Skip navigation

Distributional modelling in forestry and remote sensing

Distributional modelling in forestry and remote sensing

Wang, Mingliang (2005) Distributional modelling in forestry and remote sensing. PhD thesis, University of Greenwich.

[img] PDF
Mingliang_Wang_2005.pdf - Published Version
Restricted to Repository staff only until 16 March 2019.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (12MB)

Abstract

The use of distributional models in forestry is investigated, in terms of their capability of modelling distributions of forest mensurational attributes, for modelling and inventory purposes. Emphasis is put on: (i) the univariate and bivariate modelling of tree diameters and heights for stand-level modelling work, and (ii) heuristic methods for use and analysis of distributions which occur in multi-temporal EO imagery, (for the inventory-related tasks of land-use mapping, change detection and growth modelling).

In univariate distribution modelling, a new parameterization of the widely-used Johnson’s SB distribution is given, and new Logit-Logistic, generalised Weibull and the Burr system (XII, III, IV) models are introduced into forest modelling. The Logit-Logistic distribution is found to be the best among those compared. The use of regression-based methods of parameter estimation is also investigated.

In the domain of bivariate distribution modelling of tree diameters and heights the Plackett method (a particular form of copula) is used to construct Plackett-based bivariate Beta, S­B and Logit-Logistic distributions, (the latter two are new), which are compared with each other and the SBB­ distribution. Other copula functions, including the normal copula, are further employed (for the first time in forest modelling) to construct bivariate distributional models. With the normal copula, the superiority of the Logit-Logistic in the univariate domain is extended into the bivariate domain.

To use multi-temporal EO imagery, two pre-processing procedures are necessary: image to image co-registration, and radiometric correction. A spectral correlation-based pixel-matching method is developed to “refine” manually selected control points to achieve very accurate image co-registration. A robust non-parametric method of spectral-distribution standardization is used for relative radiometric correction between images. Finally, possibilities for further research are discussed.

Item Type: Thesis (PhD)
Additional Information: uk.bl.ethos.435520
Uncontrolled Keywords: distribution models, forestry, spatial modelling, remote sensing,
Subjects: Q Science > QA Mathematics
Q Science > QH Natural history
S Agriculture > SD Forestry
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Statistics & Operational Research Group
Last Modified: 03 Mar 2018 15:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/6337

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics