The Impact of Mapping Error on the Performance of Upscaling Agricultural Maps

Abstract

Aggregation methods are the most common way of upscaling land cover maps. To analyze the impact of land cover mapping error on upscaling agricultural maps, we utilized the Cropland Data Layer (CDL) data with corresponding confidence level data and simulated eight levels of error using a Monte Carlo simulation for two Agriculture Statistic Districts (ASD) in the U.S.A. The results of the simulations were used as base maps for subsequent upscaling, utilizing the majority rule based aggregation method. The results show that increasing error level resulted in higher proportional errors for each crop in both study areas. As a result of increasing error level, landscape characteristics of the base map also changed greatly resulting in higher proportional error in the upscaled maps. Furthermore, the proportional error is sensitive to the crop area proportion in the base map and decreases as the crop proportion increases. These findings indicate that three factors, the error level of the thematic map, the change in landscape pattern/characteristics of the thematic map, and the objective of the project, should be considered before performing any upscaling. The first two factors can be estimated by using pre-existing land cover maps with relatively high accuracy. The third factor is dependent on the project requirements (e.g., landscape characteristics, proportions of cover types, and use of the upscaled map). Overall, improving our understanding of the impacts of land cover mapping error is necessary to the proper design for upscaling and obtaining the optimal upscaled map.

Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps

“…The U.S.A. has been divided into twelve regions (Figure 1). Each ASD is a group of counties based on the geography, climate, and cropping practice within each state [24], and has been widely-used in agricultural research (e.g., [11]).…”

“…Rescaling techniques inevitably lead to errors in land cover area (e.g., [11,14]). These errors in area are not only derived from the omitted areas from the base map but also by the committed areas created in the upscaled map.…”

“…Recognizing the need for various spatial resolutions by the Earth science community, rescaling techniques have been developed to fill the resolution gaps for existing land cover maps [11]. These rescaling techniques consist of two possible outcomes: upscaling and downscaling.…”

“…The upscaling method decreases the spatial resolution while the downscaling method increases the spatial resolution [12]. These two methods have been documented as vitally necessary and efficient ways to obtain maps at different spatial resolutions to support a variety of Earth science research [11,13,14].…”

Incorporating fine‐scale environmental heterogeneity into broad‐extent models

“…There is a literature on improving data aggregation methods; however, this does not link the scale of aggregation to the relevant scale-of-effect for the organism or process of interest. Most studies which either examine the effect of data aggregation (Raj, Hamm, & Kant, 2013;Sun, Congalton, Grybas, & Pan, 2017;Wu, 2004) or propose new methods for data aggregation (Frazier, 2014;Gardner, Lookingbill, Townsend, & Ferrari, 2008) focus only on scaling up categorical representations of the landscape (i.e. land cover classes).…”