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Journal articles on the topic 'UAV multispectral images'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Rossi, Lorenzo, Irene Mammi, and Filippo Pelliccia. "UAV-Derived Multispectral Bathymetry." Remote Sensing 12, no. 23 (2020): 3897. http://dx.doi.org/10.3390/rs12233897.

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Bathymetry is considered an important component in marine applications as several coastal erosion monitoring and engineering projects are carried out in this field. It is traditionally acquired via shipboard echo sounding, but nowadays, multispectral satellite imagery is also commonly applied using different remote sensing-based algorithms. Satellite-Derived Bathymetry (SDB) relates the surface reflectance of shallow coastal waters to the depth of the water column. The present study shows the results of the application of Stumpf and Lyzenga algorithms to derive the bathymetry for a small area

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Xing, Jian, Chaoyong Wang, Ying Liu, et al. "UAV Multispectral Imagery Predicts Dead Fuel Moisture Content." Forests 14, no. 9 (2023): 1724. http://dx.doi.org/10.3390/f14091724.

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Forest floor dead fuel moisture content (DFMC) is an important factor in the occurrence of forest fires, and predicting DFMC is important for accurate fire risk forecasting. Large areas of forest surface DFMC are difficult to predict via manual methods. In this paper, we propose an unmanned aerial vehicle (UAV)-based forest surface DFMC prediction method, in which a UAV is equipped with a multispectral camera to collect multispectral images of dead combustible material on the forest surface over a large area, combined with a deep-learning algorithm to achieve the large-scale prediction of DFMC

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Oliveira, Romário Porto de, Marcelo Rodrigues Barbosa Júnior, Antônio Alves Pinto, Jean Lucas Pereira Oliveira, Cristiano Zerbato, and Carlos Eduardo Angeli Furlani. "Predicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning." Agronomy 12, no. 9 (2022): 1992. http://dx.doi.org/10.3390/agronomy12091992.

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Multispectral sensors onboard unmanned aerial vehicles (UAV) have proven accurate and fast to predict sugarcane yield. However, challenges to a reliable approach still exist. In this study, we propose to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data through the analysis of multispectral images from UAV onboard sensors. The research was conducted on five varieties of sugarcane, as a way to make a robust approach. Multispectral images were collected every 40 days and the evaluated biometric parameters were: number of tillers (NT), plant h

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Mateen, Ahmed. "LEGION BASED WEED EXTRACTION FROM UAV IMAGERY." Pakistan Journal of Agricultural Sciences 56, no. 04 (2019): 1045–52. http://dx.doi.org/10.21162/pakjas/19.8053.

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Multispectral images are the types of images which consist of many different bands of data for further processing. Vegetation Indexing has Different bands which are combined in the multispectral images and are used to accentuate the vegetated areas. In this one combination is most commonly used LIKE Ratio Vegetation Indexing (RVI). But using UAV imagery this problem has been solved because in UAV the area covered or captured by the cameras would be very wide so that the whole plot or crop can be viewed or captured from the UAV imagery. Legion is a Model used to do Scene Analysis Task, Image Se

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Sun, Mingyue, Qian Li, Xuzi Jiang, Tiantian Ye, Xinju Li, and Beibei Niu. "Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery." Sensors 22, no. 11 (2022): 3990. http://dx.doi.org/10.3390/s22113990.

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Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the mul

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Kazemi, F., and E. Ghanbari Parmehr. "EVALUATION OF RGB VEGETATION INDICES DERIVED FROM UAV IMAGES FOR RICE CROP GROWTH MONITORING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 385–90. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-385-2023.

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Abstract. The unmanned aerial vehicles (UAVs) are widely used for agricultural monitoring due to reduce the cost and time of crop monitoring via the acquisition of images with high spatial-temporal resolution. The normalized difference vegetation index (NDVI) is the most widely studied and used for mapping crop growth. A relatively expensive multispectral sensor is required to produce an NDVI map. The visible vegetation indices (VIs) derived from UAV images showed potential capabilities for predicting crop growth. The purpose of this paper is to evaluate the RGB indices to monitor the growth o

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Yang, Bo, Timothy L. Hawthorne, Hannah Torres, and Michael Feinman. "Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data." Drones 3, no. 3 (2019): 60. http://dx.doi.org/10.3390/drones3030060.

