A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites. Résumé. La cartographie précise de la végétation est essentielle entre autres pour la gestion des ressources naturelles, l’analyse écologique, et la modélisation hydrologique. Les approches d’imagerie multispectrale et hyperspectrale par télédétection se sont avérées de précieuses contributions au processus de la cartographie de la végétation, mais elles ne peuvent fournir qu’un nombre limité de caractéristiques sur la structure de la végétation, qui sont essentielles pour différencier les communautés végétales dans les forêts de composition homogènes. La télédétection par laser «light detection and ranging» (LiDAR) peut mesurer avec précision les structures verticales et horizontales de la forêt, et fournit une formidable opportunité de résoudre ce problème. Cette étude présente une stratégie qui utilise à la fois l’imagerie multispectrale aérienne et des données LiDAR pour cartographier la composition et la structure de la végétation à grandes échelles spatiales. Notre approche comprenait l’utilisation d’un algorithme du critère d’information Bayésien pour déterminer le nombre optimal de groupes de végétation dans les forêts mixtes de conifères sur deux zones d’étude dans les Sierra Nevada, en Californie, ainsi qu’une technique de classification non supervisée et une analyse post hoc pour cartographier ces groupes de végétation dans les deux zones d’étude. Les résultats montrent que la stratégie proposée peut reconnaitre quatre et sept groupes de végétation dans les deux zones d’étude respectivement. Chaque groupe de végétation a des caractéristiques uniques de structure de la végétation ou de composition des espèces de la végétation. La précision globale et le coefficient kappa des résultats de la cartographie de la végétation sont de plus de 78% et 0,64 pour les deux sites d’étude.

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