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Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environ-ment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under-and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.

The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa- the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.

In the production of gridded population maps, remotely sensed, human settlement datasets rank among the most important geographical factors to estimate population densities and distributions at regional and global scales. Within this context, the German Aerospace Centre (DLR) has developed a new suite of global layers, which accurately describe the built-up environment and its characteristics at high spatial resolution: (i) the World Settlement Footprint 2015 layer (WSF-2015), a binary settlement mask, and (ii) the experimental World Settlement Footprint Density 2015 layer (WSF-2015-Density), representing the percentage of impervious surface. This research systematically compares the effectiveness of both layers for producing population distribution maps through a dasymetric mapping approach in nine low-, middle-, and highly urbanised countries. Results indicate that the WSF-2015-Density layer can produce population distribution maps with higher qualitative and quantitative accuracies in comparison to the already established binary approach, especially in those countries where a good percentage of building structures have been identified within the rural areas. Moreover, our results suggest that population distribution accuracies could substantially improve through the dynamic preselection of the input layers and the correct parameterisation of the Settlement Size Complexity (SSC) index.

With 37 million inhabitants, Tokyo is the world's largest city in UN statistics. With this work we call this ranking into question. Usually, global city rankings are based on nationally collected population figures, which rely on administrative units. Sprawling urban growth, however, leads to morphological city extents that may surpass conventional administrative units. In order to detect spatial discrepancies between the physical and the administrative city, we present a methodology for delimiting Morphological Urban Areas (MUAs). We understand MUAs as a territorially contiguous settlement area that can be distinguished from low-density peripheral and rural hinterlands. We design a settlement index composed of three indicators (settlement area, settlement area proportion and density within the settlements) describing a gradient of built-up density from the urban center to the periphery applying a sectoral monocentric city model. We assume that the urban-rural transition can be defined along this gradient. With it, we re-territorialize the conventional administrative units. Our data basis are recent mapping products derived from multi-sensoral Earth observation (EO) data – namely the Global Urban Footprint (GUF) and the GUF Density (GUF-DenS) – providing globally consistent knowledge about settlement locations and densities. For the re-territorialized MUAs we calculate population numbers using WorldPop data. Overall, we cover the 1692 cities with > 300,000 inhabitants on our planet. In our results we compare the consistently re-territorialized MUAs and the administrative units as well as their related population figures. We find the MUA in the Pearl River Delta the largest morphologically contiguous urban agglomeration in the world with a calculated population of 42.6 million. Tokyo, in this new list ranked number 2, loses its top position. In rank-size distributions we present the resulting deviations from previous city rankings. Although many MUAs outperform administrative units by area, we find that, contrary to what we assumed, in most cases MUAs are considerably smaller than administrative units. Only in Europe we find MUAs largely outweighing administrative units in extent.

Many migratory species are facing increasing environmental and anthropogenic pressures. Important migration pathways and resting sites are gradually lost due to habitat degradation and transformation leading to reduced survival and poor reproductive success. Observations of dramatic declines in population size across taxa in conjunction with the unique challenges faced by migratory species, such as non-stationary habitat requirements, have prompted the parties of the UN Convention on the Conservation of Migratory Species (CMS) to pledge to identify, monitor, restore and conserve habitats needed for successful migration. Remote sensing approaches are among the most promising tools to pursue such identification and monitoring of spatially and temporally dynamic habitats. Yet, matching the spatial and temporal scales of available remote sensing imagery and animal habitat use remains challenging. We examined habitat use of one large avian herbivore: the greater white-fronted goose (Anser a. albifrons) which migrates annually from central Europe to its breeding grounds in the Russian arctic and back. During spring migration the animals need to maximize their energy uptake by carefully timing their migration with vegetation growth at suitable stopover foraging sites. In recent years notable changes in their migration routes and timing have been observed, the reasons for which remain unclear. We tracked well over 150 individuals by means of high-resolution GPS transmitters and accelerometers in order to identify suitable stopover sites and characterize their behavior and space-use within. We aimed to address two main questions: a) Which landscape elements are important habitat factors inside the stopover sites (local scale)? and b) Can suitable stopover sites be predicted based on the knowledge acquired at the local scale (continental scale)? Habitat factors were derived based on 20 m resolution optical (Sentinel 2 and Landsat 8) and SAR (Sentinel 1) time series. Annual summary statistics of Sentinel 1 time-series were used as proxy variables of land-use intensity and for gap-filling monthly composites of Sentinel 2 and Landsat 8. Vegetation indices from the optical time-series were used as proxies for the biophysical habitat state during the time of stopover visits. Habitat preferences and resource use of the geese was modeled by means of behaviorally informed step selection functions both on the local scale (a) and on the larger continental scale (b). Results indicate that white-fronted geese generally select for young vegetation on intensively managed agricultural fields, but there are large differences between stopovers and individuals. Building on these results, we identified the composition of habitat factors in typical stopover sites which served as the basis for continental scale modeling of stopover site selection. Further, we aim to reveal how the birds select for suitable stopover sites in relation to available alternatives. Building on these results, spatially and temporally targeted conservation schemes come into reach through the combination of remote sensing based mapping of temporal trajectories of habitat change with the now available models on stopover site selection.

