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Found 10 records similar to Annual Crop Inventory 2015

Federal

In 2013, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

Last Updated: Jul. 27, 2021
Date Published: Nov. 4, 2013
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Satellites, Agriculture, Remote sensing, Crops, Crop insurance, Forage crops, Farmlands, Geomatics, Land cover
Federal

In 2014, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

Last Updated: Jul. 27, 2021
Date Published: Mar. 12, 2015
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Crops, Agriculture, Land cover, Remote sensing
Federal

In 2011, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) expanded the process of generating annual crop inventory digital maps using satellite imagery to include British Columbia, Ontario, Quebec, and the Maritime provinces, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-5, DMC) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.

Last Updated: Jul. 27, 2021
Date Published: Nov. 4, 2013
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Remote sensing, Satellites, Agriculture, Crops, Crop insurance, Geomatics, Farmlands, Forage crops, Land cover
Federal

In 2012, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada (except Newfoundland), in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (DMC, SPOT) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.

Last Updated: Jul. 27, 2021
Date Published: Nov. 4, 2013
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Crops, Satellites, Agriculture
Federal

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 inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.

Last Updated: Jul. 27, 2021
Date Published: Nov. 4, 2013
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Satellites, Agriculture, Remote sensing, Crops, Farmlands, Forage crops, Crop insurance, Land cover, Geography
Federal

In 2010 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) continued the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5, DMC) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.

Last Updated: Jul. 27, 2021
Date Published: Nov. 4, 2013
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Remote sensing, Agriculture, Satellites, Farmlands, Crops, Crop insurance, Forage crops, Land cover, Geomatics
Federal

In 2018, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the BC Ministry of Agriculture, & the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, Guelph, and Summerland

Last Updated: Jul. 27, 2021
Date Published: Jan. 25, 2018
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Farms, Crops, Environment, Geographic data, Agriculture
Federal

In 2016, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2, Gaofen-1) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the BC Ministry of Agriculture, & the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, Guelph, and Summerland.

Last Updated: Jul. 27, 2021
Date Published: Feb. 14, 2017
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Agriculture, Satellites, Crops, Crop insurance, Farmlands, Forage crops
Federal

In 2017, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2, Gaofen-1) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the BC Ministry of Agriculture, & the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, Guelph, and Summerland

Last Updated: Jul. 27, 2021
Date Published: Jan. 25, 2018
Organization: Agriculture and Agri-Food Canada
Formats: WMS HTML GeoTIF PDF CSV ESRI REST
Keywords:  Agriculture, Satellites, Crops, Crop insurance, Farmlands, Forage crops
Federal

In 2019, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, and Guelph.

Last Updated: Jul. 27, 2021
Date Published: Feb. 18, 2020
Organization: Agriculture and Agri-Food Canada
Formats: WMS PDF CSV ESRI REST GeoTIF
Keywords:  Farms, Environment, Geographic data, Crops, Agriculture
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