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

Lechner, Alex M., Reinke, Karin J., Wang, Yan and Bastin, Lucy (2013) Interactions between landcover pattern and geospatial processing methods: effects on landscape metrics and classification accuracy. Ecological Complexity, 15 71-82. doi:10.1016/j.ecocom.2013.03.003

Author Lechner, Alex M.
Reinke, Karin J.
Wang, Yan
Bastin, Lucy
Title Interactions between landcover pattern and geospatial processing methods: effects on landscape metrics and classification accuracy
Journal name Ecological Complexity   Check publisher's open access policy
ISSN 1476-945X
Publication date 2013-09-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.ecocom.2013.03.003
Open Access Status Not yet assessed
Volume 15
Start page 71
End page 82
Total pages 12
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Subject 1105 Ecology, Evolution, Behavior and Systematics
2302 Ecological Modelling
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.
Keyword Multi-scale
Spatial resolution
Aggregation methods
Spatial uncertainty
Remote sensing
Simulation modelling
Landscape metrics
Classification accuracy
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Centre for Mined Land Rehabilitation Publications
Official 2014 Collection
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Citation counts: TR Web of Science Citation Count  Cited 19 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 24 times in Scopus Article | Citations
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