Rapport sur le comparatif des méthodes de détection d'arbre

In the presented study established single tree detection methods are benchmarked and investigated. In total eight airborne laser scanning (ALS) based detection methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types and structures. The evaluation of the detection results was carried out in a clear and reproducible way by automatically matching the detection results to precise in-situ forest inventory data. Quantitative statistical parameters such as the percentages of correctly matched trees and omission and commission errors are presented. The benchmarking results are prepared in complementary levels of information, starting with the analysis based on study area as well as detection method. Additionally investigations per forest type and an overall performance of the benchmark are presented. The best matching rate was obtained for single layered coniferous forests. Trees in lower height layers were challenging for all tested methods. The overall performance shows a matching rate of 47% which is comparable to results of other benchmarks performed in the past for other forest types. The study brings new hindsight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions

Planification stratégique forestière - guide de bonnes pratiques pour les forêts alpines

NEWFOR (NEW technologies for a better mountain FORest timber mobilization) is a research project funded by the alpine space programme. It brings together 14 institutes of the 6 countries of the alpine space with the aim of improving forest timber evaluation and mobilisation. The project considers the whole wood supply chain, from forests to wood yards, with a particular emphasis on new remote sensing technologies and geographical information systems. The report presents the major findings of the project and outlines some key issues for a better mobilisation of alpine timber ressoures

Quantification of carbon stock to understand two different forest management regimes in Kayar Khola watershed, Chitwan, Nepal

The impact of forest management activities on the ability of forest ecosystems to sequester and store atmospheric carbon is of increasing scientific and social concern. This is because a quantitative understanding of how forest management enhances carbon storage is lacking in most forest management regimes. In this paper two forest regimes, government and community-managed, in Kayar Khola watershed, Chitwan, Nepal were evaluated based on field data, very high resolution (VHR) GeoEye-1 satellite image and airborne LiDAR data. Individual tree crowns were generated using multi-resolution segmentation, which was followed by two tree species classification (Shorea robusta and Other species). Species allometric equations were used in both forest regimes for above ground biomass (AGB) estimation, mapping and comparison. The image objects generated were classified per species and resulted in 70 and 82 % accuracy for community and government forests, respectively. Development of the relationship between crown projection area (CPA), height, and AGB resulted in accuracies of R2 range from 0.62 to 0.81, and RMSE range from 10 to 25 % for Shorea robusta and other species respectively. The average carbon stock was found to be 244 and 140 tC/ha for community and government forests respectively. The synergistic use of optical and LiDAR data has been successful in this study in understanding the forest management system