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Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth\u2019s surface.\nDE Africa provides access to Landsat Collection 2 Level-2 Surface Reflectance products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.\nMore techincal information about the Landsat Surface Reflectance product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html).\nLandsat 9 product has a spatial resolution of 30 m and a temporal coverage of 2021 to present.\nLandsat Level- 2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.\nFor more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.\nThis product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).", "formats": ["OGC:WMS", "canonical"], "keywords": ["time-series", "africa", "landsat", "fractional-cover", "WOfS", "Global", "land", "Earth observation"], "providers": [{"name": "Digital Earth Africa", "organization": "Digital Earth Africa", "positionName": null, "roles": [{"name": "distributor"}], "contactInfo": {"phone": {"work": null}, "email": {"work": null}, "address": {"work": {"deliveryPoint": "GPO Box 378", "city": "Canberra", "administrativeArea": "ACT", "postalCode": "2609", "country": "Australia"}}, "url": {"url": "digitalearthafrica.org//", "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://ows.digitalearth.africa/", "name": "ls9_sr", "description": "Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth\u2019s surface.\nDE Africa provides access to Landsat Collection 2 Level-2 Surface Reflectance products over Africa. USGS Landsat Collection 2 offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant.\nMore techincal information about the Landsat Surface Reflectance product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html).\nLandsat 9 product has a spatial resolution of 30 m and a temporal coverage of 2021 to present.\nLandsat Level- 2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.\nFor more information on Landsat products, see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2-level-2-science-products.\nThis product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).", "type": "OGC:WMS", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/rwanda-hub/-/blob/rwanda/portals/Global/wms/digitalearth-ls9_sr.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "digitalearth-ls9_sr", "name": "item", "description": "digitalearth-ls9_sr", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main/items/digitalearth-ls9_sr"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "digitalearth-gm_s2_annual", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-27.86, -48.31], [-27.86, 39.0], [65.67, 39.0], [65.67, -48.31], [-27.86, -48.31]]]}, "time": "2025-06-01", "properties": {"themes": [{"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}, {"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}], "updated": "2025-06-01", "type": "dataset", "language": "en", "title": "gm_s2_annual", "description": "Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period.\nOne approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the 'geometric median' (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year.\nIn addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:\n- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.\n- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.\n- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.\nMore techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)\nThis product has a spatial resolution of 10 m and is available annually for 2017 to 2021.\nIt is derived from Surface Reflectance Sentinel-2 data. This product contains modified Copernicus Sentinel data 2017-2021.\nAnnual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year.\nFor more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312", "formats": ["OGC:WMS", "canonical"], "keywords": ["time-series", "africa", "landsat", "fractional-cover", "WOfS", "Global", "land", "Earth observation"], "providers": [{"name": "Digital Earth Africa", "organization": "Digital Earth Africa", "positionName": null, "roles": [{"name": "distributor"}], "contactInfo": {"phone": {"work": null}, "email": {"work": null}, "address": {"work": {"deliveryPoint": "GPO Box 378", "city": "Canberra", "administrativeArea": "ACT", "postalCode": "2609", "country": "Australia"}}, "url": {"url": "digitalearthafrica.org//", "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://ows.digitalearth.africa/", "name": "gm_s2_annual", "description": "Individual remote sensing images can be affected by noisy data, such as clouds, cloud shadows, and haze. To produce cleaner images that can be compared more easily across time, we can create 'summary' images or 'composites' that combine multiple images into one image to reveal the median or 'typical' appearance of the landscape for a certain time period.