Browsing by Subject "Remote Sensing"

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  • Santillo, Jordan (Helsingin yliopisto, 2022)
    Research in radar technology requires readily accessible data from weather systems of varying properties. Lack of real-world data can delay or stop progress in development. Simulation aids this problem by providing data on demand. In this publication we present a new weather radar signal simulator. The algorithm produces raw time series data for a radar signal using physically based methodology with statistical techniques incorporated for computational efficiency. From a set of user-defined scatterer characteristics and radar system parameters, the simulator solves the radar range equation for individual, representative precipitation targets in a virtual weather cell. The model addresses the question of balancing utility and performance in simulating signal that contains all the essential weather information. For our applications, we focus on target velocity measurements. Signal is created with respect to the changing position of targets, leading to a discernable Doppler shift in frequency. We also show the operation of our simulator in generating signal using multiple pulse transmission schemes. First, we establish the theoretical basis for our algorithm. Then we demonstrate the simulator's capability for use in experimentation of advanced digital signal processing techniques and data acquisition, focusing on target motion. Finally, we discuss possible future developments of the simulator and their importance in application.
  • Le, Viet (Helsingin yliopisto, 2021)
    Atmospheric aerosol particles absorb and scatter solar radiation, directly altering the Earth’s radiation budget. These particles also have a complex role in weather and climate by changing cloud physical properties such as reflectivity by acting as cloud condensation nuclei or ice nuclei. Aerosol particles in the boundary layer are important because they pose a negative impact on air quality and human health. In addition, elevated aerosol from volcanic dust or desert dust present an imminent threat to aviation safety. To improve our understanding of the role of aerosol in influencing climate and the capability to detect volcanic ash, a ground-based network of Halo Doppler lidars at a wavelength of 1565 nm is used to collect data of atmospheric vertical profiles across Finland. By comparing the theoretical values of depolarization ratio of liquid clouds with the observed values, bleed through of each lidar is detected and corrected to improve data quality. The background noise levels of these lidars are also collected to assess their stability and durability. A robust classification algorithm is created to extract aerosol depolarization ratios from the data to calculate overall statistics. This study finds that bleed through is at 0.017 ± 0.0072 for the Uto-32 lidar and 0.0121 ± 0.0071 for the Uto-32XR lidar. By examining the time series of background noise level, these instruments are also found to be stable and durable. The results from the classification algorithm show that it successfully classified aerosol, cloud, and precipitation even on days with high turbulence. Depolarization ratios of aerosol across all the sites are extracted and their means are found to be at 0.055 ± 0.076 in Uto, 0.076 ± 0.090 in Hyytiala, 0.076 ± 0.071 in Vehmasmaki and 0.041 ± 0.089 in Sodankyla. These mean depolarization ratios are found to vary by season and location. They peak during summer, when pollen is abundant, but they remain at the lowest in the winter. As Sodankylä is located in the Artic, it has aerosols with lower depolarization ratio than other sites in most years. This study found that in summer, aerosol depolarization ratio is positively correlated with relative humidity and negatively correlated with height. No conclusion was drawn as to what processes play a more important role in these correlations. This study offers an overview of depolarization ratio for aerosol at a wavelength of 1565 nm, which is not commonly reported in literature. This opens a new possibility of using Doppler lidars for aerosol measurements to support air quality and the safety of aviation. Further research can be done test the capability of depolarization ratio at this wavelength to differentiate elevated aerosol such as dust, pollution, volcanic ash from boundary layer aerosol.
