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Remote Sensing of Ecosystem Structure and Function Dynamics Due to Climate Change and Human Activities

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 4344

Special Issue Editors

Dr. Jun Zhai

E-Mail Website
Guest Editor
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People's Republic of China, Bei**g 100094, China
Interests: ecosystem assessment and management; land use and cover change; geographic information system; satellite image analysis
Dr. Yuanwei Qin

grade E-Mail Website
Guest Editor
Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019, USA
Interests: forest resources and ecosystem; forest carbon; agriculture; environmental remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Cao

E-Mail Website
Guest Editor
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Bei**g 100101, China
Interests: ecosystem services; geographic information system; ecosystem monitoring

Special Issue Information

Dear Colleagues,

The natural environment is being shaped and transformed by climate change and human activities, causing consequential alterations in ecosystem structure and function. These changes exhibit significant temporal and spatial variations in terms of their processes, rates, and extents. It is imperative to establish quantitative assessment indicators and technical methodologies to accurately characterize and comprehend the patterns and dynamics of ecological changes. This scientific foundation serves as a guide for making informed decisions concerning ecological protection, restoration, and management. The present era witnesses the emergence of multi-platform remote sensing technology, encompassing active and passive sensors integrated into satellites, unmanned aerial vehicles (UAVs), observation towers, and ground-based mobile devices. This technology has attained the capability to rapidly and accurately acquire key parameters pertaining to ecosystem structure and processes across continuous spatiotemporal scales. Consequently, it offers abundant data sources and diverse technical approaches for monitoring and assessing ecological status changes at various scales. This Special Issue is intended to provide a platform for academic exchange regarding progress in assessing ecosystem structure and function changes due to climate change and human activities, utilizing remote sensing technology. Specifically, studies including, but not limited to, the following topics are welcome:

  • Ecological remote sensing assessment models and methods;
  • The inversion of key parameters of multi-scale ecosystem structure and processes through remote sensing;
  • The application of remote sensing technology in comprehensive assessment of ecosystem patterns, quality, and functions;
  • Ecological space remote sensing monitoring and assessment;
  • Ecological protection, restoration, and management;
  • The impacts of climate change on ecosystems.

Dr. Jun Zhai
Dr. Yuanwei Qin
Dr. Wei Cao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ecosystem structure
  • ecosystem function
  • ecological protection
  • biodiversity
  • nature reserves
  • carbon sequestration
  • climate change

Published Papers (5 papers)

