A partire dal 2009, il team di osservazione della Terra della Science and Technology
Branch (STB) di Agriculture and Agri-Food Canada (AAFC) ha iniziato il processo
di generazione di mappe digitali annuali dei tipi di colture. Concentrandosi sulle province delle praterie nel 2009 e nel 2010, è stata applicata una metodologia basata su un albero decisionale (DT) utilizzando immagini satellitari ottiche (Landsat-5, AWiFS, DMC) e radar (Radarsat-2). A partire dalla stagione di crescita del 2011, questa attività è stata
estesa ad altre province a supporto di un inventario nazionale delle colture.
Ad oggi, questo approccio può fornire in modo coerente un inventario delle colture che soddisfi
l'accuratezza target complessiva di almeno l'85% a una risoluzione spaziale finale di
30 m (56 m nel 2009 e nel 2010).
Bande
Dimensioni pixel 30 metri
Bande
Nome
Min
Max
Dimensioni dei pixel
Descrizione
landcover
1
255
metri
Classificazione della copertura del suolo specifica per la coltura principale.
Tabella delle classi di copertura del suolo
Valore
Colore
Descrizione
10
#000000
Cloud
20
#3333ff
Acqua
30
#996666
Terra esposta e arida
34
#cc6699
Urbanizzato e sviluppato
35
#e1e1e1
Greenhouses
50
#ffff00
Shrubland
80
#993399
Zona umida
85
#501b50
Peatland
110
#cccc00
Prateria
120
#cc6600
Agricoltura (indifferenziata)
122
#ffcc33
Pascoli e foraggi
130
#7899f6
Too Wet to be Seeded
131
#ff9900
Fallow
132
#660000
Cereali
133
#dae31d
Orzo
134
#d6cc00
Altri cereali
135
#d2db25
Millet
136
#d1d52b
Avena
137
#cace32
Rye
138
#c3c63a
Spelta
139
#b9bc44
Triticale
140
#a7b34d
Grano
141
#b9c64e
Switchgrass
142
#999900
Sorgo
143
#e9e2b1
Quinoa
145
#92a55b
Grano invernale
146
#809769
Grano primaverile
147
#ffff99
Mais
148
#98887c
Tabacco
149
#799b93
Ginseng
150
#5ea263
Semi oleosi
151
#52ae77
Borage
152
#41bf7a
Camelina
153
#d6ff70
Canola e colza
154
#8c8cff
Semi di lino
155
#d6cc00
Senape
156
#ff7f00
Safflower
157
#315491
Girasole
158
#cc9933
Soia
160
#896e43
Pulsazioni
161
#996633
Altri battiti
162
#8f6c3d
Piselli
163
#b6a472
Ceci
167
#82654a
Fagioli
168
#a39069
Fababeans
174
#b85900
Lenticchie
175
#b74b15
Verdure
176
#ff8a8a
Pomodori
177
#ffcccc
Patate
178
#6f55ca
Barbabietole da zucchero
179
#ffccff
Altre verdure
180
#dc5424
Frutta
181
#d05a30
Frutti di bosco
182
#d20000
Mirtillo
183
#cc0000
Cranberry
185
#dc3200
Altro rosso lampone
188
#ff6666
Frutteti
189
#c5453b
Altri frutti
190
#7442bd
Vigneti
191
#ffcccc
Hops
192
#b5fb05
Zolla di erba
193
#ccff05
Erbe aromatiche
194
#07f98c
Asilo d'infanzia
195
#00ffcc
Grano saraceno
196
#cc33cc
Scagliola
197
#8e7672
Canapa
198
#b1954f
Vetch
199
#749a66
Altre colture
200
#009900
Foresta (indifferenziata)
210
#006600
Conifere
220
#00cc00
Broadleaf
230
#cc9900
Mixedwood
Proprietà immagini
Proprietà immagine
Nome
Tipo
Descrizione
landcover_class_names
STRING_LIST
Array di nomi di classificazione della copertura del suolo delle colture.
landcover_class_palette
STRING_LIST
Array di stringhe di colori con codice esadecimale utilizzate per la tavolozza di classificazione.
landcover_class_values
INT_LIST
Valore della classificazione della copertura del suolo.
A partire dal 2009, il team di osservazione della Terra della sezione di scienza e tecnologia (STB) di Agriculture and Agri-Food Canada (AAFC) ha iniziato a generare mappe digitali annuali dei tipi di colture. Concentrandosi sulle province delle praterie nel 2009 e nel 2010, è stata applicata una metodologia basata su un albero decisionale (DT) utilizzando dati ottici (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)"]]
