Mr. Lukas W. Lehnert Profile

Lukas W. Lehnert

at Philipps-Univ Marburg

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Proceedings Article | 24 October 2013 Paper

Measuring pasture degradation on the Qinghai-Tibet Plateau using hyperspectral dissimilarities and indices

Hanna Meyer, Lukas Lehnert, Yun Wang, Christoph Reudenbach, Jörg Bendix

Proceedings Volume 8893, 88931F (2013) https://doi.org/10.1117/12.2028762

KEYWORDS: Vegetation, Satellites, Absorption, Satellite imaging, Spectroscopy, Earth observing sensors, Electroluminescent displays, Climatology, Multispectral imaging, Calibration

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Despite that relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its extent is still unknown. However, livestock grazing is widely accepted as a major factor. This study investigated spectral differences of vegetation patterns along gradients of grazing intensities using plot-based hyperspectral measurements. The measurements were used to define spectral indicators for pasture degradation, which were applied to map asserted proxies for degradation from satellite images. For this purpose, hyperspectral measurements were taken at 11 sites on the north-eastern QTP using a transect design from heavy grazing and therefore asserted degradation near the settlement to less degradation with increasing distance. Potential spectral indicators for degradation were derived from the spectra by calculating the size of continuum removed absorption features and narrow-band indices (NBI). They were compared between degraded and less degraded plots. Linear regressions between proxies and each of the potential spectral indicators were calculated to assess its predictive power. The findings were transferred to larger scales by applying the indicators on two WorldView-2 (WV-2) scenes. Spectral differences between degraded and less degraded plots were obvious regarding a wide range of tested indicators. Several NBIs were considered as good indicators for vegetation cover and species numbers. WV-2 images could be successfully classified into vegetation cover whilst the estimation of species numbers was afflicted with uncertainties. The results demonstrate the potential to estimate degradation proxies using spectrometer measurements and satellite data. Applying these techniques will contribute to a better estimation of spatial degradation patterns on the QTP.

Proceedings Article | 16 October 2013 Paper

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

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

Proceedings Volume 8887, 88870I (2013) https://doi.org/10.1117/12.2028348

KEYWORDS: Vegetation, Hyperspectral imaging, Matrices, Photovoltaics, Cameras, Data analysis, Remote sensing, Accuracy assessment, Climate change, Calibration

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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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