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

J. C. Vogeler, W. B. Cohen

Abstract

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.


Keywords

wildlife habitat; forest; lidar; radar; predictive maps

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References

Ackers, S.H., Davis, R.J., Olsen, K.A., Dugger, K. M. 2015. The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based,and lidar-based habitat maps. Remote Sensing of Environment, 156, 361-373. http://dx.doi.org/10.1016/j.rse.2014.09.025

Andersen, H.E., Reutebuch, S.E., McGaughey, R.J. 2006. Active remote sensing. In Computer applications in sustainable forest management (pp.43-66). Springer Netherlands. http://dx.doi.org/10.1007/978-1-4020-4387-1_3

Andersen, H.E., Strunk, J., Temesgen, H., Atwood, D., Winterberger, K. 2012. Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska. Canadian Journal of Remote Sensing, 37(6), 596-611. http://dx.doi.org/10.5589/m12-003

Balzter, H. 2001. Forest mapping and monitoring with interferometric synthetic aperture radar (InSAR). Progress in Physical Geography, 25(2), 159-177. http://dx.doi.org/10.1177/030913330102500201

Bergen, K.M., Gilboy, A.M., Brown, D.G. 2007. Multidimensional vegetation structure in modeling avian habitat. Ecological Informatics, 2(1), 9-22. http://dx.doi.org/10.1016/j.ecoinf.2007.01.001

Broughton, R.K., Hill, R.A., Freeman, S.N., Bellamy, P.E., Hinsley, S.A. 2012. Describing habitat occupation by woodland birds with territory mapping and remotely sensed data: an example using the Marsh Tit (Poecile palustris). The Condor, 114(4), 812-822. http://dx.doi.org/10.1525/cond.2012.110171

Brown, T.K. 2002. Creating and maintaining wildlife, insect, and fish habitat structures in dead wood. General Technical Report PSW-GTR-181: 883-892.

Clawges, R., Vierling, K., Vierling, L., Rowell, E. 2008. The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest. Remote Sensing of Environment, 112(5), 2064-2073. http://dx.doi.org/10.1016/j.rse.2007.08.023

Cohen, W.B., Yang, Z., Kennedy, R. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—Tools for calibration and validation. Remote Sensing of Environment, 114(12), 2911-2924. http://dx.doi.org/10.1016/j.rse.2010.07.010

Coops, N.C., Duffe, J., Koot, C. 2010. Assessing the utility of lidar remote sensing technology to identify mule deer winter habitat. Canadian Journal of Remote Sensing, 36(2), 81-88. http://dx.doi.org/10.5589/m10-029

Culbert, P.D., Radeloff, V.C., Flather, C.H., Kellndorfer, J.M., Rittenhouse, C.D., Pidgeon, A.M. 2013. The influence of vertical and horizontal habitat structure on nationwide patterns of avian biodiversity. The Auk, 130(4), 656-665. http://dx.doi.org/10.1525/auk.2013.13007

Davis, R.J., Hollen, B., Hobson, J., Gower, J.E., Keenum, D. 2015. Northwest Forest Plan–the first 20 years (1994–2013): status and trends of northern spotted owl habitats. Gen. Tech. Rep. PNW-GTR-xxx. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. xx p.

Duncanson, L.I., Cook, B.D., Hurtt, G.C., Dubayah, R.O. 2014. An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems. Remote Sensing of Environment, 154, 378-386. http://dx.doi.org/10.1016/j.rse.2013.07.044

Farrell, S.L., Collier, B.A. , Skow, K.L., Long, A. M., Campomizzi, A.J., Morrison, M.L., Hays, K.B., Wilkins, R.N. 2013. Using LiDAR-derived vegetation metrics for high-resolution, species distribution models for conservation planning. Ecosphere, 4(3), 1-18. http://dx.doi.org/10.1890/ES12-000352.1

Garabedian, J.E., McGaughey, R.J., Reutebuch, S.E., Parresol, B.R., Kilgo, J.C., Moorman, C.E., Peterson, M.N. 2014. Quantitative analysis of woodpecker habitat using high-resolution airborne LiDAR estimates of forest structure and composition. Remote Sensing of Environment, 145,68-80. http://dx.doi.org/10.1016/j.rse.2014.01.022

García-Feced, C., Tempel, D.J., Kelly, M. 2011. LiDAR as a tool to characterize wildlife habitat: California spotted owl nesting habitat as an example. Journal of Forestry, 109(8), 436-443.