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.

Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people's physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using "essentially aspatial" data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of 'big earth data' for future environmental justice analyses.

The contribution of Antarctica's ice sheet to global sea-level rise depends on the very dynamic behavior of glaciers and ice shelves. One important parameter of ice-sheet dynamics is the location of glacier and ice-shelf fronts. Numerous remote sensing studies on Antarctic glacier and ice-shelf front positions exist, but no long-term record on circum-Antarctic front dynamics has been established so far. The article outlines the potential of remote sensing to map, extract, and measure calving front dynamics. Furthermore, this review provides an overview of the spatial and temporal availability of Antarctic calving front observations for the first time. Single measurements are compiled to a circum-Antarctic record of glacier and ice shelf retreat/advance. We find sufficient frontal records for the Antarctic Peninsula and Victoria Land, whereas on the West Antarctic Ice Sheet (WAIS), measurements only concentrate on specific glaciers and ice sheets. Frontal records for the East Antarctic Ice Sheet exist since the 1970s. Studies agree on the general retreat of calving fronts along the Antarctic Peninsula. East Antarctic calving fronts also showed retreating tendencies between 1970s until the early 1990s, but have advanced since the 2000s. Exceptions of this general trend are Victoria Land, Wilkes Land, and the northernmost Dronning Maud Land. For the WAIS, no clear trend in long-term front fluctuations could be identified, as observations of different studies vary in space and time, and fronts highly fluctuate. For further calving front analysis, regular mapping intervals as well as glacier morphology should be included. We propose to exploit current and future developments in Earth observations to create frequent standardized measurements for circum-Antarctic assessments of glacier and ice-shelf front dynamics in regard to ice-sheet mass balance and climate forcing.

Every city is&mdash, quoting Plato&mdash, divided into two, one city of the poor, the other of the rich. In this study we test whether the economic urban divide is reflected in the digital sphere of cities. Because, especially in dynamically growing cities, ready-to-use comprehensive data sets on the urban poor, as well as on the digital divide, are not existent, we use proxies: we spatially delimit the urban poor using settlement characteristics derived from remote sensing data. The digital divide is targeted by geolocated Twitter data. Based on a sample of eight cities across the globe, we spatially test whether areas of the urban poor are more likely to be digital cold spots. Over the course of time, we analyze whether temporal signatures in poor urban areas differ from formal environments. We find that the economic divide influences digital participation in public life. Less residents of morphological slums are found to be digitally oriented (&ldquo, are digitally left behind&rdquo, ) as compared to residents of formal settlements. However, among the few twitter users in morphological slums, we find their temporal behavior similar to the twitter users in formal settlements. In general, we conclude this discussion, this study exemplifies that the combination of both heterogeneous data sets allows for extending the capabilities of individual disciplines for research towards urban poverty.

The seasonal and long-term dynamics of waterbodies, particularly in (semi-)arid and cold regions of the world are strongly dependent on precipitation amounts and temperatures respectively. Even though human influences can also strongly affect the dynamics of such waterbodies, seasonal variabilities and shifts in rainfall or temperature patterns are a key driver of desiccation/filling and freezing/thawing cycles of many waterbodies around the world. Thus, the temporal dynamics of water body extents and seasonal freezing/thawing cycles of lake ice can serve as an indicator for climatic shifts and variability and can reveal respective climate impacts on the global water availability. The Global WaterPack (GWP) time series which is being processed at the German Remote Sensing Data Center (DFD) of DLR quantifies the daily dynamics of global inland waterbodies at a daily temporal resolution. The 15-year time series is based on daily optical MODIS satellite data at a spatial resolution of 250m. The dataset and its generation have been described in detail by Klein et al. (2017). The daily temporal resolution and global coverage of the GWP has high potential to reveal insights into the impact of climate change, meteorological variability and human activities on surface water dynamics. The dataset shows not only the seasonal or long-term desiccation and filling of waterbodies but also freezing and thawing cycles of lakes. The study will present selected examples of waterbodies in (semi-arid) regions with strong seasonal and multi-annual areal dynamics, and will show long- and short-term relationships to precipitation variability and shifts. In addition, we will show the temporal behavior of selected waterbodies with seasonal freezing and thawing cycles. It will be illustrated how the spatio-temporal patterns of freezing/thawing can be quantified using the Global WaterPack time series, and how observed patterns are related to temperatures. The results emphasize the added value of the high temporal (daily) resolution of the GWP which is not available in other waterbody products. This high temporal resolution is of relevance particularly in the context of impact analyses of climate change and variability where not only obvious but also subtle shifts in timing and duration of water availability are to be considered.

