IrrMapper adalah klasifikasi tahunan status irigasi di 11 negara bagian Amerika Serikat bagian Barat yang dibuat dalam skala Landsat (yaitu, 30 m) menggunakan algoritma Random Forest, yang mencakup tahun 1986 hingga saat ini.
Meskipun makalah IrrMapper
menjelaskan klasifikasi empat kelas (yaitu, lahan irigasi, lahan kering,
lahan tidak diolah, lahan basah), set data dikonversi menjadi klasifikasi biner
lahan irigasi dan non-irigasi.
'Diairi' mengacu pada deteksi irigasi apa pun selama tahun tersebut.
Model random forest IrrMapper dilatih menggunakan database geospasial yang ekstensif tentang penutupan lahan dari masing-masing empat kelas lahan yang diairi dan tidak diairi, termasuk lebih dari 50.000 lahan yang diairi yang diverifikasi oleh petugas, 38.000 lahan kering, dan lebih dari 500.000 kilometer persegi lahan yang tidak dibudidayakan.
Untuk versi 1.2, data pelatihan asli diperluas secara signifikan, model RF dibuat untuk setiap negara bagian, dan validasi serta analisis ketidakpastian yang lebih menyeluruh dilakukan. Lihat suplemen
untuk makalah kami tentang dampak irigasi terhadap aliran sungai.
Band
Ukuran Piksel 30 meter
Band
Nama
Ukuran Piksel
Deskripsi
classification
meter
Piksel yang diairi memiliki nilai 1, piksel lainnya ditutupi.
Ketchum, D.; Jencso, K.; Maneta, M.P.; Melton, F.; Jones, M.O.; Huntington, J.
IrrMapper: Pendekatan Machine Learning untuk Pemetaan Resolusi Tinggi Pertanian yang Diairi di Seluruh Amerika Serikat bagian Barat,
Remote Sens. 2020, 12, 2328. doi:10.3390/rs12142328
Ketchum, D., Hoylman, Z.H., Huntington, J. et al. Irrigation intensification impacts sustainability
of streamflow in the Western United States. Commun Earth Environ 4, 479 (2023). doi:10.1038/s43247-023-01152-2
IrrMapper adalah klasifikasi tahunan status irigasi di 11 negara bagian Amerika Serikat bagian Barat yang dibuat dalam skala Landsat (yaitu, 30 m) menggunakan algoritma Random Forest, yang mencakup tahun 1986 hingga saat ini. Meskipun makalah IrrMapper menjelaskan klasifikasi empat kelas (yaitu, lahan irigasi, lahan kering, lahan tidak diolah, lahan basah), set data dikonversi menjadi …
[null,null,[],[[["\u003cp\u003eIrrMapper is an annual dataset that classifies irrigated and non-irrigated land in the Western US from 1986 to present.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset uses a Random Forest algorithm and Landsat data for 30m resolution mapping of irrigation status.\u003c/p\u003e\n"],["\u003cp\u003eIt provides a binary classification: irrigated areas are marked with a value of 1, while non-irrigated areas are masked out.\u003c/p\u003e\n"],["\u003cp\u003eDeveloped by the University of Montana, IrrMapper is available for research, education, and non-profit use under the CC-BY-4.0 license.\u003c/p\u003e\n"],["\u003cp\u003eUsers can explore and analyze the dataset using Google Earth Engine.\u003c/p\u003e\n"]]],[],null,["# IrrMapper Irrigated Lands, Version 1.2\n\nDataset Availability\n: 1986-01-01T00:00:00Z--2024-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [University of Montana / Montana Climate Office](https://climate.umt.edu/research/irrmapper/)\n\nTags\n:\n[agriculture](/earth-engine/datasets/tags/agriculture) [landsat-derived](/earth-engine/datasets/tags/landsat-derived) \n\n#### Description\n\nIrrMapper is an annual classification of irrigation status in the 11\nWestern United States made at Landsat scale (i.e., 30 m) using the\nRandom Forest algorithm, covering years 1986 - present.\n\nWhile the [IrrMapper paper](https://www.mdpi.com/2072-4292/12/14/2328)\ndescribes classification of four classes (i.e., irrigated, dryland,\nuncultivated, wetland), the dataset is converted to a binary\nclassification of irrigated and non-irrigated.\n\n'Irrigated' refers to the detection of any irrigation during the year.\nThe IrrMapper random forest model was trained using an extensive\ngeospatial database of land cover from each of four irrigated- and\nnon-irrigated classes, including over 50,000 human-verified irrigated\nfields, 38,000 dryland fields, and over 500,000 square kilometers of\nuncultivated lands.\n\nFor version 1.2, the original training data was greatly expanded,\na RF model built for each state, and a more thorough validation and uncertainty\nanalysis undertaken. See the [supplement](https://static-content.springer.com/esm/art%3A10.1038%2Fs43247-023-01152-2/MediaObjects/43247_2023_1152_MOESM3_ESM.docx)\nto our [paper](https://www.nature.com/articles/s43247-023-01152-2) on the impacts of irrigation on streamflow.\n\n### Bands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Pixel Size | Description |\n|------------------|------------|------------------------------------------------------------------------|\n| `classification` | meters | Irrigated pixels have the value of 1, the other pixels are masked out. |\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- Ketchum, D.; Jencso, K.; Maneta, M.P.; Melton, F.; Jones, M.O.; Huntington, J.\n IrrMapper: A Machine Learning Approach for High Resolution Mapping of\n Irrigated Agriculture Across the Western U.S.,\n Remote Sens. 2020, 12, 2328. [doi:10.3390/rs12142328](https://doi.org/10.3390/rs12142328)\n\n Ketchum, D., Hoylman, Z.H., Huntington, J. et al. Irrigation intensification impacts sustainability\n of streamflow in the Western United States. Commun Earth Environ 4, 479 (2023). [doi:10.1038/s43247-023-01152-2](https://doi.org/10.1038/s43247-023-01152-2)\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.ImageCollection('UMT/Climate/IrrMapper_RF/v1_2');\nvar irr = dataset.filterDate('2023-01-01', '2023-12-31').mosaic();\n\nvar visualization = {\n min: 0.0,\n max: 1.0,\n palette: ['blue']\n};\nMap.addLayer(irr, visualization, 'IrrMapper 2023');\nMap.setCenter(-112.516, 45.262, 10);\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/UMT/UMT_Climate_IrrMapper_RF_v1_2) \n[IrrMapper Irrigated Lands, Version 1.2](/earth-engine/datasets/catalog/UMT_Climate_IrrMapper_RF_v1_2) \nIrrMapper is an annual classification of irrigation status in the 11 Western United States made at Landsat scale (i.e., 30 m) using the Random Forest algorithm, covering years 1986 - present. While the IrrMapper paper describes classification of four classes (i.e., irrigated, dryland, uncultivated, wetland), the dataset is converted to ... \nUMT/Climate/IrrMapper_RF/v1_2, agriculture,landsat-derived \n1986-01-01T00:00:00Z/2024-01-01T00:00:00Z \n31.3 -124.5 49 -99 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://climate.umt.edu/research/irrmapper/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/UMT_Climate_IrrMapper_RF_v1_2)"]]
