scispace - formally typeset
Search or ask a question
Author

Stefan Dech

Bio: Stefan Dech is an academic researcher from German Aerospace Center. The author has contributed to research in topics: Land cover & Population. The author has an hindex of 52, co-authored 337 publications receiving 9676 citations. Previous affiliations of Stefan Dech include Marshall Space Flight Center & University of Würzburg.


Papers
More filters
Journal ArticleDOI
TL;DR: 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.
Abstract: 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 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.

541 citations

Journal ArticleDOI
TL;DR: In this article, a straight forward, application-oriented approach using multi-temporal remotely sensed data to systematically monitor the spatiotemporal dynamics of the world's urban giants is presented.

396 citations

Journal ArticleDOI
TL;DR: The use of satellite remote sensing for the mapping of snow cover characteristics has a long-lasting history reaching back until the 1960s as mentioned in this paper, and many different sensors have been used in the past decades with various algorithms and respective accuracies.
Abstract: The use of satellite remote sensing for the mapping of snow-cover characteristics has a long-lasting history reaching back until the 1960s. Because snow cover plays an important role in the Earth's climate system, it is necessary to map snow-cover extent and snow mass in both high temporal and high spatial resolutions. This task can only be achieved by the use of remotely sensed data. Many different sensors have been used in the past decades with various algorithms and respective accuracies. This article provides an overview of the most common methods. The limitations, advantages and drawbacks will be illustrated while error sources and strategies on how to ease their impact will be reviewed. Beginning with a short summary of the physical and spectral properties of snow, methods to map snow extent from the reflective part of the spectrum, algorithms to estimate snow water equivalent (SWE) from passive microwave (PM) data and the combination of both spectra will be delineated. At the end, the reader should...

320 citations

Journal ArticleDOI
TL;DR: The study aims to detect similarities and differences in spatial growth in the large Indian urban agglomerations, and paints a characteristic picture of spatial pattern, gradients and landscape metrics, and thus illustrate spatial growth and future modelling of urban development in India.

313 citations

Journal ArticleDOI
TL;DR: In this paper, the Global Urban Footprint (GUF) raster map is used for the analysis of global urbanization and peri-urbanization patterns, population estimation, vulnerability assessment, or the modeling of diseases and phenomena of global change in general.
Abstract: Today, approximately 7.2 billion people inhabit the Earth and by 2050 this number will have risen to around nine billion, of which about 70% will be living in cities. The population growth and the related global urbanization pose one of the major challenges to a sustainable future. Hence, it is essential to understand drivers, dynamics, and impacts of the human settlements development. A key component in this context is the availability of an up-to-date and spatially consistent map of the location and distribution of human settlements. It is here that the Global Urban Footprint (GUF) raster map can make a valuable contribution. The new global GUF binary settlement mask shows a so far unprecedented spatial resolution of 0.4 ″ ( ∼ 12 m ) that provides – for the first time – a complete picture of the entirety of urban and rural settlements. The GUF has been derived by means of a fully automated processing framework – the Urban Footprint Processor (UFP) – that was used to analyze a global coverage of more than 180,000 TanDEM-X and TerraSAR-X radar images with 3 m ground resolution collected in 2011–2012. The UFP consists of five main technical modules for data management, feature extraction, unsupervised classification, mosaicking and post-editing. Various quality assessment studies to determine the absolute GUF accuracy based on ground truth data on the one hand and the relative accuracies compared to established settlements maps on the other hand, clearly indicate the added value of the new global GUF layer, in particular with respect to the representation of rural settlement patterns. The Kappa coefficient of agreement compared to absolute ground truth data, for instance, shows GUF accuracies which are frequently twice as high as those of established low resolution maps. Generally, the GUF layer achieves an overall absolute accuracy of about 85%, with observed minima around 65% and maxima around 98%. The GUF will be provided open and free for any scientific use in the full resolution and for any non-profit (but also non-scientific) use in a generalized version of 2.8 ″ ( ∼ 84 m ). Therewith, the new GUF layer can be expected to break new ground with respect to the analysis of global urbanization and peri-urbanization patterns, population estimation, vulnerability assessment, or the modeling of diseases and phenomena of global change in general.

277 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

14,171 citations

Journal ArticleDOI
TL;DR: In this paper, the authors created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2), including monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970-2000, using data from between 9000 and 60,000 weather stations.
Abstract: We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5–15% (0.07–0.17 °C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were ≥ 0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data.

7,558 citations

Book Chapter
01 Jan 1996
TL;DR: In this article, Jacobi describes the production of space poetry in the form of a poetry collection, called Imagine, Space Poetry, Copenhagen, 1996, unpaginated and unedited.
Abstract: ‘The Production of Space’, in: Frans Jacobi, Imagine, Space Poetry, Copenhagen, 1996, unpaginated.

7,238 citations

Journal ArticleDOI
TL;DR: This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting.
Abstract: A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables. This classifier has become popular within the remote sensing community due to the accuracy of its classifications. The overall objective of this work was to review the utilization of RF classifier in remote sensing. This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting. It is, however, sensitive to the sampling design. The variable importance (VI) measurement provided by the RF classifier has been extensively exploited in different scenarios, for example to reduce the number of dimensions of hyperspectral data, to identify the most relevant multisource remote sensing and geographic data, and to select the most suitable season to classify particular target classes. Further investigations are required into less commonly exploited uses of this classifier, such as for sample proximity analysis to detect and remove outliers in the training samples.

3,244 citations

Journal ArticleDOI
TL;DR: This paper reviews remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology that is particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples.
Abstract: A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.

2,546 citations