Forest structure analysis on very high resolution images.
Journal Title: Advances in Environmental Sciences - International Journal of the Bioflux Society - Year 2010, Vol 2, Issue 2
Abstract
Forest structure information has been of interest for a long time, being linked to the possibilities of maximizing forest production in terms of ecological stability. Forest structure parameters can be used in assessing the degree of closeness to natural ecosystems for any type of forest stand. The remote sensing methods have the advantage of large scale interpretation of results, facilitating the analysis at different levels – from tree level to landscape level, all this in the context of satellite images price decrease. The high resolution images have their advantages and disadvantages in forest structure assessments but, accurate information about the forest ecosystems can be obtained with the right processing techniques. The evolution of satellite sensors parameters has lowered the analysis level from the landscape level somewhere close to tree level; the modern very high spatial resolution satellites have a spatial resolution similar to medium scale aerial photos, with the advantage of price and multispectral characteristics. The different spectral channels can be sensitive to several forest parameters and, in the right combination, can offer an “image” of forest state and structure. Before using the data extracted from satellite images, is important to make accuracy studies and establish the correct level of analysis. In this context, the paper’s objective is to analyze the possibilities of measuring forest structure parameters on IKONOS and aerial images and establish the optimum level for information processing. The parameters taken into account were crown diameter, canopy cover index and number of trees per area unit; the forest stands are consisting of mixed forests of silver fir and Norway spruce, with a high homogeneity. The results of the study showed that a better accuracy is obtained on IKONOS rather than aerial photos, especially in the case of crown diameter and canopy cover index; in the case of number of trees per area unit, the two types of images being similar. In both cases, remote sensing methods tend to reduce the variability of the data, in comparison to ground methods. The best comparability of data is achieved at the stand level, even if the information is originating from measurements of individual trees(as in the case of crown diameter).
Authors and Affiliations
Ionut Barnoaiea
Keywords
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