Health assessment of plantations based on LiDAR canopy spatial structure parameters

Meng, Pengyu; Wang, Hong; Qin, Shuhong; Li, Xiuneng; Song, Zhenglin; Wang, Yicong; Yang, Yi; Gao, Jay

doi:10.1080/17538947.2022.2059114

Cited by 11 publications

(3 citation statements)

References 52 publications

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“…Other studies have used remote sensing to assess forest cover. Indeed, it has been shown that airborne Lidar (Laser Imaging Detection and Ranging), through the acquisition of point clouds, can detect defoliation in terms of LAI [63] and thus provide feedback on canopy and forest vigour [64]. Given the complexity of forest systems and their response to disturbances, visual or remote canopy assessment methods must always be accompanied and validated by quantitative field surveys and measurements to ensure representativeness and correlation between the data obtained.…”

Section: Tree Crown Evaluationsmentioning

confidence: 99%

Assessing Forest Vulnerability to Climate Change Combining Remote Sensing and Tree-Ring Data: Issues, Needs and Avenues

Italiano

Camarero

Colangelo

et al. 2023

Forests

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Forests around the world are facing climate change. Increased drought stress and severe heat waves in recent decades have negatively impacted on forest health, making them more vulnerable and prone to dieback and mortality phenomena. Although the term vulnerability is used to indicate an increased susceptibility of forests to climate change with a worsening of their vigour status that can compromise their ability to respond to further climate extreme events, there are still uncertainties on how to evaluate it. Indeed, evaluation of forest vulnerability is complex both because of some critical issues in the estimation methods used and because of the multiple factors influencing the response of forests to ongoing climate change. A way to assess the vulnerability to environmental stresses is by combining remote sensing and dendroecological data. However, these two approaches entail multiple uncertainties, including growth/photosynthetic relationships, carbon allocation dynamics, biases of tree-ring data and noisy remote sensing data, which require further clarification for proper monitoring of pre- and post-drought forest trajectories. Our review aims to create an overview of the current literature and knowledge to understand the critical issues, needs and possible solutions that forest vulnerability research is addressing. We focus on Mediterranean forests located in a climate warming hotspot and showing a high vulnerability to increased aridification.

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Section: Tree Crown Evaluationsmentioning

confidence: 99%

Assessing Forest Vulnerability to Climate Change Combining Remote Sensing and Tree-Ring Data: Issues, Needs and Avenues

Italiano

Camarero

Colangelo

et al. 2023

Forests

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“…Canopy cover is also an indicator of stand density the same as tree density. Furthermore, it is an important parameter in forest management and estimating forest volume [36][37][38]. Since the study area is in a tropical rainforest with dense trees, obvious vertical stratification and complex structure, the competition among tree crowns for light and living space is extremely strong, and the growth of the understory and forest floor are affected.…”

Section: Regression Model Precision Assessmentmentioning

confidence: 99%

Crown Structure Metrics to Generalize Aboveground Biomass Estimation Model Using Airborne Laser Scanning Data in National Park of Hainan Tropical Rainforest, China

Wang

et al. 2022

Forests

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Forest aboveground biomass (AGB) is an important indicator for characterizing forest ecosystem structures and functions. Therefore, how to effectively investigate forest AGB is a vital mission. Airborne laser scanning (ALS) has been demonstrated as an effective way to support investigation and operational applications among a wide range of applications in the forest inventory. Moreover, three-dimensional structure information relating to AGB can be acquired by airborne laser scanning. Many studies estimated AGB from variables that were extracted from point cloud data, but few of them took full advantage of variables related to tree crowns to estimate the AGB. In this study, the main objective was to evaluate and compare the capabilities of different metrics derived from point clouds obtained from ALS. Particularly, individual tree-based alpha-shape, along with other traditional and commonly used plot-level height and intensity metrics, have been used from airborne laser scanning data. We took the random forest and multiple stepwise linear regression to estimate the AGB. By comparing AGB estimates with field measurements, our results showed that the best approach is mixed metrics, and the best estimation model is random forest (R2 = 0.713, RMSE = 21.064 t/ha, MAE = 15.445 t/ha), which indicates that alpha-shape may be a good alternative method to improve AGB estimation accuracy. This method provides an effective solution for estimating aboveground biomass from airborne laser scanning.

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“…At present, GEDI is NASA's first spaceborne, full-waveform lidar that is specifically designed to measure ecosystem structure by providing vertical profiles of forest canopies. After its launch, recent studies have been appropriately making full use of its data, such as in mapping the canopy forest with the integration of GEDI and Landsat datasets [30], conducting plant health assessments using spatial canopy features such as canopy height and diameter at breast height [31], using spatial features of forests in wildfire management [32], and many more. Unlike gridded imagery from optical satellites such as Landsat-8, Sentinel-2, etc., GEDI is a sampling instrument that can record height measurements in spatially discrete ~25 m diameter footprints, where interstitial areas remain unsampled between footprints (60 m along-track) and between transects (600 m across-track) [33].…”

Section: Introductionmentioning

confidence: 99%

Unveiling Temperature Patterns in Tree Canopies across Diverse Heights and Types

2023

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Forests are some of the major ecosystems that help in mitigating the effects of climate change. Understanding the relation between the surface temperatures of different vegetation and trees and their heights is very crucial in understanding events such as wildfires. In this work, relationships between tree canopy temperature and canopy height with respect to vegetation types were extracted. The southern part of Sardinia Island, which has dense forests and is often affected by wildfires, was selected as the region of interest. PRISMA hyperspectral imagery has been used to map all the available vegetation types in the region of interest using the support vector machine classifier with an accuracy of >80% for all classes. The Global Ecosystem Dynamics Investigation’s (GEDI) L2A Raster Canopy Top Height product provides canopy height measurements in spatially discrete footprints, and to overcome this issue of discontinuous sampling, Random Forest Regression was used on Sentinel-1 SAR data, Sentinel-2 multispectral data, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) to estimate the canopy heights of various vegetation classes, with a root mean squared error (RMSE) value of 2.9176 m and a coefficient of determination (R2) value of 0.791. Finally, the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and emissivity product provides ground surface temperature regardless of land use and land cover (LULC) types. LST measurements over tree canopies are considered as the tree canopy temperature. We estimated the relationship between the canopy temperature of five vegetation types (evergreen oak, olive, juniper, silicicole, riparian trees) and the corresponding canopy heights and vegetation types. The resulting scatter plots showed that lower tree canopy temperatures correspond with higher tree canopies with a correlation coefficient in the range of −0.4 to −0.5 for distinct types of vegetation.

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Health assessment of plantations based on LiDAR canopy spatial structure parameters

Cited by 11 publications

References 52 publications

Assessing Forest Vulnerability to Climate Change Combining Remote Sensing and Tree-Ring Data: Issues, Needs and Avenues

Assessing Forest Vulnerability to Climate Change Combining Remote Sensing and Tree-Ring Data: Issues, Needs and Avenues

Crown Structure Metrics to Generalize Aboveground Biomass Estimation Model Using Airborne Laser Scanning Data in National Park of Hainan Tropical Rainforest, China

Unveiling Temperature Patterns in Tree Canopies across Diverse Heights and Types

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