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Open AccessArticle

An Assessment of the Cultivated Cropland Class of NLCD 2006 Using a Multi-Source and Multi-Criteria Approach

1
Stinger Ghaffarian Technologies (SGT), Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
2
InuTeq, Contractor to the USGS EROS Center, Sioux Falls, SD 57198, USA
3
USGS EROS Center, Sioux Falls, SD 57198, USA
4
Department of Geography, South Dakota State University, Brookings, SD 57007, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Giles M. Foody and Prasad S. Thenkabail
Remote Sens. 2016, 8(2), 101; https://doi.org/10.3390/rs8020101
Received: 29 July 2015 / Accepted: 13 January 2016 / Published: 28 January 2016
We developed a method that analyzes the quality of the cultivated cropland class mapped in the USA National Land Cover Database (NLCD) 2006. The method integrates multiple geospatial datasets and a Multi Index Integrated Change Analysis (MIICA) change detection method that captures spectral changes to identify the spatial distribution and magnitude of potential commission and omission errors for the cultivated cropland class in NLCD 2006. The majority of the commission and omission errors in NLCD 2006 are in areas where cultivated cropland is not the most dominant land cover type. The errors are primarily attributed to the less accurate training dataset derived from the National Agricultural Statistics Service Cropland Data Layer dataset. In contrast, error rates are low in areas where cultivated cropland is the dominant land cover. Agreement between model-identified commission errors and independently interpreted reference data was high (79%). Agreement was low (40%) for omission error comparison. The majority of the commission errors in the NLCD 2006 cultivated crops were confused with low-intensity developed classes, while the majority of omission errors were from herbaceous and shrub classes. Some errors were caused by inaccurate land cover change from misclassification in NLCD 2001 and the subsequent land cover post-classification process. View Full-Text
Keywords: land cover; agriculture; remote sensing land cover; agriculture; remote sensing
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MDPI and ACS Style

Danielson, P.; Yang, L.; Jin, S.; Homer, C.; Napton, D. An Assessment of the Cultivated Cropland Class of NLCD 2006 Using a Multi-Source and Multi-Criteria Approach. Remote Sens. 2016, 8, 101.

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