UAV Data Research Articles

Multi-Temporal UAV Data and Object-Based Image Analysis (OBIA) for Estimation of Substrate Changes in a Post-Bleaching Scenario on a Maldivian Reef

Coral reefs are declining worldwide as a result of the effects of multiple natural and anthropogenic stressors, including regional-scale temperature-induced coral bleaching. Such events have caused significant coral mortality, leading to an evident structural collapse of reefs and shifts in associated benthic communities. In this scenario, reasonable mapping techniques and best practices are critical to improving data collection to describe spatial and temporal patterns of coral reefs after a significant bleaching impact. Our study employed the potential of a consumer-grade drone, coupled with structure from motion and object-based image analysis to investigate for the first time a tool to monitor changes in substrate composition and the associated deterioration in reef environments in a Maldivian shallow-water coral reef. Three key substrate types (hard coral, coral rubble and sand) were detected with high accuracy on high-resolution orthomosaics collected from four sub-areas. Multi-temporal acquisition of UAV data allowed us to compare the classified maps over time (February 2017, November 2018) and obtain evidence of the relevant deterioration in structural complexity of flat reef environments that occurred after the 2016 mass bleaching event. We believe that our proposed methodology offers a cost-effective procedure that is well suited to generate maps for the long-term monitoring of changes in substrate type and reef complexity in shallow water.

Sand spit dynamics in a large tidal‐range environment: Insight from multiple LiDAR, UAV and hydrodynamic measurements on multiple spit hook development, breaching, reconstruction, and shoreline changes

AbstractSand spits with distal hooks have been well documented from coasts with low to moderate tidal ranges, unlike high tidal‐range environments. Datasets from 15 LiDAR and 3 UAV surveys between 2009 and 2019 on the Agon spit in Normandy (France), a setting with one of the largest tidal ranges in the world (mean spring tidal range: 11 m), combined with in‐situ hydrodynamic records between 2013 and 2017, highlight a three‐stage pattern of spit hook evolution. Stage 1 (2009–2013) commenced with the onshore migration and attachment of a swash bar, followed by persistent spit accretion updrift of the bar and erosion downdrift because of the slow speed of bar migration in this large tidal‐range environment. In stage 2 (2013–2016), three overwash events and a 220 m‐wide breach culminating in the total destruction of the spit during winter 2015–2016 involved the landward mobilization of thousands of cubic metres of sand. These events occurred during short durations (a few hours) when spring high tides coincided with relatively energetic waves, underscoring the importance of storms in rapid spit morphological change. Strong spring tidal currents maintained the breach. Stage 3 (2016–2019) has involved new hook construction through welding of a swash bar and spit longshore extension, highlighting the resilience of the spit over the 10‐year period, and involving a positive sediment balance of 244 000 m3. The three stages bring out, by virtue of the temporal density of LiDAR and UAV data used, a high detail of spit evolution relative to earlier studies in this macrotidal setting. The large tidal range strongly modulates the role of waves and wave‐generated longshore currents, the main process drivers of spit evolution, by favouring long periods of inertia in the course of the spring–neap tidal cycle, but also brief episodes of significant morphological change when storm waves coincide with spring high tides. © 2020 John Wiley & Sons, Ltd.

UAV and terrestrial laser scanner data processing for large scale topographic mapping

Integrating spatial data from different sources results in visualization, which is the last step in the process of digital basic topographic map creation. Digital elevation model and visualization will used for geomorphological mapping, geospatial database, urban planning and etc. Large scale topographic mapping in the world countries is really a prominent challenge in geospatial industries today. The purpose of this work is to integrate laser scanner data with the ones generated by aerial photogrammetry from UAV, to produce detailed maps that can used by geodetic engineers to optimize their analysis. In addition, terrestrial - based LiDAR scans and UAV photogrammetric data were collected in Sharga hill in the north zone of Mongolia. In this paper, different measurement technology and processing software systems combined for topographic mapping in the data processing scheme. UTM (Universal Transverse Mercator) projected coordinate system calculated in WGS84 reference ellipsoid. Feature compilation involving terrestrial laser scanner data and UAV data will integrated to offer Digital Elevation Models (DEM) as the main interest of the topographic mapping activity. Used UAV generate high-resolution orthomosaics and detailed 3D models of areas where no data, are available. That result issued to create topographic maps with a scale of 1:1000 of geodetic measurements. Preliminary results indicate that discontinuity data collection from UAV closely matches the data collected using laser scanner.

