Bernd Fichtelmann

A new self-learning algorithm for dynamic classification of water bodies

In many applications of remote sensing data land-water masks play an important role. In this context they can be a helpful orientation to distinguish dark areas (e.g. cloud shadows, topographic shadows, burned areas, coniferous forests) and water areas. However, water bodies cannot always be classified exactly on basis of available remote sensing data. This fact can be caused by a variety of different physical and biological factors (e.g. chlorophyll, suspended particles, surface roughness, turbid and shallow water and dynamic of water bodies) as well as atmospheric factors (e.g. haze and clouds). On the other hand the best available static water masks also show deficiencies. These are essentially caused by the fact that land-water masks represent only a temporal snapshot of the water bodies distributed worldwide and therefore these masks cannot reflect their dynamic behavior. This paper presents a dynamic self-learning water masking approach for AATSR remote sensing data in the context of integrating high-quality water masks in processing chains for deriving value-added remote sensing data products. As an advantage to conventional water masking algorithms, the proposed approach operates on basis of a static water mask as data base for deriving an optimized dynamic water mask. Significant research effort was spent to develop and validate a dynamic self-learning algorithm and a processing scheme for operational derivation of actual land-water masks as basis for operational interpretation of remote sensing data. Based on this concept actual activities and perspectives for contributions to operational monitoring systems will be presented.

Assessment for remote sensing data: accuracy of interactive data quality interpretation

Earth observation data has become a source for delivering data to monitor environmen¬tal processes. The increased availability of remote sensing data is accompanied by an increasing user demand for additional data infor¬mation to describe remote sensing data of providers (e.g. ESA, European Space Agency). In addition to cloud cover degree, the data usability parameter defined by ESA will be delivered to characterise data quality. While the cloud cover degree refers to the number of cloud pixels only the data usability parameter additionally analyses the cloud distribution. The evaluation of remote sensing data is executed by visual assessment of interpreters. This paper deals with the quantification of subjective influences during the assessment process and on the assessment of the error magnitude of the interactive data usability assessment.

Cloud classification in JPEG-compressed remote sensing data (LANDSAT 7/ETM+)

Environmental parameters required for geo-information modelling are subject to spatial and temporal dynamics. Remote sensing data can contribute to measure those parameters. For that purpose high-accuracy classifications of remote sensing data are required which can be very time-consuming due to the large data volumes involved. In many applications, however, the rapid provision of classified mass data is of higher priority than classification accuracy. One important focus on research and development efforts in the past years has been to optimise the automated interpretation of remote sensing data. Different investigators have shown that this interpretation can both be effective and efficient in JPEG compressed data with acceptable accuracy. This paper presents an operational processing chain for cloud detection in JPEG-compressed quick-look products of LANDSAT 7/ETM+-scenes (compression ratio is 10:1). Two well-developed conventional algorithms are applied to these datasets for cloud detection. Results show that the processing chain developed is stable and produces quality results with substantially compressed mass data.

Design and Implementation of Data Usability Processor into an Automated Processing Chain for Optical Remote Sensing Data

Diverse anthropogenic impacts will trigger worldwide environmental and social problems as e.g. climate change or social transformation processes. To observe these processes current information about status, direction of development and spatial or temporal dynamics of the processes are required. As the demand for current environmental information is increasing, earth observation (EO) and remote sensing (RS) techniques are moving to the focus of interest.

Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to MERIS Data

In many global applications of remote sensing land-water masks can improve the interpretation results. Their use can be of advantage to distinguish between different types of dark areas (e.g. cloud or topographic shadows, burned areas, coniferous forests, water areas). On one hand, water bodies cannot always be classified exactly on basis of available remote sensing data. On the other hand static land-water masks of different quality are available. But the main deficiencies are caused by the fact that land-water masks represent only a temporal snapshot of the water bodies. A dynamic self-learning water masking approach was developed at first for AATSR data to combine the advantages of static mask with results of pre-classifications. This paper presents the adaption of this procedure for MERIS remote sensing data. As before with AATSR data the aim consists in integrating high-quality water masks in processing chains for deriving value-added remote sensing data products. The results for some regional examples demonstrate the quality of masks and the advantages to conventional water masking algorithms. Furthermore, it will be discussed, that it is useful for a global water mask of high quality to integrate further special masks as cloud or in particular terrain shadow masks. At least, the land-water mask plays not only an important role in complex processing chains itself is the result of a complex procedure. Beside the results have shown successful transfer of a developed processing scheme for operational deriving of actual land-water masks to data of a second sensor, the adaption to further sensors or the adaption of the processor to other object types as e.g. forest will be possible in future as part of operational monitoring systems.

