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Transport infrastructure development can have significant negative impacts on natural resources over time and space, making it crucial to undertake careful consideration to minimize damage to both natural and artificial features. The objective of this study was to assess the magnitude of the environmental impacts caused by the standard gauge railway (SGR) from Nairobi Terminus to Nachu station in Kiambu county. The research approach used in collecting data, interpreting and analyzing the data was both quantitative and qualitative research. The study used a detailed GIS and remote sensing (RS) baseline environmental assessment and interviews to identify social and environmental impacts and proposed mitigation measures for the SGR project during and after implementation which is from 2016 to 2019. The results show that there was a 3.6% increase of built-up areas along the SGR line, while grassland, forest, and cropland decreased by 2.5%, 2.6%, and 13%, respectively. Increase in built-up areas has been identified to be around the railway station and also along the buffer zone The drivers of this change were mainly attributed to urbanization. Negative environmental effects of the SGR included encroachment on conservation areas, disruption of human settlement, and reduction of forest and vegetation cover. Noise and air pollution from SGR construction and operation affected wildlife, vegetation, and human settlements. To mitigate these negative impacts, this study recommends a number of measures including the wet-spraying of cement and wet drilling to reduce dust emissions during construction, frequent investigations of construction sites, afforestation, and GIS analysis to locate the most suitable SGR routes. Moreover. In conclusion, this study highlights the negative environmental impacts of the SGR project from Nairobi Terminus to Nachu station in Kiambu county. The results suggest that the project caused significant environmental degradation, particularly in terms of reduced forest and vegetation cover, and encroachment on conservation areas. The proposed mitigation measures can help reduce these negative impacts and minimize future damage caused by transport infrastructure development.

The largest actively cycling terrestrial carbon pool, soil, has been disturbed during the last centuries by human actions through decreasing woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength area for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising of five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using Specim IQ hyperspectral camera in controlled laboratory conditions, while the carbon content of was determined with a dry-oxidization analyzer. We empirically evaluate five multivariate regression methods, two of which are novel in this context, and validate a technical inference pipeline to provide reliable estimates of SOC. We investigate different ways of using the recorded spectral images for training machine learning models for estimating SOC and nitrogen content, covering both alternative strategies of extracting spectral information from the image as well as different predictive methods. Of the five multivariate regressors, two were novel in the context of SOC prediction and enable uncertainty quantification and band selection. Three of the regressors in effect performed equally well with cross-validated R2 scores of .8 and RMSE of 1% SOC. This demonstrates the feasibility of the proposed imaging configuration and computational pipeline in estimating SOC and justifies future research. The carbon and nitrogen content of the soil samples correlated strongly. Both variables were increasing together with the altitude indicating dependency on the climatic conditions from the hot lowlands to the cool highlands. In addition, the SOC and N increased with tree canopy height, canopy density and above ground biomass indicating the impact of the land cover to the soil variables. Non

Rapid and uncontrolled urbanization is a major issue in both developing and developed countries. Uncontrolled urbanization has resulted to unplanned expansion of residential and commercial areas, informal settlements, housing shortages, and unplanned land use. Understanding and quantifying urban sprawl spatiotemporal patterns is critical for informing the development of appropriate policies for effective and sustainable land use management. Using image classification and spatial metrics, this study examines the changes in Voi town's urban land use/land cover (LULC) between 1999 and 2019. The LULC was mapped using Landsat Thematic Mapper (TM):Landsat Enhanced Thematic Mapper Plus (ETM+):and Landsat Operational Land Imager (OLI) datasets using supervised maximum likelihood classification. A post classification approach was used to detect and assess LULC changes in the study area, while selected spatial metric indices quantified urban sprawl. The results of the change detection analysis revealed that Voi town has been rapidly expanding, with an urban expansion of 187.96 percent from 1999 to 2011, 183.40 percent from 2011 to 2019, and 716.1 percent from 1999 to 2019. In 1999, the built-up area comprised 1.29 percent of the total study area, 3.72 percent in 2011, and 10.53 percent in 2019. Based on spatial metrics analysis, the number of built-up area patches in 1999, 2011, and 2019 was 154, 278, and 526, respectively. An increase in the number of patches indicated fragmentation and the emergence of new built-up areas. As a result, city planners will need to plan ahead of time and implement additional measures to deal with the city's future rapid and unprecedented growth.

Tick-borne encephalitis (TBE) is an endemic infection of public health importance in Finland. We investigated the effect of ecologic factors on 2007–2017 TBE trends. We obtained domestic TBE case data from the National Infectious Diseases Register, weather data from the US National Oceanic and Atmospheric Administration, and data from the Natural Resources Institute in Finland on mammals killed by hunters yearly in game management areas. We performed a mixed-effects time-series analysis with time lags on weather and animal parameters, adding a random effect to game management areas. During 2007–2017, a total of 395/460 (86%) domestic TBE cases were reported with known place of exposure and date of sampling. Overall, TBE incidence increased yearly by 15%. After adjusting for the density of other animals and minimum temperatures, we found thatTBE incidence was positively associated with white-tailed deer density. Variation in host animal density should be considered when assessing TBE risks and designing interventions.

