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Settlement maps derived by Earth Observation data represent a critical dataset for building stock quantification. The accuracy of the settlement maps varies across the different spatial scales and across the space according to specific spatial patterns. The aim of this paper is to assess the accuracy of the settlement map at different scales, and to analyze the relationships between spatial allocation of error and built-up distribution patterns. The paper identifies two general trends. First that the building stock overestimation error increases with increasing values of spatial scattering. Second that at coarser scales the relation between building area overestimation and spatial scattering became stronger. The results have important implications when settlement maps are used to estimate the building stock.

Exposure is reported to be the biggest determinant of disaster risk, it is continuously growing and by monitoring and understanding its variations over time it is possible to address disaster risk reduction, also at the global level. This work uses Earth observation image archives to derive information on human settlements that are used to quantify exposure to five natural hazards. This paper first summarizes the procedure used within the global human settlement layer (GHSL) project to extract global built-up area from 40 year deep Landsat image archive and the procedure to derive global population density by disaggregating population census data over built-up area. Then it combines the global built-up area and the global population density data with five global hazard maps to produce global layers of built-up area and population exposure to each single hazard for the epochs 1975, 1990, 2000, and 2015 to assess changes in exposure to each hazard over 40 years. Results show that more than 35% of the global population in 2015 was potentially exposed to earthquakes (with a return period of 475 years); one billion people are potentially exposed to floods (with a return period of 100 years). In light of the expansion of settlements over time and the changing nature of meteorological and climatological hazards, a repeated acquisition of human settlement information through remote sensing and other data sources is required to update exposure and risk maps, and to better understand disaster risk and define appropriate disaster risk reduction strategies as well as risk management practices. Regular updates and refined spatial information on human settlements are foreseen in the near future with the Copernicus Sentinel Earth observation constellation that will measure the evolving nature of exposure to hazards. These improvements will contribute to more detailed and data-driven understanding of disaster risk as advocated by the Sendai Framework for Disaster Risk Reduction.

The paper discusses the potential of very high resolution (VHR) satellite imagery for post-earthquake damage assessment in comparison with the role of aerial photographs. Post-disaster optical and radar satellite data are assessed for their ability to resolve collapsed buildings, destroyed transportation infrastructure, and specific land cover changes. Optical VHR imagery has shown to be effective in quantifying building stock and for assessing damage at the building level. High-resolution synthetic aperture radar (SAR) imagery requires further research to identify optimum information extraction procedures for rapid assessment of affected buildings. Based on current technical and operational capabilities increasing efforts should be devoted to the generation of spatial datasets for disaster preparedness.

It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature-based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p

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.

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.

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.

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 ...

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

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...