Browsing by study line "Geoinformatiikka"

Sub-arctic river ecosystems are recognized as biodiversity hotspots in the region. However, gaps exist in understanding the potential impacts of the current climate change on these sensitive environments due to a lack of local data and knowledge of suitable monitoring methods. Remote sensing offers a promising approach to map river bathymetry, a critical factor in monitoring river environments. In this study, I investigated the efficacy of three remote sensing methods – multibeam echo sounding, green-wavelength airborne laser scanning, and multispectral satellite imagery – for mapping bathymetry along the Tana River, located at the border of northern Finland and Norway. Multibeam echo sounding, and laser scanning offer high-resolution and good penetration capabilities for mapping shallow river bathymetry. Satellite imagery can cover large spatial areas, often more cost-effectively than other remote sensing methods, albeit with lower spatial resolution. The analysis involved processing field survey data from two study sites with varying topography, slope, and flow velocity. I conducted the bathymetric model generation by using Inverse Distance Weighting interpolation and Lyzenga algorithm methods. The validation was carried out through error assessment. Performance evaluation of the bathymetric models and their differences were conducted by calculating linear regression and Pearson’s correlation coefficient, vertical difference, and bottom roughness. The results suggest that all three remote sensing methods successfully captured the main characteristics of the river bottom shape, aligning with the geomorphological characteristics of the study sites. The multibeam echo sounding provided densest and most coherent bathymetric models (mean R2=0.57 and RMSE=0.80). The airborne laser scanning produced bathymetric models with highest uncertainty in elevation due to noise and gaps in the data and showed better accuracy above the water surface than below (mean R2=0.27 and RMSE=1.18, compared to R2=0.98 and RMSE=0.18). The comparability of the bathymetry derived from satellite imagery against other methods was not optimal due to notably lower spatial resolution, but the satellite-based bathymetric models were able to capture the general variations in river bottom elevation (mean R2=0.48 and RMSE=0.96). The study area that was shallower, had a slow flow rate, and had a sandy bottom yielded more accurate bathymetric models. This was evidenced by strong positive correlation coefficients (mean r=0.83, compared to r=0.23 in the other site) and fewer river bottom profile differences between the models. The findings highlight the importance of comprehensive validation data and proper data pre-processing. Addressing these challenges is important for advancing the understanding of how to map and monitor sub-arctic river bathymetry.

Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation.

The African savanna elephant (Loxodonta africana) as a renowned “ecosystem engineer” modifies its habitat by sometimes destroying woody vegetation. Their destructive effect intensifies during the dry seasons, when they form larger herds and seek to consume woody plants, especially near permanent water sources. If this happens season after season in a restricted area, such as a wildlife reserve, the tree cover is reduced. Since elephants tend to make smaller trees to fall more easily than the larger ones, this “elephant problem” harms the regeneration ability of the ecosystem in a long run, even turning savannas into grasslands. With less and less trees available, elephants and other fauna in conservation areas could end up being at a fatal risk. Multi-scale vegetation structure can be studied with airborne (ALS) and terrestrial laser scanning (TLS). Although both types of LiDAR have been applied in studies on trees, most of the ALS studies concern biomass and none of the TLS research cover elephants. Tree structure on the individual tree level can be modelled using TreeQSM modelling that has not yet been applied in savanna vegetation. This study can be considered pioneering as it attempts to provide answers to these two study questions: (1) How does tree density derived from airborne laser scanning data correlate with elephant density, elephant path proximity, and river proximity? (2) How do tree architecture metrics derived from terrestrial laser scanning data correlate with elephant path proximity and river proximity? The study area is Taita Hills Wildlife Sanctuary, a small privately-owned wildlife conservancy in southeastern Kenya that falls within an area scanned with ALS in 2014. The vegetation of the reserve has been changing for many decades, and the latest changes in the vegetation cover are visible from satellite images. The “elephant problem” near the area was scientifically discussed already in 1960’s, so their damage may have been taking place for a long time. There are two datasets from the area for estimating elephant occurrence (elephant density based on elephant observation points and elephant track proximity based on elephant tracks) and one for the proximity to the river. Tree density was calculated based on detected treetops from the ALS point cloud and its correlations between the elephant predictors and the river proximity was analyzed. TLS measurements of 72 individual trees of Vachellia tortilis and Newtonia hildebrandtii were made in January and February 2020 in Taita Hills Wildlife Sanctuary. 53 were successfully modelled with TreeQSM. The correlations between the tree structure metrics and elephant density, elephant track proximity, and the river proximity were analyzed. The values for crown ratio, the metric that correlated significantly with the elephant track proximity were predicted to assess the meaning of the results in practice. The overall findings from both analyses (ALS and TLS) may suggest that trees in Taita Hills Wildlife Sanctuary may have suffered from elephant damage, since lower tree density correlates with both the elephant density estimates and the elephant track proximity. The trees scanned with TLS seem to be somewhat larger in closer proximities to the elephant tracks, while smaller trees are more able to survive in areas further away. Quantifying elephant damage in more detail, such as torn or hanging branches, was still not achieved by this study. Regardless, it can be concluded that there is enough foundation for further research on the important issue, the phenomenon that can turn dangerous to many species that were supposed to be protected.

