Forest stand delineation through remote sensing and Object-Based Image Analysis
Abstract: Forest stand delineation is an essential task of forest management planning which can be time consuming and exposed to subjectivity. The increasing availability of LiDAR data and multispectral imagery offers an opportunity to improve stand delineation by means of remotely-sensed data. Under these premises, ASTER imagery and low-density LiDAR data have been used to automatically delineate forest stands in several forests of Navarra (Spain) through Object-Based Image Analysis (OBIA). Canopy cover, mean height and the canopy model have been extracted from LiDAR data and, along with VNIR ASTER bands, introduced in OBIA for forest segmentation. The outcome of segmentation has been contrasted, on the one hand, assessing segments’ inner heterogeneity. On the other, OBIA’s segments and existing stand delineations have been compared with a new method of geometrical fitting which has been ad hoc designed for this study. Results suggest that low-density LiDAR and multispectral data, along with OBIA, are a powerful tool for stand delineation. Multispectral images have a limited predicting utility for species differentiation and, in practical terms, they help to discriminate between broad-leaved, conifer and mixed stands. The performance of ASTER data, though, could be improved with higher spatial resolution VNIR imagery, specifically sub-metric VNIR orthophotos. LiDAR data, in contrast, offers a great potential for forest structure depiction. This perspective is connected with the increasingly higher resolution datasets which are to be provided by public institutions and the rapid development of drone technology. Complexity of OBIA may limit the use of this technique for small consulting firms but it is an advisable instrument for companies and institutions involved in major forestry projects.
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