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Interactions between landcover pattern and geospatial processing methods: Effects on landscape metrics and classification accuracy

Citation

Lechner, AM and Reinke, KJ and Wang, Y and Bastin, L, Interactions between landcover pattern and geospatial processing methods: Effects on landscape metrics and classification accuracy, Ecological Complexity, 15 pp. 71-82. ISSN 1476-945X (2013) [Refereed Article]

Copyright Statement

Copyright 2013 Elsevier

DOI: doi:10.1016/j.ecocom.2013.03.003

Abstract

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric "number of patches" than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty.

Item Details

Item Type:Refereed Article
Keywords:multi-scale, spatial resolution, aggregation methods, spatial uncertainty, remote sensing, pattern-process, simulation modelling, landscape metrics, classification accuracy
Research Division:Environmental Sciences
Research Group:Environmental Science and Management
Research Field:Wildlife and Habitat Management
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Environmental Sciences
Author:Lechner, AM (Dr Alex Lechner)
ID Code:95622
Year Published:2013
Web of Science® Times Cited:19
Deposited By:Centre for Environment
Deposited On:2014-10-06
Last Modified:2014-11-11
Downloads:0

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