The number of people belonging to a language minority in Finland is increasing and people are becoming more and more spatially mobile. This has also led to an increase in transnationals and higher rates of cross-border mobility. With new methods involving social media big data, we can map spatial mobility patterns in new ways and deepen the understanding of how people relate to space. Differences in spatial mobility can for example give us an indication of the rate of integration into society. Some claim that a more spatially mobile life is a sign of success, but can we see differences in spatial mobility between people in Finland? The three language minorities considered in this thesis are Swedish, Russian, and Estonian. The history and culture of these groups are different as well as their status in Finnish society. Swedish speakers, with a national language status, have a different role in society, but do this well integrated minority differ from the other ones spatially? By using Twitter data and looking at the spatial mobility within Finland, we see where differences occur between language groups. To understand how strong ties the language groups have with neighbouring countries, we look at cross-border mobility to Estonia, Russia, and Sweden. The results show that there are differences in the spatial mobility of language minorities in Finland. Estonian speakers most frequently visit Estonia, while at the same time they are less mobile within Finland. The variation was large for Russian speakers, with some visiting Russia often and others almost never. Swedish speakers seem to have relatively weak ties to Sweden, compared to the other language groups and have very similar spatial mobility to the majority Finnish speaking population.

Urban environments are constantly changing and expanding. They grow, evolve, and adapt to society and residents’ needs. Environmental changes have an impact also on urban green such as trees. This is because the increase of building stock and expanding cityscape will target these green spaces. However, the significance of those green spaces is understood as they have a positive impact on the residents’ well-being and health. For example, urban trees are known to improve the air quality and to provide mentally relaxing environments for residents. As this importance is emphasized, changes in the areas must be monitored, which increases the importance of the change detection studies. Change detection is a comparison of two or more datasets from the same area but at different times. Principally, changes have been detected with various remote sensing methods, such as aerial- and satellite images, but as airborne laser scanning technology and multi-temporal laser scanning datasets have become more common, the use of laser scanning data has also increased. The advantage of the laser scanning method is especially in its ability to produce three-dimensional information of the area. Therefore, also vertical properties can be studied. The method’s advantage is its ability to detect changes in urban tree cover as well as in tree height. The aim of this study was to investigate how tree cover and especially canopy height have changed in the Kuninkaantammi area in Helsinki during 2008‒2015, 2015‒2017, 2017‒2020, and 2008‒2020 from multi-temporal laser scanning data. One of the starting points of this study was to find out how airborne laser scanning datasets with different sensors and survey parameters are suitable for change detection. Also, what kind of problems the differences between datasets will raise and how to reduce those problems. The study used laser scanning data from the National Land Survey of Finland and from the city of Helsinki for four different years. The canopy height models were produced of each dataset and changes were calculated as the difference of each canopy height model. The results show that multi-temporal laser scanning data require a lot of manual processing to create datasets comparable. The greatest problems were differences in point density and in classification of the data. The sparse data from the National Land Survey of Finland affected how changes were managed to be studied. Therefore, changes were detected only in general level. In addition, each dataset was classified differently which affected the usability of the classes in the datasets. The problems encountered were reduced by manual work like digitizing or by masking non-vegetation objects. The results showed that the change in the Kuninkaantammi area has been relatively large at the time of the study. Between 2008 and 2015, 12.1% of the tree cover was lost, 9.9% between 2015 and 2017, and 13.2% between 2017 and 2020. In addition, an increase in canopy height was detected. Between 2008 and 2015, 44.2% of the area had greater than 2 m increase in canopy height. Similarly, increase occurred in 11.1% and 3.5% of the area in 2015‒2017 and in 2017‒2020, respectively. Although the changes were observed at a general level, it can be concluded that the used datasets can provide valuable information about the changes in urban green that have taken place in the area.

