scholarly journals Tracking the rates and mechanisms of canopy damage and recovery following Hurricane Maria using multitemporal lidar data

2021 ◽  
Author(s):  
Veronika Leitold ◽  
Douglas C Morton ◽  
Sebastian Martinuzzi ◽  
Ian Paynter ◽  
Maria Uriarte ◽  
...  
Keyword(s):  
Forest Structure ◽  
Structural Changes ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Recovery ◽  
Height Loss ◽  
Lidar Data ◽  
List Type ◽  
Initial Height ◽  
Height Gain

ABSTRACTHurricane Maria (Category 4) snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100 to 800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13% of the study area had canopy heights ≤2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss (<-1 m, 2017-2018) followed by rapid height gain (>1 m, 2018-2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no structural changes >|1| m in either period. Canopy layers <10 m accounted for most increases in canopy height and fractional cover between 2018-2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage.MANUSCRIPT HIGHLIGHTSHurricane Maria gave forests a haircut by toppling trees and shearing branches.Regrowth after Maria was patchy, with equal areas of height gain and no change.3-D measures of forest recovery after hurricanes can improve ecosystem models.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

LiDAR remote sensing of forest structure

2003 ◽  
Vol 27 (1) ◽  
pp. 88-106 ◽  
Author(s):  
Kevin Lim ◽  
Paul Treitz ◽  
Michael Wulder ◽  
Benoît St-Onge ◽  
Martin Flood
Keyword(s):  
Remote Sensing ◽  
Forest Structure ◽  
Forest Ecosystems ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Canopy Volume

Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.

Tracking tree mortality across sites with repeat LiDAR data

2021 ◽  
Author(s):  
Toby Jackson ◽  
Matheus Nunes ◽  
Grégoire Vincent ◽  
David Coomes
Keyword(s):  
Forest Structure ◽  
Tree Mortality ◽  
Mortality Rates ◽  
Landscape Scale ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Forest Types ◽  
Large Tree ◽  
Airborne Lidar Data ◽  
Field Plots

&lt;p&gt;Repeat airborne LiDAR data provides a unique opportunity to study tree mortality at the landscape scale. We use maps of canopy height derived from repeat LiDAR (two or more scans collected a few years apart) to detect changes in forest structure. Visually, the most obvious changes are caused by large treefall events, which are difficult to study using field plots due to their rarity. While repeat LiDAR data provides exciting new possibilities, validation is a challenge, since we cannot easily determine how many trees have died and we may miss trees which are dead but still standing. I will discuss our progress so far, studying large-tree mortality rates across multiple countries and forest types.&lt;/p&gt;

scholarly journals Forest structure following tornado damage and salvage logging in northern Maine, USA

2017 ◽  
Vol 47 (4) ◽  
pp. 560-564 ◽  
Author(s):  
Shawn Fraver ◽  
Kevin J. Dodds ◽  
Laura S. Kenefic ◽  
Rick Morrill ◽  
Robert S. Seymour ◽  
...  
Keyword(s):  
Forest Structure ◽  
Structural Changes ◽  
Management Practices ◽  
Stand Structure ◽  
Woody Debris ◽  
Management Strategies ◽  
Forest Recovery ◽  
Management Options ◽  
Salvage Operation ◽  
Salvage Logging

Understanding forest structural changes resulting from postdisturbance management practices such as salvage logging is critical for predicting forest recovery and developing appropriate management strategies. In 2013, a tornado and subsequent salvage operations in northern Maine, USA, created three conditions (i.e., treatments) with contrasting forest structure: blowdown, blowdown + salvage, and control (undisturbed). We sampled forest structure in five stands representing each of these three treatments. Our results document obvious and predictable changes to forest structure caused by the blowdown and salvage operations; however, they also include unexpected findings: downed coarse woody debris volume remained quite high in the salvaged areas, although its vertical distribution was markedly reduced; salvage operations did not reduce fine woody debris volume; and the salvage operation itself reduced the abundance of upturned root masses. Our study contributes to a growing body of literature highlighting the fact that outcomes of salvage operations vary considerably from situation to situation. Nevertheless, they suggest that salvage logging has important implications for residual stand structure and regeneration potential and that these implications should be considered carefully when weighing postdisturbance management options.

scholarly journals The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2880 ◽  
Author(s):  
Qiuli Yang ◽  
Yanjun Su ◽  
Shichao Jin ◽  
Maggi Kelly ◽  
Tianyu Hu ◽  
...  
Keyword(s):  
Forest Type ◽  
The United States ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Individual Tree ◽  
Segmentation Methods ◽  
Vegetation Characteristics ◽  
Canopy Height Model ◽  
Height Model

This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics.

scholarly journals Detecting Change in Forest Structure with Simulated GEDI Lidar Waveforms: A Case Study of the Hemlock Woolly Adelgid (HWA; Adelges tsugae) Infestation

2020 ◽  
Vol 12 (8) ◽  
pp. 1304 ◽  
Author(s):  
Peter Brehm Boucher ◽  
Steven Hancock ◽  
David A Orwig ◽  
Laura Duncanson ◽  
John Armston ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Structural Changes ◽  
Hemlock Woolly Adelgid ◽  
Adelges Tsugae ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Forest Plot ◽  
Lidar Data ◽  
Spatial Coverage ◽  
Plant Area

