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This paper addresses information extraction from IKONOS imagery over the Lukole refugee camp in Tanzania. More specific, it describes automatic image analysis procedures for a rapid and reliable identification of refugee tents as well as their spatial extent. From the identified tents, the number of refugees can be derived and a map of the camp can be generated, which can be used for improving refugee camp management. Four information extraction methods have been tested and compared: supervised classification, unsupervised classification, multi-resolution segmentation and mathematical morphology analysis. The latter two procedures based on object-oriented classifiers perform best with a spatial accuracy above 85% and a statistical accuracy above 97%. These methods could be used for refugee camp information extraction in other geographical settings and on imagery with different spatial and spectral resolutions.

The paper addresses built up information extraction from high resolution imagery from two satellites, ZY-1 02C and SPOT-5. The testing is part of a larger experiment that explores spectral, textural and morphological satellite image properties aiming to extract built up information for global built up mapping and characterization. In this work we test SPOT-5 and ZY-1 02C satellites for textural properties associated to built-up. The work is exploratory in nature. We compute image textures using a ”built-up presence index (Pantex)” algorithm. We then compare the texture values with a reference built-up map derived from building footprints map by computing the Receiver Operative Characteristics (ROC). We processed four bands: ZY-HR Panchromatic, ZY Panchromatic, and two “brightness” bands derived from the ZY Pan-sharpened, and SPOT Pan-sharpened. Pan-sharpened products combine both the panchromatic and the multispectral imagery. For each dataset we use two texture windows for a total of eight tests. The study is conducted on imagery covering the city of Firenze, Italy. The landscape is characterized by dense city centres as well as rural scattered settlement. The area is deemed suitable to benchmarking imagery for built up information related to human settlements of different densities. The test shows that the information from SPOT and ZY are generating similar result. However, spatial resolution of panchromatic bands show to have great importance over other factors in extracting built up information.

What Is Special about Global Urban Remote Sensing? Qihao Weng Global Urban Observation: Needs and Requirements Global Urban Observation and Information: GEO's Effort to Address the Impacts of Human Settlements, Qihao Weng, Thomas Esch, Paolo Gamba, Dale Quattrochi, and George Xian EO Data Processing and Interpretation for Human Settlement Characterization: A Really Global Challenge, Paolo Gamba, Gianni Lisini, Gianni Cristian Iannelli, Inmaculada Dopido, and Antonio Plaza Urban Observing Sensors, Qihao Weng, Paolo Gamba, Giorgos Mountrakis, Martino Pesaresi, Linlin Lu, Thomas Kemper, Johannes Heinzel, George Xian, Huiran Jin, Hiroyuki Miyazaki, Bing Xu, Salman Quresh, Iphigenia Keramitsoglou, Yifang Ban, Thomas Esch, Achim Roth, and Christopher D. Elvidge Global Urban Footprint: Data Sets and Products Mapping Global Human Settlements Pattern Using SAR Data Acquired by the TanDEM-X Mission, Thomas Esch, Mattia Marconcini, Andreas Felbier, Achim Roth, and Hannes Taubenbock National Trends in Satellite-Observed Lighting: 1992-2012, Christopher D. Elvidge, Feng-Chi Hsu, Kimberly E. Baugh, and Tilottama Ghosh Development of a Global Built-Up Area Map Using ASTER Satellite Images and Existing GIS Data, Hiroyuki Miyazaki, Xiaowei Shao, Koki Iwao, and Ryosuke Shibasaki Building of a Global Human Settlement Layer from Fine-Scale Remotely Sensed Data, Martino Pesaresi, Vasileios Syrris, Daniele Ehrlich, Matina Halkia, Thomas Kemper, and Pierre Soille Urban Observation, Monitoring, Forecasting, and Assessment Initiatives Spatial Dynamics and Patterns of Urbanization: The Example of Chinese Megacities Using Multitemporal EO Data, Hannes Taubenbock, Thomas Esch, Michael Wiesner, Andreas Felbier, Mattia Marconcini, Achim Roth, and Stefan Dech Mapping and Monitoring of Refugees and Internally Displaced People Using EO Data, Thomas Kemper and Johannes Heinzel Assessment of Fine-Scale Built-Up Area Mapping in China, Linlin Lu, Huadong Guo, Martino Pesaresi, Daniele Ehrlich, and Stefano Ferri Climatological and Geographical Impacts on the Global Pandemic of Influenza A (H1N1) 2009, Bing Xu, Zhenyu Jin, Zhiben Jiang, Jianping Guo, Michael Timberlake, and Xiulian Ma Investigations of the Diurnal Thermal Behavior of Athens, Greece, by Statistical Downscaling of Land Surface Temperature Images and Pattern Analysis, Iphigenia Keramitsoglou Innovative Concepts and Techniques in Urban Remote Sensing Integrated Urban Sensing in the Twenty-First Century, Gunther Sagl and Thomas Blaschke Object-Based Image Analysis for Urban Studies, Vivek Dey, Bahram Salehi, Yun Zhang, and Ming Zhong Defining Robustness Measures for OBIA Framework: A Case Study for Detecting Informal Settlements, Peter Hofmann Automated Techniques for Change Detection Using Combined Edge Segment Texture Analysis, GIS, and 3D Information, Manfred Ehlers, Natalia Sofina, Yevgeniya Filippovska, and Martin Kada Fusion of SAR and Optical Data for Urban Land Cover Mapping and Change Detection, Yifang Ban, Osama Yousif, and Hongtao Hu Index

