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Journal articles on the topic 'UAV-borne remote sensing'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Bareth, G., and C. Hütt. "UPSCALING AND VALIDATION OF RTK-DIRECT GEOREFERENCED UAV-BASED RGB IMAGE DATA WITH PLANET IMAGERY USING POLYGON GRIDS FOR PASTURE MONITORING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 533–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-533-2021.

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Abstract. The monitoring of managed grasslands with remote sensing methods is becoming more important for spatial decision support. Various remote sensing data acquisition techniques are applied for that purpose in different spatial resolutions ranging from UAV-borne to satellite-based remote sensing. In the last decade, UAV-borne imaging and analysis techniques or in the focus of crop and grassland monitoring and provide very high spatial resolutions. In contrast, satellite data are only available in high to moderate spatial resolutions. In this contribution, we introduce direct georeferenced

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Wei, Lifei, Ming Yu, Yajing Liang, et al. "Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 17 (2019): 2011. http://dx.doi.org/10.3390/rs11172011.

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The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a pre

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Liu, Hong, Tao Yu, Bingliang Hu, et al. "UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring." Remote Sensing 13, no. 20 (2021): 4069. http://dx.doi.org/10.3390/rs13204069.

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Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air–ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing sy

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Dadrass Javan, Farzaneh, Farhad Samadzadegan, Ahmad Toosi, and Mark van der Meijde. "Unmanned Aerial Geophysical Remote Sensing: A Systematic Review." Remote Sensing 17, no. 1 (2024): 110. https://doi.org/10.3390/rs17010110.

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Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar

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Wei, Lifei, Can Huang, Yanfei Zhong, Zhou Wang, Xin Hu, and Liqun Lin. "Inland Waters Suspended Solids Concentration Retrieval Based on PSO-LSSVM for UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 12 (2019): 1455. http://dx.doi.org/10.3390/rs11121455.

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Suspended solids concentration (SSC) is an important indicator of the degree of water pollution. However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient. Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC. A reservoir and a polluted riverway were selected as the study areas. The spe

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Harder, Phillip, Warren D. Helgason, and John W. Pomeroy. "Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar." Cryosphere 18, no. 7 (2024): 3277–95. http://dx.doi.org/10.5194/tc-18-3277-2024.

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Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spa

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Wei, Lifei, Zhou Wang, Can Huang, et al. "Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery." IEEE Access 8 (2020): 168137–53. http://dx.doi.org/10.1109/access.2020.3023690.

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Zhong, Yanfei, Xinyu Wang, Yao Xu, et al. "Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications." IEEE Geoscience and Remote Sensing Magazine 6, no. 4 (2018): 46–62. http://dx.doi.org/10.1109/mgrs.2018.2867592.

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Liu, C., X. Zhou, Y. Zhou, and A. Akbar. "MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1469–75. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1469-2020.

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Abstract. Water quality is an important index of the ecological environment, which changes rapidly and needs to be monitored chronically. In urban ecological environment, water quality problem is not only more serious, but also more complex in time and space. Remote sensing water quality monitoring can cover a large area in a short time. Therefore, remote sensing can be adopted to make up for the shortcomings of traditional water quality monitoring methods in space coverage and temporal resolution. In order to monitor the narrow rivers in urban area, low altitude remote sensing is needed. This

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Natesan, S., G. Benari, C. Armenakis, and R. Lee. "LAND COVER CLASSIFICATION USING A UAV-BORNE SPECTROMETER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 24, 2017): 269–73. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-269-2017.

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Small fixed wing and rotor-copter unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification and precision agriculture applications. Various sensors operating in the non-visible spectrum such as multispectral, hyperspectral and thermal sensors can be used as payloads. This work presents a preliminary study on the use of unmanned aerial vehicle equipped with a compact spectrometer for land cover type characterization. When calibrated, the measured spectra by the UAV spectrometer can be processed and compared reference data to generate georefer

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Wijesingha, Jayan, Supriya Dayananda, Michael Wachendorf, and Thomas Astor. "Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters." Sensors 21, no. 8 (2021): 2886. http://dx.doi.org/10.3390/s21082886.

