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Keywords = UVA remote sensing

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24 pages, 7024 KB  
Article
Classification of Nitrogen-Efficient Wheat Varieties Based on UAV Hyperspectral Remote Sensing
by Yumeng Li, Chunying Wang, Junke Zhu, Qinglong Wang and Ping Liu
Plants 2025, 14(13), 1908; https://doi.org/10.3390/plants14131908 - 20 Jun 2025
Viewed by 502
Abstract
Aiming at tackling the challenges of traditional classification methods, which are labor-intensive, time-consuming, and inefficient, a nitrogen-efficient wheat variety classification method using support vector machine-extreme gradient boosting (SVM-XGBoost) based on unmanned aerial vehicle (UAV) hyperspectral remote sensing was proposed in this study. First, [...] Read more.
Aiming at tackling the challenges of traditional classification methods, which are labor-intensive, time-consuming, and inefficient, a nitrogen-efficient wheat variety classification method using support vector machine-extreme gradient boosting (SVM-XGBoost) based on unmanned aerial vehicle (UAV) hyperspectral remote sensing was proposed in this study. First, eight agronomic indicators closely related to wheat nitrogen efficiency were analyzed using t-SNE dimensionality reduction and hierarchical clustering, enabling the classification of 12 wheat varieties into nitrogen-efficient and nitrogen-inefficient varieties under different nitrogen stress conditions. Second, a hyperspectral feature band selection method based on least absolute shrinkage and selection operator-competitive adaptive reweighted sampling (Lasso-CARS) was employed using hyperspectral canopy data collected during the wheat heading stage with an UAV to extract feature bands relevant to nitrogen-efficient wheat classification. This approach aimed to mitigate the impact of high collinearity and noise in high-dimensional hyperspectral data on model construction. Furthermore, the SVM-XGBoost method integrated the extracted feature bands with the support vectors and decision function outputs from the preliminary SVM classification. It then leveraged XGBoost to capture nonlinear relationships and construct the final classification model using gradient-boosted trees, achieving intelligent classification of nitrogen-efficient wheat varieties. The model also selected nitrogen fertilization strategies based on the characteristics of different wheat varieties. The results demonstrated robust performance under low, high, and no nitrogen stress, with average overall accuracies of 74%, 83%, and 70% (Kappa coefficients: 0.67, 0.80, and 0.48), respectively. This study provided an efficient and accurate UAV hyperspectral remote sensing-based method for nitrogen-efficient wheat variety classification, offering a technological foundation to accelerate precision breeding. Full article
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21 pages, 28441 KB  
Article
Seismic Risk Classification of Building Clusters Using MST Clustering and UAV Remote Sensing
by Xianteng Wang, Xue Li, Zhumei Liu, Zihao Wu, Yike Xie and Zijie Han
Sensors 2025, 25(3), 744; https://doi.org/10.3390/s25030744 - 26 Jan 2025
Viewed by 983
Abstract
The fundamental attribute that is essential for the seismic capacity assessment of houses is the building structure type. Conventionally, remote sensing assessment of the seismic capacity for houses has been based on the image features of individual houses, instead of the spatial similarity [...] Read more.
The fundamental attribute that is essential for the seismic capacity assessment of houses is the building structure type. Conventionally, remote sensing assessment of the seismic capacity for houses has been based on the image features of individual houses, instead of the spatial similarity between them. To enhance the classification accuracy of house structure types, this work proposes a minimum spanning tree (MST) house clustering structure type classification method based on the spatial similarity of houses. First, the method employs the geometric characteristics of residential buildings to calculate the Gestalt factor that characterizes the visual distance. Subsequently, a Delaunay triangular mesh is constructed to create a proximity map between the houses, with the MST generated using visual distance as the weighting factor. Then, the spatial proximity similarity of house clusters is obtained through pruning. Finally, a support vector machine is employed to categorize the architectural structure of the housing complex, viz., simple houses, brick–concrete houses, and frame houses. This classification is based on the geometric, textural, height, and spatial distribution characteristics of the houses. We have conducted a remote sensing classification experiment of house structure types with Zhushan County, Hubei Province as the study area. The results show that the MST clustering method improves the classification accuracy of brick–concrete houses to 95.4% and the classification accuracy of simple houses to 93.4%. Compared to the single-family-based classification method of building structure types, the classification accuracy of frame-structure buildings is improved to 87%. The Kappa coefficient increased to 0.89. This study significantly improves the classification accuracy of building structure types by introducing spatial similarity. Furthermore, it shows the potential for spatial similarity in classifying building structure types. Full article
(This article belongs to the Special Issue Advances in Seismic Sensing and Monitoring)
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15 pages, 5873 KB  
Article
Biomass Inversion of Highway Slope Based on Unmanned Aerial Vehicle Remote Sensing and Deep Learning
by Guangcun Hao, Zhiliang Dong, Liwen Hu, Qianru Ouyang, Jian Pan, Xiaoyang Liu, Guang Yang and Caige Sun
Forests 2024, 15(9), 1564; https://doi.org/10.3390/f15091564 - 5 Sep 2024
Cited by 5 | Viewed by 1481
Abstract
Biomass can serve as an important indicator for measuring the effectiveness of slope ecological restoration, and unmanned aerial vehicle (UAV) remote sensing provides technical support for the rapid and accurate measurement of vegetation biomass on slopes. Considering a highway slope as the experimental [...] Read more.
