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Trends in UAV Remote Sensing Applications: Part II

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 33466

Special Issue Editors

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
Interests: remote sensing; lidar applications; GIS; UAV; climate change and terrestrial ecosystem
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Science, Beijing 100093, China
Interests: remote sensing; LiDAR; GIS; climate change; terrestrial ecosystems; forest structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicle (UAV) technology bridges the gaps between spaceborne, airborne, and ground-based remote sensing data. Its characteristics of light weight and low price enable affordable observations with very high spatial and temporal resolutions. Moreover, recently, the stability, flight duration, and load capacity of UAVs increased significantly with the development of flight-control and battery technology, which enable more sensor varieties (e.g., optical sensors, lidar sensors, and radar sensors) to be mounted on small UAVs. These multi-source UAV sensing data with high spatial and temporal resolution are driving new developments in the field of remote sensing applications, such as powerline inspection, forest mapping and management, terrain survey, geological disaster survey, biodiversity conservation, and hydrological modelling.

For this Special Issue, we seek submissions on all aspects of UAV remote sensing, including hardware development progresses of UAV remote sensing; novel and advanced algorithms for processing UAV remote sensing data; and applications of UAV remote sensing in, but not limited to, the fields of powerline inspection, forest mapping and management, archeology, terrain survey, geological disaster survey, biodiversity conservation, and hydrological modelling. We will also host review papers on the trends of UAV remote sensing, as well as the fusion of UAV remote sensing data with airborne and spaceborne remote sensing data for natural resource investigation.


Dr. Qinghua Guo
Dr. Yanjun Su
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV Remote sensing

Published Papers (8 papers)

