Pasha, SV and Dadhwal, Vinay Kumar and Saketh, K
(2023)
Remote Sensing for Mango and Rubber Mapping and Characterization for Carbon Stock Estimation—Case Study of Malihabad Tehsil (UP) and West Tripura District, India.
In:
Digital Ecosystem for Innovation in Agriculture: Studies in Big Data, Vol 121.
Springer, pp. 183-200.
Full text not available from this repository.
| Abstract: |
The phytomass and soil carbon pools are the two largest pools that are directly influenced by anthropogenic activities and have large spatial heterogeneity. Orchards and plantation crops contribute significantly to terrestrial C-pools but have not received adequate attention. Remote sensing (RS) with vegetation discrimination and monitoring capacity is critical to describe spatial C-pool variability. The natural forest in India has been significantly disturbed by establishing large-scale commercial and horticulture crops which contributes significantly to the current forest and tree estimate of 81Mha by the Forest Survey of India (FSI). This study estimated the area under mango and rubber in two contrasting sites, i.e. Malihabad (Uttar Pradesh) and West Tripura (Tripura) districts. We used Sentinel-2 data and machine learning algorithms to discriminate target tree species. Multi-sensor-based geophysical product of biomass was analysed for aboveground biomass (AGB). Spaceborne Lidar data from the GEDI sensor was analysed to characterize the tree height. Additional characterization of tree density was carried out by counting tree canopies on high-resolution imageries. The results of phytomass and soil pools are comparable to published estimates under similar agroclimatic settings. The demonstrated approach of simultaneous high-resolution phytomass and soil mapping with geospatial techniques significantly enhances the capability to monitor and model terrestrial carbon pools in India. |
| Item Type: |
Book Chapter
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| Subjects: |
School of Natural and Engineering Sciences > Ecology
School of Natural and Engineering Sciences > Environment |
| Divisions: |
Schools > Natural Sciences and Engineering |
| Date Deposited: |
05 Jul 2024 06:45 |
| Last Modified: |
05 Jul 2024 06:45 |
| Official URL: |
https://link.springer.com/chapter/10.1007/978-981-... |
| Related URLs: |
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| Funders: |
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| Projects: |
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| DOI: |
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| URI: |
http://eprints.nias.res.in/id/eprint/2754 |
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