scholarly journals Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review

2021 ◽  
Vol 13 (12) ◽  
pp. 2408
Author(s):  
Luo Tian ◽  
Yonghua Qu ◽  
Jianbo Qi
Keyword(s):  
Forest Canopy ◽  
Input Parameter ◽  
3D Structure ◽  
Tree Height ◽  
Heterogeneous Data ◽  
Airborne Lidar ◽  
Area Index ◽  
Cloud Data ◽  
Time Space ◽  
The Impact

The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research.

scholarly journals Optimizing the Remote Detection of Tropical Rainforest Structure with Airborne Lidar: Leaf Area Profile Sensitivity to Pulse Density and Spatial Sampling

2019 ◽  
Vol 11 (1) ◽  
pp. 92 ◽  
Author(s):  
Danilo Roberti Alves de Almeida ◽  
Scott C. Stark ◽  
Gang Shao ◽  
Juliana Schietti ◽  
Bruce Walker Nelson ◽  
...  
Keyword(s):  
Grain Size ◽  
Leaf Area ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Primary Source ◽  
Airborne Lidar ◽  
Area Index ◽  
Pulse Density ◽  
Optimal Values ◽  
And Function

Airborne Laser Scanning (ALS) has been considered as a primary source to model the structure and function of a forest canopy through the indicators leaf area index (LAI) and vertical canopy profiles of leaf area density (LAD). However, little is known about the effects of the laser pulse density and the grain size (horizontal binning resolution) of the laser point cloud on the estimation of LAD profiles and their associated LAIs. Our objective was to determine the optimal values for reliable and stable estimates of LAD profiles from ALS data obtained over a dense tropical forest. Profiles were compared using three methods: Destructive field sampling, Portable Canopy profiling Lidar (PCL) and ALS. Stable LAD profiles from ALS, concordant with the other two analytical methods, were obtained when the grain size was less than 10 m and pulse density was high (>15 pulses m−2). Lower pulse densities also provided stable and reliable LAD profiles when using an appropriate adjustment (coefficient K). We also discuss how LAD profiles might be corrected throughout the landscape when using ALS surveys of lower density, by calibrating with LAI measurements in the field or from PCL. Appropriate choices of grain size, pulse density and K provide reliable estimates of LAD and associated tree plot demography and biomass in dense forest ecosystems.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

scholarly journals Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data

2021 ◽  
Vol 13 (3) ◽  
pp. 442
Author(s):  
Khaldoun Rishmawi ◽  
Chengquan Huang ◽  
Xiwu Zhan
Keyword(s):  
Forest Structure ◽  
Infrared Imaging ◽  
Carbon Sink ◽  
3D Structure ◽  
Three Dimensional ◽  
Tree Height ◽  
Ecosystem Dynamics ◽  
Accurate Information ◽  
Area Index ◽  
Plant Area Index

Accurate information on the global distribution and the three-dimensional (3D) structure of Earth’s forests is needed to assess forest biomass stocks and to project the future of the terrestrial Carbon sink. In spite of its importance, the 3D structure of forests continues to be the most crucial information gap in the observational archive. The Global Ecosystem Dynamics Investigation (GEDI) Light Detection and Ranging (LiDAR) sensor is providing an unprecedented near-global sampling of tropical and temperate forest structural properties. The integration of GEDI measurements with spatially-contiguous observations from polar orbiting optical satellite data therefore provides a unique opportunity to produce wall-to-wall maps of forests’ 3D structure. Here, we utilized Visible Infrared Imaging Radiometer Suite (VIIRS) annual metrics data to extrapolate GEDI-derived forest structure attributes into 1-km resolution contiguous maps of tree height (TH), canopy fraction cover (CFC), plant area index (PAI), and foliage height diversity (FHD) for the conterminous US (CONUS). The maps were validated using an independent subset of GEDI data. Validation results for TH (r2 = 0.8; RMSE = 3.35 m), CFC (r2 = 0.79; RMSE = 0.09), PAI (r2 = 0.76; RMSE = 0.41), and FHD (r2 = 0.83; RMSE = 0.25) demonstrated the robustness of VIIRS data for extrapolating GEDI measurements across the nation or even over larger areas. The methodology developed through this study may allow multi-decadal monitoring of changes in multiple forest structural attributes using consistent satellite observations acquired by orbiting and forthcoming VIIRS instruments.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923 ◽  
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

scholarly journals USING AIRBORNE LIDAR DATA FOR ASSESSMENT OF FOREST FIRE FUEL LOAD POTENTIAL

2017 ◽  
Vol IV-4/W4 ◽  
pp. 255-258 ◽  
Author(s):  
M. İnan ◽  
E. Bilici ◽  
A. E. Akay
Keyword(s):  
Forest Fire ◽  
Forest Ecosystems ◽  
Deciduous Forest ◽  
Load Capacity ◽  
Tree Height ◽  
Fire Risk ◽  
Research Area ◽  
Airborne Lidar ◽  
Fuel Load ◽  
Cloud Data

