Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high-and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR) data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the OPEN ACCESSRemote Sens. 2011, 3 879 services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove ecosystem mapping, as well as the numerous methods and techniques used for data analyses, and to further discuss their potential and limitations.

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3924 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.

Digital image processing has the potential to support the identification of plant species required for site-specific weed control in grassland swards. The present study focuses on the identification of one of the most invasive and persistent weed species on European grassland, the broad-leaved dock (Rumex obtusifolius L., R.o.), in complex mixtures of perennial ryegrass with R.o. and other herbs.A total of 108 digital photographs were obtained from a field experiment under constant recording geometry and illumination conditions. An object-oriented image classification was performed. Image segmentation was done by transforming the red, green, blue (RGB) colour images to greyscale intensity images. Based on that, local homogeneity images were calculated and a homogeneity threshold (0.97) was applied to derive binary images. Finally, morphological opening was performed. The remaining contiguous regions were considered to be objects. Features describing shape, colour and texture were calculated for each of these objects. A Maximum-likelihood classification was done to discriminate between the weed species. In addition, rank analysis was used to test how combinations of features influenced the classification result.The detection rate of R.o. varied with the training dataset used for classification. Average R.o. detection rates ranged from 71 to 95% for the 108 images, which included more than 3,600 objects. Misclassifications of R.o. occurred mainly with Plantago major (P.m.). Between 9 and 16% R.o. objects were classified incorrectly as P.m. and 17-24% P.m. objects were misclassified as R.o. The classification result was influenced by the defined object classes (R.o., P.m., T.o., soil, residue vs. R.o., residue). For instance, classification rates were 86-91% and 65-82% for R.o. exclusively and R.o. against the remaining herb species, respectively.

In Gebhardt et al. (2006) an object-oriented image classification algorithm was introduced for detecting Rumex obtusifolius (RUMOB) and other weeds in mixed grassland swards, based on shape, colour and texture features. This paper describes a new algorithm that improves classification accuracy. The leaves of the typical grassland weeds (RUMOB, Taraxacum officinale, Plantago major) and other homogeneous regions were segmented automatically in digital colour images using local homogeneity and morphological operations. Additional texture and colour features were identified that contribute to the differentiation between grassland weeds using a stepwise discriminant analysis. Maximum-likelihood classification was performed on the variables retained after discriminant analysis. Classification accuracy was improved by up to 83% and Rumex detection rates of 93% were achieved. The effect of image resolution on classification results was investigated. The eight million pixel images were upscaled in six stages to create images with decreasing pixel resolution. Rumex detection rates of over 90% were obtained at almost all resolutions, and there was only moderate misclassification of other objects to RUMOB. Image processing time ranged from 45 s for the full resolution images to 2.5 s for the lowest resolution ones.

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 2531TWOPAC'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.

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Plant diseases are dynamic systems that progress or regress in spatial and temporal dimensions. Site-specific or temporally optimized disease control requires profound knowledge about the development of each stressor. The spatiotemporal dynamics of leaf rust (Puccinia recondite f. sp. tritici) and powdery mildew (Blumeria graminis f. sp. tritici) in wheat was analyzed in order to evaluate typical species-dependent characteristics of disease spread. During two growing seasons, severity data and other relevant plant growth parameters were collected in wheat fields. Spatial characteristics of both diseases were assessed by cluster analyses using spatial analysis by distance indices, whereas the temporal epidemic trends were assessed using statistical parameters. Multivariate statistics were used to identify parameters suitable for characterizing disease trends into four classes of temporal dynamics. The results of the spatial analysis showed that both diseases generally occurred in patches but a differentiation between the diseases by their spatial patterns and spread was not possible. In contrast, temporal characteristics allowed for a differentiation of the diseases, due to the fact that a typical trend was found for leaf rust which differed from the trend of powdery mildew. Therefore, these trends suggested a high potential for temporally optimized disease control. Precise powdery mildew control would be more complicated due to the observed high variability in spatial and temporal dynamics. The general results suggest that, in spite of the high variability in spatiotemporal dynamics, disease control that is optimized in space and time is generally possible but requires consideration of disease- and case-dependent characteristics.