Automated Generation of Value-Added Products for the Validation of Remote Sensing Information Based on In-Situ Data

Available in-situ (IS) data are an essential prerequisite for the validation of remote sensing (RS) data and information products. Due to the fact that IS data supply is very labour- and cost-intensive, ground-truth data acquisition needs to be operational and automated. Moreover, measurements should be taken simultaneously to data acquisition by operational RS systems. To be able to deliver representative and validated IS data applied to the validation of complimentary operational RS data, locations of measurement stations will have to be optimised to be representative for a particular geographical space. This paper presents a novel approach to provide complementary IS data simultaneously to RS data acquisition. It outlines the installation and operation of an automated IS measurement network for environmental parameters in the DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) test site. To process the data, an operational processing chain has been implemented and used for IS data volumes. IS data measurements are evaluated to establish a basis for generating and validating value-added RS products.

High Temperature Fire Experiment for TET-1 and Landsat 8 in Test Site DEMMIN (Germany)

In 2012, the German Aerospace Center (DLR) launched the small satellite TET-1 (Experimental Technology Carrier) as a test platform for new satellite technologies and as a carrier for the Multi-Spectral Camera System (MSC) with five spectral bands (Green, Red, Near Infrared, Middle Infrared, and Thermal Infrared). The MSC has been designed to provide quantitative parameters (e.g. fire radiative power, burned area) observing high-temperature events. The detection of such events provides information for operational support to fire brigades, to change detection of hotspots, to assess CO2 emissions of burning vegetation, and, finally, contributes to the monitoring programs that support climate models. In order to investigate the sensitivity and accuracy of the MSC system, a calibration and validation fire campaign was developed and executed, to derive characteristic signal changes of corresponding pixels in the MWIR and LWIR bands. The planning and execution of the validation campaign and the results are presented.

A New Approach of Geo-Rectification for Time Series Satellite Data Based on a Graph-Network

Earth observation is indisputably one of the most important data sources for current synoptic geo-data in diverse environmental applications. Nevertheless, obtained thematic information by means of Earth observation is only as good as the quality of the pre-processed data. Important pre-processing steps are e.g. data usability assessment, geo-referencing and atmospheric correction. While data usability assessment or atmospheric correction expects radiometric corrected data for a thematic interpretation, the accuracy of geo-location of interpreted data is ensured by geo-referencing. This applies especially to multi-temporal analyses of environmental processes. In this case, precise spatial allocation of data represents a prerequisite for a correct interpretation of process dynamics. The paper is dealed with a new geo-referencing algorithm. Corresponding graph-networks in reference data as well as in remote sensing data which are based on virtual object points e.g. centroids will be used for geo-rectification.

Data Usability Processor for Optical Remote Sensing Imagery: Design and Implementation into an Automated Processing Chain

A range of global environmental and social problems, such as climate change or social transformation processes, are aggravated by diverse anthropogenic impacts. To monitor, analyse and combating these processes, topical information on the status, development, spatial and temporal dynamics of them is an indispensable prerequisite. The growing, frequently rapid demand for global and regional data in relevant geographical, geometric, semantic and temporal resolution can only be met by remote sensing data the majority of which are available on an operational scale. Not only does the availability of data present a major obstacle for the above applications, but also rapid processing of the acquired remote sensing data is a severe bottleneck for the provision of the required data for, e.g. time-critical investigations. These problems can be addressed by developing an automated processing chain to derive value-added data producing from the remote sensing input data. Effective automated data processing necessitates a data quality assessment prior to actual processing. This paper deals with a processor for an automated data usability assessment that can be integrated into an automated processing chain for operative value adding.