Copernicus Global Land Cover Layers—Collection 2

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(6), P. 1044 - 1044

Published: March 24, 2020

In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, set fraction is provided depicting percentual main types in pixel. This additional continuous classification scheme represents areas heterogeneous better than standard scheme. Overall, 20 are which allow customization maps specific user needs or applications (e.g., forest monitoring, crop biodiversity and conservation, climate modeling, etc.). However, not just up-scaling, but also includes major improvements quality, reaching around 80% more overall accuracy. The processing system went into operational status allowing annual updates on scale with an implemented training validation data collection system. this paper, we provide overview changes production maps, that have led increased accuracy, including aligning Sentinel satellite grid coordinate system, improving metric extraction, adding auxiliary data, biome delineations, as well enhancing expert rules. An independent exercise confirmed improved results. addition methodological improvements, paper provides where different resources can be found, access channels product layer detailed peer-review documentation.

Language: Английский

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A 30 m global map of elevation with forests and buildings removed

Environmental Research Letters, Journal Year: 2022, Volume and Issue: 17(2), P. 024016 - 024016

Published: Jan. 20, 2022

Abstract Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation contains forest and building artifacts that limit its usefulness for applications require precise terrain heights, particular flood simulation. Here, we use machine learning remove buildings forests from the Copernicus Digital Model produce, first time, a map of with removed at 1 arc second (∼30 m) grid spacing. We train our correction algorithm on unique set reference 12 countries, covering wide range climate zones urban extents. Hence, this approach has much wider applicability compared previous DEMs trained single country. Our method reduces mean absolute vertical error built-up areas 1.61 1.12 m, 5.15 2.88 m. new is more accurate than existing maps will strengthen models where high quality information required.

Language: Английский

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Outlining where humans live, the World Settlement Footprint 2015

Scientific Data, Journal Year: 2020, Volume and Issue: 7(1)

Published: July 20, 2020

Abstract Human settlements are the cause and consequence of most environmental societal changes on Earth; however, their location extent is still under debate. We provide here a new 10 m resolution (0.32 arc sec) global map human Earth for year 2015, namely World Settlement Footprint 2015 (WSF2015). The raster dataset has been generated by means an advanced classification system which, first time, jointly exploits open-and-free optical radar satellite imagery. WSF2015 validated against 900,000 samples labelled crowdsourcing photointerpretation very high Google imagery outperforms all other similar existing layers; in particular, it considerably improves detection small rural regions better outlines scattered suburban areas. can be used at any scale observation support to applications requiring detailed accurate information presence (e.g., socioeconomic development, population distribution, risks assessment, etc.).

Language: Английский

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Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Journal of Remote Sensing, Journal Year: 2021, Volume and Issue: 2021

Published: Jan. 1, 2021

Land-cover mapping is one of the foundations Earth science. As a result combined efforts many scientists, numerous global land-cover (GLC) products with resolution 30 m have so far been generated. However, increasing number fine-resolution GLC datasets imposing additional workloads as it necessary to confirm quality these and check their suitability for user applications. To provide guidelines users, in this study, recent developments currently available (including three thematic four different types, i.e., impervious surface, forest, cropland, inland water) were first reviewed. Despite great toward improving accuracy that there decades, current still suffer from having relatively low accuracies between 46.0% 88.9% GlobeLand30-2010, 57.71% 80.36% FROM_GLC-2015, 65.59% 84.33% GLC_FCS30-2015. The reported maps vary 67.86% 95.1% eight surface reviewed, 56.72% 97.36% seven forest products, 32.73% 98.3% six cropland 15.67% 99.7% water products. consistency was then examined. showed good overall agreement terms spatial patterns but limited some vegetation classes (such shrub, tree, grassland) specific areas such transition zones. Finally, prospects also considered. With rapid development cloud computing platforms big data, Google Engine (GEE) greatly facilitates production by integrating multisource remote sensing advanced image processing classification algorithms powerful capability. synergy spectral, spatial, temporal features derived satellite stored will definitely improve spatiotemporal In general, up now, most not able achieve maximum (per class or overall) error 5%–15% required Therefore, more are needed especially which has wetland, tundra, maps.

Language: Английский

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A first Chinese building height estimate at 10 m resolution (CNBH-10 m) using multi-source earth observations and machine learning

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 291, P. 113578 - 113578

Published: April 10, 2023

Language: Английский

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High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs

Renewable Energy, Journal Year: 2021, Volume and Issue: 182, P. 659 - 684

Published: Oct. 12, 2021

The rapid uptake of renewable energy technologies in recent decades has increased the demand researchers, policymakers and planners for reliable data on spatial distribution their costs potentials. For onshore wind this resulted an active research field devoted to analysing these resources regions, countries or globally. A particular thread attempts go beyond purely technical restrictions determine realistic, feasible actual potential energy. Motivated by developments, paper reviews methods assumptions geographical, technical, economic and, finally, We address each potentials turn, including aspects related land eligibility criteria, meteorology, developments turbine characteristics such as power density, specific rotor spacing aspects. Economic assessments are central future deployment discussed a system level covering levelized depending locations, integration which often overlooked analyses. Non-technical approaches include scenicness landscape, constraints due regulation public opposition, expert stakeholder workshops, willingness pay/accept elicitations socioeconomic cost-benefit studies. different estimations, state art is critically discussed, with attempt derive best practice recommendations highlight avenues research.

