Assessment of permanent grasslands in Latvia using spectral remote sensing techniques

Permanent grasslands (meadows and pastures) are the most common agricultural land use type covering 34% (0.65 million hectares) of agricultural land in Latvia. The Common Agriculture policy (CAP) stipulates that the EU Member States have to designate permanent grasslands, ensure that farmers do not convert or plough them and that the ratio of permanent grasslands to the total agricultural area does not decrease by more than 5% in order to receive support payments. However, semi-natural grassland habitats require appropriate management activities to ensure their long-term conservation. The European Commission report (2015) required by the Birds and Habitats directives concludes that ‘grasslands and wetlands have the highest proportion of habitats with an unfavourable-bad and deteriorating status’ in the EU, while the midterm review of EU biodiversity strategy 2010-2020 highlighted that grassland habitat change presents a high risk to biodiversity. Latvia’s rural development programme (2014-2020) has identified only 47 thousand hectares of biologically valuable grasslands. These grasslands are semi-natural meadows and pastures that include species and habitat types of EU importance. 70-90% of EU importance grassland habitats in Natura 2000 sites were in poor condition in Latvia during 2012. There is a clear interest from a number of end-users (e.g. the Nature Conservation Agency, the Rural Support Service,) for grassland mapping and management practice monitoring solutions. In order to prevent loss of high nature value grasslands and increase sustainability of semi-natural grassland management, the Integrated Planning tool was developed in frames of LIFE+ project “Integrated planning tool to ensure viability of grasslands” (LIFE Viva Grass ENV/LT/00018). Spectral remote sensing technique was used for preparation of necessary inputs for the tool from Cesis Municipality in Latvia - mapping of grasslands, detection of overgrowth with shrubs/trees and spread of invasive species (Sosnowsky’s hogweed) as well as assessment of grass biomass. High spatial and spectral resolution of hyperspectral airborne data obtained with flying laboratory ARSENAL was complemented with temporal dimension of Sentinel-2 satellite data in order to achieve the best result reaching classification accuracy >90%

Analysis of trapezium system in the Virgo constellation

Veikta vairākkārtīgas zvaigžņu sistēmas BD+8°2654 kompleksa analīze, izmantojot literatūrā un datu bāzēs sastopamos novērojumus un oriģinālus augstas izšķirtspējas spektrus. Izvērtēta zvaigžņu sistēmas piederība, tā saucamajai, trapecveida sistēmai un, izmantojot augstas izšķirtspējas absorbcijas spektru, veikta vienas komponentes (HIP62831) ķīmiskā sastāva analīze. Veikta atomu un molekulu spektrālo līniju identifikācija optiskajā diapazonā, noteikts radiālais ātrums Vr = 13.3km/s, efektīvā temperatūra Teff = 4100K, brīvās krišanas paātrinājums log g = 1.3(CGS), turbulences ātrums ξ = 3km/s un dzelzs, titāna un dažu s-procesa elementu (Zr, Y, Nd) koncentrācijas: [Fe] = -0.4dex, [Ti] = -0.3dex. Precizēta HIP62831 evolūcijas stadija.The complex analysis of star system BD+8°2654, by using the given information in literature, databases and original high resolution spectra. Estimated the star system membership to, so called, trapezium system and chemical composition of one component (HIP62831) by using high resolution spectra. Identification of atomic and molecular spectral lines in optical range of spectrum, calculated radial velocity Vr = 13.3km/s, effective temperature Teff = 4100K, surface gravity log g = 1.3(CGS), microturbulence ξ = 3km/s and abundance of iron [Fe] = -0.4dex, titan [Ti] = -0.3dex and s-process elements (Zr, Y, Nd). Specified the evolutionary status of star HIP62831

