Optimal UAV Flight altitude for Multispectral monitoring of Wheat growth in Bayantsogt, Mongolia

Arthropod biomass is a key element in ecosystem functionality and a basic food item for many species. It must be estimated through traditional costly field sampling, normally at just a few sampling points. Arthropod biomass and plant productivity should be narrowly related because a large majority of arthropods are herbivorous, and others depend on these. Quantifying plant productivity with satellite or aerial vehicle imagery is an easy and fast procedure already tested and implemented in agriculture and field ecology. However, the capability of satellite or aerial vehicle imagery for quantifying arthropod biomass and its relationship with plant productivity has been scarcely addressed. Here, we used unmanned aerial vehicle (UAV) and satellite Sentinel‐2 (S2) imagery to establish a relationship between plant productivity and arthropod biomass estimated through ground‐truth field sampling in shrub steppes. We UAV‐sampled seven plots of 47.6–72.3 ha at a 4‐cm pixel resolution, subsequently downscaling spatial resolution to 50 cm resolution. In parallel, we used S2 imagery from the same and other dates and locations at 10‐m spatial resolution. We related several vegetation indices (VIs) with arthropod biomass (epigeous, coprophagous, and four functional consumer groups: predatory, detritivore, phytophagous, and diverse) estimated at 41–48 sampling stations for UAV flying plots and in 67–79 sampling stations for S2. VIs derived from UAV were consistently and positively related to all arthropod biomass groups. Three out of seven and six out of seven S2‐derived VIs were positively related to epigeous and coprophagous arthropod biomass, respectively. The blue normalized difference VI (BNDVI) and enhanced normalized difference VI (ENDVI) showed consistent and positive relationships with arthropod biomass, regardless of the arthropod group or spatial resolution. Our results showed that UAV and S2‐VI imagery data may be viable and cost‐efficient alternatives for quantifying arthropod biomass at large scales in shrub steppes. The relationship between VI and arthropod biomass is probably habitat‐dependent, so future research should address this relationship and include several habitats to validate VIs as proxies of arthropod biomass.

More than 66% of the Nepalese population has been actively dependent on agriculture for their day-to-day living. Maize is the largest cereal crop in Nepal, both in terms of production and cultivated area in the hilly and mountainous regions of Nepal. The traditional ground-based method for growth monitoring and yield estimation of maize plant is time consuming, especially when measuring large areas, and may not provide a comprehensive view of the entire crop. Estimation of yield can be performed using remote sensing technology such as Unmanned Aerial Vehicles (UAVs), which is a rapid method for large area examination, providing detailed data on plant growth and yield estimation. This research paper aims to explore the capability of UAVs for plant growth monitoring and yield estimation in mountainous terrain. A multi-rotor UAV with a multi-spectral camera was used to obtain canopy spectral information of maize in five different stages of the maize plant life cycle. The images taken from the UAV were processed to obtain the result of the orthomosaic and the Digital Surface Model (DSM). The crop yield was estimated using different parameters such as Plant Height, Vegetation Indices, and biomass. A relationship was established in each sub-plot which was further used to calculate the yield of an individual plot. The estimated yield obtained from the model was validated against the ground-measured yield through statistical tests. A comparison of the Normalized Difference Vegetation Index (NDVI) and the Green-Red Vegetation Index (GRVI) indicators of a Sentinel image was performed. GRVI was found to be the most important parameter and NDVI was found to be the least important parameter for yield determination besides their spatial resolution in a hilly region.

