Optimising the use of hyperspectral and LiDAR data for mapping reedbed habitats

Abstract

Reedbeds are important habitats for supporting biodiversity and delivering a range of ecosystem services, yet reedbeds in the UK are under threat from intensified agriculture, changing land use and pollution. To develop appropriate conservation strategies, information on the distribution of reedbeds is required. Field surveys of these wetland environments are difficult, time consuming and expensive to execute for large areas. Remote sensing has the potential to replace or complement such field surveys, yet the specific application to reedbed habitats has not been fully investigated. In the present study, airborne hyperspectral and LiDAR imagery were acquired for two sites in Cumbria, UK. The research aimed to determine the most effective means of analysing hyperspectral data covering the visible, near infrared (NIR) and shortwave infrared (SWIR) regions for mapping reedbeds and to investigate the effects of incorporating image textural information and LiDAR-derived measures of canopy structure on the accuracy of reedbed delineation. Due to the high dimensionality of the hyperspectral data, three image compression algorithms were evaluated: principal component analysis (PCA), spectrally segmented PCA (SSPCA) and minimum noise fraction (MNF). The LiDAR-derived measures tested were the canopy height model (CHM), digital surface model (DSM) and the DSM-derived slope map. The SSPCA-compressed data produced the highest reedbed accuracy and processing efficiency. The optimal SSPCA dataset incorporated 12 PCs comprised of the first 3 PCs derived from each of the spectral segments: visible (392–700 nm), NIR (701–972 nm), SWIR-1 (973–1366 nm) and SWIR-2 (1530–2240 nm). Incorporating image textural measures produced a significant improvement in the classification accuracy when using MNF-compressed data, but had no impact when using the SSPCA-compressed imagery. A significant improvement (+ 11%) in the accuracy of reedbed delineation was achieved when a mask generated by applying a 3 m threshold to the LiDAR-derived CHM was used to filter the reedbed map derived from the optimal SSPCA dataset. This paper demonstrates the value in combining appropriately compressed hyperspectral imagery with LiDAR data for the effective mapping of reedbed habitats.