Browsing by study line "Geoinformatik"

Geographical accessibility to sports facilities plays an important role when choosing a sports facility. The aim of my thesis is to examine geographical accessibility for sports facilities in Helsinki and Jyväskylä. The data of my study consists of the facilities of three different types of sports in Helsinki, Jyväskylä. The chosen types of facilities are ball parks, disc golf courses and fitness centers. I also use demographic data that cover the age groups of 7-12, 20-24 and 60-64. Mapple Analytics Ltd has produced geographical accessibility data covering whole of Finland which I also use as my data. In my thesis I analyzed geographical accessibility of sports facilities and compare the results to demographic data. Both the geographical accessibility data and demographic data is in 250 x 250 m grid level. the methods I used were Local Moran’s I and Bivariate Local Moran’s I. I applied the methods so that I combined the travel-time data and demographic data. The travel-times are from Mapple Insights API. The travel modes I have used are cycling and driving because people travel to sports facilities mostly by driving or by active methods, especially cycling. The travel-times to ball parks and fitness centers are overall good in both study regions. The good geographical accessibility is caused by that the service pattern is so dense for ball parks and fitness centers. The service pattern covers almost all of the inhabited area in both study regions. However, for some postal areas seem to have not so good geographical accessibility to ball parks. In some areas in Helsinki the geographical accessibility to disc golf course can be considered to be somewhat bad. For the chosen age groups only 20-24-year-olds have unsatisfactory travel-times to disc golf course either by cycling or driving. Other age groups do not show a similar pattern because of the different service pattern of ball parks and fitness centers. Demographic variables do not explain the travel times in this context. It is important to see which postal areas have good or bad geographical accessibility to sports facilities. This helps the future planning of sports facilities. In the future it is also possible to apply non spatial methods to the data I have collected or a similar dataset. It would also be possible to which demographic variable best explains travel-times. Because of Mapple Insighs API data is in 250 x 250 m grid level many applications can be developed using the data.

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.

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 greenery is vital to the people in our increasingly urbanizing societies. It is diverse in nature and provides numerous life improving qualities. Traditionally urban greenery has been assessed with a top-down view through the sensors of aerial vehicles and satellites. This does not equate on what is experienced down at the human level. An alternative viewpoint has emerged, with the introduction of a more human-scale viewpoint. To quantify this human-scale greenery, novel and disparate approaches have been developed. However, there is little knowledge on how these modelling methods and indices manage to capture the greenery we truly experience on the ground level. This thesis is an undertaking to better understand what the greenery experienced by people on the ground level, termed humanscale greenery (HSG), means. The goal was to grasp how the various modelling methods, indices and datasets can be best used to capture this phenomenon. Simultaneously, the study tries to better comprehend how different people experience greenery. To achieve this, human-scale greenery values were collected via interviews at randomly selected study sites across Helsinki. These values were then compared to modelled values at the same sites. The methods and indices tested include modern approaches developed specifically for HSG and traditional greenery assessment methods. Along the greenery values, sociodemographic variables were collected in the interviews and compared to each other in relation to HSG values. The modelled values were on average smaller than HSG values. All methods indicated very strong or strong linear relationships with human-scale greenery. NDVI and semantic segmentation Green View Index (GVI) had the strongest relationships and least deviation. Land use (LU) and color based GVI had the highest error deviations from HSG. The sociodemographic assessment showed hints that age might affect the amount of experienced greenery, but this is uncertain. With a random sampling of interviewees, 25–34-year-olds and less nature visiting people were more common at sites with low HSG. Based on the results obtained here, many different types of novel methods are suitable for modelling HSG with strong linear relationships. However, also traditional greenery assessment methods performed well. It is difficult to curtail the experience of greenery into a single approach. A solution could possibly be obtained via the combination of methods. The results also advocate the usage of machine learning methods for greenery image segmentation. These cannot be applied everywhere due to data coverage problems, but alternative methods can also be used to fill in gaps. The significance of age on the experience of greenery needs further research. Because urban greenery’s benefits are known, attention should also be given onto how different kinds of people are able to experience it. In the future we should also discuss the meaningfulness of assessing absolute greenery in comparison to the types and parts of greenery.