The hemlock woolly adelgid (HWA; Adelges tsugae) is an invasive insect infestation that is spreading into the forests of the northeastern United States, driven by the warmer winter temperatures associated with climate change. The initial stages of this disturbance are difficult to detect with passive optical remote sensing, since the insect often causes its host species, eastern hemlock trees (Tsuga canadensis), to defoliate in the midstory and understory before showing impacts in the overstory. New active remote sensing technologies—such as the recently launched NASA Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar—can address this limitation by penetrating canopy gaps and recording lower canopy structural changes. This study explores new opportunities for monitoring the HWA infestation with airborne lidar scanning (ALS) and GEDI spaceborne lidar data. GEDI waveforms were simulated using airborne lidar datasets from an HWA-infested forest plot at the Harvard Forest ForestGEO site in central Massachusetts. Two airborne lidar instruments, the NASA G-LiHT and the NEON AOP, overflew the site in 2012 and 2016. GEDI waveforms were simulated from each airborne lidar dataset, and the change in waveform metrics from 2012 to 2016 was compared to field-derived hemlock mortality at the ForestGEO site. Hemlock plots were shown to be undergoing dynamic changes as a result of the HWA infestation, losing substantial plant area in the middle canopy, while still growing in the upper canopy. Changes in midstory plant area (PAI 11–12 m above ground) and overall canopy permeability (indicated by RH10) accounted for 60% of the variation in hemlock mortality in a logistic regression model. The robustness of these structure-condition relationships held even when simulated waveforms were treated as real GEDI data with added noise and sparse spatial coverage. These results show promise for future disturbance monitoring studies with ALS and GEDI lidar data.

scholarly journals Regionalization of an Existing Global Forest Canopy Height Model for Forests of the Southern United States

2021 ◽  
Vol 13 (9) ◽  
pp. 1722
Author(s):  
Nian-Wei Ku ◽  
Sorin Popescu ◽  
Marian Eriksson
Keyword(s):  
United States ◽  
Large Scale ◽  
Forest Canopy ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Southern United States ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Local Data ◽  
Airborne Lidar Data

A large-scale global canopy height map (GCHM) is essential for global forest aboveground biomass estimation. Four GCHMs have recently been built using data from the Geoscience Laser Altimeter System (GLAS) sensor aboard the Ice, Cloud, and land Elevation Satellite (ICESat), along with auxiliary spatial and climate information. The main objectives of this research were to find out how well a selected GCHM agrees with airborne lidar data from locations in the southern United States and to recalibrate that GCHM to more closely match the forest canopy heights found in the region. The airborne lidar resource was built from data collected between 2010 and 2012, available from in-house and publicly available sources, for sites that included a variety of vegetation types across the southern United States. EPA ecoregions were used to provide ecosystem information for the southern United States. The airborne lidar data were pre-processed to provide lidar-derived metrics, and assigned to four height categories—namely, returns from above 0 m, 1 m, 3 m, and 5 m. The assessment phase results indicated that the 90th and 95th percentiles of the airborne lidar height values were well-suited for use in the recalibration phase of the study. Simple linear regression was used to generate a new, recalibrated GCHM. It was concluded that the characterization of the agreement of a selected GCHM with local data, followed by the recalibration of the existing GCHM to the local region, is both viable and essential for future GCHMs in studies conducted at large scales.

scholarly journals Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Cheng Wang ◽  
Shezhou Luo ◽  
Xiaohuan Xi ◽  
Sheng Nie ◽  
Dan Ma ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Voxel Size ◽  
Height Estimates ◽  
Full Waveform ◽  
Airborne Lidar Data

scholarly journals Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

2019 ◽  
Vol 11 (4) ◽  
pp. 381 ◽  
Author(s):  
Guillaume Brigot ◽  
Marc Simard ◽  
Elise Colin-Koeniguer ◽  
Alexandre Boulch
Keyword(s):  
Machine Learning ◽  
Forest Structure ◽  
Vertical Profile ◽  
Three Dimensional ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Dimensional Structure ◽  
Interferometric Synthetic Aperture Radar ◽  
The Impact

This paper presents a machine learning based method to predict the forest structure parameters from L-band polarimetric and interferometric synthetic aperture radar (PolInSAR) data acquired by the airborne UAVSAR system over the Réserve Faunique des Laurentides in Québec, Canada. The main objective of this paper is to show that relevant parameters of the PolInSAR coherence region can be used to invert forest structure indicators computed from the airborne LIDAR sensor Laser Vegetation and Ice Sensor (LVIS). The method relies on the shape of the observed generalized PolInSAR coherence region that is related to the three-dimensional structure of the scene. In addition to parameters describing the coherence shape, we consider the impact of acquisition parameters such as the interferometric baseline, ground elevation and local surface slope. We use the parameters as input a multilayer perceptron model to infer canopy features as estimated from LIDAR waveform. The output features are canopy height, cover and vertical profile class. Canopy height and canopy cover are estimated with a normalized RMSE of 13%, 15% respectively. The vertical profile was divided into 3 distinct classes with 66% accuracy.

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