Land cover changes are measured at increasingly broader spatial scales. Yet understanding and modelling change processes with a satisfactory accuracy require fine scale observations. The objective of this study is to design and test a methodology to detect tropical deforestation 'hot spots' at broad spatial scales. This methodology is designed to allow concentration of the data collection and modelling of change processes only on the areas characterized by a high rate of change. The procedure is based on a hierarchical set of decision rules with selection criteria being first measured on an exhaustive basis at a global scale and then only for the areas retained in the first sorting, with increasingly selective constraints. The first set of criteria, i.e., proportions in key land cover, landscape fragmentation, and fire activities, were derived from subcontinental scale remote sensing data. Socio-economic variables were also measured at that scale. These different variables were combined over West Africa and the northern boundary of the Central African evergreen forest to identify potential tropical deforestation fronts. Different models were used to generate maps of deforestation hot spots. These were validated with data from the literature.

There is an increasing need for data on land-cover changes at broad spatial .scales. In this study, a remote sensing-based technique for land-cover-change analysis was applied to the African continent for the last decade. Ten years of daily, continental-.scale satellite remote sensing data (front the National Oceanic and Atmospheric Administration's advanced very-high resolution radiometer) were analyzed. Deviations in. the seasonal trajectory of the land surface—characterized by its brightness temperature and a vegetation index—were interpreted in terms of land-cover change on a yearly basis, from 1982 to 1991. Land-cover-change magnitudes were measured and mapped for every year. The major categories of seasonal patterns of changes were identified. Multiyear patterns of change also were categorized, and all areas affected by a continuous decrease and gain in vegetation cover over the decade were snapped. Results were related to published high-resolution remote sensing-based studies, rainfall data, and other evidence of land-cover changes. This study's results demonstrate that inte-rannual land-cover changes in Africa mostly involve erratic variations in land-corer conditions due to interannual climatic variability and temporary nwdifncations in seasonality. Continuous, unidirectional change processes (decrease or gain in vegetation cover) affected less than 4% of sub-Saharan Africa during the study period. Althongh such linear changes are just a fraction of the total land-cover changes detected, their effect on ecosystems and sustainability of livelihood might be significant. In the semiarid regions, temporary modification of the biophysical attributes of the surface driven by variations in rainfall is the dominant process. Future work will focus on finer-scale interpretation and validations of patterns of changes.

The effect of fires on land cover was investigated with coarse spatial resolution remote sensing data on West Africa. We tested whether fires lead to land-cover changes or maintain land cover in a state of equilibrium. This was measured independently for early- and late-.season fires, for different ecological ones, and for areas characterized by different levels of land-use intensity. Spatial associations between broad-scale land-cover-change maps and fire-distribution maps were calculated. The statistical results reveal that, for a few subregions, there is a statistically significant relation between fire occurrence and land-cover changes as measured by coarse-resolution remote sensing data, whereas, at a broad .scale, there is no such relation. The main conclusion is that the scale of the data analyzed in this study is inappropriate for reaching any definitive conclusion on the effect of fires on land cover. This is due, first, to an inadequate spatial resolution to detect all fires and their associated ecological effects and second, to the heterogeneity of the population, given the size of the study area.