This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High–high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.

Understanding how species are distributed in space and time is a focal element guiding conservational efforts under ongoing climate change and the Holocene extinction. Freshwater habitats are currently one of the most threatened ecosystem types, and studies aiming to unravel factors that govern biodiversity of tropical stream micro-organisms are especially scarce. Diatoms play an important role as primary producers in streams and are widely used as ecological indicators. However, relatively little is known about which factors affect diatom communities in the tropics. Here, we studied benthic diatom diversity across 67 tropical streams spanning stream orders 1–5 in Kenya. We examined whether the hypothesis of latitudinal diversity gradient applies for benthic diatoms, i.e. whether tropical streams encompass more species than boreal streams using a comparable boreal dataset. In addition, we studied which environmental, land use and spatial factors control benthic diatom communities using redundancy analysis. We also examined the nestedness and turnover components of beta diversity, factors contributing to diatom species richness, and the uniqueness of the communities across stream orders by using boosted regression trees and local contribution to beta diversity. Finally, we studied whether environmental heterogeneity and beta diversity are related across stream orders and tested their relationship using tests of homogeneity of dispersion and regression analysis. Species richness was not higher in tropical streams than in boreal ones. Tropical diatom communities were controlled jointly by local environmental and spatial factors. Although water chemistry was the most important controlling factor, also physical variables contributed significantly to community variation. Land use had also a significant effect on diatom communities as broad leaved forest streams harboured different diatom communities compared with streams with higher human impact and conductivity, stressing the importance of forests to water quality and diatom biodiversity. Headwater streams encompassed the highest species turnover, whereas nestedness was higher in higher order streams. Species richness was significantly higher in higher order streams than in headwaters, whereas the uniqueness of the communities peaked in headwaters. Environmental heterogeneity was the highest in headwater streams and was related to high beta diversity, which highlights the importance of habitat heterogeneity to biodiversity. Our results stress the management and conservational importance of headwater streams and tropical montane forests as these environments harbour unique diatom communities important for regional diversity.

People and wildlife have co-occurred, sharing resources for thousands of years, however, over the last four decades records of human–wildlife conflict have increasingly emerged. Human–elephant conflict is a form of such conflict, resulting from negative interactions between people and elephants. Human–elephant conflict affects local community livelihood and the success of elephant conservation. Tsavo East and Tsavo West National Parks, which cover about 60% of the Taita Taveta County land area, host the single largest elephant population in Kenya. We analysed human–elephant conflict incident data over 15 years (2004–2018) in Taita Taveta County, which forms part of the Tsavo ecosystem in south-eastern Kenya. We identified eight forms of human–elephant conflict comprising elephant threat, crop raiding, property damage, injury to people, human death, elephant death, elephant injury, and livestock death. Three forms of conflict accounted for 97% of the reported incidents, namely elephant threat to humans, constituting the highest number of incidents (62.46%), followed by crop raiding (32.46%) and property damage (2.33%). Conflicts occurred throughout the year, with June to July having the highest number of incidents. Rainfall, distance from the Tsavo national parks, and human population density were used as covariates to explain HEC patterns. This study seeks to provide a detailed evaluation of the spatial–temporal patterns of human–elephant conflict in Taita Taveta County and to yield information useful for human–elephant conflict mitigation and elephant conservation.