Internet has altered the wildlife trade as it is now easy to trade animals on different online platforms. In the reptile pet trade, distinct appearance and rarity of the species are sought after attributes. Reptiles with small ranges are especially threatened by the pet trade. The Lesser Antilles are home to nearly hundred endemic lizard and snake species which are facing many threats from climate change to habitat loss. In addition, some of them are subject to international pet trade the scale of which is still not assessed properly. In this thesis I have mapped the online pet trade in endemic reptile species of the Lesser Antilles. To do this, I built an automated data collection and processing tool consisting of Python scripts. I used the tool to scrape 366 951 reptile trade advertisements from 90 distinct websites and to filter and extract information on Lesser Antillean reptiles from the collected data. The results show that most of the Lesser Antillean reptile species traded online have not yet been fully assessed for their conservation status by the International Union of the Conservation of Nature (IUCN). Overall, 9,2% off the reptiles on online sales advertisements and 21,4% of the species sold online are assessed as Threatened (i.e., at highest risk of extinction). Only 24,8% of advertisements selling Lesser Antillean reptiles online concern species that are evaluated as not Threatened by the IUCN Red List of Species. Germany was found to be the centre of trade of Lesser Antillean reptile species as the number of trade advertisements and distinct species sold was the highest there. United States was the second biggest trader of all species and the biggest trader of Convention on International Trade in Endangered Species (CITES) listed species. These results show that it is of foremost importance to evaluate the conservation status of all species that are currently traded to fully assess the threat that the pet trade possesses to reptile species. It will also be important to assess the sustainability of the reptile trade, especially in Germany and the United States. The tool used to collect and process the data in this thesis can be modified to assess the pet trade of any species on publicly accessible online platforms

Grass biomass has many important and diverse roles for ecosystems functioning, the carbon cycle, rangeland productivity and local livelihoods. Quantifying and understanding grass biomass in dynamic savanna ecosystems during dry season is important for sustainable land management and monitoring grazing pressures, especially amidst climate change. Traditional ground-based methods to assess vegetation are subjective and time consuming, while remote sensing provides efficiency in monitoring grass biomass at large scales. Grass biomass assessments using remote sensing data have been extensively conducted worldwide, but such research in African savannas remains rare. This study aimed to study connections between dry season grass biomass measured in savanna rangelands and airborne hyperspectral imagery data obtained simultaneously in LUMO Conservancy area of South-Eastern Kenya. Two modelling techniques were compared: averaged plot values (n=24) and individual sample values (n=96). Three vegetation indices (RSI, NDSI, RDSI) were computed and Generalised Additive Models (GAM) were applied to portray the relationship between measured grass biomass and VIs. The highest explanatory power for both modelling techniques was found with RSI and NDSI indices with averaged plot level values having the highest performance (D2 = 0.79, RMSE = 40.15 g/m2), with the band combination of B78 and B43 (908 nm / 667 nm). The best performing vegetation index (RSI) was used to predict grass biomass in the study area, which indicated a biomass range of 0 to 2894 g/m2. The study highlights the potential of using hyperspectral imagery to assess grass biomass in the savanna environments. However, challenges and limitations were faced related to the heterogeneous nature of savannas, varying weather conditions affected by rainfall, the temporal limits of the study, and disturbances in spectral information caused by heavily grazed areas, dead material, and preprocessing techniques. It is suggested that future research considers these factors by incorporating a broader set of variables, extending the duration of the study, exploring various preprocessing techniques, increasing the sample size, and employing additional data sources, such as active sensors and hyperspectral satellite imagery, to enhance model performance and improve accuracy.