Christina Eisfelder

Derivation of biomass information for semi-arid areas using remote-sensing data

The impact of changes in vegetation biomass on the global ecosystem and the future evolution of possible climate change is of high relevance. Above-ground biomass (AGB) influences environmental processes, such as the hydrological cycle, soil erosion and degradation, especially in semi-arid areas. Therefore, a great need exists for the development of accurate and transferable methods for biomass estimation in these areas. Remote-sensing-based biomass studies have been carried out since the early 1980s. The large majority of these have focused on forests. A reasonable number of efforts have also been undertaken for the estimation of the biomass in semi-arid regions; however, a summary of these studies is not available yet. This review article provides an overview of the remote-sensing-based research activities for AGB estimation in semi-arid regions using optical data, radar data, combined multi-sensor approaches and modelling approaches. A description of typical field measurement methods is also provided, as well as a summary and discussion of the commonly observed difficulties and challenges to be overcome in the future. Most studies were based on low- and medium-resolution optical or radar data and applied empirical relationships between the remote-sensing-derived indices and biomass field measurements. The influence of soil background on the remote-sensing signals is a major challenge. The biggest challenge, however, seems to be the transferability of the methods in time and space. Especially, empirical relationships seem to provide weak results when applied to another point in time or space. Thus, further research on the transferability of remote-sensing-based methods for biomass estimation – especially in semi-arid areas – is required. Additional analyses and research are also needed for an understanding of the relationship between the AGB and remote-sensing signals in ecosystems with scarce vegetation, towards efficient field sampling schemes, synergetic use of optical and radar data and robust models that are not dependent on extensive field sampling.

Comparison of Two Remote Sensing Based Models for Regional Net Primary Productivity Estimation—A Case Study in Semi-Arid Central Kazakhstan

Modelling net primary productivity (NPP) is an important instrument for analysing carbon exchange between atmosphere and vegetation as well as for quantification of carbon sinks and sources. Remote-sensing-based models allow for regional NPP estimation and are potentially transferable to new regions. Comparative model analyses, however, are lacking, especially for semi-arid environments. In this study, two recent remote-sensing-based NPP models were applied for the first time to a study region in semi-arid Kazakhstan: RBM, a light-use-efficiency model based on MODIS products, and BETHY/DLR, a soil-vegetation-atmosphere-transfer model. Differences in intermediate products, their influence on calculated NPP, as well as output products are evaluated and discussed. BETHY/DLR calculates higher NPP (mean annual NPP 2010 and 2011: 136.87 g C m-2 and 106.69 g C m-2) than RBM (62.14 g C m-2 and 54.61 g C m-2) and shows stronger inter-annual changes. Spatial and seasonal patterns present well phenological differences. Comparison to field data from 2011 showed better results for BETHY/DLR, though both results were highly correlated to the field observations (BETHY/DLR: R2=0.95, RMSE=8.36 g C m-2; RBM: R2=0.98, RMSE=22.49 g C m-2). The parameterization of the light use efficiency is critical for RBM; also MODIS based 16-day time steps might be too long to capture variable climatic conditions. For BETHY/DLR, the MODIS land cover product applied in this study differentiates insufficient classes within the semi-arid environment; a more detailed land cover map is needed to improve the regional analysis.

A review on derivation of biomass information in semi-arid regions based on remote sensing data

Vegetation biomass is an important ecological variable for understanding responses to the climate system and currently observed global change. It is also an important factor influencing biodiversity and environmental processes, especially in semi-arid areas. These areas cover large parts of the land surface and are especially susceptible to degradation and desertification. Therefore, a great need exists for the development of accurate and transferable methods for biomass estimation in semi-arid areas. This paper presents an overview of previously applied remote sensing based approaches for above-ground biomass estimation in semi-arid regions. Based on the literature analysis a summary and discussion of commonly observed difficulties and challenges will be presented. Further research is especially required on the transferability of remote sensing based methods for biomass estimation in semi-arid areas. Additional analyses should be directed towards efficient field sampling schemes, and the synergetic use of optical and radar data.

Towards automated forest-type mapping – a service within GSE Forest Monitoring based on SPOT-5 and IKONOS data

Object-based semi-automated segmentation and classification approaches have gained importance in the analysis of remote sensing data over the last few years. Particularly when it comes to operational processing of multi-seasonal input data, independent and robust algorithms are needed. At the German Aerospace Center (DLR) a new method for forest type classification has been developed, covering all processing steps for object-based classification. An automatic adaptation of scene-specific feature values for the classification is implemented, based on automated extraction of feasible ground data. Therefore, no manual sampling of training data is necessary. For classification of mixed forests on the basis of IKONOS data, a special algorithm was developed that can be adapted to any kind of mixed forest definition. Forest age classes are derived based on a digital surface model. The developed method can be used for area-wide forest-type classification on the basis of high and very high-resolution satellite data.

