Analysing the effect of different aggregation approaches on remotely sensed data

Raj, Rahul; Hamm, Nicholas; Kant, Yogesh

doi:10.1080/01431161.2013.781289

Cited by 25 publications

(22 citation statements)

References 21 publications

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“…The selection of an aggregation approach depends upon the objective of the research. Raj et al [20] reported that MRB is useful for monitoring agriculture at regional or national levels. Therefore, we selected MRB to test the influence of mapping error on the performance of upscaling maps.…”

Section: Upscaling Methodsmentioning

confidence: 99%

“…The effect of spatial aggregation varies using different methods, each of which is dependent on the aggregation logic [20]. The selection of an aggregation approach depends upon the objective of the research.…”

Section: Upscaling Methodsmentioning

confidence: 99%

“…MRB determines the land cover class for the coarse resolution pixels in the upscaled map by selecting the most frequently occurring class from the finer resolution map contained within each coarse pixel [32,33]. When there is more than one major class, the dominant class is selected at random [20].…”

Section: Upscaling Methodsmentioning

confidence: 99%

“…Various landscape metrics were developed in the past decades but many of them are intercorrelated [20,53]. Two metrics, Patch Per Unit area (PPU) and Square-pixel index (SqP), were used in this study.…”

Section: Landscape Metricsmentioning

confidence: 99%

“…Although diverse global or regional land cover maps have been generated, the spatial resolutions of these maps are also limited [20]. For example, the spatial resolutions of Global Land Cover data set 2000 (GLC 2000) [21], Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover [22] and Fine Resolution Observation and error.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Upscaling Methodsmentioning

confidence: 99%

Section: Upscaling Methodsmentioning

confidence: 99%