Assessing pasture quality and degradation status using hyperspectral imaging: a case study from western Tibet

Lukas W. Lehnert; Hanna Meyer; Nele Meyer; Christoph Reudenbach; Jörg Bendix

doi:10.1117/12.2028348

16 October 2013 Assessing pasture quality and degradation status using hyperspectral imaging: a case study from western Tibet

Lukas W. Lehnert, Hanna Meyer, Nele Meyer, Christoph Reudenbach, Jörg Bendix

Author Affiliations +

Proceedings Volume 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV; 88870I (2013) https://doi.org/10.1117/12.2028348
Event: SPIE Remote Sensing, 2013, Dresden, Germany

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).

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Lukas W. Lehnert, Hanna Meyer, Nele Meyer, Christoph Reudenbach, and Jörg Bendix "Assessing pasture quality and degradation status using hyperspectral imaging: a case study from western Tibet", Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 88870I (16 October 2013); https://doi.org/10.1117/12.2028348

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