Food demand is expected to rise significantly by 2050 due to the increase in population; additionally, receding water levels, climate change, and a decrease in the amount of available arable land will threaten food production. Yield precision maps, an application of precision agriculture, can be used by farmers to address these challenges by reducing input costs and optimizing yield. These maps can be created using machine learning models trained on field data (e.g., imagery data). Although performing satellite-based remote sensing to gather imagery has some advantages, using unmanned aerial vehicles (UAV)s is favorable over satellite-based approaches due to the higher spatial and temporal resolutions of the imagery. Vegetation indices (VI)s can be computed from the imagery and can represent the state or condition of vegetation. The present work performed yield prediction regression experiments that analyzed the effects of image spatial resolution (satellite vs. UAV) on prediction results, compared and ranked the prediction power of 33 VIs (and 5 raw-bands) over the growing season, and explored the optimal image acquisition date that produced the best prediction results. We gathered yield data and UAV-based multispectral imagery from a Canadian smart farm and trained random forest (RF) and linear regression (LR) machine learning models. High spatial resolution data generally led to better prediction results than lower spatial resolution data, especially for the RF model, where regardless of the VI choice or image acquisition date, good results were obtained. VIs that included the near-infrared and/or red-edge band generally performed better than the red-green-blue (RGB) VIs. The best performing VIs were: simple ratio index (near-infrared (NIR) & red-edge), normalized difference vegetation index with red-edge instead of red, normalized green index, green chlorophyll index, and simple ratio index (NIR & green). When higher spatial resolution imagery was available, optimized soil-adjusted vegetation index and renormalized difference vegetation index also performed well. We found that imagery from the middle of the growing season produced the best prediction results, even for some RGB VIs and raw-bands.

Multispectral imaging using Unmanned Aerial Vehicles (UAVs) has changed the pace of precision agriculture. Actual evapotranspiration (ETa) from the very high spatial resolution of UAV images over agricultural fields can help farmers increase their production at the lowest possible cost. ETa estimation using UAVs requires a full package of sensors capturing the visible/infrared and thermal portions of the spectrum. Therefore, this study focused on a multi-sensor data fusion approach for ETa estimation (MSDF-ET) independent of thermal sensors. The method was based on sharpening the Landsat 8 pixels to UAV spatial resolution by considering the relationship between reference ETa fraction (ETrf) and a Vegetation Index (VI). Four Landsat 8 images were processed to calculate ETa of three UAV images over three almond fields. Two flights coincided with the overpasses and one was in between two consecutive Landsat 8 images. ETrf was chosen instead of ETa to interpolate the Landsat 8-derived ETrf images to obtain an ETrf image on the UAV flight. ETrf was defined as the ratio of ETa to grass reference evapotranspiration (ETr), and the VIs tested in this study included the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Land Surface Water Index (LSWI). NDVI performed better under the study conditions. The MSDF-ET-derived ETa showed strong correlations against measured ETa, UAV- and Landsat 8-based METRIC ETa. Also, visual comparison of the MSDF-ET ETa maps was indicative of a promising performance of the method. In sum, the resulting ETa had a higher spatial resolution compared with thermal-based ETa without the need for the Albedo and hot/cold pixels selection procedure. However, wet soils were poorly detected, and in cases of continuous cloudy Landsat pixels the long interval between the images may cause biases in ETa estimation from the MSDF-ET method. Generally, the MSDF-ET method reduces the need for very high resolution thermal information from the ground, and the calculations can be conducted on a moderate-performance computer system because the main image processing is applied on Landsat images with coarser spatial resolutions.

<p>Magnetic mapping is commonly used in the academic and industrial sectors for a wide variety of objectives. To comply with a broad range of survey designs, the use of unmanned aerial vehicles (UAVs) has become frequent over the recent years. The majority of existing systems involves a magnetic acquisition equipment and its carrier (an UAV in this context) with no -or very few- connections between the two systems. Terremys is conceiving and optimizing UAVs specifically adapted for geophysical magnetic acquisitions together with the appropriate processing tools, and performs magnetic surveying in challenging environments. Terremys’ “Q6” system weights 2.5 kg in air, including UAV & instrumentation, and allows 30 min swarm or individual flights.</p><p>Rotary-wing UAVs are found to be the most adaptive systems for a wide range of contexts and constraints (extensive range of flights heights even with steep slopes). They offer more flight flexibility than fixed-wing aircrafts. One of the major problems in the use of rotary-wings UAVs for magnetic mapping is the magnetic field generated by the aircraft itself on the measurements. Towing the magnetic sensor 2 to 5 m under the aircraft reduces data positioning accuracy and decreases the performances of the UAV, which can be critical for high-resolution surveys. To overcome these problems, a deployable 1 m long boom is rigidly attached to the UAV. The UAV magnetic signal can be divided between 1-the magnetic field of the whole equipment and 2-a low to high frequency magnetic field mostly originating from the motors. The magnetization of the system is the principal source of magnetic noise. It is modelled and corrected by calibration-compensation processes permitted by the use of three-component fluxgate magnetometers. The time-varying noise depends on the motors rotational speed and is minimized by optimizing the UAV components and characteristics along with the boom’s length.</p><p>The final set-up is able to acquire magnetic data with a precision of 1 to 5 nT at any height from 1 to 150 m above ground level. The high-precision magnetic measurements are coupled with a centimetric RTK navigation system to allow for high-resolution surveying. The quality of the obtained data is similar to that obtained with ground or aerial surveys with conventional carriers and matches industrial standards. Moreover, Terremys’ systems merge in real-time data from all the aircraft instruments in order to integrate magnetic measurements, positioning information and all the UAV’s flight data (full telemetry) into a unique synchronized data file. This opens up many possibilities in terms of QA/QC, data processing and facilitates on-field workflows.</p><p>Case studies with diverse designs, flight altitudes and targets are presented to investigate the acquisition performances for different applications, as distinct as network positioning, archaeological prospecting or geological mapping.</p><p>The full integration of the magnetic sensor to the drone opens the possibility for implementation additional sensors to the system. The adjoining of other magnetic sensors would allow multi-sensors surveying and increases daily productivity. Diverse geophysical sensors can also be added, such as thermal/infrared cameras, spectrometers, radar/SAR.</p>

