Review of Satellite Remote Sensing Use in Forest Health Studies

Satellite remote sensing has been used in forest health management by providing methods for vegetation mapping, fire fuel mapping, fire risk estimates and fire detection, post-fire severity mapping, insect-infestation mapping, and relative water stress monitoring. This paper reviews the efforts in the field of satellite remote sensing for forest health, including current research activities, the satellite sensors, methods and parameters used, and their accuracy. The review concludes that the MODIS satellite data (Moderate Resolution Imaging Spectroradiometer) is more appropriate for most of the remote sensing applications for forest health than other current satellite data when considering temporal and spatial resolutions, cost, and bands. MODIS has a 1-2 day temporal and 250-1000m spatial resolutions, the data are free and cover more spectral bands than other satellites (up to 36 bands). Physical and physiological modeling (e.g., ET and biomass growth) should be developed for remote sensing of forest health. In addition, some satellite sensors such as for high temperature estimates (as high as 1800 K) and sensors of narrow bands are needed. Introduction Campbell (2002) defines remote sensing as: “the practice of deriving information about the earth’s land and water surfaces using images acquired from an overhead perspective, using electromagnetic radiation in one or more regions of the electromagnetic spectrum, reflected or emitted from the earth’s surface.” Remote sensing sensors can be instrumented on satellites, airplanes, balloons, or remote controlled vehicles. The current and past satellite remote sensing of forest health has focused on the following categories: vegetation and landscape classification, biomass mapping, invasive plants detection, fire fuel mapping and canopy or foliar water stress, fire detection and progression mapping, post fire burn area and severity mapping, and insect infestation detection. Most of the studies have analyzed spectral signatures or simple indices (calculated from reflectance data) such as the Normalized Difference Vegetation Index (NDVI). Little has been done for remote sensing of absolute forest water stress (e.g., ET) and biomass growth using physically and physiologically based algorithms. Previous studies estimated biomass by using NDVI or other simple indices using correlation and regression methods. These studies may not result in accurate biomass growth estimates for other locations and different environmental conditions (Foody et al., 2003).