Calculation of rice field embankment coefficient using high-resolution satellite imagery

Estimating rice production is vital in ensuring economic stability. One of the critical element in the estimation is the information on the width of the rice field. The uncertainty on this aspect is exacerbated by which the rice field is calculated. The current method only considers the width of the fields, without taking into account the rice field embankment. The width of the embankment is usually between 25 cm to several meters. Neglecting the contribution of this embankment to the overall width of the rice fields can result in overestimation to the rice production. In this research, the coefficient of rice field embankment is calculated. The data used are from high-resolution satellite imagery of Pleades, SPOT-6, Worldview, and GeoEye. UAV data from rice fields in Yogyakarta was used as control data. The study area covers rice fields in Bengkulu, Jambi, Riau, Bangka Belitung, Sulawesi Tenggara, Sulawesi Barat, Sulawesi Tengah, Gorontalo, Maluku, Nusa Tenggara Timur, and Papua. Two types of digitization methods were used. The first one is a rice field without any embankment incorporated. Only road or irrigation channel wider than 2.5 meters were included. The second one is a rice field with embankment feature digitized. The coefficient of the embankment is calculated by dividing the width of the embankment with the width of rice fields without embankment. For a different location, the coefficient is different, between 1% - 3 %. Types of rice fields, satellite imagery being used, and topography affect the coefficient.

Analyses of UAV and GNSS based flow velocity variations of the rock glacier Lazaun (Ötztal Alps, South Tyrol, Italy)

Flow velocities were measured on the active rock glacier Lazaun by eleven GNNS (2006–2018) and six UAV campaigns (2016–2018) to better understand the flow pattern and its possible causes. This medium-sized, active rock glacier is located W of Kurzras/Maso Corto in the Schnals/Senales Valley in the southern Ötztal Alps (South Tyrol, northern Italy). The UAV data were used to generate displacement maps by image correlation techniques, to analyse surface lowering and accumulation processes, and to interpret the geomorphology base on shaded relief images. The spatial information from UAV contributes much more to the understanding of the rock glacier flow patterns as the point based GNSS measurements even when the UAV based horizontal displacement analyses are less precise. The level of detection (LoD) for the absolute horizontal displacements derived by image correlation from UAV data ranges between 0.05 and 0.15 m. A comparison of the displacement lengths shows that the UAV derived displacement vectors deviate in average about ±0.05 m from the GNSS measured displacement lengths. The deviation in the displacement directions is smaller when the absolute displacement is higher and ranges in zones with an absolute displacement higher than 0.3 m in dependence to the data quality between 0 and 4°. Consequently, when planning a UAV based rock glacier monitoring, the achievable data accuracy, rock glacier flow velocity and the time span must be considered.Analyses of the rock glacier velocities from GNSS and UVA data show that the calculated horizontal mean velocity increased significantly from 2 mm/day in 2006 up to 6 mm/day in the period 2012 to 2016 and then decreased to values of 3–4 mm/day until 2018.We assume that internal structures and hydrogeology are essential parameters that control the flow mechanism of rock glacier Lazaun, particularly the shear horizon composed of debris and banded ice that is present at the base of the permafrost body, and the unfrozen, groundwater-bearing sediment layer between the shear horizon and the bedrock. We conclude that climate warming caused increased infiltration of meltwater into the shear horizon and that this is the main driving force for the movement of the rock glacier. Increased meltwater infiltration as result of climate warming caused an increase of flow velocity until 2016. The observed thickness loss near the front of the rock glacier indicates increased melting of permafrost ice resulting in a significant loss of permafrost ice during the last years. This process that particularly occurs in the lower part of the rock glacier is believed to be responsible for the decrease in flow velocity since 2017.

QoS-Aware UAV Coverage path planning in 5G mmWave network

Conventional UAV coverage path planning problems assume that UAVs offload sensor data only after the flight is completed. This type of “record-offload-process” approach incurs long delays and has serious risks of data loss. Emerging 5G networks enable high-throughput and low-latency network connectivity, allowing UAVs to transmit sensor data to the receiver during the flight. However, it is challenging to meet the network QoS requirements for such “online” data transfer during a flight because the performance of a 5G network may vary significantly due to channel conditions, structural blockage and movement of UAVs. In this paper, we first present a model of this new UAV path planning problem with network QoS constraint, which turns out to be a nonlinear optimization problem. We then transform the problem into two subproblems and derive solutions. We leverage an integrated full-stack simulation framework to study the end-to-end performance of UAV data transfer over a 5G mmWave network and evaluate our proposed solution. The experimental results show that our solution achieves significant reduction of data losses as well as improvement on flight time.

