Special Issue "Remote Sensing Data Fusion for Mapping Ecosystem Dynamics"

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

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editors

Dr. Carlos Alberto Silva
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Guest Editor
1. Department of Geographical Sciences, University of Maryland, College Park, MD, USA; 2. School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
Interests: LiDAR and hyperspectral remote sensing; tropical forest structure and ecology; industrial forest plantations; algorithms and tools development; data integration and change detection
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Dr. Danilo Roberti Alves de Almeida
Website
Guest Editor
Department of Forest Sciences, University of São Paulo, “Luiz de Queiroz” College of Agriculture (USP/ESALQ), Piracicaba, SP, Brazil
Interests: tropical forest; forest restoration; forest ecology; forest management; remote sensing; Lidar and hyperspectral remote sensing; unmanned aerial vehicles and forest inventory
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Dr. Eben North Broadbent
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Guest Editor
Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, USA
Interests: unmanned aerial vehicles (UAVs); forest ecology; airborne sensors; GatorEye
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Dr. Rubén Valbuena
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Guest Editor
School of Natural Sciences, Bangor University, Bangor, UK
Interests: forest ecology; remote sensing; LiDAR; forest inventory; tree size scaling theories; forest structure; competition and dominance; modelling; data fusion
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Prof. Dr. Carine Klauberg
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Guest Editor
Federal University of São João Del Rei – UFSJ, Sete Lagoas, MG, -35701-970, Brazil
Interests: forests and nontimber forest products; tropical forest ecology; remote sensing; LiDAR; forest inventory; wildfire; forest inventory; data integration; change detection; fire ecology and fire behavior modeling
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Dr. Adrian Cardil
Website
Guest Editor
Department of Vegetal Production and Forestry Science, Universitat de Lleida, Spain
Interests: Wildfire; satellite remote sensing; extreme weather events; fire management; fire ecology; global change; burn severity.
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Special Issue Information

Dear Colleagues,

We are entering an exciting era for active remote sensing of forests. Products generated from spaceborne missions, like GEDI and ICEsat-2, are starting to enable a richer understanding of terrestrial processes and ecology. However, there is a pressing need to develop and test new approaches and frameworks for efficiently combining these current and upcoming datasets. For instance, GEDI and ICESat-2 present an opportunity to obtain global-scale coverage of forest structure, but use different sampling strategies for collecting data and therefore the fusion of these datasets with wall-to-wall data, such as Landsat 8 OLI and Sentinel 2A, as well those that will be provided by the NISAR mission, will be essential for high-resolution mapping of forest attributes across landscapes.

The purpose of this Special Issue is to bring together state-of-the-art remote sensing data fusion approaches for mapping ecosystem dynamics. Review papers and research contributions are suitable. In particular, contributions covering the following subtopics are welcome:

  • Calibration of satellite data from lidar (airborne and UAV-borne) and photogrammetry 3-D derived point cloud data.
  • Machine learning and deep learning approaches for estimating forest structure attributes. Analysis of spatial and temporal changes of vegetation and associated attributes. 
  • Use of remote sensing fusion data to assess forest structure. For instance, fire damage, logging, and dynamics at the landscape scale. 
  • Remote sensing data sources to estimate fire progression and burned area. Fire simulation and fire behavior analysis based on remote sensing data.
  • New methodologies to estimate forest structure by remote sensing fusion data.
  • Estimation of the wild fauna by remote sensing fusion data.
  • Synergies among platforms (airborne, terrestrial, and spaceborne) for forest inventory and monitoring.
  • Spatial extrapolation methods, sampling design, and error propagation studies using remote sensing fusion data
Dr. Carlos Alberto Silva
Dr. Danilo Roberti Alves de Almeida
Dr. Eben North Broadbent 
Dr. Ruben Valbuena 
Dr. Carine Klauberg 
Dr. Adrian Cardil
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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 papers will be 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 2200 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

  • Remote Sensing
  • Mapping
  • Lidar
  • GEDI
  • ICESat-2
  • NISAR
  • Degradation
  • REDD+
  • Fire
  • Tools
  • Tropical forest
  • Forest restoration
  • Forest Plantation
  • Forest management
  • UAV
  • Planet Scope
  • Rapideye
  • Skysat

Published Papers (1 paper)

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Research

Open AccessArticle
Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data
Remote Sens. 2020, 12(9), 1498; https://doi.org/10.3390/rs12091498 - 08 May 2020
Abstract
Accurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) [...] Read more.
Accurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) in a selectively logged tropical forest in eastern Amazonia. Specifically, we compare results from a suite of different modelling methods with extensive field data. The calibration AGB values were derived from 85 square field plots sized 50 × 50 m field plots established in 2014 and which were estimated using airborne LiDAR data acquired in 2012, 2014, and 2017. LiDAR-derived metrics were selected based upon Principal Component Analysis (PCA) and used to estimate AGB stock and change. The statistical approaches were: ordinary least squares regression (OLS), and nine machine learning approaches: random forest (RF), several variations of k-nearest neighbour (k-NN), support vector machine (SVM), and artificial neural networks (ANN). Leave-one-out cross-validation (LOOCV) was used to compare performance based upon root mean square error (RMSE) and mean difference (MD). The results show that OLS had the best performance with an RMSE of 46.94 Mg/ha (19.7%) and R² = 0.70. RF, SVM, and ANN were adequate, and all approaches showed RMSE ≤54.48 Mg/ha (22.89%). Models derived from k-NN variations all showed RMSE ≥64.61 Mg/ha (27.09%). The OLS model was thus selected to map AGB across the time-series. The mean (±sd—standard deviation) predicted AGB stock at the landscape level was 229.10 (±232.13) Mg/ha in 2012, 258.18 (±106.53) in 2014, and 240.34 (sd ± 177.00) Mg/ha in 2017, showing the effect of forest growth in the first period and logging in the second period. In most cases, unlogged areas showed higher AGB stocks than logged areas. Our methods showed an increase in AGB in unlogged areas and detected small changes from reduced-impact logging (RIL) activities occurring after 2012. We also detected that the AGB increase in areas logged before 2012 was higher than in unlogged areas. Based on our findings, we expect our study could serve as a basis for programs such as REDD+ and assist in detecting and understanding AGB changes caused by selective logging activities in tropical forests. Full article
(This article belongs to the Special Issue Remote Sensing Data Fusion for Mapping Ecosystem Dynamics)
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