A novel vegetation index-based workflow for semi-arid, sparse woody cover mapping

Abstract

Vegetation cover maps across ecologically-fragile and particularly arid and semi-arid forest ecosystems are prerequisites for their monitoring and management. Direct and field-based measurements of vegetation cover pose serious challenges due to high costs and inaccessibility in harsh terrains, whereas multispectral remote sensing offers objective, spatially-explicit and rapid alternatives. One of the most straightforward tools is the use of broadband vegetation indices (VIs), which are mathematical derivations from multispectral bands that are correlated with various vegetation traits. There are a number of broadband VIs that reach their optimum performance by calibrating their regulatory parameters. We improved the performance of selected VIs for both greenness estimation and land-cover classification across semi-arid woodlands by optimizing their regulatory parameters. We showed this across two separate areas in highly-fragile and sparse vegetation of Zagros mountains of Iran. Regulatory parameters were optimized by multi-objective particle swarm optimization (MOPSO) for Enhanced VI (EVI) and two innovative, more complex broadband indices that use red, blue, and near-infrared multispectral bands. Then, they were applied to estimate greenness and classify vegetation, and were validated by subsets of very high-resolution optical imagery. The results suggest high accuracy of these indicators for estimating and classifying vegetation compared with the commonly-used broadband VIs. Amongst the improved VIs, the one with a more complex combination of spectral bands comparatively returned the best performance, that was 1.34 × and 1.33 × higher in greenness estimation and 1.58 × higher in classification compared with the benchmark NDVI. They also described a higher variance across systematic transects in both regions. In conclusion, both greenness estimation and classification of semi-arid, sparse woodlands were more accurate by optimizing their regulatory parameters.