/ The integration of Landsat TM and environmental GIS data sets using artificial intelligence rule-induction and decision-tree analysis is shown to facilitate the production of vegetation maps with both floristic and structural infermation. This technique is particularly suited to vegetation mapping in disturbed or hilly environments that are unsuited to either conventional remote sensing methods or GIS modeling using environmental data bases.Current concerns worldwide with the management of native forests have led to a renewed interest in the development of rapid, cost-effective methods ibr forest mapping. Before any assessment can be made of the nature of vegetation in a given area, it is necessary to know what species are predominant, how they are distributed, and their relative abundance. Basic to the description of vegetation is a knowledge of floristics, lifeform, and structure. Traditional methods of mapping these attributes using a combination of field survey and photointerpretation are both labor-intensive and slow. Hopes that digital remote sensing would reduce time and costs, yet routinely produce acceptable thematic maps of vegetation and forest type, have not yet been met. In southeast Australia, where this study was carried out, remote sensing can often successfully distinguish structural communities (Walker and others 1986, Jupp and others 1986). The identification of floristic associations and life-forms is much less successful using only remote sensing. Although the difference between the reflectance of eucalypt leaves, in particular, appears to offer promise that some species discrimination is possible using remote sensing (Figure 1), leaf reflectance varies greatly within a single tree where there are mature and juvenile leaves present along with old, insectdamaged ones. Laboratory measurements show that the spectral variability of leaves within a eucalypt species
A method is described for predicting the spatial distribution of arboreal marsupials. The approach is demonstrated using a statistical habitat association model for the greater glider (Petauroides volans), developed in the mountain ash (Eucalyptus regnans) forests of the central highlands of Victoria. The method is applied to predict the spatial distribution of P. volans in the Ada Forest Block, using forestinventory data on the values of the two significant variables in the statistical model (the age of a stand of forest and the abundance of large trees with hollows in a 3-ha area). The application of the model enabled values for the estimated probability of occurrence of P, volans (with a 95% confidence interval) to be generated for each of approximately 2200 3-ha pixels in the Ada Forest Block. A kernel smoothing procedure was then applied to allow for the spatial dependence implicit in these data. The standard measures of statistical uncertainty employed in our analysis revealed substantial variation in the predicted probability of occurrence of P. volans, even though the terms in the statistical relationship were highly significant. However, whilst the model is unable to reliably predict the occurrence of P. volans at any given 3-ha site, tests of the performance of the model showed that it performed well when the results of field surveys were aggregated over many sites. The results of our analysis emphasise the importance of including measures of uncertainty in spatial predictions generated from statistical models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.