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Relationship Between Leaf Area Index Lai Estimated By Terrestrial Lidar And Remotely Sensed Vegetation Indices As A Proxy To Forest Carbon Sequestration
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Book Synopsis RELATIONSHIP BETWEEN LEAF AREA INDEX (LAI) ESTIMATED BY TERRESTRIAL LIDAR AND REMOTELY SENSED VEGETATION INDICES AS A PROXY TO FOREST CARBON SEQUESTRATION by : Nayani Thanuja Ilangakoon
Download or read book RELATIONSHIP BETWEEN LEAF AREA INDEX (LAI) ESTIMATED BY TERRESTRIAL LIDAR AND REMOTELY SENSED VEGETATION INDICES AS A PROXY TO FOREST CARBON SEQUESTRATION written by Nayani Thanuja Ilangakoon and published by . This book was released on 2014 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leaf area index (LAI) is an important indicator of ecosystem conditions and an important key biophysical variable to many ecosystem models. The LAI in this study was measured by Leica ScanStation C 10 Terrestrial Laser Scanner (TLS) and a hand-held Li-Cor LAI-2200 Plant Canopy Analyzer for understanding differences derived from the two sensors. A total of six different LAI estimates were generated using different methods for the comparisons. The results suggested that there was a reasonable agreement (i.e., the correlations r > 0.50) considering a total of 30 plots and limited land cover types sampled. The predicted LAI from spectral vegetation indices including WDVI, DVI, NDVI, SAVI, and PVI3 which were derived from Landsat TM imagery were used to identify statistical relationships and for the development of the Bayesian inference model. The Bayesian Linear Regression (BLR) approach was used to scale up LAI estimates and to produce continuous field surfaces for the Oak Openings Region in NW Ohio. The results from the BLR provided details about the parameter uncertainties but also insight about the potential that different LAIs can be used to predict foliage that has been adjusted by removing the wooden biomass with reasonable accuracy. For instance, the modeled residuals associated with the LAI estimates from TLS orthographic projection that consider only foliage had the lowest overall model uncertainty with lowest error and residual dispersion range among the six spatial LAI estimates. The deviation from the mean LAI prediction map derived from the six estimates hinted that sparse and open areas that relate to vegetation structure were associated with the highest error. However, although in many studies TLS has been shown to hold a great potential for quantifying vegetation structure, in this study the quantified relationship between LAI and the vegetation indices did not yield any statistical relationship that needs to be further explore.
Book Synopsis Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters by : Francisco Javier García-Haro
Download or read book Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters written by Francisco Javier García-Haro and published by MDPI. This book was released on 2019-09-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.
Book Synopsis LiDAR and WorldView-2 Satellite Data for Leaf Area Index Estimation in the Boreal Forest by : Graham Wesley Pope
Download or read book LiDAR and WorldView-2 Satellite Data for Leaf Area Index Estimation in the Boreal Forest written by Graham Wesley Pope and published by . This book was released on 2012 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers. This thesis focused on estimating LAI from: i) height and density metrics derived from Light Detection and Ranging (LiDAR); ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and iii) a combination of these two remote sensing technologies. In situ measurements of LAI were calculated from digital hemispherical photographs (DHPs) and remotely sensed variables were derived from low density LiDAR and high resolution WorldView-2 data. Multiple Linear Regression (MLR) models were created using these variables, allowing forest-wide prediction surfaces to be created. Results from these analyses demonstrated: i) moderate explanatory power (i.e., R2 = 0.54) for LiDAR models incorporating metrics that have proven to be related to canopy structure; ii) no relationship when using SVIs; and iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with low ranges of LAI for model calibration. On the other hand, it was anticipated that traditional SVI relationships to LAI would be present with WorldView-2 data, a result that is not easily explained. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at a high spatial resolution for decision makers in the forestry community.
Book Synopsis Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters by : Hongliang Fang
Download or read book Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters written by Hongliang Fang and published by . This book was released on 2019 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.
