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Estimation of LAI with the LiDAR Technology: A Review
Leaf area index (LAI) is an important vegetation parameter. Active light detection and ranging (LiDAR) technology has been widely used to estimate vegetation LAI. In this study, LiDAR technology, LAI retrieval and validation methods, and impact factors are reviewed. First, the paper introduces types of LiDAR systems and LiDAR data preprocessing methods. After introducing the application of different LiDAR systems, LAI retrieval methods are described. Subsequently, the review discusses various LiDAR LAI validation schemes and limitations in LiDAR LAI validation. Finally, factors affecting LAI estimation are analyzed. The review presents that LAI is mainly estimated from LiDAR data by means of the correlation with the gap fraction and contact frequency, and also from the regression of forest biophysical parameters derived from LiDAR. Terrestrial laser scanning (TLS) can be used to effectively estimate the LAI and vertical foliage profile (VFP) within plots, but this method is affected by clumping, occlusion, voxel size, and woody material. Airborne laser scanning (ALS) covers relatively large areas in a spatially contiguous manner. However, the capability of describing the within-canopy structure is limited, and the accuracy of LAI estimation with ALS is affected by the height threshold and sampling size, and types of return. Spaceborne laser scanning (SLS) provides the global LAI and VFP, and the accuracy of estimation is affected by the footprint size and topography. The use of LiDAR instruments for the retrieval of the LAI and VFP has increased; however, current LiDAR LAI validation studies are mostly performed at local scales. Future research should explore new methods to invert LAI and VFP from LiDAR and enhance the quantitative analysis and large-scale validation of the parameters.
Journal Article
Full of fall
April Pulley Sayre explores the transformation trees undergo in fall. This book explains the leaves' initial change from green to red, yellow, and orange, the shedding of the leaves, and the leaves crumbling as winter approaches. Additional information explains the science behind this process.
Book
Simultaneous detection of carbendazim and pendimethalin residues using a compact Raman spectrometer
Surface-enhanced Raman spectroscopy (SERS) exhibits significant latent capacity in the prompt and accurate detection of minute concentrations of pesticides. In this study, carbendazim and pendimethalin were simultaneously detected within tobacco foliage by utilizing a PS@Ag composite substrate coupled with a compact Raman spectrometer. The detection limits for carbendazim is 0.1 mg/kg, and for pendimethalin, it is 5 mg/kg. The recovery rates for carbendazim within tobacco foliage ranged from 85.2% to 112.12%. And the recovery rates for pendimethalin ranged from 90.38% to 113.42%. The combination of the PS@Ag composite substrate in conjunction with a Raman spectrometer provides a highly effective means for the concurrent identification of pesticides in tobacco foliage.
Journal Article
Variations in leaf economics spectrum traits for an evergreen coniferous species
Many leaf traits strongly vary with tree size and environmental factors, but the importance of these factors to intraspecific variations of leaf traits in forest trees has rarely been simultaneously evaluated. We measured needle longevity and specific leaf area (SLA) and nitrogen (N) content of every needle age (0‐ to 4‐year old) for 65 individuals with 0.3–100 cm diameter at breast height (DBH) for an evergreen coniferous species, Pinus koraiensis Sieb. et Zucc., in Northeast China. We simultaneously evaluated the effects of tree size (DBH or tree height) and environment factors (light intensity, soil N content and water availability) on the needle longevity, SLA, foliage N content as well as the slopes of regressions of SLA and foliage N content against needle age. All of the studied leaf traits and slopes of regressions of SLA and foliage N content against needle age were significantly related to tree size. Tree height had a greater impact on SLA and area‐based leaf N content (Narea), whereas DBH was more important for needle longevity and mass‐based leaf N content (Nmass). The environment variables, light intensity, soil N content and water availability, were rather minor factors for trait variations compared with tree size. Significant influence of light intensity was found only on needle longevity, and soil N and water availability had no effects on the leaf traits. Our study clearly showed that tree size is an important driver of intraspecific variations in the key leaf traits of P. koraiensis in a natural forest. We also emphasize the importance of DBH or tree height varies depending on leaf traits, suggesting various mechanisms of size effects on the intraspecific variations in leaf traits. We suggest that ecological significance of leaf trait variations needs reconsideration incorporating tree size effect. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article.
