components of the Earth system. Conventional spaceborne or airborne remote sensing platforms have their own merits and limitations. Unmanned aerial vehicles (UAVs) [...] Read more.

Owing to usual logistic hardships related to field-based cryospheric research, remote sensing has played a significant role in understanding the frozen components of the Earth system. Conventional spaceborne or airborne remote sensing platforms have their own merits and limitations. Unmanned aerial vehicles (UAVs) have emerged as a viable and inexpensive option for studying the cryospheric components at unprecedented spatiotemporal resolutions. UAVs are adaptable to various cryospheric research needs in terms of providing flexibility with data acquisition windows, revisits, data/sensor types (multispectral, hyperspectral, microwave, thermal/night imaging, Light Detection and Ranging (LiDAR), and photogrammetric stereos), viewing angles, flying altitudes, and overlap dimensions. Thus, UAVs have the potential to act as a bridging remote sensing platform between spatially discrete in situ observations and spatially continuous but coarser and costlier spaceborne or conventional airborne remote sensing. In recent years, a number of studies using UAVs for cryospheric research have been published. However, a holistic review discussing the methodological advancements, hardware and software improvements, results, and future prospects of such cryospheric studies is completely missing. In the present scenario of rapidly changing global and regional climate, studying cryospheric changes using UAVs is bound to gain further momentum and future studies will benefit from a balanced review on this topic. Our review covers the most recent applications of UAVs within glaciology, snow, permafrost, and polar research to support the continued development of high-resolution investigations of cryosphere. We also analyze the UAV and sensor hardware, and data acquisition and processing software in terms of popularity for cryospheric applications and revisit the existing UAV flying regulations in cold regions of the world. The recent usage of UAVs outlined in 103 case studies provide expertise that future investigators should base decisions on. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Glaciology)

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23 pages, 9520 KiB

Open AccessEditor’s ChoiceArticle

Map** Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform

by Ane Alencar, Julia Z. Shimbo, Felipe Lenti, Camila Balzani Marques, Bárbara Zimbres, Marcos Rosa, Vera Arruda, Isabel Castro, João Paulo Fernandes Márcico Ribeiro, Victória Varela, Isa Alencar, Valderli Piontekowski, Vivian Ribeiro, Mercedes M. C. Bustamante, Edson Eyji Sano and Mario Barroso

Remote Sens. 2020, 12(6), 924; https://doi.org/10.3390/rs12060924 - 13 Mar 2020

Cited by 131 | Viewed by 13173

Abstract

Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened [...] Read more.

Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985–2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr⁻¹), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of map** distinct NV types in a highly seasonal and heterogeneous savanna biome. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)

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25 pages, 14879 KiB

Open AccessEditor’s ChoiceArticle

Combining InfraRed Thermography and UAV Digital Photogrammetry for the Protection and Conservation of Rupestrian Cultural Heritage Sites in Georgia: A Methodological Application

by William Frodella, Mikheil Elashvili, Daniele Spizzichino, Giovanni Gigli, Luka Adikashvili, Nikoloz Vacheishvili, Giorgi Kirkitadze, Akaki Nadaraia, Claudio Margottini and Nicola Casagli

Remote Sens. 2020, 12(5), 892; https://doi.org/10.3390/rs12050892 - 10 Mar 2020

Cited by 39 | Viewed by 6466

Abstract

The rock-cut city of Vardzia is an example of the extraordinary rupestrian cultural heritage of Georgia. The site, Byzantine in age, was carved in the steep tuff slopes of the Erusheti mountains, and due to its peculiar geological characteristics, it is particularly vulnerable [...] Read more.

The rock-cut city of Vardzia is an example of the extraordinary rupestrian cultural heritage of Georgia. The site, Byzantine in age, was carved in the steep tuff slopes of the Erusheti mountains, and due to its peculiar geological characteristics, it is particularly vulnerable to weathering and degradation, as well as frequent instability phenomena. These problems determine serious constraints on the future conservation of the site, as well as the safety of the visitors. This paper focuses on the implementation of a site-specific methodology, based on the integration of advanced remote sensing techniques, such as InfraRed Thermography (IRT) and Unmanned Aerial Vehicle (UAV)-based Digital Photogrammetry (DP), with traditional field surveys and laboratory analyses, with the aim of map** the potential criticality of the rupestrian complex on a slope scale. The adopted methodology proved to be a useful tool for the detection of areas of weathering and degradation on the tuff cliffs, such as moisture and seepage sectors related to the ephemeral drainage network of the slope. These insights provided valuable support for the design and implementation of sustainable mitigation works, to be profitably used in the management plan of the site of Vardzia, and can be used for the protection and conservation of rupestrian cultural heritage sites characterized by similar geological contexts. Full article

(This article belongs to the Special Issue Remote Sensing, Spatial Analysis, and GIS for Natural and Cultural Heritage Documentation, Monitoring, and Preservation)

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17 pages, 2072 KiB

Open AccessEditor’s ChoiceArticle

Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

by Mohammed A. Naser, Raj Khosla, Louis Longchamps and Subash Dahal

Remote Sens. 2020, 12(5), 824; https://doi.org/10.3390/rs12050824 - 3 Mar 2020

Cited by 55 | Viewed by 5674

Abstract

Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) [...] Read more.

Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI < 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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22 pages, 30065 KiB

Open AccessEditor’s ChoiceArticle

Sentinel-1 DInSAR for Monitoring Active Landslides in Critical Infrastructures: The Case of the Rules Reservoir (Southern Spain)

by Cristina Reyes-Carmona, Anna Barra, Jorge Pedro Galve, Oriol Monserrat, José Vicente Pérez-Peña, Rosa María Mateos, Davide Notti, Patricia Ruano, Agustín Millares, Juan López-Vinielles and José Miguel Azañón

Remote Sens. 2020, 12(5), 809; https://doi.org/10.3390/rs12050809 - 3 Mar 2020

Cited by 50 | Viewed by 7798

Abstract

Landslides in reservoir contexts are a well-recognised hazard that may lead to dangerous situations regarding infrastructures and people’s safety. Satellite-based radar interferometry is proving to be a reliable method to monitor the activity of landslides in such contexts. Here, we present a DInSAR [...] Read more.

Landslides in reservoir contexts are a well-recognised hazard that may lead to dangerous situations regarding infrastructures and people’s safety. Satellite-based radar interferometry is proving to be a reliable method to monitor the activity of landslides in such contexts. Here, we present a DInSAR (Differential Interferometric Synthetic Aperture Radar) analysis of Sentinel-1 images that exemplifies the usefulness of the technique to recognize and monitor landslides in the Rules Reservoir (Southern Spain). The integration of DInSAR results with a comprehensive geomorphological study allowed us to understand the typology, evolution and triggering factors of three active landslides: Lorenzo-1, Rules Viaduct and El Arrecife. We could distinguish between rotational and translational landslides and, thus, we evaluated the potential hazards related to these typologies, i.e., retrogression (Lorenzo-1 and Rules Viaduct landslides) or catastrophic slope failure (El Arrecife Landslide), respectively. We also observed how changes in the water level of the reservoir influence the landslide’s behaviour. Additionally, we were able to monitor the stability of the Rules Dam as well as detect the deformation of a highway viaduct that crosses a branch of the reservoir. Overall, we consider that other techniques must be applied to continue monitoring the movements, especially in the El Arrecife Landslide, in order to avoid future structural damages and fatalities. Full article

(This article belongs to the Special Issue Landslide Monitoring, Susceptibility, Hazard Assessment and Prediction with Remotely Sensed Big Data)

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31 pages, 14150 KiB

Open AccessEditor’s ChoiceArticle

Land-Cover Changes to Surface-Water Buffers in the Midwestern USA: 25 Years of Landsat Data Analyses (1993–2017)

by Tedros M. Berhane, Charles R. Lane, Samson G. Mengistu, Jay Christensen, Heather E. Golden, Shi Qiu, Zhe Zhu and Qiusheng Wu

Remote Sens. 2020, 12(5), 754; https://doi.org/10.3390/rs12050754 - 25 Feb 2020

Cited by 14 | Viewed by 4973

Abstract

To understand the timing, extent, and magnitude of land use/land cover (LULC) change in buffer areas surrounding Midwestern US waters, we analyzed the full imagery archive (1982–2017) of three Landsat footprints covering ~100,000 km². The study area included urbanizing Chicago, Illinois [...] Read more.

To understand the timing, extent, and magnitude of land use/land cover (LULC) change in buffer areas surrounding Midwestern US waters, we analyzed the full imagery archive (1982–2017) of three Landsat footprints covering ~100,000 km². The study area included urbanizing Chicago, Illinois and St. Louis, Missouri regions and agriculturally dominated landscapes (i.e., Peoria, Illinois). The Continuous Change Detection and Classification algorithm identified 1993–2017 LULC change across three Landsat footprints and in 90 m buffers for ~110,000 surface waters; waters were also size-binned into five groups for buffer LULC change analyses. Importantly, buffer-area LULC change magnitude was frequently much greater than footprint-level change. Surface-water extent in buffers increased by 14–35x the footprint rate and forest decreased by 2–9x. Development in buffering areas increased by 2–4x the footprint-rate in Chicago and Peoria area footprints but was similar to the change rate in the St. Louis area footprint. The LULC buffer-area change varied in waterbody size, with the greatest change typically occurring in the smallest waters (e.g., <0.1 ha). These novel analyses suggest that surface-water buffer LULC change is occurring more rapidly than footprint-level change, likely modifying the hydrology, water quality, and biotic integrity of existing water resources, as well as potentially affecting down-gradient, watershed-scale storages and flows of water, solutes, and particulate matter. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing of Land Surface Change)

