Application of UAV Photogrammetric System for Monitoring Ancient Tree Communities in Beijing

Qiu, Zixuan; Zhong-ke, Feng; Wang, Mingming; Li, Zhenru; Chen, Lu

doi:10.3390/f9120735

Cited by 30 publications

(22 citation statements)

References 63 publications

(72 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Blocks of images obtained from a low altitude give the possibility of generating very high accuracy orthophotomaps in both the visible and infrared range, even for areas which are inaccessible. In addition, images from a low altitude can also be considered as a source of accurate information about various environmental phenomena, which is why UAVs are often used to monitor agriculture [1], vegetation [2], waters [3], and others. In such works, the main task is to analyze images in order to detect selected phenomena based on a comparison of the spectral properties of the imaged objects.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles

2020

View full text Add to dashboard Cite

Images acquired at a low altitude can be the source of accurate information about various environmental phenomena. Often, however, this information is distorted by various factors, so a correction of the images needs to be performed to recreate the actual reflective properties of the imaged area. Due to the low flight altitude, the correction of images from UAVs (unmanned aerial vehicles) is usually limited to noise reduction and detector errors. The article shows the influence of the Sun position and platform deviation angles on the quality of images obtained by UAVs. Tilting the camera placed on an unmanned platform leads to incorrect exposures of imagery, and the order of this distortion depends on the position of the Sun during imaging. An image can be considered in three-dimensional space, where the x and y coordinates determine the position of the pixel and the third dimension determines its exposure. This assumption is the basis for the proposed method of image exposure compensation. A three-dimensional transformation by rotation is used to determine the adjustment matrix to correct the image quality. The adjustments depend on the angles of the platform and the difference between the direction of flight and the position of the Sun. An additional factor regulates the value of the adjustment depending on the ratio of the pitch and roll angles. The experiments were carried out for two sets of data obtained with different unmanned systems. The correction method used can improve the block exposure by up to 60%. The method gives the best results for simple systems, not equipped with lighting compensation systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles

2020

View full text Add to dashboard Cite

show abstract

“…The aims of the papers were to test the feasibility of using UAVs to rapidly identify coniferous seedlings in replanted forest-harvest using an efficient sampling-based approach, consumer-grade cameras, and straightforward image handling, such as in [1]. The tree characteristics monitored were tree height, crown width, prediction of diameter at breast height (DBH), and tree age with low cost, high efficiency, and high precision in [2]. The development of a dataset called MauFlex related to Mauritia flexuosa palm, also known as "aguaje", was a study aimed at its conservation as it is a species poorly monitored because of the difficult access to these swamps.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

“…Various data processing methods were used in this Special Issue. Image analysis workflow, where a three-step, object-based process consisting of image segmentation, automated classification using a classification and regression tree (CART) machine- Seven papers in this special issue reported results from UAV platform using RGB cameras [1][2][3][4][5][6][7], three paper used multispectral cameras [6,8,9], while one paper used a hyperspectral camera [6].…”

Section: Overview Of Contributionsmentioning

confidence: 99%

“…The Structure-from-Motion/Multi-view Stereo (SfM-MVS) method was used to detect the feature points of the image, and the scale-invariant feature transform (SIFT) algorithm was used to detect feature points and generate feature vectors. Then, matching was carried out according to the feature vectors, and the RANSAC (random sample consensus) algorithm was used to delete the connection of the conflicting geometric features of corresponding feature points in [2]. Convolutional Neural Network (CNN) based on the Deeplab v3+ architecture was completed.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Editorial for the Special Issue “Forestry Applications of Unmanned Aerial Vehicles (UAVs)”

Matese

2020

Forests

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. This special issue (SI) collects nine papers reporting research on different forestry applications using UAV imagery. The special issue covers seven Red-Green-Blue (RGB) sensor papers, three papers on multispectral imagery, and one further paper on hyperspectral data acquisition system. Several data processing and machine learning methods are presented. The special issue provides an overview regarding potential applications to provide forestry characteristics in a timely, cost-efficient way. With the fast development of sensors technology and image processing algorithms, the forestry potential applications will growing fast, but future work should consider the consistency and repeatability of these novel techniques.

show abstract

“…Only a few ground points used to interpolate terrains may be problematic and sensitive to noisy data [12,13]. Most previous studies of CHM generation using UAV-based photogrammetry alone have been conducted over flat or gentle terrains [29][30][31]; works on the CHM generation of mountainous areas under complex terrains using low-cost UAV-based photogrammetric point clouds have been rarely reported. Generally, 3D points on the surface of trees have a large slope.…”

Section: Introductionmentioning

confidence: 99%

Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network

Yan

Chen

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Tree heights are the principal variables for forest plantation inventory. The increasing availability of high-resolution three-dimensional (3D) point clouds derived from low-cost Unmanned Aerial Vehicle (UAV) and modern photogrammetry offers an opportunity to generate a Canopy Height Model (CHM) in the mountainous areas. In this paper, we assessed the capabilities of tree height estimation using UAV-based Structure-from-Motion (SfM) photogrammetry and Semi-Global Matching (SGM). The former is utilized to generate 3D geometry, while the latter is used to generate dense point clouds from UAV imagery. The two algorithms were coupled with a Radial Basis Function (RBF) neural network to acquire CHMs in mountainous areas. This study focused on the performance of Digital Terrain Model (DTM) interpolation over complex terrains. With the UAV-based image acquisition and image-derived point clouds, we constructed a 5 cm-resolution Digital Surface Model (DSM), which was assessed against 14 independent checkpoints measured by a Real-Time Kinematic Global Positioning System RTK GPS. Results showed that the Root Mean Square Errors (RMSEs) of horizontal and vertical accuracies are approximately 5 cm and 10 cm, respectively. Bare-earth Index (BEI) and Shadow Index (SI) were used to separate ground points from the image-derived point clouds. The RBF neural network coupled with the Difference of Gaussian (DoG) was exploited to provide a favorable generalization for the DTM from 3D ground points with noisy data. CHMs were generated using the height value in each pixel of the DSM and by subtracting the corresponding DTM value. Individual tree heights were estimated using local maxima algorithm under a contour-surround constraint. Two forest plantations in mountainous areas were selected to evaluate the accuracy of estimating tree heights, rather than field measurements. Results indicated that the proposed method can construct a highly accurate DTM and effectively remove nontreetop maxima. Furthermore, the proposed method has been confirmed to be acceptable for tree height estimation in mountainous areas given the strong linear correlation of the measured and estimated tree heights and the acceptable t-test values. Overall, the low-cost UAV-based photogrammetry and RBF neural network can yield a highly accurate DTM over mountainous terrain, thereby making them particularly suitable for rapid and cost-effective estimation of tree heights of forest plantation in mountainous areas.

show abstract

Application of UAV Photogrammetric System for Monitoring Ancient Tree Communities in Beijing

Cited by 30 publications

References 63 publications

Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles

Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles

Editorial for the Special Issue “Forestry Applications of Unmanned Aerial Vehicles (UAVs)”

Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network

Contact Info

Product

Resources

About