Urban Forest Growth and Gap Dynamics Detected by Yearly Repeated Airborne Light Detection and Ranging (LiDAR): A Case Study of Cheonan, South Korea

Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional changes in canopies is a helpful way to manage and understand urban forests better. Multitemporal airborne light detection and ranging (LiDAR) datasets enable us to quantify the vertical and lateral growth of trees across a landscape scale. The goal of this study is to assess the annual changes in the 3-D structures of canopies and forest gaps in an urban forest using annual airborne LiDAR datasets for 2012–2015. The canopies were classified as high canopies and low canopies by a 5 m height threshold. Then, we generated pixel- and plot-level canopy height models and conducted change detection annually. The vertical growth rates and leaf area index showed consistent values year by year in both canopies, while the spatial distributions of the canopy and leaf area profile (e.g., leaf area density) showed inconsistent changes each year in both canopies. In total, high canopies expanded their foliage from 12 m height, while forest gap edge canopies (including low canopies) expanded their canopies from 5 m height. Annual change detection with LiDAR datasets might inform about both steady growth rates and different characteristics in the changes of vertical canopy structures for both high and low canopies in urban forests.

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Leaf Area Index (LAI) Estimation in Boreal Mixedwood Forest of Ontario, Canada Using Light Detection and Ranging (LiDAR) and WorldView-2 Imagery

Remote Sensing ◽

10.3390/rs5105040 ◽

2013 ◽

Vol 5 (10) ◽

pp. 5040-5063 ◽

Cited By ~ 36

Author(s):

Graham Pope ◽

Paul Treitz

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Light Detection And Ranging ◽

Light Detection ◽

Area Index ◽

Boreal Mixedwood Forest

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Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry

Remote Sensing of Environment ◽

10.1016/j.rse.2015.02.025 ◽

2015 ◽

Vol 162 ◽

pp. 141-153 ◽

Cited By ~ 66

Author(s):

Michael Alonzo ◽

Bodo Bookhagen ◽

Joseph P. McFadden ◽

Alex Sun ◽

Dar A. Roberts

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Urban Forest ◽

Airborne Lidar ◽

Area Index

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Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data

Frontiers in Plant Science ◽

10.3389/fpls.2021.740322 ◽

2021 ◽

Vol 12 ◽

Author(s):

Behrokh Nazeri ◽

Melba M. Crawford ◽

Mitchell R. Tuinstra

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Mean Squared Error ◽

Light Detection And Ranging ◽

Coefficient Of Determination ◽

Plant Canopy ◽

Plant Structure ◽

Light Detection ◽

Area Index ◽

Row Crops

Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination (R2) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.

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Predicting forest growth based on airborne light detection and ranging data, climate data, and a simplified process-based model

Canadian Journal of Forest Research ◽

10.1139/cjfr-2012-0295 ◽

2013 ◽

Vol 43 (4) ◽

pp. 364-375 ◽

Cited By ~ 9

Author(s):

Sanna Härkönen ◽

Timo Tokola ◽

Petteri Packalén ◽

Lauri Korhonen ◽

Annikki Mäkelä

Keyword(s):

Basal Area ◽

Field Measurements ◽

Forest Growth ◽

Light Detection And Ranging ◽

Weather Data ◽

Stand Age ◽

Climate Data ◽

Light Detection ◽

Area Index ◽

Forest Inventories

Increasing use of airborne light detection and ranging (LiDAR) in forest inventories offers new possibilities to apply process-based forest models (PBM) in practice. We present a new approach, where a simplified PBM is run using inputs derived from the LiDAR data. The PBM was built by combining several existing models together, and it was tested with 52 Scots pine (Pinus sylvestris L.) dominated sample plots in Finland with the LiDAR (PBM_LIDAR) and field (PMB_FIELD) inputs. The results were compared with empirical growth predictions (EM_FIELD) and field reference growth. LiDAR-based stand variables (mean height of tree and crown base and leaf area index) were, on average, well in line with the field measurements. Basal area growth was slightly underestimated with the PBM_LIDAR (bias 4.1%, root mean square prediction error (RMSPE, 26.7%) and overestimated with the PBM_FIELD (bias –10.2%, RMSPE 33.3%), the EM_FIELD being the least biased (bias –1.9%, RMSPE of 24.6%). The bias varied with stand age and fertility. The dependence on field reference growth was highest with EM_FIELD and PBM_LIDAR (R2 = 0.47 and 0.34, respectively), and lowest with PBM_FIELD (R2 = 0.18). Despite several development needs, the approach is promising for easy incorporation of canopy and weather data into forest growth predictions.

