Julian Zeidler

Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5-5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15-30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.

Spatio-temporal robustness of fractional cover upscaling: a case study in semi-arid Savannah's of Namibia and Western Zambia

Vegetation cover is a key parameter in analyzing the state and dynamics of ecosystems. Africas semi-arid savannas are particularly prone to degradation, due to increasing population pressure as well as ongoing climatic changes. In most global land cover classifications inhomogeneous areas are aggregated into few discrete classes, delivering unsatisfying results in highly variable biomes, especially savannas with their small scale patches of woody and herbaceous vegetation and bare soil. Fractional cover(FC) classifications, which provide an estimate of sub-pixel continuous cover percentages of underlying land cover classes, and are therefore an improved thematic representation, can deliver additional information for monitoring and decision making. Prior research demonstrated that multi-scale approaches are suitable for transferring en-detail information from a small subset to a larger study area via statistical up-scaling (e.g. Random Forest). In this case study the robustness of this up-scaling approach and the limits of the spatial and temporal transferability at the very high and intermediate resolution were analysed in the Caprivi Strip in Namibia and the adjacent Western Province of Zambia. The key research questions were to quantify i) the robustness of the upscaling, ii) the loss of accuracy depending on the lag in image acquisitions, iii) the loss of accuracy dependent on the time of image acquisition in the phenological cycle. To this end 12 Worldview(WV) and all usable Landsat TM and ETM+ images, covering all phases of the vegetation cycle were obtained. The analysis showed that continuous FC mapping is a highly suitable concept for semi-arid ecosystems with gradual transitions. The optimal time for WV acquisition was at the beginning of the dry season. The RMSE was unusable for LS images recorded in the rainy season between November and March, but otherwise it was usable even for larger lags up to a month, with deviations below 15%. As long as the spatial training subset(s) cover the whole occurring range of vegetation densities, comparably small WV scenes are sufficient to reliably scale to regional results.

The global urban footprint — Processing status and cross comparison to existing human settlement products

The main goal of the TanDEM-X mission (TDM) is the generation of a global digital elevation model (DEM). The global SAR dataset, which is made available in the context of the TDM, is also used to create a global human settlement layer, the Global Urban Footprint (GUF). This paper presents a first large area cross comparison between the Global Urban Footprint and existing human settlement products, which shows promising results with an achieved confidence of 95.86% Overall, 71.15% Producers and 85.22% Users accuracy.

Exploiting Big Earth Data from Space – First Experiences with the TimeScan Processing Chain

Abstract The European Sentinel missions and the latest generation of the United States Landsat satellites provide new opportunities for global environmental monitoring. They acquire imagery at spatial resolutions between 10 and 60 m in a temporal and spatial coverage that could before only be realized on the basis of lower resolution Earth observation data ( 250 m). However, images gathered by these modern missions rapidly add up to data volume that can no longer be handled with standard work stations and software solutions. Hence, this contribution introduces the TimeScan concept which combines pre-existing tools to an exemplary modular pipeline for the flexible and scalable processing of massive image data collections on a variety of (private or public) computing clusters. The TimeScan framework covers solutions for data access to arbitrary mission archives (with different data provisioning policies) and data ingestion into a processing environment (EO2Data module), mission specific pre-processing of multi-temporal data collections (Data2TimeS module), and the generation of a final TimeScan baseline product (TimeS2Stats module) providing a spectrally and temporally harmonized representation of the observed surfaces. Technically, a TimeScan layer aggregates the information content of hundreds or thousands of single images available for the area and time period of interest (i.e. up to hundreds of TBs or even PBs of data) into a higher level product with significantly reduced volume. In first test, the TimeScan pipeline has been used to process a global coverage of 452,799 multispectral Landsat–8 scenes acquired from 2013 to 2015, a global data-set of 25,550 Envisat ASAR radar images collected 2010–2012, and regional Sentinel–1 and Sentinel–2 collections of 1500 images acquired from 2014 to 2016. The resulting TimeScan products have already been successfully used in various studies related to the large-scale monitoring of environmental processes and their temporal dynamics.

Global urban growth monitoring by means of SAR data

In the last few decades, the increasing amount of people migrating to cities resulted in the steady spatial expansion of urban agglomerations and nowadays more than half of the worlds population currently consists of urban dwellers. Accordingly, several initiatives have been recently carried out to globally monitor the actual extent of urban areas by means of Earth observation (EO) data. In this framework, the German Aerospace Center (DLR) has recently produced a global map of built-up areas at the unique spatial resolution of 12m, namely the Global Urban Footprint (GUF). Specifically, the GUF is derived from very high resolution (VHR) SAR imagery acquired in the context of the Tan-DEM-X radar mission between 2011 and 2013. In order to map urban growth occurred in the last two decades, in this paper we adapt the approach for producing the GUF to archived ESA ERS SAR and Envisat ASAR Image Mode imagery available at 30m spatial resolution from 1991 to 2012. Preliminary experimental results assess the effectiveness of the proposed method and its potential to be employed for monitoring urban growth worldwide.

