Manfred Keil

On the suitability of MODIS Time Series Metrics to Map Vegetation Types in Dry Savanna Ecosystems: A Case Study in the Kalahari of NE Namibia

The characterization and evaluation of the recent status of biodiversity in Southern Africa’s Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes. This paper presents an integrated concept for vegetation type mapping in a dry savanna ecosystem based on local scale in-situ botanical survey data with high resolution (Landsat) and coarse resolution (MODIS) satellite time series. In this context, a semi-automated training database generation procedure using object-oriented image segmentation techniques is introduced. A tree-based Random Forest classifier was used for mapping vegetation type associations in the Kalahari of NE Namibia based on inter-annual intensity- and phenology-related time series metrics. The utilization of long-term inter-annual temporal metrics delivered the best classification accuracies (Kappa = 0.93) compared with classifications based on seasonal feature sets. The relationship between annual classification accuracies and bi-annual precipitation sums was conducted using data from the Tropical Rainfall Measuring Mission (TRMM). Increased error rates occurred in years with high rainfall rates compared to dry rainy seasons. The variable importance was analyzed and showed high-rank positions for features of the Enhanced Vegetation Index (EVI) and the blue and middle infrared bands, indicating that soil reflectance was crucial information for an accurate spectral discrimination of Kalahari vegetation types. Time series features related to reflectance intensity obtained increased rank-positions compared to phenology-related metrics.

Combined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland

Abstract A key factor in the implementation of productive and sustainable cultivation procedures is the frequent and area-wide monitoring of cropland and grassland. In particular, attention is focused on assessing the actual status, identifying basic trends and mitigating major threats with respect to land-use intensity and its changes in agricultural and semi-natural areas. Here, multi-seasonal analyses based on satellite Earth Observation (EO) data can provide area-wide, spatially detailed and up-to-date geo-information on the distribution and intensity of land use in agricultural and grassland areas. This study introduces an operational, application-oriented approach towards the categorization of agricultural cropland and grassland based on a novel scheme combining multi-resolution EO data with ancillary geo-information available from currently existing databases. In this context, multi-seasonal high (HR) and medium resolution (MR) satellite imagery is used for both a land parcel-based determination of crop types as well as a cropland and grassland differentiation, respectively. In our experimental analysis, two HR IRS-P6 LISS-3 images are first employed to delineate the field parcels in potential agricultural and grassland areas (determined according to the German Official Topographic Cartographic Information System – ATKIS). Next, a stack of seasonality indices is generated based on 5 image acquisitions (i.e., the two LISS scenes and three additional IRS-P6 AWiFS scenes). Finally, a C5.0 tree classifier is applied to identify main crop types and grassland based on the input imagery and the derived seasonality indices. The classifier is trained using sample points provided by the European Land Use/Cover Area Frame Survey (LUCAS). Experimental results for a test area in Germany assess the effectiveness of the proposed approach and demonstrate that a multi-scale and multi-temporal analysis of satellite data can provide spatially detailed and thematically accurate geo-information on crop types and the cropland-grassland distribution, respectively.

Pan-European Grassland Mapping Using Seasonal Statistics From Multisensor Image Time Series

Grasslands cover approximately 40% of the Earths surface. Low-cost tools for inventory, management, and monitoring are needed because of their great expanse, diversity, and the importance for environmental processes. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and large-scale grassland management. In the context of “GIO land” (Copernicus Initial Operations land program), which is currently contracted by the European Environment Agency, a high-resolution grassland layer of 39 European countries is being created with an overall classification accuracy of better than 80%. Since grassland canopy density, chlorophyll status, and ground cover (GC) are highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use image time series to characterize the phenological dynamics of grasslands throughout the year in order to discriminate between grasslands and other vegetation with similar spectral responses. This paper describes an operational approach based on a multisensor concept that employs optical multitemporal and multiscale satellite imagery to generate the high-resolution pan-European grassland layer. The approach is based on the supervised decision tree classifier C5.0 in combination with previous image segmentation and seasonal statistics for various vegetation indices (VIs). Results from the grassland classification for Hungary are presented. The accuracy assessment for this classification was carried out using 328 independent sample points derived from a ground-based European field survey program (LUCAS) and current CORINE Land Cover data. The grassland classification approach is explained in detail on the example of Hungary where an overall accuracy of 92.2% has been reached.

Forest mapping using satellite imagery. The Regensburg map sheet 1:200 000 as example

Abstract In research on forest mapping with data from the Landsat Thematic Mapper special attention has been given to work aimed at covering large sections of Bavaria on a scale of 1:200 000. The first 1:200 000 sheet processed was the Regensburg sheet. The classification was based on signature analysis of training areas. With that information, the stands were studied according to tree species, age class and mixture distribution. Multitemporal methods were applied to separate forest from nonforest areas. In addition to the main classes of deciduous, coniferous and mixed deciduous/coniferous, the separation of spruce and pine was included.

