Estimates of bare ground and vegetation cover from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) short-wave-infrared reflectance imagery

Abstract

Abstract. The high level of success of estimating photosynthetic vegetation from multispectral satellite sensors at regional scales has not been repeated for non-photosynthetic vegetation and bare ground. Therefore regional scale estimates of total vegetation from multispectral sensors are largely underestimated with implications for a wide range of agricultural and environmental applications. Recent research using simulated data showed that the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) had the potential to provide reliable estimates of bare ground and total vegetation. This study built on that research and found that estimates of bare ground retrieved from ASTER short-wave infrared imagery using linear spectral unmixing correlated well with field measurements (RMSE < 0.1, r 2 > 0.7). Image endmember libraries required for spectral unmixing were extracted from the image data using a combination of field knowledge and the lignin and cellulose absorption index. The most reliable results were found by applying a sum-constraint to the unmixing models and tying the signatures at wavebands that corresponded to cellulose or clay-hydroxyl absorption features. The results of this research show that ASTER can improve the estimates of total vegetation extracted from satellite imagery for environmental studies at regional scales.

Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale

“…A number of studies have shown that bare soil and non-photosynthetic vegetation cannot be easily separated in visible and near-infrared (NIR) wavelength regions (e.g., [10,21,41]). While the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soils can be estimated with imaging spectroscopy sensors (e.g., [21]), the accuracy decreases with multispectral sensors (e.g., [42][43][44]). Further sources of errors in the upscaling process may be sensor-specific (e.g., radiometric resolution, signal-to-noise ratio) [45], related to ambient effects caused by illumination and observation angles, or atmospheric effects or artifacts caused by atmospheric corrections [46].…”

Estimating fractional land cover in semi-arid central Kalahari: the impact of mapping method (spectral unmixing vs. object-based image analysis) and vegetation morphology

“…Landsat/ASTER) and hyperspectral (AVIRIS/ EO-1 Hyperion) data-sets (Roberts et al 1998;Asner & Lobell 2000;Elmore et al 2000;Powell et al 2007;Myint & Okin 2009). Although MESMA of hyperspectral images has provided reliable estimates of fractional cover, it has produced modest results with multispectral imagery due to non-ideal bandwidth and spatial resolution (Okin et al 2001;Asner & Heidebrecht 2002;Gill & Phinn 2008). Besides spectral resolution, spatial resolution of imagery also impacts the accuracy of MESMA-derived fractional estimates.…”

How does the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation (FPAR) product relate to regionally developed land cover and vegetation products in a semi-arid Australian savanna?

“…More details on the woody FPC product used in this study can be found in Section 2.2.4 of this paper. At a similar spatial resolution, Gill and Phinn (2008) [22] have shown potential and limitations of using spectral unmixing of ASTER data to separate PV, NPV and bare ground at a regional scale. Attempts to estimate TVC with coarse spatial-but high temporal resolution imagery have focused on the decomposition of evergreen (woody) and seasonally green vegetation cover [27,28].…”

“…In Australia, extensive remote sensing research over the last decades on TVC mapping and monitoring has been conducted with satellite imagery and field observations [21][22][23]. Australian vegetation poses specific challenges through its dominantly vertical leaf inclination, sparse foliage, irregular crown shapes and clumping [21,24].…”