Mapping forests with Lidar provides flexible, accurate data with many uses

Kelly, Maggi; Tommaso, Stefania Di

doi:10.3733/ca.v069n01p14

Cited by 37 publications

(26 citation statements)

References 42 publications

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“…Aerial LiDAR is an active remote sensing technology that is able to penetrate the upper canopy, allowing accurate estimations of forest attributes [46,47] including canopy structure [48,49], canopy bulk density [50], and leaf area index [51][52][53]. Airborne LiDAR presents a distinct advantage over terrestrial and space borne platforms for characterizing landscapes because it is able to cover a wide area, while still maintaining high pulse coverage.…”

Section: Introductionmentioning

confidence: 99%

Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR

et al. 2016

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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...

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Section: Introductionmentioning

confidence: 99%

Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR

et al. 2016

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“…Similar situations exist in snow depth surveys using sensors; although ultrasonic snow depth sensors can be used to continuously measure snow depth, the spatial densities of sensors are minimal compared to the scale of mountains. Over the past two decades, airborne lidar is also becoming more widely used in forestry and water resources mapping (Kelly & Di Tommaso, 2015), as canopy structures can be detected and extracted using point cloud data and image-processing algorithms (Li et al, 2012;Moeser et al, 2015;Strîmbu & Strîmbu, 2015). In addition, by using change-detection techniques, spatial snow depth mapping can be retrieved from snow-on and snow-off lidar scans over the same region (Hopkinson et al, 2001).…”

Section: 1029/2018wr023758mentioning

confidence: 99%

Canopy and Terrain Interactions Affecting Snowpack Spatial Patterns in the Sierra Nevada of California

Zheng

Jin

et al. 2019

Water Resources Research

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Airborne light detection and ranging is an emerging measurement tool for snowpack estimation, and data are now emerging to better assess multiscale snow depth patterns. We used airborne light detection and ranging measurements from four sites in the southern Sierra Nevada to determine how snow depth varies with canopy structure and the interactions between canopies and terrain. We processed the point clouds into snow depth rasters at 0.5×0.5‐m2 resolution and performed statistical analysis on the processed snow depth data, terrain attributes, and vegetation attributes, including the individual tree bole locations, canopy crown area, and canopy height. We studied the snow depth at such a fine scale due in part to the spatial heterogeneity introduced by canopy interception and enhanced melting caused by tree trunks in forested areas. We found that the dominant direction of a tree well, the area around the tree bole that has shallower snowpack, is correlated with the local aspect of the terrain, and the gradient of the snow surface in a tree well is correlated with the tree's crown area. The regression‐tree based XGBoost model was fitted with the topographic variables and canopy variables, and about 71% of snow depth variability can be explained by the model.

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“…The best performing models were RF models based on LiDAR derived metrics, which took into consideration vegetation structure, topography, and spectral characteristics. The downside to this method is that LiDAR is not always freely available and can be expensive to acquire [19]. There is also a relatively high level of technical expertise needed to process LiDAR data and run RF classifiers in a programming environment such as interactive data language (IDL).…”

Section: Introductionmentioning

confidence: 99%

Evaluating free and simple remote sensing methods for mapping Chinese privet (Ligustrum sinense) invasions in hardwood forests

2020

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Chinese privet (Ligustrum sinense) is a common invasive shrub in hardwood forests of the southeastern US and has been shown to negatively affect native herbaceous and woody plants. The ability to map the distribution of L. sinense on a property could help land managers plan and budget for control operations. We evaluated whether freely available moderate resolution multispectral imageries (Landsat 8 and Sentinel 2) and open-source GIS software (QGIS with the Semi-Automatic Classification Plugin) could be effective tools for this application. These tools are widely used by remote sensing and mapping professionals; however their adoption by field-level land managers appears limited, and their utility for mapping L. sinense invasions is untested. We evaluated how satellite type, image acquisition date, classification algorithm, and L. sinense cover affected detection accuracy. We found that Sentinel 2 imagery from March tended to produce good results, especially when analyzed using the maximum likelihood algorithm. Our best classifier obtained an overall accuracy of 92.3% for areas with ≥ 40% L. sinense cover. We recommend that land managers interested in applying this tool use an adaptive process for developing training polygons and test multiple images and classification algorithms in order to achieve optimal results.

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Mapping forests with Lidar provides flexible, accurate data with many uses

Cited by 37 publications

References 42 publications

Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR

Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR

Canopy and Terrain Interactions Affecting Snowpack Spatial Patterns in the Sierra Nevada of California

Evaluating free and simple remote sensing methods for mapping Chinese privet (Ligustrum sinense) invasions in hardwood forests

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