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Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2

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Park, Suyoung, Dongryeol Ryu, Sigfredo Fuentes, Hoam Chung, Mark O’Connell, and Junchul Kim. "Dependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard." Remote Sensing 13, no. 14 (2021): 2775. http://dx.doi.org/10.3390/rs13142775.

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Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured

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Shen, Yiyang, Ziyi Yan, Yongjie Yang, Wei Tang, Jinqiu Sun, and Yanchao Zhang. "Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods." Sustainability 16, no. 2 (2024): 632. http://dx.doi.org/10.3390/su16020632.

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Rice yield estimation is vital for enhancing food security, optimizing agricultural management, and promoting sustainable development. However, traditional satellite/aerial and ground-based/tower-based platforms face limitations in rice yield estimation, and few studies have explored the potential of UAV-borne hyperspectral remote sensing for this purpose. In this study, we employed a UAV-borne push-broom hyperspectral camera to acquire remote sensing data of rice fields during the filling stage, and the machine learning regression algorithms were applied to rice yield estimation. The research

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Wu, Kunpeng, Shiyin Liu, Yu Zhu, et al. "Monitoring the Surface Elevation Changes of a Monsoon Temperate Glacier with Repeated UAV Surveys, Mainri Mountains, China." Remote Sensing 14, no. 9 (2022): 2229. http://dx.doi.org/10.3390/rs14092229.

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Due to the deep valleys, steep mountains and the influence of the Indian monsoon on the Mainri Mountains (Yunnan Province, China), it is difficult to estimate glacier change from microwave and optical remote sensing. To bridge the gap between low-quality space-borne remote sensing and scarce in situ measurements, airborne remote sensing, such as unmanned aerial vehicles (UAVs), may provide a remarkable opportunity to monitor glacier change with high-quality tools. To determine monsoon temperate glacier change, three UAV surveys were conducted on the Melang Glacier in the Mainri Mountains in No

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Bandini, Filippo, Daniel Olesen, Jakob Jakobsen, et al. "Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle." Hydrology and Earth System Sciences 22, no. 8 (2018): 4165–81. http://dx.doi.org/10.5194/hess-22-4165-2018.

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Abstract. High-quality bathymetric maps of inland water bodies are a common requirement for hydraulic engineering and hydrological science applications. Remote sensing methods, such as space-borne and airborne multispectral imaging or lidar, have been developed to estimate water depth, but are ineffective for most inland water bodies, because of the attenuation of electromagnetic radiation in water, especially under turbid conditions. Surveys conducted with boats equipped with sonars can retrieve accurate water depths, but are expensive, time-consuming, and unsuitable for unnavigable water bod

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Zheng, Z., X. Li, C. Xu, et al. "INDIVIDUAL TREE-BASED FOREST SPECIES DIVERSITY ESTIMATION USING UAV-BORNE HYPERSPECTRAL AND LIDAR DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 14, 2023): 1929–34. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1929-2023.

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Abstract. Forest biodiversity is essential in maintaining ecosystem functions and services. Recently, unmanned aerial vehicle (UAV) remote sensing technology has emerged as a cost-effective and flexible tool for biodiversity monitoring. In this study, we compared the optimal clustering algorithm, classification method (spectral angle mapper, SAM), spectral diversity metric and structural heterogeneity index for forest species diversity estimation in two complex subtropical forests, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves in China, using UAV-borne hyperspectral and

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Lioumbas, John, Thomas Spahos, Aikaterini Christodoulou, et al. "Multi-Component Remote Sensing for Mapping Buried Water Pipelines." Remote Sensing 17, no. 12 (2025): 2109. https://doi.org/10.3390/rs17122109.