The study of forest fragmentation, the break-up of forests into smaller patches, has become increasingly important due to increases in human-induced deforestation. Currently, approximately 12 million ha of forest are lost per year and 32% of this loss is tropical. There is substantial evidence showing that edge effects can alter the structure and functioning of remaining tropical forests, even hundreds of meters from the forest edge. However, implementing empirical experiments to understand the effects of fragmentation on forest structural metrics is logistically and scientifically challenging and limited to smaller areas. The use of forest models may help overcome these limitations, as they are able to quickly reproduce long-term ecological processes, as well as simulate a broad range of boundary forcings, such as biogeographical variability. This study evaluates the capability of a state-of-the-art forest dynamic model in reproducing the three-dimensional vertical distribution of plants in Amazonian forests affected by fragmentation. To achieve this, we optimized parameters driving plant demography and mortality, as well as their response to edge effects. FORMIND is an individual and process-based gap model suited for species rich vegetation communities, with the option of a fragmentation module. We modified processes and parameters in FORMIND to mimic the dynamics observed in a long-term (40 years-old) forest fragmentation experiment in the Brazilian Amazon. Forest structural metrics extracted from the FORMIND model output were compared with those obtained from terrestrial laser scans of the Amazonian Forest fragments. The resulting simulations demonstrated that, after 40 years of edge effects, the model in its original state was not capable of reproducing comparable results to those observed using the terrestrial LiDAR system. However, the addition of a new parameter capable of adjusting tree mortality at varying edge distances and inclusion of understory vegetation, drastically improved the model’s ability to replicate the three-dimensional distribution of plant material in the forest fragments. Total Plant Area Index (PAI), and PAI at varying height intervals (PAI 0-10m, PAI 10-20m, PAI 20-30m), amongst other metrics, showed consistent responses from edge effects, thus resulting in an adequate vertical plant distribution. Results demonstrate that, with the implementation of new parameters, forest models such as FORMIND have strong potential to study the mechanisms and the impact of environmental changes on forests. Models can also expand the possibilities of in-situ studies, which are limited in time and space, when calibrated carefully with suitable in-situ data, here delivered by terrestrial LiDAR.

Maantiede on hyvin visuaalinen oppiaine, ja erilaiset kartat ja kuvat ovat avainasemassa maantieteellisen tiedon esittämisessä ja ymmärtämisessä. Yhteiskunnan digitaalinen muutos ja teknologian kehitys ovat kuitenkin muokanneet maantieteen opetusta sekä opettajien käyttämiä materiaaleja merkittävästi. Digitaalisten materiaalien hyödyntämisestä onkin tullut maantieteen opettajille arkipäivää. Toisaalta digitaalisen materiaalin monimuotoisuus tarjoaa myös uusia mahdollisuuksia opettajille esimerkiksi opiskelijoiden osallistamisessa. Valtakunnalliset opetussuunnitelman perusteet painottavatkin nykyään maantieteessä digitaalisuutta sekä geomedian hyödyntämistä niin yläkoulussa kuin lukiossa. Tämä tutkielma on kvalitatiivinen tutkimus, jossa teemahaastatteluiden avulla on tutkittu maantieteen opettajien digitaalisten työkalujen käyttöä paikkatieto-opetuksessa sekä paikkatieto-opetuksen toteuttamisesta. Tutkimusta varten on haastateltu yhteensä 20 maantieteen aineenopettajaa. Haastattelujen perusteella opettajat käyttävät maantieteen opetuksessaan runsaasti digitaalista visuaalisuutta ja erilaisia digitaalisia työkaluja. Digitaalisten opetusmateriaalien käyttö korostuu erityisesti lukio-opettajilla. Sekä yläkoulun että lukion opettajat käyttävät osallistavassa geomedia opetuksessa digitaalisia työkaluja, kuten Googlen karttapalveluita. Toisaalta peruskoulun opettajat painottivat lukio-opettajia enemmän esimerkiksi diagrammien tai karttojen käsin paperille tekemistä. Paikkatieto-opetuksen toteutuksen kannalta sekä lukio- että peruskouluopettajat painottivat Googlen karttaselaimia. Sekä Google Maps että Google Earth olivat useiden käyttämiä työkaluja. Lukio-opettajilla korostui lisäksi vaativammat internetin karttapalvelut, kuten Paikkatietoikkuna tai jopa paikkatieto-ohjelmat, kuten ArcGIS Online. Opettajien valmiuteen käyttää erilaisia paikkatietosovelluksia vaikuttaa merkittävästi heidän oma osaamisensa. Kevyet matalan vaativuustason ohjelmat, kuten Googlen karttapalvelut, koetaan helpoiksi paikkatietoa opettaviksi alustoiksi ja työkaluiksi maantieteen opetuksessa. Vaativamman tason paikkatietosovellukset vaativat myös enemmän opettajan tietämystä sekä ymmärrystä paikkatiedosta. Tästä syystä monet opettajat kokevat ne usein haastaviksi tai hankaliksi opettaa. Sekä yläkoulussa että lukiossa myös aika ja maantieteen kurssien hektisyys ja kiire sekä muiden opetettavien maantieteen aiheiden priorisoiminen vaikuttavat paikkatieto-opetuksen toteutumiseen ja taitojen opettamiseen. Erityisesti lukio-opettajat kokevat maantieteen moduulien olevan niin täynnä asiaa, että paikkatieto-opetukselle ei jää kunnolla aikaa muualle, kuin viimeiseen geomedian moduuliin. Tutkimuksen perusteella voidaan yhteenvetona todeta, että paikkatieto-opetus toteutuu hyvin vaihtelevasti opettajasta sekä opetusasteesta riippuen. Erityisesti opettajan taidot sekä muut resurssit vaikuttavat merkittävästi siihen, millaisia digitaalisia työkaluja opettajat hyödyntävät paikkatieto-opetuksessa.

