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.
Highlights
Knowledge about the area and spatial distribution of land cover is critical for geo-information, environmental, and socioeconomic research [1,2,3,4,5,6,7]
In ASD4550, non-boundary pixels produced about 1.1% of the error for the simulated map at an error level of 40%
This study presented an investigation on the impacts of upscaling on crop mapping error based on the majority rule based aggregation method (MRB)
Summary
Knowledge about the area and spatial distribution of land cover is critical for geo-information, environmental, and socioeconomic research [1,2,3,4,5,6,7]. Land cover maps are fundamental data for modeling ecosystem services [8], agricultural management [9], climate change [10] and carbon cycles [11]. Various types of research and/or models require large area (continental or global scale) land cover maps, over a range of spatial resolutions [12,13]. Many of these maps are generated from remotely sensed imagery [14,15] and have been widely employed to serve scientific research [16]. Diverse global or regional land cover maps have been generated, the spatial resolutions of these maps are limited [20]
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