Browsing by Subject "Drone"

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  • Järvi, Leena; Kurppa, Mona; Kuuluvainen, Heino; Rönkkö, Topi; Karttunen, Sasu; Balling, Anna; Timonen, Hilkka; Niemi, Jarkko V.; Pirjola, Liisa (2023)
    Urban air pollutant concentrations are highly variable both in space and time. In order to understand these variabilities high-resolution measurements of air pollutants are needed. Here we present results of a mobile laboratory and a drone measurements made within a street-canyon network in Helsinki, Finland, in summer and winter 2017. The mobile lab-oratory measured the total number concentration (N) and lung-deposited surface area (LDSA) of aerosol particles, and the concentrations of black carbon, nitric oxide (NOx) and ozone (O3). The drone measured the vertical profile of LDSA. The main aims were to examine the spatial variability of air pollutants in a wide street canyon and its immediate surroundings, and find the controlling environmental variables for the observed variability's.The highest concentrations with the most temporal variability were measured at the main street canyon when the mo-bile laboratory was moving with the traffic fleet for all air pollutants except O3. The street canyon concentration levels were more affected by traffic rates whereas on surrounding areas, meteorological conditions dominated. Both the mean flow and turbulence were important, the latter particularly for smaller aerosol particles through LDSA and N. The formation of concentration hotspots in the street network were mostly controlled by mechanical processes but in winter thermal processes became also important for aerosol particles. LDSA showed large variability in the profile shape, and surface and background concentrations. The expected exponential decay functions worked better in well -mixed conditions in summer compared to winter. We derived equation for the vertical decay which was mostly con-trolled by the air temperature. Mean wind dominated the profile shape over both thermal and mechanical turbulence. This study is among the first experimental studies to demonstrate the importance of high-resolution measurements in understanding urban pollutant variability in detail.
  • Suomalainen, Juha; Oliveira, Raquel A.; Hakala, Teemu; Koivumäki, Niko; Markelin, Lauri; Näsi, Roope; Honkavaara, Eija (Elsevier, 2021)
    Remote Sensing of Environment
    Multi- and hyperspectral cameras on drones can be valuable tools in environmental monitoring. A significant shortcoming complicating their usage in quantitative remote sensing applications is insufficient robust radiometric calibration methods. In a direct reflectance transformation method, the drone is equipped with a camera and an irradiance sensor, allowing transformation of image pixel values to reflectance factors without ground reference data. This method requires the sensors to be calibrated with higher accuracy than what is usually required by the empirical line method (ELM), but consequently it offers benefits in robustness, ease of operation, and ability to be used on Beyond-Visual Line of Sight flights. The objective of this study was to develop and assess a drone-based workflow for direct reflectance transformation and implement it on our hyperspectral remote sensing system. A novel atmospheric correction method is also introduced, using two reference panels, but, unlike in the ELM, the correction is not directly affected by changes in the illumination. The sensor system consists of a hyperspectral camera (Rikola HSI, by Senop) and an onboard irradiance spectrometer (FGI AIRS), which were both given thorough radiometric calibrations. In laboratory tests and in a flight experiment, the FGI AIRS tilt-corrected irradiances had accuracy better than 1.9% at solar zenith angles up to 70◦. The system’s lowaltitude reflectance factor accuracy was assessed in a flight experiment using reflectance reference panels, where the normalized root mean square errors (NRMSE) were less than ±2% for the light panels (25% and 50%) and less than ±4% for the dark panels (5% and 10%). In the high-altitude images, taken at 100–150 m altitude, the NRMSEs without atmospheric correction were within 1.4%–8.7% for VIS bands and 2.0%–18.5% for NIR bands. Significant atmospheric effects appeared already at 50 m flight altitude. The proposed atmospheric correction was found to be practical and it decreased the high-altitude NRMSEs to 1.3%–2.6% for VIS bands and to 2.3%– 5.3% for NIR bands. Overall, the workflow was found to be efficient and to provide similar accuracies as the ELM, but providing operational advantages in such challenging scenarios as in forest monitoring, large-scale autonomous mapping tasks, and real-time applications. Tests in varying illumination conditions showed that the reflectance factors of the gravel and vegetation targets varied up to 8% between sunny and cloudy conditions due to reflectance anisotropy effects, while the direct reflectance workflow had better accuracy. This suggests that the varying illumination conditions have to be further accounted for in drone-based in quantitative remote sensing applications.
  • Pöysä, Hannu; Kotilainen, Juho; Väänänen, Veli-Matti; Kunnasranta, Mervi (2018)
    We tested the use of unmanned aerial systems (UAS) in duck brood surveys in boreal wetlands in Finland. We performed brood surveys at the same wetlands concurrently with ground-based point counts and using a UAS (multicopter; drone counts) equipped with a camera that produced high-quality images for identification of broods and ducklings. The number of broods did not differ between point counts and drone counts in three duck species, the mallard (Anas platyrhynchos), common teal (Anas crecca), and common goldeneye (Bucephala clangula). The number of ducklings was higher in drone counts than in point counts in the common teal, but no such difference was found in the mallard and common goldeneye. UAS-based images seem to be useful for estimating numbers of both broods and ducklings for different duck species, although the manual processing of images is labor intensive.
