Mapping Forest Height and Aboveground Biomass by Integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India

Nandy, Subrata; Srinet, Ritika; Padalia, Hitendra

doi:10.1029/2021gl093799

Cited by 100 publications

(44 citation statements)

References 56 publications

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“…Additionally, while smart phone GPS measurements are improving with most being at best accurate to within 4 m, an analysis of GO data found an average location accuracy of 14.5 m. Given the 13 m ICESat-2 photon pulse footprint, this means that GO Trees measurements within the footprint may not be resolvable or may be misleading. Similar results have been found in other studies (Li et al 2020, Nandy et al 2020, but additional studies should expand the types and amount of tree cover.…”

Section: Using Go Trees Data With Icesat-2supporting

confidence: 90%

“…An essential component in understanding ICESat-2's performance in measuring tree height is the comparison to ground-based measurements. As an example, a recent study (Nandy et al 2020), found a 1.1 m root mean square error (a measure of the differences between values predicted by a model and the values observed), between field-measured and ICESat-2 tree heights for trees that ranged from 14.2 to 32.4 m. This error information is critical to utilizing ICESat-2 data for aboveground biomass estimates or global land cover uses (Narine et al 2020).…”

Section: Introduction 1why Measure Tree Heightmentioning

confidence: 99%

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The potential of citizen science data to complement satellite and airborne lidar tree height measurements: lessons from The GLOBE Program

Enterkine

Campbell

Kohl

et al. 2022

Environ. Res. Lett.

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The Global Learning and Observations to Benefit the Environment (GLOBE) Program is an international science, citizen science, and education program through which volunteers in participating countries collect environmental data in support of Earth system science. Using the program’s software application, GLOBE Observer, volunteers measure tree height and optional tree circumference, which may support the interpretation of NASA and other space-based satellite data such as tree height data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI) instrument. This paper describes tree heights data collected through the GLOBE Observer application and identifies sources of error in data collection. We also illustrate how the ground-based citizen science data collected in the GLOBE Observer application can be used in conjunction with ICESat-2 tree height observations from two locations in the United States: Grand Mesa, Colorado, and Greenbelt, Maryland. Initial analyses indicate that data location accuracy and the scientific relevance of data density should be considered in order to align GLOBE tree height data with satellite-based data collections. These recommendations are intended to inform the improved implementation of citizen science environmental data collection in scientific work and to document a use case of the GLOBE Trees data for the science research community.

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Section: Using Go Trees Data With Icesat-2supporting

confidence: 90%

Section: Introduction 1why Measure Tree Heightmentioning

confidence: 99%

The potential of citizen science data to complement satellite and airborne lidar tree height measurements: lessons from The GLOBE Program

Enterkine

Campbell

Kohl

et al. 2022

Environ. Res. Lett.

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“…Many studies have analyzed the spatiotemporal variations of vegetation AGB and their responses to climate change (Stegen et al, 2011 ; Shi et al, 2015 ; Castanho et al, 2020 ; Zhou et al, 2021 ). However, most of these studies focused on grassland and forest ecosystems, and only a few were conducted on marsh ecosystems (Flannigan et al, 2000 ; Shen et al, 2016 , 2022a ; Dai et al, 2021 ; Konings et al, 2021 ; Naik et al, 2021 ; Nandy et al, 2021 ; Qin et al, 2021 ; Wang et al, 2022b ). Compared with other ecosystems, marsh ecosystems have unique environmental conditions, which may lead to different influences of climate change on AGB (Wang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

et al. 2022

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The Sanjiang Plain has the greatest concentration of freshwater marshes in China. Marshes in this area play a key role in adjusting the regional carbon cycle. As an important quality parameter of marsh ecosystems, vegetation aboveground biomass (AGB) is an important index for evaluating carbon stocks and carbon sequestration function. Due to a lack of in situ and long-term AGB records, the temporal and spatial changes in AGB and their contributing factors in the marsh of Sanjiang Plain remain unclear. Based on the measured AGB, normalized difference vegetation index (NDVI), and climate data, this study investigated the spatiotemporal changes in marsh AGB and the effects of climate variation on marsh AGB in the Sanjiang Plain from 2000 to 2020. Results showed that the marsh AGB density and annual maximum NDVI (NDVImax) had a strong correlation, and the AGB density could be accurately calculated from a power function equation between NDVImax and AGB density (AGB density = 643.57 × NDVImax4.2474). According to the function equation, we found that the AGB density significantly increased at a rate of 2.47 g·C/m2/a during 2000–2020 in marshes of Sanjiang Plain, with the long-term average AGB density of about 282.05 g·C/m2. Spatially, the largest increasing trends of AGB were located in the north of the Sanjiang Plain, and decreasing trends were mainly found in the southeast of the study area. Regarding climate impacts, the increase in precipitation in winter could decrease the marsh AGB, and increased temperatures in July contributed to the increase in the marsh AGB in the Sanjiang Plain. This study demonstrated an effective approach for accurately estimating the marsh AGB in the Sanjiang Plain using ground-measured AGB and NDVI data. Moreover, our results highlight the importance of including monthly climate properties in modeling AGB in the marshes of the Sanjiang Plain.