  • Thomas, Steven Job (Helsingin yliopisto, 2020)
    Biogenic Volatile Organic Compounds play a major role in the atmosphere by acting as precursors in the formation of secondary organic aerosols and by also affecting the concentration of ozone. The chemical diversity of BVOCs is vast but global emissions are dominated by isoprene and monoterpenes. The emissions of BVOCs from plants are affected by environmental parameters with temperature and light having significant impacts on the emissions. The Downy birch and Norway spruce trees consist of heavy and low volatile compounds but published results are limited up to observing sesquiterpenoid emissions from these two trees. In this study, the Vocus proton-transfer-reaction time-of-flight mass spectrometer is deployed in the field to examine BVOC emissions from Downy birch and Norway spruce trees. With higher mass resolution, shorter time response and lower limits of detection than conventional PTR instruments, the Vocus can effectively measure a broader range of VOCs. For the first time, real-time emissions of diterpenes and 12 different oxygenated compounds were observed from birch and spruce trees. The emission spectrum of birch was dominated by C10H17+, while for spruce C5H9+ contributed the most. The sum emissions of oxygenated compounds contributed significantly to the observed total emissions from both the trees. The emission rates of all compounds varied dramatically throughout the period due to fluctuations in temperature and light. Due to lack of data from spruce, conclusive results for temperature and light response on terpene emissions could not be drawn. For birch, the emission rates were well explained by the temperature and temperature-light algorithms. The terpene emissions modelled using both algorithms correlated similarly with experimental data making it difficult to decisively conclude if the emissions originated from synthesis or pools.
  • Garedew, Weyessa; Tesfaw Hailu, Binyam; Lemessa, Fikre; Pellikka, Petri; Pinard, Francois (Springer International Publishing AG, 2017)
    Climate Change Management
  • Kantola, Tuula; Lyytikainen-Saarenmaa, Paivi; Coulson, Robert N.; Holopainen, Markus; Tchakerian, Maria D.; Streett, Douglas A. (2016)
    Hemlock woolly adelgid (Adelges tsugae Annand, HWA) is an introduced invasive forest pest in eastern North America. Herbivory by this insect results in mortality to eastern hemlock (Tsuga canadensis L. Carr.) and Carolina hemlock (Tsuga caroliniana Engelm.). These species occur in landscapes where extreme topographic variation is common. The vegetation communities within these landscapes feature high diversity of tree species, including several other conifer species. Traditional forest inventory procedures and insect pest detection methods within these limited-access landscapes are impractical. However, further information is needed to evaluate the impacts of HWA-induced hemlock mortality. Accordingly, our goal was to develop a semi-automatic method for mapping patches of coniferous tree species that include the living hemlock component and tree mortality by the HWA using aerial images and LiDAR (light detection and ranging) to increase our understanding of the severity and pattern of hemlock decline. The study was conducted in the Linville River Gorge in the Southern Appalachians of western North Carolina, USA. The mapping task included a two-phase approach: decision-tree and support vector machine classifications. We found that about 2% of the forest canopy surface was covered by dead trees and 43% by coniferous tree species. A large portion of the forest canopy surface (over 55%) was covered by deciduous tree species. The resulting maps provide a means for evaluating the impact of HWA herbivory, since this insect was the only significant coniferous mortality agent present within the study site.
  • Junttila, Oula Samuli; Vastaranta, Mikko Antero; Hämäläinen, Jarno; Latva-käyrä, Petri; Holopainen, Markus Edvard; Hernandez-Clemente, Rocio; Hyyppä, Hannu; Navarro-Cerrillo, Rafael (2017)
    The effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Airborne thermal imagery, airborne scanning light detection and ranging (LiDAR) data and 92 field-measured sample plots were acquired at the area of interest. The surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and the logarithm of basal area at a resolution of 254 m2 (9-m radius). LiDAR-derived metrics: the standard deviations of the canopy heights, canopy height (upper percentiles and maximum height) and canopy cover percentage were most strongly negatively correlated with the surface temperature. Although forest structure has an effect on the detected surface temperature, higher temperatures were detected in severely defoliated canopies and the difference was statistically significant. We also found that the surface temperature differences between the segmented canopy and the entire plot were greater in the defoliated plots, indicating that thermal images may also provide some additional information for classifying forests health status. Based on our results, the effects of forest structure on the surface temperature captured by airborne thermal imagery should be taken into account when developing forest health mapping applications using thermal imagery.
