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Proceedings Paper

Assessing pasture quality and degradation status using hyperspectral imaging: a case study from western Tibet
Author(s): Lukas W. Lehnert; Hanna Meyer; Nele Meyer; Christoph Reudenbach; Jörg Bendix
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Paper Abstract

Alpine grasslands on the Tibetan Plateau (TP) are suffering from pasture degradation induced by over-grazing, climate change and improper livestock management. Meanwhile, the status of pastures is largely unknown especially in poor accessible western parts on the TP. The aim of this case study was to assess the suitability of hyperspectral imaging to predict quality and amount of forage on the western TP. Therefore, 18 ground- based hyperspectral images taken along two transects on a winter pasture were used to estimate leaf chlorophyll content, photosynthetic-active vegetation cover (PV) and proportion of grasses. For calibration and validation purposes, chlorophyll content of 20 grass plants was measured in situ. From the images reference spectra of grass and non-grass species were collected. PV was assessed from similarity of images to mean vegetation spectra using spectral angle mapper and threshold classifications. A set of 48 previously published hyperspectral vegetation indices (VI) was used as predictors to estimate chlorophyll content and to discriminate grass and non-grass pixels. Separation into grass and non-grass species was performed using partial least squares (PLS) discriminant analysis and chlorophyll content was estimated with PLS regression. The accuracy of the models was assessed with leave-one-out cross validation and normalised root mean square errors (nRMSE) for chlorophyll and contingency matrices for grass classification and total PV separation. Highest error rates were observed for discrimination between vegetated and non-vegetated parts (Overall accuracy = 0.85), whilst accuracies of grass and non grass separation (Overall accuracy = 0.98) and chlorophyll estimation were higher (nRMSE = 10.7).

Paper Details

Date Published: 16 October 2013
PDF: 13 pages
Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 88870I (16 October 2013); doi: 10.1117/12.2028348
Show Author Affiliations
Lukas W. Lehnert, Philipps-Univ. Marburg (Germany)
Hanna Meyer, Philipps-Univ. Marburg (Germany)
Nele Meyer, Rheinische Friedrich-Wilhelms-Univ. Bonn (Germany)
Christoph Reudenbach, Philipps-Univ. Marburg (Germany)
Jörg Bendix, Philipps-Univ. Marburg (Germany)

Published in SPIE Proceedings Vol. 8887:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XV
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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