Spatio-temporal patterns and dynamics of net primary productivity for Kazakhstan

Monitoring of net primary productivity (NPP) is especially important for the fragile ecosystems in arid and semi-arid regions. Great interest exists in observing large-scale vegetation dynamics and understanding spatial and temporal patterns of NPP in these areas. In this study we present results of NPP obtained with the model BETHY/DLR for Kazakhstan for 2003–2011 and its spatial and temporal dynamics. The spatial distribution of vegetation productivity shows a gradient from North to South and clear differences between individual vegetation classes. The monthly NPP values show the highest productivity in June. Differences between rain-fed and irrigated areas indicate the dependency on water availability. Annual NPP variability was high for agricultural areas, but showed low values for natural vegetation. The analysis of different patterns in vegetation productivity provides valuable information for the identification of regions that are vulnerable to a possible climate change. This information may thus substantially support a sustainable land management.

Investigating Fourteen Years of Net Primary Productivity Based on Remote Sensing Data for China

Net primary productivity (NPP) is an important environmental indicator that provides information about vegetation productivity and carbon fluxes. Analyses of NPP time-series allow for understanding temporal patterns and changes in vegetation productivity. These are especially important in rapidly changing environments, such as China, the world’s third largest country. In this study, we use the model BETHY/DLR (Biosphere Energy Transfer Hydrology Model) for derivation of NPP time-series for China for 14 years from 1999–2012. We analyse spatial and temporal NPP distributions. These include mean annual NPP distribution and mean productivities for different land cover classes. Monthly data provide information about temporal patterns of vegetation productivity for different regions in China and different vegetation types. Analyses of interannual NPP variability revealed considerable differences in the development of annual vegetation productivity within the analysed time period for different provinces. The decrease in NPP for the district Shanghai shows the strong influence of one of Asia’s fastest growing megacities on the environment. The NPP time-series was additionally analysed for a forest region in North China, which has been affected by forest disturbances. Our results show that the NPP data are suitable for monitoring of forest disturbance and regrowth. The analyses and results presented in this study provide valuable information about spatial and temporal variation of vegetation productivity in the various regions within China.

Land Degradation in South Africa - A Degradation Index Derived from 10 Years of net Primary Production Data

Dry regions such as arid southern Africa are strained by unfavourable climatic conditions. Intensive land use as rangeland and for livestock farming leads to additional encroachment of these ecosystems. The consequence of this long-time stress is degradation in terms of loss of the vegetative cover and productivity. Albeit these are known facts there is still a lack of objectiveness in the long term assessment of degradation on a larger scale. We present a method of applying remote sensing time-series in a vegetation model that helps to fill this gap. The approach is based on time-series of the vegetative productivity computed by our vegetation model BETHY/DLR (Biosphere Energy Transfer Hydrology Model). The used data included SPOT-VGT LAI (Leaf area index) and ECMWF meteorology time-series for the period of 1999–2010. The trend-analysis of model output and climatic input results in a new land degradation index (LDI) that distinguishes between climatic and human-induced reduction of vegetative productivity.

Cartographic Representation of Dresden’s Historical Development by Projecting a Movie onto a Solid Terrain Model

On the occasion of the 800th anniversary of the first mention of the city of Dresden (Germany) a new exhibition for the local City Museum was designed. The intention of the exhibitions concept was to use modern and innovative exhibits to attract a broad audience. A key element of the exposition is a 2.00 m by 1.50 m solid terrain model of the Dresden Elbe valley. A film, which shows the development of the depicted region since the year 8000 B.C. is projected onto the terrain model by a video projector and the help of a tilted mirror. This true 3D installation is one of the first of its kind worldwide. It facilitates much more thematic flexibility than terrain models without changing illumination. Both the terrain model and the film result from a cooperation of the City Museum of Dresden with the Institutes for Cartography and for Software- and Multimedia-Technology of the Dresden University of Technology. The scale of the solid terrain model is 1:16,250. It is four times vertically exaggerated to improve the perceptibility of small terrain features for the visitor. The projected film is a FlashMX animation, for which several tenths input data layers were prepared with the GIS software ArcGIS and the vector graphic software Freehand. Input data for both the solid terrain model and the animation were Laserscanning DEM data and ATKISDGM/- DLMdata. A major challenge of the terrain model construction was the reconstruction of the primordial terrain. As the input data showed the contemporary situation of the terrain a significant number of visible anthropogenic terrain changes, such as bridges, railway lines, motorways and man-made riverbeds had to be removed. An iterative semi-automatic approach was developed, which cuts relevant areas from the data and using a linear TIN interpolation that filled the data holes afterwards. The filtered data set was cut into a polyurethane pattern plate by using a Portatec milling machine by the Institute for Production-Technology of the Dresden University of Technology. In a stepwise process an accuracy of 1/100 mm in the three dimensions x, y and z was reached. Afterwards the solid terrain model was varnished with a special white colour, for which previous tests had shown optimum reflection behaviour.