Book Synopsis Using LiDAR and Normalized Difference Vegetation Index to Remotely Determine LAI and Percent Canopy Cover at Varying Scales by : Alicia Marie Rutledge Griffin
Download or read book Using LiDAR and Normalized Difference Vegetation Index to Remotely Determine LAI and Percent Canopy Cover at Varying Scales written by Alicia Marie Rutledge Griffin and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of airborne LiDAR (Light Detection and Ranging) as a direct method to evaluate forest canopy parameters is vital in addressing both forest management and ecological concerns. The overall goal of this study was to develop the use of airborne LiDAR in evaluating canopy parameters such as percent canopy cover (PCC) and leaf area index (LAI) for mixed pine and hardwood forests (primarily loblolly pine, Pinus taeda, forests) of the southeastern United States. More specific objectives were to: (1) Develop scanning LiDAR and multispectral imagery methods to estimate PCC and LAI over both hardwood and coniferous forests; (2) investigate whether a LiDAR and normalized difference vegetation index (NDVI) data fusion through linear regression improve estimates of these forest canopy characteristics; (3) generate maps of PCC and LAI for the study region, and (4) compare local scale LiDAR-derived PCC and regional scale MODIS-based PCC and investigate the relationship. Scanning LiDAR data was used to derive local scale PCC estimates, and TreeVaW, a LiDAR software application, was used to locate individual trees to derive an estimate of plot-level PCC. A canopy height model (CHM) was created from the LiDAR dataset and used to determine tree heights per plot. QuickBird multispectral imagery was used to calculate the NDVI for the study area. LiDAR- and NDVI-derived estimates of plot-level PCC and LAI were compared to field observations for 53 plots over 47 square kilometers. Linear regression analysis resulted in models explaining 84% and 78% of the variability associated with PCC and LAI, respectively. For these models to be of use in future studies, LiDAR point density must be 2.5 m. The relationship between regional scale PCC and local scale PCC was investigated by resizing the local scale LiDAR-derived PCC map to lower resolution levels, then determining a regression model relating MODIS data to the local values of PCC. The results from this comparison showed that MODIS PCC data is not very accurate at local scales. The methods discussed in this paper show great potential for improving the speed and accuracy of ecological studies and forest management.
Book Synopsis The Normalized Difference Vegetation Index by : Nathalie Pettorelli
Download or read book The Normalized Difference Vegetation Index written by Nathalie Pettorelli and published by Oxford University Press, USA. This book was released on 2013-10 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a coherent review of NDVI including its origin, its availability, its associated advantages and disadvantages, and its possible applications in ecology, environmental monitoring, wildlife management, and conservation.
Book Synopsis Photosynthesis of Leaf Canopies by : Cornelis Teunis de Wit
Download or read book Photosynthesis of Leaf Canopies written by Cornelis Teunis de Wit and published by . This book was released on 1965 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of a procedure to calculate the effect of certain environmental factors on the rate of photo-synthesis imposed mainly geometrical problems, which were solved in such a way that the actual calculation could be carried out by means of a computer. The calculation procedures have been used to study the. relative importance of the variables under various conditions. The results for a standard set of conditions, have been summarized in order to make it possible to estimate the daily photosynthesis at any time and place for a wide range of photosynthesis functions without a computer.
Book Synopsis Leaf Area Index in Riparian Forests by : Travis Axe
Download or read book Leaf Area Index in Riparian Forests written by Travis Axe and published by . This book was released on 2018 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote Sensing technology has expanded tremendously over the past few decades and has created value when integrated into environmental concepts and practices. But there is unmet potential for bolstering ecosystem services and creating additional value for society. Impediments such as the cost and complexity of the technology, and the difficulty of readily assimilating it into a decision-making process, must be overcome to facilitate broader use. This study demonstrates the capacity for an emerging and inexpensive remote sensing technology to estimate an important ecological indicator and then discusses the broader implications for societal value. First, we compare the estimation of effective leaf area index (LAI[subscript]e) of heterogeneous riparian forests between two remote sensing methodologies: discrete-return Airborne Laser Scanning (ALS) and airborne structure-from-motion (SfM). LAI[subscript]e is an indispensable component of process-based ecological research and can be associated with a variety of ecosystem services. SfM data acquisition is more frequent and inexpensive compared to ALS, but its capabilities less explored. Two point-cloud data files for each technology were evaluated using respective field-measured reference data. SfM shows promise: a combinational linear regression revealed that the distribution elevation values of upper-canopy point returns and the elevation values representing mid and max stand-level, when paired grey-level co-occurrence matrix (GLCM), can estimate LAI[subscript]E (r2 = 0.62). Although it did not perform as well as ALS, which has more data representing light attenuation behavior (r2 = 0.66), SfM as an alternative methodology for remotely sensing ecological data has demonstrated potential and warrants further investigation. Next, we discuss how remotely sensed ecological information like LAI[subscript]e can create value for society. We provide a primer on the ways in which society values the environment and how these values may be perceived and quantified, and the dynamic behavior that exists between them. We then introduce a major policy tool used in quantifying these values, benefit cost analysis, and why it is useful for framing environmental issues and how remote sensing can contribute to its outcomes. Finally, we review remote sensing applications used in increasing our understanding of society’s interaction with the environment and existing opportunities for value addition.