Journal Article
Design and Control of Autonomous Rover for Foliage Navigation
Implementation of UGV in civilian applications is getting trendy and normalized due to its solid achievement in executing mission under harsh environments. The UGV foliage environment-oriented applications even attracted the people’s attention in correspond sectors to ease their tasks with guaranteed performance. Given this, the development of the autonomous rover from sensors and control aspects with the consideration of foliage environment interest people to enhance its functionality. To ensure the sensor is not easily affected by the environment factor, the radar and the camera are selected to provide the environment perception to the rover. The approach for a linear Proportional-Derivative (PD) controller with the dynamic modelling of the UGV is also discussed in this paper. The study shows preliminary results that are working.
Journal Article
Deposition and water repelling of temperature-responsive nanopesticides on leaves
Pesticides are widely used to increase agricultural productivity, however, weak adhesion and deposition lead to low efficient utilization. Herein, we prepare a nanopesticide formulation (tebuconazole nanopesticides) which is leaf-adhesive, and water-dispersed via a rapid nanoparticle precipitation method, flash nanoprecipitation, using temperature-responsive copolymers poly-(2-(dimethylamino)ethylmethylacrylate)-
b
-poly(ε-caprolactone) as the carrier. Compared with commercial suspensions, the encapsulation by the polymer improves the deposition of TEB, and the contact angle on foliage is lowered by 40.0°. Due to the small size and strong van der Waals interactions, the anti-washing efficiency of TEB NPs is increased by 37% in contrast to commercial ones. Finally, the acute toxicity of TEB NPs to zebrafish shows a more than 25-fold reduction as compared to commercial formulation indicating good biocompatibility of the nanopesticides. This work is expected to enhance pesticide droplet deposition and adhesion, maximize the use of pesticides, tackling one of the application challenges of pesticides.
Weak adhesion is a common hindrance to efficient utilization of pesticides in agricultural applications. Here, authors demonstrate leaf-adhesive tebuconazole nanopesticides which can be water-dispersed via flash nanoprecipitation using temperature-responsive copolymers PDMAEMA-b-PCL as the carrier.
Journal Article
The Detection Method of Potato Foliage Diseases in Complex Background Based on Instance Segmentation and Semantic Segmentation
Potato early blight and late blight are devastating diseases that affect potato planting and production. Thus, precise diagnosis of the diseases is critical in treatment application and management of potato farm. However, traditional computer vision technology and pattern recognition methods have certain limitations in the detection of crop diseases. In recent years, the development of deep learning technology and convolutional neural networks has provided new solutions for the rapid and accurate detection of crop diseases. In this study, an integrated framework that combines instance segmentation model, classification model, and semantic segmentation model was devised to realize the segmentation and detection of potato foliage diseases in complex backgrounds. In the first stage, Mask R-CNN was adopted to segment potato leaves in complex backgrounds. In the second stage, VGG16, ResNet50, and InceptionV3 classification models were employed to classify potato leaves. In the third stage, UNet, PSPNet, and DeepLabV3+ semantic segmentation models were applied to divide potato leaves. Finally, the three-stage models were combined to segment and detect the potato leaf diseases. According to the experimental results, the average precision (AP) obtained by the Mask R-CNN network in the first stage was 81.87%, and the precision was 97.13%. At the same time, the accuracy of the classification model in the second stage was 95.33%. The mean intersection over union (MIoU) of the semantic segmentation model in the third stage was 89.91%, and the mean pixel accuracy (MPA) was 94.24%. In short, it not only provides a new model framework for the identification and detection of potato foliage diseases in natural environment, but also lays a theoretical basis for potato disease assessment and classification.
Journal Article
Nanoparticles in Plants: Uptake, Transport and Physiological Activity in Leaf and Root
Due to their unique characteristics, nanoparticles are increasingly used in agricultural production through foliage spraying and soil application. The use of nanoparticles can improve the efficiency of agricultural chemicals and reduce the pollution caused by the use of agricultural chemicals. However, introducing nanoparticles into agricultural production may pose risks to the environment, food and even human health. Therefore, it is crucial to pay attention to the absorption migration, and transformation in crops, and to the interaction with higher plants and plant toxicity of nanoparticles in agriculture. Research shows that nanoparticles can be absorbed by plants and have an impact on plant physiological activities, but the absorption and transport mechanism of nanoparticles is still unclear. This paper summarizes the research progress of the absorption and transportation of nanoparticles in plants, especially the effect of size, surface charge and chemical composition of nanoparticle on the absorption and transportation in leaf and root through different ways. This paper also reviews the impact of nanoparticles on plant physiological activity. The content of the paper is helpful to guide the rational application of nanoparticles in agricultural production and ensure the sustainability of nanoparticles in agricultural production.
Journal Article