Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 9, 2013
Publication Date: May 1, 2013
Citation: Thorp, K.R., French, A.N., Rango, A. 2013. Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA). Remote Sensing of Environment. 132:120-130. Interpretive Summary: Encroachment of invasive shrubs into grassland areas on rangelands in the southwestern United States is a problem that has been directly linked to human activities in the region. Loss of grasslands threatens the viability of livestock production and has been shown to severely and permanently alter the hydrology and biodiversity of rangeland areas. Remote sensing from airborne and satellite platforms is a technology that can help monitor, map, and perhaps remediate this threat. Novel remote sensing instrumentation and data processing routines are currently being developed, but these technologies need proper evaluation before they can be reliably implemented. In this study, a new remote sensing data processing approach was evaluated for monitoring vegetation cover in rangeland environments. The main findings were 1) that the new analysis technique was reliably able to map shrub and grass vegetation over rangeland areas and 2) that the results of the new analysis technique were largely independent of the spatial resolution of the remote sensing images. The results of the study will be useful to rangeland and remote sensing scientists aiming to monitor and map large rangeland areas for assessments of vegetation cover change. Results will also be useful for scientists at the National Aeronautics and Space Administration (NASA), who are currently developing the "HyspIRI" remote sensing satellite and preparing for its launch.
Technical Abstract: Encroachment of invasive shrubs into grassland areas on rangelands in the southwestern United States threatens the viability of livestock production and can severely alter hydrology and biodiversity. Novel remote sensing technologies may provide useful information for monitoring and remediating this threat. The objectives were to investigate multiple endmember spectral mixture analysis (MESMA) as an approach to map rangeland vegetation using hyperspectral remote sensing imagery and to test the sensitivity of MESMA to alternative image spatial resolutions and spectral waveband combinations. Data from two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) overflights at the Jornada Experimental Range in southwestern New Mexico were used in the analysis. Endmember spectra were selected from a library of ground-based spectral observations collected with a field spectroradiometer. A 4-endmember MESMA was conducted for both AVIRIS images at their native spatial resolutions using 113 10-nm wavebands from 422 to 2339 nm. Additional MESMAs were conducted at 10 multiples of the images' native spatial resolution and for 6 alternative combinations of spectral waveband subregions. Maps of endmember fractional cover for green shrub vegetation, nonphotosynthetic grass vegetation, and bare soil were comparable to an earlier vegetation classification map of Jornada. MESMA fractional cover estimates for the green vegetation endmember were positively correlated with the normalized difference vegetation index (NDVI) with correlation coefficients (r) greater than 0.58. Correlation coefficients between the sum of the green and nonphotosynthetic vegetation endmembers and the cellulose absorption index (CAI) were greater than 0.59. Correlation coefficients between MESMA fractional green vegetation cover and NDVI for independent multispectral images were greater than 0.57. Despite obvious losses in spatial detail at coarser image spatial resolutions, MESMA results for images with spatial resolution degraded by a factor of 10 (~150 m) were quite similar to aggregated results for MESMA at the native spatial resolution (~15 m). Additionally, MESMA results were shown to be substantially more sensitive to the spectral wavebands used in the analysis as compared to the spatial resolution of the images. Considered together, the MESMA results at Jornada indicate that fine spectral resolution with hyperspectral remote sensing is substantially more important than incremental changes in image spatial scale from 15 m to 150 m.