Thilo Wehrmann

TiSeG: A Flexible Software Tool for Time-Series Generation of MODIS Data Utilizing the Quality Assessment Science Data Set

Time series generated from remotely sensed data are important for regional to global monitoring, estimating long-term trends, and analysis of variations due to droughts or other extreme events such as El Nintildeo. Temporal vegetation patterns including phenological states, photosynthetic activity, or biomass estimations are an essential input for climate modeling or the analysis of the carbon cycle. However, long-term analysis requires accurate calibration and error estimation, i.e., the quality of the time series determines its usefulness. Although previous attempts of quality assessment have been made with NOAA-AVHRR data, a first rigorous concept of data quality and validation was introduced with the MODIS sensors. This paper presents the time-series generator (TiSeG), which analyzes the pixel-level quality-assurance science data sets of all gridded MODIS land (MODLand) products suitable for time-series generation. According to user-defined settings, the tool visualizes the spatial and temporal data availability by generating two indices, the number of invalid pixels and the maximum gap length. Quality settings can be modified spatially and temporally to account for regional and seasonal variations of data quality. The user compares several quality settings and masks or interpolates the data gaps. This paper describes the functionality of TiSeG and shows an example of enhanced vegetation index time-series generation with numerous settings for Germany. The example indicates the improvements of time series when the quality information is employed with a critical weighting between data quality and the necessary quantity for meaningful interpolation.

Influence of image fusion approaches on classification accuracy: a case study

While many studies in the field of image fusion of remotely sensed data aim towards deriving new algorithms for visual enhancement, there is little research on the influence of image fusion on other applications. One major application in earth science is land cover mapping. The concept of sensors with multiple spatial resolutions provides a potential for image fusion. It minimises errors of geometric alignment and atmospheric or temporal changes. This study focuses on the influence of image fusion on spectral classification algorithms and their accuracy. A Landsat 7 ETM+ image was used, where six multispectral bands (30 m) were fused with the corresponding 15 m panchromatic channel. The fusion methods comprise rather common techniques like Brovey, hue‐saturation‐value transform, and principal component analysis, and more complex approaches, including adaptive image fusion, multisensor multiresolution image fusion technique, and wavelet transformation. Image classification was performed with supervised methods, e.g. maximum likelihood classifier, object‐based classification, and support vector machines. The classification was assessed with test samples, a clump analysis, and techniques accounting for classification errors along land cover boundaries. It was found that the adaptive image fusion approach shows best results with low noise content. It resulted in a major improvement when compared with the reference, especially along object edges. Acceptable results were achieved by wavelet, multisensor multiresolution image fusion, and principal component analysis. Brovey and hue‐saturation‐value image fusion performed poorly and cannot be recommended for classification of fused imagery.

Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification

We present a novel and innovative automated processing environment for the derivation of land cover (LC) and land use (LU) information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain) enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT) for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS), as introduced by the Open Geospatial Consortium (OGC), are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network) enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data

During recent years, synthetic aperture radar (SAR) data have been increasingly used for flood mapping. New radar satellites especially, such as TerraSAR-X, Radarsat-2 and COSMO-SkyMed, provide high-resolution data with high potential for fast and reliable detection of inundated areas. This article compares three simple approaches to derive water areas from SAR data in relation to the German–Vietnamese project, Water-related Information System for the Sustainable Development of the Mekong Delta (WISDOM). Two methods are pixel based and use histogram-based grey-level thresholds, as well as a homogeneity criterion for classification. The third approach is object based and applies characteristic attributes of water objects such as grey value, texture and relations to neighbouring objects. Further discussed are the influence of a variation of the thresholds and the challenges to validate water masks derived from active remote-sensing data. We implemented one of the introduced approaches for surface water derivation in a water mask processor for automatic water mask calculation from radar satellite imagery (WaMaPro). This fully automatic processing chain was developed to process TerraSAR-X and Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) imagery in order to meet the demands for automatic flood monitoring.

MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data

Estimating forest area at a national scale within the United Nations program of Reducing Emissions from Deforestation and Forest Degradation (REDD) is primarily based on land cover information using remote sensing technologies. Timely delivery for a country of a size like Mexico can only be achieved in a standardized and cost-effective manner by automatic image classification. This paper describes the operational land cover monitoring system for Mexico. It utilizes national-scale cartographic reference data, all available Landsat satellite imagery, and field inventory data for validation. Seven annual national land cover maps between 1993 and 2008 were produced. The classification scheme defined 9 and 12 classes at two hierarchical levels. Overall accuracies achieved were up to 76%. Tropical and temperate forest was classified with accuracy up to 78% and 82%, respectively. Although specifically designed for the needs of Mexico, the general process is suitable for other participating countries in the REDD+ program to comply with guidelines on standardization and transparency of methods and to assure comparability. However, reporting of change is ill-advised based on the annual land cover products and a combination of annual land cover and change detection algorithms is suggested.

Generation and Assessment of MODIS Time Series using Quality Information

Monitoring and modeling extensive Earth surface processes for regional to global applications such as carbon budgeting or biomass estimation requires time series derived from remotely sensed imagery. Time series are also needed for discrimination of long-term land cover change from short-term variations, mapping of vegetation dynamics and improved land cover mapping and update. The results of these applications, however, clearly depend on the quality of the time series. Cloud coverage, high aerosol content, adverse view and illumination angles, or sensor defects affect and corrupt the data and may lead to false conclusions. Value-added MODIS data contain detailed pixel level quality information. This source of meta-data highly suits for data analysis or generation of time series. A software package, called Time Series Generator (TiSeG), has been developed to analyze data quality and estimate the quality of time series to be generated. TiSeG meets the challenge to weight the data quality against the quantity of available data for meaningful time series construction.

An automated object-based classification approach for updating CORINE land cover data

In this paper, an object based classification approach for land cover and land use classes is presented, and first test results are shown. Recently, there is an increasing demand for information on actual land cover resp. land use from planning, administration and science institutions. Remote sensing provides timely information products in different geometric and thematic scales. The effort to manually classify land use data is still very high. Therefore a new approach is required to incorperate automated image classification to human image understanding. The proposed approach couples object-based clasification technique -a rather new trend in image classification - with machine learning capacities (Support Vector Classifier) depending on information levels. To ensure spatial and spectral transferability of the classification scheme, the data has to be passed through several generalisation levels. The segmentation generates homogeneous and contiguous image objects. The hierarchical rule type uses direct and derived spectral attributes combined with spatial features and information extracted from the metadata. The identified land cover objects can be converted into the current CORINE classes after classification.

A Water-Related Web-Based Information System for the Sustainable Development of the Mekong Delta

One goal of the WISDOM (Water-related Information System for the Sustainable development of the Mekong Delta) project lies in the development and implementation of an innovative water information system containing all the outcomes and results of the different research disciplines involved in the bi-lateral German-Vietnamese project. The topics stem from natural and social sciences and cover all major issues that come into play in an Integrated Water Resources Management (IWRM) system. In line with the IWRM principles, the envisaged information system communicates all research results to local stakeholders and decision makers. It is a web-based geo information system, additionally incorporating non-geographic data such as statistics, literature and reports, legal documents, institutional mappings and others. One major challenge, therefore, lies in the management of heterogeneous data for easy access, so that non-GIS expert users can easily find information to support them in their decision-making. This chapter gives an overview of the main requirements that constitute the system’s architecture and describes the layered architecture with its data management tier, logic tier and presentation tier. A short discussion on the sustainable operation, after the project ends, is also given in the outlook section.

Reply to Mas et al.: Comment on Gebhardt et al. MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data. Remote Sens. 2014, 6, 3923–3943

Mas, J.F. et al. have submitted a paper [1] for publication, which aims to respond to a paper published by Gebhardt et al. [2]. Mas, J.F. et al. had received a consultancy in 2013 to assess the quality of the early prototype products partly described in Gebhardt et al. in 2014. This consultancy, although a formal non-disclosure agreement had not been demanded, was awarded under the mutual understanding that the data handed over to Mas et al. constitute the early development phase of the program. Therefore, Mas et al. had been asked to give an assessment on the quality of the prototypes to obtain a proof of concept for the proposed workflow of MAD-Mex. It was clear that this assessment would suffer from limited availability of high quality training and validation data available in 2013. Mas et al. finally did not execute the consultancy due to the limited vector processing capacities in their lab. In October 2014, we sent the latest products, version 4.2 of the MAD-Mex products, including the more than 200,000 validation points gathered from independent expert interpreters of all Mexican ecosystems. Mas et al. did not respond to this transfer or to our request to collaborate in the quality control and assessment of MAD-Mex.