Use Of Aerial Imagery Research Articles

Impact of Tile Size and Tile Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification

Popular geo-computer vision works make use of aerial imagery, with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near the image boundaries. In this work, the impact of three tile sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km2 of the Spanish territory that was labelled with binary road information) under twelve different scenarios, with each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on data from novel areas covering approximately 825 km2. The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of distinct levels of the fixed factors (tile size, tile overlap, and neural network architecture) on them. Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and a higher amount of tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap, and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.

Use of Machine Learning Techniques on Aerial Imagery for the Extraction of Photovoltaic Data within the Urban Morphology

Locating and quantifying photovoltaic (PV) installations is a time-consuming and labor-intensive process, but it is necessary for monitoring their distribution. In the absence of existing data, the use of aerial imagery and automated detection algorithms can improve the efficiency and accuracy of the data collection process. This study presents a machine learning approach for the analysis of PV installations in urban areas based on less complex and resource-intensive models to target the challenge of data scarcity. The first objective of this work is to develop a model that can automatically detect PV installations from aerial imagery and test it based on the case study of Crevillent, Spain. Subsequently, the work estimates the PV capacity in Crevillent, and it compares the distribution of PV installations between residential and industrial areas. The analysis utilizes machine learning techniques and existing bottom-up data to assess land use and building typology for PV installations, identifying deployment patterns across the town. The proposed approach achieves an accuracy of 67% in detecting existing PV installations. These findings demonstrate that simple machine learning models still provide a reliable and cost-effective way to obtain data for decision-making in the fields of energy and urban planning, particularly in areas with limited access to existing data. Combining this technology with bottom-up data can lead to more comprehensive insights and better outcomes for urban areas seeking to optimize and decarbonize their energy supply while minimizing economic resources.

THE EARTH FROM ABOVE: FROM BELOW

The evolution of aerial surveying from its nascent stages in the early 20th century to the contemporary era of satellite imagery is a narrative of technological and methodological advancement. Initially, the ability to interpret landscapes from above was rudimentary, limited by the infancy of aeronautical activities. However, as aviation developed, particularly spurred by wartime needs, the significance of aerial perspectives grew. The use of photography from aircrafts became a pivotal method for understanding and documenting the Earth's surface, aiding in military strategies through the development of camouflage techniques. This paper explores the historical context in which aerial photography emerged as a critical tool for mapping and reconnaissance, highlighted by examples of camouflage used during World War II and the innovative use of aerial imagery in art and education, such as at the Pratt Institute of Art. It further discusses the transition from manual, piecemeal photographic surveys to the modern era's comprehensive, real-time digital mapping technologies. This transition not only marks a significant technological leap but also a shift in our perceptual understanding of the Earth's surface, as detailed through the examination of aerial imagery of airstrips and the conceptual art project "Earth From Above." This project, which organizes aerial images of airstrips and includes simulations of fictional runways, exemplifies the blend of artistic interpretation and geographic analysis. The paper concludes by reflecting on the philosophical implications of this evolution, drawing on Gaston Bachelard's notions of scale and the miniature, to underscore the changing relationship between humans and their ability to conceptualize and visualize their environment from aerial perspectives.

Delineating wetland vegetation species using a UAV-mounted multi-spectral camera and computer-aided classification

Agricultural runoff can be harmful to the environment by increasing soil erosion and flooding, while adding excess nutrients such as phosphorus to downstream waterways. Stormwater detention ponds are a common way to mitigate flood risk and improve water quality in urban areas. This concept has since been adapted to protect susceptible areas downstream of agriculture. Wetland vegetation can be planted around these ponds to uptake nutrients and slow water flow. Vegetation mapping is a frequent practice within land management to monitor health and species distribution. This process could be less labor-intensive if paired with the use of aerial imagery and computer-aided classification. Unmanned Aerial Vehicle (UAV) acquired imagery allows for high temporal and spatial resolutions that can map the environment in high detail and accuracy. Here we present a detailed workflow for species-level mapping from data acquisition in the field through image processing and analysis.

Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery

The construction of contour levees for rice irrigation represents a major landscape management activity with impacts on irrigation water use efficiency, crop management decisions, and food production. However, levee distribution information traditionally relies on local field surveys because remote sensing approaches are complicated by irregular spacing, shape, and landscape variability within the field. In this paper the authors develop a deep learning approach capable of identifying rice fields with contour style levee irrigation practices from open-source aerial imagery. To generate a levee-identification scheme, a hybrid ResNet/Unet model is built from the commonly known Residual Network (ResNet) architecture for multi-layer deep learning strategies. The model takes a 320 × 320 RGB aerial landscape image from the US National Agricultural Imagery Program as input along with label data to then generate a probability map of the distribution of farm fields that use contour levees within the image. In performing this task, the model generates a 0.991 receiver operating characteristic curve score. The model continues to perform well under the introduction of clouds, data augmentation, or minor reductions in spatial resolution. Throughout these tests, the model performed within 0.2 of its original score, except for when the image quality was reduced to 60 m wherein the model score dropped to 0.691. Via these tests the model demonstrates potential to function well given different spatial extents or potential satellite remote sensing with moderate (10 m) resolutions. This model provides a proof-of-concept for the use of aerial imagery and a deep learning strategy for irrigation-type mapping practices.

Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys

Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: “adult caribou”, “calf caribou”, and “ghost caribou” (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96–0.99, P value < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers’ annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities.

Remote detection of Fusarium crown rot in broadacre bread wheat and durum wheat through use of aerial imagery

Context The cereal disease Fusarium crown rot (FCR), caused by the fungal pathogen Fusarium pseudograminearum, is a worldwide major constraint to winter cereal production, especially in Australia’s northern grain region of New South Wales and Queensland. Aims Detection of the disease is labour-intensive and often not spatially quantifiable; hence, the aim of this study was to provide methods for in-crop FCR detection on a broadacre scale. Methods A replicated field experiment across three locations in northern New South Wales explored the use of thermal and multispectral imagery and hyperspectral reflectance data for the spatial detection of FCR in three bread wheat (Triticum aestivum L.) and three durum wheat (T. durum Desf.) varieties in the presence and absence of inoculation with F. pseudograminearum. Key results Canopy temperature was 0.30–0.90°C higher in two-thirds of field sites inoculated with the pathogen during early wheat growth in a slightly wetter than normal season. Some multispectral indices including normalised difference red edge, normalised difference vegetation index, near infrared and red edge also demonstrated the ability to identify inoculated versus uninoculated treatments as early as the first node stage (GS31). Conclusions Although positive identification was achieved with remote detection, environmental conditions (i.e. soil-water availability and ambient temperature) and physiological maturity influenced the accuracy of the technology for detecting FCR infection, particularly in wetter early-season conditions. Implications Early spatial detection of FCR infection on a broadacre scale could allow producers to manage this disease spatially through better agronomic decisions.

Asbestos roofing recognition by use of convolutional neural networks and high-resolution aerial imagery. Testing different scenarios

Asbestos has been widely used in the economy due to the mineral's unique physical and chemical properties. At the beginning of the 21st century, research confirmed that asbestos is a carcinogen. In 1997, in Poland it was forbidden to produce and use products containing asbestos. Statistics on the import of asbestos to Poland were collected, but unfortunately, there are no statistics on the quantity of asbestos products in use. Over 90% of still used products containing asbestos are asbestos-cement tiles. Therefore, various methods of estimating the number of these roofs are being sought to eliminate them safely from the use by the end of 2032. Our previous study has used CNN to test asbestos cement roofs identification in one commune. The purpose of the study is to present the possibilities of using the new artificial neural network architecture enhanced by inception-net which was developed to identify asbestos cement roofs based on high-resolution aerial images. In addition to our previous study, another commune was added to the research area. This study was conducted in two communes in Poland: Chęciny and Baranów. The study used orthophotos with a spatial resolution of 25 cm. Information on asbestos cement roofs was obtained during fieldwork. A new architecture of convolutional neural network (CNN) with a feature extraction block based on inception-net was used, which was not tested in our previous study. The classification was performed with the use of aerial imagery in the RGB composition since the results obtained before were promising. In this research, four different classification scenarios were tested: (1) the new network architecture with an inception-net-based network was trained and validated on signatures obtained for the same commune as in our previously published results (Krówczyńska et al., 2020) [18]; (2) the new network was trained and validated on signatures obtained for another commune in Poland; (3) the new network architecture with an inception-net-based network was trained on a dataset that combined both image signatures from two communes, from which training and validation signatures were selected randomly; (4) the new network architecture testing image signatures derived from one commune in another which was not seen by CNN. Moreover, the high-resolution imagery in those two communes was taken under different conditions. The choice of final model selection was done on the basis of the validation dataset loss function value (lowest is the best). The overall accuracy of the classification of different scenarios tested ranges from 88.0% to 93.0%. The presented research results indicate that there is a possibility to map asbestos cement roofs with high accuracy using the high-resolution aerial imagery taken under different conditions and CNN with inception-net using image signatures from different areas. This method may be used for decision-making communities to estimate the amount of asbestos cement roofing still in use to issue policies that will enable to safely eliminate those carcinogenic building materials from the environment.

