Sören Hese

Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest

The 2012 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society (GRSS) aimed at investigating the potential use of very high spatial resolution (VHR) multi-modal/multi-temporal image fusion. Three different types of data sets, including spaceborne multi-spectral, spaceborne synthetic aperture radar (SAR), and airborne light detection and ranging (LiDAR) data collected over the downtown San Francisco area were distributed during the Contest. This paper highlights the three awarded research contributions which investigate (i) a new metric to assess urban density (UD) from multi-spectral and LiDAR data, (ii) simulation-based techniques to jointly use SAR and LiDAR data for image interpretation and change detection, and (iii) radiosity methods to improve surface reflectance retrievals of optical data in complex illumination environments. In particular, they demonstrate the usefulness of LiDAR data when fused with optical or SAR data. We believe these interesting investigations will stimulate further research in the related areas.

Robust Extraction of Urban Land Cover Information From HSR Multi-Spectral and LiDAR Data

This paper focuses on the description and demonstration of a simple, but effective object-based image analysis (OBIA) approach to extract urban land cover information from high spatial resolution (HSR) multi-spectral and light detection and ranging (LiDAR) data. Particular emphasis is put on the evaluation of the proposed method with regard to its generalization capabilities across varying situations. For this purpose, the experimental setup of this work includes three urban study areas featuring different physical structures, four sets of HSR optical and LiDAR input data, as well as statistical measures to enable the assessment of classification accuracies and methodological transferability. The results of this study highlight the great potential of the developed approach for accurate, robust and large-area mapping of urban environments. Users and producers accuracies observed for all maps are almost consistently above 80%, in many cases even above 90%. Only few larger class-specific errors occur mainly due to the simple assumptions on which the method is based. The presented feature extraction workflow can therefore be used as a template or starting point in the framework of future urban land cover mapping efforts.

From land cover-graphs to urban structure types

Urban structure types (UST) are an initial interest and basic instrument for monitoring, controlling and modeling tasks of urban planners and decision makers during ongoing urbanization processes. This study focuses on a method to classify UST from land cover (LC) objects, which were derived from high resolution satellite images. The topology of urban LC objects is analyzed by implementing neighborhood LC-graphs. Various graph measures are examined by their potential to distinguish between different UST, using the machine learning classifier random forest. Additionally the influence of different parameter settings of the random forest model, the reduction of training samples, and the graph measure importance is analyzed. An independent test set is classified and validated, achieving an overall accuracy of 87%. It was found that the height of the building with the highest node degree has a strong impact on the classification result.

High spatial resolution image object classification for terrestrial oil spill contamination mapping in West Siberia.

This work is a part of the OSCaR pilot study (Oil Spill Contamination mapping in Russia). A synergetic concept for an object based and multi temporal mapping and classification system for terrestrial oil spill pollution using a test area in West Siberia is presented. An object oriented image classification system is created to map contaminated soils, vegetation and changes in the oil exploration well infrastructure in high resolution data. Due to the limited spectral resolution of Quickbird data context information and image object structure are used as additional features building a structural object knowledge base for the area. The distance of potentially polluted areas to industrial land use and infrastructure objects is utilized to classify crude oil contaminated surfaces. Additionally the potential of Landsat data for dating of oil spill events using change indicators is tested with multi temporal Landsat data from 1987, 1995 and 2001. OSCaR defined three sub-projects: (1) high resolution mapping of crude oil contaminated surfaces, (2) mapping of industrial infrastructure change, (3) dating of oil spill events using multi temporal Landsat data. Validation of the contamination mapping results has been done with field data from Russian experts provided by the Yugra State University in Khanty-Mansiyskiy. The developed image object structure classification system has shown good results for the severely polluted areas with good overall classification accuracy. However it has also revealed the need for direct mapping of hydrocarbon substances. Oil spill event dating with Landsat data was very much limited by the low spatial resolution of Landsat TM 5 data, small scale character of oil spilled surfaces and limited information about oil spill dates.

Graph-Based Mapping of Urban Structure Types From High-Resolution Satellite Image Objects—Case Study of the German Cities Rostock and Erfurt

Ongoing urbanization processes have increased the demand for monitoring, controlling, and modeling services, with urban structure types as an initial interest. While urban land cover (LC) can be derived directly from high-resolution satellite images, urban land use (LU) is achieved through analyzing a combination of structural, functional, spatial, morphological, and topological attributes of the various LC classes. The objective of this letter is to distinguish urban LU classes on the basis of distances between buildings incorporated into a graph-based concept. The method was developed using cadastral data (ALK) for the German city of Rostock, then applied to the LC building objects derived from Quickbird data. Building distribution was examined and distances between buildings were used as an attribute for graph generation. Two graph measures (beta index, clustering coefficient) were analyzed resulting in two groups of LU categories. Transferability to a different urban area was tested without adaptions. Similar building distribution and LC extraction quality were found to be crucial for transferability tests. Distances between buildings are an important property for deriving LU classes, but should be accompanied by additional LC attributes to improve LU separability.

