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Evaluation of the Initial Thematic Output from a Continuous Change-Detection Algorithm for Use in Automated Operational Land-Change Mapping by the U.S. Geological Survey

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SGT Inc., Contractor to the U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd St., Sioux Falls, SD 57198, USA
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U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd St., Sioux Falls, SD 57198, USA
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Inuteq., Contractor to the U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd St., Sioux Falls, SD 57198, USA
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Author to whom correspondence should be addressed.
Current address: Department of Geosciences, MS 1053, Science Building 125, Texas Tech University, Lubbock, TX 79409, USA
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Remote Sens. 2016, 8(10), 811; https://doi.org/10.3390/rs8100811
Received: 3 May 2016 / Revised: 13 September 2016 / Accepted: 19 September 2016 / Published: 1 October 2016
The U.S. Geological Survey (USGS) has begun the development of operational, 30-m resolution annual thematic land cover data to meet the needs of a variety of land cover data users. The Continuous Change Detection and Classification (CCDC) algorithm is being evaluated as the likely methodology following early trials. Data for training and testing of CCDC thematic maps have been provided by the USGS Land Cover Trends (LC Trends) project, which offers sample-based, manually classified thematic land cover data at 2755 probabilistically located sample blocks across the conterminous United States. These samples represent a high quality, well distributed source of data to train the Random Forest classifier invoked by CCDC. We evaluated the suitability of LC Trends data to train the classifier by assessing the agreement of annual land cover maps output from CCDC with output from the LC Trends project within 14 Landsat path/row locations across the conterminous United States. We used a small subset of circa 2000 data from the LC Trends project to train the classifier, reserving the remaining Trends data from 2000, and incorporating LC Trends data from 1992, to evaluate measures of agreement across time, space, and thematic classes, and to characterize disagreement. Overall agreement ranged from 75% to 98% across the path/rows, and results were largely consistent across time. Land cover types that were well represented in the training data tended to have higher rates of agreement between LC Trends and CCDC outputs. Characteristics of disagreement are being used to improve the use of LC Trends data as a continued source of training information for operational production of annual land cover maps. View Full-Text
Keywords: Continuous Change Detection and Classification; USGS Land Cover Trends; training data; Landsat; high-resolution imagery; land cover mapping Continuous Change Detection and Classification; USGS Land Cover Trends; training data; Landsat; high-resolution imagery; land cover mapping
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Pengra, B.; Gallant, A.L.; Zhu, Z.; Dahal, D. Evaluation of the Initial Thematic Output from a Continuous Change-Detection Algorithm for Use in Automated Operational Land-Change Mapping by the U.S. Geological Survey. Remote Sens. 2016, 8, 811.

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