Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation

Abstract

Abstract. Robust predictive models of the effects of habitat change on species abundance over large geographical areas are a fundamental gap in our understanding of population distributions, yet are urgently required by conservation practitioners. Predictive models based on underpinning relationships between environmental predictors and the individual organism are likely to require measurement of spatially fine-grained predictor variables. Further, models must show spatial generality if they are to be used to predict the consequences of habitat change over large geographical areas.Remote sensing techniques using airborne scanning laser altimetry (LiDAR) and high resolution multi-spectral imagery allow spatially fine-grained predictor variables to be measured over large geographical areas and thus facilitate testing of the spatial generality of organism-habitat models. These techniques are considered using the skylark as an example species. A range image segmentation system for LiDAR data is described which allows measurement of skylark habitat predictor variables such as within-field vegetation height, boundary height and shape for individual fields within the LiDAR image. Additional variables such as field vegetation type and fractional vegetation ground cover may be obtained from coregistered multi-spectral data. These techniques could have wide application in testing the generality of relationships between populations and habitats, and in ecological monitoring of change in habitat structures and the associated effects on wildlife, over large geographical areas.

Mesocarnivores respond to fine-grain habitat structure in a mosaic landscape comprised by commercial forest plantations in southern Chile

“…The reflected light is detected by the sensor and is digitized, creating a record of returns that are a function of the distance between the sensor and the intercepted object. Thus, since studies linking wildlife to habitat structure at fine resolution are still scarce across in landscapes, LiDAR-derived data would become a new avenue to perform such task (Lefsky et al, 2002;Mason et al, 2003;Vierling et al, 2008) as supported by recent research (e.g., Goetz et al, 2007;Müller and Brandl, 2009; see Vierling et al, 2008 for a review).…”

“…Recently, high-resolution remote sensing imagery has improved our ability to characterize habitat heterogeneity over larger spatial extents than can be accomplished in the field, allowing for detailed characterization of habitat structure (Mason et al, 2003). Remote sensing Light Detection and Ranging (LiDAR) offers a costeffective method to obtain high-resolution environmental information on forest structure including understory vegetation density, canopy height profiles, canopy cover, and biomass Hyde et al, 2006;Vierling et al, 2008).…”

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

“…Maps highlighting important areas for species distributions may be useful for forecast models identifying potential impacts of climate change (Maclean et al, 2008), the prioritization of conservation resources (Graf et al, 2005), trade-off analyses for landscape planning, monitoring habitat change through time , local scale management planning (Graf et al, 2009), among many other potential applications (Figure 1). The challenge of habitat modeling/mapping is balancing generality, detail, and accuracy (Mason et al, 2003). While general coarse grain maps may be appropriate for large scale planning such as climate change impacts and fragmentation of landscapes, fine grain predictions may be needed for local scale land management decisions.…”

“…While understanding these relationships is a vital first step, it is also important to exploit the full value of the spatial predictors by translating these relationships into usable tools for broader applications in the form of maps. Predicted habitat maps provide a valuable tool for management and conservation planning (Mason et al, 2003). Maps highlighting important areas for species distributions may be useful for forecast models identifying potential impacts of climate change (Maclean et al, 2008), the prioritization of conservation resources (Graf et al, 2005), trade-off analyses for landscape planning, monitoring habitat change through time , local scale management planning (Graf et al, 2009), among many other potential applications (Figure 1).…”

Remote Sensing for Threatened and Endangered Species Habitat Assessment on Military Lands: A Literature Review

“…) and stand density and volume (Maclean and Krabil 1986;Nilsson 1996;Naesset 1997;Lefsky et al 1999a;Lefsky et al 1999b;Holmgren et al 2003b). LIDAR has also been used specifically to assess forest structure as it relates to habitat preference (Hinsley et al 2002;Leyva et al 2002;Hill et al 2003;Mason et al 2003).…”