Biomass can serve as an important indicator for measuring the effectiveness of slope ecological restoration, and unmanned aerial vehicle (UAV) remote sensing provides technical support for the rapid and accurate measurement of vegetation biomass on slopes. Considering a highway slope as the experimental area, in this study, we integrate UAV data and Sentinel-2A images; apply a deep learning method to integrate remote sensing data; extract slope vegetation features from vegetation probability, vegetation indices, and vegetation texture features; and construct a slope vegetation biomass inversion model. The R2 of the slope vegetation biomass inversion model is 0.795, and the p-value in the F-test is less than 0.01, which indicates that the model has excellent regression performance and statistical significance. Based on laboratory biomass measurements, the regression model error is small and reasonable, with RMSE = 0.073, MAE = 0.064, and SE = 0.03. The slope vegetation biomass can be accurately estimated using remote-sensing images with a high precision and good applicability. This study will provide a methodological reference and demonstrate its application in estimating vegetation biomass and carbon stock on highway slopes, thus providing data and methodological support for the simulation of the carbon balance process in slope restoration ecosystems. Full article
(This article belongs to the Special Issue UAV Application in Forestry)
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19 pages, 4397 KB  
Article
Sh-DeepLabv3+: An Improved Semantic Segmentation Lightweight Network for Corn Straw Cover Form Plot Classification
by Yueyong Wang, Xuebing Gao, Yu Sun, Yuanyuan Liu, Libin Wang and Mengqi Liu
Agriculture 2024, 14(4), 628; https://doi.org/10.3390/agriculture14040628 - 18 Apr 2024
Cited by 5 | Viewed by 3923
Abstract
Straw return is one of the main methods for protecting black soil. Efficient and accurate straw return detection is important for the sustainability of conservation tillage. In this study, a rapid straw return detection method is proposed for large areas. An optimized Sh-DeepLabv3+ [...] Read more.
Straw return is one of the main methods for protecting black soil. Efficient and accurate straw return detection is important for the sustainability of conservation tillage. In this study, a rapid straw return detection method is proposed for large areas. An optimized Sh-DeepLabv3+ model based on the aforementioned detection method and the characteristics of straw return in Jilin Province was then used to classify plots into different straw return cover types. The model used Mobilenetv2 as the backbone network to reduce the number of model parameters, and the channel-wise feature pyramid module based on channel attention (CA-CFP) and a low-level feature fusion module (LLFF) were used to enhance the segmentation of the plot details. In addition, a composite loss function was used to solve the problem of class imbalance in the dataset. The results show that the extraction accuracy is optimal when a 2048 × 2048-pixel scale image is used as the model input. The total parameters of the improved model are 3.79 M, and the mean intersection over union (MIoU) is 96.22%, which is better than other comparative models. After conducting a calculation of the form–grade mapping relationship, the error value of the area prediction was found to be less than 8%. The results show that the proposed rapid straw return detection method based on Sh-DeepLabv3+ can provide greater support for straw return detection. Full article
(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)
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20 pages, 10127 KB  
Article
Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model
by Zhangxi Ye, Jiahao Wei, Yuwei Lin, Qian Guo, Jian Zhang, Houxi Zhang, Hui Deng and Kaijie Yang
Remote Sens. 2022, 14(6), 1523; https://doi.org/10.3390/rs14061523 - 21 Mar 2022
Cited by 52 | Viewed by 10298
Abstract
Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning [...] Read more.
Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A–D) in the study area were selected to evaluate the performance of the U2-Net model in olive crown extraction in different scenarios, and the U2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U2-Net model increased by 14.03–23.97 percentage points, 7.57–12.85 percentage points, and 8.15–14.78 percentage points, respectively. In addition, the U2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R2) higher than 0.93 in all four subareas, suggesting that the U2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees. Full article
(This article belongs to the Special Issue UAV Applications for Forest Management: Wood Volume, Biomass, Mapping)
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18 pages, 2679 KB  
Article
Satellite Monitoring of Environmental Solar Ultraviolet A (UVA) Exposure and Irradiance: A Review of OMI and GOME-2
by Alfio V. Parisi, Damien Igoe, Nathan J. Downs, Joanna Turner, Abdurazaq Amar and Mustapha A. A Jebar
Remote Sens. 2021, 13(4), 752; https://doi.org/10.3390/rs13040752 - 18 Feb 2021
Cited by 18 | Viewed by 5341
Abstract
Excessive exposure to solar ultraviolet (UV) radiation has damaging effects on life on Earth. High-energy short-wavelength ultraviolet B (UVB) is biologically effective, influencing a range of dermal processes, including the potentially beneficial production of vitamin D. In addition to the damaging effects of [...] Read more.
