A novel algorithm of individual tree crowns segmentation considering three-dimensional canopy attributes using UAV oblique photos

The rapid, up-to-date, cost-effective acquisition and tracking of intertidal topography are the fundamental basis for timely, high-priority protection and restoration of the intertidal zone. The low cost, ease of use, and flexible UAV-based photogrammetry have revolutionized the monitoring of intertidal zones. However, the capability of the RTK-assisted UAV photogrammetry without ground control points, the impact of flight configuration difference, the presence of surface water in low-lying intertidal areas on the photogrammetric accuracy, and the potential of UAV/satellite Synergy remain unknown. In this paper, we used an RTK-assisted UAV to assess the impact of the above-mentioned considerations quantitatively on photogrammetric results in the context of annual monitoring of the Chongming Dongtan Nature Reserve, China based on an optimal flight combination. The results suggested that (1) RTK-assisted UAVs can obtain high-accuracy topographic data with a vertical RMSE of 3.1 cm, without the need for ground control points. (2) The effect of flight altitude on topographic accuracy was most significant and also nonlinear. (3) The elevation obtained by UAV photogrammetry was overestimated by approximately 2.4 cm in the low-lying water-bearing regions. (4) The integration of UAV and satellite observations can increase the accuracy of satellite-based waterline methods by 51%. These quantitative results not only provide scientific insights and guidelines for the balance between accuracy and efficiency in utilizing UAV-based intertidal monitoring, but also demonstrate the great potential of combined UAV and satellite observations in identifying coastal erosion hotspots. This establishes high-priority protection mechanisms and promotes coastal restoration.

Section: Uncertainty Caused By Photogrammetric Configurationsmentioning

confidence: 99%

UAV Photogrammetry in Intertidal Mudflats: Accuracy, Efficiency, and Potential for Integration with Satellite Imagery

Chen

Tian

et al. 2023

“…To test the accuracy of the crown parameters extraction, the measured values of the crown parameters were compared with the estimates extracted by the model, and the RMSE (root mean square error) and rRMSE (relative RMSE) were used to evaluate the effect of the crown parameters extraction (Equations ( 7) and ( 8)) [10].…”

Section: Accuracy Evaluationmentioning

confidence: 99%

“…As a lightweight and flexible remote sensing platform, the unmanned aerial vehicle (UAV) can carry various types of sensors, including RGB, multispectral, and hyperspectral cameras to acquire high-resolution remote sensing data [7]. Therefore, it has the ability to capture forest information comprehensively and quickly [8], which has great potential for application in forest tree species surveys and individual tree parameters measurement [9,10].…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Species Identification and Crown Parameters Extraction Based on Mask R-CNN: Assessing the Applicability of Unmanned Aerial Vehicle Optical Images

Yao,

Chai,

Lei

et al. 2023

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and spectral features. As such, they have attracted extensive attention in the field of forest resource investigation, but their applicability for end-to-end individual tree species identification is unclear. Based on the Mask R-CNN instance segmentation model, this study utilized UAV hyperspectral images to generate spectral thinning data, spectral dimensionality reduction data, and simulated multispectral data, thereby evaluating the importance of high-resolution spectral information, the effectiveness of PCA dimensionality reduction processing of hyperspectral data, and the feasibility of multispectral data for individual tree identification. The results showed that the individual tree species identification accuracy of spectral thinning data was positively correlated with the number of bands, and full-band hyperspectral data were better than other hyperspectral thinning data and PCA dimensionality reduction data, with Precision, Recall, and F1-score of 0.785, 0.825, and 0.802, respectively. The simulated multispectral data are also effective in identifying individual tree species, among which the best result is realized through the combination of Green, Red, and NIR bands, with Precision, Recall, and F1-score of 0.797, 0.836, and 0.814, respectively. Furthermore, by using Green–Red–NIR data as input, the tree crown area and width are predicted with an RMSE of 3.16m2 and 0.51m, respectively, along with an rRMSE of 0.26 and 0.12. This study indicates that the Mask R-CNN model with UAV optical images is a novel solution for identifying individual tree species and extracting crown parameters, which can provide practical technical support for sustainable forest management and ecological diversity monitoring.

“…where TP denotes the number of successfully detected objects, FP denotes the number of successfully detected backgrounds, FN denotes the number of objects that were missed [46], IoU reflects the degree of overlap between the prediction map and the ground truth, Precision reflects the model's ability to distinguish negative samples, Recall reflects the model's ability to identify positive samples, and F1-score reflects the model's robustness.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Method for Extracting Photovoltaic Panels from High-Resolution Optical Remote Sensing Images Guided by Prior Knowledge

Liu,

Huo,

et al. 2023

The extraction of photovoltaic (PV) panels from remote sensing images is of great significance for estimating the power generation of solar photovoltaic systems and informing government decisions. The implementation of existing methods often struggles with complex background interference and confusion between the background and the PV panels. As a result, the completeness and edge clarity of PV panel extraction results are compromised. Moreover, most previous studies have overlooked the unique color characteristics of PV panels. To alleviate these deficiencies and limitations, a method for extracting photovoltaic panels from high-resolution optical remote sensing images guided by prior knowledge (PKGPVN) is proposed. Firstly, aiming to address the problems related to missed extractions and background misjudgments, a Photovoltaic Index (PVI) based on visible images in the three-band is constructed to serve as prior knowledge to differentiate between PV panels and non-PV panels. Secondly, in order to strengthen information interaction between shallow features and deep features and enhance the accuracy and integrity of results, a Residual Convolution Hybrid Attention Module (RCHAM) is introduced into the skip-connection of the encoding–decoding structure. Finally, for the purpose of reducing the phenomenon of blurred edges, a multilevel Feature Loss (FL) function is designed to monitor the prediction results at different scales. Comparative experiments are conducted with seven methods, including U-Net, on publicly available datasets. The experimental results show that our PKGPVN achieves superior performance in terms of evaluation metrics such as IoU (above 82%), Precision (above 91%), Recall (above 89%), and F1-score (above 90%) on the AIR-PV dataset. Additionally, the ablation experiments illustrate the effectiveness of our key parts. The proposed method reduces the phenomena of missed extractions and background misjudgments effectively while producing highly accurate results with clear boundaries.