Recent studies on day-care staff have reported on problems in hiring qualified staff, and in increased resignations in existing staff. These problems are connected to an increase in workload and stress, and reduced wellbeing at work. When workload and challenges in day-care work increase, there can even be a risk of diminishing the pedagogical quality of education. The problems seem to occur differently and in different magnitudes in different day-care units, which indicates learning conditions’ possible segregation. In the case of schools, the socioeconomic status of nearby population has been noticed to affect children’s predisposed abilities to learn, and their support requirements in learning. This effect can be assumed to affect early childhood education similarly, which would lead to day-cares in socioeconomically disadvantaged areas to require extra resources and staff to compensate for the children’s increased support requirements. If those extra resources are not available, the staff will experience increased workload and stress, which will cause problems in the long term. The city is known to be somewhat socioeconomically segregated, and if this is mirrored in day-cares so that the backgrounds of children in day-cares get segregated, it may also start to affect the quality of education. In this case the unevenly distributed challenges would cause institutional segregation of learning conditions in early childhood education. The institutional segregation of early childhood education or schools has not been studied much in Finland. Earlier studies on Finnish schools have been able to explain differences between schools through differences in children’s backgrounds, and there has not been a reason to doubt the institutional equality of schools’ quality. The basic principle of the Finnish early childhood education and school system is to provide every child with equal conditions and opportunities to grow and learn. These equal conditions equalise segregation in the population by offering equally high-quality education in both disadvantaged and well-off areas of the city. However, if the segregation of children’s backgrounds is accompanied by the segregation of learning conditions in day-cares, there is a risk of the cumulation of both socioeconomic disadvantage and lower quality of education. In this case, the quality would decrease exactly where it would be most needed. In my thesis I study whether there is differentiation in problems related to hiring or keeping staff in the day-cares in Helsinki, through the numbers of resigned and unqualified staff in each unit. I also look at whether this segregation of day-care units is at all related to the socioeconomic segregation of the city’s population. In the study I utilize HR data from the city of Helsinki and socioeconomic population data from Statistics Finland, which I join onto spatial data of day-cares’ locations. I use this combined dataset to study the segregation of day-cares and its connections to socioeconomic segregation using quantitative statistical methods and spatial analysis methods. The results indicate that there is perceivable segregation in the staff of day-cares in Helsinki, but socioeconomic segregation is able to statistically explain the patterns only slightly. Therefore, mostly other phenomena seem to cause the differentiation in staff related problems, but these phenomena are not yet known. In terms of institutional segregation, the early childhood education system in Helsinki seems to still be quite equal. However, more knowledge about the subject is needed, because both the results in this study, as well as previous studies show some worrying signals pointing to the possibility of institutional segregation. In addition, intense public discourse around the topic of early childhood education, and a wide-ranging worker’s strike, including day-care staff, seem demonstrative of the seriousness of these challenges in day-cares.