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High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have qu

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Xu, Sizhe, Xingang Xu, Clive Blacker, et al. "Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices and Feature Variable Optimization with Information Fusion of Multiple-Sensor Images from UAV." Remote Sensing 15, no. 3 (2023): 854. http://dx.doi.org/10.3390/rs15030854.

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LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram

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Huo, Langning, Iryna Matsiakh, Jonas Bohlin, and Michelle Cleary. "Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images." Remote Sensing 17, no. 2 (2025): 271. https://doi.org/10.3390/rs17020271.

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Multispectral imagery from unmanned aerial vehicles (UAVs) can provide high-resolution data to map tree mortality caused by pests or diseases. Although many studies have investigated UAV-imagery-based methods to detect trees under acute stress followed by tree mortality, few have tested the feasibility and accuracy of detecting trees under chronic stress. This study aims to develop methods and test how well UAV-based multispectral imagery can detect pine needle disease long before tree mortality. Multispectral images were acquired four times through the growing season in an area with pine tree

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Shin, Jung-il, Won-woo Seo, Taejung Kim, Joowon Park, and Choong-shik Woo. "Using UAV Multispectral Images for Classification of Forest Burn Severity—A Case Study of the 2019 Gangneung Forest Fire." Forests 10, no. 11 (2019): 1025. http://dx.doi.org/10.3390/f10111025.

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Unmanned aerial vehicle (UAV)-based remote sensing has limitations in acquiring images before a forest fire, although burn severity can be analyzed by comparing images before and after a fire. Determining the burned surface area is a challenging class in the analysis of burn area severity because it looks unburned in images from aircraft or satellites. This study analyzes the availability of multispectral UAV images that can be used to classify burn severity, including the burned surface class. RedEdge multispectral UAV image was acquired after a forest fire, which was then processed into a mo

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YE, Huichun, Senzheng CHEN, Anting GUO, Chaojia NIE, and Jingjing WANG. "A dataset of UAV multispectral images for a banana Fusarium wilt survey." China Scientific Data 9, no. 2 (2024): 1–5. http://dx.doi.org/10.11922/11-6035.csd.2023.0008.zh.

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Banana Fusarium wilt, also known as "banana cancer", currently poses a significant threat to banana production worldwide. Timely and accurate identification of Fusarium wilt disease is crucial for effective disease control and optimizing agricultural planting structure. To explore the use of unmanned aerial vehicle (UAV) remote sensing for identifying banana wilt disease, in this study, we obtained comprehensive experimental data on wilted banana plants in a banana plantation in Long'an County, Guangxi. The dataset includes UAV multispectral reflectance data and ground survey data on the incid

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Zhang, Jian, Chufeng Wang, Chenghai Yang, et al. "Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring." Remote Sensing 12, no. 7 (2020): 1207. http://dx.doi.org/10.3390/rs12071207.

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The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 2

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ANDRADE JUNIOR, ADERSON SOARES DE, EDSON ALVES BASTOS, CARLOS ANTONIO FERREIRA DE SOUSA, RAPHAEL AUGUSTO DAS CHAGAS NOQUELI CASARI, and BRAZ HENRIQUE NUNES RODRIGUES. "WATER STATUS EVALUATION OF MAIZE CULTIVARS USING AERIAL IMAGES." Revista Caatinga 34, no. 2 (2021): 432–42. http://dx.doi.org/10.1590/1983-21252021v34n219rc.

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ABSTRACT The objective of this study was to evaluate the water status of maize cultivars through thermal and vegetation indexes generated from multispectral aerial images obtained from an unmanned aerial vehicle (UAV), and correlate them with physiological indicators and soil water contents. The application of three water regimes based on the reference evapotranspiration (ETo) (30%, 90%, and 150% ETo) was evaluated for two maize cultivars (AG-1051 and BRS-Caatingueiro). An UAV was used to acquire thermal and multispectral images. The indexes evaluated were CWSI, CI-G, CI-RE, CIV, NDVI and OSAV

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Ren, Xiang, Min Sun, Xianfeng Zhang, and Lei Liu. "A Simplified Method for UAV Multispectral Images Mosaicking." Remote Sensing 9, no. 9 (2017): 962. http://dx.doi.org/10.3390/rs9090962.