Design and Study UAV Data Link Terminal with One Station Controls Several Vehicles Dynamic Networking

Considering that there are UAVdynamic networking and collaborative communication requirements under complex mission planning conditions, this paper designs a UAV data link communication terminal with One Station Controls Several Vehicles dynamic networking. Firstly, this paper analyzes the structure of One Station Controls Several Vehicles dynamic networking and cooperative communication system in UAV data link, and combines the characteristics of dynamic networking and task cooperative communication to design a One Station Controls Several Vehicles dynamic networking communication protocol stack and design the structure and function of the man-machine data link, the terminal device hardware is realized by the FPGA chip. This paper focuses on digital zero-IF signal processing unit design and hardware implementation process. Finally, combined with the terminal to build an indoor test environment, this paper tests the network function and performance. The test results prove that the UAV data link terminal in One Station Controls Several Vehicles dynamic networking is effective, and it is practical.

Analiza vplivov na kakovost izdelkov UAV

Unmanned aerial vehicles, equipped with various sensors and devices, are increasingly used to acquire geospatial data in geodesy, geoinformatics, and environmental studies. In this context, a new research and professional field has been developed – UAV photogrammetry – dealing with photogrammetry data acquisition and data processing, acquired by unmanned aerial vehicles. In this study, we analyse the selected factors that impact the quality of data provided using UAV photogrammetry, with the focus on positional accuracy; they are discussed in three groups: (a) factors related to the camera properties and the quality of images; (b) factors related to the mission planning and execution; and (c) factors related to the indirect georeferencing of images using ground control points. These selected factors are analysed based on the detailed review of relevant scientific publications. Additionally, the influence of the number of ground control points and their spatial distribution on point clouds' positional accuracy has been investigated for the case study. As the conclusion, key findings and recommendations for UAV photogrammetric projects are given; we have highlighted the importance of suitable lighting and weather conditions when performing UAV missions for spatial data acquisition, quality equipment, appropriate parameters of UAV data acquisition, and a sufficient number of ground control points, which should be determined with the appropriate positional accuracy and their correct distribution in the field.

A new-one-time-pad stream cipher: new design, hardware implementation and its application in security of UAVs

UAVs are used in military and civilian fields. Security of UAV Data Link is still unsatisfactory especially if a strong adversary model is considered. This paper describes a new design of AENOTP, tested for security in UAVs Data Link. AENOTP algorithm combines NOTP with AE technique to ensure encryption and authentication along with data integrity. AE is a technique that ensures the security of the transported data. The design and analysis of AENOTP gives a fast and enhanced stream cipher AE architecture based on the unpredictability concept. Implementation of AENOTP measures encryption and decryption of any sent and received data. AENOTP shows high speed of generating the key stream bits of the OTP stream cipher and high speed of the proposed algorithm of 75 Mbits/sec. It achieves high level of security for any transmitted data. It can easily resist different attacks such as linear and differential attacks.

A new-one-time-pad stream cipher: new design, hardware implementation and its application in security of UAVs

UAVs are used in military and civilian fields. Security of UAV Data Link is still unsatisfactory especially if a strong adversary model is considered. This paper describes a new design of AENOTP, tested for security in UAVs Data Link. AENOTP algorithm combines NOTP with AE technique to ensure encryption and authentication along with data integrity. AE is a technique that ensures the security of the transported data. The design and analysis of AENOTP gives a fast and enhanced stream cipher AE architecture based on the unpredictability concept. Implementation of AENOTP measures encryption and decryption of any sent and received data. AENOTP shows high speed of generating the key stream bits of the OTP stream cipher and high speed of the proposed algorithm of 75 Mbits/sec. It achieves high level of security for any transmitted data. It can easily resist different attacks such as linear and differential attacks.

