Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments

Abstract

Post-fire vegetation response is influenced by the interaction of natural and anthropogenic factors such as topography, climate, vegetation type and restoration practices. Previous research has analyzed the relationship of some of these factors to vegetation response, but few have taken into account the effects of pre-fire restoration practices. We selected three wildfires that occurred in Bandelier National Monument (New Mexico, USA) between 1999 and 2007 and three adjacent unburned control areas. We used interannual trends in the Normalized Difference Vegetation Index (NDVI) time series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess vegetation response, which we define as the average potential photosynthetic activity through the summer monsoon. Topography, fire severity and restoration treatment were obtained and used to explain post-fire vegetation response. We applied parametric (Multiple Linear Regressions-MLR) and non-parametric tests (Classification and Regression Trees-CART) to analyze effects of fire severity, terrain and pre-fire restoration treatments (variable used in CART) on post-fire vegetation response. MLR results showed strong relationships between vegetation response and environmental factors (p < 0.1), however the explanatory factors changed among treatments. CART results showed that beside fire severity and topography, pre-fire treatments strongly impact post-fire vegetation response. OPEN ACCESSRemote Sensing 2012, 4 599Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response.

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

“…The dNBR shows greater capacity to assess fire severity levels than dNDVI [21,[24][25][26]. The dNBR method demonstrates efficiency in mapping burned areas within tundra ecosystems [27], temperate forests [12,28], heathland vegetation types [29], pine flatwood forests [30], and boreal forests [13], although some studies have obtained inconsistent results [31][32][33].…”

Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data

“…NDVI is widely used for assessing vegetation cover and condition [41][42][43][44][45]. In general, a pixel is considered as vegetation pixel if its NDVI value is more than 0.3.…”

Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

“…Buma [230] examined this hypothesis using MODIS time series data from 2000 to 2010 in the area of burned forest in Colorado's Routt National Forest, USA, and demonstrated that NDVI is poorly correlated with forest recovery represented by seedling density in burned areas. Therefore, studies on post-fire forest recovery should consider the inclusion of structural forest ground variables, such as seedling recruitment, percent of cover, tree diameter and height, directly to remotely sensed parameters [34,175,230] (Table 9). To date, however, very few studies in the literature have attempted to tie post-fire ground variables to remotely sensed data with different metrics and spatial scales in either boreal forests or other ecosystems.…”

“…data and methods over the widely spatial and temporal ranges of post-fire-affected environments, particularly in characterizing and evaluating the patterns of how forest ecosystems respond to fire disturbances [2,20,34]. This paper reviews the methods and remotely sensed data used for modeling post-fire effects and forest recovery patterns, with a greater focus on examples of boreal forests, as well as the existing optical remote sensing data and methods that can be potentially applied to the aftermath of fires in this ecosystem.…”