Volumes 211-212 of the French Journal of Photogrammetry and Remote Sensing (Revue Française de Photogrammétrie et Télédétection) are dedicated to forests. Results of the Newfor projects were presented in two articles :
- Monnet, J.-M., Chirouze, É., Mermin, É. 2015. Estimation de paramètres forestiers par données LiDAR aéroporté et imagerie satellitaire RapidEye – Étude de sensibilité. Revue Française de Photogrammétrie et Télédétection, 211-212, 71-80. PDF
Abstract. Regarding the accuracy of forest estimates from airborne laser scanning data, several important parameters have already been investigated separately, and in various forest contexts. Comparing the results in order to determine the optimal setting for the accuracy and cost of LiDAR-based inventories thus remains difficult. This article presents an assessment of the sensitivity of prediction accuracy with regards to ground truth data and to the use of spectral information from LiDAR intensity or RapidEye satellite imagery. With data fusion the error of basal area estimates is 16.5%, 23.7% for stem density and 11.7% for mean diameter. The size and diameter threshold of the forest plots greatly impact prediction accuracy. The position accuracy and sample number of forest plots are crucial points as they influence both prediction error and its estimation.
- Monnet, J.-M., Munoz, A. 2015. Comparaison de méthodes de spatialisation pour l’agrégation par parcelle des estimations de paramètres forestiers par LiDAR aéroporté. Revue Française de Photogrammétrie et Télédétection, 211-212, 93-102. PDF
Abstract. While the use of field plots and airborne LiDAR data for the estimation of forest parameters has been intensively investigated in the past ten years, the issue of the evaluation of their accuracy at the compartment level (surface of a few hectares) remains poorly documented. Based on a full-calliper inventory of 35 compartments representing 380 ha, the present study compares different strategies for the mapping of LiDAR predictions and their aggregation by compartment. Results show that the prediction error decreases between estimations at the plot and those at the compartment levels : from 15 to 6.4% for basal area, 26 to 7.7% for stem density and 6.5 to 3.4% for mean diameter. At the compartment level, a LiDAR-based inventory thus displays an accuracy similar to a full-calliper inventory, for basal area. For the mapping step, it is crucial to respect the size of field plots used for calibration, whereas for the aggregation step the handling of compartment borders remains tricky for all forest parameters.