Abstract

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.

Highlights

  • Land cover change (LCC) is one of the most important topics for global environmental change studies

  • We developed a method to assess the quality of the cultivated cropland class mapped in National Land Cover Database (NLCD) 2006 using a multi-source and multi-criteria approach

  • Path/rows with the most commission or omission errors included path/row (KA), (WA)where wherethe the cultivated cropland accounts for less path/row (KA), 42/35(CA), (CA), and and 44/27

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Summary

Introduction

Land cover change (LCC) is one of the most important topics for global environmental change studies. Information on LCC is essential to understanding the relationship and feedbacks between land cover and the climate system, and its impacts on environmental and socioeconomic processes [1,2,3]. Changes in agricultural lands link closely to both natural and anthropogenic drivers. Studies of agricultural land change require accurate and spatially explicit estimates of cropland area, types, and qualities over large geographic regions. In the United States, two national digital classification maps contain agricultural classes: the National Land Cover Database (NLCD) [4,5,6]. Cropland Data Layer (CDL) [7]. These two datasets have been widely used for many applications, often to quantify change across time. Wright and Wimberly [8] quantified the grassland

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