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Research

19 pages, 9937 KiB  
Article
Evolution and Spatiotemporal Response of Ecological Environment Quality to Human Activities and Climate: Case Study of Hunan Province, China
by Jiawei Hui and Yongsheng Cheng
Remote Sens. 2024, 16(13), 2380; https://doi.org/10.3390/rs16132380 - 28 Jun 2024
Viewed by 217
Abstract
Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must [...] Read more.
Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–**angtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystem Structure and Function Dynamics Due to Climate Change and Human Activities)
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31 pages, 62358 KiB  
Article
Comprehensive Ecological Risk Changes and Their Relationship with Ecosystem Services of Alpine Grassland in Gannan Prefecture from 2000–2020
by Zhan** Ma, **long Gao, Tiangang Liang, Zhibin He, Senyao Feng, Xuanfan Zhang and Dongmei Zhang
Remote Sens. 2024, 16(12), 2242; https://doi.org/10.3390/rs16122242 - 20 Jun 2024
Viewed by 447
Abstract
Alpine grassland is one of the most fragile and sensitive ecosystems, and it serves as a crucial ecological security barrier on the Tibetan Plateau. Due to the combined influence of climate change and human activities, the degradation of the alpine grassland in Gannan [...] Read more.
Alpine grassland is one of the most fragile and sensitive ecosystems, and it serves as a crucial ecological security barrier on the Tibetan Plateau. Due to the combined influence of climate change and human activities, the degradation of the alpine grassland in Gannan Prefecture has been increasing recent years, causing increases in ecological risk (ER) and leading to the grassland ecosystem facing unprecedented challenges. In this context, it is particularly crucial to construct a potential grassland damage index (PGDI) and assessment framework that can be used to effectively characterize the damage and risk to the alpine grassland ecosystem. This study comprehensively uses multi-source data to construct a PGDI based on the grassland resilience index, landscape ER index, and grass–livestock balance index. Thereafter, we proposed a feasible framework for assessing the comprehensive ER of alpine grassland and analyzed the responsive relationship between the comprehensive ER and comprehensive ecosystem services (ESs) of the grassland. There are four findings. The first is that the comprehensive ER of the alpine grassland in Gannan Prefecture from 2000–2020 had a low distribution in the southeast and a high distribution trend in the northwest, with medium risk (29.27%) and lower risk (27.62%) dominating. The high-risk area accounted for 4.58% and was mainly in Lintan County, the border between Diebu and Zhuoni Counties, the eastern part of **ahe County, and the southwest part of Hezuo. Second, the comprehensive ESs showed a pattern of low distribution in the northwest and high distribution in the southeast. The low and lower services accounted for only 9.30% of the studied area and were mainly distributed in the west of Maqu County and central Lintan County. Third, the Moran’s index values for comprehensive ESs and ER for 2000, 2005, 2010, 2015, and 2020 were −0.246, −0.429, −0.348, −0.320, and −0.285, respectively, thereby indicating significant negative spatial autocorrelation for all aspects. Fourth, ER was caused by the combined action of multiple factors. There are significant differences in the driving factors that affect ER. Landscape index is the first dominant factor affecting ER, with q values greater than 0.25, followed by DEM and NDVI. In addition, the interaction between diversity index and NDVI had the greatest impact on ER. Overall, this study offers a new methodological framework for the quantification of comprehensive ER in alpine grasslands. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystem Structure and Function Dynamics Due to Climate Change and Human Activities)
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19 pages, 7159 KiB  
Article
Comparison between Satellite Derived Solar-Induced Chlorophyll Fluorescence, NDVI and kNDVI in Detecting Water Stress for Dense Vegetation across Southern China
by Chunxiao Wang, Lu Liu, Yuke Zhou, **aojuan Liu, Jiapei Wu, Wu Tan, Chang Xu and **aoqing **ong
Remote Sens. 2024, 16(10), 1735; https://doi.org/10.3390/rs16101735 - 14 May 2024
Cited by 1 | Viewed by 721
Abstract
In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing [...] Read more.
In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI’s capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughts. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystem Structure and Function Dynamics Due to Climate Change and Human Activities)
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20 pages, 5201 KiB  
Article
Impacts of Extreme Precipitation and Diurnal Temperature Events on Grassland Productivity at Different Elevations on the Plateau
by Hexuan An, Jun Zhai, **aoyan Song, Gang Wang, Yu Zhong, Ke Zhang and Wenyi Sun
Remote Sens. 2024, 16(2), 317; https://doi.org/10.3390/rs16020317 - 12 Jan 2024
Cited by 1 | Viewed by 882
Abstract
The impact of extreme climate events on vegetation growth and ecosystem function has garnered widespread attention, particularly in plateau regions, which are facing increasingly severe environmental pressures. This study employs the Events Coincidence Analysis (ECA) method to examine the impacts of extreme climate [...] Read more.
The impact of extreme climate events on vegetation growth and ecosystem function has garnered widespread attention, particularly in plateau regions, which are facing increasingly severe environmental pressures. This study employs the Events Coincidence Analysis (ECA) method to examine the impacts of extreme climate events on the Net Primary Productivity (NPP) of vegetation in plateau regions. Specifically, we focus on the unique phenomenon of asymmetric daytime and nighttime warming and evaluate the compounding effect of extreme precipitation with extreme temperature events. The results indicate that grassland NPP has higher overall sensitivity and Coincidence Rates (CR) to extreme precipitation events compared to extreme temperature events. Specifically, extreme drought events significantly negatively impact grassland NPP, and the sensitivity of NPP increases with the severity of extreme drought events. In lower elevations (<3200 m), grassland NPP shows a strong response to extreme precipitation events, with sensitivity decreasing with increasing elevation, especially for alpine meadows. Extreme high-temperature events contributed significantly to NPP in mid to high elevations (3000–5000 m). Compound extreme climate events amplify the average coincidence degree with NPP anomalies, with extreme precipitation events playing a major role in compound effects. The CR of compound climate events can reach above 0.6, reflected in the anomaly increase of NPP in temperate grasslands at higher elevations caused by compound events of extremely wet and extremely high temperatures. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystem Structure and Function Dynamics Due to Climate Change and Human Activities)
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22 pages, 5247 KiB  
Article
Synergistic Application of Multiple Machine Learning Algorithms and Hyperparameter Optimization Strategies for Net Ecosystem Productivity Prediction in Southeast Asia
by Chaoqing Huang, Bin Chen, Chuanzhun Sun, Yuan Wang, Junye Zhang, Huan Yang, Shengbiao Wu, Peiyue Tu, MinhThu Nguyen, Song Hong and Chao He
Remote Sens. 2024, 16(1), 17; https://doi.org/10.3390/rs16010017 - 20 Dec 2023
Viewed by 1096
Abstract
The spatiotemporal patterns and shifts of net ecosystem productivity (NEP) play a pivotal role in ecological conservation and addressing climate change. For example, by quantifying the NEP information within ecosystems, we can achieve the protection and restoration of natural ecological balance. Monitoring the [...] Read more.
The spatiotemporal patterns and shifts of net ecosystem productivity (NEP) play a pivotal role in ecological conservation and addressing climate change. For example, by quantifying the NEP information within ecosystems, we can achieve the protection and restoration of natural ecological balance. Monitoring the changes in NEP enables a more profound understanding and prediction of ecosystem alterations caused by global warming, thereby providing a scientific basis for formulating policies aimed at mitigating and adapting to climate change. The accurate prediction of NEP sheds light on the ecosystem’s response to climatic variations and aids in formulating targeted carbon sequestration policies. While traditional ecological process models provide a comprehensive approach to predicting NEP, they often require extensive experimental and empirical data, increasing research costs. In contrast, machine-learning models offer a cost-effective alternative for NEP prediction; however, the delicate balance in algorithm selection and hyperparameter tuning is frequently overlooked. In our quest for the optimal prediction model, we examined a combination of four mainstream machine-learning algorithms with four hyperparameter-optimization techniques. Our analysis identified that the backpropagation neural network combined with Bayesian optimization yielded the best performance, with an R2 of 0.68 and an MSE of 1.43. Additionally, deep-learning models showcased promising potential in NEP prediction. Selecting appropriate algorithms and executing precise hyperparameter-optimization strategies are crucial for enhancing the accuracy of NEP predictions. This approach not only improves model performance but also provides us with new tools for a deeper understanding of and response to ecosystem changes induced by climate change. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystem Structure and Function Dynamics Due to Climate Change and Human Activities)
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