Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 671-678, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/671/2016/
doi:10.5194/isprs-archives-XLI-B7-671-2016
 
21 Jun 2016
COMPREHENSIVE SPECTRAL SIGNAL INVESTIGATION OF A LARCH FOREST COMBINING GROUND- AND SATELLITE-BASED MEASUREMENTS
J. M. Landmann1, M. Rutzinger1,2, M. Bremer1, and K. chmidtner2 1Dept. of Geography, University of Innsbruck, 6020 Innsbruck, Austria
2Institute of Interdisciplinary Mountain Research (Austrian Academy of Science), 6020 Innsbruck, Austria
Keywords: Data Fusion, Spectral Signal, Field Spectrometry, Landsat 8, OLI, Classification, Larix decidua Abstract. Collecting comprehensive knowledge about spectral signals in areas composed by complex structured objects is a challenging task in remote sensing. In the case of vegetation, shadow effects on reflectance are especially difficult to determine. This work analyzes a larch forest stand (Larix decidua MILL.) in Pinnis Valley (Tyrol, Austria). The main goal is extracting the larch spectral signal on Landsat 8 (LS8) Operational Land Imager (OLI) images using ground measurements with the Cropscan Multispectral Radiometer with five bands (MSR5) simultaneously to satellite overpasses in summer 2015. First, the relationship between field spectrometer and OLI data on a cultivated grassland area next to the forest stand is investigated. Median ground measurements for each of the grassland parcels serve for calculation of the mean difference between the two sensors. Differences are used as “bias correction” for field spectrometer values. In the main step, spectral unmixing of the OLI images is applied to the larch forest, specifying the larch tree spectral signal based on corrected field spectrometer measurements of the larch understory. In order to determine larch tree and shadow fractions on OLI pixels, a representative 3D tree shape is used to construct a digital forest. Benefits of this approach are the computational savings compared to a radiative transfer modeling. Remaining shortcomings are the limited capability to consider exact tree shapes and nonlinear processes. Different methods to implement shadows are tested and spectral vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Greenness Index (GI) can be computed even without considering shadows.
Conference paper (PDF, 3707 KB)


Citation: Landmann, J. M., Rutzinger, M., Bremer, M., and chmidtner, K.: COMPREHENSIVE SPECTRAL SIGNAL INVESTIGATION OF A LARCH FOREST COMBINING GROUND- AND SATELLITE-BASED MEASUREMENTS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 671-678, doi:10.5194/isprs-archives-XLI-B7-671-2016, 2016.

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