Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels
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...
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doaj-bc649e6337974a488a5a12adf9283ce32020-11-25T03:55:05ZengMDPI AGRemote Sensing2072-42922020-06-01122048204810.3390/rs12122048Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale SuperpixelsCharlie Marshak0Marc Simard1Laura Duncanson2Carlos Alberto Silva3Michael Denbina4Tien-Hao Liao5Lola Fatoyinbo6Ghislain Moussavou7John Armston8Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USABiospheric Sciences Lab, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USAMinistère des Forêts, de la Mer et de l’Environnement, Libreville 3241, GabonDepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USAWe 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.https://www.mdpi.com/2072-4292/12/12/2048NISARGEDIICESat-2L-band SARALOS-2/PALSAR-2microwave remote sensing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Charlie Marshak Marc Simard Laura Duncanson Carlos Alberto Silva Michael Denbina Tien-Hao Liao Lola Fatoyinbo Ghislain Moussavou John Armston |
spellingShingle |
Charlie Marshak Marc Simard Laura Duncanson Carlos Alberto Silva Michael Denbina Tien-Hao Liao Lola Fatoyinbo Ghislain Moussavou John Armston Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels Remote Sensing NISAR GEDI ICESat-2 L-band SAR ALOS-2/PALSAR-2 microwave remote sensing |
author_facet |
Charlie Marshak Marc Simard Laura Duncanson Carlos Alberto Silva Michael Denbina Tien-Hao Liao Lola Fatoyinbo Ghislain Moussavou John Armston |
author_sort |
Charlie Marshak |
title |
Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels |
title_short |
Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels |
title_full |
Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels |
title_fullStr |
Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels |
title_full_unstemmed |
Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels |
title_sort |
regional tropical aboveground biomass mapping with l-band repeat-pass interferometric radar, sparse lidar, and multiscale superpixels |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-06-01 |
description |
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. |
topic |
NISAR GEDI ICESat-2 L-band SAR ALOS-2/PALSAR-2 microwave remote sensing |
url |
https://www.mdpi.com/2072-4292/12/12/2048 |
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