COMPREHENSIVE SPECTRAL SIGNAL INVESTIGATION OF A LARCH FOREST COMBINING GROUND- AND SATELLITE-BASED MEASUREMENTS
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 (<i>Larix...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/671/2016/isprs-archives-XLI-B7-671-2016.pdf |
| Summary: | 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 (<i>Larix decidua</i> 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. |
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| ISSN: | 1682-1750 2194-9034 |