Quality Control of Outsourced LiDAR Data Acquired with a UAV: A Case Study

Over the last few decades, we witnessed a revolution in acquiring very high resolution and accurate geo-information. One of the reasons was the advances in photonics and LiDAR, which had a remarkable impact in applications requiring information with high accuracy and/or elevated completeness, such as flood modelling, forestry, construction, and mining. Also, miniaturization within electronics played an important role as it allowed smaller and lighter aerial cameras and LiDAR systems to be carried by unmanned aerial vehicles (UAV). While the use of aerial imagery acquired with UAV is becoming a standard procedure in geo-information extraction for several applications, the use of LiDAR for this purpose is still in its infancy. In several countries, companies have started to commercialize products derived from LiDAR data acquired using a UAV but not always with the necessary expertise and experience. The LIDAR-derived products’ price has become very attractive, but their quality must meet the contracted specifications. Few studies have reported on the quality of outsourced LiDAR data acquired with UAV and the problems that need to be handled during production. There can be significant differences between the planning and execution of a commercial project and a research field campaign, particularly concerning the size of the surveyed area, the volume of the acquired data, and the strip processing. This work addresses the quality control of LiDAR UAV data through outsourcing to develop a modelling-based flood forecast and alert system. The contracted company used the Phoenix Scout-16 from Phoenix LiDAR Systems, carrying a Velodyne VLP-16 and mounted on a DJI Matrice 600 PRO Hexacopter for an area of 560 ha along a flood-prone area of the Águeda River in Central Portugal.

Cost-effective use of aerial imagery to quantify faunal-habitat associations across multiple spatial scales

We investigated the utility of publicly available map products to identify faunal-habitat associations to inform fisheries management. The influence of structural and abiotic variables on the abundance of 4 focal species (red drum Sciaenops ocellatus, spotted seatrout Cynoscion nebulosus, blue crab Callinectes sapidus, and brown shrimp Farfantepenaeus aztecus) was examined at multiple spatial scales in the Mission-Aransas estuary, Texas, USA. Structural habitat data were derived from the 2004 Benthic Atlas of Texas and paired with species abundance and hydrographic data collected by the Texas Parks and Wildlife Department’s Fisheries Independent Monitoring program. Blue crab and brown shrimp distributions at the ecosystem scale were driven primarily by abiotic factors such as salinity and distance to inlet, but structural habitat became relatively more important at smaller spatial scales. Variation in spotted seatrout abundance was primarily driven by seagrass and saltmarsh extent at the ecosystem scale but was driven by abiotic factors at the sub-region scale. Red drum distribution was driven primarily by abiotic factors, and association with structural habitats was bay-specific; there was no clear pattern in the influence of spatial scale on the relative importance of structural versus abiotic habitat variables for red drum. Results demonstrate that leveraging existing mapping efforts can allow fisheries managers to incorporate habitat data into fisheries management when funding or logistical challenges may otherwise prevent habitat monitoring at management-relevant scales. More research is warranted to investigate the temporal limitations to this approach, such as identifying the ‘shelf life’ of historical mapping products for utility in fisheries management.

