Mapping a burned forest area from Landsat TM data by multiple methods

Abstract

Forest fire is one of the dominant disturbances in boreal forests. It is the primary process responsible for organizing the physical and biological attributes of the boreal biome, shaping landscape diversity and influencing biogeochemical cycles. The Greater Hinggan Mountain of China is rich in forest resources while suffers from a high incidence of forest fires simultaneously. In this study, focusing on the most serious forest fire in the history of P. R. China which occurred in this region, we made use of two Landsat-5 TM (Thematic Mapper) images, and proposed to map the overall burned area and burned forest area by multiple methods. During the mapping, the fire perimeter, as well as rivers, roads and urban areas were first extracted and masked visually, and then four indices of Normalized Difference Vegetation Index, Enhanced Vegetation Index, Vegetation Fractional Cover and Disturbance Index were calculated. For each index, the optimal threshold for separating burned from unburned forest area was determined using their histograms. For comparison, threshold segmentation using single-band reflectance was performed, in addition to a Maximum Likelihood Classifier (MLC) based supervised classification of all features and forest area alone; their accuracies were also evaluated and analysed. Among all the methods compared here, mapping by EVI threshold segmentation proved to be optimal by the comparisons of overall accuracy (99.78%) and the kappa coefficient (0.9946). Finally, the calculated burned area and burned forest area were compared with the values from official statistics. Compared with the classical methods used to report official statistics on burned areas, the remote sensing-based mapping is more objective and efficient, less labour-and time-consuming, and more repeatable.

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

“…In addition to land management data, remote sensing platforms can provide spatially explicit data on historical disturbances, including wildfire (Davies, Ilavajhala, Wong, & Justice, ). In particular, Landsat satellite imagery has been successfully used to estimate a range of proxies for fire attributes at a relatively fine‐scale, including fire severity (Eidenshink et al, ; Escuin, Navarro, & Fernández, ), active fire detection (Schroeder et al, ), and assessment of fire‐affected areas (Chen, Moriya, Sakai, Koyama, & Cao, ). Altogether, these novel data sources provide a wealth of opportunities to quantify how anthropogenic factors influence communities and populations over regional scales.…”

Mapping and Tracking Forest Burnt Areas in the Indio Maiz Biological Reserve Using Sentinel-3 SLSTR and VIIRS-DNB Imagery

“…As depicted in Figure 2, after preprocessing the acquired images, the following step was to derive spectral indices commonly used to identify burnt areas in satellite images. Since Sentinel-2 MSI and Sentinel-3 SLSTR imagery possess visible and infrared bands with similar bandwidth, we calculated the normalized difference vegetation index (NDVI) [45,46], normalized difference water index (NDWI) [47,48], and normalized burn ratio (NBR) [49,50] for each scene of Sentinel-2 and Sentinel-3 ( Table 2). The next step was to create a composite with the red, near infrared (NIR), shortwave infrared (SWIR), and SWIR 2 bands, along with the spectral indices, and perform the segmentation of each scene using the eCognition Developer [51].…”

Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery

“…In addition, since 2008, the US Geological Survey (USGS) has provided more than 11 million current and historical Landsat images free of charge to users over the Internet (https://earthexplorer.usgs.gov/), with ease of access. Thus, Landsat imagery has been widely used in monitoring the natural land cover changes caused by floods [4], forest fires [5,6], and glacial melting [7].…”