Integrated use of Landsat and Corona data for long-term monitoring of forest cover change and improved representation of its patch size distribution

Forest cover change has profound impact on global carbon cycle, hydrological processes, energy balance, and biodiversity. The primary goal of this dissertation is to improve forest cover change characterization by filling a number of knowledge gaps in forest change studies. These include use of Corona data to extend satellite based forest cover change mapping back to pre-Landsat years in the 1960s, quantification of forest cover change over four decades (1960s – 2005) for a major forested province in China using Corona and Landsat data, and development of more accurate patch size-frequency modeling methods for improved representation of forest disturbances in ecosystem and other spatially explicit models. With comprehensive data coverages in the 1960s, Corona data can be used to extend Landsat-based forest change analysis by up to a decade. The usefulness of such data, however, is hindered by poor geolocation accuracy and lack of multi-spectral bands. In this study, it was demonstrated that combined use of texture features and the advanced support vector machines allowed forest mapping with accuracies of up to 95% using Corona data. Further, a semi-automated method was developed for rapid registration of Corona images with residual errors as low as 100 m. These methods were used to assess the forest cover in the 1960s in Sichuan, a major forest province in China. Together with global forest cover change products derived using Landsat data, these results revealed that the forest cover in Sichuan Province was reduced from 45.19% in the 1960s to 38.98% by 1975 and further down to 28.91% by 1990. It then stayed relatively stable between 1990 and 2005, which contradicted trends reported by inventory data. The turning point between sharp decreases before 1990 and the stable period after 1990 likely reflected transitions in forest policies from focuses on timber production to forest conservation. Representation of forest disturbances in spatially explicit ecosystem models typically relies on patch size-frequency models to allocate an appropriate amount of disturbances to each patch size level. Existing patch size-frequency models, however, do not provide accurate representation of the total disturbance area nor the patch sizes at each frequency level. In this study, a hierarchical method was developed for modeling patch size-frequency distribution. Evaluation of this method over China revealed that it greatly improved the accuracy in representing the patch size at different frequency levels and reduced error in total disturbance area estimation over existing methods from around 40% to less than 10%. The significance of this dissertation is the contribution to improve the characterization of forest cover change by extending the satellite-based forest cover change monitoring back to the 1960s and developing a more accurate patch size distribution model to represent the forest disturbance in ecosystem models. The work in the dissertation has a broader impact beyond developing methods and models, as they provide essential basis to understand the relationship between the long-term change of forest and the socioeconomic transitions. They also improve the capacities of ecosystem and other spatially explicit models to simulate the vegetation dynamics and the resultant biodiversity and carbon dynamics