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Disentangle the short-term forest degradation over most fire-affected parts of Western Himalaya, India

Disentangle the short-term forest degradation over most fire-affected parts of Western Himalaya, India
Disentangle the short-term forest degradation over most fire-affected parts of Western Himalaya, India
The tropical forest contributes around 5% to 15% of atmospheric carbon emissions, which are mostly anthropogenic. But there are large uncertainties in the quantification of these emissions from its sources. The remote-sensing data offers a practical opportunity to monitor and assess different forest disturbances. Western Himalayan forest is often affected by fire events, mostly during (pre-monsoon) dry and warm periods. In this study, we present a way to monitor the forest degradation condition using spectral mixture analysis (SMA) and surface reflectance of Landsat-8 data from 2014 to 2019. The Normalized Degradation Fraction Index (NDFI) has been performed by using spectral end member fractions of green vegetation (GV), non-photosynthetic vegetation (NPV), soil, and shade in the Google Earth Engine (GEE) cloud platform. The NDFI shows considerable spatial correspondences with clusters of fire spots during the pre-monsoon period. Around 3% to 9% of the forest burned area transformed to partially to highly degraded forest. The overall trend of degradation fraction (NDFI) over total forest cover shows a significant negative trend over a considerable area. Thus, Landsat-8-based SMA and NDFI demonstrate a potential way to identify forest degradation mediated by forest fires, although remote sensing-based approaches are limited in their capacity to accurately detect forest disturbances. Furthermore, field-based studies are needed to monitor the potentialities of the NDFI approach in forest degradation identification.
87-102
CRC Press
Bar, Somnath
1e199d14-4020-46ef-9dfa-733fe5fa6082
Parida, Bikash Ranjan
21c6f8e7-5d6c-4d46-86e3-4e7160b4d1b5
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Parida, Bikash Ranjan
Pandey, Arvind Chandra
Behera, Mukunda Dev
Kumar, Navneet
Bar, Somnath
1e199d14-4020-46ef-9dfa-733fe5fa6082
Parida, Bikash Ranjan
21c6f8e7-5d6c-4d46-86e3-4e7160b4d1b5
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Parida, Bikash Ranjan
Pandey, Arvind Chandra
Behera, Mukunda Dev
Kumar, Navneet

Bar, Somnath, Parida, Bikash Ranjan and Dash, Jadunandan (2022) Disentangle the short-term forest degradation over most fire-affected parts of Western Himalaya, India. In, Parida, Bikash Ranjan, Pandey, Arvind Chandra, Behera, Mukunda Dev and Kumar, Navneet (eds.) Handbook of Himalayan Ecosystems and Sustainability: Spatio-Temporal Monitoring of Forests and Climate. 1st ed. CRC Press, pp. 87-102. (doi:10.1201/9781003268383-7).

Record type: Book Section

Abstract

The tropical forest contributes around 5% to 15% of atmospheric carbon emissions, which are mostly anthropogenic. But there are large uncertainties in the quantification of these emissions from its sources. The remote-sensing data offers a practical opportunity to monitor and assess different forest disturbances. Western Himalayan forest is often affected by fire events, mostly during (pre-monsoon) dry and warm periods. In this study, we present a way to monitor the forest degradation condition using spectral mixture analysis (SMA) and surface reflectance of Landsat-8 data from 2014 to 2019. The Normalized Degradation Fraction Index (NDFI) has been performed by using spectral end member fractions of green vegetation (GV), non-photosynthetic vegetation (NPV), soil, and shade in the Google Earth Engine (GEE) cloud platform. The NDFI shows considerable spatial correspondences with clusters of fire spots during the pre-monsoon period. Around 3% to 9% of the forest burned area transformed to partially to highly degraded forest. The overall trend of degradation fraction (NDFI) over total forest cover shows a significant negative trend over a considerable area. Thus, Landsat-8-based SMA and NDFI demonstrate a potential way to identify forest degradation mediated by forest fires, although remote sensing-based approaches are limited in their capacity to accurately detect forest disturbances. Furthermore, field-based studies are needed to monitor the potentialities of the NDFI approach in forest degradation identification.

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Published date: 2 December 2022

Identifiers

Local EPrints ID: 477958
URI: http://eprints.soton.ac.uk/id/eprint/477958
PURE UUID: 5a4607b6-5e2b-4776-a6f1-786d69b95792
ORCID for Somnath Bar: ORCID iD orcid.org/0000-0003-1679-6130
ORCID for Jadunandan Dash: ORCID iD orcid.org/0000-0002-5444-2109

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Date deposited: 16 Jun 2023 17:01
Last modified: 06 Jun 2024 02:18

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Contributors

Author: Somnath Bar ORCID iD
Author: Bikash Ranjan Parida
Author: Jadunandan Dash ORCID iD
Editor: Bikash Ranjan Parida
Editor: Arvind Chandra Pandey
Editor: Mukunda Dev Behera
Editor: Navneet Kumar

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