Remote sensing techniques have been used effectively for measuring the overall loss of terrestrial ecosystem production and biodiversity due to the forest fire. The current research focuses on assessing the impact of forest fire severity on terrestrial ecosystem productivity using different burn indices in Uttarakhand, India. Satellite-based land surface temperature (LST) was calculated for pre-fire (2014) and fire (2016) year using MODerate Resolution Imaging Spectroradiometer (MODIS) to identify the burn area hotspots across all eco-regions in Uttarakhand. In this study, spatial and temporal changes of different vegetation and burn area indices i.e Normalized Burn Ratio (NBR), Burnt Area Index (BAI), Normalized Multiband Drought Index (NMDI), Soil Adjusted Vegetation Index (SAVI), Global Environmental Monitoring Index (GEMI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI)were estimated for both pre-fire and fire years. Additionally, two Light Use Efficiency (LUE) models i.e Carnegie- Ames-Stanford-Approach (CASA) and Vegetation Photosynthesis Model (VPM) were selected to quantify the terrestrial Net Primary Productivity (NPP) in pre-fire and fire years across all biomes of the study area.The present approach appears to be promising and has a potential in quantifying the loss of ecosystem productivity due to forest fires. A detailed field observation data is required for further training, and testing of remotely sensed fire maps for future research.
Forests are basic components of the global carbon cycle, and forest fires are a serious threat to indigenous forests that degrade net primary productivity (NPP), gross primary productivity (GPP) and carbon sequestration services (Dixon et al., 1994). According to Intergovernmental Panel on Climate Change (IPCC, 1992), burning of forest biomass produce a significant amount of CO2, which is 10% of the annual global methane and 10-20% of the global N2O emissions leading to the change in atmospheric chemistry in the long run. Forest fires perturb the carbon sequestration capacity of a green canopy and dismantle the surface energy balance of the ecosystem by emitting several greenhouse gases which affecting the spatiotemporal dynamics of carbon pools, thermal properties of regional/global climate and Net Ecosystem Productivity (NEP) (e.g., Flannigan et al., 2000;Amiro et al., 2001;Amiro et al., 2003). Several environmental indicators, including NPP, GPP, NEP etc. has been used widely to analyze the anthropogenic and climatic effects on ecosystem on the local and global scale.
NPP is the net amount of carbon fixed by a green canopy from the atmosphere in a given space and time through photosynthesis and plant respiration (Potter et al., 1993). It is one of the most understood ecosystem processes to analyse the anthropogenic and climatic effects on an ecosystem (Potter et al., 1993;Chu et al., 2016). In addition to this, NPP is useful in capturing vegetation changes across the world and is deployed to track the unprecedented modifications in different biomes (Field et al., 1995;Brouwers and Coops, 2016). Presently, NPP is highly favored as an ecosystem indicator for measuring the capacity of an ecosystem to act as a carbon source or sink (Amiro et al., 2001).
The remotely sensed data has been widely utilized in the past few decades to extract the valuable information about the dynamics of terrestrial ecosystem productivity and vegetation phenological pattern from coarser to finer scales (Field, 1998). The availability (free data) of remote sensing data makes it suitable for estimating extent and monitoring of fire events in the developing countries under scarce data conditions. Over the last few decades, several remote sensing based spectral indices have been explored around the globe to assess the impact of fire on forest ecosystem at local, regional and global scales (Milne, 1986;White et al., 1996;Lentile et al., 2006;Chen et al., 2011;Chen et al., 2015). Different indices have their strengths and shortcomings for assessing and mapping the forest fire severity. Normalized Difference Vegetation Index (NDVI) is the most commonly used index that detects the green vegetative cover (Chuvieco et al., 2004) but it is sensitive to attenuation from atmosphere and aerosols (Carlson and Ripley, 1997). Soil-Adjusted Vegetation Index (SAVI) is modified NDVI with reduced soil background effect making it very sensitive to discriminating vegetation amount in sparsely vegetated areas (Huete 1988;Chenhbouni et al., 1994). The Enhanced Vegetation Index (EVI) has improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal with less atmospheric influences (Jiang et al., 2008). The Global Environmental Monitoring Index (GEMI) is sensitive in discriminating burned area and is least affected by soil variations, atmospheric variations, and illumination conditions than NDVI (Pinty and Verstraete 1992;Pereira 1999). Besides, the Burned Area Index (BAI) easily discriminate the burned and low reflectance areas depending on the temporal performance of behavior charcoal after forest fires (Martin, 2006). Normalized Multiband Drought Index (NMDI) uses the difference between two liquid water absorption bands (1.64 mm and 2.13 mm), as the soil and vegetation water sensitive band. Strong differences between two water absorption bands in response to soil and leaf water content has the potential to estimate water content of both soil and vegetation. Therefore, this improved drought index is expected to offer more accurate assessments of drought severity and fire conditions (Wang and Qu, 2007;Wang et al., 2008). Normalized Burn Ratio (NBR) combines the information on the near-infrared (NIR) band centred at approximately 0.8 mm and a shortwave infrared (SWIR) band centred at approximately 2.1 mm to map the burned areas and burn scar (Key andBenson, 1999, 2005;Miller and Yool, 2002;Cocke et al., 2005). As, NIR and SWIR spectral bands have the most change among reflective spectral bands (White et al., 1996;Wagtendonk et al., 2004), therefore, NBR would be one of the most discriminating for burn effects during forest fires. Apart from the mentioned spectral burn indices, the land surface temperature (LST) that uses the thermal bandwidth to detect water, energy interaction between earth and atmosphere, is used as a significant burn indicator to assess the forest fire destruction
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