An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data

Gao, Ting; Gao, Zhong‐Hua; Sun, Bin; Qin, Pengyao; Li, Yifu; Yan, Ziyu

doi:10.3390/rs14174317

Cited by 14 publications

(6 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In precision agriculture, UAVs, along with several technologies, such as sensors and companion computers, are used to estimate the characteristics of the local culture. They can determine culture vigor using multispectral or hyperspectral cameras [30], potential local water stress using thermal cameras [31], pest and disease severity using computer-vision techniques [32], or by reconstructing three-dimensional maps using LiDAR (light detection and ranging) sensors to estimate the density of local vegetation [33], among others. For instance, precision olive growing represents an essential agricultural management method for the countries of southern Europe that aims to maximize the yield of olive groves, offering benefits from productive, qualitative and environmental points of view.…”

Section: Uavs Applied To Precision Agriculturementioning

confidence: 99%

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

et al. 2023

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.

show abstract

Section: Uavs Applied To Precision Agriculturementioning

confidence: 99%

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The spurious local maxima detected in the CHM are eliminated by the application of a smoothing filter, which will increase tree detection accuracy [ 64 ]. Voronoi tessellation-based algorithm is particularly suitable for dense areas of conifer [ 65 ] and broadleaf forests [ 66 ]. The Voronoi tessellation algorithm was considered suitable for our study area due to the dense canopy of the old larch plantation.…”

Section: Review Of Literaturementioning

confidence: 99%

UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes

Karthigesu,

Owari,

Tsuyuki

et al. 2023

Sensors

View full text Add to dashboard Cite

Old plantations are iconic sites, and estimating stand parameters is crucial for valuation and management. This study aimed to estimate stand parameters of a 115-year-old Japanese larch (Larix kaempferi (Lamb.) Carrière) plantation at the University of Tokyo Hokkaido Forest (UTHF) in central Hokkaido, northern Japan, using unmanned aerial vehicle (UAV) photogrammetry. High-resolution RGB imagery was collected using a DJI Matrice 300 real-time kinematic (RTK) at altitudes of 80 and 120 m. Structure from motion (SfM) technology was applied to generate 3D point clouds and orthomosaics. We used different filtering methods, search radii, and window sizes for individual tree detection (ITD), and tree height (TH) and crown area (CA) were estimated from a canopy height model (CHM). Additionally, a freely available shiny R package (SRP) and manually digitalized CA were used. A multiple linear regression (MLR) model was used to estimate the diameter at breast height (DBH), stem volume (V), and carbon stock (CST). Higher accuracy was obtained for ITD (F-score: 0.8–0.87) and TH (R2: 0.76–0.77; RMSE: 1.45–1.55 m) than for other stand parameters. Overall, the flying altitude of the UAV and selected filtering methods influenced the success of stand parameter estimation in old-aged plantations, with the UAV at 80 m generating more accurate results for ITD, CA, and DBH, while the UAV at 120 m produced higher accuracy for TH, V, and CST with Gaussian and mean filtering.

show abstract

“…In image-based species recognition, there are two main types of species recognition: one is species recognition based on ordinary optical images [40][41][42][43][44], and the other is species recognition based on remote sensing images [45][46][47][48][49]. The current research mainly focuses on the species recognition of ordinary optical images; however, both species recognition from optical images and species recognition from aerial images are important.…”

Section: Image Species Recognition Based On Cnnmentioning

confidence: 99%

Plant and Animal Species Recognition Based on Dynamic Vision Transformer Architecture

2022

View full text Add to dashboard Cite

Automatic prediction of the plant and animal species most likely to be observed at a given geo-location is useful for many scenarios related to biodiversity management and conservation. However, the sparseness of aerial images results in small discrepancies in the image appearance of different species categories. In this paper, we propose a novel Dynamic Vision Transformer (DViT) architecture to reduce the effect of small image discrepancies for plant and animal species recognition by aerial image and geo-location environment information. We extract the latent representation by sampling a subset of patches with low attention weights in the transformer encoder model with a learnable mask token for multimodal aerial images. At the same time, the geo-location environment information is added to the process of extracting the latent representation from aerial images and fused with the token with high attention weights to improve the distinguishability of representation by the dynamic attention fusion model. The proposed DViT method is evaluated on the GeoLifeCLEF 2021 and 2022 datasets, achieving state-of-the-art performance. The experimental results show that fusing the aerial image and multimodal geo-location environment information contributes to plant and animal species recognition.

show abstract

An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data

Cited by 14 publications

References 60 publications

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes

Plant and Animal Species Recognition Based on Dynamic Vision Transformer Architecture

Contact Info

Product

Resources

About