Application of UAV and ArcGIS in Soil and Water Conservation Monitoring of slag area—Taking a slag area of hydroelectric power station in Dadu River as an example

In the process of slag area in hydropower projects, the ground will be disturbed strongly, and a large amount of spoil slag will be generated, resulting in serious soil erosion. Therefore, the monitoring of soil and water conservation in the slag area of hydropower projects is particularly important. Tradition al ground monitoring cannot fully meet the needs of current monitoring work. UAV technology is more efficient than traditional technology,. Through periodic aerial photography and post-modeling, combining with ArcGIS, provides enhanced data analysis support for UAV data analysis. Taking the 1# slag yard of a hydropower station in Dadu River as an example, this paper discusses the applicability of UAV and ArcGIS technology in soil and water conservation monitoring of large hydropower project slag area. The effective way of maintaining information, while improving the monitoring efficiency of soil and water conservation, can ensure that the monitoring data and the Soil erosion error control are within 10%, making up for the traditional technical ways that is insufficient in the monitoring of soil and water conservation in the large-scale hydropower project slag yard.In recent years, the state has paid more and more attention to the construction of ecological civilization. As an important part of ecological civilization construction, soil and water conservation work has become more and more important. As an important part of soil and water conservation work, soil and water conservation monitoring is the basis for ecological restoration and comprehensive management of soil and water conservation, and is a powerful guarantee for national ecological construction decision-making [1]. In recent years, the application of remote sensing technology for UAVs is for production and construction. The project of soil and water conservation monitoring work provides a new technical way [2-4]. UAV operation has high automation and precision, and it is targeted. Some models have reached the application-level standards, and with the rapid progress of software and hardware technology, it is greatly improving the post-processing applicability of aerial image data, making drones widely used in aerial photography, agriculture, plant protection and monitoring. Torres-Sánchez et al [5], Francisco et al [6] use low-altitude high-resolution drone remote sensing imagery to carry out on precision agriculture and agricultural pest control. Mitch Bryson et al. [7] used low-altitude visible drone images to study vegetation type classification based on vegetation color and texture characteristics. Sebastian d’Oleire—Oltmanns et al. [8] used high-altitude drone aerial imagery to complete the monitoring of soil erosion in Morocco, providing a reference for soil and water.

INTERACTIVE 3D CITY VISUALIZATION FROM STRUCTURE MOTION DATA USING GAME ENGINE

Abstract. Developing a 3D city model is always a challenging task, whether on how to obtain the 3D data or how to present the model to users. Lidar is often used to produce real-world measurement, resulting in point clouds which further processed into a 3D model. However, this method possesses some limitation, e.g. tedious, expensive works and high technicalities, which limits its usability in a smaller area. Currently, there exists pipeline utilize point-clouds from Lidar data to automate the generation of 3D city model. For example, 3dfier (http://github.com/tudelft3d/3dfier) is a software capable of generating LoD 1 3D city model from lidar point cloud data. The resulting CityGML file could further be used in a 3D GIS viewer to produce an interactive 3D city model. This research proposed the use of Structure from Motion (SfM) method to obtain point cloud from UAV data. Using SfM to generate point clouds means cheaper and shorter production time, as well as more suitable for smaller area compared to LiDAR. 3Dfier could be utilized to produce 3D model from the point cloud. Subsequently, a game engine, i.e. Unity 3D, is utilized as the visualization platform. Previous works shows that a game engine could be used as an interactive environment for exploring virtual world based on real-world measurement and other data, such as parcel boundaries. This works shows that the process of generating 3D city model could be achieved using the proposed pipeline.

Study of extracting natural resources of Chinese medicinal materials planted area in Luoning of Henan province based on UAV of low altitude remote sensing technology and remote sensing image of satellite

The study is aimed to effectively obtain the planting area of traditional Chinese medicine resources. The herbs used as the material for traditional Chinese medicine are mostly planted in natural environment suitable mountainous areas. The UAV low altitude remote sensing data were used as the samples and the GF-2 remote sensing images were applied for the data source to extract the planting area of Salvia miltiorrhiza and Artemisia argyi in Luoning county combined with field investigation. Remote sensing satellite data of standard processing obtain specific remote sensing data coverage. The UAV data were pre-processed to visually interpret the species and distribution of traditional Chinese medicine resources in the sample quadrat. Support vector machine( SVM) was used to classify and estimate the area of traditional Chinese medicine resources in Luoning county,confusion matrix was used to determine the accuracy of spatial distribution of traditional Chinese medicine resources. The result showed that the application of UAV of low altitude remote sensing technology and remote sensing image of satellite in the extraction of S. miltiorrhiza and other varieties planting area was feasible,it also provides a scientific reference for poverty alleviation policies of the traditional Chinese medicine Industry in local areas.Meanwhile,research on remote sensing classification of Chinese medicinal materials based on multi-source and multi-phase high-resolution remote sensing images is actively carried out to explore more effective methods for information extraction of Chinese medicinal materials.