Cork oak woodland land-cover types classification: a comparison between UAV sensed imagery and field survey

ABSTRACT This work assesses the use of aerial imagery for the vegetation cover characterization in cork oak woodlands. The study was conducted in a cork oak woodland in central Portugal during the summer of 2017. Two supervised classification methods, pixel-based and object-based image analysis (OBIA), were tested using a high spatial resolution image mosaic. Images were captured by an unmanned aerial vehicle (UAV) equipped with a red, green, blue (RGB) camera. Four different vegetation covers were distinguished: cork oak, shrubs, grass and other (bare soil and tree shadow). Results have been compared with field data obtained by the point-intercept (PI) method. Data comparison reveals the reliability of aerial imagery classification methods in cork oak woodlands. Results show that cork oak was accurately classified at a level of 82.7% with pixel-based method and 79.5% with OBIA . 96.7% of shrubs were identified by OBIA, whereas there was an overestimation of 21.7% with pixel approach. Grass presents an overestimation of 22.7% with OBIA and 12.0% with pixel-based method. Limitations rise from using only spectral information in the visible range. Thus, further research with the use of additional bands (vegetation indices or height information) could result in better land-cover type classification.

Post-Hype Uses of Drones in News Reporting: Revealing the Site and Presenting Scope

Camera-equipped drones have emerged as an increasingly commonplace tool for media to acquire aerial imagery. Previous research has mainly focused on the innovative aspects and creative potential of the technology. This article argues that early optimistic projections reflected a novelty effect, typical of a culturally embedded idea that new and better technologies continuously replace older ones. Using a historical theory which distinguishes techno-optimistic innovation discourse from actual observations of technology in use, photojournalists were interviewed on the role of drones in news reporting. The results show that the practitioners historicise drones, relating them to previous aerial technologies, and they reflect on current and future uses of drones in journalism based on a notion of phases, where early hype gives way to subsequent drone fatigue. Drones are seen by many as a more convenient tool to do things that journalism has done before, but the convenience increases the use of aerial imagery. The results also show that, although photojournalists see a wide range of potential uses, there are also limitations, including the ideals of the invisible observer, safety concerns, and the perils of over-aesthetic imagery. The post-hype uses of drone photography were summarized in two categories: (a) revealing the site, establishing ‘this happened here’ and (b) presenting scope, or showing how vast or large something is.

BLOB-Based AOMs: A Method for the Extraction of Crop Data from Aerial Images of Cotton

The use of aerial imagery in agriculture is increasing. Improvements in unmanned aerial systems (UASs) and the hardware and software used to analyze imagery are presenting new options for agricultural studies. One of the challenges associated with improving crop performance under water deficit conditions is the increased variability in the growth and development inherent in low water settings. The nature of plant growth and development under water deficits makes it difficult to monitor the response to environmental changes. Small field and plot-level experiments are often variable enough that averages of seasonal crop characteristics may be of limited value to the researcher. This variability leads to a desire to resolve fields on finer temporal and spatial scales. While UAS imagery provides an ability to monitor the crop on a useful temporal scale, the spatial scale is still difficult to resolve. In this study, an automated computer software framework was developed to facilitate resolving field and plot-level crop imagery to finer spatial resolutions. The method uses a Binary Large Object (BLOB)-based algorithm to automate the generation of areas of measurement (AOMs) as a tool for crop analysis. The use of the BLOB-based system is demonstrated in the analysis of plots of cotton grown in Lubbock, Texas, during the summer of 2018. The method allowed the creation and analysis of 1133 AOMs from the plots and the extraction of agronomic data that described plant growth and development.

Comparison of photogrammetric canopy models from archived and made-to-order aerial imagery in forest inventory

In remote sensing-based forest inventories 3D point cloud data, such as acquired from airborne laser scanning, are well suited for estimating the volume of growing stock and stand height, but tree species recognition often requires additional optical imagery. A combination of 3D data and optical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric 3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories, due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereo-photogrammetric canopy modeling can also be applied to previously acquired imagery, such as for aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory. One canopy model was based on standard archived imagery acquired primarily for ortho-mosaic production, and another was based on aerial imagery whose acquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested in the estimation of growing stock volume, volumes of main tree species, basal area and diameter and height. Despite the better quality of the latter canopy model, the difference of the accuracy of the forest estimates based on the two different data sets was relatively small for most variables (differences in RMSEs were 0–20%, depending on variable). However, the estimates based on stereo-photogrammetrically oriented aerial data retained better the original variation of the forest variables present in the study area.