Summary Precision agriculture has increasingly incorporated the use of Unmanned Aerial Vehicles (UAVs) equipped with multispectral cameras. This study examined the influence of different UAV flight altitudes on the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Red Edge (NDRE), Optimized Soil-Adjusted Vegetation Index (OSAVI), and Leaf Chlorophyll Index (LCI), indices critical to crop monitoring and health assessment. The experiment was conducted on a 2-hectare winter wheat field at the Institute of Field and Vegetable Crops in Novi Sad, Serbia. The field was divided into 400 plots, each containing different wheat varieties subjected to twenty distinct combinations of artificial mineral fertilizer (NPK) treatments. A DJI P4 Multispectral drone was employed to capture images at altitudes of 30, 60, and 90 meters on three separate dates, corresponding to different plant growth stages: May 9, May 20, and June 6, 2022. All other operating parameters were held constant. The data were processed using the DJI Terra and Pix4D software to generate orthomosaic maps, which were subsequently analyzed using ArcGIS (v10.5, ESRI, Redlands, CA, USA) to calculate the multispectral index values for each plot. The results were statistically analyzed using the STATISTICA Tibco software. The analysis revealed significant differences in the index values based on the UAV flight altitude (p < 0.05). This research underscores the centrality of selecting the optimal UAV flight altitude to ensure the accuracy and reliability of data. While higher altitudes enable UAVs to cover larger areas in a single flight, factors such as image resolution, wind conditions, and the precision of crop health indicators must be considered. These findings offer valuable insights for agricultural professionals seeking to improve crop monitoring and ultimately enhance agricultural productivity through more effective UAV deployment.

Recently near-ground remote sensing using unmanned aerial vehicles (UAV) witnessed wide applications in obtaining field information. In this research, four Rapideye satellite images and eight RGB images acquired from UAV were used from early June to the end of July, 2015 covering two experimental winter wheat fields, in order to monitor wheat canopy growth status and analyze the correlation among satellite images based normalized difference vegetation index (NDVI) with UAV’s RGB images based visible-band difference vegetation index (VDVI) and ground variables of the sampled grain protein contents. Firstly, through image interpretation of UAV’s multi-temporal RGB images with fine spatial resolution, the wheat canopy color changes could be intuitively and clearly monitored. Subsequently, by monitoring the changes of satellite images based NDVI as well as VDVI values and UAV’s RGB images based VDVI values, the conclusions were made that these three vegetation indices demonstrated the same and synchronized trend of increasing at the early stage of wheat growth season, reaching up to peak values at the same timing, and starting to decrease since then. The results of the correlation analysis between NDVI of satellite images and sampled grain protein contents show that NDVI has good predicative capability for mapping grain protein content before ripening growth stage around June7, 2015, while the reliability of using satellite image based NDVI to predict grain protein contents becomes worse as ripening stage approaches. The regression analysis between UAV’s RGB image based VDVI and satellite image based VDVI as well as NDVI showed good coefficients of determination. It is concluded that it is feasible and practical to temporally complement satellite remote sensing by using UAV’s RGB images based vegetation indices to monitor wheat growth status and to map within-field spatial variations of grain protein contents for small scale farmlands. Keywords: satellite remote sensing, UAV remote sensing, wheat growth monitoring, wheat lodging; wheat protein content, multi-temporal images, NDVI DOI: 10.25165/j.ijabe.20171005.3180 Citation: Du M M, Noguchi N, Itoh A, Shibuya Y. Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle. Int J Agric & Biol Eng, 2017; 10(5): 1–13.

