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Title: Remote sensing for continuous cover forestry : quantifying spatial structure and canopy gap distribution
Author: Gaulton, Rachel
ISNI:       0000 0004 2728 8710
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2009
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The conversion of UK even-aged conifer plantations to continuous cover forestry (CCF), a form of forest management that maintains forest cover over time and avoids clear-cutting, requires more frequent and spatially explicit monitoring of forest structure than traditional systems. Key aims of CCF management are to increase the spatial heterogeneity of forest stands and to make increased use of natural regeneration, but judging success in meeting these objectives and allowing an adaptive approach to management requires information on spatial structure at a within-stand scale. Airborne remote sensing provides an alternative approach to field survey and has potential to meet these monitoring needs over large areas. An integral part of CCF is the creation of canopy gaps, allowing regeneration by increasing understorey light levels. This study examined the use of airborne lidar and passive optical data for the identification and characterisation of canopy gaps within UK Sitka spruce (Picea sitchensis) plantations. The potential for using the distribution of canopy and gaps within a stand to quantify spatial heterogeneity and allow the detection of changes in spatial structure, between stands and over time, was assessed. Detailed field surveys of six study plots, located in three UK spruce plantations, allowed assessment of the accuracy of gap delineation from remotely sensed data. Airborne data (multispectral, hyperspectral and lidar) were acquired for all sites. A novel approach to the delineation of gaps from lidar data was developed, delineating gaps directly from the lidar point cloud, avoiding the interpolation errors (and associated under-estimation of gap area) resulting from conversion to a canopy height model. This method resulted in improved accuracy of delineation compared to past techniques (overall accuracy of 78% compared to field gap delineations), especially when applied to lidar data collected at relatively low point densities. However, lidar data can be costly to acquire and provides little information about the presence of natural regeneration or other understorey vegetation within gaps. For these reasons, the potential of passive optical (and in particular, hyperspectral) data for gap delineation was also considered. The use of spectral indices, based on shortwave infrared reflectance or hyperspectral characteristics of the red- edge and chlorophyll absorption well, were shown to enhance the discrimination of canopy and gap and reduce the influence of illumination conditions. An average overall accuracy of 71% was obtained using hyperspectral characteristics for gap delineation, suggesting the use of optical data compares reasonably to results from lidar. Methods based on shortwave infrared (SWIR) reflectance were shown to be sensitive to within gap vegetation type, with SWIR reflectance being lower in the presence of natural regeneration. Potential for using optical data to classify within gap vegetation type was also demonstrated. Methods of quantifying spatial structure through the use of indices describing variations in gap size, shape and distribution were found to allow the detection of structural differences between stands and changes over time. Gap distribution based indices were also found to be strongly related to alternative methods based on relative tree positions, suggesting significant potential for consistent monitoring of structural changes during conversion of plantations to CCF. Remotely sensed delineations of canopy gap distribution may also allow spatially explicit modelling of understorey light conditions and potential for regeneration, providing further information to aid the effective management of CCF forests.
Supervisor: Malthus, Tim. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Geoscience ; LIDAR