Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps

Graf, Roland Felix; Mathys, L.; Bollmann, Kurt

doi:10.1016/j.foreco.2008.08.021

Cited by 113 publications

(86 citation statements)

References 36 publications

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“…In their pioneering study on habitat quality for great tits and blue tits in the United Kingdom, Hinsley et al (2002) reported a significant relationship between mean chick mass and vegetation height derived from lidar data. In an analysis of the suitability of a forest reserve in the Alps as habitat for capercaillie, Graf et al (2009) reported good relationships between presenceÁ absence patterns and lidar-derived metrics of horizontal and vertical canopy structure. Bradbury et al (2005) provide a brief review of other studies linking bird characteristics to estimates of forest structure obtained from lidar data.…”

Section: Lidar Metrics As Predictors Of Forest Attributesmentioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

111

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Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and modelbased inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

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Section: Lidar Metrics As Predictors Of Forest Attributesmentioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

111

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“…Relationships between habitat quality, vegetation structure, and birds have been studied using ALS data, but in many studies, only a few species and structural parameters (e.g. canopy height) have been analysed, and the studies have covered relatively small areas (Hill et al 2004;Bradbury et al 2005;Graf et al 2009). However, more recent work (e.g.…”

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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“…The possibility to obtain detailed and continuous maps of ''new'' environmental descriptors from ALS data offers great opportunities across a range of research disciplines in ecology, natural resource management and conservation (Graf et al 2009, Martinuzzi et al 2009, Wing et al 2012). …”

Section: Discussionmentioning

confidence: 99%

“…The main contribution towards this result was through quantification of cover, rather than the improvement in forage predictions. Direct inclusion of structural variables is a common approach to ALS based habitat studies (Graf et al 2009, Coops et al 2010, Melin et al 2013), but the ecological links are not always obvious. Habitat selection studies that lack detailed field data on forage and cover availability typically characterize habitat as ''open'' or ''dense'' (Godvik et al 2009, Ciuti et al 2012, Tolon et al 2012) and assume these are ''forage'' and ''cover'' habitat types respectively.…”

Section: Als Improves Understanding Of Habitat Selectionmentioning

confidence: 99%

“…In these studies, laser data have been used directly to interpret the physical structure of the habitat relevant to each species or species assemblages. Despite the fundamental importance of forage and cover in understanding animal ecology, there has been no formal analysis linking structural information of habitat to forage resources, and few relating ALS derived cover variables to habitat selection (Graf et al 2009, Melin et al 2014.…”

Section: Introductionmentioning

confidence: 99%

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Improving broad scale forage mapping and habitat selection analyses with airborne laser scanning: the case of moose

et al. 2014

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Abstract. Determining the spatial distribution of large herbivores is a key challenge in ecology and management. However, our ability to accurately predict this is often hampered by inadequate data on available forage and structural cover. Airborne laser scanning (ALS) can give direct and detailed measurements of vegetation structure. We assessed the effectiveness of ALS data to predict (1) the distribution of browse forage resources and (2) moose (Alces alces) habitat selection in southern Norway. Using ground reference data from 153 sampled forest stands, we predicted available browse biomass with predictor variables from ALS and/or forest inventory. Browse models based on both ALS and forest inventory variables performed better than either alone. Dominant tree species and development class of the forest stand remained important predictor variables and were not replaced by the ALS variables. The increased explanatory power from including ALS came from detection of canopy cover (negatively correlated with forage biomass) and understory density (positively correlated with forage biomass). Improved forage estimates resulted in improved predictive ability of moose resource selection functions (RSFs) at the landscape scale, but not at the home range scale. However, when also including ALS cover variables (understory cover density and canopy cover density) directly into the RSFs, we obtained the highest predictive ability, at both the landscape and home range scales. Generally, moose selected for high browse biomass, low amount of understory vegetation and for low or intermediate canopy cover depending on the time of day, season and scale of analyses. The auxiliary information on vegetation structure from ALS improved the prediction of browse moderately, but greatly improved the analysis of habitat selection, as it captured important functional gradients in the habitat apart from forage. We conclude that ALS is an effective and valuable tool for wildlife managers and ecologists to estimate the distribution of large herbivores.

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Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps

Cited by 113 publications

References 36 publications

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

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

Improving broad scale forage mapping and habitat selection analyses with airborne laser scanning: the case of moose

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