Urban vegetation has traditionally been mapped through traditional ways of remote sensing like laser scanning and aerial photography. However, it has been stated that the bird view examination of vegetation cannot fully represent the amount of green vegetation that the citizens observe on street level. Recent studies have raised human perspective methods like street view images and measuring of green view next to more traditional ways of mapping vegetation. Green view index states the percentage of green vegetation in street view on certain location. The purpose for this study was to create a green view dataset of Helsinki city through street view imagery and to reveal the differences between human perspective and aerial perspective in vegetation mapping. Street view imagery of Helsinki was downloaded from Google street view application interface. The spatial extent of the data was limited by the availability of street view images of summer months. Several green view maps of Helsinki were created based on the green view values calculated on the street view images. In order to understand the differences between human perspective and the aerial view, the green view values were compared with the regional land cover dataset of Helsinki trough linear regression. Areas with big differences between the datasets were examined visually through the street view imagery. Helsinki green view was also compared internationally with other cities with same kind of data available. It appealed that the green view of Helsinki was divided unequally across the city area. The lowest green view values were found in downtown, industrial areas and the business centers of the suburbs. Highest values were located at the housing suburbs. When compared with the land cover, it was found that the green view has a weak correlation with low vegetation and relatively high correlation with taller vegetation such as trees. Differences between the datasets were mainly concentrated on areas where the vegetation was not visible from the street by several reasons. Main sources of errors were the oldest street view images and the flaws in image classification caused by other green objects and shadows. Even though Helsinki has many parks and other green spaces, the greenery visible to the streets isn’t always that high. The green view dataset created in this study helps to understand the spatial distribution of street greenery and brings human perspective next to more traditional ways of mapping city vegetation. When combined with previous city greenery datasets, the green view dataset can help to build up more holistic understanding of the city greenery in Helsinki

Uuden tieliikennelain mukanaan kunnille tuomat velvoitteet, kuten liikenteenohjaukseen käytetyn välineistön (esim. liikennemerkkien) ylläpitovastuu, siirtyy kunnille 1.6.2020. Kenttäinventoimalla suoritettava liikennemerkkien kunnon ja sijainnin selvittäminen on usein työlästä ja tuottaa kustannuksia. Tässä tutkimuksessa selvitetään, miten näitä voidaan automatisoidusti inventoida panoraamakuvilta. Samalla verrataan panoraamakuvien ja niistä luotujen osakokonaisuuksien (pilkottujen kuvien) soveltuvuutta kyseiseen tarkoitukseen. Tunnistuksen tuloksena syntyviä havaintoja verrataan Väyläviraston ylläpitämään avoimeen liikennemerkkiaineistoon sekä tunnistettujen kohteiden sijainti lasketaan kuvilta. Työssä tutustutaan myös eri kohteentunnistusalgoritmien toimintaan sekä selvitetään, miten liikennemerkkien automaattisessa tunnistuksessa on onnistuttu muissa tutkimuksissa. Aineistona toimii Inkoosta otettujen panoraamakuvien lisäksi Mapillaryn toimittamat kuva-aineistot, joita käytetään YOLOv3-kohteentunnistusalgoritmin koulutukseen ja testaukseen. Työssä esitellään myös YOLOv3-koulutuksen toteuttaminen ja käydään läpi tarvittavat ohjelmistot sen implementoinnissa toiseen työhön. Koulutus vaatii riittävän GPU:n lisäksi erilaisia ohjelmia sekä runsaasti kuva-aineistoa, jotta ylisovittamisen riskiltä vältytään. Tulosten perusteella pilkotut kuvat tuottavat paremman tuloksen verrattuna panoramakuviin. Pilkotuilta kuvilta jokainen ajoreitin varrella ollut kärkikolmio tunnistettiin, kun taas panoraamakuvilta tämä ei onnistunut. Lisäksi algoritmin kyky sijoittaa kärkikolmion sijainti kuvalle oli varsin hyvä saavuttaen keskimäärin IoU-arvon 0,86, kun se panoraamoilla oli 0,52. Samoin tulosten luotettavuutta kuvaavat Precision- ja Recall-arvot olivat huomattavasti korkeammat kuin panoraamakuvilla. Työssä havaittiin lisäksi, että Väyläviraston avoimesta aineistosta puuttuu useita kärkikolmioita. Kuvilta onnistuttiin myös laskemaan muutaman metrin tarkkuudella kärkikolmioiden sijainti maastossa. Tutkimuksen perusteella kohteentunnistusalgoritmit tuottavat merkittävää hyötyä kohteiden automaattisessa tunnistuksessa. Algoritmien hyödyntämistä tulevaisuudessa mahdollistaa lisääntyvä kuva-aineistojen määrä sekä laskentatehon kasvu. Hyödyntämällä kohteentunnistusalgoritmeja kuntien on mahdollista helpottaa uuden tieliikennelain velvoitteiden noudattamista. Tämän myötä algoritmien suosio voi kasvaa tulevaisuudessa. Kohteentunnistusalgoritmien implementointiin tarvitaan kuitenkin ohjeistusta ja käyttötapauksia, joita tämä tutkimus tuloksillaan edistää.