The GAC (Global Area Coverage) by the NOAA-AVHRR satellites represents an excellent data set for studying global and regional patterns of variations in surface conditions driven in part by climatic variation. In this pilot study we examined whether biodiversity ‘hotspots’, defined from peak concentrations of neoendemics as well as geographically relict forms, differ in ecoclimatic stability from surrounding areas under present-day climatic conditions. Coefficients of variation of the ratio between brightness surface temperature (Ts) and the Normalized Difference Vegetation Index (NDVI) based upon 10 years' monthly composited scenes of tropical Africa revealed distinctive geographical patterns of interannual variability in surface conditions. Regions with a predominance of old species are characterized by spatial uniformity in the ecoclimatic variability, while regions where rapidly radiating groups dominate are spatially complex in this respect. However, the exact ‘hotspots’, with peak concentrations of endemic species, are characterized by a local reduction in ecoclimatic variability, or placement on the boundary to a stable region. This relationship was supported statistically by comparing ecoclimatic profiles across montane forests representing ‘hotspots’, and those of other montane forests. It is suggested that, because of interactions between prevailing atmospheric flows, topography and vegetation, the impact of extreme weather is moderated locally. The correlation between current stability and aggregates of neoendemics as well as old relics indicate that local moderation of climatic extremes persist through shifting climatic periods, permitting populations of unique species to survive in these places. The results are used to identify study sites for better ground truthing and for paleoclimatological studies which may be useful for more thorough studies of these relationships.

This paper reports about the feasibility to automatically map the settlements of Brazil with the panchromatic, 2.5 m data of the HRC camera of the CBERS-2B satellite. Using a combined textural and morphological image analysis in a fully automated workflow an equal error rate of 0.18 ± 0.09 and a minimal error rate of 0.06 ± 0.06 could be obtained for an area of 1.4 million km2 of the most densely inhabited parts of Brazil. The method provides in addition to the presence of buildings information about the building density and average size., JRC.G.2-Global security and crisis management

Previous attempts to map land cover at broad scales were based on single year time series and usually on the Normalized Difference Vegetation Index ( NDVI) Single years of data lack statistical representativity and the NDVI is partially driven by short-term climatic characteristics. We investigate two approaches to produce land-cover classifications that are not excessively influenced by short-term climatic variability: (i) averaging a climate-driven variable over several years, and (ii) measuring a more climate-independent variable. We test, compare and combine these two approaches for the African continent using 8 years of AVHRR Global Area Coverage (GAC) data. Our results demonstrate that times series of the ratio between surface temperature and NDVI are less influenced by interannual variations in climatic conditions than NDVI time series and thus produce more stable land-cover classifications. This finding is consistent with the biophysical interpretations of these two variables. When data a...

Most previous applications of coarse scale remote sensing data for land-cover mapping and land-cover change analysis were based on multi-temporal Normalized Difference Vegetation Index (NDVI) data. Recent empirical studies have documented that the combination of measurements of thermal infrared radiation (e.g., land brightness temperature, Ts) and vegetation indices (VI) improves the mapping and monitoring of land cover at broad scales. We investigate the biophysical justification for such a combination, using 10 years of Advanced Very High Resolution Radiometer (AVHRR) global area coverage ( GAC) data over the African continent. First, we review recent findings on the biophysical interpretation of the TS-VI space. Second, we analyse the seasonal time trajectories of different biomes in the TS-NDVI space. Third, we measure the relative role of multi-temporal NDVI and Ts data in the discrimination of land cover classes for land-cover mapping. Fourth, we analyse trajectories of land-cover change in...

The analysis of multi-temporal AVHRRGAC data over Africa reveals that the integration of thermal information with a vegetation index clearly increases biome discrimination at a continental scale. Multi-temporal series of the ratio between surface temperature and the normalised difference vegetation index have the greatest potential to yield a realistic land-cover classification.