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Qi, Guanghui, Chunyan Chang, Wei Yang, Peng Gao, and Gengxing Zhao. "Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach." Remote Sensing 13, no. 16 (2021): 3100. http://dx.doi.org/10.3390/rs13163100.

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Soil salinization is a significant factor affecting corn growth in coastal areas. How to use multi-source remote sensing data to achieve the target of rapid, efficient and accurate soil salinity monitoring in a large area is worth further study. In this research, using Kenli District of the Yellow River Delta as study area, the inversion of soil salinity in a corn planting area was carried out based on the integration of ground imaging hyperspectral, unmanned aerial vehicles (UAV) multispectral and Sentinel-2A satellite multispectral images. The UAV and ground images were fused, and the partia

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Song, Bonggeun, and Kyunghun Park. "Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index." Remote Sensing 12, no. 3 (2020): 387. http://dx.doi.org/10.3390/rs12030387.

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In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalize

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Tian, Bingquan, Hailin Yu, Shuailing Zhang, et al. "Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition." Agriculture 14, no. 9 (2024): 1452. http://dx.doi.org/10.3390/agriculture14091452.

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In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were

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Wang, Changwei, Yongchong Chen, Zhipei Xiao, et al. "Cotton Blight Identification with Ground Framed Canopy Photo-Assisted Multispectral UAV Images." Agronomy 13, no. 5 (2023): 1222. http://dx.doi.org/10.3390/agronomy13051222.

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Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at a higher accuracy. We selected a cotton field in Shihezi, Xinjiang in China to acquire multispectral images with an unmanned airborne vehicle (UAV); then, fifty-three 50 cm by 50 cm ground framed plots were set with defined coordinates, and a photo of its cotton canopy was taken of each and converted to the L*a*b* colo

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Yu, Junru, Yu Zhang, Zhenghua Song, et al. "Estimating Leaf Area Index in Apple Orchard by UAV Multispectral Images with Spectral and Texture Information." Remote Sensing 16, no. 17 (2024): 3237. http://dx.doi.org/10.3390/rs16173237.

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The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was prop

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Chen, Pei-Chun, Yen-Cheng Chiang, and Pei-Yi Weng. "Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification." Agriculture 10, no. 9 (2020): 416. http://dx.doi.org/10.3390/agriculture10090416.

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An unmanned aerial vehicle (UAV) was used to capture high-resolution aerial images of crop fields. Software-based image analysis was performed to classify land uses. The purpose was to help relevant agencies use aerial imaging in managing agricultural production. This study involves five townships in the Chianan Plain of Chiayi County, Taiwan. About 100 ha of farmland in each township was selected as a sample area, and a quadcopter and a handheld fixed-wing drone were used to capture visible-light images and multispectral images. The survey was carried out from August to October 2018 and aeria

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Kaimaris, Dimitris, and Aristoteles Kandylas. "Small Multispectral UAV Sensor and Its Image Fusion Capability in Cultural Heritage Applications." Heritage 3, no. 4 (2020): 1046–62. http://dx.doi.org/10.3390/heritage3040057.

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For many decades the multispectral images of the earth’s surface and its objects were taken from multispectral sensors placed on satellites. In recent years, the technological evolution produced similar sensors (much smaller in size and weight) which can be placed on Unmanned Aerial Vehicles (UAVs), thereby allowing the collection of higher spatial resolution multispectral images. In this paper, Parrot’s small Multispectral (MS) camera Sequoia+ is used, and its images are evaluated at two archaeological sites, on the Byzantine wall (ground application) of Thessaloniki city (Greece) and on a mo

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Yang, Xin, Shichen Gao, Qian Sun, et al. "Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images." Agriculture 12, no. 7 (2022): 970. http://dx.doi.org/10.3390/agriculture12070970.

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Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and inefficient. The analysis on the accuracy of subdivision categories is insufficient for multi-grade crop lodging. In this study, a classification method of maize lodging extents was proposed based on deep learning algorithms and unmanned aerial vehicle (UAV) RGB and multispectral images. The characteristic variation of three lodging extents in RG

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Sineglazov, Victor, and Kyrylo Lesohorskyi. "Orthophotomosaicing Framework for Thermal and Multispectral Images Collected with a UAV for Intelligent Systems." Electronics and Control Systems 2, no. 84 (2025): 21–28. https://doi.org/10.18372/1990-5548.84.20189.