Spectroscopy of binary stars in the disk and halo populations of Galaxy

Darbā veikta Galaktikas diska un halo populācijas dubultzvaigžņu salīdzinoša analīze. Tika modelēti zvaigžņu izlases augstas izšķirtspējas absorbcijas spektri optiskajā diapazonā ar mērķi aprēķināt atmosfēras ķīmisko sastāvu, precizēt kodolsintēzes procesus un piederību zvaigžņu populācijai. Aprēķinātas dzelzs un dzelzs grupas elementu koncentrācijas: HD30443 [Fe/H]=-1.17, HD202851 [Fe/H]=-0.85 and NGC2420X [Fe/H]=-0.25. Konstatēta paaugstināta smago elementu koncentrācija pētīto zvaigžņu atmosfērās. Noteiktas oglekļa izotopu attiecības HD30443 (12C/13C~2.5), HD202851 (12C/13C~8) atmosfērās un izvērtēta piederība zvaigžņu populācijām: HD30443 – halo, HD202851 – biezā diska, NGC2420X – diska populācija.This work presents a comparative analysis of binary stars in the disk and halo populations of the Galaxy. High resolution absorption spectra were modeled for a selection of stars to determine the chemical composition of the stellar atmospheres, to specify fusion process and membership of stellar population. The average abundancies of iron and iron group elements were calculated: HD30443 [Fe/H]=-1.17, HD202851 [Fe/H]=-0.85 and NGC2420X [Fe/H]=-0.25. The results show enhanced abundances of heavy elements for HD30443, HD202851 and NGC2420X. Carbon isotope ratio was estimated for HD30443 (12C/13C~2.5) and HD202851 (12C/13C~8). Membership of stellar population was evaluated for HD30443 as halo, HD202851 – thick disk and NGC2420X – disk population

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

Changes in the ungulate population density in the wild has impacts on both the wildlife and human society. In order to control the ungulate population movement, monitoring systems such as camera trap networks have been implemented in a non-invasive setup. However, such systems produce a large number of images as the output, hence making it very resource consuming to manually detect the animals. In this paper, we present a new dataset of wild ungulates which was collected in Latvia. Moreover, we demonstrate two methods, which use RetinaNet and Faster R-CNN as backbones, respectively, to detect the animals in the images. We discuss the optimization of training and impact of data augmentation on the performance. Finally, we show the result of aforementioned tune networks over the real world data collected in Latvia

Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84–0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing

Towards Automated Detection and Localization of Red Deer <i>Cervus elaphus</i> Using Passive Acoustic Sensors during the Rut

Passive acoustic sensors have the potential to become a valuable complementary component in red deer Cervus elaphus monitoring providing deeper insight into the behavior of stags during the rutting period. Automation of data acquisition and processing is crucial for adaptation and wider uptake of acoustic monitoring. Therefore, an automated data processing workflow concept for red deer call detection and localization was proposed and demonstrated. The unique dataset of red deer calls during the rut in September 2021 was collected with four GPS time-synchronized microphones. Five supervised machine learning algorithms were tested and compared for the detection of red deer rutting calls where the support-vector-machine-based approach demonstrated the best performance of −96.46% detection accuracy. For sound source location, a hyperbolic localization approach was applied. A novel approach based on cross-correlation and spectral feature similarity was proposed for sound delay assessment in multiple microphones resulting in the median localization error of 16 m, thus providing a solution for automated sound source localization—the main challenge in the automation of the data processing workflow. The automated approach outperformed manual sound delay assessment by a human expert where the median localization error was 43 m. Artificial sound records with a known location in the pilot territory were used for localization performance testing

Towards Automated Detection and Localization of Red Deer Cervus elaphus Using Passive Acoustic Sensors during the Rut

Passive acoustic sensors have the potential to become a valuable complementary component in red deer Cervus elaphus monitoring providing deeper insight into the behavior of stags during the rutting period. Automation of data acquisition and processing is crucial for adaptation and wider uptake of acoustic monitoring. Therefore, an automated data processing workflow concept for red deer call detection and localization was proposed and demonstrated. The unique dataset of red deer calls during the rut in September 2021 was collected with four GPS time-synchronized microphones. Five supervised machine learning algorithms were tested and compared for the detection of red deer rutting calls where the support-vector-machine-based approach demonstrated the best performance of &minus;96.46% detection accuracy. For sound source location, a hyperbolic localization approach was applied. A novel approach based on cross-correlation and spectral feature similarity was proposed for sound delay assessment in multiple microphones resulting in the median localization error of 16 m, thus providing a solution for automated sound source localization&mdash;the main challenge in the automation of the data processing workflow. The automated approach outperformed manual sound delay assessment by a human expert where the median localization error was 43 m. Artificial sound records with a known location in the pilot territory were used for localization performance testing

Separability of mowing and ploughing events on short temporal baseline sentinel-1 coherence time series

Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications.Peer reviewe