Consistent metropolitan boundaries for the remote sensing of urban land

This paper introduces an internationally consistent definition of metropolitan areas to the literature regarding the remote sensing of urban land use or land cover. In the cross-comparison of land use or land cover for explicitly bounded urban areas, the observed ‘economic’ definition is argued to hold distinct potential merits over administrative or agglomeration-based boundaries, which typically underpin other studies. To illustrate the proposed merits as well as their implications for the remote sensing literature, the empirical analysis considers the case of 687 European metropolitan areas. Across these metropolitan areas, whose boundaries are defined jointly by the OECD and the European Commission, land cover and land use are segmented in a fusion of imagery from radar and optical sensors in Sentinel satellites. Segmentation is achieved using deep learning in a well-established model architecture. The analytical focus is on built-up areas that are in a residential use or in a commercial or industrial use. Map classifications and accuracy measures are obtained for cities as well as their respective commuting zones as these together embody metropolitan areas. The results underline that not only land use area estimates but also map classification accuracy vary widely across individual metropolitan areas. Whereas classification accuracy to some degree varies for metropolitan areas within as well as between countries, classification accuracy is positively associated with population size and built-up area density as regression analysis confirms. Additionally, the extent of built-up areas in distinct uses is shown to vary across different types of metropolitan (sub-)areas. This study's findings highlight the typically unobserved role that study area definition and selection may play in affecting outcomes in remote sensing studies in urban settings, as relevant to both studies of single as well as multiple urban areas. The consistent comparison of remote sensing outcomes across metropolitan areas may further promote generalization in a growing and global field and potentially supports better-informed policy making processes.</p

High-resolution mapping of 33 years of material stock and population growth in Germany using Earth Observation data

Global societal material stock in buildings and infrastructure have accumulated rapidly within the last decades, along with population growth. Recently, an approach for nation-wide mapping of material stock at 10 m spatial resolution, using freely available and globally consistent Earth Observation (EO) imagery, has been introduced as an alternative to cost-intensive cadastral data or broad-scale but thematically limited nighttime light-based mapping. This study assessed the potential of EO data archives to create spatially explicit time series data of material stock dynamics and their relation to population in Germany, at a spatial resolution of 30 m. We used Landsat imagery with a change-aftereffect-trend analysis to derive yearly masks of land surface change from 1985 onward. Those served as an input to an annual reverse calculation of six material stock types and building volume-based annual gridded population, based on maps for 2018. Material stocks and population in Germany grew by 13% and 4%, respectively, showing highly variable spatial patterns. We found a minimum building stock of ca. 180 t/cap across all municipalities and growth processes characterized by sprawl. A rapid growth of stocks per capita occurred in East Germany after the reunification in 1990, with increased building activity but population decline. Possible over- or underestimations of stock growth cannot be ruled out due to methodological assumptions, requiring further research.Peer Reviewe

Continental-scale land cover mapping at 10 m resolution over Europe (ELC10)

Widely used European land cover maps such as CORINE are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a high resolution (10 m) land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A Random Forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across 8 land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10-m land cover maps including S2GLC and FROM-GLC10. We found that atmospheric correction of Sentinel-2 and speckle filtering of Sentinel-1 imagery had minimal effect on enhancing classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The conversion of LUCAS points into homogenous polygons under the Copernicus module increased accuracy by <1%, revealing that Random Forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies - the difference between 5K and 50K LUCAS points is only 3% (86 vs 89%). At 10-m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g. tree planting)

Mapping Landcover Change and Population Displacement of Lakshmipur District, Bangladesh due to Riverbank Erosion From 2001-2021: A Geospatial Approach

