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INTEGRATION OF GIS DATA AND CLASSIFIED SATELLITE IMAGERY FOR REGIONAL FOREST ASSESSMENT
Author(s) -
He Hong S.,
Mladenoff David J.,
Radeloff Volker C.,
Crow Thomas R.
Publication year - 1998
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/1051-0761(1998)008[1072:iogdac]2.0.co;2
Subject(s) - ecoregion , geography , forest inventory , deciduous , forest ecology , ecology , satellite imagery , forest dynamics , remote sensing , tree canopy , temperate rainforest , environmental science , canopy , physical geography , forest management , ecosystem , forestry , archaeology , biology
New methods are needed to derive detailed spatial environmental data for large areas, with the increasing interest in landscape ecology and ecosystem management at large scales. We describe a method that integrates several data sources for assessing forest composition across large, heterogeneous landscapes. Multitemporal Landsat Thematic Mapper (TM) satellite data can yield forest classifications with spatially detailed information down to the dominant canopy species level in temperate deciduous and mixed forests. We stratified a large region (10 6 ha) by ecoregions (10 3 –10 4 ha). Within each ecoregion, plot‐level, field inventory data were aggregated to provide information on secondary and sub‐canopy tree species occurrence, and tree age class distributions. We derived a probabilistic algorithm to assign information from a point coverage (forest inventory sampling points) and a polygon coverage (ecoregion boundaries) to a raster map (satellite land cover classification). The method was applied to a region in northern Wisconsin, USA. The satellite map captures the occurrence and the patch structure of canopy dominants. The inventory data provide important secondary information on age class and associated species not available with current canopy remote sensing. In this way we derived new maps of tree species distribution and stand age reflecting differences at the ecoregion scale. These maps can be used in assessing forest patterns across regional landscapes, and as input data in models to examine forest landscape change over time. As an example, we discuss the distribution of eastern white pine ( Pinus strobus ) as an associated species and its potential for restoration in our study region. Our method partially fills a current information gap at the landscape scale. However, its applicability is also limited to this scale.