Fildişi Sahili BNETD 2020 Arazi Örtüsü Haritası, Fildişi Sahili Hükümeti tarafından ulusal bir kurum olan Ulusal Çalışma Ofisi Teknikleri ve Geliştirme'ye (BNETD-CIGN) bağlı Coğrafi Bilgi ve Dijital Merkezi aracılığıyla Avrupa Birliği'nin teknik ve mali desteğiyle üretilmiştir. Haritayı oluşturmak için kullanılan metodoloji şeffaf, katılımcı ve uluslararası standartlara uygundur.
Bu haritayı geliştirmek için 2020'ye ait uydu görüntülerinden (Sentinel 2) oluşan bir mozaik, Google Earth Engine aracılığıyla işlendi ve denetimli sınıflandırma algoritmasını (Random Forest) eğitmek için sahada toplanan verilerle desteklendi.
Ülke genelinde 10 Kasım-9 Aralık 2022 ve 26 Ocak-13 Şubat 2023 tarihleri arasında iki saha kampanyası düzenlendi. Paydaşlar tarafından benimsenen veri toplama yöntemleri ve belirli arazi kullanım sınıflarının tanımları bazen farklılık gösterebileceğinden bu görevlere birden fazla iş ortağı kuruluşundan 33 kişi katıldı.
EUDR durum tespiti süreci kapsamında, EUDR ile ilgili ürünler üreten arazilerin coğrafi konum verileri, 2020 orman örtüsü verileriyle birlikte kullanılabilir. Bu sayede, arazinin 2020 son tarihinden önce ormanlık bir alanda bulunma riski değerlendirilebilir. Bunun için, FAO'nun orman tanımıyla uyumlu ve 2020 kesme tarihiyle belirlenmiş orman örtüsü verileri gerekir. Fildişi Sahili'nin 2020 arazi örtüsü haritası bu ihtiyaçları karşılamaktadır.
Gerçekten de, arazi örtüsü haritasındaki sınıflar birleştirilerek FAO'nun orman tanımıyla uyumlu bir orman/orman dışı haritası oluşturulabilir.
2020 arazi örtüsü verilerine, meta verilere ve yönteme erişmek için ESRI çözümleri kullanılarak bir platform geliştirildi. Afrika GeoPortal'dan alınan bu çözümler, veri analizi ve görselleştirme için kullanılıyor:
Fildişi Sahili BNETD 2020 Arazi Örtüsü Haritası, Fildişi Sahili Hükümeti tarafından ulusal bir kurum olan Ulusal Çalışma Ofisi Teknikleri ve Kalkınma'nın (BNETD-CIGN) Coğrafi Bilgi ve Dijital Merkezi aracılığıyla Avrupa Birliği'nin teknik ve mali desteğiyle üretilmiştir. Haritayı oluşturmak için kullanılan metodoloji …
[null,null,[],[[["\u003cp\u003eThe Cote d'Ivoire BNETD 2020 Land Cover Map provides a detailed classification of land cover across Cote d'Ivoire for the year 2020, created using Sentinel 2 satellite imagery and ground-truthed data.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset, produced by the Ivorian Government with support from the European Union, follows international standards and offers 10-meter resolution for analysis.\u003c/p\u003e\n"],["\u003cp\u003eUsers can access the dataset through Google Earth Engine and the Africa GeoPortal for visualization and analysis, with the possibility of overlaying it with other data for due diligence purposes like assessing deforestation risk.\u003c/p\u003e\n"],["\u003cp\u003eThe land cover classifications include a variety of forest types, plantations, agricultural areas, water bodies, and human infrastructure, allowing for comprehensive land use assessments.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available under a CC-BY-4.0 license, encouraging open access and use for research, education, and non-profit purposes.\u003c/p\u003e\n"]]],[],null,["# Cote d'Ivoire BNETD 2020 Land Cover Map\n\nDataset Availability\n: 2020-01-01T00:00:00Z--2021-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [BNETD-CIGN](https://africageoportal.maps.arcgis.com/home/user.html?user=bnetdcignCI)\n\nCadence\n: 1 Year\n\nTags\n:\n[classification](/earth-engine/datasets/tags/classification) [deforestation](/earth-engine/datasets/tags/deforestation) [forest](/earth-engine/datasets/tags/forest) [landcover](/earth-engine/datasets/tags/landcover) [landuse-landcover](/earth-engine/datasets/tags/landuse-landcover) \n\n#### Description\n\nThe Cote d'Ivoire BNETD 2020 Land Cover Map was produced by the Ivorian\nGovernment through a national institution, the Center for Geographic\nInformation and Digital from the National Study Office Techniques and\nDevelopment (BNETD-CIGN), with technical and financial support from the\nEuropean Union. The methodology used to produce the map was transparent,\nparticipatory and in line with international standards.\n\nTo develop this map, a mosaic of satellite images (Sentinel 2) from 2020 was\nprocessed via Google Earth Engine and supplemented with data collected in the\nfield, to train a supervised classification algorithm (Random Forest).