Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3D point data with high spatial resolution and accuracy. Individual tree segmentations, usually derived from the canopy height model, are used to derive individual tree structural attributes such as tree height, crown diameter, canopy-based height, and others. In this study, we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. We experimentally applied the new algorithm to segment trees in a mixed conifer forest in the Sierra Nevada Mountains in California. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm detected 86 percent of the trees ("recall"), 94 percent of the segmented trees were correct ("precision"), and the overall F-score is 0.9. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data.

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Light detection and ranging (lidar) data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California's Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA) of a canopy height model (CHM). The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m 2 ), discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r 2 : 0.93-0.96). Tree detection rates increased for more dominant trees (8-100 percent). The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest structure.

Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. Ground-based field surveying is a conventional method for mapping and characterizing downed logs but is limited. In addition, optical remote sensing methods have not been able to map these ground targets due to the lack of optical sensor penetrability into the forest canopy and limited sensor spectral and spatial resolutions. Lidar (light detection and ranging) sensors have become a more viable and common data source in forest science for detailed mapping of forest structure. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA) and classification algorithms. Downed log objects were successfully delineated and classified from lidar derived metrics using an OBIA framework. 73% of digitized downed logs were completely or partially classified correctly. Over classification occurred in areas with large numbers of logs clustered in close proximity to one another and in areas with vegetation and tree canopy. The OBIA methods were found to be effective but inefficient in terms of automation and analyst's time in the delineation and classification of downed logs in the lidar data.

Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical changes, and it has contributed to uncharacteristically large and severe fires. The abundance of ladder fuels makes it difficult to return these forests to their natural fire regime or to meet management objectives. Despite the importance of ladder fuels, methods for quantifying them are limited and imprecise. LiDAR (Light Detection and Ranging), a form of active remote sensing, is able to estimate many aspects of forest structure across a landscape. This study investigates a new method for quantifying ladder fuel in the field (using photographs with a calibration banner) and remotely (using LiDAR data). We apply these new techniques in the Klamath Mountains of Northern California to predict ladder fuel levels across the study area. Our results demonstrate a new utility of LiDAR data to identify fire hazard and areas in need of fuels reduction.Remote Sens. 2016, 8, 766 2 of 23 90% of trees are killed), which have been increasing [10,11]. These altered contemporary fire patterns are due to a number of factors, but considerable increases in surface and ladder fuels play an important role [12]. Ladder fuels allow fire to transition into overstory tree crowns by providing greater vertical fuel continuity, and fire burning ladder fuels also preheats canopy fuels that have not yet ignited [13]. Additionally, dense ladder fuels can make suppression more difficult and increase wildland firefighter exposure to hazardous conditions, especially when fire behavior shifts unexpectedly, by inhibiting escape to safety zones [14]. In addition to the biophysical influences of ladder fuels on fire behavior and fire management, they can also reduce habitat quality through decreasing accessibility and foraging efficiency for wildlife and tribal subsistence gathering [15].Despite the importance of ladder fuels to fire behavior, effects, and firefighter safety, they have not been directly quantified except in a few cases [13,16,17], all of which are sampled on the ground, with no explicit connection to remote sensing. Because ladder fuels are below the canopy, passive remote sensing platforms are not able to capture their composition, except in very open forests. In most cases, fire models utilize a surrogate for ladder fuels, which is comprised of a combination of canopy base height (CBH) and fuel model (sometimes with an adjustment of fire behavior) [18]. CBH is the height above which there is enough fuel per unit volume to carry the fire upward. The fuel density necessary to carry fire is most commonly 0.012 kg·m −3 [19], but other thresholds have been suggested and used [20][21][22][23].In addition to a variable fuel density threshold, estimations for CBH are commonly based on allometric equations, moving them further from a consistent direct measurement. Although activ...