Structural Biases and Sensitivities of Vegetation Indices

Abstract

Since the epoch of climate change, observation of forest post-disturbance regeneration by satellite remote sensing has become a major research frontier. However, the monotonic saturation effects of specific reflectance bands may hinder the interpretation of post-disturbance vegetation indexing. We examine how spectral vegetation enhancement index limitations negate widespread implementation. The structural biases and sensitivities of four vegetation indices with potential usefulness for observing post-disturbance forest regeneration are assessed and clarified: the normalized difference vegetation index (NDVI), normalized burn ratio (NBR), near-infrared vegetation index (VI_NIR), and the infrared vegetation index (VI_IR). Index structures are partitioned in calculation space to model every possible output. Simulated burned, unburned, and global vegetation computational domains for each index are assessed using complex statistical visualizations. Cross-comparison among indices shows that NDVI and NBR exhibit saturation given the upper range of simulated near-infrared (NIR) reflectance inputs (> 0.30) while VI_NIR and VI_IR display increasing variability given lower inputs in the Green (> 0.07) and Shortwave-infrared (SWIR) (> 0.10), regions of the electromagnetic spectrum. NDVI and NBR display potential for vegetation class separability, while VI_NIR and VI_IR also display a linear association with forest post-disturbance regeneration stages. VI_NIR and VI_IR display significant potential for observing forest post-disturbance regeneration compared to traditional vegetation indices.