A Multi-Scale Approch for Retreiving Proportional Cover of Life Forms

This study presents a multi-scale procedure to derive continuous proportional cover of woody vegetation in savanna ecosystems. QuickBird data was classified to define a continuous training and validation data set of woody cover proportions. Using a regression tree algorithm based on Landsat TM data, this woody cover information was extrapolated to an area of approximately 185 km times 185 km. The resulting 30 m map of the Namibian North-eastern Kalahari Woodland was aggregated to 250 m and 500 m resolutions. Comparisons of the global MODIS VCF product with the regionally adjusted multi-scale fractional cover map indicate that VCF tree cover is generally underestimated in the study area and confusions between tree and dense shrub cover occur.

Differentiation of Crop Types and Grassland by Multi-Scale Analysis of Seasonal Satellite Data

The implementation of productive and sustainable cultivation procedures is a major effort regarding the agricultural production in the European Community. However, political, economic and environmental factors impact the cultivation strategies directly and indirectly, and therewith strongly determine the condition and transformation of the cultivated and natural landscape. To assess the actual status, identify basic trends and mitigate major threats with respect to the agricultural production and its impact on the cultural and natural landscape, a frequent and area-wide monitoring of cropland and grassland is required. Satellite-based earth observation (EO) provides ideal capabilities for the area-wide and spatially detailed provision of up-to-date geo-information on the agricultural land use and the properties of the cultivated landscape. A specific benefit of EO is given by analysing multi-seasonal data acquisitions. Intra-annual time series facilitate the analysis of the phenological behaviour of the main crop and grassland types – key information with respect to the characterisation of the land use intensity and its impacts on the environment. The presented approach focuses on a seasonal analysis of multi-scale EO time series to classify main crop types and differentiate between cropland and grassland for given areas of interest on the basis of field parcels. The areas of interest are typically existing land use / land cover (LULC) data sets (e.g. national topographic data, CORINE Land Cover, etc.) that show a limited resolution in the semantic and/or spatial domain. Hence, the presented approach is primarily designed to improve the level of thematic/geometric detail for given LULC data sets.

Investigation of forest areas in Germany and Brazil using SAR data of the SIR-C/X-SAR and other SAR missions

In a PI project for ecology and vegetation monitoring multifrequency SAR data of the SIR-C/X-SAR mission in April and October 1994 are under investigation for forest mapping and rainforest monitoring. Test sites are German forest areas in the Harz Mountains and in Bavaria. Within a cooperation with the National Brasilian Space Research Institute (INPE), a region in the state of Acre in the Brasilian rainforest is included. Investigations in the Harz are based on experience with multifrequency polarimetric. AIRSAR data which have been registered in 1991 by an JPL aircraft. In Acre in the Southwestern Amazon, X-SAR data of April and October 1994 are used for studies of the deforestation dynamics, in comparison with ERS-1 SAR data. Classifications of forest/nonforest areas are in introduced, based on different SAR data. Using SIR-C datasets, the different information content of X-, C- and L-band data is discussed for a rainforest area.

Modeling of impervious surface in Germany using Landsat images and topographic vector data

The constant expansion of urban agglomerations in most countries is closely associated with a significant increase of impervious surface (IS). In Europe, robust and cost-effective methods for detection and quantification of IS on a continental or national scale are still rare. Thus, our study focuses on determining the percentage of impervious surface (PIS) for whole Germany based on a combined analysis of Landsat images and vector data on roads and railway networks, using Support Vector Machines (SVM) and GIS functionalities. We developed a procedure which provides functionalities for 1) the modeling of IS for built-up areas (PISB) based on optical earth observation data, 2) the combination of PISB with vector data providing additional information on small-scale infrastructure (PIST) and 3) the spatial aggregation of the combined product (PISBT) to the administrative units of municipalities. Compared to reference data sets of four cities, the results showed a mean absolute error of 19.4 % and a mean standard deviation of 17.3 %. The mean PIS of the total of residential, industrial and transportation-related areas in Germany comes up to 43.0 %, with a minimum in the federal state of Brandenburg (39.3 %) and a maximum in Hessen (46.1 %).

Mapping of grassland using seasonal statistics derived from multi-temporal satellite images

Grasslands cover about 40 % of the earths surface [1]. Due to its great expanse and diversity, low-cost tools for inventory, management and monitoring are needed. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and to support large scale grassland management. In the context of “GIO land” (Copernicus initial operations land), which is currently implemented by the European Environment Agency (EEA) the permanent grasslands of 39 countries in Europe has to be mapped with an overall classification accuracy of more than 80 % [2]. Since grassland canopy density, chlorophyll status and ground cover is highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use time series to characterize the phenological dynamics of grasslands throughout the year to be able to discriminate among them and other vegetation which shows similar spectral response such as crops. The article outlines the adopted classification method using multi-temporal, multi-scale and multi-source remotely sensed data. The approach is based on the supervised decision Tree (DT) classifier C5 in combination with previous image segmentation and seasonal statistics of bio-physical parameters. In this paper the results of entire Hungary are presented. The accuracy assessment of the grassland classification was carried out using 340 sample points derived from a ground-based European field survey program. The multi-temporal grassland classification of Hungary reached an overall accuracy of 92.2 %.