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This research deconstructs complexity as a key challenge of intergovernmental digitalisation projects. While much of the literature acknowledges that the fundamental restructuring coupled with technical capacity that these joint projects require leads to increased complexity, little is known about how different types of complexity interact within the collaborative process. Using established concepts of substantive, strategic, and institutional complexity, we apply complexity theory in collaborative digital environments. To do so, eight digital projects are analysed that differ by state structure and government level. Using a cross-case design with 50 semi-structured expert interviews, we find that each digitalisation project exhibits all types of complexity and that these complexities overlap. However, clear differences emerge between national and local level projects, suggesting that complexity in digitalisation processes presents different challenges for collaborative digitalisation projects across contexts. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper is funded under the TROPICO (Transforming into Open, Innovative and Collaborative Governments), www.tropico-project.eu. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 726840.

Clean water is necessary for all living organisms, but human activities and climate change have deteriorated water quality in many regions. It is important to monitor water bodies to take the necessary measures. Conventional contact measurements have been carried out for decades. However, they are time-consuming and expensive and can cover only a small number of water bodies. This is insufficient as changes in waterbodies are often rapid and occur over large areas. Remote sensing methods have been introduced to fill these gaps in temporal and spatial coverage. On the other hand, there is a need to study parameters that impact the formation of water-leaving signal in order to increase the accuracy of remote sensing products. One such substance that requires further investigation is suspended particulate matter. It consists of mineral and organic particles suspended in the water column due to natural processes (waves, erosion, river transport) and human activities (trawling, dredging, construction). The high content of suspended particles increases the water turbidity, consequently reducing the light availability. In turn, this worsens aquatic organisms' living conditions and alters physical processes (like radiative heating) in the water environment. Suspended particles can contain harmful substances such as organic pollutants and heavy metals. We studied the behaviour of heavy metal-rich sediments in Portman Bay (one of the most polluted areas of the Mediterranean), in southern Spain, after trawling the sea bottom. Coarse and heavy particles and flocs settled quickly. Fine particles remained in suspension for a longer time. Therefore, the content of heavy metals in the water increased. From an optical point of view, the suspended particles mainly scatter light. The optical properties of suspended particles (light absorption, scattering, backscattering/scattering ratio) were studied in several Estonian coastal areas. The backscattering ratio, which is used as a constant in bio-optical remote sensing algorithms, was highly variable, depending on the conditions (algal bloom, storm). Such variability was also observed for other optical properties. It was found that small particles resuspended by a storm aggregated into larger flocs (> 30 μm), affecting the particles' optical properties in the Pärnu Bay, which has very low salinity and little organic content. This study shows that using an assumption that the backscattering ratio is a fixed constant reduces the accuracy of remote sensing products. Puhas vesi on eluks vajalik kõikidele elusolenditele, kuid inimtegevuse ja kliimamuutuste mõjul on paljudes piirkondades veekvaliteet halvenenud. Veekogude seisundi hindamiseks ja vajalike meetmete kasutusele võtmiseks, on tarvis neid seirata. Aastakümneid on tehtud kontaktmõõtmisi, kuid need on ajakulukad ja kallid ning nendega on keeruline jälgida kiireid muutusi suurtel aladel. Viimastel aastakümnetel on kasutusele võetud kaugseire meetodid, mis võimaldavad neid puudujääke täita. Selleks, et mõista mida kaugseire instrument mõõdab on vaja teada parameetreid, mis kaugseire signaali ja selle interpreteerimist