Incorporating topographic and climatic GIS data into satellite image analysis of an alpine tundra ecosystem, front range, Colorado rocky mountains

Abstract

A series of digital climate indices have been developed in support of a remote sensing study of complex alpine vegetation patterns at the Niwot Ridge Long‐Term Ecological Research site in the Colorado Front Range, USA. Using an integrated remote sensing‐GIS approach, critical factors which control the distribution of vegetation such as precipitation, temperature, wind, soil moisture and snow accumulation have been derived from a 16 year archive of digital Landsat imagery, geomorphometry from a digital elevation model, and meteorological station data. Five climatic indices were implemented: (i) Orogenic Precipitation Index, (ii) Slope‐Aspect Index, (iii) Snow Probability Index, (iv) Insolation Index, and (v) Growing Degree Days. These indices were tested individually and together with a Landsat TM image and topographic measures from a DEM to assess their significance for increasing the accuracy and precision of maximum likelihood landcover classification with respect to the hierarchical Braun‐Blanquet vegetation classification system. Growing Degree Days and Orogenic Precipitation Index had the highest classification accuracies among the individual climate indices, with the highest overall accuracies obtained using all five climate indices together with several Landsat TM bands (74% to 83% at the highest and lowest levels of precision tested, respectively). These results were favourable for the sensor resolutions and classification algorithms used in this complex environment, and provide additional promise for future studies involving higher resolution remote sensing and topographic data sets together with more sophisticated classification algorithms.