Abstract
Forest operates as sink–source of the atmospheric CO2; hence, they form the integral part of terrestrial global carbon cycle. Biomass and primary productivity are the crucial dynamic biophysical parameters for understanding the ecosystem functioning in any forested landscape. The present study was performed in Aglar watershed situated in outer Indian Himalayan range. We performed geospatial modeling of plot-level field data on forest above ground biomass (AGB) by correlating it with textural, spectral and linearly transformed variables retrieved from Landsat 8 OLI data using of random forest (RF) machine learning algorithm. We also applied recursive feature elimination function (RFE) to obtain the variables contributing most in AGB prediction. A combination of 24 among 96 variables was identified as the most effective variables. Ground-based AGB varied from 62.54 to 470.98 Mg ha−1, whereas RF-modeled AGB ranged from 48.5 to 407.73 Mg ha−1. Results indicated that RFE selected variables were able to predict AGB with R2 of 0.84, RMSE of 42.03 Mg ha−1, MAE of 34.68 and %RMSE of 19.49 Mg ha−1 which was accepted considering the terrain complexity. Light use efficiency approach was used to model monthly NPP using Landsat 8 OLI data. The results indicated that Quercus mixed forest had highest carbon assimilation (95,148,073.9 gC) followed by Pinus roxburghii (1,863,187.7 gC), Cedrus deodara (5,752,954.1 gC) and mixed forest (2,634,737.1 gC). The seasonal pattern of NPP indicated that its strike peaked in October, whereas December and January were the lean months, suggesting that NPP is governed by climatic factors, viz. PAR, precipitation and temperature. Such watershed-level study in complex Himalayan terrain would help to understand complex biogeochemical processes in basins and ecosystem services provided by the forests.
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It is a Ph.D. research work. The data can be shared with terms and conditions with persons with valid research interests for verification and validation.
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Acknowledgements
Authors are grateful to Dr. Prakash Chauhan Director, Indian Institute of Remote Sensing, ISRO, for time to time encouragements and facilities. We thank Dr. Hitendra Padalia Head, Forestry & Ecology Department Indian Institute of Remote Sensing for his cooperation and administrative support. We are thankful to Dr. S. P. Aggarwal Head Water Resource Department and Dr. Praveen Thakur Senior Scientist Water Resource Department, IIRS, for maintaining AWS and providing meteorological data. We are also thankful to the authorities of Forest department of Uttarakhand, more specially to Chief Conservator of Forest, Uttarakhand, and Divisional Forest Officer of Mussoorie Forest Division for allowing us to conduct this study. We thank Range officers of the concerned ranges for their extended cooperation while collecting the field data. Srishti Gwal is also thankful to University Grant Commission for providing funds for this project. We are thankful to the anonymous reviewers for their valuable comments which have enhanced the worth of the work.
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The present work is funded by University Grant Commission, Government of India (Grant No. 21/12/2014(ii)EU-V).
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Srishti Gwal has performed data compilation, simulation, analysis and has drafted the manuscript. Sarnam Singh and Stutee Gupta contributed in reviewing and editing the manuscript. Shikha Anand supported during field data accumulation of this project.
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Gwal, S., Singh, S., Gupta, S. et al. Understanding forest biomass and net primary productivity in Himalayan ecosystem using geospatial approach. Model. Earth Syst. Environ. 6, 2517–2534 (2020). https://doi.org/10.1007/s40808-020-00844-4
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DOI: https://doi.org/10.1007/s40808-020-00844-4