Remote sensing is an effective tool in urban extent mapping and monitoring. In this study, a procedure to detect urban expansion is presented using SPOT5 panchromatic image with 2.5m resolution. First of all, the urban extents of different images are extracted with a PANTEX methodology, which use the anisotropic rotation-invariant textural grey-level co-occurrence measures as the presence of built-up areas. The change detection is performed on the two built-up index images using principal component analysis technique. Finally, the accuracy of this procedure is assessed with visual analysis results. The method shows good performance according to the overall accuracy of 91.18% (Kappa coefficient=0.8719).

On April 14, 2010, an earthquake with a magnitude of 6.9 hit the Yushu prefecture in the Qinghai province of China. A strategy for rapid building damage assessment using mono-temporal post-earthquake very high resolution imagery is presented. Integrating automatic and manual interpretation, the strategy ensures the reliability, timeliness and interpretability of damage assessment products. This product played an important role in post-disaster response and relief activities.

This paper describes a framework for an image processing procedure for operational agricultural crop area estimation. This operational framework has been conceived within the development of an Advanced Agricultural Information System (AAIS) for the “Regione del Veneto “ (RdV ‐ Veneto Region) in northeastern Italy. The objective of this program is to develop the ability to generating timely and accurate area estimates and production information for four major agricultural crops: soybeans, sugar beets, corn, and small grains. AAIS uses state of the art methods in remote sensing and geographic information systems (GIS) technology and integrates a variety of data types including satellite imagery. This paper describes the methodology developed for image and ancillary data processing for the production of crop area statistics. Using a combination of standard unsupervised classification and GIS operations that incorporate knowledge about the agricultural system, a “sequential masking” classification pr...

This paper reviews the applications of NOAA-AVHRR 1 km data for environmental monitoring. This topic is very significant in view of the development of 1 km global datasets. Research papers reviewed herein largely describe application of AVHRR data for North America, Sahelian Africa and tropical forests of both Africa and South America. All 5 AVHRR bands have found some level of use for land-cover studies. However, the majority of the land characterization research papers used AVHRR/ NDVI as the main data source. Multi-temporal NDVI datasets have found wide use to describe vegetation phenology. AVHRR thermal bands have also been employed by a number of researchers for both surface temperature mapping and for land-cover discrimination especially in tropical rain forests. The most common data analysis techniques employed in these papers were regressions between bands and supervised classification procedures. The review indicated that land-cover mapping and direct estimation of land-cover parameters ...

A method for analyzing the urbanization process from multitemporal SPOT 5 panchromatic images is presented. A region-based local Mutual Information change indicator is proposed to perform the change analysis of large scenes. Experiments are conducted for mapping the urbanization of Tangshang, China, in between 2003 and 2008. The results show the efficiency of the change detection method., JRC.G.2-Global security and crisis management

This paper addresses built up change detection using texture features derived from SPOT Panchromatic imagery and principal component analysis (PCA). SPOT imagery combines the adequate ground sampling distance (GSD) and large area coverage that extends beyond cities to include the peri-urban areas where urban sprawl (built-up change) typically occurs. This study uses SPOT 1 and SPOT 5 panchromatic data over Casablanca. The texture based “Built up area presence index” is used to provide a likelihood of built up area. The changes in texture in t 0 and t 1 are then quantified using PCA. The procedure is simple to implement and requires minimum manual intervention. The analysis shows that the method is reliable for rapid assessments built up changes but that these detected changes need to be visually inspected if operational products have to be derived. The technique performs adequately when changes occur over large areas. The Casablanca study results are encouraging despite the coarse GSD of SPOT 1 and the different illumination and viewing angle. The procedure should be tested over cities in different geographical regions. Finer resolution input data and data with similar acquisition and illumination angle are likely to yield better the results.

Cataloged from PDF version of article. We propose a new procedure for quantitative evaluation of object detection algorithms. The procedure consists of a matching stage for finding correspondences between reference and output objects, an accuracy score that is sensitive to object shapes as well as boundary and fragmentation errors, and a ranking step for final ordering of the algorithms using multiple performance indicators. The procedure is illustrated on a building detection task where the resulting rankings are consistent with the visual inspection of the detection maps. (C) 2009 Elsevier B.V. All rights reserved.

