LORENZLIDAR · Classification of Forest Structural Types with LiDAR Remote Sensing Applied to Study Tree Size-Density Scaling Theories

Classification of Forest Structural Types with LiDAR Remote Sensing Applied to Study Tree Size-Density Scaling Theories

Light detection and ranging (LIDAR) remote sensing provides detailed measurements of vegetation height, density and spatial heterogeneity. Until recently, the acquisition of airborne LIDAR surveys was considered too costly for operational applications at broad scales. However, today a number of national surveying programmes are producing publicly-available LIDAR datasets covering entire countries. These low-density datasets are also demonstrably useful for forest inventory and ecological applications. As national LIDAR surveys are becoming more common, unique opportunities exist for generating habitat indicator variables and classifications that can be consistently obtained throughout entire countries. There is still a need to clarify and harmonize what indicator variables and classifications can be derived from LIDAR datasets and employed for transnational comparisons and monitoring. Objectives: LORENZLIDAR tested the feasibility of a simple methodology for classification of forest structure from low-density LIDAR datasets acquired by nation-wide programmes, adapting it to assure its validity across biogeographical regions in Europe. The methodology developed was built upon an analysis framework based on Lorenz curve analysis. The Lorenz curve is a method widely used in econometrics to measure the degree of wealth or income inequality in a society. LORENZLIDAR adapted the method to forest ecosystems and ecology, and to the assessment of its structural properties using LIDAR. Conclusions: LORENZLIDAR concluded with a two-tier methodology for forest structure classification. The higher tier are parameters can be extracted from a Lorenz curve constructed from the sizes of trees in a forest, and it is used to determine the shape of the tree diameter distribution. In a lower tier, mean diameter (QMD) and stem density (N) were used to discriminate young/mature and sparse/dense subtypes . The resulting forest structural types relate to tree size-density scaling relationships: metabolic scaling or demographic equilibrium. Furthermore, we used similar structural predictors derived from LIDAR to predict the forest classes, obtaining reliable classification accuracies. The simplicity of the developed two-tier approach paves the way toward transnational assessments of forest structure across bioregions.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз