2009 begann das Earth Observation Team des Science and Technology Branch (STB) bei Agriculture and Agri-Food Canada (AAFC) mit der Erstellung digitaler Karten für jährliche Erntetypen. Für die Prairie-Provinzen in den Jahren 2009 und 2010 wurde eine auf Entscheidungsbäumen (Decision Tree, DT) basierende Methodik angewendet, bei der optische (Landsat-5, AWiFS, DMC) und radarbasierten (Radarsat-2) Satellitenbilder verwendet wurden. Seit der Anbausaison 2011 wurde diese Aktivität auf andere Provinzen ausgeweitet, um ein nationales Pflanzeninventar zu unterstützen.
Bisher kann mit diesem Ansatz ein Pflanzeninventar erstellt werden, das die allgemeine Zielgenauigkeit von mindestens 85% bei einer endgültigen räumlichen Auflösung von 30 m (56 m in den Jahren 2009 und 2010) erreicht.
Bänder
Pixelgröße 30 Meter
Bänder
Name
Min.
Max.
Pixelgröße
Beschreibung
landcover
1
255
Meter
Klassifizierung der Bodenbedeckung für die Hauptkultur.
Klassentabelle für die Landbedeckung
Wert
Farbe
Beschreibung
10
#000000
Cloud
20
#3333ff
Wasser
30
#996666
Offenes Land und Ödland
34
#cc6699
Städtisch und entwickelt
35
#e1e1e1
Greenhouses
50
#ffff00
Shrubland
80
#993399
Feuchtgebiet
85
#501b50
Peatland
110
#cccc00
Wiese
120
#cc6600
Landwirtschaft (nicht differenziert)
122
#ffcc33
Weide und Futtermittel
130
#7899f6
Zu nass für die Aussaat
131
#ff9900
Brachland
132
#660000
Getreide
133
#dae31d
Barley
134
#d6cc00
Andere Getreidesorten
135
#d2db25
Hirse
136
#d1d52b
Hafer
137
#cace32
Rye
138
#c3c63a
Buchstabierte
139
#b9bc44
Triticale
140
#a7b34d
Wheat
141
#b9c64e
Switchgrass
142
#999900
Sorghum
143
#e9e2b1
Quinoa
145
#92a55b
Winterweizen
146
#809769
Sommerweizen
147
#ffff99
Aus Mais
148
#98887c
Tabak
149
#799b93
Ginseng
150
#5ea263
Ölsaaten
151
#52ae77
Borage
152
#41bf7a
Camelina
153
#d6ff70
Raps
154
#8c8cff
Leinsamen
155
#d6cc00
Senf
156
#ff7f00
Safflower
157
#315491
Sonnenblume
158
#cc9933
Sojabohnen
160
#896e43
Pulsiert
161
#996633
Andere Pulses
162
#8f6c3d
Erbsen
163
#b6a472
Aus Kichererbsen
167
#82654a
Bohnen
168
#a39069
Fababeans
174
#b85900
Linsen
175
#b74b15
Gemüse
176
#ff8a8a
Tomaten
177
#ffcccc
Kartoffeln
178
#6f55ca
Zuckerrüben
179
#ffccff
Anderes Gemüse
180
#dc5424
Obst
181
#d05a30
Beeren
182
#d20000
Heidelbeere
183
#cc0000
Cranberry
185
#dc3200
Andere Beeren
188
#ff6666
Obstplantagen
189
#c5453b
Andere Früchte
190
#7442bd
Weinberge
191
#ffcccc
Aus Hopfen
192
#b5fb05
Rollrasen
193
#ccff05
Kräuter
194
#07f98c
Kindergarten
195
#00ffcc
Buchweizen
196
#cc33cc
Glanzgras
197
#8e7672
Hanf
198
#b1954f
Wicken
199
#749a66
Andere Kulturen
200
#009900
Wald (undifferenziert)
210
#006600
Nadelbäume
220
#00cc00
Broadleaf
230
#cc9900
Mischholz
Bildattribute
Bildattribute
Name
Typ
Beschreibung
landcover_class_names
STRING_LIST
Array mit Namen für die Klassifizierung der Landbedeckung von Ackerland.
landcover_class_palette
STRING_LIST
Array von Hex-Code-Farbstrings, die für die Klassifizierungspalette verwendet werden.
2009 begann das Earth Observation Team des Science and Technology Branch (STB) bei Agriculture and Agri-Food Canada (AAFC) mit der Erstellung jährlicher digitaler Karten für Nutzpflanzen. In den Jahren 2009 und 2010 wurde in den Prärieprovinzen eine auf Entscheidungsbäumen (Decision Tree, DT) basierende Methodik angewendet, bei der optische (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)"]]
