Forest information from hyperspectral sensing

Abstract

Remote sensing of forests is a major application for Canada's new hyperspectral satellite (HERO). Hyperspectral remote sensing can provide forest information products for applications in forest inventory, forest chemistry, and for some Kyoto Protocol information products. Through several projects, we have demonstrated that airborne and satellite hyperspectral sensing can provide accurate maps of west coast forest species. High correlations have been demonstrated between ground measurements of foliar nitrogen and estimates derived from hyperspectral sensing. Data for these experiments have included airborne AVIRIS and casi, and satellite Hyperion data. To achieve operational accuracies of 80% or better for forest species classification, it is essential that the hyperspectral data are well calibrated. Also, sensor artifacts, such as smile, keystone, and striping, must be corrected.Methods have also been developed for mapping forest health status bioindicators: leaf chlorophyll, nitrogen, and water content. Methods have also been developed for determining canopy fractions to levels as low as 20%. If HERO generates a 1% improvement in forest product sales, this would amount to a benefit of $700 million annually in Canada alone. This paper will report on the current state of hyperspectral sensing of forests, and present the hyperspectral sensor specifications needed for forest applications.

Evaluating the Potential of Satellite Hyperspectral Resurs-P Data for Forest Species Classification

“…The airborne hyperspectral image with lower spectral resolution (62 spectral bands of CASI against 116 of Resurs-P) and higher spatial resolution (1 m of CASI against 30 m of Resurs-P) yielded the best classification accuracies (CASI overall accuracy of 87% against Resurs-P overall accuracy of 67%). This confirms a recent study (Goodenough et al, 2004) showing that airborne hyperspectral data can be used in species classification with higher accuracy than satellite hyperspectral data. However, Townsend and Foster (2002) demonstrated the classification using satellite Hyperion data was superior to that using airborne AVIRIS data for pine and maple forest.…”

A practical enhanced-resolution integrated optical-digital imaging camera (PERIODIC)

“…Hyper-spectral or multi-spectral imaging techniques provide the means to extract additional information from the scene in a wide variety of applications, ranging from remote sensing [5,6] to biomedical imaging [7,8] to target recognition and tracking [9,10]. Hyper-spectral imaging suffers from a significant drawback in that the traditional approach to hyper-spectral imaging is not well suited for imaging motion.…”

Combining Hyperspectral Remote Sensing and Physical Modeling for Applications in Land Ecosystems

“…Forest biomass and carbon have been estimated employing both multi-sensor techniques [6] and AVIRIS data using partial least squares (PLS) regression [7]. Estimates of the chemical concentrations (chlorophyll, nitrogen, lignin, water content) of forest canopies can be made using hyperspectral data [8].…”