\nOne approach is to create a geomedian. A geomedian is based on a high-dimensional statistic called the 'geometric median' (Small 1990), which effectively trades a temporal stack of poor-quality observations for a single high-quality pixel composite with reduced spatial noise (Roberts et al. 2017). In contrast to a standard median, a geomedian maintains the relationship between spectral bands. This allows further analysis on the composite images, just as we would on the original satellite images (e.g. by allowing the calculation of common band indices like NDVI). An annual geomedian image is calculated from the surface reflectance values drawn from a calendar year.\nIn addition, surface reflectance varabilities within the same time period can be measured to support characterization of the land surfaces. The median absolute deviation (MAD) is a robust measure (resilient to outliers) of the variability within a dataset. For multi-spectral Earth observation, deviation can be measured against the geomedian using a number of distance metrics.  Three of these metrics are adopted to highlight different types of changes in the landscape:\n- Euclidean distance (EMAD), which is more sensitive to changes in target brightness.\n- Cosine (spectral) distance (SMAD), which is more sensitive to changes in target spectral response.\n- Bray Curtis dissimilarity (BCMAD), which is more sensitive to the distribution of the observation values through time.\nMore techincal information about the GeoMAD product can be found in the User Guide (https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html)\nThis product has a spatial resolution of 10 m and is available annually for 2017 to 2021.\nIt is derived from Surface Reflectance Sentinel-2 data. This product contains modified Copernicus Sentinel data 2017-2021.\nAnnual geomedian images enable easy visual and algorithmic interpretation, e.g. understanding urban expansion, at annual intervals. They are also useful for characterising permanent landscape features such as woody vegetation. The MADs can be used on their own or together with geomedian to gain insights about the land surface, e.g. for land cover classificiation and for change detection from year to year.\nFor more information on the algorithm, see https://doi.org/10.1109/TGRS.2017.2723896 and https://doi.org/10.1109/IGARSS.2018.8518312", "type": "OGC:WMS", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/rwanda-hub/-/blob/rwanda/portals/Global/wms/digitalearth-gm_s2_annual.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "digitalearth-gm_s2_annual", "name": "item", "description": "digitalearth-gm_s2_annual", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main/items/digitalearth-gm_s2_annual"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "digitalearth-s2_l2a", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-27.0, -47.7], [-27.0, 38.85], [65.01, 38.85], [65.01, -47.7], [-27.0, -47.7]]]}, "time": "2025-06-01", "properties": {"themes": [{"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}, {"scheme": "https://lsc-hubs.org/categories/", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}], "updated": "2025-06-01", "type": "dataset", "language": "en", "title": "s2_l2a", "description": "Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth\u2019s surface.\nDE Africa provides Sentinel 2 Level-2A surface reflectance data from European Commission's Copernicus Programme. Sentinel-2 is an Earth observation mission that systematically acquires optical imagery at up to 10 m spatial resolution. The mission is based on a constellation of two identical satellites in the same orbit, 180\u00b0 apart for optimal coverage and data delivery. Together, they cover all Earth's land surfaces, large islands, inland and coastal waters every 3-5 days. Each of the Sentinel-2 satellites carries a wide swath high-resolution multispectral imager with 13 spectral bands.\nThis product has a temporal coverage of 2017 to current and is updated as new images are acquired. Images in different spectral bands are provided at spatial resolutions of 10, 20 or 60 m. The surface reflectance values are scaled to be between 0 and 10,000.