Annekatrin Metz

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.

Synergetic use of TerraSAR-X and Radarsat-2 time series data for identification and characterization of grassland types - a case study in Southern Bavaria, Germany

In the context of global change, alteration of landscapes and loss of biodiversity, the monitoring of habitats, vegetation types and their changes have become extraordinary important. In this paper, first results from a study that analyses the differentiability of NATURA2000 habitats and HNV grassland with imaging radar data are presented. Therefore, Kennaugh elements derived from TerraSAR-X and Radarsat-2 dual pol (VV/VH) time series data are used, both separately and in combination, to model the distribution of these classes with the Maximum-Entropy principle. The preliminary results show that the multi-frequency approach enables - compared to single frequency analyses - a finer differentiation between scatterers in the size of 3-6 cm (e.g. 7120, 7230 and HNV grassland).

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.

Global urban growth monitoring by means of SAR data

In the last few decades, the increasing amount of people migrating to cities resulted in the steady spatial expansion of urban agglomerations and nowadays more than half of the worlds population currently consists of urban dwellers. Accordingly, several initiatives have been recently carried out to globally monitor the actual extent of urban areas by means of Earth observation (EO) data. In this framework, the German Aerospace Center (DLR) has recently produced a global map of built-up areas at the unique spatial resolution of 12m, namely the Global Urban Footprint (GUF). Specifically, the GUF is derived from very high resolution (VHR) SAR imagery acquired in the context of the Tan-DEM-X radar mission between 2011 and 2013. In order to map urban growth occurred in the last two decades, in this paper we adapt the approach for producing the GUF to archived ESA ERS SAR and Envisat ASAR Image Mode imagery available at 30m spatial resolution from 1991 to 2012. Preliminary experimental results assess the effectiveness of the proposed method and its potential to be employed for monitoring urban growth worldwide.

Mapping crop distribution in administrative districts of southwest Germany using multi-sensor remote sensing data

In the face of global change, concepts for sustainable land management are increasingly requested, among others to cope with the rapidly increasing energy demand. High resolution land use classifications can contribute spatially explicit information suitable for land use planning. In this study, the coverage of cereal crops was derived for two regions in Baden-Wuerttemberg and Rhineland-Palatinate - Germany, as well as in the Alsace - France, by classifying multitemporal and multi-scale remote sensing data. The presented methodology shall be used as basic input for high resolution bio-energy potential calculations. Segmentation of pan-merged 15 m Landsat 7 ETM+ data and pre-classification with CORINE data was applied to derive homogenous objects assumed to approximate the field boundaries of agricultural areas. Seven acquisitions of moderate resolution IRS-P6 AWiFS data (60 m) recorded during the vegetation period of 2007 were used for the subsequent classification of the objects. Multiple classification and regression trees (random forest) were selected as classification algorithm due to their ability to consider non-linear distributions of class values in the feature space. Training and validation was based on a subset of 1724 samplings of the official European land use survey LUCAS (Land Use/ Cover Area Frame Statistical Survey). Altogether, the object based approach resulted in an overall accuracy of 74 %. The use of 15 m Landsat for mapping field objects were identified to be one major obstacle caused by the characteristically small agricultural units in Southwest Germany. Improvements were also achieved by correcting the LUCAS samples for location errors.

From top-down land use planning intelligence to bottom-up stakeholder engagement for smart cities - a case study: DECUMANUS service products

Intelligence delivered by earth observation (EO) satellites performs a vital role in supporting ICT enabled urban governance, and the creation of decision making tools delivering integrated urban planning. This paper reviews the DECUMANUS project experience, detailing the development of the EO derived tools, and evaluating the service products that facilitate the deployment of top-down expertise in land use planning. The central purpose of the paper is to assess the potential for use of these DECUMANUS high resolution EO images and data, also to support bottom-up participatory planning, promoting co-design. It is concluded: 1) EO derived images and associated data offer great opportunity to deliver top-down decision making tools, which combined with auxiliary data, including participatory sensing data, effectively support integrated urban planning; 2) EO derived images also offer substantial potential as communication tools, enabling citizens to make more informed and responsible choices and participate in co-designed urban planning.