Graf, R.F., Bollmann, K., Suter, W., Bugmann, H. 2005. The importance of spatial scale in habitat models: capercaillie in the Swiss Alps. Landscape Ecology, 20(6), 703-717. http://dx.doi.org/10.1007/s10980-005-0063-7

Graf, R.F., Mathys, L., Bollmann, K. 2009. Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps. Forest Ecology and Management, 257(1), 160-167. http://dx.doi.org/10.1016/j.foreco.2008.08.021

Goetz, S.J., Steinberg, D., Betts, M.G., Holmes, R.T., Doran, P.J., Dubayah, R., Hofton, M. 2010. Lidar remote sensing variables predict breeding habitat of a Neotropical migrant bird. Ecology, 91(6), 1569-1576. http://dx.doi.org/10.1890/09-1670.1

Hagar, J.C. 2007. Wildlife species associated with nonconiferous vegetation in Pacific Northwest conifer forests: A review. Forest Ecology and Management, 246(1), 108-122. http://dx.doi.org/10.1016/j.foreco.2007.03.054

Haggard, M., Gaines, W.L. 2001. Effects of standreplacement fire and salvage logging on a cavitynesting bird community in eastern Cascades, Washington. Northwest Science, 75, 387-396.

Hatten, J.R. 2014. Mapping and monitoring Mount Graham red squirrel habitat with Lidar and Landsat imagery. Ecological Modelling, 289(10), 106-123. http://dx.doi.org/10.1016/j.ecolmodel.2014.07.004

Huang, S., Crabtree, R.L., Potter, C., Gross, P. 2009. Estimating the quantity and quality of coarse woody debris in Yellowstone post-fire forest ecosystem from fusion of SAR and optical data. Remote Sensing of Environment, 113(9), 1926-1938. http://dx.doi.org/10.1016/j.rse.2009.05.001

Hudak, A.T., Crookston, N.L., Evans, J.S., Falkowski, M.J., Smith, A.M., Gessler, P.E., Morgan, P. 2006. Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data. Canadian Journal of Remote Sensing, 32(2), 126-138. http://dx.doi.org/10.5589/m06-007

Imhoff, M.L., Sisk, T.D., Milne, A., Morgan, G., Orr, T. 1997. Remotely sensed indicators of habitat heterogeneity: use of synthetic aperture radar in mapping vegetation structure and bird habitat. Remote Sensing of Environment, 60(3), 217-227. http://dx.doi.org/10.1016/S0034-4257(96)00116-2

Johnson, D.H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology, 61(1), 65-71. http://dx.doi.org/10.2307/1937156

Kankare, V., Vauhkonen, J., Tanhuanpää, T., Holopainen, M., Vastaranta, M., Joensuu, M., Krooks,A., Hyyppa, J., Hyyppa, H., Alho, P., Viitala, R. 2014. Accuracy in estimation of timber assortments and stem distribution–A comparison of airborne and terrestrial laser scanning techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 97, 89-97. http://dx.doi.org/10.1016/j.isprsjprs.2014.08.008

Kasischke, E.S., Melack, J.M., Dobson, M.C. 1997. The use of imaging radars for ecological applications—a review. Remote Sensing of Environment, 59, 141-156. http://dx.doi.org/10.1016/S0034-4257(96)00148-4

Kennedy, R.E., Andréfouët, S., Cohen, W.B., Gómez, C., Griffiths, P., Hais, M., Healey, S.P., Helmer, E.H., Hostert, P., Lyons, M.B., Meigs, G.W., Pflugmacher, D., Phinn, S.R., Powell, S.L., Scarth, P., Sen, S., Schroeder, T.A., Schneider, A., Sonnenschein, R., Vogelmann, J.E., Wulder, M.A., Zhu, Z. 2014. Bringing an ecological view of change to Landsatbased remote sensing. Frontiers in Ecology and the Environment, 12(6), 339-346. http://dx.doi.org/10.1890/130066

Kim, Y., Yang, Z., Cohen, W.B., Pflugmacher, D., Lauver, C.L., Vankat, J.L. 2009. Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment, 113(11), 2499-2510. http://dx.doi.org/10.1016/j.rse.2009.07.010

Lim, K., Treitz, P., Wulder, M., St-Onge, B., Flood, M. 2003. LiDAR remote sensing of forest structure. Progress in physical geography, 27(1), 88-106. http://dx.doi.org/10.1191/0309133303pp360ra

MacArthur, R.H., MacArthur, J.W. 1961. On bird species diversity. Ecology, 42(3), 594-598. http://dx.doi.org/10.2307/1932254

