Cite as:
Lehnert, L.; Meyer, H.; Meyer, N.; Reudenbach, C. &amp; 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. (
Resource Owner(s):
Individual: Lukas Lehnert
Individual: Hanna Meyer
Individual: Nele Meyer
Individual: Christoph Reudenbach
Individual: Jörg Bendix
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/>
| remote sensing | Tibetan Plateau | Pasture degradation | Partial least square regression | field spectrometry | Linear spectral unmixing | EnMAP |
Literature type specific fields:
Journal: Ecological Indicators
Volume: 39
Page Range: 54-64
Metadata Provider:
Individual: Lukas Lehnert
Online Distribution:
Download File:

Quick search

  • Publications:
  • Datasets: