Dynamic World, Near real-time global 10 m land use land cover mapping

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: June 9, 2022

Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times between image processing dataset release. We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) use (LULC) classification leveraging deep learning 10 m Sentinel-2 imagery. utilize highly scalable cloud-based system to apply this provide open, continuous feed of LULC predictions parallel acquisitions. This first-of-its-kind NRT product, we collectively refer as Dynamic World, accommodates variety user needs ranging from extremely up-to-date data custom composites representing user-specified date ranges. Furthermore, the nature product's outputs enables refinement, extension, even redefinition classification. In combination, these unique attributes enable unprecedented flexibility diverse community users across disciplines.

Language: Английский

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Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(18), P. 3062 - 3062

Published: Sept. 18, 2020

Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use cover (LULC) provide a new perspective in remote sensing analysis. Jointly, these sources permit researchers to improve operational classification change detection, guiding better reasoning landscape intrinsic processes, as deforestation agricultural expansion. However, the results their have not yet been synthesized order coherent guidance on effect different well identify promising approaches issues which affect performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, future possibilities L8/OLI S2/MSI LULC mapping detection. particular, highlight possibility using medium-resolution (Landsat-like, 10–30 m) time series multispectral optical provided by harmonization between sensors cube architectures analysis-ready that are permeated publicizations, open policies, science principles. We also reinforce potential exploring more spectral bands combinations, especially three Red-edge two Near Infrared Shortwave S2/MSI, calculate vegetation indices sensitive phenological variations were less frequently applied long time, but turned since mission. Summarizing peer-reviewed papers can guide scientific community data, enable detailed knowledge detection landscapes, natural scenarios.

Language: Английский

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Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(16), P. 4101 - 4101

Published: Aug. 21, 2022

The European Space Agency’s Sentinel satellites have laid the foundation for global land use cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s Cover (WC) Esri’s Land (Esri) products first time in order to inform adoption application these maps going forward. For year 2020, three LULC show strong spatial correspondence (i.e., near-equal area estimates) water, built area, trees crop classes. However, relative one another, WC is biased towards over-estimating grass cover, Esri shrub scrub DW snow ice. Using ground truth data minimum unit 250 m2, we found that had highest overall (75%) compared (72%) (65%). Across all maps, water was most accurately mapped class (92%), followed by (83%), tree (81%) crops (78%), particularly biomes characterized temperate boreal forests. classes lowest accuracies, tundra biome, included (47%), (34%), bare (57%) flooded vegetation (53%). When using from LUCAS (Land Use/Cover Area Frame Survey) <100 (71%) (66%) (63%), highlighting ability resolve landscape elements more Esri. Although not analyzed our study, discuss advantages due its frequent near real-time delivery both categorical predictions probability scores. recommend should involve critical evaluation their suitability respect purpose, such as aggregate changes ecosystem accounting versus site-specific change detection monitoring, considering trade-offs between thematic resolution, versus. local accuracy, class-specific biases whether analysis necessary. also emphasize importance estimating areas pixel-counting alone but adopting best practices design-based inference estimation quantify uncertainty given study area.

Language: Английский

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Anthropogenic Land Use and Land Cover Changes—A Review on Its Environmental Consequences and Climate Change

Journal of the Indian Society of Remote Sensing, Journal Year: 2022, Volume and Issue: 50(8), P. 1615 - 1640

Published: June 7, 2022

Language: Английский

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What is the relationship between land use and surface water quality? A review and prospects from remote sensing perspective

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 29(38), P. 56887 - 56907

Published: June 16, 2022

Language: Английский

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Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(7), P. 1938 - 1938

Published: April 4, 2023

This study provides an overview of the techniques, shortcomings, and strengths remote sensing (RS) applications in effective retrieval monitoring water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, solids among others. To be effectively retrieved by RS, these WQPs are categorized optically active or inactive based on their influence optical characteristics measured RS sensors. offer opportunity for decisionmakers to quantify monitor a spatiotemporal scale effectively. The use has been explored many studies using empirical, analytical, semi-empirical, machine-learning algorithms. spectral signatures have applied estimation two categories namely, microwave Optical which heavily WQPs, is further grouped spaceborne airborne sensors platform they board. choice particular sensor used any application depends various factors including cost, spatial, spectral, temporal resolutions images. Some known satellite literature reviewed this paper include Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Operational Land Imager.