Identification of land surface temperature and albedo trends in AVHRR Pathfinder data from 1982 to 2005 for northern Siberia

The arctic regions are highly vulnerable to climate change. Climate models predict an increase in global mean temperatures for the upcoming century. The arctic environment is subject to significant changes of the land surface. Especially the changes of vegetation pattern and the phenological cycle in the taiga–tundra transition area are of high importance in climate change research. This study focuses on time series and trend analysis of land surface temperature, albedo, snow water equivalent, and normalized difference vegetation index information in the time period of 1982–2005 for northern Siberia. The findings show strong dependencies between these parameters and their inter-annual dynamics, which indicate changes in vegetation growing period. We found a strong negative correlation between land surface temperature and albedo conditions for the beginning (60–90%) of the growing season for selected hot spot trend regions in northern Siberia.

Evaluation of red-edge spectral information for biotope mapping using RapidEye

Mapping of Landscape Protection Areas with regard to user requirements for detailed land cover and biotope classes has been limited by the spatial and temporal resolution of Earth observation data. With the new spatial high resolution RapidEye data providing an additional channel in the red-edge region potentially new possibilities for vegetation mapping should be investigated. The presented work is part of the ENVILAND-2 project, which focuses on the complementary use of RapidEye and TerraSAR-X data to derive land cover and biotope classes as needed by the environmental agencies. The goal is to semi-automatically update the corresponding maps by utilising more Earth observation data and less field work derived information. The red-edge spectral region located between the red and near infrared (NIR) wavelengths, has proven to held valuable information on vegetation type, age and condition. In this study the goal is to evaluate the red-edge spectral information compared to the shorter and longer wavelength of the RapidEye sensor. This is done with regard to the classification capability of different land cover classes. Four RapidEye images were used covering two study sites: 1. Rostocker Heide, Mecklenburg-Vorpommern and 2. Elsteraue, Saxony. The spectral bands were analysed for redundant information by using regression and hypothesis testing. For the rededge band and for every class combination present in the study area different separability measurements like divergence or Bhattacharyya distance were computed. As result there are for every class a separability values. The separability values are provided for all spectral bands. A comparison of the values showed the applicability of the red-edge for the classification. Results have shown that additional red-edge information leads to similar class separability for vegetation classes as using red and NIR spectral information. Some specific classes can be classified with a higher accuracy by additional using the red-edge information.

Pan-arctic land cover mapping and fire assessment for the ESA Data User Element Permafrost

The paper presents first results of a pan-boreal scale land cover harmonization and classification. A methodology is presented that combines global and regional vegetation datasets to extract percentage cover information for different vegetation physiognomy and barren for the pan-arctic region within the ESA Data User Element Permafrost. Based on the legend description of each land cover product the datasets are harmonized into four LCCS (Land Cover Classification System) classifiers which are linked to the MODIS Vegetation Continuous Field (VCF) product. Harmonized land cover and Vegetation Continuous Fields products are combined to derive a best estimate of percentage cover information for trees, shrubs, herbaceous and barren areas for Russia. Future work will concentrate on the expansion of the developed methodology to the pan-arctic scale. Since the vegetation builds an isolation layer, which protects the permafrost from heat and cold temperatures, a degradation of this layer due to fire strongly influences the frozen conditions in the soil. Fire is an important disturbance factor which affects vast processes and dynamics in ecosystems (e.g. biomass, biodiversity, hydrology, etc.). Especially in North Eurasia the fire occupancy has dramatically increased in the last 50 years and has doubled in the 1990s with respect to the last five decades. A comparison of global and regional fire products has shown discrepancies between the amounts of burn scars detected by different algorithms and satellite data

Expanding an urban structure type mapping approach from a subarea to the entire city of Berlin

Each city exhibits recurring patterns consisting of similar building types, vegetation structures, and open spaces, enabling environmental and socio-economic investigations of the urban fabric. In this study, urban structure types (UST) of the city of Berlin are mapped on the basis of a prior land cover classification utilizing a synergistic approach of knowledge based classification and Random Forests. The results are then compared to the outcomes of a previous analysis regarding a subarea of the utilized high spatial resolution airborne data. Results show that UST classification based on a combination of prototype objects and Random Forests is suitable to generate accurate UST maps for these areas with only minor adaptations. Future analyses will focus on transferring the processes to different German cities and data of several sensors.

An object-based multisensoral approach for the derivation of urban land use structures in the city of Rostock, Germany

The present work is part of the Enviland-2 research project, which investigates the synergism between radar- and optical satellite data for ENVIronment and LAND use applications. The urban work package of Enviland aims at the combined analysis of RapidEye and TerraSAR-X data for the parameterization of different urban land use structures. This study focuses on the development of a transferable, object-based rule set for the derivation of urban land use structures at block level. The data base consists of RapidEye and TerraSAR-X imagery, as well as height information of a LiDAR nDSM (normalized Digital Surface Model) and object boundaries of ATKIS (Official Topographic Cartographic Information System) vector data for a study area in the city of Rostock, Germany. The classification of various land cover units forms the basis of the analysis. Therefore, an object-based land cover classification is implemented that uses feature level fusion to combine the information of all available input data. Besides spectral values also shape and context features are employed to characterize and extract specific land cover objects as indicators for the prevalent land use. The different land use structures are then determined by typical combinations and constellations of the extracted land use indicators and land cover proportions. Accuracy assessment is done by utilizing the available ATKIS information. From this analysis the land use structure classes residential, industrial/commercial, other built-up, allotments, sports facility, forest, grassland, other green spaces, squares/parking areas and water are distinguished with an overall accuracy of 63.2 %.