Canopy Scale High-Resolution Forest Biophysical Parameter (LAI, fAPAR, and fCover) Retrieval Through Machine Learning and Cloud Computation Approach

Paper


Bandopadhyay, Subhajit, Barnali, Das, Sánchez, Alexander Cotrina, Banerjee, Sankar Prasad, Banerjee, Bikram P. and Ghosh, Subhasis. 2023. "Canopy Scale High-Resolution Forest Biophysical Parameter (LAI, fAPAR, and fCover) Retrieval Through Machine Learning and Cloud Computation Approach." 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS). Hyderabad, India 27 - 29 Jan 2023 Hyderabad, India. https://doi.org/10.1109/MIGARS57353.2023.10064558
Paper/Presentation Title

Canopy Scale High-Resolution Forest Biophysical Parameter (LAI, fAPAR, and fCover) Retrieval Through Machine Learning and Cloud Computation Approach

Presentation TypePaper
AuthorsBandopadhyay, Subhajit, Barnali, Das, Sánchez, Alexander Cotrina, Banerjee, Sankar Prasad, Banerjee, Bikram P. and Ghosh, Subhasis
Journal or Proceedings TitleProceedings of 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)
Journal Citation1, pp. 1-4
Number of Pages4
Year2023
Place of PublicationHyderabad, India
Digital Object Identifier (DOI)https://doi.org/10.1109/MIGARS57353.2023.10064558
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/10064558
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/10064385/proceeding
Conference/Event2023 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.

KeywordsForest; LAI; Random Forest; Biophysical; India
ANZSRC Field of Research 2020401302. Geospatial information systems and geospatial data modelling
401304. Photogrammetry and remote sensing
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Byline AffiliationsUniversity 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
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