mõjutavad. Üheks selliseks aineks on heljum. Heljum koosneb veemassis hõljuvatest mineraalsetest (setted) ja orgaanilistest osakestest ja see jõuab veemassi looduslike protsesside (lainetus, erosioon, jõgede transport) ja inimtegevuse (traalimine, süvendamine, ehitamine) tagajärjel. Kõrge heljumi sisaldus veemassis muudab vee häguseks, mõjutades valguse jõudmist sügavamatesse veekihtidesse, mis omakorda halvendab vee, kui elukeskkonna seisundit. Heljum võib sisaldada kahjulikke aineid, nagu orgaanilised saasteained ja raskemetallid. Seetõttu uurisime Portmani lahes (Vahemere üks reostatumaid alasid), Lõuna-Hispaanias põhjasetteid, milles on kõrge raskemetallide sisaldus, ning kuidas need setted jõuavad veesambasse traalimise mõjul. Suured osakesed settisid kiiresti. Väikesed osakesed, millega liitusid raskemetallid, jäid veemassi pikemaks ajaks. Seetõttu raskemetallide sisaldus vees tõusis. Eesti rannikualadel uuriti heljumi optilisi omadusi (valguse neeldumist, hajumist, tagasihajumise/hajumise suhet). Leiti, et tagasihajumise/hajumise suhe, mida kasutatakse bio-optilistes kaugseire algoritmides konstandina on tegelikult väga muutlik ning sõltub hetke tingimustest (vetikaõitseng, torm). Sellist varieeruvust täheldati ka muude optiliste omadustega seoses. Leiti, et väga väikese soolsuse ja vähese orgaanika sisaldusega Pärnu lahes agregeerisid väikesed tormi mõjul veemassi keerutatud osakesed suuremateks osakesteks (> 30 μm) mõjutades vee optilisi omadusi ning seega ka kaugseire signaali. Väitekirja elektrooniline versioon ei sisalda publikatsioone https://www.ester.ee/record=b5553931

In the eastern Baltic region, the abundance of Scots pine (Pinus sylvestris L.) has been predicted to shift due to changes in height growth and competitiveness. Under such conditions, the relationships between tree growth and meteorological/climatic conditions can provide valuable information on the ecological plasticity and adaptability of local populations. Regarding the climatic dependence of productivity and competitiveness, height increment is an informative proxy, although data gathering is laborious. In this study, meteorological sensitivity of the height increment of Scots pine across the climatic gradient of north-eastern Baltic region was assessed by the means of timeseries deconstruction and generalized additive regression. Previously published time series from sites in Latvia, Estonia, and Finland were reanalysed. The local linear weather–growth relationships showed gradual changes in sensitivity to low summer temperature to conditions related to water availability in summer in Finland and the Baltics, respectively. The regional generalization of responses indicated the prevalence of temporary and spatially stationary effects of temperature in winter and summer, which had a complex influence (direct and carry-over effects). The prevailing linearity of the regional responses suggested that, regarding height growth, the studied populations appeared adapted to a wider climatic gradient than the studied one.

Emerald ash borer (EAB), Agrilus planipennis, is a phloem-boring beetle, native to East Asia that has become a serious invasive pest of ash (Fraxinus spp.) trees in North America and European Russia since the early 2000s. In 2019, EAB was detected in Ukraine. It had spread over 300 km from the entry point over two years and killed hundreds of Fraxinus excelsior and F. pennsylvanica trees. EAB poses a threat to the ash forests of neighboring European countries, which have already been damaged by the invasive fungus Hymenoscyphus fraxineus. The purpose of this research was (i) to reveal the traits of EAB and the climatic variables that affect its survival; (ii) to predict the EAB expansion range in Ukraine and westward; and (iii) to compare the most significant bioclimatic variables in the native, invasive ranges of EAB, as well as outside these ranges. The results demonstrated the following: (i) in all ranges, EAB has adapted to the seasonal temperature variations; (ii) the MaxEnt model predicted the potential distribution of EAB with high accuracy (AUC = 0.988); the predicted area of EAB invasion covered 87%, 48%, and 32% in Luhansk, Kharkiv, and Donetsk regions, respectively; and (iii) the ranges of climatic variables in EAB-inhabited regions demonstrated the high ecological plasticity of this pest. However, the predictions could be improved by considering forest structure, as well as the localization of roads.

