Abstract
For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R2 = 0.52, p-value <0.001), and avian species richness among habitats (R2 = 0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R2 = 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R2 = 0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas.
Highlights
It is difficult to monitor and map patterns of wildlife diversity and abundance efficiently across broad areas
To calculate second-order contrast, we summarized the pixel values within the moving window in a gray-level co-occurrence matrix (GLCM) and we calculated the texture statistic based on this matrix [56]
Grasshopper sparrow density was most strongly related to the standard deviation of second-order contrast calculated from the air photo in a 51651 moving window (R2 = 0.52, p-value,0.001, Table 4, Fig. 4)
Summary
It is difficult to monitor and map patterns of wildlife diversity and abundance efficiently across broad areas. Variation in foliage-height diversity was originally used to predict avian diversity patterns and niche partitioning among species [3] Since this seminal work by MacArthur and MacArthur, ecologists have linked foliage-height diversity to biodiversity in habitats around the world [7,12,13,14,15,16]. Field-measured foliage-height diversity provides valuable fine grained information about habitat heterogeneity, it is logistically difficult to collect at large extents. This limits its use for management and conservation applications, which typically occur at broad-scales [17,18,19]
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