Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.660182
Title: Remote sensing for continuous cover forestry
Author: Olaya-Gonzalez, G. P.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2008
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Abstract:
The study was carried out at a test area in central Scotland, situated within the Queen Elizabeth II Forest Part (lat. 56°10’ N, long. 4° 23’ W). Six plots containing three different species (Norway spruce, European larch and Sessile oak), characterised by their different light regimes, were established within the area for the measurement of forest variables by using a forest inventory approach and hemispherical photography> The remote sensing data consisted of Landsat ETM+ imagery, small footprint multi-return lidar dataset over the study area, one Airborne Thematic Mapper (ATM) image, and aerial photography with same acquisition data as the lidar data. Landsat ETM+ imagery was used for the spectral characterisation of the species under study and the evaluation of phonological change as a factor to consider for future Landsat imagery acquisitions. Three approaches were used for the discrimination between species: raw data, NDVI, and Principal Component Analysis (PCA). Early summer imagery differentiated species best, although no single date is ideal, and a combination of two or three datasets covering phonological cycles will be optimal for the differentiation. Although the approaches used helped the characterization of forest species, especially the discrimination between spruces, larch and the deciduous specie oak, further work is needed in order to define an optimum approach to discriminate between spruces species. In general, the useful ranges of the indices were small, so a careful and accurate preprocessing of the imagery is highly recommended. Lidar, ATM, and aerial photography were analysed for the characterisation of vertical and horizontal forest structure. A slope-based algorithm was developed for the extraction of ground elevation and three heights from multiple return lidar data and the production of a Digital Terrain Model (DTM) and Digital Surface Model (DSM) of the area under study, and for the comparison of the predicted lidar tree heights with the true tree heights, following by the building of a Digital Canopy Model (DCM) for the determination of percentage canopy cover and tree crown delineation. Mean height and individual tree height were estimated for all sample plots. Lidar underestimated tree heights by an average of 1.49 m. The standard deviation of the lidar estimates was 3.58 m and the mean standard error was 0.38. For spruce and larch plots, lidar measurements explained 92% of the variance associated with the mean height of dominant trees. For the deciduous plots, regression models explained 75% of the mean height variance for dominant trees.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.660182  DOI: Not available
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