Where We Live—A Summary of the Achievements and Planned Evolution of the Global Urban Footprint

The TerraSAR-X (TSX) mission provides a distinguished collection of high resolution satellite images that shows great promise for a global monitoring of human settlements. Hence, the German Aerospace Center (DLR) has developed the Urban Footprint Processor (UFP) that represents an operational framework for the mapping of built-up areas based on a mass processing and analysis of TSX imagery. The UFP includes functionalities for data management, feature extraction, unsupervised classification, mosaicking, and post-editing. Based on >180.000 TSX StripMap scenes, the UFP was used in 2016 to derive a global map of human presence on Earth in a so far unique spatial resolution of 12 m per grid cell: the Global Urban Footprint (GUF). This work provides a comprehensive summary of the major achievements related to the Global Urban Footprint initiative, with dedicated sections focusing on aspects such as UFP methodology, basic product characteristics (specification, accuracy, global figures on urbanization derived from GUF), the user community, and the already initiated future roadmap of follow-on activities and products. The active community of >250 institutions already working with the GUF data documents the relevance and suitability of the GUF initiative and the underlying high-resolution SAR imagery with respect to the provision of key information on the human presence on earth and the global human settlements properties and patterns, respectively.

Earth observation-supported service platform for the development and provision of thematic information on the built environment — the TEP-Urban project

The Sentinel fleet will provide a so-far unique coverage with Earth observation (EO) data and therewith new opportunities for the implementation of methodologies to generate innovative geo-information products and services. It is here where the TEP Urban project is supposed to initiate a step change by providing an open and participatory platform based on modern Information and Communication Technologies (ICTs) that enable any interested user to easily exploit EO data pools, in particular those of the Sentinel missions, and derive thematic information on the status and development of the built environment from these data. Key component of TEP Urban project is the implementation of a web-based platform employing distributed high-level computing infrastructures and providing key functionalities for i) high-performance access to satellite imagery and derived thematic data, ii) modular and generic state-of-the-art pre-processing, analysis, and visualization techniques, iii) customized development and dissemination of algorithms, products and services, and iv) networking and communication. This contribution introduces the main facts about the TEP Urban project, including a description of the general objectives, the platform systems design and functionalities, and the preliminary portfolio products and services available at the TEP Urban platform.

The Landsat soil composite mapping processor (SCMAP): AN OPUS product

The primary objective of the SCMaP is to supply value added information about soils at three levels: 1) the spatial distribution of exposed soils; 2) temporal statistics of those soils; and, 3) a reflectance soil composite map. The SCMaP is designed for temperate climatic regions that comprise areas of extensive crop based agriculture where soils are commonly covered by vegetation. For the SCMaP satellite based multi-temporal optical imagery is used to generate per-pixel composite images that reflect maximum and minimum photosynthetically active vegetativion. Applying pre-determined thresholds to the maximum and minimum composites an exposed soil mask generated that can be used to build a reflectance soil composite image. The SCMaP has been designed towards free and open access high spatial muti-spectral Landsat and Sentinel 2 data, with results shown here for archived Landsat (4,5,7) imagery from 2010–2014 for all of Germany.

Exploiting Earth observation data pools for urban analysis - The TEP URBAN project

Large amounts of Earth observation (EO) data have been collected to date, to increase even more rapidly with the upcoming Sentinel data. All this data contains unprecedented information, yet it is hard to retrieve, especially for nonremote sensing specialists. As we live in an urban era, with more than 50% of the world population living in cities, urban studies can especially benefit from the EO data. Information is needed for sustainable development of cities, for the understanding of urban growth patterns or for studying the threats of natural hazards or climate change. Bridging this gap between the technology-driven EO sector and the information needs of environmental science, planning, and policy is the driver behind the TEP-Urban project. Modern information technology functionalities and services are tested and implemented in the Urban Thematic Exploitation Platform (U-TEP). The platform enables interested users to easily exploit and generate thematic information on the status and development of the environment based on EO data and technologies. The beta version of the web platform contains value added basic earth observation data, global thematic data sets, and tools to derive user specific indicators and metrics. The code is open source and the architecture of the platform allows adding of new data sets and tools. These functionalities and concepts support the four basic use scenarios of the U-TEP platform: explore existing thematic content; task individual on-demand analyses; develop, deploy and offer your own content or application; and, learn more about innovative data sets and methods.

Earth observation-based service platforms - a new instrument to provide geo-information for urban and regional planning

This paper introduces concepts for the utilization of modern information technology functionalities, bundled in form of dedicated service platforms, to bridge the gap between the technology-driven EO sector and the information needs of environmental science, planning, and policy. Thereby the intended service platforms generally aim at opening up new opportunities by systematically exploring: unique EO capabilities in Europe; Big Data perspective; high-level IT-infrastructures; massive processing power; vast expert knowledge; new media and ways of communication; and increasing connectivity and networks. Key components of such systems are currently developed and tested in the projects OPUS-GMES (Operational Platform for the Provision and Processing of Sentinel Data in Support of Copernicus Geo-Information Services) and TEP Urban (Urban Thematic Exploitation Platform), both coordinated by DLR. The two projects include the implementation of an open, web-based platform employing distributed high-level computing infrastructures (Platform as a Service - PaaS) as well as providing key functionalities for i) high-performance access to thematic data (Information as a Service - InaaS), ii) modular and generic state-of-the art pre-processing, analysis, and visualization (Software as a Service - SaaS), iii) customized development and dissemination of algorithms, products and services, and iv) networking and communication.