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Current high spatial resolution satellite sensors lack the spectral resolution required for many quantitative remote sensing applications and, given the limited spectral resolution, they only allow the calculation of a limited number of vegetation indices and remote sensing products. Additionally if short revisit time is required for management app...
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... INS/GPS data from the autopilot at the exact time of image acquisition is used to filter out the images acquired during turns and outside the region of interest, as well as images with excessive roll. The exterior orientation (EO) is used also as an initial approximation for the aerotriangulation (AT). In order to provide a good synchronization of the autopilot data (UTC time) and the image acquisition, a second GPS (model Copernicus, Trimble, USA) was used as time source. The image trigger was captured together to the UTC time from the auxiliary GPS receiver using a dedicated microcontroller. The pulse per second (PPS) signal from the GPS was used to ensure time accuracies better than 10ms. Automatic tie points are extracted using the SIFT algorithm (Lowe, 2004) and self made software. This algorithm has shown very robust results as compared with automatic tie point extraction with other photogrammetric software. The images are loaded together with the auxiliary data into the Leica Photogrammetric Suite (LPS, Leica Geosystems, Switzerland), where uniformly-distributed ground control points (GCP) were measured throughout the block. Once the model definition is complete it is possible to run the triangulation adjusting the weight assigned to EO, GCP and image measurements. When the AT is accepted it is possible to create the orthomosaic from the images using ready available high spatial resolution DEM. At present, orthomosaics of 600 images have been successfully generated following this methodology ( figure 7). Several airborne and field campaigns were performed with the multispectral camera using the six 10nm FWHM bands. The average flight height was 150 m yielding 20 cm ground resolution imagery. The Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974) was calculated to assess the estimation of crown leaf area index (cLAI) over a variety field of olive trees (figure 8a). Using an empirical relationship obtained by field measurements of cLAI showed good agreement ( r 2 =0.88, RMSE =0.13), allowing to map the field variability of cLAI for the different olive varieties (figure 8b). The Transformed Chlorophyll Absorption in Reflectance Index (TCARI) (Haboudane, 2002) normalized by the Optimized Soil-Adjusted Vegetation Index (OSAVI) (Rondeaux et al., 1996) to obtain TCARI/OSAVI is demonstrated to successfully minimize soil background and leaf area index variation in crops, providing predictive relationships for chlorophyll concentration estimation with narrow-band imagery in open tree canopy orchards (Zarco-Tejada et al., 2004). The leaf-level radiative transfer model PROSPECT (Jacquemoud & Baret, 1990) was linked with the canopy-level Forest LIGHT Interaction Model (FLIGHT) (North, 1996) and SAILH (Verhoef, 1984) to obtain predicting algorithms for chlorophyll concentration (Cab) from the airborne TCARI/OSAVI index. The comparison between field measured Cab in olive trees at the same variety field and airborne-estimated Cab yielded a RMSE of 4.2 μ g/cm2 and r 2 =0.89 (Berni et al., 2009) showing the capabilities of this system for estimating chlorophyll content at the crown level. The Photochemical Reflectance Index (PRI) (Gamon et al., 1992) was calculated to assess its potential capability for water stress detection from the UAV platform. The PRI index was calculated with the MCA-6 camera using additional 10 nm FWHM filters centered at 530 and 570 nm wavelengths. A flight was conducted over an experimental field of olive trees with deficit irrigation treatments (see Suárez et al., 2009). Water stress levels were quantified by comparing the image- derived PRI and the simulated non-stress PRI (sPRI) obtained through radiative transfer. PRI simulation was conducted using PROSPECT-FLIGHT and PROSPECT-SAILH radiate transfer models. The PRI values of the deficit irrigation treatments (figure 10) were consistently higher than the modelled PRI for the study sites, correlating well with tree xylem water potential ( r 2 =0.84), thus enabling the identification of individual