On the Use of Unmanned Aerial Systems for Environmental Monitoring

Manfreda, Salvatore; McCabe, Matthew F.; Miller, Pauline E.; Lucas, Richard; Madrigal, Victor Pajuelo; Mallinis, Giorgos; Dor, Eyal Ben; Helman, David; Estes, Lyndon; Ciraolo, Giuseppe; Müllerová, Jana; Tauro, Flavia; Lima, M. Isabel De; Lima, João L.M.P. de; Sabater, Francesc; Caylor, K. K.; Kohv, Marko; Perks, Matthew; Ruiz-Pérez, Guiomar; Su, Zhongbo; Vico, Giulia; Szabó, Brigitta

doi:10.3390/rs10040641

Cited by 589 publications

(444 citation statements)

References 152 publications

(174 reference statements)

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“…In recent years, the development of UAV-based hyperspectral recording systems has made rapid progress [6]. In comparison to manned aircraft based systems, the sensors are smaller, lighter, and less costly during acquisition and processing [7]. The great potential of this technology has been demonstrated [8].Hyperspectral snapshot camera systems are frequently used, as they are easier to handle in comparison to pushbroom cameras, especially when mosaicking images [6].…”

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confidence: 99%

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

et al. 2018

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The efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400-1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R 2 LAI = 0.79, RMSE LAI [m 2 m −2 ] = 0.18, R 2 CHL = 0.77, RMSE CHL [µg cm −2 ] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R 2 yield = 0.88, RMSE yield [dt ha −1 ] = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield.From the farmer's perspective, the most important economic parameter is achieved yields. An overdose of N fertilizer, within the legal limits, results higher costs without adding value in terms of additional yield. Further possible regulations for the application of fertilizers should only have a limited negative impact on yields. With controlled experiments, directly comparing the harvested yield resulting from different N applications, one can identify the effects of reduced fertilization. Moreover, new concepts of monitoring these effects during vegetative growth enables the development of precision farming applications, especially created for efficient N fertilization [3].Remote sensing technology at various scales has often proved to be a suitable tool for agricultural crop monitoring [4]. In particular, UAV-supported remote sensing enables very precise monitoring of individual areas through lower flight altitudes and high-resolution data [5]. In recent years, the development of UAV-based hyperspectral recording systems has made rapid progress [6]. In comparison to manned aircraft based systems, the sensors are smaller, lighter, and less costly during acquisition and processing [7]. The great potential of this technology has been demonstrated [8].Hyper...

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confidence: 99%

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

et al. 2018

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“…In recent years, space-borne and airborne remote sensing technologies have been developing rapidly and monitoring of topographic displacements and deformations, depending on construction activities or natural disasters has become possible by temporal change detection analysis. With the development of airborne laser scanning (ALS) technology, threedimensional (3D) description of the topographic surface became easier by means of very high resolution (VHR), rapid achievable and accurate point clouds that could not been provided by previous remote sensing technologies (Deng et al, 2007;Darwin et al, 2014;Höhle, 2017;Manfreda et al, 2018a). Considering high surface description potential, point cloud thought was adapted to photogrammetric image processing following ALS (Teizer et al, 2005;Rosnell and Honkavaara, 2012).…”

Section: Introductionmentioning

confidence: 99%

Precise Monitoring of Temporal Topographic Change Detection via Unmanned Air Vehicle

Karakis¹,

Sefercik²,

Bilir³

et al. 2019

Bulletin of the Mineral Research and Exploration

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Nowadays, fast developing space-borne and airborne remote sensing technologies became indispensable for land related engineering disciplines such as mapping, geology, environment, mining and forestry. The new technologies, provide more qualified and rapid achievable outcomes, are adopted permanently. The description of the topographic surface became easier by means of very high resolution (VHR), rapid achievable and accurate point clouds acquired by digital photogrammetry and airborne laser scanning (ALS). Optical unmanned air vehicle (UAV), one of the most actual photogrammetric techniques, is much in demand for varied purposes. UAVs provide high resolution data using the advantage of lower flight altitudes. In this study, a construction activity and its environmental influences in Bulent Ecevit University Central Campus were monitored by an optical hand-made UAV. In the application, the temporal change was detected by generating contour-lines, digital terrain models (DTMs) and differential DTMs (DiffDTM) of the topography. By DiffDTMs, temporal changes on the topography were visualized in color height scale where the contour-lines presents the change of morphological structure.