Information on the distribution of inland waters bodies and their seasonal variability is important for supporting water and land management and informed decision making. Furthermore, such data can be crucial basis for scientific analyses in the field of regional and global environmental research. Key functions and services of wetlands for example are closely related to the temporal variations of surface water availability and inundation cycles. Over the past years the mapping of temporal water dynamics from earth observation data has received increasing attention. Open data policies as well as the progress in computing power and processing techniques have played a major role for this development. In the past decades there has been several optical instruments available collecting data on global scale with very high temporal resolution, such as MODIS or SPOT-VGT/Proba-V. With the launch of Sentinel-3 and Suomi-NPP such observations at global scale and high temporal resolution has been secured for the near future. Recently, many studies already underlined the relevance of mapping water with high temporal resolution presenting essential results and interesting findings. In our research we are presenting DLRs Global WaterPack, a MODIS-based 250m time series dataset of surface water dynamics with daily temporal resolution. Using examples of lakes and reservoirs from around the Earth, the potential of the Global WaterPack to capture relevant parameters such as inundation frequency and duration, timing of flooding and water retreat, as well as freezing and thawing cycles, is presented. Results are compared with high resolution spatial reference data and in-situ measurements. The application potential of the Global WaterPack time series for monitoring and assessing changes is discussed. Furthermore, we discuss this potential based on selected examples which underline the added value of water surface detection at high temporal resolution in the context of climate and environmental change. Short and long-term dynamics of lakes and reservoirs and the underlying climatic or anthropogenic processes often cannot be determined in detail in regards to time by assessing interrupted time series or multi-temporal watermask snapshot observations. The entire MODIS time series from 06/2002-today is currently being processed and the dataset will be tested as input for global hydrological models. Additionally, quantification of uncertainty is being planned and will be part of future development.

In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques.

Information on the spatio-temporal dynamics of inland water bodies is of high value for many applications, for example in the context of water and land management or for ecosystem service assessments. In this study, different approaches to delineate inland water bodies from MODIS 250 m time series were compared. Here, the performance of different input bands and indices, of trainings pixel selection methods, and of dynamic threshold definition approaches were assessed with the goal to find an optimized approach applicable for global inland water body detection based on moderate spatial and high temporal resolution MODIS data. The results of the tested approaches were cross validated with high resolution Landsat-8 classifications. The results show amongst others that a combination of near infrared band (NIR) and difference index (NIR – red band) performed best for most of the globally distributed test regions and that single band approaches revealed higher commission errors.

Rice is the most important food crop in Asia and the mapping and monitoring of paddy rice fields is an important task in the context of food security, food trade policy and greenhouse gas emissions modelling. Two countries where rice is of special significance are China, the largest producer and importer of rice, and Vietnam, where rice exports contribute a fifth to the GDP. Both countries are facing increasing pressure in terms of food security due to population and economic growth while agricultural areas are confronted with urban encroachment and the limits of yield increase. Despite the importance of knowledge about rice production the countries official land cover products and rice production statistics are of varying quality and sometimes even contradict each other. Available remote sensing studies focused either on time-series analysis from optical sensors or from Synthetic Aperture Radar (SAR) sensors – the studies using optical sensors faced problems due to either the spatial or temporal resolution and the persistent cloud cover while SAR studies found the limited data availability and large image size to be the biggest drawbacks. We try to address these issues by proposing a paddy rice mapping approach that combines medium spatial resolution, temporally dense time-series from the optical MODIS sensors and high spatial resolution time-series from the recently launched Sentinel-1 SAR sensor. We used the 250m resolution MOD13Q1 and MYD13Q1 products as a basis for our medium resolution rice map. Prevalent cloud cover introduces noise into these timeseries which we reduced by applying a Savitzky-Golay filter. We then derived a number of time-series temporal and phenological metrics for multiple years and classified rice areas with One Class Support Vector Machines. In a next step we used this medium resolution rice map to mask Sentinel-1 Interferometric Wide Swath images and create SAR time-series from which we again derived temporal and phenological metrics and classified rice areas with machine learning algorithms to arrive at a 10m resolution rice map. This method allows concurrent, accurate and high resolution mapping of paddy rice areas from freely available data with limited requirements towards processing infrastructure and can be used as a basis for greenhouse gas and crop modelling as well as providing viable information for decision makers regarding food security, food trade, bioeconomy and mitigation after crop failure. Results of our paddy rice classification will be presented for selected study sites in China and Vietnam.

The aim of this study is the contextualization of urban growth and sprawl experienced in the coastal conurbation of Mersin in southern Turkey. For this purpose, an interdisciplinary approach has been developed by using methods of remote sensing and social sciences. The sample areas, identified by using a series of satellite images available for the period 1987-2009, have been examined using a multi-temporal change detection approach in combination with available statistics and on-site excursions in order to understand the pattern of development. What is evident from this study is that an interdisciplinary perspective on the examination of urban growth and sprawl provides us with timely results that can be employed in urban and regional planning activities.