Forest fire incidences are one of the most detrimental disasters that may cause long terms effects on forest ecosystems in many parts of the world. In order to minimize environmental damages of fires on forest ecosystems, the forested areas with high fire risk should be determined so that necessary precaution measurements can be implemented in those areas. Assessment of forest fire fuel load can be used to estimate forest fire risk. In order to estimate fuel load capacity, forestry parameters such as number of trees, tree height, tree diameter, crown diameter, and tree volume should be accurately measured. In recent years, with the advancements in remote sensing technology, it is possible to use airborne LIDAR for data estimation of forestry parameters. In this study, the capabilities of using LIDAR based point cloud data for assessment of the forest fuel load potential was investigated. The research area was chosen in the Istanbul Bentler series of Bahceköy Forest Enterprise Directorate that composed of mixed deciduous forest structure.

scholarly journals Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska

2016 ◽  
Author(s):  
Karina Williams ◽  
Jemma Gornall ◽  
Anna Harper ◽  
Andy Wiltshire ◽  
Debbie Hemming ◽  
...  
Keyword(s):  
Input Parameter ◽  
Model Development ◽  
Crop Productivity ◽  
Crop Model ◽  
Test Model ◽  
Area Index ◽  
Crop Varieties ◽  
Weather And Climate ◽  
Set Up ◽  
The Impact

Abstract. The JULES-crop model (Osborne et al., 2015) is a parameterisation of crops within the Joint UK Land Environment Simulator (JULES), which aims to simulate both the impact of weather and climate on crop productivity and the impact of crop-lands on weather and climate. In this evaluation paper, observations of maize at three FLUXNET sites in Nebraska (US-Ne1, US-Ne2, US-Ne3) are used to test model assumptions and make appropriate input parameter choices. JULES runs are performed for the irrigated sites (US-Ne1 and US-Ne2) both with the crop model switched off (prescribing leaf area index (LAI) and canopy height) and with the crop model switched on. These are compared against GPP and carbon pool FLUXNET observations. We use the results to point to future priorities for model development and describe how our methodology can be adapted to set up model runs for other sites and crop varieties. The implications of our results on the choice of parameters and settings to be used in global runs of JULES-crop are also discussed.

scholarly journals Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

2021 ◽  
Vol 13 (1) ◽  
pp. 705-716
Author(s):  
Qiuji Chen ◽  
Xin Wang ◽  
Mengru Hang ◽  
Jiye Li
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Tree Height ◽  
Mining Area ◽  
Research Area ◽  
Airborne Lidar ◽  
Shaanxi Province ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data

Abstract The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for K-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.

scholarly journals INFLUENCE OF FLIGHT CONFIGURATION USED FOR LiDAR DATA COLLECTION ON INDIVIDUAL TREES DATA EXTRACTION IN FOREST PLANTATIONS

FLORESTA ◽  
2014 ◽  
Vol 44 (2) ◽  
pp. 279 ◽  
Author(s):  
Mauricio Muller ◽  
Ana Paula Baungarten Kersting ◽  
Nelson Yoshihiro Nakajima ◽  
Roberto Tuyoshi Hosokawa ◽  
Nelson Carlos Rosot
Keyword(s):  
Data Collection ◽  
Cost Benefit Analysis ◽  
Tree Height ◽  
Flight Time ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Forest Plantations ◽  
Operational Parameters ◽  
Individual Trees ◽  
The Impact

AbstractIn the last decades, several studies have been conducted aiming to the extraction of forest variables from LiDAR data. Although such studies have indicated great potential, the high cost associated with LiDAR data acquisition process inhibits the technology to become an operational technique for forestry applications. The cost of a LiDAR survey, as for any other data collection techniques, is composed of fixed and variable costs. The variable portion, which can be optimized, is dependent, among other factors, on the number of flight hours. The flight time is mainly dependent on the flight configuration used for the survey. The objective of this paper is to investigate the impact of using different operational parameters on different species of forest plantations, to provide inputs for an adequate cost-benefit analysis. The different configurations are evaluated in terms of the number of individual trees automatically detected, individual height and volume, using the forest inventory as the reference data. The experiments have shown that compatible results are obtained using different configurations with flight time varying by a factor of 3.5 to 10 times. Also, for a given point density, preference should be given to the configuration based on a lower flying height.Keywords: Airborne LiDAR; remote sensing; progressive densification; forest mensuration; operational parameters; tree height; volume. ResumoInfluência da configuração de voo para aquisição de dados LiDAR na extração de dados de árvores individuais em plantios florestais. Nas últimas décadas, vários estudos têm sido realizados visando à utilização dos dados obtidos através da tecnologia LiDAR (Light Detection And Ranging) na obtenção de variáveis florestais. Embora tais estudos indiquem alto potencial do LiDAR para aplicações florestais, o elevado custo do levantamento dificulta sua operacionalização no meio florestal. O custo de um levantamento LiDAR, como em outros tipos de levantamentos, é composto de custos fixos e variáveis. A parcela de custos variáveis, a qual pode ser otimizada, encontra-se associada, dentre outros fatores, ao número de horas de voo. O tempo de voo depende principalmente da configuração de voo utilizada no levantamento. Este artigo visa analisar a influência da utilização de diferentes parâmetros operacionais para diferentes espécies de plantios florestais, objetivando prover insumos para uma adequada análise custo-benefício. As configurações são avaliadas em termos do número de árvores automaticamente identificadas, altura individual e volume, tendo como referência os dados do inventário florestal. Os experimentos realizados demonstraram que resultados comparáveis são obtidos com a utilização de diferentes configurações com tempo de voo variando de um fator de 3,5 a 10 vezes. Observou-se também que para uma dada densidade de pontos deve-se dar preferência à configuração que utilize menor altura de voo.Palavras-chave:      Laser scanner; sensoriamento remoto; parâmetros operacionais; altura; volume.