Several EU policy papers on security research have called for improved dialogue between security policymakers, social science researchers and science and technology researchers working in the area of security. In this paper, geospatial concepts and geospatial technology are introduced into this dialogue in order to improve the efficacy of communication. The explanatory power of these concepts has already been described theoretically. Their quantitative application using GIS and spatial statistical techniques poses challenges. Several technical issues still have to be addressed through a three-way dialogue in order to assist in monitoring European security policy. Generally, increases in the scientific knowledge base and in technology, are seen as enhancing state capabilities and political coordination

The objective of this document is to summarize the current work conducted within GMOSS regarding the use of Earth Observation (EO) and geospatial data to derive indicators for the presence of populations and their characteristics. The objective of this paper is to create a link between the Earth Observation products generated and the demand for information at the policy level.

The Regione del Veneto (Italy) is cooperating with the University of California, Santa Barbara and other researchers in Italy and the U.S.A. to develop a system of econometric crop production modeling. Five crops are to be included in this project: small grains (wheat and barley), corn, sugar beets, soybeans, orchards and vineyards. A critical part of the crop yield modeling process is the identification of crops using multispectral satellite data. This paper explores two strategies to improve crop classification accuracies: (1) use of ancillary data stored in digital format and (2) use of multitemporal data. Ancillary information stored on digital files were used in this research to remove (mask) non‐agricultural areas from satellite image data. Comparison between the classification of masked and unmasked images showed that improvement ranged from 3% to 26% depending on crop type. The multidate classification was performed by compiling an image of transformed spectral bands and three TM‐5 bands....

Very high resolution (VHR) Ikonos images were analysed to assess the state of activity of a diamond mine extraction site in Africa. The methodology uses a dynamic approach, based on an object-oriented classification of two sets of satellite remote sensing data that were acquired four months apart. The data was processed according to a supervised maximum likelihood classification system, using fuzzy sets of membership functions. Additionally, field information gathered from experts in the sector of diamond mining was used. The results confirm the usefulness of VHR satellite imagery for the identification of both artisanal and industrial diamond mining. The bi-temporal dataset allows information on the evolution of the activity to be derived during a first period., JRC.G.2-Support to external security

This paper tackles the need of enhanced population data for disaster management and aid delivery studies in developing countries. It analyses the usefulness of a set of spatial data layers, including medium resolution satellite imagery, for population density estimations in rural Zimbabwe. The exercise conducted on a 185 × 185km area at a grid cell size of 150m allowed us to develop a methodology that can be extended to the whole of Zimbabwe.

As an important urban agglomeration of China, the Jing-Jin-Tang area has experi- enced intense urbanization since the 1980s. This study explores the spatiotemporal dynamics of urban areas in this region using multitemporal Landsat images. An enhanced built-up (BU) index method was applied to extract BU areas with an overall accuracy ranging from 75% to 91.35%. Seven spatial metrics were used to discern urban growth patterns at city and county levels. The results indicate that all cities witnessed a rapid growth of BU areas with different spatial patterns. Beijing has been aggregating since the 1990s and a large homogeneous urban patch has formed. The construction and development of metropolitan Beijing and Tianjin started in the early 1980s and became almost fully developed by the end of 1990. Tangshan, like many medium-sized cities in China, is still enduring a development process with an accelerating pace. The metropolitan areas of Beijing and Tianjin have been greatly developed with BU densities exceeding 90% since 2000, compared with Tangshan's 55% in 2010. These results provide spatial information on the evolution of urban extent in the period of 1990s to 2010s in this region. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original pub- lication, including its DOI. (DOI: 10.1117/1.JRS.8.084592) The Beijing-Tianjin-Tangshan (a.k.a. Jing-Jin-Tang) urban agglomeration is mainly com- posed of three adjacent cities: Beijing, Tianjin, and Tangshan in northern China. It is located at the heart of the Bohai Economic Rim, and plays a vital role in the nation's economic growth. 3 This area has experienced intense urbanization since the national economic reform of the late 1980s. The rapid growth of built-up (BU) areas has brought great pressure to local environment and has led to serious environmental problems such as water shortage, air/water pollution, and vegetation degradation, especially in overpopulated areas. Air pollution results in significant neg- ative health impacts on the large population in cities like Beijing. 4 Dumping solid waste on the

In the aftermath of man-made crises, refugees (or internally displaced persons) cluster in relatively safe areas that rapidly become refugee camps. The maintenance of these camps is one of the chal...