Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Rural Land-Use Trends in the Conterminous United States, 1950-2000.

In order to understand the magnitude, direction, and geographic distribution of land-use changes, we evaluated land-use trends in U.S. counties during the latter half of the 20th century. Our paper synthesizes the dominant spatial and temporal trends in population, agriculture, and urbanized land uses, using a variety of data sources and an ecoregion classification as a frame of reference. A combination of increasing attractiveness of nonmetropolitan areas in the period 1970–2000, decreasing household size, and decreasing density of settlement has resulted in important trends in the patterns of developed land. By 2000, the area of low-density, exurban development beyond the urban fringe occupied nearly 15 times the area of higher density urbanized development. Efficiency gains, mechanization, and agglomeration of agricultural concerns has resulted in data that show cropland area to be stable throughout the Corn Belt and parts of the West between 1950 and 2000, but decreasing by about 22% east of the Mississippi River. We use a regional case study of the Mid-Atlantic and Southeastern regions to focus in more detail on the land-cover changes resulting from these dynamics. Dominating were land-cover changes associated with the timber practices in the forested plains ecoregions and urbanization in the piedmont ecoregions. Appalachian ecoregions show the slowest rates of landcover change. The dominant trends of tremendous exurban growth, throughout the United States, and conversion and abandonment of agricultural lands, especially in the eastern United States, have important implications because they affect large areas of the country, the functioning of ecological systems, and the potential for restoratio

Earth Resources Laboratory research and technology

The accomplishments of the Earth Resources Laboratory's research and technology program are reported. Sensors and data systems, the AGRISTARS project, applied research and data analysis, joint research projects, test and evaluation studies, and space station support activities are addressed

Impervious surface estimation using remote sensing images and gis : how accurate is the estimate at subdivision level?

Impervious surface has long been accepted as a key environmental indicator linking development to its impacts on water. Many have suggested that there is a direct correlation between degree of imperviousness and both quantity and quality of water. Quantifying the amount of impervious surface, however, remains difficult and tedious especially in urban areas. Lately more efforts have been focused on the application of remote sensing and GIS technologies in assessing the amount of impervious surface and many have reported promising results at various pixel levels. This paper discusses an attempt at estimating the amount of impervious surface at subdivision level using remote sensing images and GIS techniques. Using Landsat ETM+ images and GIS techniques, a regression tree model is first developed for estimating pixel imperviousness. GIS zonal functions are then used to estimate the amount of impervious surface for a sample of subdivisions. The accuracy of the model is evaluated by comparing the model-predicted imperviousness to digitized imperviousness at the subdivision level. The paper then concludes with a discussion on the convenience and accuracy of using the method to estimate imperviousness for large areas