ArticlePloS one2017
Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Significant Land Cover Transitions and Regional Acceleration at the Continental Scale of Africa over the Last Four Decades.Sensors (Basel, Switzerland) · 2026Article
- Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure.Heliyon · 2024Article
- Analyzing Land Use/Land Cover Changes Using Google Earth Engine and Random Forest Algorithm and Their Implications to the Management of Land Degradation in the Upper Tekeze Basin, Ethiopia.TheScientificWorldJournal · 2024Article
- Monitoring forest cover and land use change in the Congo Basin under IPCC climate change scenarios.PloS one · 2024Article
- Nonparametric assessment of mangrove ecosystem in the context of coastal resilience in Ghana.Ecology and evolution · 2023Article
- Soil and vegetation water content identify the main terrestrial ecosystem changes.National science review · 2023Article
- Article
- Agricultural SandboxNL: A national-scale database of parcel-level processed Sentinel-1 SAR data.Scientific data · 2022Article
- Towards a concrete landscape: Assessing the efficiency of land consumption in the Greater Accra Region, Ghana.PloS one · 2022Article
- Tracking forest loss and fragmentation between 1930 and 2020 in Asian elephant (Elephas maximus) range in Nepal.Scientific reports · 2021Article
- Spectral responses in rangelands and land cover change by livestock in regions of the Caatinga biome, Brazil.Scientific reports · 2021Article
- Supporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics.Scientific reports · 2020Article
- Remotely sensed thermal decay rate: an index for vegetation monitoring.Scientific reports · 2020Article
- Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China.Sensors (Basel, Switzerland) · 2020Article
- Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in Liberia and Gabon.PloS one · 2020Article
- Household electricity access in Africa (2000-2013): Closing information gaps with model-based geostatistics.PloS one · 2019Article
- Article
- Validity of environmental audits using GigaPanInternational journal of health geographics · 2018Article
- Predicting residential structures from open source remotely enumerated data using machine learning.PloS one · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.