\nSentinel-2 Level-2A data are provided by the European Space Agency (ESA).  Data prior to 2017 are processed from Level-1C to Level-2A with ESA's Sen2Cor software by Sinergise. All images are converted to Cloud Optimised GeoTIFF format by Element 84, Inc.\nFor more information on the Sentinel-2 Level-2A surface reflectance product, see https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm\nThis product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).", "formats": ["OGC:WMS", "canonical"], "keywords": ["time-series", "africa", "landsat", "fractional-cover", "WOfS", "Global", "Land", "Earth observation"], "providers": [{"name": "Digital Earth Africa", "organization": "Digital Earth Africa", "positionName": null, "roles": [{"name": "distributor"}], "contactInfo": {"phone": {"work": null}, "email": {"work": null}, "address": {"work": {"deliveryPoint": "GPO Box 378", "city": "Canberra", "administrativeArea": "ACT", "postalCode": "2609", "country": "Australia"}}, "url": {"url": "digitalearthafrica.org//", "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://ows.digitalearth.africa/", "name": "s2_l2a", "description": "Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth\u2019s surface.\nDE Africa provides Sentinel 2 Level-2A surface reflectance data from European Commission's Copernicus Programme. Sentinel-2 is an Earth observation mission that systematically acquires optical imagery at up to 10 m spatial resolution. The mission is based on a constellation of two identical satellites in the same orbit, 180\u00b0 apart for optimal coverage and data delivery. Together, they cover all Earth's land surfaces, large islands, inland and coastal waters every 3-5 days. Each of the Sentinel-2 satellites carries a wide swath high-resolution multispectral imager with 13 spectral bands.\nThis product has a temporal coverage of 2017 to current and is updated as new images are acquired. Images in different spectral bands are provided at spatial resolutions of 10, 20 or 60 m. The surface reflectance values are scaled to be between 0 and 10,000.\nSentinel-2 Level-2A data are provided by the European Space Agency (ESA).  Data prior to 2017 are processed from Level-1C to Level-2A with ESA's Sen2Cor software by Sinergise. All images are converted to Cloud Optimised GeoTIFF format by Element 84, Inc.\nFor more information on the Sentinel-2 Level-2A surface reflectance product, see https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm\nThis product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).", "type": "OGC:WMS", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/rwanda-hub/-/blob/rwanda/portals/Global/wms/digitalearth-s2_l2a.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "digitalearth-s2_l2a", "name": "item", "description": "digitalearth-s2_l2a", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main/items/digitalearth-s2_l2a"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "10.11648-j.aff.20160506.13", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[29.02, -2.92], [29.02, -1.14], [30.82, -1.14], [30.82, -2.92], [29.02, -2.92]]]}, "time": "2016", "properties": {"themes": [{"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}, {"scheme": "https://lsc-hubs.org/categories/", "concepts": []}], "updated": "2025-06-01", "type": "dataset", "created": "2016-01-01", "language": "english", "title": "Pedological Characterization of Soils Developed from Volcanic Parent Materials of Northern Province of Rwanda", "description": "Soils formed from volcanic materials have high potential for agricultural production and support high human population densities. This study was carried out on soils developed from volcanic parent materials of Northern Province of Rwanda aiming largely on pedological characterization but to a certain extent on assessment of potentials of soils for production of major crops in the area. Three representative pedons namely Kinigi Pedon 1 (KNG-P1), Kinigi Pedon 2 (KNGP2) and Gahunga Pedon 1 (GHNG-P1) were identified and described. Sixteen soil samples were collected from different pedogenic horizons and analyzed in the laboratory for physico-chemical properties. Pedons were classified using USDA Soil Taxonomy and FAO-WRB for Soil Resources. Potentials and limitations of the soils were also identified. Results show that soils were shallow to very deep and well drained. Topsoils were very dark coloured with colour values of \u2264 2 in all pedons. Texture was generally loamy with