Spatial Analysis of Livestock Grazing and Forest Service Management in the High Uintas Wilderness, Utah

This case study addresses the Forest Service reauthorization for grazing of domestic sheep in Utah’s High Uintas Wilderness, USA. It provides an approach using spatial analysis and aerial imagery to evaluate the lands capable of being grazed based on Forest Service criteria and field surveys. The resulting model and analysis demonstrated that the Forest Service has not applied its own criteria. This has led to the Forest Service overestimating the amount of land and numbers of sheep that can be supported in the study area. Past field studies show this has resulted in environmental damage by grazing sheep. Our analysis concludes that the numbers of domestic sheep should be greatly reduced to protect these lands and wilderness values. Limitations of the study include the lack of a suitably detailed soil survey to determine erosion susceptibility, a lack of ground cover data, a lack of Forest Service data for the level of grazing use, or utilization, and the lack of a Forest Service quantitative measurement of vegetation production in each plant community and soil type. In the end, our use of aerial imagery, GIS determinations of areas of steep slopes and dense forests, and our measurement of vegetation production in the dominant soil types showed most of the land is not capable for grazing domestic sheep even in the absence of this other data.

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

There is a long history of the use of aerial imagery for archaeological research, but the application of multisensor image data has only recently been facilitated by the development of unmanned aerial vehicles (UAVs). Two archaeological sites in the East Midlands U.K. that differ in age and topography were selected for survey using multisensor imaging from a fixed-wing UAV. The aim of this study was to determine optimum methodology for the use of UAVs in examining archaeological sites that have no obvious surface features and examine issues of ground control target design, thermal effects, image processing and advanced filtration. The information derived from the range of sensors used in this study enabled interpretation of buried archaeology at both sites. For any archaeological survey using UAVs, the acquisition of visible colour (RGB), multispectral, and thermal imagery as a minimum are advised, as no single technique is sufficient to attempt to reveal the maximum amount of potential information.

Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm

The unique set of physical and chemical properties of asbestos has led to its many industrial applications worldwide, of which roofing and facades constitute approximately 80% of currently used asbestos-containing products. Since asbestos-containing products are harmful to human health, their use and production have been banned in many countries. To date, no research has been undertaken to estimate the total amount of asbestos–cement products used at the country level in relation to regions or other administrative units. The objective of this paper is to present a possible new solution for developing the spatial distribution of asbestos–cement products used across the country by applying the supervised machine learning algorithm, i.e., Random Forest. Based on the results of a physical inventory taken on asbestos–cement products with the use of aerial imagery, and the application of selected features, considering the socio-economic situation of Poland, i.e., population, buildings, public finance, housing economy and municipal infrastructure, wages, salaries and social security benefits, agricultural census, entities of the national economy, labor market, environment protection, area of built-up surfaces, historical belonging to annexations, and data on asbestos manufacturing plants, best Random Forest models were computed. The selection of important variables was made in the R v.3.1.0 program and supported by the Boruta algorithm. The prediction of the amount of asbestos–cement products used in communes was executed in the randomForest package. An algorithm explaining 75.85% of the variance was subsequently used to prepare the prediction map of the spatial distribution of the amount of asbestos–cement products used in Poland. The total amount was estimated at 710,278,645 m2 (7.8 million tons). Since the best model used data on built-up surfaces which are available for the whole of Europe, it is worth considering the use of the developed method in other European countries, as well as to assess the environmental risk of asbestos exposure to humans.

Changes in Vegetation and Geomorphological Condition 10 Years after Riparian Restoration

