An Effective Leaf Area Index Estimation Method for Wheat from UAV-Based Point Cloud Data

Leaf area index (LAI) is one of the most important structural parameters of forestry studies which manifests the ability of the green vegetation interacted with the solar illumination. Classic understanding about LAI is to consider the green canopy as integration of horizontal leaf layers. Since multi-angle remote sensing technique developed, LAI obliged to be deliberated according to the observation geometry. Effective LAI could formulate the leaf-light interaction virtually and precisely. To retrieve the LAI/effective LAI from remotely sensed data therefore becomes a challenge during the past decades. Laser scanning technique can provide accurate surface echoed coordinates with densely scanned intervals. To utilize the density based statistical algorithm for analyzing the voluminous amount of the 3-D points data is one of the subjects of the laser scanning applications. Computational geometry also provides some mature applications for point cloud data (PCD) processing and analysing. In this paper, authors investigated the feasibility of a new application for retrieving the effective LAI of an isolated broad leaf tree. Simplified curvature was calculated for each point in order to remove those non-photosynthetic tissues. Then PCD were discretized into voxel, and clustered by using Gaussian mixture model. Subsequently the area of each cluster was calculated by employing the computational geometry applications. In order to validate our application, we chose an indoor plant to estimate the leaf area, the correlation coefficient between calculation and measurement was 98.28 %. We finally calculated the effective LAI of the tree with 6 × 6 assumed observation directions.

Download Full-text

A Method for Quantifying Understory Leaf Area Index in a Temperate Forest through Combining Small Footprint Full-Waveform and Point Cloud LiDAR Data

Remote Sensing ◽

10.3390/rs13153036 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3036

Author(s):

Jinling Song ◽

Xiao Zhu ◽

Jianbo Qi ◽

Yong Pang ◽

Lei Yang ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Point Cloud ◽

Temperate Forest ◽

Lidar Data ◽

Point Cloud Data ◽

Area Index ◽

Waveform Data ◽

Cloud Data ◽

Full Waveform

Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the three-dimensional structures of vegetation, while full-waveform data contain more detailed information on the interactions between laser pulses and vegetation; both types have been widely used to estimate various forest canopy structural parameters, including leaf area index (LAI). Here, we present a new method for quantifying understory LAI in a temperate forest by combining the advantages of both types of LiDAR data. To achieve this, we first estimated the vertical distribution of the gap probability using point cloud data to automatically determine the height boundary between overstory and understory vegetation at the plot level. We then deconvolved the full-waveform data to remove the blurring effect caused by the system pulse to restore the vertical resolution of the LiDAR system. Subsequently, we decomposed the deconvolved data and integrated the plot-level boundary height to differentiate the waveform components returned from the overstory, understory, and soil layers. Finally, we modified the basic LiDAR equations introducing understory leaf spectral information to quantify the understory LAI. Our results, which were validated against ground-based measurements, show that the new method produced a good estimation of the understory LAI with an R2 of 0.54 and a root-mean-square error (RMSE) of 0.21. Our study demonstrates that the understory LAI can be successfully quantified through the combined use of point cloud and full-waveform LiDAR data.

Download Full-text

UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

Remote Sensing ◽

10.3390/rs13040803 ◽

2021 ◽

Vol 13 (4) ◽

pp. 803

Author(s):

Lingchen Lin ◽

Kunyong Yu ◽

Xiong Yao ◽

Yangbo Deng ◽

Zhenbang Hao ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Point Cloud ◽

Forest Canopy ◽

Estimation Method ◽

Vertical Direction ◽

Estimation Accuracy ◽

3D Point Cloud ◽

Area Index ◽

Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

Download Full-text

Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat

Remote Sensing ◽

10.3390/rs12152378 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2378

Author(s):

Yang Song ◽

Jinfei Wang ◽

Jiali Shang ◽

Chunhua Liao

Keyword(s):

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Point Cloud ◽

Low Cost ◽

Simple Algorithm ◽

Yield Potential ◽

Yield Estimation ◽

Area Index ◽

Cloud Data

Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale.

Download Full-text

Comparison of LiDAR-based Models for True Leaf Area Index and Effective Leaf Area Index Estimation in Young Beech Forests

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun202068030559 ◽

2020 ◽

Vol 68 (3) ◽

pp. 559-566

Author(s):

Zdeněk Patočka ◽

Kateřina Novosadová ◽

Pavel Haninec ◽

Radek Pokorný ◽

Tomáš Mikita ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Regression Models ◽

Canopy Structure ◽

Optical Methods ◽

Distribution Model ◽

Area Index ◽

Index Estimation ◽

Effective Leaf Area Index

The leaf area index (LAI) is one of the most common leaf area and canopy structure quantifiers. Direct LAI measurement and determination of canopy characteristics in larger areas is unrealistic due to the large number of measurements required to create the distribution model. This study compares the regression models for the ALS-based calculation of LAI, where the effective leaf area index (eLAI) determined by optical methods and the LAI determined by the direct destructive method and developed by allometric equations were used as response variables. LiDAR metrics and the laser penetration index (LPI) were used as predictor variables. The regression models of LPI and eLAI dependency and the LiDAR metrics and eLAI dependency showed coefficients of determination (R2) of 0.75 and 0.92, respectively; the advantage of using LiDAR metrics for more accurate modelling is demonstrated. The model for true LAI estimation reached a R2 of 0.88.

Download Full-text

Leaf area index estimation using top-of-canopy airborne RGB images

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2020.102282 ◽

2021 ◽

Vol 96 ◽

pp. 102282

Author(s):

Rahul Raj ◽

Jeffrey P. Walker ◽

Rohit Pingale ◽

Rohit Nandan ◽

Balaji Naik ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Area Index ◽

Rgb Images ◽

Index Estimation

Download Full-text

Drone-Based Sensing for Leaf Area Index Estimation of Citrus Canopy

Lecture Notes in Civil Engineering - Proceedings of UASG 2019 ◽

10.1007/978-3-030-37393-1_9 ◽

2020 ◽

pp. 79-89 ◽

Cited By ~ 1

Author(s):

Rahul Raj ◽

Saurabh Suradhaniwar ◽

Rohit Nandan ◽

Adinarayana Jagarlapudi ◽

Jeffrey Walker

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Area Index ◽

Index Estimation

Download Full-text

Leaf area index estimation in a pine plantation with LAI-2000 under direct sunlight conditions: relationship with inventory and hydrologic variables

Forest Systems ◽

10.5424/fs/2011201-10009 ◽

2011 ◽

Vol 20 (1) ◽

pp. 108 ◽

Cited By ~ 2

Author(s):

A. Molina ◽

A. D. Del-Campo

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Pine Plantation ◽

Direct Sunlight ◽

Area Index ◽

Index Estimation

Download Full-text

Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

Remote Sensing ◽

10.3390/rs12010050 ◽

2019 ◽

Vol 12 (1) ◽

pp. 50 ◽

Cited By ~ 2

Author(s):

Mahyar Aboutalebi ◽

Alfonso F. Torres-Rua ◽

Mac McKee ◽

William P. Kustas ◽

Hector Nieto ◽

...

Keyword(s):

Remote Sensing ◽

Surface Area ◽

Point Cloud ◽

Spatial Information ◽

Point Cloud Data ◽

Area Index ◽

Cloud Data ◽

Fractional Cover ◽

Spatially Distributed

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

Download Full-text