Book Synopsis Multi-scale Mapping and Accuracy Assessment of Leaf Area Index for Vegetation Study in Southern Illinois by : Kushendra Narayan Shah
Download or read book Multi-scale Mapping and Accuracy Assessment of Leaf Area Index for Vegetation Study in Southern Illinois written by Kushendra Narayan Shah and published by . This book was released on 2013 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing interest of modeling global carbon cycling during the past two decades has driven this research to map leaf area index (LAI) at multiple spatial resolutions by combining LAI field observations with various sensor images at local, regional, and global scale. This is due to its important role in process based models that are used to predict carbon sequestration of terrestrial ecosystems. Although a substantial research has been conducted, there are still many challenges in this area. One of the challenges is that various images with spatial resolutions varying from few meters to several hundred meters and even to 1 km have been used. However, a method that can be used to collect LAI field measurements and further conduct multiple spatial resolution mapping and accuracy assessment of LAI is not available. In this study, a pilot study in a complex landscape located in the Southern Illinois was carried out to map LAI by combining field observations and remotely sensed images. Multi-scale mapping and accuracy assessment of LAI using aerial photo, Landsat TM and MODIS images were explored by developing a multi-scale sampling design. The results showed that the sampling design could be used to collect LAI observations to create LAI products at various spatial resolutions and further conduct accuracy assessment. It was also found that the TM derived LAI maps at the original and aggregated spatial resolutions successfully characterized the heterogeneous landscape and captured the spatial variability of LAI and were more accurate than those from the aerial photo and MODIS. The aerial photo derived models led to not only over- and under-estimation, but also pixilated maps of LAI. The MODIS derived LAI maps had an acceptable accuracy at various spatial resolutions and are applicable to mapping LAI at regional and global scale. Thus, this study overcame some of the significant gaps in this field.
Book Synopsis Remote Sensing of Green Leaf Area Index in Maize and Soybean by : Anthony Lawrence Nguy-Robertson
Download or read book Remote Sensing of Green Leaf Area Index in Maize and Soybean written by Anthony Lawrence Nguy-Robertson and published by . This book was released on 2013 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation seeks to explore alternative methodologies for estimating green leaf area index (LAI) and crop developmental stages. Specifically this research [1] developed an approach for creating a Moderate Resolution Imaging Spectroradiometer (MODIS) high spatial resolution product for estimating green LAI on the base of data collected using two different close-range sensors. It was determined that the vegetation indices (VIs) Wide Dynamic Range Vegetation Index (WDRVI) and Enhanced Vegetation Index 2 (EVI2) were capable of accurate estimation of green LAI from MODIS 250 m data using models developed from hyperspectral (RMSE
Book Synopsis Quantifying Forest Structure Parameters and Their Changes from LiDAR Data and Satellite Imagery in the Sierra Nevada by : Qin Ma
Download or read book Quantifying Forest Structure Parameters and Their Changes from LiDAR Data and Satellite Imagery in the Sierra Nevada written by Qin Ma and published by . This book was released on 2018 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sierra Nevada forests have provided many economic benefits and ecological services to people in California, and the rest of the world. Dramatic changes are occurring in the forests due to climate warming and long-term fire suppression. Accurate mapping and monitoring are increasingly important to understand and manage the forests. Light Detection and Range (LiDAR), an active remote sensing technique, can penetrate the canopy and provide three-dimensional estimates of forest structures. LiDAR-based forest structural estimation has been demonstrated to be more efficient than field measurements and more accurate than those from passive remote sensing, like satellite imagery. Research in this dissertation aims at mapping and monitoring structural changes in Sierra Nevada forests by taking the advantages of LiDAR. We first evaluated LiDAR and fine resolution imagery-derived canopy cover estimates using different algorithms and data acquisition parameters. We suggested that LiDAR data obtained at 1 point/m2 with a scan angle smaller than 12°were sufficient for accurate canopy cover estimation in the Sierra Nevada mix-conifer forests. Fine resolution imagery is suitable for canopy cover estimation in forests with median density but may over or underestimate canopy cover in extremely coarse or dense forests. Then, a new LiDAR-based strategy was proposed to quantify tree growth and competition at individual tree and forest stand levels. Using this strategy, we illustrated how tree growth in two Sierra Nevada forests responded to tree competition, original tree sizes, forest density, and topography conditions; and identified that the tree volume growth was determined by the original tree sizes and competitions, but tree height and crown area growth were mostly influenced by water and space availability. Then, we calculated the forest biomass disturbance in a Sierra Nevada forest induced by fuel treatments using bi-temporal LiDAR data and field measurements. Using these results as references, we