Canopy Scale High-Resolution Forest Biophysical Parameter (LAI, fAPAR, and fCover) Retrieval Through Machine Learning and Cloud Computation Approach
Paper
Paper/Presentation Title | Canopy Scale High-Resolution Forest Biophysical Parameter (LAI, fAPAR, and fCover) Retrieval Through Machine Learning and Cloud Computation Approach |
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Presentation Type | Paper |
Authors | Bandopadhyay, Subhajit, Barnali, Das, Sánchez, Alexander Cotrina, Banerjee, Sankar Prasad, Banerjee, Bikram P. and Ghosh, Subhasis |
Journal or Proceedings Title | Proceedings of 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS) |
Journal Citation | 1, pp. 1-4 |
Number of Pages | 4 |
Year | 2023 |
Place of Publication | Hyderabad, India |
Digital Object Identifier (DOI) | https://doi.org/10.1109/MIGARS57353.2023.10064558 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10064558 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10064385/proceeding |
Conference/Event | 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS) |
Event Details | 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS) Delivery In person Event Date 27 to end of 29 Jan 2023 Event Location Hyderabad, India |
Abstract | High-resolution Forest biophysical parameter estimation is crucial to understand forest structural and functional variability. Moreover, mapping high-resolution biophysical products is significant to capture accurate forest carbon fluxes and understand seasonal variability. The existing biophysical products are coarse in spatial resolution and unable to capture intra-annual variability. In this study, we proposed a random forest machine learning approach embedded in Google Earth Engine to retrieve three major forest biophysical parameters. The training samples were distributed in a 70:30 ratio for model training and validation. The outcome of the work shows promising results that hold a good agreement with SNAP-derived biophysical variables, whereas the agreement is moderate-to-poor for MODIS and VIIRS biophysical products. As shown in the model, NIR performs as the most sensitive band for forest biophysical estimation. We believe our proposed approach would significantly support the existing methods for improved high-resolution forest biophysical component estimation. |
Keywords | Forest; LAI; Random Forest; Biophysical; India |
ANZSRC Field of Research 2020 | 401302. Geospatial information systems and geospatial data modelling |
401304. Photogrammetry and remote sensing | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Southampton, United Kingdom |
Indian Institute Of Technology Delhi (IITD), India | |
University of Campania Luigi Vanvitelli, Italy | |
iMerit Technology Services, India | |
Agriculture Victoria | |
Auburn University, United States |
https://research.usq.edu.au/item/z307z/canopy-scale-high-resolution-forest-biophysical-parameter-lai-fapar-and-fcover-retrieval-through-machine-learning-and-cloud-computation-approach
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