Excessive exposure to solar ultraviolet (UV) radiation has damaging effects on life on Earth. High-energy short-wavelength ultraviolet B (UVB) is biologically effective, influencing a range of dermal processes, including the potentially beneficial production of vitamin D. In addition to the damaging effects of UVB, the longer wavelength and more abundant ultraviolet A (UVA) has been shown to be linked to an increased risk of skin cancer. To evaluate this risk requires the monitoring of the solar UVA globally on a time repetitive basis in order to provide an understanding of the environmental solar UVA irradiance and resulting exposures that humans may receive during their normal daily activities. Satellite-based platforms, with the appropriate validation against ground-based instrumentation, can provide global monitoring of the solar UVA environment. Two satellite platforms that currently provide data on the terrestrial UVA environment are the ozone monitoring instrument (OMI) and the global ozone monitoring experiment (GOME-2). The objectives of this review are to provide a summary of the OMI and GOME-2 satellite-based platforms for monitoring the terrestrial UVA environment and to compare the remotely sensed UVA data from these platforms to that from ground-based instrumentation. Full article
(This article belongs to the Collection Feature Papers for Section Environmental Remote Sensing)
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11 pages, 4910 KB  
Article
A Low-Cost Smartphone Sensor-Based UV Camera for Volcanic SO2 Emission Measurements
by Thomas Charles Wilkes, Tom David Pering, Andrew John Samuel McGonigle, Giancarlo Tamburello and Jon Raffe Willmott
Remote Sens. 2017, 9(1), 27; https://doi.org/10.3390/rs9010027 - 1 Jan 2017
Cited by 43 | Viewed by 13445
Abstract
Recently, we reported on the development of low-cost ultraviolet (UV) cameras, based on the modification of sensors designed for the smartphone market. These units are built around modified Raspberry Pi cameras (PiCams; ≈USD 25), and usable system sensitivity was demonstrated in the UVA [...] Read more.
Recently, we reported on the development of low-cost ultraviolet (UV) cameras, based on the modification of sensors designed for the smartphone market. These units are built around modified Raspberry Pi cameras (PiCams; ≈USD 25), and usable system sensitivity was demonstrated in the UVA and UVB spectral regions, of relevance to a number of application areas. Here, we report on the first deployment of PiCam devices in one such field: UV remote sensing of sulphur dioxide emissions from volcanoes; such data provide important insights into magmatic processes and are applied in hazard assessments. In particular, we report on field trials on Mt. Etna, where the utility of these devices in quantifying volcanic sulphur dioxide (SO2) emissions was validated. We furthermore performed side-by-side trials of these units against scientific grade cameras, which are currently used in this application, finding that the two systems gave virtually identical flux time series outputs, and that signal-to-noise characteristics of the PiCam units appeared to be more than adequate for volcanological applications. Given the low cost of these sensors, allowing two-filter SO2 camera systems to be assembled for ≈USD 500, they could be suitable for widespread dissemination in volcanic SO2 monitoring internationally. Full article
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27 pages, 3473 KB  
Article
Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing
by Chudong Huang and Xinyue Ye
Sustainability 2015, 7(7), 9478-9504; https://doi.org/10.3390/su7079478 - 17 Jul 2015
Cited by 27 | Viewed by 7094
Abstract
The coupling relationship between urban vegetation and land surface temperature (LST) has been heatedly debated in a variety of environmental studies. This paper studies the urban vegetation information and LST by utilizing a series of remote sensing imagery covering the period from 1990 [...] Read more.
The coupling relationship between urban vegetation and land surface temperature (LST) has been heatedly debated in a variety of environmental studies. This paper studies the urban vegetation information and LST by utilizing a series of remote sensing imagery covering the period from 1990 to 2007. Their coupling relationship is analyzed, in order to provide the basis for ecological planning and environment protection. The results show that the normalized difference vegetation index (NDVI), urban vegetation abundance (UVA) and urban forest abundance (UFA) are negatively correlated with LST, which means that both urban vegetation and urban forest are capable in decreasing LST. The apparent influence of urban vegetation and urban forest on LST varies with the spatial resolution of the imagery, and peaks at the resolutions ranging from 90 m to 120 m. Full article
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