Implementation of a general linear model using LiDAR derived explanatory variables: a case study in Scotland

S. Flaherty; P. W. W. Lurz; G. Patenaude

doi:10.1117/12.977541

19 October 2012 Implementation of a general linear model using LiDAR derived explanatory variables: a case study in Scotland

S. Flaherty, P. W. W. Lurz, G. Patenaude

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Proceedings Volume 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV; 853128 (2012) https://doi.org/10.1117/12.977541
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

Abstract

The native Eurasian red squirrel is considered endangered in the UK and under strict legal protection. Long term habitat management is a key goal of the UK conservation strategy. The importance of forest structural parameters for red squirrels habitat mapping was previously demonstrated: a General Linear Model (GLM) was used to relate the number of cones stripped by squirrels to mean canopy closure, mean tree height and total number of trees at the plot level, all significant predictors and explaining 43% of the variance in the number of stripped cones. The main aim of this study is to implement the GLM using LiDAR derived explanatory variables and to assess habitat suitability at Abernethy Forest, one of the proposed red squirrel strongholds in the UK. LiDAR-based GLM performance was explored by assessing the correlation between field-predicted and LiDAR-predicted number of stripped cones (Spearman rank correlation coefficient = 0.59; n=32, P< 0.00). Finally, habitat suitability maps were generated. Results suggest that when forest structure is considered, only 27% of the total forest area at Abernethy is suitable for red squirrel.

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S. Flaherty, P. W. W. Lurz, and G. Patenaude "Implementation of a general linear model using LiDAR derived explanatory variables: a case study in Scotland", Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 853128 (19 October 2012); https://doi.org/10.1117/12.977541

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KEYWORDS

LIDAR

Animal model studies

Performance modeling

Forestry

Raster graphics

Remote sensing

Vegetation

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