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40 pages, 3499 KiB

Open AccessFeature PaperEditor’s ChoiceReview

The Spatial and Spectral Resolution of ASTER Infrared Image Data: A Paradigm Shift in Volcanological Remote Sensing

by Michael S. Ramsey and Ian T.W. Flynn

Remote Sens. 2020, 12(4), 738; https://doi.org/10.3390/rs12040738 - 23 Feb 2020

Cited by 24 | Viewed by 7774

Abstract

During the past two decades, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on the Terra satellite has acquired nearly 320,000 scenes of the world’s volcanoes. This is ~10% of the data in the global ASTER archive. Many of these scenes [...] Read more.

During the past two decades, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on the Terra satellite has acquired nearly 320,000 scenes of the world’s volcanoes. This is ~10% of the data in the global ASTER archive. Many of these scenes captured volcanic activity at never before seen spatial and spectral scales, particularly in the thermal infrared (TIR) region. Despite this large archive of data, the temporal resolution of ASTER is simply not adequate to understand ongoing eruptions and assess the hazards to local populations in near real time. However, programs designed to integrate ASTER into a volcanic data sensor web have greatly improved the cadence of the data (in some cases, to as many as 3 scenes in 48 h). This frequency can inform our understanding of what is possible with future systems collecting similar data on the daily or hourly time scales. Here, we present the history of ASTER’s contributions to volcanology, highlighting unique aspects of the instrument and its data. The ASTER archive was mined to provide statistics including the number of observations with volcanic activity, its type, and the average cloud cover. These were noted for more than 2000 scenes over periods of 1, 5 and 20 years. Full article

(This article belongs to the Special Issue ASTER 20th Anniversary)

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20 pages, 4249 KiB

Open AccessEditor’s ChoiceArticle

A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery

by Alejandra Mora-Soto, Mauricio Palacios, Erasmo C. Macaya, Iván Gómez, Pirjo Huovinen, Alejandro Pérez-Matus, Mary Young, Neil Golding, Martin Toro, Mohammad Yaqub and Marc Macias-Fauria

Remote Sens. 2020, 12(4), 694; https://doi.org/10.3390/rs12040694 - 20 Feb 2020

Cited by 63 | Viewed by 16251

Abstract

Giant kelp (Macrocystis pyrifera) is the most widely distributed kelp species on the planet, constituting one of the richest and most productive ecosystems on Earth, but detailed information on its distribution is entirely missing in some marine ecoregions, especially in the [...] Read more.

Giant kelp (Macrocystis pyrifera) is the most widely distributed kelp species on the planet, constituting one of the richest and most productive ecosystems on Earth, but detailed information on its distribution is entirely missing in some marine ecoregions, especially in the high latitudes of the Southern Hemisphere. Here, we present an algorithm based on a series of filter thresholds to detect giant kelp employing Sentinel-2 imagery. Given the overlap between the reflectances of giant kelp and intertidal green algae (Ulvophyceae), the latter are also detected on shallow rocky intertidal areas. The kelp filter algorithm was applied separately to vegetation indices, the Floating Algae Index (FAI), the Normalised Difference Vegetation Index (NDVI), and a novel formula (the Kelp Difference, KD). Training data from previously surveyed kelp forests and other coastal and ocean features were used to identify reflectance threshold values. This procedure was validated with independent field data collected with UAV imagery at a high spatial resolution and point-georeferenced sites at a low spatial resolution. When comparing UAV with Sentinel data (high-resolution validation), an average overall accuracy ≥ 0.88 and Cohen’s kappa ≥ 0.64 coefficients were found in all three indices for canopies reaching the surface with extensions greater than 1 hectare, with the KD showing the highest average kappa score (0.66). Measurements between previously surveyed georeferenced points and remotely-sensed kelp grid cells (low-resolution validation) showed that 66% of the georeferenced points had grid cells indicating kelp presence within a linear distance of 300 m. We employed the KD in our kelp filter algorithm to estimate the global extent of giant kelp and intertidal green algae per marine ecoregion and province, producing a high-resolution global map of giant kelp and intertidal green algae, powered by Google Earth Engine. Full article

(This article belongs to the Special Issue Drone-Based Ecological Conservation)

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