Journal of Data Science

Land use land cover (LULC) change in the agriculture, is a critical area of concern as it directly impacts food security, environmental health, and economic stability. One of the leading LULC data products is the U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL). Produced annually by the USDA National Agricultural Statistics Service (NASS) using satellite imagery, the CDL provides crop-specific data with an estimated classification accuracy of 85% to 95% for major crop types across the U.S. However, several limitations inherent to the CDL, such as crop underestimation bias, pixel misclassification, and difficulty distinguishing certain vegetation types, have raised questions about the accuracy of LULC change estimates derived from this dataset. In this paper, we introduce the R package cdlsim, designed to quantify the sensitivity of CDL-derived metrics through simulations of CDL data at the patch level using NASS published accuracy statistics. We present a case study utilizing landscape metrics calculated with the popular landscapemetrics R package to demonstrate the utility of cdlsim in quantifying the sensitivity of metrics to random perturbations in the data. The case study examines a mixed agricultural and grassland landscape in South Dakota, illustrating how our package enables researchers to achieve a more nuanced representation of land-use change.