water- stressed trees. Finally, thermal imagery of 40 cm spatial resolution was acquired over the same experimental field used for PRI validation (figure 11). The high spatial resolution allows isolating the tree crowns temperature from the soil and shadows enabling the retrieval of vegetation temperature. Canopy temperature yielded a relationship with water potential of r 2 =0.82 which suggest that this can be a valuable tool to track water stress on heterogeneous crops. This work demonstrated that it is possible to generate quantitative remote sensing products by means of a UAV equipped with commercial off-the-shelf (COTS) thermal and multispectral imaging sensors. Laboratory and field calibration methods provided 6-band 10 nm FWHM multispectral imagery with RMSE of 1.17% in ground reflectance and less that 0.2m spatial resolution. For the thermal camera, atmospheric correction methods based on MODTRAN radiative transfer model showed the successful estimation of surface temperature images of 40 cm spatial resolution, yielding RMSE < 1 K. Photogrammetric techniques were required to register the frame-based imagery to map coordinates. Cameras were geometrically characterized with their intrinsic parameters. These techniques along with position and attitude data gathered from the autopilot enabled the generation of large mosaics semi-automatically with minimum use of ground control points. Appropriate bandset configurations selected for the multispectral camera enabled the calculation of several traditional narrowband vegetation indices (NDVI, TCARI/OSAVI and PRI), which were linked to biophysical parameters using quantitative methods based on physical approaches such as PROSPECT, SAILH, and FLIGHT models. The high spatial, spectral and temporal resolution provided at high turnaround times, make this platform particularly suitable for a number of applications, including precision farming or irrigation scheduling, where time-critical management is required. Financial support from the Spanish Ministry of Science and Innovation (MCI) for the projects AGL2005-04049, EXPLORA INGENIO AGL2006-26038-E/AGR, CONSOLIDER CSD2006-67, and AGL2003-01468 is gratefully acknowledged and in-kind support provided by Bioiberica through the project PETRI PET2005-0616. Technical support from UAV Navigation and Tetracam Inc. is also acknowledged. A. Vera, D. Notario, G. Sepulcre-Cantó, M. Guillén, C. Trapero, I. Calatrava, and M. Ruiz Bernier are acknowledged for measurements and technical support in field and airborne campaigns. Berk, A.; Anderson, G.; Acharya, P.; Chetwynd, J.; Bernstein, L.; Shettle, E.; Matthew, M.; Adler-Golden, S. (1999) Modtran4 user's manual. Berni, J.; Zarco-Tejada, P.; Suarez, L.; Fererez, E. (2009) Thermal and narrow-band multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions On Geoscience And Remote Sensing , 47, 722-738. Bouguet, J. (2001) Camera calibration toolbox for matlab. (accessed 15 Apr. 2009) Gamon, J.; Penuelas, J.; Field, C. (1992) A narrow-waveband spectral index that tracks diurnal changes in potosynthetic efficiency. Remote Sensing of Environment , 41, 35-44. Haboudane, D. and Miller, J. R. and Tremblay, N. and Zarco- Tejada, P. J. and Dextraze, L. (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing Of Environment , 81, 416-426. Jacquemoud, S.; Baret, F. (1990) Prospect: a model of leaf optical properties spectra. Remote Sensing of Environment , 34, 75-91. Lowe, D. (2004) Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. , 60, 91-110. North, P.R.J. (1996) Three-dimensional forest light interaction model using a montecarlo method. IEEE Transactions on Geosciences and Remote Sensing , 34, 946-956. Rondeaux, G.; Steven, M.; Baret, F. (1996) Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment , 55(2), 95 − 107. Rouse JW, Haas RH, Schell JA, Deering DW, Harlan JC (1974) Monitoring the vernal advancements and retrogradation of natural vegetation. In: MD UG, editor. NASA/GSFS final report. . p. 371. Suárez, L.; Zarco-Tejada, P.; Berni, J.; González-Dugo, V.; Fereres, E. (2009) Modelling PRI for water stress detection using radiative transfer models. Remote Sensing of Environment , 113, 730-744. Verhoef, W. (1984) Light scattering by leaf layers with application to canopy reflectance modeling: the sail model. Remote Sensing of Environment , 16, 125-141. Zarco-Tejada, P.; Miller, J.; Morales, A.; Berjon, A.; Aguera, J. (2004) Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing Of Environment , 90, ...