The breeding of economically important forest tree species in the Baltic Sea region has contributed notably to the availability of quality wood for bioeconomy. Accordingly, the altered stand dynamics of improved trees should be identified and incorporated in growth models to accurately reflect these gains. Such advanced models can be used for assessment of different alternatives, e.g. strategies for increased carbon sequestration. We tested and modified dynamic forms of the King-Prodan height growth function based on the remeasured National Forest Inventory plots in Latvia to predict the growth of improved Scots pine, Norway spruce and silver birch forest reproductive material (FRM) categories ‘qualified’ and ‘tested’ using height measurements from progenies of 371, 390, and 690 open-pollinated families, respectively. Both categories had steeper growth trajectories at young age compared to an unmodified function. Growth of category ‘tested’ for pine and birch exceeded that of category ‘qualified’ across the modelled age range, while trajectories mainly overlapped for spruce on lower site indices. The functions with FRM category-specific multipliers more accurately reflect the actual growth of improved stands, advancing planning of timely management activities like thinning. The single model with category-specific set of multipliers may be easy applicable in practice or incorporated in growth simulators without increased complexity for end-users. However, the predictions are limited to the sites with medium and high site indices, where improved planting stock is typically used. Keywords: GADA approach, dynamic modelling, tree breeding, FRM categories

Received: February 1st, 2023 ; Accepted: April 25th, 2023 ; Published: May 10th, 2023 ; Correspondence: mikael.anakkala@helsinki.fi Interest in forage maize (Zea mays L.) cultivation for livestock feed has grown in northern conditions. In addition, it is important to develop methods and tools to monitor crop development and other characteristics of the crop. For these purposes UAVs are very efficient and versatile tools. UAVs can be equipped with a variety of sensors like lidar or different types of cameras. Several studies have been conducted where data collected by UAVs are used to estimate different crop properties like yield and biomass. In this research, a forage maize field experiment was studied to examine how well the aerial multispectral data correlated with the different properties of the vegetation. The field test site is located in Helsinki, Finland. A multispectral camera (MicaSense Rededge 3) was used to take images from five spectral bands (Red, Green, Blue, Rededge and NIR). All the images were processed with Pix4D software to generate orthomosaic images. Several vegetation indices were calculated from the five spectral bands. During the growing season, crop height, chlorophyll content, leaf area index (LAI), fresh and dry matter biomass were measured from the vegetation. From the five spectral bands, Rededge had the highest correlation with fresh biomass (R2 = 0.273). The highest correlation for a vegetation index was found between NDRE and chlorophyll content (R2 = 0.809). A multiple linear regression (MLR) model using selected spectral bands and vegetation indices as inputs showed high correlations with the field measurements.

Received: May 9th, 2023 ; Accepted: September 16th, 2023 ; Published: September 21st, 2023 ; Correspondence: eda.merisalu@emu.ee Digitization is actively spreading and widely implemented in the occupational health and safety (OHS) systems. Progressive technologies make it possible to effectively implement digitalised systems in all the elements of management cycle - from planning to monitoring, evaluation and solutions in the different economy sectors. Aim. The purpose of the article is to give a review on the current state of digital transformation in OHS policy of European countries and Ukraine and identify the positive and negative aspects of digitization in OHS management systems. Method. The review is based on full research articles, reports and conference proceedings in Scopus, Web of Science and Google Scholar database, where the keywords ‘digitization’, ‘occupational health’, ‘safety’, ‘robots’ and ‘management systems’ for literature search have used. Results. In total 51 sources were under analysis, including 23 full research articles and 28 publications from EU-OSHA, ILO and OECD. The database formation and use of big data systems and control tools for monitoring and assessing work environment, digital guidelines, legislation and e-teaching platforms are the main digital instruments in OHS management systems. Use of workplace navigator programs helps to collect necessary information and make right regulations by the law. Use of new digital technologies allows more effectively perform OHS inspection, service and risk prevention functions, and diminish work related injuries and illnesses. Conclusions. A new ideology of public policy in developing digitalised OHS management systems (DOHSS) will make the evidence-based solutions effective and achieve high safety standards and stimulate business growth of specific industries on the national and EU level. The best European experiences of digital transformation into OHS management systems can be implemented also in Ukraine.

