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Environmental Health and Protection

Mathematics, Physics, and Computer Science Faculty Articles and Research

Vegetation index

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A Feasibility Study On The Application Of Tvdi On Accessing Wildfire Danger In The Korean Peninsula, Kwang Nyun Kim, Seung Hee Kim, Myoung Soo Won, Keun Chang Jang, Won Jun Choi, Yun Gon Lee Dec 2019

A Feasibility Study On The Application Of Tvdi On Accessing Wildfire Danger In The Korean Peninsula, Kwang Nyun Kim, Seung Hee Kim, Myoung Soo Won, Keun Chang Jang, Won Jun Choi, Yun Gon Lee

Mathematics, Physics, and Computer Science Faculty Articles and Research

Wildfire is a major natural disaster affecting socioeconomics and ecology. Remote sensing data have been widely used to estimate the wildfire danger with an advantage of higher spatial resolution. Among the several wildfire related indices using remote sensing data, Temperature Vegetation Dryness Index (TVDI) assesses wildfire danger based on both Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Although TVDI has physical advantages by considering both weather and vegetation condition, previous studies have shown TVDI does not performed well compare to other wildfire related indices over the Korean Peninsula. In this study we have attempted multiple modification to …


Estimation Of The Relationship Between Satellite-Derived Vegetation Indices And Live Fuel Moisture Towards Wildfire Risk In Southern California, Kristen Whitney, Seung Hee Kim, Shenyue Jia, Menas Kafatos Aug 2018

Estimation Of The Relationship Between Satellite-Derived Vegetation Indices And Live Fuel Moisture Towards Wildfire Risk In Southern California, Kristen Whitney, Seung Hee Kim, Shenyue Jia, Menas Kafatos

Mathematics, Physics, and Computer Science Faculty Articles and Research

Southern California possesses a Mediterranean climate having semi-arid to arid characteristics and contains shrubland areas at high risk to wildfire. To assess wildfire danger, fire agencies have been monitoring the moisture of vegetation, called live fuel moisture (LFM), using field-based sampling. Unfortunately, spatial and temporal resolution of live fuel moisture data are significantly limited because sampling is labor intensive. Remote sensing satellite data has been used to monitor vegetation moisture content and health of shrublands. Therefore, a potential approach to overcome the limitations of manual measurements of live fuel moisture is to use vegetation indices (VIs) derived from satellite data. …