Abstract
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.
Highlights
Mapping land cover and monitoring land cover change are important for a variety of societal and scientific purposes, including land management, natural resource management, ecological studies, sustainable development, climate modeling, urban planning, habitat monitoring, and many others [1,2,3,4,5]
The Continuous Change Detection and Classification (CCDC) algorithm [6] was developed to support continuous monitoring with Landsat data to take advantage of the multi-decadal Landsat archive housed by the U.S Geological Survey (USGS) and is expected to play a central role in LCMAP mapping and monitoring activities
The results we present do not provide a statistical description of error in the CCDC Land Cover maps; they provide levels of agreement with maps generated from the LC Trends project and characterize features associated with common categories of disagreement between LC Trends and CCDC maps
Summary
Mapping land cover and monitoring land cover change are important for a variety of societal and scientific purposes, including land management, natural resource management, ecological studies, sustainable development, climate modeling, urban planning, habitat monitoring, and many others [1,2,3,4,5]. Assessment, and Projection (LCMAP) initiative to develop an expanded operational capacity for land cover mapping and monitoring to support these needs. The Continuous Change Detection and Classification (CCDC) algorithm [6] was developed to support continuous monitoring with Landsat data to take advantage of the multi-decadal Landsat archive housed by the USGS and is expected to play a central role in LCMAP mapping and monitoring activities. The USGS Land Cover LC Trends project plays an integral role in the development of the current capability for continuous monitoring by providing a reliable, consistent land cover product and related change assessments [8,9,10]. LC Trends data were generated through manual interpretation and were developed for the nominal years of 1973, 1980, 1986, 1992, and 2000 [7] These data offer a basis for both training and initial testing of CCDC land cover classification
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