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... Remote sensing (RS) is used to generate high spatial/spectral/radiometric/temporal resolutions required for effective precision agriculture [5]. Some of the applications of RS in agriculture include monitoring crop health over the growing season, predicting crop yield, and determining the schedule, frequency, and quantity of inputs (e.g., application of irrigation water, fertilizer, pesticide, seed, fuel, labor, etc.) [2,[6][7][8]. Vegetation indices (VIs) are one of the products derived from satellite remote sensing or multispectral sensors mounted on unmanned aerial vehicles (UAVs). Currently, there are over 100 VIs that can be computed from different light spectra, instruments, platforms, and resolutions [9]. ...
Sustainable crop production requires adequate and efficient management practices to reduce the negative environmental impacts of excessive nitrogen (N) fertilization. Remote sensing has gained traction as a low-cost and time-efficient tool for monitoring and managing cropping systems. In this study, vegetation indices (VIs) obtained from an unmanned aerial vehicle (UAV) were used to detect corn ( Zea mays L.) response to varying N rates (ranging from 0 to 208 kg N ha ⁻¹ ) and fertilizer application methods (liquid urea ammonium nitrate (UAN), urea side-dressing and slow-release fertilizer). Four VIs were evaluated at three different growth stages of corn (V6, R3, and physiological maturity) along with morphological traits including plant height and leaf chlorophyll content (SPAD) to determine their predictive capability for corn yield. Our results show no differences in grain yield (average 13.2 Mg ha ⁻¹ ) between furrow-applied slow-release fertilizer at ≥156 kg N ha ⁻¹ and 208 kg N ha ⁻¹ side-dressed urea. Early season remote-sensed VIs and morphological data collected at V6 were least effective for grain yield prediction. Moreover, multivariate grain yield prediction was more accurate than univariate. Late-season measurements at the R3 and mature growth stages using a combination of normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) in a multilinear regression model showed effective prediction for corn yield. Additionally, a combination of NDVI and normalized difference red edge index (NDRE) in a multi-exponential regression model also demonstrated good prediction capabilities.
... Besides autonomous fixed-wing UAVs, rotorcraft UAVs (both single-and multi-rotor) have also been widely developed in the past. In the documented works by [66] and [67], the researchers have modified a commercially available RC helicopter, Benzin Acrobatic ( Fig. 12(a)), to be equipped with an MCA-6 six-band multi-spectral camera used for thermal and multi-spectral sensing of wheat water stress through thermal imaging and NDVI measurements. In Spain, the MD4-1000 UAV from Microdrones ( Fig. 12(b)) equipped with a similar MCA-6 multi-spectral camera from Tetracam Inc. was used for mapping weeds in sunflower crop through accurate orthomosaic-ed multi-spectral UAV images [68], [69]. ...
The applications of remote sensing technologies to precision agriculture were reviewed. The different uses of conventional satellites and unmanned aerial vehicles (UAVs) for data collection were also examined. The ways in which the collected data have been used in terms of precision agriculture are laid out in further detail. Modern UAVs provide a range of advantages over conventional satellites in data collection and analysis. In addition, precision agriculture itself is a wide topic; and practitioners, researchers, and policy planners need to account for a complex value chain with diverse stakeholders. Thus, in order to further the discussion in a meaningful manner, the later sections of this article provide additional analysis on topics deemed crucial for the development of precision agriculture: a discussion on key technological limitations for scaling, a corresponding list of suggestions for the development of solutions, and a brief overview on the current social-economical landscape of the agriculture industry.
... However, due to the long monitoring time and small coverage area, it cannot be widely promoted. Compared with the above-described methods, UAV platforms are characterized by small size, lightweight and low cost [22,23]. UAVs can be equipped with visible light and thermal infrared detection equipment and perform near-real-time data transmission and processing, enabling rapid and large-scale inspection of embankment hazards. ...
Piping is a major factor contributing to river embankment breaches, particularly during flood season in small and medium rivers. To reduce the costs of earth rock embankment inspections, avoid the need for human inspectors and enable the quick and widespread detection of piping hazards, a UAV image-acquisition function was introduced in this study. Through the collection and analysis of thermal infrared and visible (TIR & V) images from several piping field simulation experiments, temperature increases, and diffusion centered on the piping point were discovered, so an automatic algorithm for piping identification was developed to capture this phenomenon. To verify the identification capabilities, the automatic identification algorithm was applied to detect potential piping hazards during the 2022 flooding of the Dingjialiu River, Liaoning, China. The algorithm successfully identified all five piping hazard locations, demonstrating its potential for detecting embankment piping.
