There has been considerable work done within the NASA LCLUC program and other ecosystem and land science programs on closed canopy tropical forest LCLUC dynamics. Now, the important next stage for research on drivers and dynamics is in three new types of LCLUC and landscapes: 1) industrial forests (as in this solicitation), 2) open forests, woodlands and savannas, and 3) trees outside of forests, principally in agricultural landscapes. It is the aim of this proposal to focus on the first aforementioned area of interest. This requires new and innovative analysis and methods, first to develop new methods for detecting industrial forests in time series of remote sensing data, and second to analyze the spatial patterns to understand underlying LCLUC processes and drivers. Hence this project is largely basic LCLUC research but will do both development work and apply the remote sensing methods to create some early prototype monitoring datasets and maps. This proposal is responding to the ROSES LCLUC solicitation for 2012 under the element, Global mapping of industrial forests from Landsat observations. The proposal aims to develop continental products that delineate areas of industrial forests in the Asia-Pacific region. We focus on the most important form of new LCLUC in the tropical forests - the increasing area and size of new tree plantations, many being displaced from the temperate zone into new source regions in the tropics. We will utilize Landsat data including historical and new Landsat 8 datasets. Our aim is research and development of new methods for industrial forest detection and monitoring, with the goal of deploying a method that can be made operational, and providing in this project a first order Asia-Pacific assessment. Although social science is not required of this element, we shall examine the trends and geographic shifts in industrial forests to understand drivers of change to inform better finance and econometric models of LCLUC. We will use develop and test two methods of remote sensing analysis using Landsat data. The first method is based on continuous-fields analysis that produces a fractional cover dataset that can be used to demarcate industrial forests. Landsat data are processed to Forest Fractional Cover (fC). This initial detection is calibrated by using texture analysis, spectral analyses and visual interpretation. The second remote sensing analysis method will use a suite of vegetation indices (NDVI, EVI, ARVI, SARVI, SAVI, MSAVI2) and multi-temporal change detection analyses to detect patterns of clearing and re-growth consistent with industrial forests. We will also exploit new opportunities to use very high spatial resolution data to detect forest cover and individual tree crowns in industrial forests. We will test two types of methods for detection of industrial forest areas using high-resolution data: 1) Geographic Object-Based Image Analysis methods, and 2) watershed image segmentation coupled with rule-based classification of segmented images. We organize the project research design into a series of nine tasks. Task 1 is the stratification of the Asian-Pacific Region for industrial forest source areas. Task 2 is analytical assessment of forest investment and policy targets for production areas. Task 3 is the development of pilot project sites in India, Thailand, Indonesia, Malaysia, and Vietnam. Task 4 is methods development for Landsat data. Task 5 is methods development for high-resolution data. Task 6 is validation of the pilot area data products. Task 7 is a test of continental application for the Asia Pacific Region. Task 8 is re-validation of the continental assessment. Task 9 is a pattern to process analysis. This project will bring insights into the processes that drive LCLUC in industrial forest dynamics. The work will lend insight into whether the geographic shift is occurring, and define the magnitude and how these processes affect industrial forests.