Master`s Thesis Environmental Governance and Adaptation to Climate Change Curriculum Nitrogen (N) is a crucial nutrient for crop growth production. The input of N in the form of fertilisers increases crop yield. On the other hand, agricultural nitrogen inputs can cause N leaching and the loss of N to the atmosphere, contributing to global warming. The main objectives of the study were to evaluate whether and how different crop species and fertilisation norms affect nitrous oxide (N2O) emissions and soil microbiome using the closed chamber method and quantitative polymerase chain reaction (qPCR) analysis. The study was done in the IOSDV (International Organic Nitrogen Long-term Fertilisation Experiment) experimental field. The crop species studied were barley, sorghum, and wheat. The fertiliser treatment is constituted of mineral N fertilisation and mineral N fertilisation combined with farmyard manure amendment. Three N fertiliser treatment norms were used: 0, 80 and 160 kg ha−1 . Samples were collected over seven months, from April 2022 to October 2022. qPCR was used to measure the abundance of bacterial and archaeal specific16S rRNA, nitrification (bacterial, archaeal and COMAMMOX amoA genes), denitrification (nirK, nirS, nosZI and nosZII genes) and dissimilatory nitrate reduction to ammonium (DNRA; nrfA gene) marker genes from the soil samples. The results of this study indicate that different fertilisation rates influence N2O emissions and the highest N2O emissions are emitted from the highest N fertiliser treatment (160 kg ha−1). Crop species did not have a significant effect on N2O emissions. Sorghum plots with mineral N fertilisation combined with farmyard manure amendment had slightly higher average N2O emissions compared to sorghum plots with mineral N fertilisation without manure. The microbial analysis showed nitrification was the primary process resulting in N2O emissions. Lämmastik (N) on kriitilise tähtsusega toitaine taimekasvatuses. Lämmastiku lisamine väetisena tõstab põllukultuuride saagikust. Teisalt, lämmastiku lisamine võib kaasa tuua lämmastiku leostumise ja lämmastiku kaod atmosfääri, panustades globaalsesse kliima soojenemisse. Käesoleva uurimustöö põhieesmärk oli hinnata, kas ja kuidas erinevad põllukultuurid ja väetusnromid mõjutavad dilämmastikoksiidi (N2O) vooge ja mulla mikrobioomi kasutades staatilise pimekambri meetodit ja kvantitatiivset polümeraasi ahelreaktsiooni (qPCR). Uurimistöö tehti IOSDV (International Organic Nitrogen Long-term Fertilisation Experiment) katsepõllul. Uuritud põllukultuurid olid oder, sorgo ja nisu. Väetistöötlus koosnes väetamisest mineraalse lämmastikuga ja tahesõnnikuga kombineeritud mineraalväetamisest. Katses kasutati kolme erinevat mineraalväetise normi: 0, 80 ja 160 kg ha−1 N. Proove koguti seitsme kuu vältel aprillist oktoobrini aastal 2022. Mullaproovidest määrati bakterite ja arhede 16S rRNA, nitrifikatsiooni (bakterite, arhede ja COMAMMOX amoA geenid), denitrifikatsiooni (nirK, nirS, nosZI ja nosZII) ja dissimilatoorse nitraadi reduktsioon ammooniumiks (DNRA; nrfA geen) markergeenide arvukus reaal-aja polümeraasi ahelreaktsiooni (qPCR) meetodil. Töö tulemustest järeldus, et erinevad väetise normed mõjutasid N2O emissioonide kontsentratsiooni ja suurimad N2O emissioonid tulenesid suurima väetisenormiga lappidelt (160 kg ha−1). Põllukultuurid ei omanud statistiliselt olulist mõju N2O emissioonidele. Mineraalse lämmastiku ja sõnniku kooskasutamisel sorgo lapil olid keskmised N2O vood veidi suuremad võrreldes sorgo lappidega, kus kasutati vaid mineraalset lämmastikväetist. Mikrobioloogilised analüüsid näitasid, et suurimad N2O emissioonid tulenesid nitrifikatsiooni protsessist.