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In this paper, a framework for orthophotomosaicing of multispectral and thermal images collected by unmanned aerial vehicles is presented. The proposed framework is based on a two-stage data preprocessing and mosaicing orthophotographic restoration of images captured with a route-planned unmanned aerial vehicle collection. The super-resolution and image restoration step is handled via a two-pathway U-net image restoration artificial neural network. The framework simplifies the process and makes the collected data less sensitive to noise via image restoration and upscaling steps. The framework

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Avtar, Ram, Stanley Anak Suab, Mohd Shahrizan Syukur, Alexius Korom, Deha Agus Umarhadi, and Ali P. Yunus. "Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm." Remote Sensing 12, no. 18 (2020): 3030. http://dx.doi.org/10.3390/rs12183030.

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The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored

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Guo, Yahui, J. Senthilnath, Wenxiang Wu, Xueqin Zhang, Zhaoqi Zeng, and Han Huang. "Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform." Sustainability 11, no. 4 (2019): 978. http://dx.doi.org/10.3390/su11040978.

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Unmanned aerial vehicle (UAV) equipped with multispectral cameras for remote sensing (RS) has provided new opportunities for ecological and agricultural related applications for modelling, mapping, and monitoring. However, when the multispectral images are used for the quantitative study, they should be radiometrically calibrated, which accounts for atmospheric and solar conditions by converting the digital number into a unit of scene reflectance that can be directly used in quantitative remote sensing (QRS). Indeed, some of the present applications using multispectral images are processed wit

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Zhang, Yanchao, Wen Yang, Ying Sun, Christine Chang, Jiya Yu, and Wenbo Zhang. "Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification." Remote Sensing 13, no. 8 (2021): 1411. http://dx.doi.org/10.3390/rs13081411.

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Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine lear

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Duan, Jiaqi, Hong Wang, Yuhang Yang, Mingwang Cheng, and Dan Li. "Rice Growth Parameter Estimation Based on Remote Satellite and Unmanned Aerial Vehicle Image Fusion." Agriculture 15, no. 10 (2025): 1026. https://doi.org/10.3390/agriculture15101026.

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Precise monitoring of the leaf area index (LAI) and soil–plant analysis development (SPAD, which represents chlorophyll content) at the field level is crucial for enhancing crop yield and formulating agricultural management strategies. Currently, most studies use multispectral sensors mounted on unmanned aerial vehicles (UAVs) to obtain images, whereby the spectral information is utilized to estimate rice growth parameters. Considering the cost of multispectral sensors and factors influencing rice growth parameters, this study integrated satellite remote sensing images with UAV visible-light i

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Lin, Hong, Zhuqun Chen, Zhenping Qiang, Su-Kit Tang, Lin Liu, and Giovanni Pau. "Automated Counting of Tobacco Plants Using Multispectral UAV Data." Agronomy 13, no. 12 (2023): 2861. http://dx.doi.org/10.3390/agronomy13122861.

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Plant counting is an important part in precision agriculture (PA). The Unmanned Aerial Vehicle (UAV) becomes popular in agriculture because it can capture data with higher spatiotemporal resolution. When it is equipped with multispectral sensors, more meaningful multispectral data is obtained for plants’ analysis. After tobacco seedlings are raised, they are transplanted into the field. The counting of tobacco plant stands in the field is important for monitoring the transplant survival rate, growth situation, and yield estimation. In this work, we adopt the object detection (OD) method of dee

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Wan, Liang, Yijian Li, Haiyan Cen, et al. "Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape." Remote Sensing 10, no. 9 (2018): 1484. http://dx.doi.org/10.3390/rs10091484.

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Remote estimation of flower number in oilseed rape under different nitrogen (N) treatments is imperative in precision agriculture and field remote sensing, which can help to predict the yield of oilseed rape. In this study, an unmanned aerial vehicle (UAV) equipped with Red Green Blue (RGB) and multispectral cameras was used to acquire a series of field images at the flowering stage, and the flower number was manually counted as a reference. Images of the rape field were first classified using K-means method based on Commission Internationale de l’Éclairage (CIE) L*a*b* space, and the result s

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Ma, Qian, Wenting Han, Shenjin Huang, Shide Dong, Guang Li, and Haipeng Chen. "Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images." Sensors 21, no. 6 (2021): 1994. http://dx.doi.org/10.3390/s21061994.