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Using airborne lidar to predict Leaf Area Index in cottonwood trees and refine riparian water-use estimates

Journal of Arid Environments ◽

10.1016/j.jaridenv.2007.04.010 ◽

2008 ◽

Vol 72 (1) ◽

pp. 1-15 ◽

Cited By ~ 38

Author(s):

A. Farid ◽

D.C. Goodrich ◽

R. Bryant ◽

S. Sorooshian

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Water Use ◽

Airborne Lidar ◽

Area Index ◽

Cottonwood Trees

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Quantifying the Effects of Normalisation of Airborne LiDAR Intensity on Coniferous Forest Leaf Area Index Estimations

Remote Sensing ◽

10.3390/rs9020163 ◽

2017 ◽

Vol 9 (2) ◽

pp. 163 ◽

Cited By ~ 10

Author(s):

Haotian You ◽

Tiejun Wang ◽

Andrew Skidmore ◽

Yanqiu Xing

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Coniferous Forest ◽

Airborne Lidar ◽

Area Index

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Optimising prediction of forest leaf area index from discrete airborne lidar

Remote Sensing of Environment ◽

10.1016/j.rse.2017.08.002 ◽

2017 ◽

Vol 200 ◽

pp. 220-239 ◽

Cited By ~ 21

Author(s):

Grant D. Pearse ◽

Justin Morgenroth ◽

Michael S. Watt ◽

Jonathan P. Dash

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Airborne Lidar ◽

Area Index

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AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b2-35-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 35-42 ◽

Cited By ~ 1

Author(s):

A. W. Lyda ◽

X. Zhang ◽

C. L. Glennie ◽

K. Hudnut ◽

B. A. Brooks

Keyword(s):

Change Detection ◽

Point Cloud ◽

Near Field ◽

Three Dimensional ◽

Light Detection And Ranging ◽

Data Sets ◽

Light Detection ◽

Icp Algorithm ◽

Detection Algorithms ◽

Change Detection Algorithms

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

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Comparisons of Herbicide Treated and Cultivated Herbicide-Resistant Corn

International Journal of Agronomy ◽

10.1155/2010/798127 ◽

2010 ◽

Vol 2010 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

H. Arnold Bruns ◽

Hamed K. Abbas

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Growth Rates ◽

Negative Impact ◽

Growth Yield ◽

Yield Component ◽

Dry Matter Accumulation ◽

Negative Effects ◽

Area Index ◽

Relative Growth Rates

Four glyphosate resistant corn (Zea maysL.) hybrids, a glufosinate-ammonium resistant hybrid, and a conventional atrazine resistant hybrid gown at Stoneville, MS in 2005, 2006, and 2007 with furrow irrigation were treated with their respective herbicides and their growth, yield, and mycotoxin incidence were compared with untreated cultivated plots. Leaf area index (LAI) and dry matter accumulation (DMA) were collected on a weekly basis beginning at growth stage V3 and terminating at anthesis. Crop growth rates (CRGs) and relative growth rates (RGRs) were calculated. Plots were later harvested, yield and yield component data collected, and kernel samples analyzed for aflatoxin and fumonisin. Leaf area index, DMA, CRG, and RGR were not different among the herbicide treated plots and from those that were cultivated. Curves for LAI and DMA were similar to those previously reported. Aflatoxin and fumonisin were relatively low in all plots. Herbicide application or the lack thereof had no negative impact on the incidence of kernel contamination by these two mycotoxins. Herbicides, especially glyphosate on resistant hybrids, have no negative effects on corn yields or kernel quality in corn produced in a humid subtropical environment.

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