Classification of grassland types by means of multi-seasonal TerraSAR-X and RADARSAT-2 imagery

The management and protection of grassland biodiversity is of utmost importance as they play a key role in the carbon and hydrological cycle. Therefore, the analysis of their dynamics is of great value given the current ongoing intensification of agricultural land use. To this aim, in this paper we present a novel approach for monitoring grassland dynamics based on polarimetric high-resolution SAR imagery, which is i) capable of handling either dual- or quad-polarization multi-temporal data and ii) supports targeted classification. Results based on dualpol TerraSAR-X as well as dual- and quadpol Radarsat-2 data acquired over a test area in Bavaria (Germany) in 2011 are extremely promising and confirm the effectiveness of the proposed approach.

European Area Frame Sampling Based on Very High Resolution Images

Initiated in 2007, the Area Frame Sampling Europe subtask of the Seasonal and Annual Change Monitoring Service (SATChMo) Core Service in the geoland2 project delivered its final products in 2012. Three of these are described in this paper: (i) an Area Frame Sampling scheme design that aims at optimizing the statistical accuracy when extrapolating a land cover classification at reasonable cost, (ii) a semi-automatic classification tool that is able to discriminate 10 land cover classes on VHR samples with 0.25ha minimum mapping unit (MMU), and (iii) a highly automatized change detection tool based on Multivariate Alteration Detection (MAD) approach that additionally employs Normalised Difference Vegetation Index (NDVI) and texture characteristics. This later step, as well as giving change/no-change mask provides directions of changes in three main categories, artificialization, revegetation, and devegetation. The algorithms were cast into the form of production chains starting with data acquisition and processing, through the main processing to validation and product dissemination via Spatial Data Infrastructure servers. The whole process was tested on representative set of 114 sites from across the European Union (EU).

Earth observation-supported service platform for the development and provision of thematic information on the built environment — the TEP-Urban project

The Sentinel fleet will provide a so-far unique coverage with Earth observation (EO) data and therewith new opportunities for the implementation of methodologies to generate innovative geo-information products and services. It is here where the TEP Urban project is supposed to initiate a step change by providing an open and participatory platform based on modern Information and Communication Technologies (ICTs) that enable any interested user to easily exploit EO data pools, in particular those of the Sentinel missions, and derive thematic information on the status and development of the built environment from these data. Key component of TEP Urban project is the implementation of a web-based platform employing distributed high-level computing infrastructures and providing key functionalities for i) high-performance access to satellite imagery and derived thematic data, ii) modular and generic state-of-the-art pre-processing, analysis, and visualization techniques, iii) customized development and dissemination of algorithms, products and services, and iv) networking and communication. This contribution introduces the main facts about the TEP Urban project, including a description of the general objectives, the platform systems design and functionalities, and the preliminary portfolio products and services available at the TEP Urban platform.

Exploiting Earth observation data pools for urban analysis - The TEP URBAN project

Large amounts of Earth observation (EO) data have been collected to date, to increase even more rapidly with the upcoming Sentinel data. All this data contains unprecedented information, yet it is hard to retrieve, especially for nonremote sensing specialists. As we live in an urban era, with more than 50% of the world population living in cities, urban studies can especially benefit from the EO data. Information is needed for sustainable development of cities, for the understanding of urban growth patterns or for studying the threats of natural hazards or climate change. Bridging this gap between the technology-driven EO sector and the information needs of environmental science, planning, and policy is the driver behind the TEP-Urban project. Modern information technology functionalities and services are tested and implemented in the Urban Thematic Exploitation Platform (U-TEP). The platform enables interested users to easily exploit and generate thematic information on the status and development of the environment based on EO data and technologies. The beta version of the web platform contains value added basic earth observation data, global thematic data sets, and tools to derive user specific indicators and metrics. The code is open source and the architecture of the platform allows adding of new data sets and tools. These functionalities and concepts support the four basic use scenarios of the U-TEP platform: explore existing thematic content; task individual on-demand analyses; develop, deploy and offer your own content or application; and, learn more about innovative data sets and methods.