\nTwo field campaigns were conducted, from 10 November to 9 December 2022 and\nfrom 26 January to 13 February 2023, throughout the country. These missions\ninvolved 33 people from multiple partner organizations because the data\ncollection methods and definitions of certain land use classes\nadopted by stakeholders may sometimes differ.\n\nAs part of the EUDR due diligence process, geolocation data for plots of\nland producing EUDR-relevant products could be overlaid with 2020 forest\ncover data in order to assess the risk that the plot is located in an area\nthat was forested before the 2020 cut-off date. To do this, forest cover\ndata aligned with the FAO definition of forests and the 2020 cut-off date\nis required. The 2020 land cover map of Cote d'Ivoire meets these needs.\nIndeed, the classes in the land cover map can be combined to create a\nforest/non-forest map that is aligned with the FAO definition of forests.\n\nA platform for accessing 2020 land cover data, metadata and the methodology\nhas been developed using ESRI solutions, from Africa GeoPortal, for data\nanalysis and visualization:\n\nThe address is: \u003chttps://bit.ly/carte-ci-2020\u003e\n\nDocumentation:\n\n- [Detailed documentation](https://africageoportal.maps.arcgis.com/sharing/rest/content/items/26a717d4c13f4f3db2c6056f7e5c0bab/data)\n\n- [Methodology in French](https://africageoportal.maps.arcgis.com/sharing/rest/content/items/76dc18767b89472eb89e8aa54e08a6c9/data)\n\n### Bands\n\n\n**Pixel Size**\n\n10 meters\n\n**Bands**\n\n| Name | Min | Max | Pixel Size | Description |\n|------------------|-----|-----|------------|------------------|\n| `classification` | 1 | 23 | meters | Land Cover class |\n\n**classification Class Table**\n\n| Value | Color | Description |\n|-------|---------|-------------------------------------------------------------------------------------------------------------------|\n| 1 | #276300 | Dense forest (Forêt dense) |\n| 2 | #59D757 | Light forest (Forêt claire) |\n| 3 | #569D6E | Forest gallery (Forêt galerie) |\n| 4 | #79CFAD | Secondary forest/degraded forest (Forêt secondaire/forêt dégradée) |\n| 5 | #34734C | Mangrove |\n| 6 | #B4FFAD | Forest plantation/Reforestation (Plantation forestière/Reboisement) |\n| 7 | #6EFA9A | Swamp forest/Forest on hydromorphic soil (Forêt marécageuse/Forêt sur sol hydromorphe) |\n| 8 | #D68589 | Coffee Plantation (Plantation de Café) |\n| 9 | #EBD37F | Cocoa Plantation (Plantation de Cacao) |\n| 10 | #D0E09D | Rubber plantation (Plantation d'Hévéa) |\n| 11 | #E8BEFF | Oil palm plantation (Plantation de Palmier à huile) |\n| 12 | #E751FE | Coconut Plantation (Plantation de Coco) |\n| 13 | #F3BFF2 | Cashew plantation (Plantation d'Anacarde) |\n| 14 | #9DFD00 | Fruit plantation / Arboriculture (Plantation fruitière / Arboricultures) |\n| 15 | #F2F38D | Agricultural development/Other crops/Orchards/Fallow land (Aménagement agricole/Autres cultures/Vergers/Jachères) |\n| 16 | #B6D322 | Tree savannah (Savane arborée) |\n| 17 | #E2FE5F | Shrub formations/ Thickets (Formations arbustives/ Fourrés) |\n| 18 | #F9FDCC | Herbaceous formations (Formations herbacées) |\n| 19 | #4A70C0 | Body of water, Courses and waterways (Plan d'eau, Cours et voies deau) |\n| 20 | #BEFFE8 | Swampy area (Zone marécageuse) |\n| 21 | #D20A02 | Human habitat, Infrastructure (Habitat humain, Infrastructures) |\n| 22 | #DBECEF | Rocky outcrop (Affleurement rocheux) |\n| 23 | #DCDCDC | Bare ground (Sol nu) |\n\n### Terms of Use\n\n**Terms of Use**\n\n[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)\n\n### Citations\n\nCitations:\n\n- BNETD Land Cover Map 2020.\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar dataset = ee.Image('BNETD/land_cover/v1/2020').select('classification');\n\nMap.setCenter(-5.4400, 7.5500, 7);\n\nMap.addLayer(dataset, {}, \"Cote d'Ivoire Land Cover Map 2020\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/BNETD/BNETD_land_cover_v1) \n[Cote d'Ivoire BNETD 2020 Land Cover Map](/earth-engine/datasets/catalog/BNETD_land_cover_v1) \nThe Cote d'Ivoire BNETD 2020 Land Cover Map was produced by the Ivorian Government through a national institution, the Center for Geographic Information and Digital from the National Study Office Techniques and Development (BNETD-CIGN), with technical and financial support from the European Union. The methodology used to produce the map ... \nBNETD/land_cover/v1, classification,deforestation,forest,landcover,landuse-landcover \n2020-01-01T00:00:00Z/2021-01-01T00:00:00Z \n4.3603 -8.602 10.74 -2.493 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://africageoportal.maps.arcgis.com/home/user.html?user=bnetdcignCI)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/BNETD_land_cover_v1)"]]