Maclean, I., Austin, G.E., Rehfisch, M.M, Blew, J.A.N., Crowe, O., Delany, S., Devos, K., Deceuninck, B., Gunther, K., Laursen, K., Roomen, M., Wahl, J. 2008. Climate change causes rapid changes in the distribution and site abundance of birds in winter.Global Change Biology, 14(11), 2489-2500. http://dx.doi.org/10.1111/j.1365-2486.2008.01666.x

Maltamo, M., Packalén, P., Yu, X., Eerikäinen, K., Hyyppä, J., Pitkänen, J. 2005. Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data. Forest Ecology and Management, 216(1-3), 41-50. http://dx.doi.org/10.1016/j.foreco.2005.05.034

Mason, D.C., Andersen, G.Q.A., Bradbury, R.B., Cobby, D.M., Davenport, I.J., Vandepoll, M., Wilson, J.D. 2003. Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation. International Journal of Remote Sensing, 24(12), 2515-2532.

http://dx.doi.org/10.1080/014311602100100848

Martinuzzi, S., Vierling, L.A., Gould, W.A., Falkowski, M.J., Evans, J.S., Hudak, A.T., Vierling, K.T. 2009. Mapping snags and understory shrubs for a LiDARbased assessment of wildlife habitat suitability. Remote Sensing of Environment, 113(12), 2533-2546. http://dx.doi.org/10.1016/j.rse.2009.07.002

McGaughey, R.J. 2009. FUSION/LDV: Software for LiDAR data analysis and visualization. US Department of Agriculture, Forest Service, Pacific Northwest Research Station: Seattle, WA, USA, 123.

Merrick, M.J., Koprowski, J.L., Wilcox, C. 2013. Into the third dimension: benefits of incorporating LiDAR data in wildlife habitat models. USDA Forest Service Proc, 67, 389-395.

Michel, P., Jenkins, J., Mason, N., Dickinson, K.J. M., Jamieson, I.G. 2008. Assessing the ecological application of lasergrammetric techniques to measure fine-scale vegetation structure. Ecological Informatics, 3(4-5), 309-320. http://dx.doi.org/10.1016/j.ecoinf.2008.07.002

National Aeronautics and Space Administration (NASA). NASA Science Missions: Global Ecosystem Dynamics Investigation Lidar. Retrieved June 2015, from: http://science.nasa.gov/missions/gedi/.

National Aeronautics and Space Administration (NASA). Ice Cloud and Land Elevation Satellite-2:Mission Overview. Retrieved June 2015, from http://icesat.gsfc.nasa.gov/icesat2/mission_overview.php.

Nelson, R., Keller, C., Ratnaswamy, M. 2005. Locating and estimating the extent of Delmarva fox squirrel habitat using an airborne LiDAR profiler. Remote Sensing of Environment, 96(3-4), 292-301.

http://dx.doi.org/10.1016/j.rse.2005.02.012

Osborne, P.E., Alonso, J.C., Bryant, R.G. 2001. Modelling landscape-scale habitat use using GIS and remote sensing: a case study with great bustards. Journal of applied ecology, 38(2), 458-471.

http://dx.doi.org/10.1046/j.1365-2664.2001.00604.x

Pflugmacher, D., Cohen, W.B., Kennedy, R.E., Yang, Z. 2014. Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics. Remote Sensing of Environment, 151, 124-137. http://dx.doi.org/10.1016/j.rse.2013.05.033

Pistolesi, L.I., Ni-Meister, W., McDonald, K.C. 2015. Mapping wetlands in the Hudson Highlands ecoregion with ALOS PALSAR: an effort to identifypotential swamp forest habitat for golden-winged warblers. Wetlands Ecology and Management, 23(1),95-112. http://dx.doi.org/10.1007/s11273-014-9381-3

Popescu, S.C., Wynne, R.H., Scrivani, J.A. 2004. Fusion of small-footprint lidar and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia, USA. Forest Science, 50(4), 551-565.

Poulin, J.F., Villard, M.A., Edman, M., Goulet, P.J., Eriksson, A.M. 2008. Thresholds in nesting habitat requirements of an old forest specialist, the Brown Creeper (Certhia americana), as conservation targets. Biological Conservation, 141(4), 1129-1137. http://dx.doi.org/10.1016/j.biocon.2008.02.012

Scott, J.M., Davis, F., Csuti, B., Noss, R., Butterfield, B., Groves, C., Andersen, H., Caicco, S., D’Erchia, F., Edwards, T.C. Jr., Ulliman, J., Wright, R.G. 1993. Gap analysis: a geographic approach to protection of biological diversity. Wildlife monographs, 123, 3-41.