Language: Английский

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DKDFN: Domain Knowledge-Guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classification

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2022, Volume and Issue: 186, P. 170 - 189

Published: Feb. 24, 2022

Language: Английский

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Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis

Journal Of Big Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: June 2, 2023

Abstract Classification and analysis of high-resolution satellite images using conventional techniques have been limited. This is due to the complex characteristics imagery. These are characterized by features such as spectral signatures, texture shape, spatial relationships temporal changes. In this research, we present performance evaluation deep learning approaches based on Convolutional Neural Networks vision transformer towards achieving efficient classification remote sensing images. The CNN-based models explored include ResNet, DenseNet, EfficientNet, VGG InceptionV3. were evaluated three publicly available EuroSAT, UCMerced-LandUse NWPU-RESISC45 datasets containing categories achieve promising results in accuracy, recall, precision F1-score. demonstrates feasibility Deep Learning in-homogeneous

Language: Английский

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Challenges and Opportunities in Remote Sensing for Soil Salinization Mapping and Monitoring: A Review

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(10), P. 2540 - 2540

Published: May 12, 2023

Meeting current needs without compromising future generations’ ability to meet theirs is the only path toward achieving environmental sustainability. As most valuable natural resource, soil faces global, regional, and local challenges, from quality degradation mass losses brought on by salinization. These issues affect agricultural productivity ecological balance, undermining sustainability food security. Therefore, timely monitoring accurate mapping of salinization processes are crucial, especially in semi-arid arid regions where climate variability impacts have already reached alarming levels. Salt-affected has enormous potential thanks recent progress remote sensing. This paper comprehensively reviews sensing assess The review demonstrates that large-scale salinity estimation based tools remains a significant challenge, primarily due data resolution acquisition costs. Fundamental trade-offs constrain practical applications between resolution, spatial temporal coverage, costs, high accuracy expectations. article provides an overview research work related using By synthesizing highlighting areas further investigation needed, this helps steer efforts, insight for decision-making resource management, promotes interdisciplinary collaboration.

Language: Английский

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National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 304, P. 114069 - 114069

Published: Feb. 24, 2024

Spatially explicit and detailed information on tree species composition is critical for forest management, nature conservation the assessment of ecosystem services. In many countries, attributes are monitored regularly through sample-based inventories. combination with satellite imagery, data from such inventories have a great potential developing large-area maps. Here, high temporal resolution Sentinel-1 Sentinel-2 has been useful extracting vegetation phenology, that may also be valuable improving mapping. The objective this study was to map main in Germany using combined time series, identify address challenges related use National Forest Inventory (NFI) remote sensing applications. We generated cloud free series 5-day intervals imagery combine those monthly backscatter composites. Further, we incorporate topography, meteorology, climate account environmental gradients. To NFI training machine learning models, following challenges: 1) link pixels variable radius plots, which precise area unknown, 2) efficiently utilize mixed-species plots model validation. past, accuracies pixel-level maps were often estimated solely homogeneous pure-species stands. study, assess how well generalize mixed plot conditions. Our results show mapping large, environmentally diverse landscapes. Classification accuracy pure stands ranged between 72% 97% (F1-score) five dominant species, while less frequent remained challenging. When including assessment, decreased by 4–14 percentage points most groups. highlights importance mixed-forest when validating Based these results, discuss potentials remaining at national level. findings allow further improve national-level medium provide guidance similar approaches other countries where ground-based inventory available.

Language: Английский

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