10m Sentinel-2A Cloud-Free Composite &amp;ndash; Southern Africa 2016

Ramoino, Fabrizio; Tutunaru, Florin; Pera, Fabrizio; Arino, Olivier

doi:10.20944/preprints201706.0009.v1

Cited by 2 publications

(5 citation statements)

References 5 publications

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“…Therefore, programs such as REDD + (Reduction of Emission from Deforestation and Degradation) and Ethiopia's voluntary commitment to the Bonn Challenge could benefit from this approach and its decision-support tools. The approach employs freely accessible data including Sentinel-2 land cover (Ramoino et al ., 2016), DEM, EVI, water occurrence (Gorelick et al ., 2017) and soil depth (Hengl et al ., 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Lakes and reservoirs are affected by the human activities occurring around them. First, the water bodies layer was created from a land cover map released by the European Space Agency (Ramoino et al ., 2016). This map, available for free, was created using Sentinel-2.…”

Section: Methodsmentioning

confidence: 99%

“…2C). The present land-use layer was created using the Sentinel-2 land cover (Ramoino et al ., 2016) and agricultural land cover (IWMI, 2015). The outputs generated by this step indicate whether the land identified as suitable for exclosure corresponds to current degraded agricultural land or to current natural or perennial vegetation.…”

Section: Methodsmentioning

confidence: 99%

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Multicriteria decision-support system to assess the potential of exclosure-based conservation in Ethiopia

Kibret

Haileslassie

Bori

et al. 2020

Renew. Agric. Food Syst.

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Land degradation is a global challenge that affects lives and livelihoods in many communities. Since 1950, about 65% of Africa's cropland, on which millions of people depend, has been affected by land degradation caused by mining, poor farming practices and illegal logging. One-quarter of the land area of Ethiopia is severely degraded. As part of interventions to restore ecosystem services, exclosures have been implemented in Ethiopia since the 1980s. But the lack of tools to support prioritization and more efficient targeting of areas for large-scale exclosure-based interventions remains a challenge. Within that perspective, the overarching objectives of the current study were: (i) to develop a Geographic Information System-based multicriteria decision-support tool that would help in the identification of suitable areas for exclosure initiatives; (ii) to provide spatially explicit information, aggregated by river basin and agroecology, on potential areas for exclosure interventions and (iii) to conduct ex-ante analysis of the potential of exclosure areas for improving ecosystem services in terms of increase in above-ground biomass (AGB) production and carbon storage. The results of this study demonstrated that as much as 10% of Ethiopia's land area is suitable for establishing exclosures. This amounts to 11 million hectares (ha) of land depending on the criteria used to define suitability for exclosure. Of this total, a significant proportion (0.5–0.6 million ha) is currently under agricultural land-use systems. In terms of propriety river basins, we found that the largest amount of suitable area for exclosures falls in the Abay (2.6 million ha) and Tekeze (2.2 million ha) river basins, which are hosts to water infrastructure such as hydropower dams and are threatened by siltation. Ex-ante analysis of ecosystem services indicated that about 418 million tons of carbon can be stored in the AGB through exclosure land use. Ethiopia has voluntarily committed to the Bonn Challenge to restore 15 million ha of degraded land by 2025. The decision-support tool developed by the current study and the information so generated go toward supporting the planning, implementation and monitoring of these kinds of local and regional initiatives.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Multicriteria decision-support system to assess the potential of exclosure-based conservation in Ethiopia

Kibret

Haileslassie

Bori

et al. 2020

Renew. Agric. Food Syst.

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“…The corresponding times are summarized in Table 1. We consider seasons a reasonable trade-off between a time frame that is significantly shorter than one year used in standard multi-temporal image aggregation approaches (Sentinel-2 cloudless, 2017, Ramoino et al, 2017), but still long enough to have the chance to gather at least some cloud-free input information. Furthermore, for areas that are affected by seasonal land cover changes, we assume intra-seasonal changes to be less significant than inter-seasonal changes.…”

Section: Validation Of the Methodsmentioning

confidence: 99%

“…Finally, most methods exploiting multi-temporal imagery usually rely on rather long time series, e.g. about 1 year for a global cloud-free Sentinel-2 mosaic (May 2016 to April 2017) (Sentinel-2 cloudless, 2017) or a cloud-free Sentinel-2 mosaic of the southern extent of the African continent (January 2016 to December 2016) (Ramoino et al, 2017). In these cases, temporal stability of the land cover cannot be ensured, which renders multi-temporal cloud-free mosaics an inadequate resource for fine-grained monitoring or change detection approaches.…”

Section: Introductionmentioning

confidence: 99%

Aggregating Cloud-Free Sentinel-2 Images With Google Earth Engine

Schmitt

Hughes

Qiu

et al. 2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Cloud coverage is one of the biggest concerns in spaceborne optical remote sensing, because it hampers a continuous monitoring of the Earth’s surface. Based on Google Earth Engine, a web- and cloud-based platform for the analysis and visualization of large-scale geospatial data, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for user-defined areas of interest and time periods, which can be significantly shorter than the one-year time frames that are commonly used in other multi-temporal image aggregation approaches. We demonstrate the feasibility of our workflow for several cities spread around the globe and affected by different amounts of average cloud cover. The experimental results confirm that our results are better than the results achieved by standard approaches for cloud-free image aggregation.</p>

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10m Sentinel-2A Cloud-Free Composite – Southern Africa 2016

Cited by 2 publications

References 5 publications

Multicriteria decision-support system to assess the potential of exclosure-based conservation in Ethiopia

Multicriteria decision-support system to assess the potential of exclosure-based conservation in Ethiopia

Aggregating Cloud-Free Sentinel-2 Images With Google Earth Engine

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