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Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to

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Balsi, Marco, Salvatore Esposito, Paolo Fallavollita, Maria Grazia Melis, and Marco Milanese. "Preliminary Archeological Site Survey by UAV-Borne Lidar: A Case Study." Remote Sensing 13, no. 3 (2021): 332. http://dx.doi.org/10.3390/rs13030332.

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Preliminary analysis of an archaeological site requires the acquisition of information by several diverse diagnostic techniques. Remote sensing plays an important role especially in spatially extended and not easily accessible sites for the purposes of preventive and rescue archaeology, landscape archaeology, and intervention planning. In this paper, we present a case study of a detailed topographic survey based on a light detection and ranging (LiDAR) sensor carried by an unmanned aerial vehicle (UAV; also known as drone). The high-resolution digital terrain model, obtained from the cloud of

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Brook, A., M. Polinova, D. Kopel, et al. "REMOTE SENSING TECHNIQUES TO ASSESS POST-FIRE EFFECTS AT THE HILLSLOPE AND SUB-BASIN SCALES VIA MULTI-SCALE MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 135–41. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-135-2017.

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Post-fire environmental footprint is expected at varying scales in space and in time and demands development of multi-scale monitoring approaches. In this paper, a spatially and temporally explicit multi-scale model that reveals the physical and morphological indicators affecting hillslope susceptibility at varying scales, is explained and demonstrated. The qualitative and quantitative suitability classification procedures are adapted to translate the large-scale space-borne data supplied by satellite systems (Landsat OLS8 and Sentinel 2 and 3) to local scale produced by a regional airborne su

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Zhou, Xiaoteng, Chun Liu, Yun Xue, et al. "Radiometric calibration of a large-array commodity CMOS multispectral camera for UAV-borne remote sensing." International Journal of Applied Earth Observation and Geoinformation 112 (August 2022): 102968. http://dx.doi.org/10.1016/j.jag.2022.102968.

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Li Jiahui, 李加慧, 谭奋利 Tan Fenli, 曾晨欣 Zeng Chenxin та 季轶群 Ji Yiqun. "无人机载超低空宽覆盖遥感相机光学系统设计". Acta Optica Sinica 41, № 14 (2021): 1422001. http://dx.doi.org/10.3788/aos202141.1422001.

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Abrahams, Mishkah, Mbulisi Sibanda, Timothy Dube, Vimbayi G. P. Chimonyo, and Tafadzwanashe Mabhaudhi. "A Systematic Review of UAV Applications for Mapping Neglected and Underutilised Crop Species’ Spatial Distribution and Health." Remote Sensing 15, no. 19 (2023): 4672. http://dx.doi.org/10.3390/rs15194672.

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Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluatin

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Xu, Dandan, Haobin Wang, Weixin Xu, Zhaoqing Luan, and Xia Xu. "LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives." Forests 12, no. 5 (2021): 550. http://dx.doi.org/10.3390/f12050550.

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Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resoluti

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Wijesingha, Jayan, Thomas Astor, Damian Schulze-Brüninghoff, and Michael Wachendorf. "Mapping Invasive Lupinus polyphyllus Lindl. in Semi-natural Grasslands Using Object-Based Image Analysis of UAV-borne Images." PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 88, no. 5 (2020): 391–406. http://dx.doi.org/10.1007/s41064-020-00121-0.

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Abstract Knowledge on the spatio-temporal distribution of invasive plant species is vital to maintain biodiversity in grasslands which are threatened by the invasion of such plants and to evaluate the effect of control activities conducted. Manual digitising of aerial images with field verification is the standard method to create maps of the invasive Lupinus polyphyllus Lindl. (Lupine) in semi-natural grasslands of the UNESCO biosphere reserve “Rhön”. As the standard method is labour-intensive, a workflow was developed to map lupine coverage using an unmanned aerial vehicle (UAV)-borne remote

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Wang, Zhuo, Haiwei Li, Shuang Wang, and Liyao Song. "Spatial-spectral adaptive generalization driven high-precision BRDF modeling for extensible areas using UAV-Borne remote sensing." ISPRS Journal of Photogrammetry and Remote Sensing 227 (September 2025): 12–30. https://doi.org/10.1016/j.isprsjprs.2025.05.032.

