Discrimination of Senescent Vegetation Cover from Landsat-8 OLI Imagery by Spectral Unmixing in the Northern Mixed Grasslands. Issue 2 (4th March 2019)

• Record Type: Journal Article
• Authors: Yu, Xiaolei; Guo, Qingxia; Chen, Qiuji; Guo, Xulin
• Is Part Of: Canadian journal of remote sensing. Volume 45:Issue 2(2019)
• Page Start: 192
• Page End: 208
• Publication Date: 2019-03-04
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2019.1605586
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: The mixed grasslands of North America are ecosystems with a high volume of dead biomass. This characteristic underlies key ecosystem features such as the rate of carbon and nutrient uptake, heat flux exchange between the surface and the atmosphere, and wildlife habitat. Senescent vegetation is an important forage resource for grazing animals and is related to natural fire frequency and intensity. Therefore, quantitative estimation of photosynthetic vegetation (PV), senescent vegetation (NPV), and bare soil (BS) fraction is important for natural resource management. The authors propose an approach for extracting PV, NPV, and BS endmembers from the normalized difference vegetation index–dead fuel index (NDVI–DFI) plane by using the Landsat-8 imagery. The constrained linear spectral unmixing model was applied to discriminate NPV, PV, and BS using original spectral bands, NDVI–DFI indices, and original spectral plus NDVI and DFI indices. As a comparison, the traditional NDVI–SWIR32 was also investigated. Results showed that the DFI performed better than the SWIR32 to predict NPV from spectral unmixing. Index selection has a significant effect on NPV and BS cover fraction estimation. Choice of equation setup has a significant effect on the PV estimation. The methods proposed here can be applied to grassland ecosystems across the northern mixed grasslands region.