Laboratory for
Climatology and Remote Sensing

Abstract:

The thermal structure of a megadiverse mountain ecosystem in southern Ecuador is examined on the basis of temperature measurements inside the natural mountain forest and at open sites along an altitudinal gradient from 1700m to 3200m. The data is used to calculated maps of monthly average minimum, mean and maximum 2m air temperature for the period 1999-2007, needed by ecological projects. The maps are generated by combining the straightforward detrending technique by using a Digital Elevation Model and satellite-based land cover classification. The results reveal a clear thermal differentiation over the year, partly triggered by the change of synoptic weather situation but also by land cover effects. Thermal amplitudes are particularly low during the main rainy season when cloudiness and air humidity are high but markedly pronounced in the relative dry season when daily irradiance and outgoing nocturnal radiation causes distinct differences between the land cover units. Particularly the lower pasture areas gained by slash and burn of the natural forest exhibit the most extreme thermal conditions while the atmosphere inside the mountain forest is slightly cooler due to the regulating effects of the dense vegetation. Thus, clearing the forest clearly reduces the thermal regulation function (regulating ecosystem services) of the ecosystem which might become problematic under future global warming.

Abstract:

Though the relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its extent is still unknown. Due to the enormous spatial extent, remote sensing provides the only possibility to investigate pasture degradation via frequently used proxies such as vegetation cover and aboveground biomass (AGB). However, unified remote sensing approaches are still lacking. This study tests the appli- cability of hyper- and multispectral in situ measurements to map vegetation cover and AGB on regional scales. Using machine learning techniques, it is tested whether the full hyperspectral information is needed or if multispectral information is sufficient to accurately estimate pasture degradation prox- ies. To regionalize pasture degradation proxies, the transferability of the locally derived ML-models to high resolution multispectral satellite data is assessed. 1183 hyperspectral measurements and vegeta- tion records were performed at 18 locations on the QTP. Random Forests models with recursive feature selection were trained to estimate vegetation cover and AGB using narrow-band indices (NBI) as predic- tors. Separate models were calculated using NBI from hyperspectral data as well as from the same data resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral measurements were used (cross validated R2 = 0.89). In contrast, errors in AGB estimations were consid- erably higher (cross validated R2 = 0.32). Only small differences in accuracy were observed between the models based on hyperspectral compared to multispectral data. The application of the models to satellite images generally resulted in an increase of the estimation error. Though this reflects the challenge of applying in situ measurements to satellite data, the results still show a high potential to map pasture degradation proxies on the QTP. Thus, this study presents robust methodology to remotely detect and monitor pasture degradation at high spatial resolutions.