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Wijesingha, Jayan, Thomas Astor, Damian Schulze-Brüninghoff, Matthias Wengert, and Michael Wachendorf. "Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy." Remote Sensing 12, no. 1 (2020): 126. http://dx.doi.org/10.3390/rs12010126.

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The timely knowledge of forage quality of grasslands is vital for matching the demands in animal feeding. Remote sensing (RS) is a promising tool for estimating field-scale forage quality compared with traditional methods, which usually do not provide equally detailed information. However, the applicability of RS prediction models depends on the variability of the underlying calibration data, which can be brought about by the inclusion of a multitude of grassland types and management practices in the model development. Major aims of this study were (i) to build forage quality estimation models

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Wengert, Matthias, Hans-Peter Piepho, Thomas Astor, Rüdiger Graß, Jayan Wijesingha, and Michael Wachendorf. "Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing." Remote Sensing 13, no. 14 (2021): 2751. http://dx.doi.org/10.3390/rs13142751.

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Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LA

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Wei, Lifei, Ming Yu, Yanfei Zhong, Ji Zhao, Yajing Liang, and Xin Hu. "Spatial–Spectral Fusion Based on Conditional Random Fields for the Fine Classification of Crops in UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 7 (2019): 780. http://dx.doi.org/10.3390/rs11070780.

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The fine classification of crops is critical for food security and agricultural management. There are many different species of crops, some of which have similar spectral curves. As a result, the precise classification of crops is a difficult task. Although the classification methods that incorporate spatial information can reduce the noise and improve the classification accuracy, to a certain extent, the problem is far from solved. Therefore, in this paper, the method of spatial–spectral fusion based on conditional random fields (SSF-CRF) for the fine classification of crops in UAV-borne hype

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Vogel, Sebastian, Robin Gebbers, Marcel Oertel, and Eckart Kramer. "Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing." Sensors 19, no. 20 (2019): 4593. http://dx.doi.org/10.3390/s19204593.

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On a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of so

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Liang, Rubing, Keren Dai, Xianlin Shi, et al. "Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery." Remote Sensing 13, no. 7 (2021): 1330. http://dx.doi.org/10.3390/rs13071330.

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The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpreta

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Wengert, Matthias, Jayan Wijesingha, Damian Schulze-Brüninghoff, Michael Wachendorf, and Thomas Astor. "Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data." Remote Sensing 14, no. 9 (2022): 2068. http://dx.doi.org/10.3390/rs14092068.

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Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicles (UAVs) and miniaturized hyperspectral sensors, a novel tool for remote sensing of g

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Wengert, Matthias, Jayan Wijesingha, Damian Schulze-Brüninghoff, Michael Wachendorf, and Thomas Astor. "Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data." Remote Sensing 14, no. 9 (2022): 2068. http://dx.doi.org/10.3390/rs14092068.

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Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicles (UAVs) and miniaturized hyperspectral sensors, a novel tool for remote sensing of g

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Tsai, Meng Lun, Kai Wei Chiang, Cheng Fang Lo, and Jiann Yeou Rau. "Directly Georeferenced Ground Feature Points with UAV Borne Photogrammetric Platform without Ground Control." Applied Mechanics and Materials 284-287 (January 2013): 1523–27. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1523.

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In order to facilitate applications such as environment detection or disaster monitoring, developing a quickly and low cost system to collect near real time spatial information is very important. Such a rapid spatial information collection capability has become an emerging trend in the technology of remote sensing and mapping application. In this study, a fixed-wing UAV based spatial information acquisition platform is developed and evaluated. The proposed UAV based platform has a direct georeferencing module including an low cost INS/GPS integrated system, low cost digital camera as well as o

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Hu, Jie, Jie Peng, Yin Zhou, et al. "Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images." Remote Sensing 11, no. 7 (2019): 736. http://dx.doi.org/10.3390/rs11070736.

