The assessment of woodland condition in the New Forest using airborne remote sensing
Authors: Sumnall, M.J., Hill, R. and Hinsley, S.A.
Conference: RSPSoc Annual Conference
Dates: 10-12 September 2012
Abstract:In the UK woodland covers 10% of the land area, but little of this is in a completely natural state. Those woodlands which resemble original forests are more highly prized in nature conservation terms. For the management of any woodland an indication of ‘quality’ is required. Woodland quality assessments are currently conducted via fieldwork, where the researcher assesses forest structure and composition, deadwood, tree regeneration, and ground vegetation. Such detailed fieldwork is hampered by cost, spatial coverage, objectiveness and repeatability. This project proposes the use of a sensor-fusion approach to surveying forests, with the development of habitat condition indicators derived from remotely sensed (RS) data. Estimation of forest compositional and structural characteristics were made from airborne hyperspectral, small footprint LiDAR capturing discrete return (DR) and full waveform (FW) data. Combining RS data is expected to offer detailed and complimentary information about the structure and composition of woodlands.
A 5x5 km study area was established in the New Forest, Hampshire, UK (Lat. 50.84° N, Long. 1.50° W) which contained a variety of forest structural types, including managed plantation, semi-ancient coniferous and deciduous woodland. Field operations were conducted across 21 field plots located across a stratification of forest structural properties. Field plots were 30x30m in size and recorded a number of forest inventory metrics relating to condition. The hyperspectral and LiDAR data were acquired simultaneously, under both leaf-on and leaf-off conditions in 2010. Metrics related to overstorey species type, LiDAR point distribution, return intensity (DR only), amplitude and width (FW only). For each plot over 100 metrics were extracted from RS data, using both individual tree crown and area-based approaches. RS metrics were used for direct comparison with field data and in multiple stepwise regression analysis to model forest structure. For example, in comparison with field data, dominant height and canopy cover were derived with an RMSE of 1.31m and 0.10%. Significant relationships (at p<0.001, n=21) were identified with tree stem count, R2 = 0.803; mean DBH, R2 = 0.911; tree crown base height, R2 = 0.928; snag volume R2 = 0.902; and number of seedlings (deciduous plots only), R2 =0.853.
Although validation is still required, these results suggest that a combination of RS can describe the structural components of woodland. Further study will examine the estimation of additional facets of vegetation structure, including the number of canopy layers and shrub layer characteristics, key information for assessing woodland condition and habitat quality.
Source: Manual
Preferred by: Ross Hill