Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data (original) (raw)
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To achieve the overall objective of restoring natural environment and sustainable resource usability, each forest management practice effect needs to be predicted using a simulation model. Previous simulation efforts were typically confined to public land. Comprehensive forest management practices entail incorporating interactions between public and private land. To make inclusion of private land into management planning feasible at the regional scale, this study uses a new method of combining Forest Inventory and Analysis (FIA) data with remotely sensed forest group data to retrieve detailed species composition and age information for the Missouri Ozark Highlands. Remote sensed forest group and land form data inferred from topography were integrated to produce distinct combinations (ecotypes). Forest types and size classes were assigned to ecotypes based on their proportions in the FIA data. Then tree species and tree age determined from FIA subplots stratified by forest type and size class were assigned to pixels for the entire study area. The resulting species composition map can improve simulation model performance in that it has spatially explicit and continuous information of dominant and associated species, and tree ages that are unavailable from either satellite imagery or forest inventory data. In addition, the resulting species map revealed that public land and private land in Ozark Highlands differ in species composition and stand size. Shortleaf pine is a co-dominant species in public land, whereas it becomes a minor species in private land. Public forest is older than private forest. Both public and private forests have deviated from historical forest condition in terms of species composition. Based on possible reasons causing the deviation discussed in this study, corresponding management avenues that can assist in restoring natural environment were recommended.
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Forest Ecology and Management, 2005
Canada's ability to sustainably manage approximately 10% of the global forest cover is a critical environmental and economic issue. The capacity to meet such demands and to deliver on national and international commitments regarding forest management is enabled through collaboration between federal, provincial, and territorial agencies. A principal collaborator is the National Forest Inventory (NFI); a systematic photo-plot based monitoring system designed specifically for reporting purposes and as an important input for scientific models. Satellite imagery is illustrated here as a support data set to ensure the quality of the NFI, for auditing the photo-plot contents, and to detect spatial biases. The Canadian Forest Service, in collaboration with the Canadian Space Agency and other federal and provincial agencies, is producing a national land cover database of the forested area of Canada (Earth Observation for Sustainable Development of Forests (EOSD)) using Landsat-7 ETM+ data for circa 2000 conditions. The integration between the plot-based NFI with classified EOSD data is presented for central British Columbia, an area comprising 6 Landsat scenes and 324 2 km  2 km photo-plots. Traditional accuracy assessment measures based on the analysis of coincidence matrices are reported as levels of agreement for hierarchically aggregated land cover categories (overall agreements of 91%, 79%, 64% and 26% for 3, 4, 6 and 20 classes respectively) to demonstrate coincidence between the different data products. Local agreement between NFI and EOSD is demonstrated as a means of photo-plot auditing while spatial biases are detected through investigations of geographic pattern in the coincidence values. The illustrated approaches may be expanded or applied to different mapped attributes (e.g., biomass) that are of utility to those attempting to characterize large areas in a consistent and rigorous fashion. #
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Silva Fennica, 2005
Geostatistically based methods that utilize textural information are frequently used to analyze remote sensing (RS) images. The role of these methods in analyzing forested areas increased rapidly during the last several years following advancements in high-resolution RS technology. The results of numerous applications of geostatistical methods for processing RS forest images are encouraging. This paper summarizes such results. Three closely related topics are reviewed: 1) specific properties of geostatistical measures of spatial variability calculated from digital images of forested areas, 2) determination of biophysical forest parameters using semivariograms and characterization of forest ecosystem structure at the stand level, and 3) forest classification methods based on spatial information.