Language: Английский

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World Settlement Footprint 3D - A first three-dimensional survey of the global building stock

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 270, P. 112877 - 112877

Published: Jan. 8, 2022

Settlements, and in particular cities, are at the center of key future challenges related to global change sustainable development. Widely used indicators assess efficiency sustainability settlement development compactness density built-up area. However, scale, a temporally consistent spatially detailed survey distribution concentration building stock – meaning total area volume buildings within defined spatial unit or settlement, commonly referred as does not yet exist. To fill this data knowledge gap, an approach was developed map characteristics world's so far unprecedented level detail for every single on our planet. The resulting World Settlement Footprint 3D dataset quantifies fraction, area, average height, measuring grid with 90 m cell size. is generated using modified version human settlements mask derived from Sentinel-1 Sentinel-2 satellite imagery 10 resolution, combination 12 digital elevation radar collected by TanDEM-X mission. underlying, automated processing framework includes three basic workflows: one estimating mean height based analysis differences along potential edges, second module determining fraction each cell, third part combining information order determine gridding. Optionally, simple model (level 1) can be regions where footprints available. A comprehensive validation campaign models obtained 19 (~86,000 km2) street-view samples indicating number floors >130,000 individual 15 additional cities documents that novel provides valuable and, first time, globally both, large urban agglomerations well small-scale rural settlements. Thus, new represents promising baseline wide range previously impossible environmental, socioeconomic, climatological studies worldwide.

Language: Английский

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Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(18), P. 3053 - 3053

Published: Sept. 18, 2020

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on fine-grained feature level which can help us to better understand the of land surfaces taking object into account. To extract features and objects, most popular deep-learning model for image analysis is commonly used: convolutional neural network (CNN). this review, we provide comprehensive overview impact deep learning EO applications reviewing 429 studies segmentation detection CNNs. We extensively examine distribution study sites, employed sensors, used datasets CNN architectures, give thorough Our main finding that CNNs an advanced transition phase from computer vision EO. Upon this, argue near future, analyze will have significant research. With focus Part II, complete methodological review provided I.

Language: Английский

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Assessing the Impact of Sea Level Rise and Resilience Potential in the Caribbean

World Bank, Washington, DC eBooks, Journal Year: 2020, Volume and Issue: unknown

Published: Sept. 22, 2020

No AccessOther papers22 Sep 2020Assessing the Impact of Sea Level Rise and Resilience Potential in Caribbean360° Background PaperAuthors/Editors: Alessio Giardino, Tim Leijnse, Luisa Torres Duenas, Panos Athanasiou, Marjolijn HaasnootAlessio Haasnoothttps://doi.org/10.1596/36417SectionsAboutPDF (6 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: The Caribbean region suffers major economic losses from natural hazards such as flooding due storms, cyclones, extreme waves, winds precipitation, coastal erosion, volcanic eruptions landslides. Consequently, typical at most small states, when a disaster strikes, large part population, infrastructure businesses, generally concentrated areas, are directly or indirectly affected. Climate change sea level rise (SLR), combination with socio-economic growth, likely exacerbate this situation, which is already critical for many these countries. particular, effect SLR will lead more frequent intense events chronical direct on local regional economies. study, estimation effects terms erosion sandy beaches was carried out 18 countries aim deriving proxies evaluate resilient potential each country their adaptation. (change in) risk resulting estimated until 2100 under different scenarios pathways. Previous bookNext book FiguresreferencesRecommendeddetails View Published: September 2020 Copyright & Permissions Related RegionsLatin America CaribbeanRelated TopicsEnvironmentUrban DevelopmentWater Resources PDF DownloadLoading ...

Language: Английский

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A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land, Journal Year: 2021, Volume and Issue: 10(3), P. 231 - 231

Published: Feb. 25, 2021

Bare soil is a critical element in the urban landscape and plays an essential role environments. Yet, separation of bare other land cover types using remote sensing techniques remains significant challenge. There are several sensing-based spectral indices for barren detection, but their effectiveness varies depending on patterns climate conditions. Within this research, we introduced modified index (MBI) shortwave infrared (SWIR) near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed was tested two different Thailand Vietnam, where there large areas during agricultural fallow period, obstructing between areas. extracted MBI achieved higher overall accuracy about 98% kappa coefficient over 0.96, compared to (BSI), normalized (NDBaI), dry (DBSI). results also revealed that considerably contributes classification. We suggest detection tropical climatic regions.

Language: Английский

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