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“…Post-fire remote sensing applications aim to provide timely spatial and spectral information regarding forest structure, composition, and vigor during regeneration [1]. Moderate resolution satellite imagery does not provide optimal spatial resolution for individual tree detection (IDT), or in cases where high-spatial resolution is available, and may be costly [2]. Satellite remote sensing also suffers from a lack of flexibility in temporal resolution and is susceptible to atmospheric noise [3].…”

Section: Introductionmentioning

confidence: 99%

“…Unmanned aerial systems provide an excellent alternative to satellite imagery, providing higher spatial resolution and temporal frequency at a much more cost-effective rate than manned aircraft and satellite sensors [2,5]. Limitations to their use include small payload capacity, limited battery life, and a lack of professional standards for their use [2,5]. There is a critical need to establish standard protocols to produce reproducible, repeatable workflows.…”

Section: Introductionmentioning

confidence: 99%

UAS-GEOBIA Approach to Sapling Identification in Jack Pine Barrens after Fire

White

Bomber

Hupy

et al. 2018

Drones

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Jack pine (pinus banksiana) forests are unique ecosystems controlled by wildfire. Understanding the traits of revegetation after wildfire is important for sustainable forest management, as these forests not only provide economic resources, but also are home to specialized species, like the Kirtland Warbler (Setophaga kirtlandii). Individual tree detection of jack pine saplings after fire events can provide information about an environment's recovery. Traditional satellite and manned aerial sensors lack the flexibility and spatial resolution required for identifying saplings in early post-fire analysis. Here we evaluated the use of unmanned aerial systems and geographic object-based image analysis for jack pine sapling identification in a region burned during the 2012 Duck Lake Fire in the Upper Peninsula of Michigan. Results of this study indicate that sapling identification accuracies can top 90%, and that accuracy improves with the inclusion of red and near infrared spectral bands. Results also indicated that late season imagery performed best when discriminating between young (<5 years) jack pines and herbaceous ground cover in these environments.Drones 2018, 2, 40 2 of 15 as a valid and low-cost method to generate both orthomosaics and digital surface models (DSMs) derived from 2D image sequences [5,6]. In their study of SfM derived IDT, Reference [7] performed ITD using UAS-SfM derived canopy height models based on algorithms designed for LiDAR data processing. The authors achieved the most accurate results for smoothing window size (SWS) at 3 × 3 irrespective of the fixed window size (FWS). In their assessment of models utilizing SWS and FWS of 3 × 3, the authors achieved a statistical F-scores greater than 0.80. Reference [8] reconstructed poplar saplings using digital photographs and terrestrial LiDAR (T-LiDAR), finding that T-LiDAR was more accurate at 3D construction than digital photographs, but at a much higher cost. Reference [9] examined the potential contribution hyperspectral imagery makes to IDT, achieving accuracies between 40% and 95% in tree detection. A comparison of LiDAR and SfM technology by Reference [10] indicated achieved accuracies of 96% and 80%, respectively, and the authors concluded that the technologies were capable of producing equally acceptable results for plot level estimates. These studies indicate that photogrammetric methods can provide accurate results for identifying tree crowns; however, none of these studies addressed sapling identification in natural environments. Additionally, processing photogrammetric datasets like SfM and LiDAR are computationally intensive for large areas. Finally, Reference [11] developed a land cover classification using multi-view data using a conditional random field (CRF) model, leading to accuracy improvements between 6% and 16.4% for a variety of classification methods. While these methods show promise of integrating multiple image view points for constructing classifications, we posit that there is still a need to develop robust low-cost (...

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On the Use of Unmanned Aerial Systems for Environmental Monitoring

Cited by 589 publications

References 152 publications

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

Precise Monitoring of Temporal Topographic Change Detection via Unmanned Air Vehicle

UAS-GEOBIA Approach to Sapling Identification in Jack Pine Barrens after Fire

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