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Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed

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Bhadra, S., V. Sagan, C. Nguyen, M. Braud, A. L. Eveland, and T. C. Mockler. "AUTOMATIC EXTRACTION OF SOLAR AND SENSOR IMAGING GEOMETRY FROM UAV-BORNE PUSH-BROOM HYPERSPECTRAL CAMERA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 131–37. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-131-2022.

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Abstract. Calculating solar-sensor zenith and azimuth angles for hyperspectral images collected by UAVs are important in terms of conducting bi-directional reflectance function (BRDF) correction or radiative transfer modeling-based applications in remote sensing. These applications are even more necessary to perform high-throughput phenotyping and precision agriculture tasks. This study demonstrates an automated Python framework that can calculate the solar-sensor zenith and azimuth angles for a push-broom hyperspectral camera equipped in a UAV. First, the hyperspectral images were radiometric

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Avilés-Viñas, Jaime, Roberto Carrasco-Alvarez, Javier Vázquez-Castillo, et al. "An Accurate UAV Ground Landing Station System Based on BLE-RSSI and Maximum Likelihood Target Position Estimation." Applied Sciences 12, no. 13 (2022): 6618. http://dx.doi.org/10.3390/app12136618.

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Earth observation with unmanned aerial vehicles (UAVs) offers an extraordinary opportunity to bridge the gap between field observations and traditional air and space-borne remote sensing. In this regard, ground landing stations (GLS) systems play a central role to increase the time and area coverage of UAV missions. Bluetooth low energy (BLE) technology and the received signal strength indicator (RSSI) techniques have been proposed for target location during UAV landing. However, these RSSI-based techniques present a lack of precision due to the propagation medium characteristics, which leads

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Luo, Lili, Qinrui Chang, Yifan Gao, Danyao Jiang, and Fenling Li. "Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves." Remote Sensing 14, no. 9 (2022): 2271. http://dx.doi.org/10.3390/rs14092271.

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To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimat

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Díaz-Delgado, Ricardo, Gábor Ónodi, György Kröel-Dulay, and Miklós Kertész. "Enhancement of Ecological Field Experimental Research by Means of UAV Multispectral Sensing." Drones 3, no. 1 (2019): 7. http://dx.doi.org/10.3390/drones3010007.

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Although many climate research experiments are providing valuable data, long-term measurements are not always affordable. In the last decades, several facilities have secured long-term experiments, but few studies have incorporated spatial and scale effects. Most of them have been implemented in experimental agricultural fields but none for ecological studies. Scale effects can be assessed using remote sensing images from space or airborne platforms. Unmanned aerial vehicles (UAVs) are contributing to an increased spatial resolution, as well as becoming the intermediate scale between ground me

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Rodríguez-Puerta, Francisco, Rafael Alonso Ponce, Fernando Pérez-Rodríguez, et al. "Comparison of Machine Learning Algorithms for Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne LiDAR Data and Multispectral Images." Drones 4, no. 2 (2020): 21. http://dx.doi.org/10.3390/drones4020021.

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Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensin

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Kuswidiyanto, Lukas Wiku, Hyun-Ho Noh, and Xiongzhe Han. "Plant Disease Diagnosis Using Deep Learning Based on Aerial Hyperspectral Images: A Review." Remote Sensing 14, no. 23 (2022): 6031. http://dx.doi.org/10.3390/rs14236031.

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Plant diseases cause considerable economic loss in the global agricultural industry. A current challenge in the agricultural industry is the development of reliable methods for detecting plant diseases and plant stress. Existing disease detection methods mainly involve manually and visually assessing crops for visible disease indicators. The rapid development of unmanned aerial vehicles (UAVs) and hyperspectral imaging technology has created a vast potential for plant disease detection. UAV-borne hyperspectral remote sensing (HRS) systems with high spectral, spatial, and temporal resolutions h

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Zhou, Xiaoteng, Chun Liu, Akram Akbar, Yun Xue, and Yuan Zhou. "Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality." Remote Sensing 13, no. 22 (2021): 4591. http://dx.doi.org/10.3390/rs13224591.

