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Estimation of Forest Biomass from SAR

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 9983

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Dr. Maciej J. Soja

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Guest Editor

MJ Soja Consulting, West Hobart, Tasmania, Australia and University of Tasmania, Hobart, Tasmania, Australia
Interests: forest mapping; forest change detection; forest biomass estimation; remote sensing; synthetic aperture radar (SAR); interferometry; tomography; electromagnetic modeling

Dr. Henrik J. Persson

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Forest Resource Management, Swedish University of Agricultural Sciences, Umea, Sweden
Interests: airborne laser scanning; LiDAR; radar; multi-spectral imagery; forest mapping; forest disturbances
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Dr. Stefano Tebaldini

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Politecnico di Milano, Department of Information, Electronics, and Bioengineering, Milan, Italy
Interests: radar remote sensing; diffraction tomography; inverse problems; EM imaging; SAR processing; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shaun Quegan

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School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
Interests: carbon cycle measurements and modeling; data assimilation; forest dynamics; forest observations from space; forest biomass; SAR

Prof. Dr. Lars M. H. Ulander

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Chalmers University of Technology, Department of Space, Earth and Environment, Gothenburg, Sweden
Interests: radar; SAR; signal processing; interferometry; tomography; electromagnetic modeling; forestry; biomass

Special Issue Information

Dear Colleagues,

Aboveground biomass (AGB) of forests is an essential climate variable and maps of its global distribution are urgently needed for accurate and reliable carbon cycle and climate modeling, carbon accounting, and forest management. Large-scale measurement of AGB is challenging due to the complex nature of forests: it depends not only on the geometrical structure and physical properties of trees and vegetation, but also on ground topography, moisture content and wood density, which are not possible to estimate with current remote sensing methods.

Synthetic aperture radar (SAR) has a demonstrated sensitivity to biomass. This potential led to the European Space Agency’s selection of BIOMASS, a P-band (432–438 MHz) SAR, for its 7^th Earth Explorer mission. The mission aims to unravel the global variability of this essential—yet poorly known—property of the biosphere.

We would like to invite you to participate in a Special Issue of Remote Sensing focusing on forest biomass estimation from SAR. We wish to cover a wide range of approaches, present the latest results, and discuss new findings. In order to provide a comprehensive overview of the state of the art in this field, we encourage contributions using different methods and SAR imaging modes (including polarimetry, interferometry, and tomography), different frequency bands and sensor platforms, and addressing a broad range of forest biomes.

In order to maintain the high quality of published articles, the received manuscripts will undergo scrutiny through a peer review process and 10–15 excellent papers presenting novel methods and exciting results will be published. The submission deadline is 30 June 2020.

Dr. Maciej J. Soja
Dr. Henrik J. Persson
Dr. Stefano Tebaldini
Prof. Dr. Shaun Quegan
Prof. Dr. Lars M. H. Ulander
Guest Editors

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 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

forests
aboveground biomass (AGB)
mapping
estimation
synthetic aperture radar (SAR)
interferometry
tomography
polarimetry

Published Papers (2 papers)

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22 pages, 4630 KiB

Open AccessArticle

Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels

by Charlie Marshak, Marc Simard, Laura Duncanson, Carlos Alberto Silva, Michael Denbina, Tien-Hao Liao, Lola Fatoyinbo, Ghislain Moussavou and John Armston

Remote Sens. 2020, 12(12), 2048; https://doi.org/10.3390/rs12122048 - 25 Jun 2020

Cited by 9 | Viewed by 4199

Abstract

We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions. Full article

(This article belongs to the Special Issue Estimation of Forest Biomass from SAR)

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28 pages, 11109 KiB

Open AccessArticle

The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation

by Francesco Banda, Davide Giudici, Thuy Le Toan, Mauro Mariotti d’Alessandro, Kostas Papathanassiou, Shaun Quegan, Guido Riembauer, Klaus Scipal, Maciej Soja, Stefano Tebaldini, Lars Ulander and Ludovic Villard

Remote Sens. 2020, 12(6), 985; https://doi.org/10.3390/rs12060985 - 19 Mar 2020

Cited by 19 | Viewed by 4910

Abstract

BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements. Full article

(This article belongs to the Special Issue Estimation of Forest Biomass from SAR)

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