Digital hemispherical photographs and Sentinel-2 multi-spectral imagery for mapping leaf area index at regional scale over a tropical deciduous forest

Tropical Ecology - Trang 1-13 - 2024
Mukunda Dev Behera1, J. S. R. Krishna1, Somnath Paramanik1, Shubham Kumar1, Soumit K. Behera2, Sonik Anto2, Shiv Naresh Singh2, Anil Kumar Verma2, Saroj K. Barik2, Manas Ranjan Mohanta1,3, Sudam Charan Sahu3, Chockalingam Jeganathan4, Prashant K. Srivastava5, Biswajeet Pradhan6
1Spatial Analysis and Modelling Laboratory, Centre for Oceans, Rivers, Atmosphere, and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur, India
2Plant Ecology and Climate Change Science Division, CSIR-National Botanical Research Institute, Lucknow, India
3Department of Botany, Maharaja Sriram Chandra Bhanja Deo University, Baripada, India
4Department of Remote Sensing, Birla Institute of Technology, Mesra, India
5Institute of Environment & Sustainable Development, Banaras Hindu University, Varanasi, India
6Centre for Advanced Modelling and Geospatial Information Systems, School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia

Tóm tắt

The leaf area index (LAI) provides valuable input for modeling climate and ecosystem processes. However, ground-based observations are necessitated across various phenophases from dense tropical forests for a better understanding in terms of their contribution to carbon fixation. In this study, Digital Hemispherical Photography (DHP) was used for LAI observation from Similipal Biosphere Reserve, and to predict high-resolution LAI using Random Forest Machine Learning approach. Observations were taken from ninety-three Elementary sampling units (ESUs) corresponding to the beginning and end of leaf fall seasons across moist deciduous, dry deciduous, and semi-evergreen forests. LAI demonstrated high values for dry deciduous, followed by semi-evergreen and moist deciduous forests for the start of the leaf fall season, whereas moist deciduous forests demonstrated high values during the end of the leaf fall season. Satellite-based spectral reflectance bands of Sentinel-2 and vegetation indices (VIs) were used as predictor variables, wherein the band-7, band-8, band-12, enhanced vegetation index (EVI), and Red-edge based EVI were evaluated as the most dominant responsive variables for LAI estimation. Random Forest (RF) model provided good accuracy (R2 = 0.64, RMSE = 0.62) with observed DHP-based LAI. However, a comparison of RF model-based predicted LAI with global LAI products (MOD15A2H and VNP15A2H) provided a moderate correlation. Such studies demonstrate the potential of site or region-specific case studies to evaluate coarser-resolution global LAI products for possible improvement.

Tài liệu tham khảo

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