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“…However, the LiDAR data is available as strips for the limited region for airborne missions and is discontinuous for spaceborne missions (Narine et al, 2019;Dubayah et al, 2020;Duncanson et al, 2020) that pose a grave source of uncertainty in biomass estimation. LiDAR canopy height, in combination with other remote sensing techniques, is successfully used to estimate forest AGB with high accuracy levels (Dhanda et al, 2017;Nandy et al, 2021). A combination of spectral variables from Sentinel-2 and ICESat-2 data is used to interpolate canopy height using a random forest algorithm by Nandy et al (2021) that provided an improved estimate of forest height and AGB in sub-tropical forests.…”

Section: Introductionmentioning

confidence: 99%

“…LiDAR canopy height, in combination with other remote sensing techniques, is successfully used to estimate forest AGB with high accuracy levels (Dhanda et al, 2017;Nandy et al, 2021). A combination of spectral variables from Sentinel-2 and ICESat-2 data is used to interpolate canopy height using a random forest algorithm by Nandy et al (2021) that provided an improved estimate of forest height and AGB in sub-tropical forests. However, such similar studies utilizing SAR and LiDAR data for forest AGB estimation in the species-rich Western Ghats located in the tropical regions is very limited.…”

Section: Introductionmentioning

confidence: 99%

Improving Forest Above-Ground Biomass Retrieval Using Multi-Sensor L- and C- Band SAR Data and Multi-Temporal Spaceborne LiDAR Data

Musthafa

Singh

2022

Front. For. Glob. Change

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Due to the great structural and species diversity of tropical forests and limitations of the methods used to estimate aboveground biomass, there is uncertainty in quantifying its carbon sequestration potential. Measuring carbon sequestered in the terrestrial ecosystem and monitoring its dynamics is one of the key objectives in sustainable development goals. Synthetic Aperture Radar (SAR) has evolved as a key satellite technology in measuring and monitoring terrestrial carbon sink stored as biomass in plants. This study attempts to model forest above-ground biomass (AGB) using a random forest machine-learning approach where the predictor variables are from C-band (Radarsat-2), L-band (ALOS-2/PALSAR-2), and multi-temporal spaceborne LiDAR data from the GEDI platform. Training and validation data for the machine learning approach are obtained from the field measured inventory campaigns to evaluate the modeled forest biomass accuracies. The results show that variables from L-band (HH, HV), C-band (HV), and canopy height from the GEDI LiDAR platform performed effectively to model forest AGB with the coefficient of determination (R2) of 0.81 and root mean squared error (rmse) of 19.35 Mg/ha (%rmse – 17.17). In the case of single frequency SAR data, the analysis shows that the model derived from the L-band SAR data and LiDAR performed comparably better than the combination of C-band SAR and LiDAR data with an R2 of 0.78 and rmse of 21.36 Mg/ha (%rmse – 18.94). The results, thus, demonstrate the potential of SAR data (both single frequency and multiple frequencies) in combination with GEDI LiDAR data in effectively modeling AGB over highly biodiverse tropical forest regions.

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Mapping Forest Height and Aboveground Biomass by Integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India

Cited by 100 publications

References 56 publications

The potential of citizen science data to complement satellite and airborne lidar tree height measurements: lessons from The GLOBE Program

The potential of citizen science data to complement satellite and airborne lidar tree height measurements: lessons from The GLOBE Program

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

Improving Forest Above-Ground Biomass Retrieval Using Multi-Sensor L- and C- Band SAR Data and Multi-Temporal Spaceborne LiDAR Data

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