Adrià, D., Serge, W., Erik, M., David, G., Stephen, P., & Zoltan, S. (2021). 2019년 고해상도 글로벌 산업 및 소규모 농장 야자수 지도 (버전 v1) [데이터 세트]. Zenodo.
doi:10.5281/zenodo.4473715
이 데이터 세트는 2019년의 10m 글로벌 산업 및 소규모 농장 야자수 지도입니다. 팜유 농장이 감지된 지역을 포함합니다. 분류된 이미지는 Sentinel-1 및 Sentinel-2 반년 합성물을 기반으로 하는 컨볼루션 신경망의 출력입니다. 자세한 내용은 도움말을 참고하세요.
[null,null,[],[[["\u003cp\u003eThe BIOPAMA/GlobalOilPalm/v1 dataset provides a 10-meter resolution global map of industrial and smallholder oil palm plantations for the year 2019.\u003c/p\u003e\n"],["\u003cp\u003eIt uses Sentinel-1 and Sentinel-2 satellite data processed through a convolutional neural network to identify oil palm plantation areas.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset distinguishes between industrial and smallholder plantations, and it is available through the Google Earth Engine platform.\u003c/p\u003e\n"],["\u003cp\u003eThe classification includes three categories: Industrial closed-canopy, Smallholder closed-canopy, and other land covers/uses.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset is licensed under CC-BY-4.0 and is valuable for research, conservation, and land use planning related to oil palm cultivation.\u003c/p\u003e\n"]]],[],null,["# Global Map of Oil Palm Plantations\n\nDataset Availability\n: 2019-01-01T00:00:00Z--2019-12-31T00:00:00Z\n\nDataset Provider\n:\n\n\n [Biopama programme](https://doi.org/10.5281/zenodo.4473715)\n\nTags\n:\n [agriculture](/earth-engine/datasets/tags/agriculture) [biodiversity](/earth-engine/datasets/tags/biodiversity) [conservation](/earth-engine/datasets/tags/conservation) [crop](/earth-engine/datasets/tags/crop) [global](/earth-engine/datasets/tags/global) [landuse](/earth-engine/datasets/tags/landuse) [palm](/earth-engine/datasets/tags/palm) [plantation](/earth-engine/datasets/tags/plantation) \nbiopama \n\n#### Description\n\nThe dataset is a 10m global industrial and smallholder oil palm map for 2019.\nIt covers areas where oil palm plantations were detected. The classified images are the output\nof a convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites.\n\nSee [article](https://essd.copernicus.org/articles/13/1211/2021/) for additional information.\n\n### Bands\n\n**Bands**\n\n| Name | Pixel Size | Description |\n|------------------|------------|----------------------------|\n| `classification` | 10 meters | Oil Palm class description |\n\n**classification Class Table**\n\n| Value | Color | Description |\n|-------|---------|--------------------------------------------------------------------|\n| 1 | #ff0000 | Industrial closed-canopy oil palm plantations |\n| 2 | #ef00ff | Smallholder closed-canopy oil palm plantations |\n| 3 | #696969 | Other land covers and/or uses that are not closed-canopy oil palm. |\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- Adrià, D., Serge, W., Erik, M., David, G., Stephen, P., \\& Zoltan, S. (2021). High resolution global industrial and smallholder oil palm map for 2019 (Version v1) \\[Data set\\]. Zenodo.\n [doi:10.5281/zenodo.4473715](https://doi.org/10.5281/zenodo.4473715)\n\n### DOIs\n\n- \u003chttps://doi.org/10.5281/zenodo.4473715\u003e\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\n// Import the dataset; a collection of composite granules from 2019.\nvar dataset = ee.ImageCollection('BIOPAMA/GlobalOilPalm/v1');\n\n// Select the classification band.\nvar opClass = dataset.select('classification');\n\n// Mosaic all of the granules into a single image.\nvar mosaic = opClass.mosaic();\n\n// Define visualization parameters.\nvar classificationVis = {\n min: 1,\n max: 3,\n palette: ['ff0000','ef00ff', '696969']\n};\n\n// Create a mask to add transparency to non-oil palm plantation class pixels.\nvar mask = mosaic.neq(3);\nmask = mask.where(mask.eq(0), 0.6);\n\n// Display the data on the map.\nMap.addLayer(mosaic.updateMask(mask),\n classificationVis, 'Oil palm plantation type', true);\nMap.setCenter(-3.0175, 5.2745,12);\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/BIOPAMA/BIOPAMA_GlobalOilPalm_v1) \n[Global Map of Oil Palm Plantations](/earth-engine/datasets/catalog/BIOPAMA_GlobalOilPalm_v1) \nThe dataset is a 10m global industrial and smallholder oil palm map for 2019. It covers areas where oil palm plantations were detected. The classified images are the output of a convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. See article for additional information. \nBIOPAMA/GlobalOilPalm/v1, agriculture,biodiversity,conservation,crop,global,landuse,palm,plantation \n2019-01-01T00:00:00Z/2019-12-31T00:00:00Z \n-90 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [https://doi.org/10.5281/zenodo.4473715](https://doi.org/https://doi.org/10.5281/zenodo.4473715)\n- [https://doi.org/10.5281/zenodo.4473715](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/BIOPAMA_GlobalOilPalm_v1)"]]
