Lehnert, L.; Meyer, H.; Meyer, N.; Reudenbach, C. & Bendix, J. (2014): <b>A hyperspectral indicator system for rangeland degradation on the Tibetan Plateau: A case study towards spaceborne monitoring</b>. <i>Ecological Indicators</i> <b>39</b>, 54-64.
Resource Description
Title:
A hyperspectral indicator system for rangeland degradation on the Tibetan Plateau: A case study towards spaceborne monitoring
FOR816dw ID:
4
Publication Date:
2014-01-01
License and Usage Rights:
PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)
Resource Owner(s):
Individual:
Lukas Lehnert
Contact:
email:
lukas.lehnert <at> staff.uni-marburg.de
Germany
Individual:
Hanna Meyer
Contact:
email:
webmaster <at> lcrs.de
Individual:
Nele Meyer
Contact:
email:
webmaster <at> lcrs.de
Individual:
Christoph Reudenbach
Contact:
email:
webmaster <at> lcrs.de
Individual:
Jörg Bendix
Contact:
email:
bendix <at> staff.uni-marburg.de
Deutschhausstraße 12
Room No. 02 A 48
35032 Marburg
Faculty of Geography
Germany
Abstract:
The Tibetan Plateau suffers from progressive degradation caused by over-grazing due to improper live-<br/>
stock management, global climate change and herbivory from small mammals. Therefore, a robust<br/>
indicator system for rangeland degradation has to be developed and tested. This paper investigates local<br/>
patterns of degradation at two sites (Lake Namco and Mt. Kailash) in Xizang province (China) that are cov-<br/>
ered by vegetation types typical of a large portion of the plateau. The suitability of a two-indicator system<br/>
is analysed using hyperspectral field measurements, and its transferability to spaceborne data is tested.<br/>
The indicators are (1) land-cover fractions derived from linear spectral unmixing and (2) chlorophyll<br/>
content as a proxy for nutrient and water availability calculated using hyperspectral vegetation indices<br/>
and partial least squares regression. Because cattle remain near settlements overnight in the local semi-<br/>
nomadic pastoral system, it can be expected that grazing intensity is highest near the settlement and<br/>
declines with increasing distance. Therefore, we tested the effect of distance on both indicators using a<br/>
Spearman correlation analysis. The predicted chlorophyll content and land cover fractions of the indica-<br/>
tor system were in good agreement with field observations (correlation coefficients between 0.70 and<br/>
0.98). High correlations between distance from settlements and land-cover fractions at both study sites<br/>
demonstrated that the land-cover fraction is a reliable indicator for degradation. A positive correlation<br/>
between distance from settlements and photosynthetically active vegetation (PV) revealed over-grazing<br/>
patterns at the first site. Furthermore, the chlorophyll indicator was proven suitable because chlorophyll<br/>
concentration declined with increasing distance from settlements. This underlines the over-grazing pat-<br/>
tern because cattle excrement was the only external source of nutrients in the ecosystem and it was<br/>
positively correlated with grazing intensity. However, at the second site, we found a significant positive<br/>
effect of distance on the amount of photosynthetically non-active vegetation; no effect of distance on PV<br/>
and chlorophyll content was found. Therefore, no evidence of pasture degradation was detected at the<br/>
second site. Regarding the potential use of satellite data for degradation monitoring, we found that (1) the<br/>
land-cover indicator derived from multispectral data was more robust than using noise-filtered hyper-<br/>
spectral information and (2) the chlorophyll amount indicator was estimated from simulated EnMAP<br/>
data with low error rates. Because the proposed two-indicator system can be derived from multi- and<br/>
hyperspectral satellite data and combines site conditions and local plant cover, it provides a time-saving<br/>
and robust method to measure pasture degradation across large areas, assuming that respective satellite<br/>
data are available.<br/>
Keywords:
| remote sensing | Tibetan Plateau | Pasture degradation | Partial least square regression | field spectrometry | Linear spectral unmixing | EnMAP |