bulk densities of < 0.47 g/cm3 in Pedons KNG-P1 and KNG-P2 while they ranged from 0.94 to 1.34 g/cm3 in Pedon GHNG-P1. Topsoils were medium acid (KNG-P1, KNG-P2) and mildly alkaline (GHNG-P1), with high to very high OC ranging from 3.97 to 13.03%. CECsoil was high (> 32 cmol (+)/kg) in Pedons KNG-P1 and KNG-P2. Base saturation was low (< 30%) in Pedons KNG-P1 and KNG-P2 while it was high (> 50%) in Pedon GHNG-P1. pHNaF was > 9.5 in Pedons KNG-P1 and KNG-P2 reflecting exchange complex dominated by amorphous materials and/or humus complexes. Phosphorus retention capacity ranged from 6.25% to 99.58% and only Pedons KNG-P1 and KNG-P2 met the \u201candic properties\u201d requirement of PRC \u2265 85%. Melanic index values indicated that these two pedons were characterized more by fulvic than humic acids. Nutrient imbalance with reference to basic cations was common in all studied soils, implying suboptimal nutrient uptake and toxicity. Fe2O3, SiO2 and Al2O3 were the dominant oxides in the studied soils. Degree of weathering of studied soils was low as indicated by their weathering indices. Using field and laboratory data, Pedons KNG-P1 and KNG-P2 classified as Andisols/Andosols and GHNG-P1 as Mollisols/Phaeozems. Land units represented by Pedons KNGP1 and KNG-P2 were rated as marginally suitable while land unit represented by Pedon GHNG-P1 was rated as moderately suitable for the major crops of the area. Application of P fertilizers coupled with efficient placement was recommended to enhance P, and soil conservation should be underscored in study area.", "formats": ["application/xlsx", "canonical"], "keywords": ["Pedological Characterization", "Soil Morphology and Genesis", "Nutrient Balance", "Total Elemental Composition", "Indices of Degree of Weathering", "Soil Classification", "Rwanda", "Sub-national", "land suitability", "Soil"], "providers": [{"name": "SYLVERE SIRIKARE", "organization": "RAB", "positionName": null, "roles": [{"name": "Soil scientist"}], "contactInfo": {"phone": {"work": null}, "email": {"work": "sylvere.sirikare@rab.gov.rw"}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": null, "country": "Rwanda"}}, "url": {"url": null, "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "10.11648/j.aff.20160506.13", "name": "Citation", "description": null, "type": null, "rel": null}, {"href": "10.11648/j.aff.20160506.13", "name": "xlsx", "description": "10.11648/j.aff.20160506.13", "type": "application/xlsx", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/rwanda-hub/-/blob/rwanda/portals/RW/LSC/data/10.11648-j.aff.20160506.13.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "10.11648-j.aff.20160506.13", "name": "item", "description": "10.11648-j.aff.20160506.13", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main/items/10.11648-j.aff.20160506.13"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "10.3390-rs13244985", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[29.02, -2.92], [29.02, -1.14], [30.82, -1.14], [30.82, -2.92], [29.02, -2.92]]]}, "time": "2021", "properties": {"themes": [{"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}, {"scheme": "https://lsc-hubs.org/categories/", "concepts": []}], "updated": "2025-06-01", "type": "dataset", "created": "2021-01-01", "language": "English", "externalIds": [{"value": "https://doi.org/10.3390/rs13244985"}], "title": "UAV-Based Mapping of Banana Land Area for Village-Level Decision-Support in Rwanda", "description": "Crop monitoring is crucial to understand crop production changes, agronomic practice decision-support, pests/diseases mitigation, and developing climate change adaptation strategies. Banana, an important staple food and cash crop in East Africa, is threatened by Banana Xanthomonas Wilt (BXW) disease. Yet, there is no up-to-date information about the spatial distribution and extent of banana lands, especially in Rwanda, where banana plays a key role in food security and livelihood. Therefore, delineation of banana-cultivated lands is important to prioritize resource allocation for optimal productivity. We mapped the spatial extent of smallholder banana farmlands by acquiring and processing high-resolution (25 cm/px) multispectral unmanned aerial vehicles (UAV) imageries, across four villages in Rwanda. Georeferenced ground-truth data on different land cover classes were combined with reflectance data and vegetation indices (NDVI, GNDVI, and EVI2) and compared using pixel-based supervised multi-classifiers (support vector