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Multi-platform and Multi-scale Forest Inventory from Remote Sensing Perspectives

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 543

Special Issue Editor


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Guest Editor
School of Aviation and Transportation Technology, Purdue Polytechnic Institute, Purdue University, West Lafayette, IN 47907, USA
Interests: unmanned aerial systems; geospatial data collection and analysis; unmanned aerial system remote sensing applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remotely Piloted Aircraft (RPAs), commonly referred to as drones, have firmly established themselves as a valid remote sensing platform. These platforms, capable of flying on demand and in often otherwise inaccessible environments, have proven themselves a niche data product when a high spatial scale is required by the researcher. While some may see RPAs completely replacing the need for other traditional remote sensing platforms such as satellites and traditional fixed-wing aircraft, past and present research trends reveal that remote sensing research continues at multiple scales, and employs multiple forms of remote sensing platforms. Forestry, and forest inventory research, by its very nature is a multi-scale endeavor that is intimately associated with the utilization of remotely sensed data to match the needs of the research, whether that involves assessing thousands of hectares at the landscape level, or examining individual species at the stand level. Increasingly, foresters are looking to assess forest health and inventories using a scalable approach, employing a wide array of platforms ranging from RPAs, ultra-lights, to satellite technology, which may all be applied in conjunction with one another.

This Special Issue of Remote Sensing seeks to compile a collection of studies engaging in forestry research, particularly those in the realm of forest inventory that utilize multi-platform and multi-scaled approaches. This Special Issue welcomes the submission of papers that are inclusive and present unique approaches with regard to a combination of platforms employed at multiple scales. The scope of this Special Issue is vast, but contributions ought to focus on the components of various remote sensing platforms and sensors at multiple scales, and address technological and regulatory challenges in utilizing UAS for forest inventory over large areas. Other topics might include the challenges and solutions involved in utilizing multiple forms of sensor technologies at varying scales. Suggested article topics include, but are not limited to, the following:

  • Forestry health
  • Forest inventory
  • Remote sensor technology
  • RPA platform regulatory challenges
  • Unique rs platform approaches (ultra-light)
  • Multi remote sensing platform studies
  • Multi-scale forest inventory practices
  • Precision/accuracy approaches

Prof. Dr. Joseph Hupy
Guest Editor

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

  • UAS
  • RPA
  • drone
  • satellite
  • photogrammetry
  • forest inventory
  • forest health
  • biomass
  • stand count

Published Papers (1 paper)

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Research

27 pages, 6641 KiB  
Article
Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data
by Rula Sa, Yonghui Nie, Sergey Chumachenko and Wenyi Fan
Remote Sens. 2024, 16(12), 2250; https://doi.org/10.3390/rs16122250 - 20 Jun 2024
Viewed by 307
Abstract
Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. [...] Read more.
Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated. Full article
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