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection. Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal. Introduction. Climate changes are affecting the world, making wildfire understanding vital, and Estonia might experience an increase in the frequency of wildfires in the forthcoming years. Despite efforts to integrate fire management actions and research, many findings remain in "grey literature." This gap calls for formal research to evaluate the efficacy of these strategies. Recently, Estonian initiatives seek to foster collaboration between research and government entities. This thesis thus aims to contribute to the topic by developing knowledge on remote sensing, particularly UAV-based approaches, to improve fire management frameworks. Methods. The study involved various Estonian locations. Paper I examined how UAV flight parameters affect data quality in Lahemaa National Park. Papers II and IV estimated aboveground biomass using multispectral imagery and hyperspectral data from the Agricultural Research Centre in Kuusiku, employing diverse machine learning methods and approaches. Paper III predicted soil moisture in Lavassaare Natural Reserve using optical UAV data and partial least squares regression. Lastly, Paper V maps Rosa rugosa occurrence across Estonian coast using UAVs, integrating these findings into a satellite dataset. Results and Conclusions. The study unveiled an optimal UAV collection strategy for Estonian forests, ensuring canopy and forest floor reconstruction. Machine learning and AutoML frameworks proved effective in estimating biomass, paving the way for automated algorithms. Developing distinct models or conducting field surveys at various periods emerged as the most effective approach for accurate soil moisture modelling. Lastly, a UAV-based methods aided post-fire monitoring and species mapping, allowing assessments beyond surveyed areas. Thus, the importance of these datasets shifted across management stages. Multispectral data aided pre-fire soil moisture estimation, while post-fire used photogrammetry for exposed areas. Integrating diverse remote sensing data improved modelling results, overcoming transferability challenges. Complementary use of RS and field data enhanced modelling, particularly in complex environments. Findings underlined potential in merging remote sensing data for precise variable modelling, enhancing accuracy and agreement toward improved fire management systems. Sissejuhatus. Kliimamuutused mõjutavad maailma, muutes metsatulekahjude mõistmise hädavajalikuks, ning Eestis võib lähiaastatel sageneda metsatulekahjude esinemissagedus. Hoolimata jõupingutustest integreerida tuleohjamise meetmeid ja teadustööd, jäävad paljud uuringud "halli kirjandusse". See lünk nõuab formaalset uurimustööd nende strateegiate tõhususe hindamiseks. Hiljutised Eesti algatused püüavad edendada koostööd teadus- ja valitsusasutuste vahel. Seetõttu eesmärk ongi käesoleva doktoritööga arendada teadmisi kaugseire valdkonnas, eriti mehitamata õhusõidukite (UAV) põhiste lähenemiste kaudu, et parandada tuleohjamise raamistikke. Materjal ja metoodika. Uuring hõlmas erinevaid Eesti asukohti. Artikkel I uuris, kuidas UAV lendude parameetrid mõjutavad andmete kvaliteeti Lahemaa rahvuspargis. Artiklid II ja IV hindasid ülemise maa biomassi kasutades multispektraalseid ja hüperspektraalseid andmeid Põllumajandusuuringute Keskusest Kuusikust, rakendades mitmekesiseid masinõppe meetodeid ja lähenemisi. Artikkel III prognoosis pinnase niiskust Lavassaare looduskaitsealal, kasutades optilisi UAV andmeid ja osalist vähimruutude regressiooni. Lõpuks, artikkel V kaardistas Rosa rugosa esinemist üle Eesti rannajoone, kasutades UAV-sid ja ühendades need leiud satelliidi andmestikuga. Tulemused ja järeldused. Uuring näitas optimaalset UAV andmete kogumise strateegiat Eesti metsades, tagades krooni ja metsapõranda rekonstrueerimise võrreldes laserskaneerimise andmekvaliteediga. Masinõppe ja AutoML raamistikud osutusid tõhusaks ülemise maa biomassi hindamisel, avades tee automatiseeritud algoritmidele. Erinevate mudelite arendamine või välitööde teostamine erinevatel perioodidel osutus kõige tõhusamaks meetodiks täpse pinnase niiskuse modelleerimisel. Lõpuks aitas UAV-põhine meetod jälgida tulekahjude järelvalvet ja liikide kaardistamist, võimaldades hindamist ka välitööde piirkondade kaugemale. Seega muutus nende andmete tähtsus tuleohjamise etappide vahel. Multispektraalsed andmed aitasid ennetavalt hinnata pinnase niiskust, samas kui järeltulekahjude kontekstis kasutati fotogrammetriat paljastatud alade jaoks. Erinevate kaugseire andmete integreerimine parandas modelleerimistulemusi, ületades ülekantavuse väljakutsed. Kaugseire ja välitööde andmete komplementaarne kasutamine täiustas modelleerimist, eriti keerulistes keskkondades. Leiud rõhutasid kaugseire andmete ühendamise potentsiaali täpseks muutuva muutuja modelleerimiseks, suurendades täpsust ja kokkulepet paremate tuleohjamissüsteemide poole. Publication of this thesis is supported by the Estonian University of Life Sciences and by the Doctoral School of Earth Sciences and Ecology, created under the auspices of the European Social Fund.