Evaluation of the U.S. Geological Survey Landsat Burned Area Essential Climate Variable across the Conterminous U.S. Using Commercial High-Resolution Imagery

Abstract

The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution . Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation.

An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data

“…It was found that, for the standard NASA BA product [8], derived from the MODIS sensor, the BA omission error was above 65% (at a global scale) [9]. Using sensors, such as Landsat or Sentinel-2, with higher spatial resolution (20-30m instead of 500m), but worse temporal resolution (5-16 days instead of 1-2 days), the performances improved and omission errors equal to around 40% [10,11], 26.5% [12], and 22% [13] were obtained (generally, at smaller spatial scales).…”

“…Undetected clouds represent the major source of commission errors; it was found that commission error was above 40% (at a global scale) for the standard NASA BA product [8] , while it ranged between 48% and 19% for operational BA products derived from higher resolution sensors [10][11][12][13].…”

Quantitative Remote Sensing of Land Surface Variables: Progress and Perspective

“…The Landsat Burned Area Essential Climate Variable (BAECV) product is a 30 m resolution burned area product over the conterminous United States (CONUS) generated by the U.S. Geological Survey. Vanderhoof et al [14] assessed the BAECV with burned area reference maps derived from high-resolution satellite images. A maximum likelihood supervised classifier trained with manually created burned data was used to generate the reference maps.…”

Global Analysis of Burned Area Persistence Time with MODIS Data

“…Cropland land cover was not considered in our analysis, since the MODIS burned area product is not suited to capturing the size and heterogeneity of cropland burned areas [18,99] because of its geometrical resolution, and because other agricultural practices (e.g., tilling) often immediately follow the fires [100], and have similar spectral signatures [101]. Omission errors in the cropland class of the MCD12Q1 land cover product, therefore, led to the inclusion of MCD64A1 detections over agricultural areas in the burned population ba.…”