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Evaluation of Multi-Platform LiDAR-Based Leaf Area Index Estimates Over Row Crops
thesisposted on 09.03.2021, 00:23 by Behrokh Nazeri
Leaf Area Index (LAI) is an important variable for both for characterizing plant canopy and as an input to many crop models. It is a dimensionless quantity broadly defined as the total one-sided leaf area per unit ground area, and is estimated over agriculture row crops by both direct and indirect methods. Direct methods, which involve destructive sampling, are laborious and time-consuming, while indirect methods such as remote sensing-based approaches have multiple sources of uncertainty. LiDAR (Light Detection and Ranging) remotely sensed data acquired from manned aircraft and UAVs’ have been investigated to estimate LAI based on physical/geometric features such as canopy gap fraction. High-resolution point cloud data acquired with a laser scanner from any platform, including terrestrial laser scanning and mobile mapping systems, contain random noise and outliers. Therefore, outlier detection in LiDAR data is often useful prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, description of plant complexity, and local point densities, which are crop dependent. This dissertation first explores the effectiveness of using LiDAR data to estimate LAI for row crop plants at multiple times during the growing season from both a wheeled vehicle and an Unmanned Aerial Vehicle (UAV). Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data and ground reference obtained from an in-field plant canopy analyzer and leaf area derived from destructive sampling. LAI estimates obtained from support vector regression (SVR) models with a radial basis function (RBF) kernel developed using the wheel-based LiDAR system and UAVs are promising, based on the value of the coefficient of determination (R2) and root mean squared error (RMSE) of the residuals.
This dissertation also investigates approaches to minimize the impact of outliers on discrete return LiDAR acquired over crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform. Two methods are explored to detect and remove the outliers from the plant datasets. The first is based on surface fitting to noisy point cloud data based on normal and curvature estimation in a local neighborhood. The second utilizes the deep learning framework PointCleanNet. Both methods are applied to individual plants and field-based datasets. To evaluate the method, an F-score and LAI are calculated both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust to changes in point densities, level of noise, and shapes. Also, the predicted LAI was improved for the wheel-based vehicle data based on the R2 value and RMSE of residuals.
The quality of the extracted features depends on the point density and laser penetration of the canopy. Extracting appropriate features is a critical step to have accurate prediction models. Deep learning frameworks are increasingly being used in remote sensing applications. In the last objective of this study, a feature extraction approach is investigated for encoding LiDAR data acquired by UAV platforms multiple times during the growing season over sorghum and maize plant breeding experiments. LAI estimates obtained with these inputs are used to develop support vector regression (SVR) models using plant canopy analyzer data as the ground reference. Results are compared to models based on estimates from physically-based features and evaluated in terms of the coefficient determination (R2). The effects of experimental conditions, including flying height, sensor characteristics, and crop type, are also investigated relative to the estimates of LAI.