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Research

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22 pages, 115750 KiB  
Article
Comprehensive Analysis of the Object Detection Pipeline on UAVs
by Leon Amadeus Varga, Sebastian Koch and Andreas Zell
Remote Sens. 2022, 14(21), 5508; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215508 - 01 Nov 2022
Cited by 2 | Viewed by 1836
Abstract
An object detection pipeline comprises a camera that captures the scene and an object detector that processes these images. The quality of the images directly affects the performance of the object detector. Current works focus on independently improving the image quality or object [...] Read more.
An object detection pipeline comprises a camera that captures the scene and an object detector that processes these images. The quality of the images directly affects the performance of the object detector. Current works focus on independently improving the image quality or object detection models but neglect the importance of joint optimization of the two subsystems. This paper aims to tune the detection throughput and accuracy of existing object detectors in the remote sensing scenario by optimizing the input images tailored to the object detector. We empirically analyze the influence of two selected camera calibration parameters (camera distortion correction and gamma correction) and five image parameters (quantization, compression, resolution, color model, and additional channels) for these applications. For our experiments, we utilize three Unmanned Aerial Vehicle (UAV) data sets from different domains and a mixture of large and small state-of-the-art object detector models to provide an extensive evaluation of the influence of the pipeline parameters. Finally, we realize an object detection pipeline prototype on an embedded platform for a UAV and give a best practice recommendation for building object detection pipelines based on our findings. We show that not all parameters have an equal impact on detection accuracy and data throughput. Using a suitable compromise between parameters, we can achieve higher detection accuracy for lightweight object detection models while keeping the same data throughput. Full article
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications: Part II)
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20 pages, 9215 KiB  
Article
UAV-Mounted GPR for Object Detection Based on Cross-Correlation Background Subtraction Method
by Shuxian Wu, Longxiang Wang, Xiaozhen Zeng, Feng Wang, Zichang Liang and Hongxia Ye
Remote Sens. 2022, 14(20), 5132; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205132 - 14 Oct 2022
Cited by 1 | Viewed by 1598
Abstract
Unmanned aerial vehicle (UAV) ground-penetrating radar (GPR) is an important research topic for target detection in many fields. In this paper, we develop a UAV-mounted GPR system with a frequency band at 150 MHz–309 MHz. However, the received signal in the complex background [...] Read more.
Unmanned aerial vehicle (UAV) ground-penetrating radar (GPR) is an important research topic for target detection in many fields. In this paper, we develop a UAV-mounted GPR system with a frequency band at 150 MHz–309 MHz. However, the received signal in the complex background is covered by various clutter and interference, leading to the serious obscuring of the target. To meet this challenge, a cross-correlation-based background subtraction (CCBS) method and an interference suppression technique are adopted in combination for more accurate detection. The CCBS method processes the raw echo by establishing a background-removal model and using the similarity between each A−Scan and a reference wave. In addition, a Butterworth filter is adopted to get rid of the active electromagnetic interference beyond the working frequencies of the system; then, a lateral Doppler filtering (LDF) technique is introduced to suppress the passive interference generated by the rotation of the UAV rotor itself. Moreover, a practical method for estimating the dielectric constant is introduced by the calibration process of the measured radar echo. Numerical simulations and experimental results by our UAV-GPR system demonstrate that the proposed method has presented a better performance than the traditional methods, and the system has great potential in detecting deeply buried targets. Full article
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications: Part II)
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23 pages, 9256 KiB  
Article
Is an Unmanned Aerial Vehicle (UAV) Suitable for Extracting the Stand Parameters of Inaccessible Underground Forests of Karst Tiankeng?
by Wei Shui, Hui Li, Yongyong Zhang, Cong Jiang, Sufeng Zhu, Qianfeng Wang, Yuanmeng Liu, Sili Zong, Yunhui Huang and Meiqi Ma
Remote Sens. 2022, 14(17), 4128; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174128 - 23 Aug 2022
Cited by 3 | Viewed by 1484
Abstract
Unmanned aerial vehicle (UAV) remote sensing technology is gradually playing a role alternative to traditional field survey methods in monitoring plant functional traits of forest ecology. Few studies focused on monitoring functional trait ecology of underground forests of inaccessible negative terrain with UAV. [...] Read more.
Unmanned aerial vehicle (UAV) remote sensing technology is gradually playing a role alternative to traditional field survey methods in monitoring plant functional traits of forest ecology. Few studies focused on monitoring functional trait ecology of underground forests of inaccessible negative terrain with UAV. The underground forests of tiankeng were discovered and are known as the inaccessible precious ecological refugia of extreme negative terrain. The aim of this research proposal is to explore the suitability of UAV technology for extracting the stand parameters of underground forests’ functional traits in karst tiankeng. Based on the multi-scale segmentation algorithm and object-oriented classification method, the canopy parameters (crown width and densities) of underground forests in degraded karst tiankeng were extracted by UAV remote sensing image data and appropriate features collection. First, a multi-scale segmentation algorithm was applied to attain the optimal segmentation scale to obtain the single wood canopy. Second, feature space optimization was used to construct the optimal feature space set for the image and then the k-nearest neighbor(k-NN) classifier was used to classify the image features. The features were classified into five types: canopy, grassland, road, gap, and bare land. Finally, both the crown densities and average crown width of the trees were calculated, and their accuracy were verified. The results showed that overall accuracy of object-oriented image feature classification was 85.60%, with 0.72 of kappa coefficient. The accuracy of tree canopy density extraction was 82.34%, for which kappa coefficient reached 0.91. The average canopy width of trees in the samples from the tiankeng-inside was 5.38 m, while that of the outside samples was 4.83 m. In conclusion, the canopy parameters in karst tiankeng were higher than those outside the tiankeng. Stand parameters extraction of karst tiankeng underground forests based on UAV remote sensing was relatively satisfactory. Thus, UAV technology provides a new approach to explore forest resources in inaccessible negative terrain such as karst tiankengs. In the future, we need to consider UAVs with more bands of cameras to extract more plant functional traits to promote the application of UAV for underground forest ecology research of more inaccessible negative terrain. Full article
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications: Part II)
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22 pages, 5816 KiB  
Article
Comparison of Low-Cost Commercial Unpiloted Digital Aerial Photogrammetry to Airborne Laser Scanning across Multiple Forest Types in California, USA
by James E. Lamping, Harold S. J. Zald, Buddhika D. Madurapperuma and Jim Graham
Remote Sens. 2021, 13(21), 4292; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214292 - 25 Oct 2021
Cited by 7 | Viewed by 4029
Abstract
Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. [...] Read more.
Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurements, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this study was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models (DTMs) were comparable to lidar DTMS across most sites and nadir vs. off-nadir imagery collection (R2 = 0.74–0.99), although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest (R2 = 0.17) due to high canopy density occluding the ground from the image sensor. Surface and canopy height models were shown to have less agreement to lidar (R2 = 0.17–0.69), with off-nadir imagery surface models at high canopy density sites having the lowest agreement with lidar. UAS DAP models predicted key forest metrics with varying accuracy compared to field data (R2 = 0.53–0.85), and were comparable to predictions made using lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions. Full article
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications: Part II)
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25 pages, 42236 KiB  
Article
Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance
by Shuaipeng Fei, Muhammad Adeel Hassan, Zhonghu He, Zhen Chen, Meiyan Shu, Jiankang Wang, Changchun Li and Yonggui Xiao
Remote Sens. 2021, 13(12), 2338; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122338 - 15 Jun 2021
Cited by 39 | Viewed by 3441
Abstract
Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning [...] Read more.
Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat. Full article
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications: Part II)
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18 pages, 2791 KiB  
Article
Classification of Street Tree Species Using UAV Tilt Photogrammetry
by Yutang Wang, Jia Wang, Shuping Chang, Lu Sun, Likun An, Yuhan Chen and Jiangqi Xu
Remote Sens. 2021, 13(2), 216; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020216 - 10 Jan 2021
Cited by 16 | Viewed by 3478
Abstract
As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies [...] Read more.
As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and used four types of commonly-used machine learning classification algorithms as classifiers for tree species classification. The results show that random forest (RF), support vector machine (SVM), and back propagation (BP) neural network provide better classification results when using combined parameters for tree species classification, compared with those using individual tree attributes alone; however, the K-nearest neighbor (KNN) algorithm produced the opposite results. The best combination for classification is the BP neural network using combined attributes, with a classification precision of 89.1% and F-measure of 0.872, and we conclude that this approach best meets the requirements of street tree surveys. The results also demonstrate that optical UAV tilt photogrammetry combined with a machine learning classification algorithm is a low-cost, high-efficiency, and high-precision method for tree species classification. Full article
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications: Part II)
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21 pages, 8953 KiB  
Article
Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications
by Tianyu Hu, Xiliang Sun, Yanjun Su, Hongcan Guan, Qianhui Sun, Maggi Kelly and Qinghua Guo
Remote Sens. 2021, 13(1), 77; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010077 - 28 Dec 2020
Cited by 75 | Viewed by 13382
Abstract
Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has [...] Read more.
Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management. Full article
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications: Part II)
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Review