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Urban river networks have the characteristics of medium and micro scales, complex water quality, rapid change, and time–space incoherence. Aiming to monitor the water quality accurately, it is necessary to extract suitable features and establish a universal inversion model for key water quality parameters. In this paper, we describe a spectral- and spatial-feature-integrated ensemble learning method for urban river network water quality grading. We proposed an in situ sampling method for urban river networks. Factor and correlation analyses were applied to extract the spectral features. Moreov

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Merlaud, Alexis, Frederik Tack, Daniel Constantin, et al. "The Small Whiskbroom Imager for atmospheric compositioN monitorinG (SWING) and its operations from an unmanned aerial vehicle (UAV) during the AROMAT campaign." Atmospheric Measurement Techniques 11, no. 1 (2018): 551–67. http://dx.doi.org/10.5194/amt-11-551-2018.

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Abstract. The Small Whiskbroom Imager for atmospheric compositioN monitorinG (SWING) is a compact remote sensing instrument dedicated to mapping trace gases from an unmanned aerial vehicle (UAV). SWING is based on a compact visible spectrometer and a scanning mirror to collect scattered sunlight. Its weight, size, and power consumption are respectively 920 g, 27 cm × 12 cm × 8 cm, and 6 W. SWING was developed in parallel with a 2.5 m flying-wing UAV. This unmanned aircraft is electrically powered, has a typical airspeed of 100 km h−1, and can operate at a maximum altitude of 3 km. We present S

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Tuominen, S., R. Näsi, E. Honkavaara, et al. "TREE SPECIES RECOGNITION IN SPECIES RICH AREA USING UAV-BORNE HYPERSPECTRAL IMAGERY AND STEREO-PHOTOGRAMMETRIC POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (October 20, 2017): 185–94. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-185-2017.

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Recognition of tree species and geospatial information of tree species composition is essential for forest management. In this study we test tree species recognition using hyperspectral imagery from VNIR and SWIR camera sensors in combination with 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum forest with a high number of tree species was used as a test area. The imagery was acquired from the test area using UAV-borne cameras. Hyperspectral imagery was calibrated for providing a radiometrically corrected reflectance mosaic, which was tested along with

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Lausch, Angela, Jussi Baade, Lutz Bannehr, et al. "Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics." Remote Sensing 11, no. 20 (2019): 2356. http://dx.doi.org/10.3390/rs11202356.

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In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardi

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Zhang, Hengheng, Christian Rolf, Ralf Tillmann, et al. "Comparison of scanning aerosol lidar and in situ measurements of aerosol physical properties and boundary layer heights." Aerosol Research 2, no. 1 (2024): 135–51. http://dx.doi.org/10.5194/ar-2-135-2024.

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Abstract. The spatiotemporal distribution of aerosol particles in the atmosphere has a great impact on radiative transfer, clouds, and air quality. Modern remote sensing methods, as well as airborne in situ measurements by unpiloted aerial vehicles (UAV) or balloons, are suitable tools to improve our understanding of the role of aerosol particles in the atmosphere. To validate the measurement capabilities of three relatively new measurement systems and to bridge the gaps that are often encountered between remote sensing and in situ observation, as well as to investigate aerosol particles in an

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Zhao, Yingxiang, Lumei Zhou, Xiaoli Wang, Fan Wang, and Gang Shi. "Highway Crack Detection and Classification Using UAV Remote Sensing Images Based on CrackNet and CrackClassification." Applied Sciences 13, no. 12 (2023): 7269. http://dx.doi.org/10.3390/app13127269.

