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

Abstract

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'analysé ecologique, 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. Cetté etude présente une stratégie qui utiliseà la fois l'imagerie multispectrale aérienne et des données LiDAR pour cartographier la composition et la structure de la végétationà grandeséchelles 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...

Fuels treatment and wildfire effects on runoff from Sierra Nevada mixed‐conifer forests

“…The bedrock underlying both the Lewis Fork fireshed and Big Sandy headwater consists of plutonic Early Cretaceous Bass Lake Tonalite (Bateman, 1989). Fireshed vegetation transitions from lower-elevation pine-oak to the higher-elevation mixed-conifer forest that covered the headwater sites (Su et al, 2016).…”

“…Fuels treatments consisted of forest thinning by removing a fraction of the trees below 76.2-cm diameter at breast height, mastication, and prescribed ground burning. Changes in forest vegetation were determined by differences in leaf area index (LAI), canopy cover, and shrub cover using a combination of light detection and ranging, colour-infrared aerial imagery, and vegetation-plot data collected before and after treatments Su et al, 2016).…”

“…The map consisted of stands, or polygons, classified into vegetation types that captured gradients in tree species composition and forest structure. Classification used both multispectral aerial imagery and light detection and ranging-derived metrics (Su et al, 2016). The forest landscapes were divided into vegetation types.…”

Vegetation structure determination using LIDAR data and the forest growth parameters

“…In practice, we understand this to be the height of mean trunk of woody plant species given. In addition to mean height, the top height of vegetation is presented in [3,5,8,11]. Its definition is not unified at the present time, but we can say that it is as a rule the mean height of a certain number of the thickest trees of vegetation given.…”

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

“…Different types of remote sensing data: multi-spectral, hyper-spectral, radar, or LiDAR obtained from satellites or aircrafts have been exploited for the detection and mapping of vegetation at local or large scale [8][9][10][11][12][13][14][15][16][17][18][19]. Major techniques used for the detection, classification, and mapping of vegetation using remote sensing imagery are vegetation indices [20,21], spectral mixture analysis [22], temporal image-fusion [23,24], texture based measures [25], and supervised classification using machine learning classifiers such as maximum likelihood [26], random forests [27,28], decision trees [29], support vector machines [30], fuzzy learning [31], and neural networks [32][33][34].…”