  • Änäkkälä, Mikael (Helsingin yliopisto, 2020)
    The number of drones has increased in both the private and corporate sectors. There is also an interest in the use of drones in agriculture since by using them the large fields can be monitored easily. Automatic flight systems of drones are simple to use. More accurate overview of the field can be got by utilizing the drones than by making observations from the side of the field. With aerial photographs the measures for the field can be planned further. For example, based on the photos pesticide spraying or fertilize spreading can be planned for the field. Drones can also be used to estimate crop biomasses. With drones the development of the crops is possible to observe as a timeseries during the growing season. The aim of this study was to explore the use of multispectral images and 3D models in crop monitoring. Crop leaf area index (LAI), biomass and chlorophyll content were measured. There were 8 different plants/fertilization levels in this study. In this study, a multispectral camera and a RGB-camera were used to estimate crops features. With a multispectral camera the reflectance values of the vegetation, which described how much of the incoming sun radiation was reflected back from the vegetation, were able to determine. The multispectral camera had five spectral bands (blue, green, red, red edge and NIR). Based on these bands NDVI vegetation index was calculated. The reflectance values and vegetation indices were compared to the dry matter mass, LAI, and chlorophyll content determinations of the vegetation. From the images of the RGB-camera 3D-models were created to calculate crop volumes. Calculated volumes were compared to crop dry matter mass and LAI measurements. Linear regression analysis was used to examine the relationship between the variables calculated from the images and the parameters determined from the crops on the field. According to these results, the variables determined from the multispectral images explained the dry matter mass and leaf area index of the crop slightly less than the 3D-models determined from the RGB images. The strongest determined dependence of the data recorded by the multispectral camera was between the faba bean LAI and NDVI (R2 = 0,85). The relationship between the reflection/index data of multispectral camera and crop parameter was weak: average coefficient of determination for dry matter mass of the crop was 0.15, for chlorophyll content 0.14, and for LAI 0,21. The highest coefficient of determination for 3D model of crop volume was between the dry matter mass of oats (R2 = 0.91). The mean coefficient of dependence was 0.69 for the relationship between the plant dry matter masses and 3D model volumes. The mean coefficient of determination for the relationship between the leaf area index of plants and the 3D model volumes was 0.57. Based on these results, from the multispectral camera data, the NDVI index was best suited to determine the crops dry matter mass, leaf area index, and chlorophyll content. However, there were differences in the dependencies between different spectral bands/NDVI index and plant properties determined from different crops. 3D models produced stronger dependences for estimating crop dry matter mass and leaf area index than the quantities determined from multispectral images. Analyzing the data with more sophisticated calculation methods utilizing the values of several spectral bands and the indices in the same time would probably have been a more efficient method to analyzing the data than the current used linear regression used in this study. Removing errors, caused by external factors, from multispectral images was found to be very difficult. Especially reflectance values of dry soil differed clearly from vegetations values. Further studies are needed to develop vegetation indices that can reduce errors caused by external factors. In addition, data processing of images should be developed to utilize multiple spectral bands and vegetation indices to determine the relationship between crop characteristics and variables measured from images. In addition, different plant species imaging techniques should be investigated, as different plants have different reflection values.
