NDVI signatures of regional in time and in space thermal anomalies in SW USA

Abstract

The selective variance reduction technique that applies linear regression models to the principal components of multi-temporal night monthly averaged land surface temperature (LST) imagery splits the variance associated to elevation, latitude, longitude in SW USA for the year 2007. The reconstructed multi-temporal imagery indicate the positive or negative deviation (thermal anomaly) from the elevation, latitude, longitude predicted LST. The spatial pattern of thermal anomalies is revealed by K-means clustering. The thermal anomaly clusters are parametrically represented according to both the (a) monthly averaged deviation from the elevation, latitude, longitude predicted LST as well as (b) the monthly averaged positive normalized difference vegetation index in an attempt to determine the vegetation density temporal variability per thermal anomaly cluster. This research effort will contribute to aridity and terrain evaluation studies, allowing terrain characterization and planning in the context of the upcoming climatic change.