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dc.contributor.authorPriyadarshani, GGH
dc.contributor.authorWelikanna, DR
dc.date.accessioned2024-03-14T03:29:26Z
dc.date.available2024-03-14T03:29:26Z
dc.date.issued2023-09
dc.identifier.urihttp://ir.kdu.ac.lk/handle/345/7336
dc.description.abstractSri Lanka is one of the few surviving countries in the world with an extensive natural forest cover, however, most of the existing forest has been impacted by changing environmental conditions and escalating disturbances. To preserve our forest environment, investigating its temporal resilience is important. Forest Resilience is the capacity of forests to recover from disturbances in which they experience undesired shifts from their original state to available alternative stable. This research study mainly focused to analyse the resilience of forests Wilpattu National Park and Kanneliya Rain Forest with a time series of the Landsat 8/9 and Sentinel 1 satellite imagery during the period of the year 2017 to 2022 by generating Forest Resilience Index (FRI). In this study, Landsat 8/9 and Sentinel 1 satellite images were used to create the NDVI, LAI and RVI layers. Then time series analysis was conducted with values of NDVI, LAI and RVI. The final outcomes were Forest Resilience Indices that were generated with NDVI and RVI. The FRI for Wilpattu National Park is 0.7827NDVI + 0.2173RVI and for Kanneliya Rain Forest is 0.7853NDVI + 0.2147RVI. The validation was conducted with generated FRI for the Upper Wilpattu area and it was succeeded. This analysis helped to analyse the temporal variability indicating the resilient dynamics of the Sri Lankan forests.en_US
dc.language.isoen_USen_US
dc.subjectForest Resilience Indexen_US
dc.subjectLAIen_US
dc.subjectNDVIen_US
dc.subjectRVIen_US
dc.subjectTime Series Analysisen_US
dc.titleDevelopment of a Forest Resilience Index Combining Multispectral and Microwave Vegetation Indicesen_US
dc.typeArticle Full Texten_US
dc.identifier.facultyFaculty of Built Environment and Spatial Sciencesen_US
dc.identifier.journalIRC - KDUen_US
dc.identifier.pgnos21-26en_US


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