Riparian restoration is an important objective for landscape managers seeking to redress the widespread degradation of riparian areas and the ecosystem services they provide. This study investigated the long-term outcomes of ‘one-off’ restoration activities undertaken in the Upper Murrumbidgee Catchment, NSW, Australia. The objective of the restoration was to protect and enhance riparian vegetation and control erosion, and consequently reduce sediment and nutrient delivery into the Murrumbidgee River. To evaluate the outcomes 10 years after restoration, rapid riparian vegetation and geomorphological assessments were undertaken at 29 sites spanning the four different restoration methods used (at least five replicates per treatment), as well as at nine comparable untreated sites. We also trialed the use of aerial imagery to compare width of riparian canopy vegetation and projective foliage cover prior to restoration with that observed after 10 years. Aerial imagery demonstrated the width of riparian canopy vegetation and projective foliage cover increased in all restored sites, especially those with native plantings. The rapid assessment process indicated that 10 years after riparian restoration, the riparian vegetation was in a better condition at treated sites compared to untreated sites. Width of riparian canopy vegetation, native mid-storey cover, native canopy cover and seedling recruitment were significantly greater in treated sites compared to untreated sites. Geomorphological condition of treated sites was significantly better than untreated sites, demonstrating the importance of livestock exclusion to improve bank and channel condition. Our findings illustrate the value of ‘one-off’ restoration activities in achieving long-term benefits for riparian health. We have demonstrated that rapid assessments of the vegetation and geomorphological condition can be undertaken post-hoc to determine the long-term outcomes, especially when supported with analysis of historical aerial imagery.

Experimental Study of the Levels of Street Lighting Using Aerial Imagery and Energy Efficiency Calculation

This article describes an innovative method for measuring lighting levels and other lighting parameters through the use of aerial imagery of towns and cities. Combined with electricity consumption data from smart electricity meters, it was possible to measure the energy efficiency of public lighting installations. The results of this study also confirmed that lighting measurements, installation material, luminaire position, and electricity consumption data can be easily integrated into geographic information systems (GIS). The main advantage of this new methodology is that it provides information about lighting installations in large areas in less time than more conventional procedures. It is thus a more effective way of obtaining the data required to calculate the energy efficiency of lighting levels and electricity consumption. There is even the possibility of generating street lighting maps that provide local administrations with up-to-date information regarding the status of public lighting installations in their city. In this way, modifications or improvements can be made to achieve greater energy savings and, if necessary, to correct the distribution or configuration of public lighting systems to make them more efficient and sustainable. This research studied levels of street lighting and calculated the energy efficiency in various streets of Deifontes (Granada), through the use of aerial imagery.

Data-driven precision agricultural applications using field sensors and Unmanned Aerial Vehicle

Abstract: Unmanned Aerial Vehicle (UAVs) and crop sensors are the most widely used remote sensing tool in precision agriculture. Use of UAVs in precision agriculture is attracting increasing interest due to its unique non-destructive approach. In recent years, dramatic evolution of precision agriculture technology has been driven by technologies such as sensors and controllers, telematics, and UAVs. An intriguing area in the field of precision agriculture and UAVs is big volume of data and its analysis that have not been dealt with in depth. The objective of this study was to validate the crop data acquisition procedure and the crop relationship with different type of data acquisition technique. In particular, this paper will compare ground based active optical sensor data collection with UAVs imagery for site-specificnitrogen management. To accomplish these objectives randomized complete block plantation experimental design was used with four treatments and four replicates. The plots were 12 rows wide at 38” spacing and were 125 ft. in length. GreenSeeker field sensor and Micasense sensor for UAVs was used to evaluate the Normalized Difference Vegetation Index. The software used to analyze the data were Microsoft ® Excel ®  2013, Statistical Analysis Software (ver.9.4) and ESRI ArcGIS (ver. 10.3). The results showed that UAVs assessed NDVI are good indicator of crop nutrition along with the ground based crop sensors. The result of the statistical data analysis showed that NDVI values are dependent on nitrogen application rate. The average NDVI value for no nitrogen application was recorded 0.54 whereas for240 lb./acre nitrogen application it was noted to be 0.76. Crucially, this correlation holds true for definite extent of nitrogen application rate. Because there was not any significant change in NDVI for 160 lb./acre and240 lb./acre. The NDVI values being 0.74 and 0.76 respectively. The results are significant because it shows the potential of further validating the use of aerial imagery derived NDVI for real time application of crop nutrient. This research has also proven that UAVs are reliable platform for nutrient assessment and making crop management decisions. Keywords: Digital image, NDVI, Nitrogen, ground based sensor, crop DOI: 10.33440/j.ijpaa.20180101.0004 Citation: Pathak R, Barzin R, Bora G C. Data-driven precision agricultural applications using field sensors and Unmanned Aerial Vehicle (UAVs). Int J Precis Agric Aviat, 2018; 1(1): 19 –23.