Remote sensing and forest inventory for wildlife habitat assessment

McDermid, Gregory J.; Hall, Rosalie J.; Sánchez‐Azofeifa, Arturo; Franklin, Steven E.; Stenhouse, Gord; Kobliuk, T.; LeDrew, E.

doi:10.1016/j.foreco.2009.03.005

Cited by 90 publications

(89 citation statements)

References 27 publications

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“…It is a relevant parameter for various ecological issues such as wildlife habitat modeling and it is also becoming more and more important in sustainable forest management [1][2][3]. For example, in close-to-nature forest management pure stands are replaced by heterogeneous, mixed stands.…”

Section: Introductionmentioning

confidence: 99%

Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data

2012

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Abstract:Tree species diversity is a key parameter to describe forest ecosystems. It is, for example, important for issues such as wildlife habitat modeling and close-to-nature forest management. We examined the suitability of 8-band WorldView-2 satellite data for the identification of 10 tree species in a temperate forest in Austria. We performed a Random Forest (RF) classification (object-based and pixel-based) using spectra of manually delineated sunlit regions of tree crowns. The overall accuracy for classifying 10 tree species was around 82% (8 bands, object-based). The class-specific producer's accuracies ranged between 33% (European hornbeam) and 94% (European beech) and the user's accuracies between 57% (European hornbeam) and 92% (Lawson's cypress). The object-based approach outperformed the pixel-based approach. We could show that the 4 new WorldView-2 bands (Coastal, Yellow, Red Edge, and Near Infrared 2) have only limited impact on classification accuracy if only the 4 main tree species (Norway spruce, Scots pine, European beech, and English oak) are to be separated. However, classification accuracy increased significantly using the full spectral resolution if further tree species were included. Beside the impact on overall classification accuracy, the importance of the spectral bands was evaluated with two measures provided by RF. An in-depth analysis of the RF output was carried out to evaluate the impact of reference data quality and the resulting reliability of final class assignments. Finally, an extensive literature review on tree species classification comprising about 20 studies is presented. OPEN ACCESSRemote Sens. 2012, 4 2662

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Section: Introductionmentioning

confidence: 99%

Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data

2012

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“…Earth Observation studies have often led to land cover data that is derived from satellite images (Gottschalk et al 2005). An increasingly important field of remote sensing applications is wildlife habitat assessment and modelling (Clawges et al 2008;McDermid et al 2009;Tattoni et al 2012;Melin et al 2013). Birds have often been used as an indicator group in studies searching for structural and functional features that best describe the biodiversity values of landscapes (Dauber et al 2003;Jeanneret et al 2003;Jones et al 2013;Zellweger et al 2013Zellweger et al , 2014Morelli et al 2014).…”

Section: Introductionmentioning

confidence: 99%

How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale

et al. 2014

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Context Biodiversity and ecosystem functioning underpins the delivery of all ecosystem services and should be accounted for in all decision-making related to the use of natural resources and areas. However, biodiversity and ecosystem services are often inadequately accounted for in land use management decisions. Objective We studied a boreal forest ecosystem by linking citizen-science bird data with detailed information on forest characteristics from airborne laser scanning (ALS). In this paper, we describe this method, and evaluate how similar kinds of biological data sets combined with remote sensing can be used for ecosystem assessments at landscape scale. Methods We analysed data for 41 boreal forest bird species and for 14 structural ALS-based forest parameters.Results The results support the use of the selected method as a basis for quantifying spatially-explicit biodiversity indicators for ecosystem assessments, while suggestions for improvements are also reported. Finally, we evaluate the capacity of those indicators to describe biodiversity-ecosystem service relationships, for example with carbon trade-offs. The results showed clear distinctions between the different species as measured, for example, by above-ground forest biomass at the observation sites. We also assess how Electronic supplementary material The online version of this article

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“…Nature resources management and biological conservation assessments require spatially explicit environmental data that come from remote sensing or derived thematic layers. Most of these studies assume that the selected geospatial data are an effective representation of the ecological target (such as habitat) and provide an appropriate source of information to the objectives (McDermid et al 2009). For example, biodiversity has been frequently studied indirectly through associations with land cover, which represents that mapping land cover has been often used as a surrogate for habitats (Foody 2008).…”

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confidence: 99%

“…For example, biodiversity has been frequently studied indirectly through associations with land cover, which represents that mapping land cover has been often used as a surrogate for habitats (Foody 2008). The suitability of these assumptions is a current scientific concern and a dynamic research issue (Glenn and Ripple 2004;McDermid et al 2009;Thogmartin et al 2004). Some studies (McDermid et al 2009) have evaluated the suitability of general-purpose land cover classifications and compared to other data sources like vegetation inventory or specificpurpose maps: they show the constraints of general-purpose remote sensing land cover maps for explain wildlife habitat patterns and recommend the use of specific-purpose databases based on remote sensing along with field measurements.…”

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confidence: 99%

“…The suitability of these assumptions is a current scientific concern and a dynamic research issue (Glenn and Ripple 2004;McDermid et al 2009;Thogmartin et al 2004). Some studies (McDermid et al 2009) have evaluated the suitability of general-purpose land cover classifications and compared to other data sources like vegetation inventory or specificpurpose maps: they show the constraints of general-purpose remote sensing land cover maps for explain wildlife habitat patterns and recommend the use of specific-purpose databases based on remote sensing along with field measurements. Then, traditional or general-purpose land cover maps may not be appropriate proxies of habitats, as we will show after assessing the suitability of the CORINE Land Cover product (European Environment Agency, http://www.eea.europa.eu/publications/COR0-landcover, last accessed May 2011) in Europe on this target (Section 4).…”

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confidence: 99%

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Assessing Loss of Biodiversity in Europe Through Remote Sensing: The Necessity of New Methodologies

Sánchez¹,

Rego²,

Sánchez³

et al. 2011

Biodiversity Loss in a Changing Planet

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Remote sensing and forest inventory for wildlife habitat assessment

Cited by 90 publications

References 27 publications

Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data

Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data

How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale

Assessing Loss of Biodiversity in Europe Through Remote Sensing: The Necessity of New Methodologies

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