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Remote Sens. 2015, 7(4), 3446-3466; doi:10.3390/rs70403446

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

1
Department of Life and Environmental Sciences, Bournemouth University, Talbot Campus, Fern Barrow, Poole, Dorset BH12 5BB, UK
2
Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Norbert Pfeifer, András Zlinszky, Hermann Heilmeier, Bernhard Höfle, Bálint Czúcz, Heiko Balzter and Prasad S. Thenkabail
Received: 19 November 2014 / Accepted: 18 March 2015 / Published: 24 March 2015
(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)
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Abstract

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 (2000–2012). 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 m2–17.77 m2), 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 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. View Full-Text
Keywords: remote sensing; lidar; airborne laser scanning; habitat; forest; bird ecology remote sensing; lidar; airborne laser scanning; habitat; forest; bird ecology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hill, R.A.; Hinsley, S.A. Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships. Remote Sens. 2015, 7, 3446-3466.

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