models-SVM, classification and regression trees-CART, and random forest\u2013RF), based on varying ground-truth data richness. Results show that RF consistently outperformed other classifiers regardless of data richness, with overall accuracy above 95%, producer\u2019s/user\u2019s accuracies above 92%, and kappa coefficient above 0.94. Estimated banana farmland areal coverage provides concrete baseline for extension-delivery efforts in terms of targeting banana farmers relative to their scale of production, and highlights opportunity to combine UAV-derived data with machine-learning methods for rapid landcover classification.", "formats": ["canonical"], "keywords": ["Rwanda", "banana", "machine learning", "UAV", "remote sensing", "land cover mapping", "precision agriculture", "food security", "BXW", "National", "crop", "Crop"], "providers": [{"name": "Svetlana Gaidashova", "organization": "RAB", "positionName": null, "roles": [{"name": "Banana program cordinator"}], "contactInfo": {"phone": {"work": null}, "email": {"work": "svetlana.gaidashova@rab.gov.rw"}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": null, "country": "Rwanda"}}, "url": {"url": null, "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "; https://doi.org/10.3390/rs13244985", "name": "Citation", "description": null, "type": null, "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/rwanda-hub/-/blob/rwanda/portals/RW/LSC/data/10.3390-rs13244985.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390-rs13244985", "name": "item", "description": "10.3390-rs13244985", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main/items/10.3390-rs13244985"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "16qTiVPupblpZeyavwKjwFUP5zp8bUu9L-view", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[29.02, -2.92], [29.02, -1.14], [30.82, -1.14], [30.82, -2.92], [29.02, -2.92]]]}, "time": "2013", "properties": {"themes": [{"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}, {"scheme": "https://lsc-hubs.org/categories/", "concepts": []}], "updated": "2025-06-01", "type": "dataset", "created": "2013-01-01", "language": "english", "externalIds": [{"value": "https://drive.google.com/file/d/16qTiVPupblpZeyavwKjwFUP5zp8bUu9L/view?usp=share_link"}], "title": "Soil health", "description": "The data contains 599 data points collected from 320 plot in Gishwati and Bugasera regions. The data contins Carobon, Nitrogen and texture", "formats": ["application/csv", "canonical"], "keywords": ["Land health", "soil health", "soil properties", "Rwanda", "catchment", "soil quality", "Soil"], "providers": [{"name": "Ermias Betemariam", "organization": "CIFOR-ICRAF", "positionName": null, "roles": [{"name": "Researher"}], "contactInfo": {"phone": {"work": null}, "email": {"work": "e.betemariam@cgiar.org"}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": null, "country": "Rwanda"}}, "url": {"url": null, "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://drive.google.com/file/d/16qTiVPupblpZeyavwKjwFUP5zp8bUu9L/view?usp=share_link", "name": "csv", "description": "https://drive.google.com/file/d/16qTiVPupblpZeyavwKjwFUP5zp8bUu9L/view?usp=share_link", "type": "application/csv", "rel": null}, {"href": "https://git.wur.nl/isric/lsc-hubs/rwanda-hub/-/blob/rwanda/portals/RW/LSC/data/16qTiVPupblpZeyavwKjwFUP5zp8bUu9L-view.yml", "name": "Source of the record", "description": null, "type": "canonical", "rel": "canonical"}, {"rel": "self", "type": "application/geo+json", "title": "16qTiVPupblpZeyavwKjwFUP5zp8bUu9L-view", "name": "item", "description": "16qTiVPupblpZeyavwKjwFUP5zp8bUu9L-view", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main/items/16qTiVPupblpZeyavwKjwFUP5zp8bUu9L-view"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://rwanda.lsc-hubs.org/cat/collections/metadata:main"}]}, {"id": "RW17", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[29.02, -2.92], [29.02, -1.14], [30.82, -1.14], [30.82, -2.92], [29.02, -2.92]]]}, "time": "2020", "properties": {"themes": [{"scheme": "https://inspire.ec.europa.eu/metadata-codelist/SpatialScope", "concepts": []}, {"scheme": "https://lsc-hubs.org/tags/", "concepts": []}, {"scheme": "https://lsc-hubs.org/categories/", "concepts": []}], "updated": "2025-06-01", "type": "dataset", "created": "2020-01-01", "language": "english", "title": "FOREST LANDSCAPE RESTORATION-Technical Packages for Rwanda", "description": "The evaluation FLR actions are: agroforestry integrated with maize-beans crops, agroforestry integrated with