Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

https://www.fs.fed.us/pnw/pubs/journals/pnw_2012_kennedy.pdf

Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan

Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data

We estimate the age of humid lowland tropical forests in Rondonia, Brazil, from a somewhat densely spaced time series of Landsat images (1975-2003) with an automated procedure, the Threshold Age Mapping Algorithm (TAMA), first described here. We then estimate a landscape-level rate of aboveground woody biomass accumulation of secondary forest by combining forest age mapping with biomass estimates from the Geoscience Laser Altimeter System (GLAS). Though highly variable, the estimated average biomass accumulation rate of 8.4 Mg ha-1 yr-1 agrees well with ground-based studies for young secondary forests in the region. In isolating the lowland forests, we map land cover and general types of old-growth forests with decision tree classification of Landsat imagery and elevation data. We then estimate aboveground live biomass for seven classes of old-growth forest. TAMA is simple, fast, and self-calibrating. By not using between-date band or index differences or trends, it requires neither image normalization nor atmospheric correction. In addition, it uses an approach to map forest cover for the self-calibrations that is novel to forest mapping with satellite imagery; it maps humid secondary forest that is difficult to distinguish from old-growth forest in single-date imagery; it does not assume that forest age equals time since disturbance; and it incorporates Landsat Multispectral Scanner imagery. Variations on the work that we present here can be applied to other forested landscapes. Applications that use image time series will be helped by the free distribution of coregistered Landsat imagery, which began in December 2008, and of the Ice Cloud and land Elevation Satellite Vegetation Product, which simplifies the use of GLAS data. Finally, we demonstrate here for the first time how the optical imagery of fine spatial resolution that is viewable on Google Earth provides a new source of reference data for remote sensing applications related to land cover.

https://www.fs.fed.us/research/publications/iitf/HelmerLefskyRoberts.2009.JRS033505.pdf

Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System

Tourism, forest conversion, and land transformations in the Angkor basin, Cambodia

Fires associated with tropical deforestation, land conversion, and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rond6nia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 was examined in a 94 370-ha area of active deforestation to map land cover change. The Tasseled Cap (TC) transformation was used to enhance the contrast between forest, cleared areas, and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 78.1% (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opened to land exhibiting regrowth with infrequent clearing.

Detection of Deforestation and Land Conversion in Rondonia, Brazil Using Change Detection Techniques

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Detection of Deforestation and Land Conversion in Rondonia, Brazil Using Change Detection Techniques