From Event: SPIE Optical Engineering + Applications, 2016
Remote sensing techniques such as change detection are widely used for mapping and monitoring forest cover to detect the declining health and vigor of forests. These techniques rely on the assumption that the biophysical variation in the forest introduces a corresponding variation in its reflectance. The biophysical variations are assessed by foresters, but these assessment techniques are expensive and cannot be performed frequently to identify a specific level of change in the forest, for example, infection due to gypsy moths that results in forest defoliation. Further, the interaction of atmosphere, sensor characteristics, and phenology that are inherent in the remotely sensed images makes it difficult to separate biophysical changes from observational effects. We have addressed these limitations by developing a method to model the spectral reflectance properties of forests with varying degrees of defoliation using the Digital Image and Remote Sensing Image Generation (DIRSIG) tool. This paper discusses the in-canopy radiative approach and the impact of defoliation on the reflectance and radiance observed by sensors such as Landsat. The results indicate that the relative variation in forest reflectance between a non-defoliated and a 30% defoliated deciduous forest can be as high as 10% in the NIR spectral band. A function can be fit to predict the level of defoliation from the relative variation in radiance. The modeling and analysis techniques can be extended to assess the impact of atmospheric factors and sensor characteristics relative to the biophysical changes as well as for assessing other biophysical variables in forests.
Rajagopalan Rengarajan and John R. Schott, "Modeling forest defoliation using simulated BRDF and assessing its effect on reflectance and sensor reaching radiance," Proc. SPIE 9975, Remote Sensing and Modeling of Ecosystems for Sustainability XIII, 997503 (Presented at SPIE Optical Engineering + Applications: August 31, 2016; Published: 19 September 2016); https://doi.org/10.1117/12.2235391.
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