Oilseed crops are among the product groups with a supply deficit in the world. The sunflower oil crisis experienced after 2020 ha increased the importance of sunflower cultivation. The most important stages in agricultural applications are to understand whether the plant is healthy in the early stages before it is formed and to prevent negative results in harvest. With the developing technology, the use of unmanned aerial vehicles (UAVs) and multispectral cameras in agricultural applications has gained enormous importance. Thanks to UAVs, agricultural temporal resolution can be adjusted according to the user's request, and spatial resolution can be adjusted according to the ability of the sensor used and the flight altitude. Spectral resolution is directly proportional to the number of bands and the band wavelength. We performed correlation analysis in this study by comparing the accuracy of the band values with ground measurements made with a spectroradiometer. We measured the sunflower in its vegetative, R-3, and R-5 phases and found that there was a strong correlation (r=0.894) in the green band, r=0.845 in the red, r=0.789 in the red edge (RE) band, and r=0.725 in the near infrared band (NIR). The results show a strong connection between the spectral bands and the spectroradiometer measurements, especially in the green and red bands.

The use of unmanned aerial vehicles allows the countries that use them to significantly reduce the loss of manpower and equipment during the combat mission and at the same time significantly increase the effectiveness of the use of high-precision and conventional means of destruction. The greatest experience in the use of unmanned aerial vehicles was acquired by countries that are actually advanced in terms of military technology (in particular, the USA, Israel, Turkey, etc.), which took an active part in armed conflicts in the Middle East, the North Caucasus, etc. In addition, in modern conditions, the threat of uncontrolled spread of the use of unmanned aerial vehicles of a light class, which can be used for the purpose of carrying out terrorist acts on important state and military facilities, is growing. Unmanned aerial vehicles have become so important to success on the battlefield that they are sometimes used by the military to destroy enemy drones. In addition, it is with the help of unmanned aerial vehicles that one side receives the coordinates of military targets and command posts of the opposite side, which are subsequently destroyed by accurate artillery strikes. In the article, based on the analysis of modern wars and armed conflicts, combat experience and features of the use of unmanned aerial vehicles of the armed forces of the russian federation, an analysis of unmanned aerial vehicles for typical tasks, in particular, conducting reconnaissance, adjusting fire, striking and electronic warfare, was carried out. In particular, the conducted analysis indicates a tendency to increase the scale of use of unmanned aerial vehicles by the armed forces of the russian federation in conditions of a full-scale armed conflict (not excluded due to the end of stocks of high-precision missiles), in contrast to the experience of the combat use of individual unmanned aerial vehicles in the East of the country and the expansion of the range of tasks.

The conducted SWOT analysis made it possible to develop problematic issues that are proposed during the operation of unmanned aerial vehicles, proposals were made for the creation of a mobile complex of the use of unmanned aerial vehicles based on the chassis of a truck. Formulation of the problem. During hostilities, unmanned aerial vehicles (UAVs) were widely used. The massive use of the latest robotic (automated) weapons and military equipment on the battlefield is changing the nature of units' actions. Currently, almost every combat unit has a UAV application group. The high efficiency of their use is confirmed by high results on the battlefield. The security and defense forces of Ukraine are armed with dozens of types of UAVs for various purposes and with various capabilities for lifting cargo and carrying explosives. The use of UAV combat units in the field. Launches of lethal vehicles are carried out by hand or with the help of launch devices. With this mobile application group, the BPLA, although it is at a distance from the line of the next combat encounter, does not have sufficient security and protection from enemy fire. Preparation for use, maintenance of UAVs and charging of batteries in stationary conditions the day before, which reduces the efficiency of actions.The deployment of mobile groups of UAV operators is carried out on vehicles that are available to the military unit at the time, and which are not adapted for the high-quality performance of the combat mission by the group. UAV operators and equipment are not protected from exposure to low ambient temperatures and have no protection from small arms and shrapnel damage. Time indicators of deployment on the terrain of the complex for launching UAVs and folding after the completion of a combat mission need improvement. Analysis of recent research and publications. Scientific studies on improving the actions of units equipped with UAVs were carried out in the National Guard even before the beginning of the full-scale invasion of the Russian Federation. For example, in [1-2], data are given regarding the need for intelligence information and the development of a rational procedure for the use of intelligence unmanned aerial vehicles. In [3], the methodological apparatus for researching the problems of using information technologies and telecommunication systems in the process of military management is presented. The purpose of the article is to justify the need to create a mobile complex for the use of unmanned aerial vehicles based on a truck chassis. Provide recommendations on the creation of a mobile complex for the use of unmanned aerial vehicles.