Due to the geographical setting, the Lakshmipur district of Bangladesh experiences adverse effects of global climate change that include but are not limited to natural disasters such as tropical cyclones, storm surges, coastal flooding, and riverbank erosion. While riverbank erosion is an implicit impact of climate change, it directly affects human settlements, agricultural activity, and the overall livelihoods of people in this area. Examining the spatiotemporal changes in land cover and population due to riverbank erosion in this region could help us better understand the dynamics of human-environmental relations. This study aimed to classify land cover for every five years from 2001-2021, examine land cover changes in this area from 2001-2021, map populations in Lakshmipur district for every five years from 2001-2021, and estimate population displacement due to riverbank erosion every five years from 2001-2021. Landsat 5 TM 30 m satellite Imagery from 2001-2011 and Landsat 8 OLI 30 m resolution Imagery from 2016-2021 were used to classify landcover and observed landcover changes from 2001-2021. We classified Imagery using the smile random forest classifier in Google Earth Engine and calculated landcover change using the Change Detection Wizard in ArcGIS Pro 3.0. The overall classification accuracies range between 79.04% to 87 %. Our landcover change result suggests that from 2001-2021 fallow land/agricultural land has lost the largest area of land, 341.81 sq km, to homestead forest and waterbody among all the classes. To map populations vector and raster-based dasymetric mapping approaches were used. The vector and raster-based binary dasymetric mapping and population displacement calculation were carried out in ArcGIS Pro 3.0. The findings suggest that the lowest number of population displacements (1844 people using vector-based approach and 5241 raster-based approach) happened from 2001-2006, and the highest number (86107 people using vector-based approach, 63453 using raster-based approach) were displaced between 2016-2021. INDEX WORDS: Landcover Change, Population Mapping, Google Earth Engine, Riverbank Erosion, Dasymetric mapping, Population displacement, Lakshmipur District, Bangladesh

Urban Change Pattern Exploration of Megacities Using Multitemporal Nighttime Light and Sentinel-1 SAR Data

During the last 20 years, fast urbanization activities have been highly concentrated in just few countries (e.g., China, India, and Nigeria) and have led to the emergence of large urban aggregations, with high population density. Still, very few researches have focused on this dynamic phenomenon with a global perspective using multisource remote sensing data. In this article, combining radar and spectral sensors of different spatial resolution, a novel approach based on a novel hierarchical biclustering technique is proposed and proved to be effective in discriminating the underlying change patterns without pre-estimating the number of clusters. To this aim, experimental results focused on newly emerging megalopolis in China, India, and Nigeria, as well as on the highly urbanized and stable Lombardy region in Italy, are presented. The analysis of the results allows us to understand, in a global and comparative perspective, the spatiotemporal differentiation of urban density and how cities are changing and evolving in the building volume and, to some extent, their economic level

Mapping Europe into local climate zones

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives

Sleep Variability and Nighttime Activity among Tsimane Forager‐Horticulturalists

Objectives A common presumption in sleep research is that “normal” human sleep should show high night‐to‐night consistency. Yet, intra‐individual sleep variation in small‐scale subsistence societies has never been studied to test this idea. In this study, we assessed the degree of nightly variation in sleep patterns among Tsimane forager‐horticulturalists in Bolivia, and explored possible drivers of the intra‐individual variability. Methods We actigraphically recorded sleep among 120 Tsimane adults (67 female), aged 18–91, for an average of 4.9 nights per person using the Actigraph GT3X and Philips Respironics Actiwatch 2. We assessed intra‐individual variation using intra‐class correlations and average deviation from each individual\u27s average sleep duration, onset, and offset times ( ). Results Only 31% of total variation in sleep duration was due to differences among different individuals, with the remaining 69% due to nightly differences within the same individuals. We found no statistically significant differences in Tsimane sleep duration by day‐of‐the‐week. Nightly variation in sleep duration was driven by highly variable sleep onset, especially for men. Nighttime activities associated with later sleep onset included hunting, fishing, housework, and watching TV. Conclusions In contrast to nightly sleep variation in the United States being driven primarily by “sleeping‐in” on weekends, Tsimane sleep variation, while comparable to that observed in the United States, was driven by changing “bedtimes,” independent of day‐of‐the‐week. We propose that this variation may reflect adaptive responses to changing opportunity costs to sleep/nighttime activity

Urban surface temperature time series estimation at the local scale by spatial-spectral unmixing of satellite observations

The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3) observations, to provide high spatio-temporal resolution LST estimates in cities