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Measuring and monitoring linear woody features in agricultural landscapes through earth observation data as an indicator of habitat availability

International Journal of Applied Earth Observation and Geoinformation


The loss of natural habitats and the loss of biological diversity is a global problem affecting all ecosystems including agricultural landscapes. Indicators of biodiversity can provide standardized measures that make it easier to compare and communicate changes to an ecosystem. In agricultural landscapes the amount and variety of available habitat is directly correlated with biodiversity levels. Linear woody features (LWF), including hedgerows, windbreaks, shelterbelts as well as woody shrubs along fields, roads and watercourses, play a vital role in supporting biodiversity as well as serving a wide variety of other purposes in the ecosystem. Earth observation can be used to quantify and monitor LWF across the landscape. While individual features can be manually mapped, this research focused on the development of methods using line intersect sampling (LIS) for estimating LWF as an indicator of habitat availability in agricultural landscapes. The methods are accurate, efficient, repeatable and provide robust results. Methods were tested over 9.5 Mha of agricultural landscape in the Canadian Mixedwood Plains ecozone. Approximately 97,000 km of LWF were estimated across this landscape with results useable both at a regional reporting scale, as well as mapped across space for use in wildlife habitat modelling or other landscape management research. The LIS approach developed here could be employed at a variety of scales in particular for large regions and could be adapted for use as a national scale indicator of habitat availability in heavily disturbed agricultural landscape. Crown Copyright (C) 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (

Author(s): Pasher, J; McGovern, M; Putinski, V

Journal: International Journal of Applied Earth Observation and Geoinformation

Year: 2016


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