maize-beans-fodder, agroforestry integrated with wheat-Irish potato, woodlot plantations, and protective forests along riversides and roadsides.", "formats": ["application/txt", "canonical"], "keywords": ["agroforestry", "forest", "income", "national", "land management", "Land"], "providers": [{"name": "Dr. Jules Rutebuka, Dr Alain Ndoli", "organization": "International Union for Conservation of Nature (IUCN)", "positionName": null, "roles": [{"name": "Reg Programme officer, Sustainable Agriculture-Land systems"}], "contactInfo": {"phone": {"work": null}, "email": {"work": "jules.rutebuka@iucn.org"}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": null, "country": "Rwanda"}}, "url": {"url": null, "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://www.iucn.org/resources/publication/forest-landscape-restoration-opportunity-assessment-rwanda", "name": "Citation", "description": "MoE, 2020. Forest landscape restoration Technical Packages. 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However, the most common benefits mentioned were the following:\uf0b7 69% of the respondents mentioned that improved soil conservation due to terracing on one hand and the other, a high chance of organic manures stability on the bench leading to higher nutrient availability, and thus resulting in consistent yield increment.\uf0b7 82% of respondents mentioned that the RT benefited them in having increased productivity without further land degradation. 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The analysis of this study was expected to deliver economic and financial benefits through Cost-Benefit Analysis (CBA) of the possible catchment restoration options. This report provides information on socio-economic characteristics of the sample farmers, encountered challenges/ issues and suggested solutions, and Cost-Benefit Analysis of landscape restoration options: Mukungwa and Akagera Lower Catchments in Rwanda. From the assessment results, it is evident that the main assets owned by most of the people living in the study regions are phones, hoes, houses and mattresses. 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Sustainable agricultural intensification, in the context of increasing cropping intensities, is a prerequisite for deliberate land management strategies that deliver multiple ecosystem goods (food, energy, income sources, etc.) and services (especially improving soil conditions) on the same land, as well as system resilience, if adopted at scale. Tree based ecosystem approaches (TBEAs) are among such multi-functional land management strategies. Knowledge on the multi-functionality of TBEAs and on their scaling up, however, remains severely limited due to several methodological challenges. This study aims at offering an analytical perspective to view multi-functional TBEAs as an integral part of sustainable agricultural intensification. The study proposes a conceptual framework to guide the analysis of socio-economic data and applies it to cross-site analysis of TBEAs in extremely densely populated Rwanda. Heterogeneous TBEAs were identified across Rwanda\u2019s different agro-ecological zones to meet locally-specific smallholders\u2019 needs for a set of ecosystem goods and services on the same land. The sustained adoption of TBEAs would be guaranteed if farmers subjectively recognize their compatibility and synergy with sustainable intensification of existing farming systems, supported by favorable institutional conditions.", "formats": ["WWW:LINK", "application/txt", "canonical"], "keywords": ["East African Highlands", "agroforestry", "based ecosystem approaches (TBEAs)", "ecosystem services", "functionality", "heterogeneity", "landscape", "multi", "population pressure", "smallholder farmers", "sustainable intensification", "tree", "National", "field experimentation", "Land"], "providers": [{"name": "Vicky Ruganzu", "organization": "RAB", "positionName": null, "roles": [{"name": "Integrated Soil Fertility Program coordinator"}], "contactInfo": {"phone": {"work": null}, "email": {"work": "rugavicky@gmail.com"}, "address": {"work": {"deliveryPoint": null, "city": null, "administrativeArea": null, "postalCode": null, "country": "Rwanda"}}, "url": {"url": null, "protocol": null, "name": null, "description": null, "function": null}}}]}, "links": [{"href": "https://www.mdpi.com/2071-1050/10/5/1360/htm", "name": "Citation", "description": "Iiyama, M., Mukuralinda, A., Ndayambaje, J. 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