  • Jokiniemi, Juha (Helsingin yliopisto, 2022)
    Dronejen käyttö on lisääntynyt voimakkaasti viimeisten vuosien aikana. Ensimmäiset dronet on kehitetty jo 1900-luvun alussa. Myös lämpökameroita on käytetty jo useamman vuosikymmenen ajan. Näiden yhteiskäyttö on yleistynyt 2010-luvun aikana. Dronen ja lämpökameran avulla on tutkittu erityisesti kasvien lämpöstressiä. Tavoitteena oli selvittää, millaisia käyttömahdollisuuksia droneen kiinnitetyllä lämpökameralla on ja ovatko kameran antamat tulokset riittävän tarkkoja esimerkiksi täsmäviljelyssä hyödyntämiseen. Lisäksi tutkittiin kuvausprosessin käyttökelpoisuutta ja lämpökameran kalibrointia. Tutkimuksessa käytettiin lämpökameran rinnalla kolmea muuta eri lämpötilan mittausmenetelmää. Tutkimus suoritettiin Helsingin yliopiston Viikin tutkimustilalla. Kuvattavana kohteina olivat nurmi, jonka kasvuaste oli Zadoksin (BBCH) asteikolla 12–13, edellisenä syksynä kultivoitu maa sekä kynnetty maa. Tutkimuksessa oli käytössä itserakennettu drone (Tarot Ironman:n runko) sekä Flir Duo Pro R -lämpökamera. Tutkimus suoritettiin touko-kesäkuussa 2022. Lämpökuvien käsittely tehtiin Pix4D ja Matlab-ohjelmilla. Lämpökameralla saatiin kuvattua kaikki peltolohkot. Jokaisesta koelohkosta mitattiin vertailulämpötilat, jotta voitiin tutkia ilmasta otetun kuvan paikkaansa pitävyyttä. Kontrollipisteet mitattiin GCP-pisteiden (Ground Control Point) läheisyydestä kolmen metrin etäisyydeltä merkkitolpasta. Dronella otettujen lämpökuvien ja Ahlbornin mittarin tulosten välinen korrelaatiokerroin oli 0,67; joka on kohtalaisen korkea. Flir-käsilämpökameran ja dronella otettujen lämpökuvien välinen korrelaatio ei osoittautunut tilastollisesti merkitseväksi. Tähän vaikutti luultavasti Flir-käsilämpökameralla otettujen mittauspisteiden epätarkkuus kunnollisen kuvaustelineen puuttuessa. Maaperäskannerin tuottaman lämpökartan ja dronella otettujen lämpökuvien välinen korrelaatio oli -0,11. Tutkimuksessa havaittiin myös kameran kulma-anturissa olevan jotain häiriötä, koska kaikki sen ottamat kuvat olivat virtuaalisella karttatasolla 90 astetta väärässä kulmassa. Tämä saatiin korjattua kuvankäsittelyohjelmalla. Dronen lämpökameran kalibrointi todettiin riittäväksi tutkimuksen olosuhteissa. Droneen kiinnitetty lämpökamera on riittävän tarkka mitattaessa lämpötiloja ilmasta, jos olosuhteet ovat kameralle oikeat. Tulevia kuvauksia varten kasvustoon tulisi saada lisää kiintopisteitä, jotta analysointiohjelma saisi muodostettua kohdealueelta luotettavan lämpökuvan. Myös säähän olisi kiinnitettävä huomiota, sillä vähäinenkin pilvisyys vaikuttaa lopputulokseen kameran ominaisuuksista johtuen. Myös maasta mitattujen kontrollipisteiden tarkkuuteen tulisi kiinnittää enemmän huomiota, sillä niillä on suuri vaikutus tuloksiin, koska maan lämpötila voi vaihdella hyvin pienenkin alueen sisällä. Tässäkin tutkimuksessa vierekkäisten mittauspisteiden välillä oli jopa useiden asteiden lämpötilaeroja.
  • Xu, Shan; Zaidan, Martha A.; Honkavaara, Eija; Hakala, Teemu; Viljanen, Niko; Porcar-Castell, Albert; Liu, Zhigang; Atherton, Jon (IEEE, 2020)
    IEEE International Symposium on Geoscience and Remote Sensing IGARSS
    Leaf angle distribution (LAD) is a key canopy structural parameter, playing an important role in light transfer. LAD can be estimated from fixed point of view photography, however this is time consuming and spatially limited. Recently, Terrestrial LiDAR Scanning (TLS) has been used to estimate LAD through 3D canopy space. The downside of TLS it is more costly than the cameras used in the photographic method. We propose a cost effective method to estimate LAD from drone based photogrammetry. We compare LAD estimates in different water treatment plots. Results show that LAD can be obtained from photogrammetric point clouds. Leaf angles were enhanced in stressed plots, presumably due to wilting. Further, the leaf azimuth distribution was not random but concentrated around 0 and 180 degrees. In summary, drone based photogrammetry can be used to estimate remote sensing parameters such as LAD paving the way for cost effective trait estimation.
  • Oliveira, R.A.; Khoramshahi, E.; Suomalainen, J.; Hakala, T.; Viljanen, N.; Honkavaara, E. (2018)
    The use of drones and photogrammetric technologies are increasing rapidly in different applications. Currently, drone processing workflow is in most cases based on sequential image acquisition and post-processing, but there are great interests towards real-time solutions. Fast and reliable real-time drone data processing can benefit, for instance, environmental monitoring tasks in precision agriculture and in forest. Recent developments in miniaturized and low-cost inertial measurement systems and GNSS sensors, and Real-time kinematic (RTK) position data are offering new perspectives for the comprehensive remote sensing applications. The combination of these sensors and light-weight and low-cost multi- or hyperspectral frame sensors in drones provides the opportunity of creating near real-time or real-time remote sensing data of target object. We have developed a system with direct georeferencing onboard drone to be used combined with hyperspectral frame cameras in real-time remote sensing applications. The objective of this study is to evaluate the real-time georeferencing comparing with post-processing solutions. Experimental data sets were captured in agricultural and forested test sites using the system. The accuracy of onboard georeferencing data were better than 0.5 m. The results showed that the real-time remote sensing is promising and feasible in both test sites. © Authors 2018. CC BY 4.0 License.