SNR estimation algorithm for UAV data link based on deep learning

SNR estimation algorithm for UAV data link based on deep learning

Energy Efficiency Optimization for UAV-Assisted Backscatter Communications

Future Internet-of-Things (IoT) has high demand for energy-saving communications, especially in remote areas and smart cities. To meet this demand, we propose novel Unmanned Aerial Vehicle-assisted backscatter communications, where a UAV first collects data from multiple terrestrial backscattering tags via time division multiple access, and then flies into the coverage region of a terrestrial base station to upload its collected data to its associated base station. To determine the optimal UAV data collection location, we first analyze the system average outage probability, and then optimize the energy efficiency with the optimal backscattering location through Golden Section method under UAV energy constraint. Our analytical and simulation results illustrate that there is a trade-off between UAV data collection location and the outage probability, and the optimal UAV data collection location to achieve maximum energy efficiency needs to be closer to the tags for lower UAV transmit power.

UAV survey at archaeological earthwork sites in the Brazilian state of Acre, southwestern Amazonia

AbstractUnmanned aerial vehicles (UAVs) are increasingly used for many scientific applications, including archaeological surveys. We test the suitability and practicability of UAV surveying in the tropical lowlands of Brazil and techniques for visualizing the resulting digital elevation models, specifically the Red Relief Image Map (RRIM). We present the results of UAV surveys conducted at four diverse archaeological earthwork sites situated in interfluvial southwestern Amazonia, in the state of Acre. The elevation models produced from UAV derived point clouds display clear patterns in the site layouts and reveal subtle intra‐site earthwork features that are not easily discernible on the ground. Our study demonstrates that UAVs are cost efficient and give highly detailed results for topographic mapping and visualization of archaeological features when vegetation cover is sufficiently low and sparse. The rapid data capture and lack of spatial sampling bias of the UAV data collection is a significant advantage compared to conventional mapping methods. Furthermore, UAV surveying and UAV derived data processing do not require expensive technologies or specialized user expertise, since open‐source software and easy‐to‐use toolkits are readily available.

Multiscale assessment of northern forest characteristics based on ultra-high resolution data

Abstract. The algorithms for quantitative estimates of various structural and functional parameters of forest ecosystems, particularly boreal forests, on high resolution remote sensing data are actively developing since the mid-2000s. For monitoring of forest ecosystems located at the Northern limit of distribution, effective not only lidar data but also the optical data obtained by unmanned aerial vehicles (UAV’s) with ultra-low altitude photography and derived products resulting from modern algorithms for the photogrammetric processing.High-detail remote sensing from UAV’s is a key level of monitoring of Northern forests at a large-scale level, ensuring the correct transition from sub - satellite ground-based studies to thematic products obtained from multi-time Hyper-and multispectral data of medium and relatively high resolution (MODIS, LANDSAT, Sentinel-2).When planning and conducting specific case studies based on UAV data, special attention should be paid to the justification of the survey methodology. In particular, the choice of a strictly defined high-altitude echelon of the survey determines the recognition of the objects of study and the possibility of reliable determination of its properties and features. To study the parameters of forest ecosystems at the level of individual trees and at the level of forest plantations, we selected two different-height echelons of survey from ultra-low altitudes: from 50 m, which allowed us to obtain ultra-high-detailed data for each sample area provided by detailed ground-based studies with sub-tree account, and from 100 m-to obtain derived characteristics of forest communities within the area equivalent to 3 pixels of thematic MODIS products with a spatial resolution of 250 m. The data of optical survey with UAV were obtained in July 2018 for 22 plots located in the central part of the Kola Peninsula and representative of different types of North taiga stands and their dynamics under climate change.At the stage of preprocessing images were obtained dense point clouds, characterizing both vertical and horizontal structure of stands. Digital terrain and terrain models and tree canopy models were obtained after cloud filtering and classification. Algorithms of automated segmentation and classification have been developed and tested to obtain such characteristics of stands as the height of individual trees, the area of crown projections, the projective cover of the tree-shrub layer. The obtained characteristics are aggregated by cells of a regular network with the dimension corresponding to the spatial resolution of Sentinel-2 and Landsat-8 data.The main results of the works are digital spatial datasets for 22 sample plots: raw data with very high resolution imagery (optical images with very high resolution, dense point clouds, RGB-orthophoto) and create based on a thematic derivative products (digital terrain model, topography, tree canopy cover; map of the heights and projections of the crowns of trees, percent cover of tree and shrub vegetation).