scholarly journals Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China

Forests ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 746 ◽  
Author(s):  
Jie Zou ◽  
Peng Leng ◽  
Wei Hou ◽  
Peihong Zhong ◽  
Ling Chen ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Stand Density ◽  
Tree Height ◽  
Area Ratio ◽  
Optical Methods ◽  
Larix Gmelinii ◽  
Tree Age ◽  
Site Conditions ◽  
Area Index ◽  
Plant Area Index

Accurate in situ leaf area index (LAI) estimates of forest plots are required to validate currently-used LAI map products. Woody-to-total area ratio ( α ) is a crucial parameter in converting the plant area index estimates of forest plots obtained by optical methods into LAI. Although optical methods for estimating the α of forest canopy have been proposed, their performance has never been assessed. In this study, five Larix gmelinii Rupr. forest plots with contrasting plot characteristics (i.e., tree age, tree height, management activities, stand density, and site conditions) were selected. The performance of two commonly used optical methods, namely, multispectral canopy imager (MCI) and digital hemispherical photography (DHP), in estimating the α of L. gmelinii forest plots was evaluated by using the reference α of the selected forest plots. The reference α of forest plots was measured via destructive method by harvesting two or three representative trees in each plot. Large variations were observed amongst the reference α of the selected forest plots (ranging from 0% to 56%). These α were also highly correlated with the site conditions and management activities in these plots. The effective α ( α e ) or α estimated using the leaf-on and leaf-off periods MCI or DHP images with or without consideration of the clumping effects of canopy element and woody components were 1.57 to 4.63 times the reference α in the five plots. The overestimation of α or α e was mainly caused by the preferential shading of woody components by the shoots in the leaf-on canopy. Accurate α estimates for the L. gmelinii forest plots with errors of less than 20% can be obtained from MCI when the clumping effects of canopy element and woody components are considered in the estimation.

scholarly journals Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map

2020 ◽  
Vol 12 (16) ◽  
pp. 2559 ◽  
Author(s):  
Yuzhen Zhang ◽  
Shunlin Liang
Keyword(s):  
Aboveground Biomass ◽  
Land Surface ◽  
Forest Canopy ◽  
Forest Cover ◽  
Airborne Lidar ◽  
Estimation Methods ◽  
Area Index ◽  
Temperature And Precipitation ◽  
Error Removal ◽  
Forest Aboveground Biomass

Many advanced satellite estimation methods have been developed, but global forest aboveground biomass (AGB) products remain largely uncertain. In this study, we explored data fusion techniques to generate a global forest AGB map for the 2000s at 0.01-degree resolution with improved accuracy by integrating ten existing local or global maps. The error removal and simple averaging algorithm, which is efficient and makes no assumption about the data and associated errors, was proposed to integrate these ten forest AGB maps. We first compiled the global reference AGB from in situ measurements and high-resolution AGB data that were originally derived from field data and airborne lidar data and determined the errors of each forest AGB map at the pixels with corresponding reference AGB values. Based on the errors determined from reference AGB data, the pixel-by-pixel errors associated with each of the ten AGB datasets were estimated from multiple predictors (e.g., leaf area index, forest canopy height, forest cover, land surface elevation, slope, temperature, and precipitation) using the random forest algorithm. The estimated pixel-by-pixel errors were then removed from the corresponding forest AGB datasets, and finally, global forest AGB maps were generated by combining the calibrated existing forest AGB datasets using the simple averaging algorithm. Cross-validation using reference AGB data showed that the accuracy of the fused global forest AGB map had an R-squared of 0.61 and a root mean square error (RMSE) of 53.68 Mg/ha, which is better than the reported accuracies (R-squared of 0.56 and RMSE larger than 80 Mg/ha) in the literature. Intercomparison with previous studies also suggested that the fused AGB estimates were much closer to the reference AGB values. This study attempted to integrate existing forest AGB datasets for generating a global forest AGB map with better accuracy and moved one step forward for our understanding of the global terrestrial carbon cycle by providing improved benchmarks of global forest carbon stocks.

Sign in / Sign up

Export Citation Format

Share Document