... The main benefits (detailed descriptions in (Anderson and Gaston, 2013;Assmann et al., 2019;Rango et al., 2006) that make drone technology suitable for rangeland monitoring are that it provides data with exceptional detail at centimeter to decimeter spatial resolutions (i. e., may be equivalent to pure pixels of ecosystem features) and at userdefined temporal resolutions (Berni et al., 2009;Gallacher, 2019). In addition, miniaturized drone multispectral sensors with narrow spectral bandwidths and high radiometric quality are readily available (Aasen et al., 2018;Anderson and Gaston, 2013;Assmann et al., 2019). ...
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The purpose of the study is to determine the needs of modern Russian agriculture for specialists with certain, most-in-demand, digital competencies. The research methodology is based on the application of the expert assessments method, the method of random statistical selection of experts, and the scientific generalization method. The field of the research is modern digital technologies in agriculture, as well as the corresponding competencies of Russian agricultural university graduates. The study period is 2021–2022. Having acquired competencies of a modern agricultural specialist at the university should help graduates to integrate into the production process as quickly as possible, the latter undergoing qualitative changes due to the transition to a new technological order based on the use of digital technologies. The study of the current curricula of Russian agricultural universities has shown their lack of adequacy regarding the modern requirements of agricultural production. It indicates the need to examine the curricula. Taking into account the fact that digital technologies are rapidly improving and being introduced into agricultural production, further research in this area should be conducted annually in order to increase the compliance of training at universities according to modern requirements of agricultural production.
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Estimating the quantitative distribution of wind erosion rates is one of the most important requirements for managing affected environments and optimizing locations to control wind erosion. This study develops high-resolution maps for wind erosion with unmanned aerial vehicles or UAV images in the Sistan region—an area in southeastern Iran with severe wind erosion. Aerial imaging by UAV was done during period of erosive winds. Changes in the amount of wind erosion were measured for 7 months. Digital elevation models or DEM with a spatial resolution of 6 mm, orthophoto mosaic images with a resolution of 3 mm, were prepared before and after the erosion event. Three erosive facies consisting of surface, edge, and blowout were identified. The amount of erosion in different geomorphological landscapes or facies was measured according to differences of DEMs (DOD). The effect of physical factors of the geomorphological landscapes on wind erosion was investigated by calculating the correlation between the erosion, roughness, and slope in the geomorphological landscapes. The results showed that the highest and lowest mean of eroded soil were 22 mm and 4 mm in the blowout and surface facies, respectively. The average rate of wind erosion was 201 t/ha during the study period, which indicates the high intensity of wind erosion in the Sistan plain. Overall, UAV—as an aerial imaging technique collecting ground data—can be a helpful tool in the aeolian geomorphology especially for collecting data for measuring the rate of soil erosion by the wind in the aeolian landscapes located in remote regions.
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Extreme hydro-meteorological events become an increasing risk in high mountain environments, resulting in erosion events that endanger human infrastructure and life. Vegetation is known to be an important stabilizing factor; however, little is known about the spatial patterns of species composition in glacial forelands. This investigation aims to differentiate sparse vegetation in a steep alpine environment in the Austrian part of the Central Eastern Alps using low-cost multispectral cameras on an unmanned aerial vehicle (UAV). Highly resolved imagery from a consumer-grade UAV proved an appropriate basis for the SfM-based modeling of the research area as well as for vegetation mapping. Consideration must be paid to changing light conditions during data acquisition, especially with multispectral sensors. Different approaches were tested, and the best results were obtained using the Random Forest (RF) algorithm with the target class discrimination based on the RGB orthomosaic and the DEM as supplementary dataset. Our work contributes to the field of biogeomorphic research in proglacial areas as well as to the field of small-scale remote sensing and vegetation measuring. Our findings show that the occurrence of vegetation patches differs in terms of density and diversity within this relatively recent deglaciated environment.