Schroeder, R.L. 1983. Habitat suitability index models:Downy Woodpecker (No. FWS/OSB-82/10.38). Fish and Wildlife Service Fort Collins Co Western Energy and Land Use Team.

Schroeder, T.A., Wulder, M.A., Healey, S.P., Moisen, G.G. 2012. Detecting post-fire salvage logging from Landsat change maps and national fire survey data. Remote Sensing of Environment, 122, 166-174.

http://dx.doi.org/10.1016/j.rse.2011.10.031

Sousa, P.J. 1983. Habitat suitability index models: Lewis’ woodpecker (No. FWS/OBS-82/10.32). Fish and Wildlife Service Fort Collins Co Western Energy and Land Use Team.

Sousa, P.J. 1987. Habitat Suitability Index Models: Hairy Woodpecker (No. FWS-82 (10.146)). National Ecology Research Center Fort Collins Co.

Su, J.G., Bork, E.W. 2007. Characterization of diverse plant communities in Aspen Parkland rangeland using LiDAR data. Applied Vegetation Science, 10(3), 407-416. http://dx.doi.org/10.1111/j.1654-109X.2007.tb00440.x

Swatantran, A., Dubayah, R., Goetz, S., Hofton, M., Betts, M.G., Sun, M., Simard, M., Holmes, R. 2012. Mapping migratory bird prevalence using remote sensing data fusion. PLoS One, 7(1), e28922. http://dx.doi.org/10.1371/journal.pone.0028922

Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E., Steininger, M. 2003. Remote sensing for biodiversity science and conservation. Trends in ecology & evolution, 18(6), 306-314.

http://dx.doi.org/10.1016/S0169-5347(03)00070-3

United States Fish and Wildlife Service (USFWS). 1980. Habitat Evaluation Procedures Handbook. USFWS Division of Ecological Services 101-103 ESM. Retrieved May 2015, from http://www.fws.gov/policy/ESMindex.html.

United States Geological Survey (USGS). 2015. Habitat Suitability Index. Retrieved May 2015, from http://www.nwrc.usgs.gov/wdb/pub/hsi/hsiindex_byauthor.htm.

Vierling, L.A., Vierling, K.T., Adam, P., Hudak, A.T., 2013. Using satellite and airborne lidar to model woodpecker habitat occupancy at the landscape scale. PLoS OONE, 8, e80988. http://dx.doi.org/10.1371/journal.pone.0080988

Vierling, K.T., Vierling, L.A., Gould, W.A., Martinuzzi, S., Clawges, R.M. 2008. Lidar: shedding new light on habitat characterization and modeling. Frontiers in Ecology and the Environment, 6(2), 90-98. http://dx.doi.org/10.1890/070001

Vogeler, J.C. 2014. The Use of Remote Sensing for Characterizing Forests in Wildlife Habitat Modeling. Dissertation, Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon.

Vogeler, J.C., Hudak, A.T., Vierling, L.A., Vierling, K.T. 2013. Lidar-derived canopy architecture predicts Brown Creeper occupancy of two western coniferous forests. The Condor, 115(3), 614-622.

http://dx.doi.org/10.1525/cond.2013.110082

Vogeler, J.C., Yang, Z., Cohen, W.B. 2016. Mapping post-fire habitat characteristics through the fusion of remote sensing tools. Remote Sensing of Environment, 173, 294-303. http://dx.doi.org/10.1016/j.rse.2015.08.011

Wing, M.G., Eklund, A., Sessions, J. 2010. Applying LiDAR technology for tree measurements in burned landscapes. International Journal of Wildland Fire, 19(1), 104-114. http://dx.doi.org/10.1071/WF08170

Wing, B.M., Ritchie, M.W., Boston, K., Cohen, W.B., Gitelman, A., Olsen, M.J. 2012. Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sensing of Environment, 124, 730-741. http://dx.doi.org/10.1016/j.rse.2012.06.024

Yang, X., Schaaf, C., Strahler, A., Kunz, T., Fuller, N., Betke, M., Wu, Z., Wang, Z., Theriault, D., Culvenor, D., Jupp, D., Newnham, G., Lovell, J. 2013. Study of bat flight behavior by combining thermal image analysis with a LiDAR forest reconstruction. Canadian Journal of Remote Sensing, 39(1), S112-S125.

http://dx.doi.org/10.5589/m13-034

Zhao, K., Popescu, S., Nelson, R. 2009. Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers. Remote Sensing of Environment, 113(1), 182-196. http://dx.doi.org/10.1016/j.rse.2008.09.009

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