Abstract
Satellite-based hyperspectral sensors provide spectroscopic information in relatively narrow contiguous spectral bands over a large area which can be useful in forestry applications. This study evaluates the potential of satellite hyperspectral Resurs-P data for forest species mapping. Firstly, a comparative study between top of canopy reflectance obtained from the Resurs-P, from the airborne hyperspectral scanner CASI and from field measurement (FieldSpec ASD 4) on selected vegetation cover types is conducted. Secondly, Resurs-P data is tested in classification and verification of different forest species compartments. The results demonstrate that satellite hyperspectral Resurs-P sensor can produce useful informational and show good performance for forest species classification comparable both with forestry map and classification from airborne CASI data, but also indicate that developments in pre-processing steps are still required to improve the mapping level.
Highlights
One of the main applications of remote sensing in the last decade is monitoring of forest cover (Xie et al, 2008)
The objectives of the study were: 1/ to compare spectral response between Resurs-P, CASI and ASD data, and 2/ to test whether the classification of hyperspectral Resurs-P satellite data can reveal approximately the same forest species achieved from airborne hyperspectral CASI data
An airborne hyperspectral CASI image was acquired in spectral range of 368 – 1041 nm with spatial resolution of 1 m on June 5th 2015 (Table 1)
Summary
One of the main applications of remote sensing in the last decade is monitoring of forest cover (Xie et al, 2008). Classification and discrimination of forest types from local to global scales at a given time point or over a continuous period need high spectral and spatial resolution images generated by remote sensors. Several studies revealed that forest application results from satellite hyperspectral data had a good match with results from airborne hyperspectral data (Townsend & Foster, 2002; Smith 2003). Some authors stressed that hyperspectral satellite data demonstrated poor performance related to airborne data in such tasks as forest species mapping and canopy water content (Ustin et al, 2002; Ceballos et al, 2015)
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