Multi-temporal mesoscale hyperspectral data of mixed agricultural and grassland regions for anomaly detection

Abstract

Flight-based hyperspectral imaging systems have the potential to provide valuable information for ecosystem and environmental studies, as well as aid in land management and land health monitoring. This paper examines a series of images taken over the course of three years that were radiometrically referenced allowing for quantitative comparisons of changes in vegetation health and land usage. The study area is part of a geologic carbon sequestration project located in north-central Montana, approximately 580ha in extent, at a site requiring permission from multiple land owners to access, making ground based validation difficult. Classification based on histogram splitting of the biophysically based parameters utilizing the entire three years of data is done to determine the major classes present in the data set in order to show the constancy between data sets taken over multiple years. Additionally, a method of anomaly detection for both single and multiple data sets, using Median Absolute Deviations (MADs), is presented along with a method of determining the appropriate size of area for a particular ecological system. Detection of local anomalies within a single data set is examined to determine, on a local scale, areas that are different from the surrounding area and depending on the specific MAD cutoff between 50–70% of the anomalies were located. Additionally, the detection and identification of persistent (anomalies that occur in the same location over multiple data sets) and non-persistent anomalies was qualitatively investigated.