Progression of hyperspectral remote sensing for estimation of forest health to comply with SDGs

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dc.coverageAsia
dc.coverageIndia
dc.creatorSharma, L. K., Gupta, R.
dc.date2023-04-27T13:57:18Z
dc.date2023-04-27T13:57:18Z
dc.date2022
dc.date2023-02-24T03:59:17.0000000Z
dc.date.accessioned2026-06-27T22:45:23Z
dc.descriptionForests offer many crucial services to sustain life on Earth as they produce timber, maintain hydrological status and biodiversity conservation, sequesters carbon dioxide (CO2), which play a role in mitigating climate change.Forest’s health is an important parameter to maintain in the dire face of tremendous anthropogenic and natural pressure over many parts of the world. Remote sensing technology offers an opportunity to assess forest health at a local, regional to a global scale. New advancements such as hyperspectral remote sensing (HRS) can provide improved forest health estimation and monitoring. The present study classified hyperspectral Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data into four broad classes forests, agriculture, fallow land and water bodies. We used the supervised classification method spectral angle mapper (SAM) to classify hyperspectral image based on endmembers produced. Then, we derived the greenness index, leaf pigment index, canopy water and light use efficiency index and dry or senescent carbon index parameters to monitor the health status of forest tropical of the Shoolpaneshwar wildlife sanctuary (SWS), Gujarat, India. The classification map shows that the dense forest cover in SWS is mainly found in the inner parts of SWS, while the outer parts are occupied with agriculture and fallow land. The health parameters mapsrevealed that the outside zones have low forest health, while the inner forest of SWS has good health. This research effectively uses advanced remote sensing hyperspectral data for forest health monitoring, which helps planning and sustainable forest management. Keywords: Forest health, hyperspectral, AVIRIS-NG, Remote sensing ID: 3623691
dc.format10p.
dc.formatapplication/pdf
dc.identifierhttps://openknowledge.fao.org/handle/20.500.14283/cc1591en
dc.identifierhttp://www.fao.org/3/cc1591en/cc1591en.pdf
dc.identifier.urihttp://hdl.handle.net/123456789/261994
dc.languageEnglish
dc.publisherFAO ;
dc.rightsNon-FAO
dc.rightsCC BY NC SA 3.0 IGO
dc.titleProgression of hyperspectral remote sensing for estimation of forest health to comply with SDGs
dc.titleXV World Forestry Congress, 2-6 May 2022
dc.typeArticle

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