2009'dan itibaren, Agriculture and Agri-Food Canada'daki (AAFC) Bilim ve Teknoloji Şubesi'nin (STB) Dünya Gözlem Ekibi, yıllık ürün türü dijital haritaları oluşturma sürecine başladı. 2009 ve 2010'da Prairie eyaletlerine odaklanarak optik (Landsat-5, AWiFS, DMC) ve radar (Radarsat-2) tabanlı uydu görüntüleri kullanılarak karar ağacı (KA) tabanlı bir metodoloji uygulandı. 2011 yetiştirme sezonundan itibaren bu etkinlik, ulusal bir ürün envanterini desteklemek amacıyla diğer illere de genişletilmiştir.
Bu yaklaşım, bugüne kadar 30 m'lik nihai bir konumsal çözünürlükte (2009 ve 2010'da 56 m) en az% 85'lik genel hedef doğruluğunu karşılayan bir ürün envanteri sunmayı başardı.
Bantlar
Piksel Boyutu 30 metre
Bantlar
Ad
Min.
Maks.
Piksel Boyutu
Açıklama
landcover
1
255
metre
Ana ürüne özgü arazi örtüsü sınıflandırması.
landcover Class Table
Değer
Renk
Açıklama
10
#000000
Cloud
20
#3333ff
Su
30
#996666
Çıplak Arazi ve Çorak
34
#cc6699
Kentsel ve Gelişmiş
35
#e1e1e1
Greenhouses
50
#ffff00
Shrubland
80
#993399
Sulak arazi
85
#501b50
Peatland
110
#cccc00
Otlak
120
#cc6600
Tarım (ayrıştırılmamış)
122
#ffcc33
Otlak ve Yemler
130
#7899f6
Çok Islak Olduğu İçin Tohum Ekilemiyor
131
#ff9900
Fallow
132
#660000
Tahıllar
133
#dae31d
Arpa
134
#d6cc00
Diğer Tahıllar
135
#d2db25
Millet
136
#d1d52b
Yulaf
137
#cace32
Çavdar
138
#c3c63a
Kavuzlu
139
#b9bc44
Triticale
140
#a7b34d
Buğday
141
#b9c64e
Switchgrass
142
#999900
Sorgum
143
#e9e2b1
Kinoa
145
#92a55b
Kışlık Buğday
146
#809769
İlkbahar Buğdayı
147
#ffff99
Mısır
148
#98887c
Tütün
149
#799b93
Ginseng
150
#5ea263
Yağlı tohumlar
151
#52ae77
Borage
152
#41bf7a
Camelina
153
#d6ff70
Kanola ve Kolza
154
#8c8cff
Keten tohumu
155
#d6cc00
Hardal
156
#ff7f00
Safflower
157
#315491
ayçiçeği
158
#cc9933
Soya fasulyesi
160
#896e43
Baklagiller
161
#996633
Diğer Pulseler
162
#8f6c3d
Bezelyeler
163
#b6a472
Nohut
167
#82654a
Fasulye
168
#a39069
Fababeans
174
#b85900
Mercimek
175
#b74b15
sebzeler
176
#ff8a8a
Domatesler
177
#ffcccc
Patatesler
178
#6f55ca
Şeker pancarı
179
#ffccff
Diğer Sebzeler
180
#dc5424
meyveler
181
#d05a30
Orman meyveleri
182
#d20000
Yaban Mersini
183
#cc0000
Yaban Mersini
185
#dc3200
Diğer Berry
188
#ff6666
Meyve Bahçeleri
189
#c5453b
Diğer Meyveler
190
#7442bd
Üzüm bağları
191
#ffcccc
Hops
192
#b5fb05
Çim
193
#ccff05
Otlar
194
#07f98c
Çocuk bakımevi
195
#00ffcc
Karabuğday
196
#cc33cc
Kanarya tohumu
197
#8e7672
Kenevir
198
#b1954f
Vetch
199
#749a66
Diğer Mahsuller
200
#009900
Orman (farklılaşmamış)
210
#006600
İğne Yapraklı
220
#00cc00
Broadleaf
230
#cc9900
Mixedwood
Resim Özellikleri
Resim Özellikleri
Ad
Tür
Açıklama
landcover_class_names
STRING_LIST
Tarım arazisi yüzey örtüsü sınıflandırma adları dizisi.
landcover_class_palette
STRING_LIST
Sınıflandırma paleti için kullanılan onaltılık renk kodu dizeleri dizisi.
