Locating and estimating the extent of Delmarva fox squirrel habitat using an airborne LiDAR profiler

Nelson, Ross; Keller, Cherry; Ratnaswamy, Mary J.

doi:10.1016/j.rse.2005.02.012

Cited by 70 publications

(46 citation statements)

References 35 publications

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“…Lidar data have been shown to be particularly important for forest habitat studies. Nelson et al (2005) used structural lidar metrics to model habitat suitability for small mammals such as squirrels. Mü ller and Brandl (2009) demonstrated that the richness and composition of assemblages of forest-dwelling beetles are strongly correlated with structural lidar metrics.…”

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

112

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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

112

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“…A common approach in assessing habitat with airborne lidar data is to derive a geospatial model of one or more elements of forest vertical or horizontal structure, and then to use such models for predicting habitat suitability for a specific organism based on known habitat requirements. Examples of individual species' habitats assessed by this method include the Delmarva fox squirrel (Sciurus niger cinereus) in Delaware [4], the mule deer (Odocoileus hemionus) in British Columbia [5], the red-cockaded woodpecker (Picoides borealis) in South Carolina [6], and the brown creeper (Certhia americana) in Idaho [7]. An alternative to this approach is to utilise species distribution and abundance data to quantify habitat requirements directly from lidar data for occupied areas [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

Hill

Hinsley

2015

Remote Sensing

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Structure is a fundamental physical element of habitat, particularly in woodlands, and hence there has been considerable recent uptake of airborne lidar data in forest ecology studies. This paper investigates the significance of lidar data characteristics when modelling organism-habitat relationships, taking a single species case study in a mature woodland ecosystem. We re-investigate work on great tit (Parus major) habitat, focussing on bird breeding data from 1997 and 2001 (years with contrasting weather conditions and a demonstrated relationship between breeding success and forest structure). We use a time series of three lidar data acquisitions across a 12-year period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). The lidar data characteristics assessed include time-lag with field data (up to 15 years), spatial sampling density (average post spacing in the range of 1 pulse per 0.14 m 2 -17.77 m 2 ), approach to processing (raster or point cloud), and the complexity of derived structure metrics (with a total of 33 metrics assessed, each generated separately using all returns and only first returns). Ordinary least squares regression analysis was employed to investigate relationships between great tit mean nestling body mass, calculated per brood, and the various canopy structure measures from all lidar datasets. For the 2001 bird breeding data, the relationship between mean nestling body mass and mean canopy height for a sample area around each nest was robust to the extent that it could be detected strongly and with a OPEN ACCESS Remote Sens. 2015, 7 3447 high level of statistical significance, with relatively little impact of lidar data characteristics. In 1997, all relationships between lidar structure metrics and mean nestling body mass were weaker than in 2001 and more sensitive to lidar data characteristics, and in almost all cases they were opposite in trend. However, whilst the optimum habitat structure differed between the two study years, the lidar-derived metrics that best characterised this structure were consistent: canopy height percentiles and mean overstorey canopy height (calculated using all returns or only first returns) and the standard deviation of canopy height (calculated using all returns). Overall, our results suggest that for relatively stable woodland habitats, ecologists should not feel prohibited in using lidar data to explore or monitor organism-habitat relationships because of perceived data quality issues, as long as the questions investigated, the scale of analysis, and the interpretation of findings are appropriate for the data available.

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“…Vogeler et al (2013) found the upper canopy as represented by lidar, to be the driving factor in the occupancy of a late-seral specialist, the brown creeper (Certhia americana). The continuous nature of lidar data as opposed to field sampled vegetation data also allows for extraction of landscape metrics in studies examining relationships at larger habitat selection scales (Nelson et al, 2005).…”

Section: Lidar Wildlife Habitat Studiesmentioning

confidence: 99%

A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models

Vogeler

Cohen

2016

Rev. Teledetec.

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Spatially explicit maps of wildlife habitat relationships have proven to be valuable tools for conservation and management applications including evaluating how and which species may be impacted by large scale climate change, ongoing fragmentation of habitat, and local land-use practices. Studies have turned to remote sensing datasets as a way to characterize vegetation for the examination of habitat selection and for mapping realized relationships across the landscape. Potentially one of the more difficult habitat types to try to characterize with remote sensing are the vertically and horizontally complex forest systems. Characterizing this complexity is needed to explore which aspects may represent driving and/or limiting factors for wildlife species. Active remote sensing data from lidar and radar sensors has thus caught the attention of the forest wildlife research and management community in its potential to represent three dimensional habitat features. The purpose of this review was to examine the applications of active remote sensing for characterizing forest in wildlife habitat studies through a keyword search within Web of Science. We present commonly used active remote sensing metrics and methods, discuss recent advances in characterizing aspects of forest habitat, and provide suggestions for future research in the area of new remote sensing data/techniques that could benefit forest wildlife studies that are currently not represented or may be underutilized within the wildlife literature. We also highlight the potential value in data fusion of active and passive sensor data for representing multiple dimensions and scales of forest habitat. While the use of remote sensing has increased in recent years within wildlife habitat studies, continued communication between the remote sensing, forest management, and wildlife communities is vital to ensure appropriate data sources and methods are understood and utilized, and so that creators of mapping products may better realize the needs of secondary users.Key words: wildlife habitat, forest, lidar, radar, predictive maps. Revisión de la teledetección activa y la fusión de datos para la caracterización de bosques en modelos especie-hábitatResumen: Se ha probado que los mapas que muestran explícitamente las relaciones especie-hábitat constituyen herramientas valiosas en aplicaciones de conservación y gestión, incluyendo la evaluación sobre qué especies y de qué forma se pueden ver afectadas por el cambio climático a gran escala, la fragmentación progresiva del hábitat y los usos del suelo a nivel local. Diversos estudios se han centrado en utilizar la teledetección como herramienta que permite caracterizar la vegetación para el análisis de la selección del hábitat y para cartografiar las relaciones con el entorno natural. Uno de los tipos de hábitats más difíciles de caracterizar mediante teledetección son los sistemas forestales verticales y horizontales complejos. Su caracterización es necesaria para estudiar los aspectos determinantes y/o limitantes...

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Locating and estimating the extent of Delmarva fox squirrel habitat using an airborne LiDAR profiler

Cited by 70 publications

References 35 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

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models

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