Christoph Reudenbach

Climate variability rather than overstocking causes recent large scale cover changes of Tibetan pastures

The Tibetan Plateau (TP) is a globally important “water tower” that provides water for nearly 40% of the world’s population. This supply function is claimed to be threatened by pasture degradation on the TP and the associated loss of water regulation functions. However, neither potential large scale degradation changes nor their drivers are known. Here, we analyse trends in a high-resolution dataset of grassland cover to determine the interactions among vegetation dynamics, climate change and human impacts on the TP. The results reveal that vegetation changes have regionally different triggers: While the vegetation cover has increased since the year 2000 in the north-eastern part of the TP due to an increase in precipitation, it has declined in the central and western parts of the TP due to rising air temperature and declining precipitation. Increasing livestock numbers as a result of land use changes exacerbated the negative trends but were not their exclusive driver. Thus, we conclude that climate variability instead of overgrazing has been the primary cause for large scale vegetation cover changes on the TP since the new millennium. Since areas of positive and negative changes are almost equal in extent, pasture degradation is not generally proceeding.

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

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).

Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique

ABSTRACT Estimating rainfall areas and rates from geostationary satellite images has the opportunity of both, a high spatial and a high temporal resolution which cannot be achieved by other satellite-based systems until now. Most recent retrieval techniques are solely based on spectral channels of the satellites. These retrievals can be classified as ‘purely pixel-based’ because no information about the neighbourhood pixels is included. Assuming that precipitation is highly correlated with cloud processes and therefore with cloud texture, textural information derived from the neighbourhood of a pixel might give valuable information about the cloud type and hence about a respective probability of the rainfall rate. To study the potential of textural variables to improve optical rainfall retrieval techniques, rainfall areas and rainfall rates were estimated over Germany for the year 2010 using a neural network approach. In addition to the spectral predictor variables from Meteosat Second Generation (MSG), different Grey Level Co-occurance Matrix (GLCM) based textural variables were calculated from all MSG channels. Using recursive feature selection, models were trained and their performance was compared to spectral-only models. Contrary to the expectations, the performance of the models did not increase when textural information was included.

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

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.

Mean Number of Storm Days

Precipitation events are the main driving force for hydrological processes; for this reason, correctly compiling the distribution of precipitation in the study area is given high priority. Therefore, three models for assessing precipitation were implemented in DANUBIA: a mesoscale atmosphere model, an interpolation model based on station data and a satellite-supported rainfall retrieval. The satellite-based derivation of precipitation takes place using data from the European Meteosat system. In a first step, the boundaries of the raining cloud areas are delineated. Second, the precipitation rate is assigned considering the precipitation processes identified before.

Precipitation Dynamics of Convective Clouds

The dynamics of precipitation processes represent a typical example for a multi-scale geo-system. The gaps between the scales of the cloud microphysics, resolutions of cloud models and remote sensing methods have to be closed by upscaling or parameterisation techniques. The section gives an overview over the governing processes for the generation of precipitation and the importance of the cloud physics for the modelling and remote sensing of precipitation processes. An example of a cloud model simulation is presented, and a possible approach for parameterisations of convective precipitation processes for infrared remote sensing is given.