The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content.

In recent years, low-altitude and low-volume plant protection operations using unmanned aerial vehicle (UAV) sprayer developed rapidly in China with the advantages of high efficiency, labour saving, high safety, high terrain adaptability, high flexibility, water and chemicals saving, and high intelligence. With the UAV application technology in field crops is becoming more and more mature, aerial spraying operations in orchards are promising and in the ascendant, but a high risk of UAV spray drift is appearing due to high working height and fine droplets sprayed in slope orchards, highlighting the necessity of the study on the spray drift characteristics of UAV chemicals application for fruit trees. Therefore, based on previous research, a novel type of measuring method of spray drift for UAV chemicals application in orchard was proposed in this study and an artificial orchard test stand (vineyard) and 3 airborne drift frame collectors were designed and built, and a set of field drift test bench was firstly used to collect aerial spray drift droplets at different downwind distances, together with ground drift collectors and canopy deposition collectors. An airborne drift index (ADX) of UAV’s spray was initially applied for quantitative analysis to compare spray drift characteristics of different models of unmanned aircrafts and variable operation parameters. Fluorescence tracer Pyranine water solution was prepared at the concentration of 0.1% as the spray liquid. Four typical types of plant protection UAV (a single-rotor oil-powered helicopter, a 6-rotor motor drone and two models of 8-rotor motor drones) equipped with conventional hollow cone nozzle ‘TR 80-0067’ and air-induction anti-drift nozzle ‘IDK 120-015’were tested in the artificial vineyard, and results of canopy deposition distribution, ground sediment drift, near-ground drift, and airborne drift were obtained and analysed, and different sampling collectors for spray drift were evaluated and compared. The results showed that: Under the environmental conditions that the nominal crosswind speed was 2.4-3.6 m/s, the temperature was 29.8-34.3 ℃ and relative humidity was 10.7%-30.6%, at the flight height of 1.5 m (3.5 m from the ground) and the speed of 2.0 m/s the air-induction nozzle IDK can significantly reduce the level of downwind spray drift of UAV, optimize the uniformity of deposition distribution and increase the effective utilization rate of chemicals; There was no significant difference in the drift characteristics of the 4 types of unmanned aircraft, and the vortex generated by the combination of the rotor’s downwash airflow and the external wind was an important factor on spray drift; Buffer zone of UAV aerial spraying operation in vineyards should be set at at least 15 m; The lower the canopy deposition rate (P 0), the larger the average average drift rate (AADR) and 90% cumulative drift distancex90% of the field drift test bench (P 0), the greater the ADX value (P 0) all indicated the higher spray drift risk, respectively; Both these sampling collectors and their evaluation index could assess the downwind drift characteristics effectively; the relationship between the UAV spray drift rate βdep% and the downwind distance x was described by the exponential function. The results of this study are expected to provide references and data supports for the R&D of UAV dedicated for orchard spraying, the formulation of standards on spray drift field measuring method for UAV orchard operations and the selection of aerial application working parameters in orchards.