2009'dan itibaren, Agriculture and Agri-Food Canada'daki (AAFC) Bilim ve Teknoloji Şubesi'nin (STB) Dünya Gözlem Ekibi, yıllık ürün türü dijital haritaları oluşturma sürecini başlattı. 2009 ve 2010'da Prairie eyaletlerine odaklanılarak optik (Landsat-5, …
[null,null,[],[[["\u003cp\u003eThe Annual Crop Inventory (ACI) dataset provides annual crop type maps for Canada, starting from 2009 and updated yearly.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset utilizes a Decision Tree methodology and satellite imagery (optical and radar) to classify cropland with an accuracy target of at least 85%.\u003c/p\u003e\n"],["\u003cp\u003eIt offers a 30-meter resolution and includes a comprehensive land cover classification with values representing various crop types, land uses, and infrastructure.\u003c/p\u003e\n"],["\u003cp\u003eThe ACI dataset is provided by Agriculture and Agri-Food Canada and is openly accessible under the OGL-Canada-2.0 license.\u003c/p\u003e\n"],["\u003cp\u003eUsers can access and analyze the dataset using Google Earth Engine for research, education, and non-profit purposes.\u003c/p\u003e\n"]]],["Agriculture and Agri-Food Canada (AAFC) initiated annual crop type mapping in 2009, utilizing optical and radar satellite imagery. The dataset, available from 2009 to 2023, employs a Decision Tree methodology to classify land cover, including specific crops. With a 30-meter pixel size and annual cadence, this inventory achieves at least 85% overall accuracy. The classification includes 255 different crops or land types, with a landcover band representing them.\n"],null,["# Canada AAFC Annual Crop Inventory\n\nDataset Availability\n: 2009-01-01T00:00:00Z--2023-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [Agriculture and Agri-Food Canada](https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9)\n\nCadence\n: 1 Year\n\nTags\n:\n [agriculture](/earth-engine/datasets/tags/agriculture) [canada](/earth-engine/datasets/tags/canada) [crop](/earth-engine/datasets/tags/crop) [landcover](/earth-engine/datasets/tags/landcover) \naafc \n\n#### Description\n\nStarting in 2009, the Earth Observation Team of the Science and Technology\nBranch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process\nof generating annual crop type digital maps. Focusing on the Prairie\nProvinces in 2009 and 2010, a Decision Tree (DT) based methodology was\napplied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based\nsatellite images. Beginning with the 2011 growing season, this activity has\nbeen extended to other provinces in support of a national crop inventory.\nTo date this approach can consistently deliver a crop inventory that meets\nthe overall target accuracy of at least 85% at a final spatial resolution of\n30m (56m in 2009 and 2010).\n\n### Bands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Min | Max | Pixel Size | Description |\n|-------------|-----|-----|------------|-----------------------------------------------|\n| `landcover` | 1 | 255 | meters | Main crop-specific land cover classification. |\n\n**landcover Class Table**\n\n| Value | Color | Description |\n|-------|---------|--------------------------------|\n| 10 | #000000 | Cloud |\n| 20 | #3333ff | Water |\n| 30 | #996666 | Exposed Land and Barren |\n| 34 | #cc6699 | Urban and Developed |\n| 35 | #e1e1e1 | Greenhouses |\n| 50 | #ffff00 | Shrubland |\n| 80 | #993399 | Wetland |\n| 85 | #501b50 | Peatland |\n| 110 | #cccc00 | Grassland |\n| 120 | #cc6600 | Agriculture (undifferentiated) |\n| 122 | #ffcc33 | Pasture and Forages |\n| 130 | #7899f6 | Too Wet to be Seeded |\n| 131 | #ff9900 | Fallow |\n| 132 | #660000 | Cereals |\n| 133 | #dae31d | Barley |\n| 134 | #d6cc00 | Other Grains |\n| 135 | #d2db25 | Millet |\n| 136 | #d1d52b | Oats |\n| 137 | #cace32 | Rye |\n| 138 | #c3c63a | Spelt |\n| 139 | #b9bc44 | Triticale |\n| 140 | #a7b34d | Wheat |\n| 141 | #b9c64e | Switchgrass |\n| 142 | #999900 | Sorghum |\n| 143 | #e9e2b1 | Quinoa |\n| 145 | #92a55b | Winter Wheat |\n| 146 | #809769 | Spring Wheat |\n| 147 | #ffff99 | Corn |\n| 148 | #98887c | Tobacco |\n| 149 | #799b93 | Ginseng |\n| 150 | #5ea263 | Oilseeds |\n| 151 | #52ae77 | Borage |\n| 152 | #41bf7a | Camelina |\n| 153 | #d6ff70 | Canola and Rapeseed |\n| 154 | #8c8cff | Flaxseed |\n| 155 | #d6cc00 | Mustard |\n| 156 | #ff7f00 | Safflower |\n| 157 | #315491 | Sunflower |\n| 158 | #cc9933 | Soybeans |\n| 160 | #896e43 | Pulses |\n| 161 | #996633 | Other Pulses |\n| 162 | #8f6c3d | Peas |\n| 163 | #b6a472 | Chickpeas |\n| 167 | #82654a | Beans |\n| 168 | #a39069 | Fababeans |\n| 174 | #b85900 | Lentils |\n| 175 | #b74b15 | Vegetables |\n| 176 | #ff8a8a | Tomatoes |\n| 177 | #ffcccc | Potatoes |\n| 178 | #6f55ca | Sugarbeets |\n| 179 | #ffccff | Other Vegetables |\n| 180 | #dc5424 | Fruits |\n| 181 | #d05a30 | Berries |\n| 182 | #d20000 | Blueberry |\n| 183 | #cc0000 | Cranberry |\n| 185 | #dc3200 | Other Berry |\n| 188 | #ff6666 | Orchards |\n| 189 | #c5453b | Other Fruits |\n| 190 | #7442bd | Vineyards |\n| 191 | #ffcccc | Hops |\n| 192 | #b5fb05 | Sod |\n| 193 | #ccff05 | Herbs |\n| 194 | #07f98c | Nursery |\n| 195 | #00ffcc | Buckwheat |\n| 196 | #cc33cc | Canaryseed |\n| 197 | #8e7672 | Hemp |\n| 198 | #b1954f | Vetch |\n| 199 | #749a66 | Other Crops |\n| 200 | #009900 | Forest (undifferentiated) |\n| 210 | #006600 | Coniferous |\n| 220 | #00cc00 | Broadleaf |\n| 230 | #cc9900 | Mixedwood |\n\n### Image Properties\n\n**Image Properties**\n\n| Name | Type | Description |\n|-------------------------|-------------|----------------------------------------------------------------------|\n| landcover_class_names | STRING_LIST | Array of cropland landcover classification names. |\n| landcover_class_palette | STRING_LIST | Array of hex code color strings used for the classification palette. |\n| landcover_class_values | INT_LIST | Value of the land cover classification. |\n\n### Terms of Use\n\n**Terms of Use**\n\n[OGL-Canada-2.0](https://spdx.org/licenses/OGL-Canada-2.0.html)\n\n### Citations\n\nCitations:\n\n- Agriculture and Agri-Food Canada Annual Crop Inventory. {YEAR}\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('AAFC/ACI');\nvar crop2016 = dataset\n .filter(ee.Filter.date('2016-01-01', '2016-12-31'))\n .first();\nMap.setCenter(-103.8881, 53.0372, 10);\nMap.addLayer(crop2016, {}, '2016 Canada AAFC Annual Crop Inventory');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/AAFC/AAFC_ACI) \n[Canada AAFC Annual Crop Inventory](/earth-engine/datasets/catalog/AAFC_ACI) \nStarting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, ... \nAAFC/ACI, agriculture,canada,crop,landcover \n2009-01-01T00:00:00Z/2023-01-01T00:00:00Z \n36.83 -135.17 62.25 -51.24 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/AAFC_ACI)"]]
