A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors
The Cropland Data Layers (CDL) are high-resolution geo-referenced data products made available by the U.S. Department of Agriculture. However, the CDL lacks in its ability to be employed as a tool to identify the impact of the gradually evolving drivers of land use change, e.g., climate change, due to its limited historical depth. We implement a robust, phenology-based satellite image classification algorithm to identify historical cropland allocation in eastern South Dakota and North Dakota predating the initial CDL by 13 and 22 years, respectively. Five major land covers, i.e., corn, soybeans, wheat, alfalfa and grass (including native grass, hay and pasture) are identified using archived Landsat-5 surface reflectance data, while achieving CDL-like accuracy. The long-term rate of grassland loss during 1985–2011 is found to be significantly lower (26,781 hectares or 1.5% annually) relative to the near-term rate of grassland loss during 2006-’11 (84,545 hectares or 5.2% annually). We find similar discrepancy in regional corn expansion rates. Our value-added raster data provide opportunities for improved identification of land use drivers, whereas relying solely on the CDL’s restricted historical extent may lead to biased land use change estimates and misguide policy.
Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors
The Cropland Data Layers (CDL) are high-resolution geo-referenced data products made available by the U.S. Department of Agriculture. However, the CDL lacks in its ability to be employed as a tool to identify the impact of the gradually evolving drivers of land use change, e.g., climate change, due to its limited historical depth. We implement a robust, phenology-based satellite image classification algorithm to identify historical cropland allocation in eastern South Dakota and North Dakota predating the initial CDL by 13 and 22 years, respectively. Five major land covers, i.e., corn, soybeans, wheat, alfalfa and grass (including native grass, hay and pasture) are identified using archived Landsat-5 surface reflectance data, while achieving CDL-like accuracy. The long-term rate of grassland loss during 1985–2011 is found to be significantly lower (26,781 hectares or 1.5% annually) relative to the near-term rate of grassland loss during 2006-’11 (84,545 hectares or 5.2% annually). We find similar discrepancy in regional corn expansion rates. Our value-added raster data provide opportunities for improved identification of land use drivers, whereas relying solely on the CDL’s restricted historical extent may lead to biased land use change estimates and misguide policy.
Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors
Arora, Gaurav (author) / Wolter, Peter T. (author)
Journal of Land Use Science ; 13 ; 59-80
2018-03-04
22 pages
Article (Journal)
Electronic Resource
English
Characterizing major agricultural land change trends in the Western Corn Belt
| Online Contents | 2016
Analysis of Factors Contributing to "Walking Along Roadway" Crashes
| British Library Online Contents | 1999
The U2U Corn Growing Degree Day tool: Tracking corn growth across the US Corn Belt
| DOAJ | 2017