Jump to: Research

16 pages, 5713 KiB  
Review
Social Network and Bibliometric Analysis of Unmanned Aerial Vehicle Remote Sensing Applications from 2010 to 2021
by Jingrui Wang, Shuqing Wang, Dongxiao Zou, Huimin Chen, Run Zhong, Hanliang Li, Wei Zhou and Kai Yan
Remote Sens. 2021, 13(15), 2912; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152912 - 24 Jul 2021
Cited by 9 | Viewed by 2449
Abstract
Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the [...] Read more.
Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the past decade, we screened related publications from the Web of Science core database from 2010 to 2021, built co-author networks, a discipline interaction network, a keywords timeline view, a co-citation cluster, and detected burst citations using bibliometrics and social network analysis. Our results show that: (1) The number of UAV RS publications had an increasing trend, with explosive growth in the past five years. The number of papers published by China and the United States (US) is far ahead in this field; (2) The US has currently the greatest influence in this field through the largest number of international cooperations. Cooperation is mainly concentrated in countries and institutions with a large number of publications but is not widely distributed. (3) The application of UAV RS involves multiple interdisciplinary subjects, among which “Environmental Science and Ecology” ranks first; (4) Future research trends of UAV RS are expected to be related to artificial intelligence (e.g., artificial neural networks-based research). This paper provides a scientific basis and guidance for future developments of UAV RS applications, which can help the research community to better grasp the developments of this field. Full article
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications: Part II)
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