Cite as:
Meyer, H.; Lehnert, L.; Wang, Y.; Reudenbach, C.; Nauss, T. &amp; Bendix, J. (2017): <b>From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information?</b>. <i>International Journal of Applied Earth Observation and Geoinformation</i> <b>55</b>, 21-31.

Resource Description

Title: From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information?
FOR816dw ID: 281
Publication Date: 2017-03-15
License and Usage Rights: PAK 823-825 data user agreement. (
Resource Owner(s):
Individual: Hanna Meyer
Individual: Lukas Lehnert
Individual: Yun Wang
Individual: Christoph Reudenbach
Individual: Thomas Nauss
Individual: Jörg Bendix
Though the relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its<br/> extent is still unknown. Due to the enormous spatial extent, remote sensing provides the only possibility<br/> to investigate pasture degradation via frequently used proxies such as vegetation cover and aboveground<br/> biomass (AGB). However, unified remote sensing approaches are still lacking. This study tests the appli-<br/> cability of hyper- and multispectral in situ measurements to map vegetation cover and AGB on regional<br/> scales. Using machine learning techniques, it is tested whether the full hyperspectral information is<br/> needed or if multispectral information is sufficient to accurately estimate pasture degradation prox-<br/> ies. To regionalize pasture degradation proxies, the transferability of the locally derived ML-models to<br/> high resolution multispectral satellite data is assessed. 1183 hyperspectral measurements and vegeta-<br/> tion records were performed at 18 locations on the QTP. Random Forests models with recursive feature<br/> selection were trained to estimate vegetation cover and AGB using narrow-band indices (NBI) as predic-<br/> tors. Separate models were calculated using NBI from hyperspectral data as well as from the same data<br/> resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared<br/> to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and<br/> AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral<br/> measurements were used (cross validated R2 = 0.89). In contrast, errors in AGB estimations were consid-<br/> erably higher (cross validated R2 = 0.32). Only small differences in accuracy were observed between the<br/> models based on hyperspectral compared to multispectral data. The application of the models to satellite<br/> images generally resulted in an increase of the estimation error. Though this reflects the challenge of<br/> applying in situ measurements to satellite data, the results still show a high potential to map pasture<br/> degradation proxies on the QTP. Thus, this study presents robust methodology to remotely detect and<br/> monitor pasture degradation at high spatial resolutions.<br/>
| biomass | Tibetan Plateau | Pasture degradation | Hyperspectral measurements | Random forests | Qinghai-Tibet Plateau | Regionalization | Vegetation cover |
Literature type specific fields:
Journal: International Journal of Applied Earth Observation and Geoinformation
Volume: 55
Page Range: 21-31
Metadata Provider:
Individual: Lukas Lehnert
Online Distribution:
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