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This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models a

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Leblon, B., A. LaRocque, E. Gallant, K. Clyne, and A. Douglas. "EELGRASS BED MAPPING WITH MULTISPECTRAL UAV IMAGERY IN ATLANTIC CANADA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 649–56. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-649-2022.

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Abstract. Eelgrass (Zostera marina L.) is a marine angiosperm that grows throughout coastal regions in Atlantic Canada. This study aimed to assess the capability of UAV multispectral imagery to map the presence of eelgrass beds within two estuaries in Atlantic Canada (Souris River and Richibucto River). The images were mosaicked using Agisoft and calibrated in reflectance. The corrected images were classified using a non-parametric supervised classifier (Random Forests). The input features of the classification were the UAV band reflectance and associated bathymetric ratios and vegetation indi

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Ge, Chentian, Chao Zhang, Yuan Zhang, Zhekui Fan, Mian Kong, and Wentao He. "Synergy of UAV-LiDAR Data and Multispectral Remote Sensing Images for Allometric Estimation of Phragmites Australis Aboveground Biomass in Coastal Wetland." Remote Sensing 16, no. 16 (2024): 3073. http://dx.doi.org/10.3390/rs16163073.

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Quantifying the vegetation aboveground biomass (AGB) is crucial for evaluating environment quality and estimating blue carbon in coastal wetlands. In this study, a UAV-LiDAR was first employed to quantify the canopy height model (CHM) of coastal Phragmites australis (common reed). Statistical correlations were explored between two multispectral remote sensing data (Sentinel-2 and JL-1) and reed biophysical parameters (CHM, density, and AGB) estimated from UAV-LiDAR data. Consequently, the reed AGB was separately estimated and mapped with UAV-LiDAR, Sentinel-2, and JL-1 data through the allomet

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Sona, Giovanna, Daniele Passoni, Livio Pinto, et al. "UAV MULTISPECTRAL SURVEY TO MAP SOIL AND CROP FOR PRECISION FARMING APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1023–29. http://dx.doi.org/10.5194/isprsarchives-xli-b1-1023-2016.

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New sensors mounted on UAV and optimal procedures for survey, data acquisition and analysis are continuously developed and tested for applications in precision farming. Procedures to integrate multispectral aerial data about soil and crop and ground-based proximal geophysical data are a recent research topic aimed to delineate homogeneous zones for the management of agricultural inputs (i.e., water, nutrients). Multispectral and multitemporal orthomosaics were produced over a test field (a 100 m x 200 m plot within a maize field), to map vegetation and soil indices, as well as crop heights, wi

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Sona, Giovanna, Daniele Passoni, Livio Pinto, et al. "UAV MULTISPECTRAL SURVEY TO MAP SOIL AND CROP FOR PRECISION FARMING APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1023–29. http://dx.doi.org/10.5194/isprs-archives-xli-b1-1023-2016.

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New sensors mounted on UAV and optimal procedures for survey, data acquisition and analysis are continuously developed and tested for applications in precision farming. Procedures to integrate multispectral aerial data about soil and crop and ground-based proximal geophysical data are a recent research topic aimed to delineate homogeneous zones for the management of agricultural inputs (i.e., water, nutrients). Multispectral and multitemporal orthomosaics were produced over a test field (a 100 m x 200 m plot within a maize field), to map vegetation and soil indices, as well as crop heights, wi

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Abi Kaberou, Alain. "Comparative use of UAV and Satellite Images in discrimination and estimation of cashew plantation areas." Journal of Geomatics 18, no. 2 (2024): 71–81. http://dx.doi.org/10.58825/jog.2024.18.2.147.

