Measuring Spatial and Vertical Heterogeneity of Grasslands Using Remote Sensing Techniques

Abstract

Grassland heterogeneity, defined by its components of spatial pattern, vertical structure, and species composition, is one of the most important indicators of prairie habitat. Maintaining grassland under conservation without disturbance may result in homogeneity at multiple spatial scales that could reduce wildlife diversity as a consequence. Therefore, monitoring grassland conditions that contribute to diversity can be critical for wildlife habitat and ecological integrity. Remote sensing, with multi-spatial, multi-spectral, and multi-temporal resolutions plus newly developed analytical techniques, provides a potential tool for measuring grassland heterogeneity under different management regimes quickly, efficiently, and at low cost. The objectives of this study were 1) to evaluate the heterogeneity of grassland under grazing and conservation management practices spatially and vertically, and 2) to investigate the feasibility of using remotely sensed data to measure grassland heterogeneity. The study area was Grasslands National Park of Canada and its surrounding pastures. Field data were collected in the 1999 growing season by measuring the grassland vertical profile in a fixed spatial array. A Landsat Enhanced Thematic Mapper Plus (ETM+) image was acquired for the same year. A grey level co-occurrence matrix (GLCM) texture analysis was applied to the Landsat ETM+ imagery to compare the grasslands under grazing and those under the conservation practice. The results derived from field measurement show that the variation of vertical structures of grasslands differ significantly under grazing and conservation management regimes. Optical remote sensing data could detect the spatial variation of grasslands under these two management practices. Texture analysis is effective at 15 m resolution, which confirmed other studies that grassland heterogeneity is at about 15 meter.

Detecting Climate Effects on Vegetation in Northern Mixed Prairie Using NOAA AVHRR 1-km Time-Series NDVI Data

“…Considerable research has been conducted in GNP. Using remote sensing techniques, C 4 species cover [11], biophysical parameters [12,13] and spatial and vertical heterogeneity [2,14], as well as CO 2 exchange rate [15] have been estimated. These studies have shown the promise of using satellite imagery to study vegetation in GNP.…”

Identification of grassland management practices from leaf area index time series

“…In addition, the functional role of grasslands (nitrogen and carbon storage) changes with agricultural practices and this affects environmental behavior. 9,10 Yet, monitoring grassland management with remote sensing is still limited both by the low revisit frequency of high spatial resolution sensors and the low spatial resolution of high revisit frequency sensors. 5,6 In this context, the identification of agricultural practices on grassland in farming systems, such as grazing and mowing, is of prime importance for environmental protection and sustainable agriculture.…”

Improving GIS-based Wildlife-Habitat Analysis

“…Obtaining better correlations of species use patterns within a biotope may require the use of higher resolution imagery, or techniques such as spectral (textural) analysis (e.g., Guo et al 2004;Bellis et al 2008) or LiDAR (Vierling et al 2008;Goetz et al 2010) that provide an alternate means of quantifying internal heterogeneity. Recall also that even if higher resolution imagery is viewed by the researcher at too small a mapping scale (i.e., not at the scale viewed by the organism), it still may be difficult for the researcher to discern this heterogeneity and thus to appreciate its potential influence on the species.…”