found that Landsat imagery-derived vegetation indices were suitable for quantifying canopy cover changes and biomass disturbances in forests with median density. Large uncertainties existed in applying the vegetation indices to quantify disturbance in extremely dense forests or forests only disturbed in the understory. Last, we assessed vegetation losses caused by the American Fire in 2013 using a new LiDAR point based method. This method was able to quantify fire-induced forest structure changes in basal area and leaf area index with lower uncertainties, compared with traditional LiDAR metrics and satellite imagery-derived vegetation indices. The studies presented in this dissertation can provide guidance for forest management in the Sierra Nevada, and potentially serve as useful tools for forest structural change monitoring in the rest of the world.
Book Synopsis Vegetation Monitoring by : Caryl L. Elzinga
Download or read book Vegetation Monitoring written by Caryl L. Elzinga and published by DIANE Publishing. This book was released on 1998-05 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This annotated bibliography documents literature addressing the design and implementation of vegetation monitoring. It provides resources managers, ecologists, and scientists access to the great volume of literature addressing many aspects of vegetation monitoring: planning and objective setting, choosing vegetation attributes to measure, sampling design, sampling methods, statistical and graphical analysis, and communication of results. Over half of the 1400 references have been annotated. Keywords pertaining to the type of monitoring or method are included with each bibliographic entry. Keyword index.
Book Synopsis Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales by : Genesis T. Yengoh
Download or read book Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales written by Genesis T. Yengoh and published by Springer. This book was released on 2015-11-11 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report examines the scientific basis for the use of remotely sensed data, particularly Normalized Difference Vegetation Index (NDVI), primarily for the assessment of land degradation at different scales and for a range of applications, including resilience of agro-ecosystems. Evidence is drawn from a wide range of investigations, primarily from the scientific peer-reviewed literature but also non-journal sources. The literature review has been corroborated by interviews with leading specialists in the field. The report reviews the use of NDVI for a range of themes related to land degradation, including land cover change, drought monitoring and early warning systems, desertification processes, greening trends, soil erosion and salinization, vegetation burning and recovery after fire, biodiversity loss, and soil carbon. This SpringerBrief also discusses the limits of the use of NDVI for land degradation assessment and potential for future directions of use. A substantial body of peer-reviewed research lends unequivocal support for the use of coarse-resolution time series of NDVI data for studying vegetation dynamics at global, continental and sub-continental levels. There is compelling evidence that these data are highly correlated with biophysically meaningful vegetation characteristics such as photosynthetic capacity and primary production that are closely related to land degradation and to agroecosystem resilience.
Book Synopsis Improving the Relationship Between Remotely Sensed Vegetation Indices and Crop LAI by : Yanghui Kang
Download or read book Improving the Relationship Between Remotely Sensed Vegetation Indices and Crop LAI written by Yanghui Kang and published by . This book was released on 2013 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Remote Sensing of Drought by : Brian D. Wardlow
Download or read book Remote Sensing of Drought written by Brian D. Wardlow and published by CRC Press. This book was released on 2012-04-24 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote Sensing of Drought: Innovative Monitoring Approaches presents emerging remote sensing-based tools and techniques that can be applied to operational drought monitoring and early warning around the world. The first book to focus on remote sensing and drought monitoring, it brings together a wealth of information that has been scattered throughout the literature and across many disciplines. Featuring contributions by leading scientists, it assembles a cross-section of globally applicable techniques that are currently operational or have potential to be operational in the near future. The book explores a range of applications for monitoring four critical components of the hydrological cycle related to drought: vegetation health, evapotranspiration, soil moisture and groundwater, and precipitation. These applications use remotely sensed optical, thermal, microwave, radar, and gravity data from instruments such as AMSR-E, GOES, GRACE, MERIS, MODIS, and Landsat and implement several advanced modeling and data assimilation techniques. Examples show how to integrate this information into routine drought products. The book also examines the role of satellite remote sensing within traditional drought monitoring, as well as current challenges and future prospects. Improving drought monitoring is becoming increasingly important in addressing a wide range of societal issues, from food security and water scarcity to human health, ecosystem services, and energy production. This unique book surveys innovative remote sensing approaches to provide you with new perspectives on large-area drought monitoring and early warning.