Fusarium wilt (Panama disease) of banana currently threatens banana production areas worldwide. Timely monitoring of Fusarium wilt disease is important for the disease treatment and adjustment of banana planting methods. The objective of this study was to establish a method for identifying the banana regions infested or not infested with Fusarium wilt disease using unmanned aerial vehicle (UAV)-based multispectral imagery. Two experiments were conducted in this study. In experiment 1, 120 sample plots were surveyed, of which 75% were used as modeling dataset for model fitting and the remaining were used as validation dataset 1 (VD1) for validation. In experiment 2, 35 sample plots were surveyed, which were used as validation dataset 2 (VD2) for model validation. An UAV equipped with a five band multispectral camera was used to capture the multispectral imagery. Eight vegetation indices (VIs) related to pigment absorption and plant growth changes were chosen for determining the biophysical and biochemical characteristics of the plants. The binary logistic regression (BLR) method was used to assess the spatial relationships between the VIs and the plants infested or not infested with Fusarium wilt. The results showed that the banana Fusarium wilt disease can be easily identified using the VIs including the green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), normalized difference vegetation index (NDVI), and normalized difference red-edge index (NDRE). The fitting overall accuracies of the models were greater than 80%. Among the investigated VIs, the CIRE exhibited the best performance both for the VD1 (OA = 91.7%, Kappa = 0.83) and VD2 (OA = 80.0%, Kappa = 0.59). For the same type of VI, the VIs including a red-edge band obtained a better performance than that excluding a red-edge band. A simulation of imagery with different spatial resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m resolutions) showed that good identification accuracy of Fusarium wilt was obtained when the resolution was higher than 2 m. As the resolution decreased, the identification accuracy of Fusarium wilt showed a decreasing trend. The findings indicate that UAV-based remote sensing with a red-edge band is suitable for identifying banana Fusarium wilt disease. The results of this study provide guidance for detecting the disease and crop planting adjustment.

ABSTRACTWeed mapping at very early phenological stages of crop and weed plants for site-specific weed management can be achieved by using ultra-high spatial and high spectral resolution imagery provided by multispectral sensors on-board an unmanned aerial vehicle (UAV). These UAV images cannot cover the whole field, resulting in the need to take a sequence of multiple overlapped images. Therefore, the overlapped images must be oriented and ortho-rectified to create an accurate ortho-mosaicked image of the entire field for further classification. Because the spatial quality of ortho-mosaicked images mainly depend on the flight altitude and percentage of overlap, this paper describes the effect of flight parameters using a multirotor UAV and a multispectral camera on the mosaicking workflow. The objective is to define the best configuration for the mission planning to generate accurate ortho-images. A set of flights with a range of altitudes (30, 40, 50, 60, 70, 80, and 90 m) above ground level (AGL) and two end-lap and side-lap settings (60–30% and 70–40%) were studied. The spatial accuracy of ortho-mosaics was evaluated taking into consideration the ASPRS test. The results showed that the best flight setting to keep the spatial accuracy in the bundle adjustment was 70–40% overlap and altitudes AGL ranging from 60 to 90 m. At these flight altitudes, the spatial resolution was quite similar, making it possible to optimize the mission planning, flying at a higher altitude and increasing the area overflow without decreasing the ortho-mosaic spatial quality. This study has relevant implications for further use in detecting weed seedlings in crops.

The study of vegetation status is of great importance as it reveals environmental factors and their relationship with the availability of water resources, components that are relevant in the planning, drafting and execution of numerous civil engineering projects. Detecting changes in vegetation cover often requires data from space-based platforms, which may have limitations in terms of spatial or temporal resolution and high costs for acquiring high-resolution data. For relatively small areas, the use of Unmanned Aerial Vehicles (UAVs) provides a solution. These devices facilitate the acquisition of land cover information with high spatial and temporal resolution through the use of photographic cameras or specialized sensors. This research analyzes the vegetation status within a study area characterized by distinct climatic seasons (dry and humid) within a warm sub-tropical climate. The primary focus lies in detecting changes in vegetation cover and evaluating the overall state of the vegetation. To accomplish this objective, a multirotor UAV equipped with a multispectral camera that captures RED and NIR bands is deployed. Various vegetation indices based on distance and slope are applied to access the vegetation’s condition. The analysis of the results indicates that it is possible to evaluate the vegetation cover dynamics using the very high-resolution data obtained with the VANT approach (10 cm per pixel). Notably, the distance-based vegetation indices demonstrate superior efficacy in delineating the vegetation status and the stress induced by the scarcity of water resources during the transition between dry and humid seasons.