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Cashew plantations generate significant interest in Benin due to their high socioeconomic value for the population. A thorough understanding of the spatial distribution of these plantations is crucial for comprehending their environmental and socioeconomic impacts. In this study, various types of multi-sensor imagery were compared to assess each sensor's capabilities in mapping plantation areas. The study was conducted in the Savè commune, a major industrial cashew-producing region. Multispectral sensors from Landsat-8 Operational Land Imager (OLI), Sentinel-2A, and UAV multispectral platforms

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Aziz, Nor Hafiza, Rohayu Haron Narashid, Tajul Rosli Razak, et al. "Utilizing Spectral Indices on UAV Multispectral Images for Paddy Healthiness Detection: A Case Study in Perlis, Malaysia." E3S Web of Conferences 557 (2024): 03005. http://dx.doi.org/10.1051/e3sconf/202455703005.

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The increasing global population has brought challenges in expanding and maintaining the productivity levels of paddy. Nowadays, the use of Unmanned Aerial Vehicles (UAV) and multispectral sensors in precision farming has become a prevalent approach in the agriculture sector to enhance efficiency, production, and sustainability in various agricultural activities, including paddy cultivation. In addition, the red edge spectral in multispectral sensor which reflects the rapid change in vegetation is the most suitable for crop studies and very significant to be applied in the computation of spect

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Tidula, Tito Jun T., Willie Jones B. Saliling, and Renel M. Alucilja. "Evaluation of plant reflectance response with elevation using multispectral images captured by an unmanned aerial vehicle (UAV)." Journal of Agricultural Research, Development, Extension, and Technology 2, no. 1 (2020): 1–12. https://doi.org/10.5281/zenodo.8245558.

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The survival, development and productivity of plants can be affected by elevation. Remote sensing has been used to study altitudinal gradient and plant reflectance. Plant reflectance is an important factor for determining plant health and phenology. This study presents a technique to support a better understanding of how plant reflectance is associated with elevation. In particular, this study determined the effect of elevation on reflectance of pineapple. This study was conducted at Polomolok, South Cotabato, Philippines. The Unmanned Aerial Vehicle (UAV) platform, eBee Ag, onboard the Parrot

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Zhang, Qiangzhi, Xiwen Luo, Lian Hu, et al. "Method and Experiments for Acquiring High Spatial Resolution Images of Abnormal Rice Canopy by Autonomous Unmanned Aerial Vehicle Field Inspection." Agronomy 13, no. 11 (2023): 2731. http://dx.doi.org/10.3390/agronomy13112731.

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The yield and quality of rice are closely related to field management. The automatic identification of field abnormalities, such as diseases and pests, based on computer vision currently mainly relies on high spatial resolution (HSR) images obtained through manual field inspection. In order to achieve automatic and efficient acquisition of HSR images, based on the capability of high-throughput field inspection of UAV remote sensing and combining the advantages of high-flying efficiency and low-flying resolution, this paper proposes a method of “far-view and close-look” autonomous field inspect

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Lee, Geunsang, Jeewook Hwang, and Sangho Cho. "A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images." Applied Sciences 11, no. 8 (2021): 3472. http://dx.doi.org/10.3390/app11083472.

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Unmanned aerial vehicles (UAVs) equipped with high-resolution multispectral cameras have increasingly been used in urban planning, landscape management, and environmental monitoring as an important complement to traditional satellite remote sensing systems. Interest in urban regeneration projects is on the rise in Korea, and the results of UAV-based urban vegetation analysis are in the spotlight as important data to effectively promote urban regeneration projects. Vegetation indices have been used to obtain vegetation information in a wide area using the multispectral bands of satellites. UAV

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Daniels, Louis, Eline Eeckhout, Jana Wieme, Yves Dejaegher, Kris Audenaert, and Wouter H. Maes. "Identifying the Optimal Radiometric Calibration Method for UAV-Based Multispectral Imaging." Remote Sensing 15, no. 11 (2023): 2909. http://dx.doi.org/10.3390/rs15112909.

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The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same

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Mia, Md Suruj, Ryoya Tanabe, Luthfan Nur Habibi, et al. "Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data." Remote Sensing 15, no. 10 (2023): 2511. http://dx.doi.org/10.3390/rs15102511.

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Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (UAV)-based remote sensing studies, but weather data have not been considered in modeling. The aim of this study was to explore the potential of multimodal deep learning on rice yield prediction accuracy using UAV multispectral images at the heading stage, along with weather data. The effects of the CNN architectures, layer depths, and weather data i

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Zhang, Pengpeng, Bing Lu, Jiali Shang, et al. "Ensemble Learning for Oat Yield Prediction Using Multi-Growth Stage UAV Images." Remote Sensing 16, no. 23 (2024): 4575. https://doi.org/10.3390/rs16234575.