Author :National Academies of Sciences, Engineering, and Medicine Publisher :National Academies Press ISBN 13 :0309492432 Total Pages :29 pages Book Rating :4.3/5 (94 download)
Book Synopsis Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space by : National Academies of Sciences, Engineering, and Medicine
Download or read book Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-06-18 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: We live on a dynamic Earth shaped by both natural processes and the impacts of humans on their environment. It is in our collective interest to observe and understand our planet, and to predict future behavior to the extent possible, in order to effectively manage resources, successfully respond to threats from natural and human-induced environmental change, and capitalize on the opportunities â€" social, economic, security, and more â€" that such knowledge can bring. By continuously monitoring and exploring Earth, developing a deep understanding of its evolving behavior, and characterizing the processes that shape and reshape the environment in which we live, we not only advance knowledge and basic discovery about our planet, but we further develop the foundation upon which benefits to society are built. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (National Academies Press, 2018) provides detailed guidance on how relevant federal agencies can ensure that the United States receives the maximum benefit from its investments in Earth observations from space, while operating within realistic cost constraints. This short booklet, designed to be accessible to the general public, provides a summary of the key ideas and recommendations from the full decadal survey report.
Book Synopsis A Voxel Matching Method for Effective Leaf Area Index Estimation in Temperate Deciduous Forests from Leaf-on and Leaf-off Airborne LiDAR Data by : Zhu, Xi
Download or read book A Voxel Matching Method for Effective Leaf Area Index Estimation in Temperate Deciduous Forests from Leaf-on and Leaf-off Airborne LiDAR Data written by Zhu, Xi and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The quantification of leaf area index (LAI) is essential for modeling the interaction between atmosphere and biosphere. The airborne LiDAR has emerged as an effective tool for mapping plant area index (PAI) in a landscape consisting of both woody and leaf materials. However, the discrimination between woody and leaf materials and the estimation of effective LAI (eLAI) have, to date, rarely been studied at landscape scale. We applied a voxel matching algorithm to estimate eLAI of deciduous forests using simulated and field LiDAR data under leaf-on and leaf-off conditions. We classified LiDAR points as either a leaf or a woody hit on leaf-on LiDAR data by matching the point with leaf-off data. We compared the eLAI result of our voxel matching algorithm against the subtraction method, where the leaf-off effective woody area index (eWAI) is subtracted from the effective leaf-on PAI (ePAI). Our results, which were validated against terrestrial LiDAR derived eLAI, showed that the voxel matching method, with an optimal voxel size of 0.1 m, produced an unbiased estimation of terrestrial LiDAR derived eLAI with an R2 of 0.70 and an RMSE of 0.41 (RRMSE: 20.1%). The subtraction method, however, yielded an R2 of 0.62 and an RMSE of 1.02 (RRMSE: 50.1%) with a significant underestimation of 0.94. Reassuringly, the same outcome was observed using a simulated dataset. In addition, we evaluated the performance of 96 LiDAR metrics under leaf-on conditions for eLAI prediction using a statistical model. Based on the importance scores derived from the random forest regression, nine of the 96 leaf-on LiDAR metrics were selected. Cross-validation showed that eLAI could be predicted using these metrics under leaf-on conditions with an R2 of 0.73 and an RMSE of 0.27 (RRMSE: 17.4%). The voxel matching method yielded a slightly lower accuracy (R2: 0.70, RMSE:0.41, RRMSE: 20.1%) than the statistical model. We, therefore, suggest that the voxel matching method offers a new opportunity for the estimating eLAI and other ecological applications that require the classification between leaf and woody materials using airborne LiDAR data. It potentially allows transferability to different sites and flight campaigns