  • Auvinen, Markus (Helsingfors universitet, 2017)
    The exploitation of remotely piloted aircraft systems has sharply increased for both amateur and professional purposes. Rapid technological development is driven by consumer grade applications. On the professional side, RPAS technologies have already been widely used in different fields of environmental resource management, especially in agriculture. Due to the recent price development, technology has become also attainable for less financially productive purposes, such as environmental surveys. By applying RPAS technologies in environmental surveys of forest industry, a substantial amount of fieldwork can be avoided thus leading to financial savings and possibly to more comparative measurements and results. To test this hypothesis, three different sub-disciplines of forest industry environmental surveys were conducted by both traditional fieldwork and by applying manual and automatic remote sensing methods. Aerial imagery was recorded with a multispectral sensor attached to consumer grade remotely piloted aircraft. Sub-discipline specific attributes were measured and compared to estimates derived from aerial imagery and three dimensional models. It was proved that by using even relatively low-priced instruments the quality of the data was more than adequate for remote sensing purposes. An automated workflows to derive measurements from the subjects of interest did not perform satisfactory, but manual interpretation of imagery gave promising results. It can be assumed that RPAS technologies are able to provide savings for conducting environmental surveys. Manual interpretation was at that moment seen superior to automated workflows.
  • Junttila, Oula Samuli; Vastaranta, Mikko Antero; Linnakoski, Riikka Marjaana; Sugano, Junko; Kaartinen, Harri; Kukko, Antero; Holopainen, Markus Edvard; Hyyppä, Hannu; Hyyppä, Juha (2017)
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Climate change is increasing the amount and intensity of disturbance events, i.e. drought, pest insect outbreaks and fungal pathogens, in forests worldwide. Leaf water content (LWC) is an early indicator of tree stress that can be measured remotely using multispectral terrestrial laser scanning (MS-TLS). LWC affects leaf reflectance in the shortwave infrared spectrum which can be used to predict LWC from spatially explicit MS-TLS intensity data. Here, we investigated the relationship between LWC and MS-TLS intensity features at 690 nm, 905 nm and 1550 nm wavelengths with Norway spruce seedlings in greenhouse conditions. We found that a simple ratio of 905 nm and 1550 nm wavelengths was able to explain 84% of the variation (R2) in LWC with a respective prediction accuracy of 0.0041 g/cm2. Our results showed that MS-TLS can be used to estimate LWC with a reasonable accuracy in environmentally stable conditions.
  • Honkavaara, Eija; Nasi, Roope; Viljanen, Niko; Oliveira, Raquel A.; Suomalainen, Juha; Khoramshahi, Ehsan; Hakala, Teemu; Nevalainen, Olli; Markelin, Lauri; Vuorinen, Matti; Kankaanhuhta, Ville; Lyytikäinen-Saarenmaa, Päivi; Haataja, Lauri (2020)
    Various biotic and abiotic stresses are threatening forests. Modern remote sensing technologies provide powerful means for monitoring forest health, and provide a sustainable basis for forest management and protection. The objective of this study was to develop unmanned aerial vehicle (UAV) based spectral remote sensing technologies for tree health assessment, particularly, for detecting the European spruce bark beetle (Ips typographus L.) attacks. Our focus was to study the early detection of bark beetle attack, i.e. the “green attack” phase. This is a difficult remote sensing task as there does not exist distinct symptoms that can be observed by the human eye. A test site in a Norway spruce (Picea abies (L.) Karst.) dominated forest was established in Southern-Finland in summer 2019. It had an emergent bark beetle outbreak and it was also suffering from other stress factors, especially the root and butt rot (Heterobasidion annosum (Fr.) Bref. s. lato). Altogether seven multitemporal hyper- and multispectral UAV remote sensing datasets were captured from the area in August to October 2019. Firstly, we explored deterioration of tree health and development of spectral symptoms using a time series of UAV hyperspectral imagery. Secondly, we trained assessed a machine learning model for classification of spruce health into classes of “bark beetle green attack”, “root-rot”, and “healthy”. Finally, we demonstrated the use of the model in tree health mapping in a test area. Our preliminary results were promising and indicated that the green attack phase could be detected using the accurately calibrated spectral image data.