Using aboveground vegetation attributes as proxies for mapping peatland belowground carbon stocks

Peatlands are key reservoirs of belowground carbon (C) and their monitoring is important to assess the rapid changes in the C cycle caused by climate change and direct anthropogenic impacts. Frequently, information of peatland area and vegetation type estimated by remote sensing has been used along with soil measurements and allometric functions to estimate belowground C stocks. Despite the accuracy of such approaches, there is still the need to find mappable proxies that enhance predictions with remote sensing data while reducing field and laboratory efforts. Therefore, we assessed the use of aboveground vegetation attributes as proxies to predict peatland belowground C stocks. First, the ecological relations between remotely detectable vegetation attributes (i.e. vegetation height, aboveground biomass, species richness and floristic composition of vascular plants) and belowground C stocks were obtained using structural equation modeling (SEM). SEM was formulated using expert knowledge and trained and validated using in-situ information. Second, the SEM latent vectors were spatially mapped using random forests regressions with UAV-based hyperspectral and structural information. Finally, this enabled us to map belowground C stocks using the SEM functions parameterized with the random forests derived maps.This SEM approach resulted in higher accuracies than a direct application of a purely data-driven random forests approach with UAV data, with improvements of r2 from 0.39 to 0.54, normalized RMSE from 31.33% to 20.24% and bias from −0.73 to 0.05. Our case study showed that: (1) vegetation height, species richness and aboveground biomass are good proxies to map peatland belowground C stocks, as they can be estimated using remote sensing data and hold strong relationships with the belowground C gradient; and (2) SEM is facilitates to incorporate theoretical knowledge in empirical modeling approaches.

A CALIBRATION WORKFLOW FOR “PROSUMER” UAV CAMERAS

Abstract. High-end consumer quadcopter UAVs or so-called “prosumer devices”, have made inroads into the mapping industry over the past few years, arguably displacing more expensive purpose-built systems. In particular, the DJI Phantom series quadcopters, marketed primarily for videography, have shown considerable promise due to their relatively high-quality cameras. Camera pre-calibration has long been a part of the aerial photogrammetric workflow with calibration certificates being provided by operators for every project flown. Most UAV data, however, is processed today in Structure-from-Motion software where the calibration is generated “on-the-fly” from the same image-set being used for mapping. Often the scenes being mapped and their flight-plans are inappropriate for calibration as they do not have enough variation in altitude to produce a good focal-length solution, and do not have cross-strips to improve the estimation of the principal point. What we propose is a new type of flight-plan that can be run on highly textured scenes of varying height prior to mapping missions that will significantly improve the estimation of the interior orientation parameters and, as a consequence, improve the overall accuracy of projects undertaken with these sorts of UAV systems. We also note that embedded manufacturer camera profiles, which correct for distortion automatically, should be removed prior to all photogrammetric processing, something that is often overlooked as these profiles are not made visible to the end user in most image conversion software, particularly Adobe’s CameraRAW.

ESTIMATING CROP DENSITY FROM MULTI-SPECTRAL UAV IMAGERY IN MAIZE CROP

Abstract. In this study we exploit UAV data for estimating Fractional Vegetation Cover (FVC) of maize crop at the early stages of the growing season. UAV survey with a MicaSense RedEdge multispectral sensor was carried out on July 13th, 2017 over a maize field in Italy; simultaneous RGB in situ pictures were collected to build a reference dataset of FVC over 15 ESU (Elementary Sampling Units) distributed over the field under investigation. The approach proposed for classification of UAV data is based on local contrast enhancement techniques applied to a vegetation index (NDVI-Normalized Difference Vegetation Index) to capture signal from small plants at the early development stage. The output fc map is obtained over grid cells over 70 × 70 cm size. The approach proposed here, based on contextual analysis, reduced artefacts due to illumination conditions by better enhancing signal from vegetation compared to, for example, simple band combination such as vegetation index alone (e.g. NDVI). Validation accomplished by a point comparison between estimated (from UAV) and in situ measured FVC values provided R2 = 0.73 and RMSE = 6%.