Three-dimensional characterization of pine forest type and red-cockaded woodpecker habitat by small-footprint, discrete-return lidar

Smart, Lindsey S.; Swenson, Jennifer J.; Christensen, Norman L.; Sexton, Joseph O.

doi:10.1016/j.foreco.2012.06.020

Cited by 68 publications

(37 citation statements)

References 56 publications

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“…For the black-capped vireo (Vireo atricapilla) in the Fort Hood Military Reservation, Texas, mean height, canopy cover, and edge density were useful predictive variables, although not as important as vegetation and soil type [52]. Measures of forest vertical structure (e.g., mean and standard deviation of canopy height) and horizontal patterns of vertical structure (assessed by both semivariograms and lacunarity analysis), together with elevation, land-cover and hydrography data were found useful in predictive distribution modelling for the red-cockaded woodpecker (Picoides borealis, Vieillot) in a forested catchment in North Carolina [31]. For the red-naped sapsucker (Sphyrapicus nuchalis) in northern Idaho, key airborne lidar variables for predicting breeding site selection were foliage height diversity, the distance between major strata in the canopy vertical profile, and vegetation density close to the ground [17].…”

Section: Assessment Of Results Against Study Aimsmentioning

confidence: 95%

“…Over recent years, both the nature of airborne lidar systems and subsequent data processing have become increasingly sophisticated. Standard metrics extracted from lidar data for forest structure and habitat assessment have included the mean, maximum, standard deviation, and coefficient of variation of canopy height in regular grid cells or sample areas relating to, for example, field plots, count stations, pitfall trap locations, or territories [30,31]. Other frequently extracted canopy structure metrics from airborne lidar have included measures of skewness and kurtosis, height percentiles, and the percentage of returns (or return energy) within specified height bands [5,19].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Assessment Of Results Against Study Aimsmentioning

confidence: 95%

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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“…For example, Mitasova et al (2005) used a simultaneous spline approximation and topographic analysis for LiDAR elevation data in open-source GIS to create accurate relief models. LiDAR, in combination with inventory data, has also been used to evaluate the forest habitats of birds and other wildlife (Graf et al 2009, Tattoni et al 2012, Wilsey et al 2012, Smart et al 2012.…”

Section: Discussionmentioning

confidence: 99%

Making objective forest stand maps of mixed managed forest with spatial interpolation and multi-criteria decision analysis

Destan¹,

Yilmaz²,

Şahin³

2013

iForest

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© iForest -Biogeosciences and Forestry IntroductionInformation about extant forest communities is of considerable importance in forest management planning. The stand is the smallest production and silvicultural unit, and the most crucial part of this information. The location of planning units, their size and silvicultural features are used to select management objectives and determine the working cycle, management regulations and silvicultural goals. Thus, the precision of decisions to be implemented and the planning to be realized are both closely related to the accuracy of the stand maps. Moreover, map accuracy depends on the type of forest inventory, the sample size, and the techniques used to evaluate the inventory results.Stand maps have previously been produced subjectively (Feng et al. 2006) in conjunction with current forest management practices. In conventional management planning, stands are defined through the planning process. The type of of the management objectives is a component of this process (Gunnarsson et al. 1998). However, the use of computers has changed this conventional approach, and spatially and temporally dynamic description and treatment units can readily be produced (Holmgren & Thuresson 1997). Thus, dynamic forest planning can be achieved with the use of geopositioned field plot data and computers (Wallerman et al. 2002).The sampling methods used in forest inventories vary according to different countries' forestry objectives and forest structures. Systematic sampling methods are recommended for large and homogeneous forest areas and have been implemented in all production and conservation areas of Turkey since 1964. However, if this sampling method is implemented with an insufficient number of sample plots and/or in heterogeneous forests, the results will be highly questionable because they may fail to reflect the true forest composition. Although Sherman (1996), Aurbi & Debouzie (2000), Flores et al. (2003) andD'Orazio (2003) used remote sensing data to improve the results of such inventories, the desired outcome was never achieved in practice.Spatial interpolation methods, which have been classified by Li & Heap (2008) as nongeostatistical, geostatistical and combined, came into use at the end of the 1960s and have been investigated for use in forest management. As described in Akhavan et al. (2010), Guibal (1973 was the first study to use kriging in the forest inventory process. Jost (1993) also used geostatistical methods to compare conventional inventory results based on systematic sampling with the results of the kriging method. Geostatistically based methods that utilize textural information are frequently used to analyze remote-sensing (RS) images (Zawadzki et al. 2005). The quality and quantity of these methods have increased through advances in the computer sciences and in the science of geographical information systems. These methods allow the use of a variety of ecological and technical parameters in the assessment of inventory results. Spatial interpolation and multi-...

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“…Especially the combination of LiDAR (Light Detection And Ranging) data and high resolution images has been proved to be useful in mapping tree crowns and measuring individual tree structure (Holmgren et al 2008, Hou and Walz 2014, Morsdorf et al 2004, Smart et al 2012. However, the advantages of remote sensing technology in habitat mapping are not fully utilized.…”

Section: Introductionmentioning

confidence: 99%

An integrated approach for landscape contrast analysis with particular consideration of small habitats and ecotones

Hou¹,

Walz²

2016

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Citation: Hou W, Walz U (2016) An integrated approach for landscape contrast analysis with particular consideration of small habitats and ecotones. Nature Conservation 14: 25-39. doi: 10.3897/natureconservation.14.7010 AbstractHabitat diversity is considered as an expression of biodiversity at landscape level in addition to genetic and species diversity. Thus, effective methods for measuring habitat pattern at landscape level are crucial for understanding the ecological processes. In this paper we propose to extend the commonly used model Patch Corridor Matrix Model (PCMM) for spatial pattern analysis. Originally, this model assumes discrete structures within the landscape without explicit consideration of "gradients" between patches. The gradients, often called "ecotones", can be considered as "soft edges" which have a profound influence on adjacent ecosystems. Another part of information that has often been ignored are "small habitats" inside patches (e.g. hedgerows, tree rows, copse, and scattered trees), which leads to within-patch heterogeneity being underestimated. In this paper, the concept of landscape contrast is used to integrate the discrete and gradient landscape representations by incorporating small habitats and ecotones in methods to measure landscape heterogeneity. A height gradient is used to define the ecotones between forest and field. Then, patch contrast (i.e. Edge Contrast Index (ECON)) is calculated based on the height difference between adjacent vegetation patches. Artificial elements (e.g. traffic roads) are considered as barriers which are assigned with the highest edge contrast value. At the landscape level, a metric called Area-Weighted Edge Contrast (AWEC) is introduced to describe the landscape structure. The edge effects of ecotones, small habitats, and traffic roads are incorporated in the calculation of AWEC. Our test examples show that incorporation of ecotones and small habitats can smooth "edge effects" among patches and result in a more realistic quantification of habitat contrast. The contrast concept is especially useful in a vegetated landscape with less human impact. It could be understood as an additional interpretation to fragmentation of habitats with permeable edges among them. Consequently, this presented approach may enhance the understanding of the relationship between landscape pattern and process.

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Three-dimensional characterization of pine forest type and red-cockaded woodpecker habitat by small-footprint, discrete-return lidar

Cited by 68 publications

References 56 publications

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

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

Making objective forest stand maps of mixed managed forest with spatial interpolation and multi-criteria decision analysis

An integrated approach for landscape contrast analysis with particular consideration of small habitats and ecotones

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