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Accurate crop yield prediction is crucial for optimizing cultivation practices and informing breeding decisions. Integrating UAV-acquired multispectral datasets with advanced machine learning methodologies has markedly refined the accuracy of crop yield forecasting. This study aimed to construct a robust and versatile yield prediction model for multi-genotyped oat varieties by investigating 14 modeling scenarios that combine multispectral data from four key growth stages. An ensemble learning framework, StackReg, was constructed by stacking four base algorithms—ridge regression (RR), support v

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Ortega-Terol, Damian, David Hernandez-Lopez, Rocio Ballesteros, and Diego Gonzalez-Aguilera. "Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images." Sensors 17, no. 10 (2017): 2352. http://dx.doi.org/10.3390/s17102352.

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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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Andrés, Sarmiento, Taboada G., and Morocho Villie. "An Approach for identifying Crops Types using UAV Images in the Ecuadorian Sierra." Latin-American Journal of Computing 6, no. 2 (2019): 23–30. https://doi.org/10.5281/zenodo.5724957.

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Spectral signature analysis allows identification of the different types of terrestrial objects and characterizes behaviour of different kinds of vegetation. In Ecuador usually phenological analysis (state of vegetal growing) and crop type are based on acquired manually information. This does not allow taking agile decisions over crops management. The advantages for using UAV images propose a significant change to the current methodologies. This paper presented a correlation study of crop spectral signature using multispectral images from a UAV. Ecuadorian Sierra was the study zone to differen

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Zisi, Theodota, Thomas Alexandridis, Spyridon Kaplanis, et al. "Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches." Journal of Imaging 4, no. 11 (2018): 132. http://dx.doi.org/10.3390/jimaging4110132.

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Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The d

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Wu, Jinlong, Xin Wu, and Ronghui Miao. "Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-Net." Agriculture 15, no. 14 (2025): 1471. https://doi.org/10.3390/agriculture15141471.

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Quickly and accurately identifying weed areas is of great significance for improving weeding efficiency, reducing pesticide residues, protecting soil ecological environment, and increasing crop yield and quality. Targeting low detection efficiency in complex agricultural environments and inability of multispectral input in weed recognition of minor grain based on unmanned aerial vehicles (UAVs), a semantic segmentation model for buckwheat weeds based on MSU-Net (multispectral U-shaped network) was proposed to explore the influence of different band optimizations on recognition accuracy. Five s

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Li, Zongpu, Zhiyun Xiao, Yulong Zhou, and Tengfei Bao. "Typical Crop Classification of Agricultural Multispectral Remote Sensing Images by Fusing Multi-Attention Mechanism ResNet Networks." Sensors 25, no. 7 (2025): 2237. https://doi.org/10.3390/s25072237.

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Traditional crop classification methods have three critical limitations: (1) dependency on labor-intensive field surveys with limited spatial coverage, (2) susceptibility to human subjectivity during manual data collection, and (3) the inability to capture fine-grained spectral variations due to the lack of multispectral analysis. This research introduces an enhanced crop classification and identification model based on a residual ResNet network. This model leverages multispectral remote sensing images from unmanned aerial vehicles (UAVs) to accurately classify complex crop planting structures

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Cheshkova, A. F., and V. S. Riksen. "Strawberry Disease Detection Using Multispectral UAV Imagery." Agricultural Machinery and Technologies 19, no. 2 (2025): 45–52. https://doi.org/10.22314/2073-7599-2025-19-2-45-52.

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Accurate, timely, and non-invasive diagnosis of plant diseases is essential in the industrial cultivation of strawberries, as it helps minimize yield losses and reduce treatment costs. With the advancement of unmanned aerial vehicles and sensor technologies, remote sensing has emerged as a promising tool for monitoring crop health and detecting diseases. Early detection is especially important for sensitive crops such as garden strawberries. (Research purpose) The research aims to evaluate the potential of using multispectral sensors and unmanned aerial vehicles for detecting fungal diseases i

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Modak, Sourav, Jonathan Heil, and Anthony Stein. "Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network." Remote Sensing 16, no. 5 (2024): 874. http://dx.doi.org/10.3390/rs16050874.

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Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limite

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