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Cracks are a common type of road distress. However, the traditional manual and vehicle-borne methods of detecting road cracks are inefficient, with a high rate of missed inspections. The development of unmanned aerial vehicles (UAVs) and deep learning has led to their use in crack detection and classification becoming an increasingly popular topic. In this paper, an aerial drone is used to efficiently and safely collect road data. However, this also brings many challenges. For example, flying too high or too fast may produce poor quality images, with unclear cracks that may be ignored or misju

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Kezoudi, Maria, Christos Keleshis, Panayiota Antoniou, et al. "The Unmanned Systems Research Laboratory (USRL): A New Facility for UAV-Based Atmospheric Observations." Atmosphere 12, no. 8 (2021): 1042. http://dx.doi.org/10.3390/atmos12081042.

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The Unmanned Systems Research Laboratory (USRL) of the Cyprus Institute is a new mobile exploratory platform of the EU Research Infrastructure Aerosol, Clouds and Trace Gases Research InfraStructure (ACTRIS). USRL offers exclusive Unmanned Aerial Vehicle (UAV)-sensor solutions that can be deployed anywhere in Europe and beyond, e.g., during intensive field campaigns through a transnational access scheme in compliance with the drone regulation set by the European Union Aviation Safety Agency (EASA) for the research, innovation, and training. UAV sensor systems play a growing role in the portfol

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Handique, B. K., C. Goswami, C. Gupta, et al. "HIERARCHICAL CLASSIFICATION FOR ASSESSMENT OF HORTICULTURAL CROPS IN MIXED CROPPING PATTERN USING UAV-BORNE MULTI-SPECTRAL SENSOR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 67–74. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-67-2020.

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Abstract. Assessment of horticultural crops under mixed cropping system has been a challenge, both for horticulturists and also to the remote sensing communities. But the recent developments in wide range of sensors onboard Unmanned Aerial Vehicles (UAVs) has opened up new possibilities in identification, mapping and monitoring of horticultural crops. This paper presents the results made from a pilot exercise on horticultural crop discrimination using Parrot Sequoia multi-spectral sensor onboard a UAV. This exercise was carried out in Nongkhrah village, Ri-Bhoi district of Meghalaya state loca

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Liao, Lihua, Lin Cao, Yaojian Xie, Jianzhong Luo, and Guibin Wang. "Phenotypic Traits Extraction and Genetic Characteristics Assessment of Eucalyptus Trials Based on UAV-Borne LiDAR and RGB Images." Remote Sensing 14, no. 3 (2022): 765. http://dx.doi.org/10.3390/rs14030765.

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Phenotype describes the physical, physiological and biochemical characteristics of organisms that are determined or influenced by genes and environment. Accurate extraction of phenotypic data is a prerequisite for comprehensive forest phenotyping in order to improve the growth and development of forest plantations. Combined with the assessments of genetic characteristics, forest phenotyping will help to accelerate the breeding process, improve stress resistance and enhance the quality of the planted forest. In this study, we disposed our study in Eucalyptus trials within the Gaofeng forest far

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Døssing, Arne, Eduardo Lima Simoes da Silva, Guillaume Martelet, et al. "A High-Speed, Light-Weight Scalar Magnetometer Bird for km Scale UAV Magnetic Surveying: On Sensor Choice, Bird Design, and Quality of Output Data." Remote Sensing 13, no. 4 (2021): 649. http://dx.doi.org/10.3390/rs13040649.

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Magnetic surveying is a widely used and cost-efficient remote sensing method for the detection of subsurface structures at all scales. Traditionally, magnetic surveying has been conducted as ground or airborne surveys, which are cheap and provide large-scale consistent data coverage, respectively. However, ground surveys are often incomplete and slow, whereas airborne surveys suffer from being inflexible, expensive and characterized by a reduced signal-to-noise ratio, due to increased sensor-to-source distance. With the rise of reliable and affordable survey-grade Unmanned Aerial Vehicles (UAV

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