Characterizing major agricultural land change trends in the Western Corn Belt

Abstract

a b s t r a c tIn this study we developed annual corn/soybean maps for the Western Corn Belt within the United States using multi-temporal MODIS NDVI products from 2001 to 2015 to support long-term cropland change analysis. Based on the availability of training data (cropland data layer from the USDA-NASS), we designed a cross-validation scheme for 2006-2015 MODIS data to examine the spatial generalization capability of a neural network classifier. Training data points were derived from a three-state subregion consisting of North Dakota, Nebraska, and Iowa. Trained neural networks were applied to the testing sub-region (South Dakota, Kansas, Minnesota, and Missouri) to generate corn/soybean maps. Using a default threshold value (neural network output signal P 0.5), the neural networks performed well for South Dakota and Minnesota. Overall accuracy was higher than 80% (kappa > 0.55) for all testing years from 2006 to 2015. However, we observed high variation of classification performance for Kansas (overall accuracy: 0.71-0.82) and Missouri (overall accuracy: 0.65-0.77) for various testing years. We developed a threshold-moving method that decreases/increases threshold values of neural network output signals to match MODIS-derived corn/soybean acreage with the NASS acreage statistics. Over 70% of testing states and years showed improved classification performance compared to the use of a default 0.5 threshold. The largest improvement of kappa value was about 0.08. This threshold-moving method was used to generate MODIS-based annual corn/soybean map products for 2001-2015. A non-parametric Mann-Kendall test was then used to identify areas that showed significant (p < 0.05) upward/downward trends. Areas showing fast increase of corn/soybean intensities were mainly located in North Dakota, South Dakota, and the west portion of Minnesota. The highest annual increase rate for a 5-km moving window was about 6.8%. Ó

Multi-Year Mapping of Maize and Sunflower in Hetao Irrigation District of China with High Spatial and Temporal Resolution Vegetation Index Series

“…To further verify the accuracy of crop identification results, the correct identification percentage of different crops and Kappa coefficient (Table 6) were calculated using the verification points in 2014 and 2015 (Figure 1). The correct identification rates of different crops were all greater than 70% and the Kappa value of consistency test was 0.62, which indicated that the classifier performance was acceptable [15]. We also selected a specific micro-scale area (19.36 ha) of Hetao Irrigation District to analyze the classification results; the detailed description of this area can be referred to Ren et al [4].…”

Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

“…The land use differed by year in the study region [30,31], and a cropland mask was prepared to identify spatial pixels where the land use was classified as cropland throughout the period from 2001-2016 (Figure 1a). A crop-specific mask for corn and soybean would have been preferable as its use could improve the accuracy of crop yield forecast.…”

Cropping pattern changes diminish agroecosystem services in North and South Dakota, USA

“…Nationwide (Lark et al., ; Wright et al., ) and regional analyses (Lu et al., ; Shao, Taff, Ren, & Campbell, ; Wright & Wimberly, ) identify North and South Dakota as having some of the greatest land use changes in the United States over the last several decades. These types of analyses are improving due to increasing availability of datasets, tools, and new methodology for geospatial analyses.…”