Detecting vegetation change with satellite remote sensing over 2007 Georgia wildfire regions

Min Li; John Jianhe Qu; Xianjun Hao

doi:10.1117/1.2973659

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1 August 2008 Detecting vegetation change with satellite remote sensing over 2007 Georgia wildfire regions

Min Li, John Jianhe Qu, Xianjun Hao

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J. of Applied Remote Sensing, 2(1), 021505 (2008). https://doi.org/10.1117/1.2973659

Abstract

The wildfires which occurred in April 2007 in southern Georgia lasted for almost two months. Approximately 386,722 acres were burned. In this paper, we explored the strategy to use MODIS products to study fire impacts on vegetation. Vegetation variations caused by the fires are studied using these MODIS products: 8-day composite fire products, 16-day composite vegetation indices, and land cover types. Several tiles of MODIS products from dates immediately before and after the fire were employed to monitor vegetation changes. Unburned control plots were selected to establish new variables QNDVI and QEVI which are used to evaluate the vegetation recovery status. The results show that vegetation indices: NDVI and EVI decreased dramatically after the fires. The variations of QNDVI and QEVI indicate that vegetation status still has a disparity between fire spots and surrounding undisturbed area, even though commonly used vegetation indices of fire spots have attained pre-fire levels. The appropriate distance outside the fire spots for selecting control plots is related to the size of the burned area. The larger the burned area is, the bigger distance we should choose for control plots. EVI and QEVI are better indicators to show vegetation changes due to fires. The method presented in this paper can be employed to monitor vegetation change to fires as well as to indicate different recovery rate. It also can be useful to identify the fire severity and assess the ecological consequence of fires.

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Min Li, John Jianhe Qu, and Xianjun Hao "Detecting vegetation change with satellite remote sensing over 2007 Georgia wildfire regions," Journal of Applied Remote Sensing 2(1), 021505 (1 August 2008). https://doi.org/10.1117/1.2973659

Published: 1 August 2008

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