Tropical montane forests are known to support many endemic species with restricted geographic ranges. Many of these species are however, faced with numerous threats, most notably from habitat loss and degradation, invasive alien species, and climate change. Examples include Taita Apalis and Taita Thrush. Taita Apalis (Apalis fuscigularis) and Taita Thrush (Turdus helleri) are species of birds listed as Critically Endangered by the Government of Kenya and the International Union for Conservation of Nature (IUCN). They are endemic to Taita Hills' cloud forests in southeastern Kenya and protected under Wildlife Conservation and Management Act. As they face high risk of extinction, exploring their habitat suitability is imperative for their protection. To determine the current spatial distribution and the key ecogeographical explanatory factors and conditions affecting species distribution and indirect effects on species survival and reproduction, we employed Maximum Entropy (MaxEnt) modelling. This study was conducted in Ngangao and Vuria forests in June and July 2019 and 2020. Ngangao forest is gazetted as forest reserve and managed by the Kenya Forest Service whereas Vuria is nongazetted and thus remains without official protection status. Ecogeographical explanatory variables; climatic, remote sensing-, LIDAR-, topography-and landscape-based variables were used in modelling and separate models were produced. 23 occurrence records of Taita Apalis and 30 of Taita Thrush from Ngangao and 21 of Taita Apalis from Vuria forests were used in the modelling. According to the models, less than 7% of the total area of Ngangao and Vuria forests was predicted as suitable habitat for Taita Apalis and Taita Thrush. This shows that these two species are more vulnerable to extinction from demographic stochasticity. Consequently, managing their habitats is critical for their long-term persistence. LIDAR-based canopy height range and elevation greatly influenced Taita Apalis distribution in Ngangao forest, with areas of high elevation (1620-1750 m a.s.l.) and having open middle-storey preferred. Elevation, slope and topographic wetness index (twi) were the major determinants of Taita Thrush distribution in Ngangao, where gentle sloping areas with moderately dry surfaces within high elevation (1620-1730 m a.s.l.) were favoured. Mean annual temperature, Euclidean distance to the forest edge, slope and land cover type greatly influenced the distribution of Taita Apalis in Vuria, with gentle sloping areas within forest interior made up of indigenous vegetation preferred. This study proposes reforesting open and degraded sites next to areas predicted as highly suitable for the two species; establishment of agroforestry belts based on indigenous trees on the boundaries of the two forests to reduce grazing and firewood collection pressure and enhance resilience to the edge effects; and enhancing forest protection through Participatory Forest Management.

African mountains are characterized by high levels of biodiversity and provide ecosystem services to millions of people. Due to steep environmental gradients, growing human populations and geographical isolation, these coupled socio-ecological systems are highly vulnerable to climate change impacts. The capacity of local stakeholders to anticipate future changes and to assess their potential impacts are paramount for enhancing adaptation and resilience. Here we apply a participatory scenario development framework in two parts of the Eastern Afromontane Biodiversity Hotspot: Taita Hills in Kenya and Jimma rural area in Ethiopia. In each area, we facilitated local stakeholders in envisioning adaptation scenarios under projected climate changes by mid-21st century, and assessed the potential impacts of these pathways on land use and land cover. In the Taita Hills, under a business-as-usual scenario, human population and activities concentrate at high elevation, triggering cascade effects on remnant forest cover, biodiversity and ecosystem services. Alternative adaptation scenarios envisage reforestation associated with either improved agricultural practices or ecosystem restoration. In the Jimma area, rising temperatures are expected to disrupt traditional coffee production under a business-as-usual scenario, resulting in the loss of coffee-forest canopies and reduction of forest-dependent biodiversity. Alternative adaptation scenarios envisage either expansion of commercial coffee plantations or expansion of agroforestry, including traditional coffee farming. In the both Taita and Jimma, adaptation pathways present trade-offs between provisioning, supporting and regulating services, and between livelihoods and biodiversity conservation. Our findings encourage the use of multidisciplinary, bottom-up approaches for developing locally tailored, climate-smart and sustainable adaptation pathways.

Land cover change takes place in sub-Saharan Africa as forests and shrublands are converted to agricultural lands in order to meet the needs of growing population. Changes in land cover also impact carbon sequestration in vegetation cover with an influence on climate on continental scale. The impact of land cover change on tree aboveground carbon stocks was studied in Taita Hills, Kenya. The land cover change between 1987 and 2011 for four points of time was assessed using SPOT satellite imagery, while the carbon density in various land cover types was assessed with field measurements, allometric biomass functions and airborne laser scanning data. Finally, the mean carbon densities of land cover types were combined with land cover maps resulting in carbon stock values for given land cover types for each point of time studied. Expansion of croplands has been taking place since 1987 and before on the cost of thickets and shrublands, especially on the foothills and lowlands. Due to the land cover changes, the carbon stock of trees was decreasing until 2003, after which there has been an increase. The findings of the research is supported by forest transition model, which emphasizes increase of awareness of forests' role in providing ecosystem services, such as habitats for pollinators, water harvesting and storage at the same time when economic reasons in making land-use choices between cropland and woodland, and governmental legislation supports trees on farms. Land cover change takes place in sub-Saharan Africa as forests and shrublands are converted to agricultural lands in order to meet the needs of growing population. Changes in land cover also impact carbon sequestration in vegetation cover with an influence on climate on continental scale. The impact of land cover change on tree aboveground carbon stocks was studied in Taita Hills, Kenya. The land cover change between 1987 and 2011 for four points of time was assessed using SPOT satellite imagery, while the carbon density in various land cover types was assessed with field measurements, allometric biomass functions and airborne laser scanning data. Finally, the mean carbon densities of land cover types were combined with land cover maps resulting in carbon stock values for given land cover types for each point of time studied. Expansion of croplands has been taking place since 1987 and before on the cost of thickets and shrublands, especially on the foothills and lowlands. Due to the land cover changes, the carbon stock of trees was decreasing until 2003, after which there has been an increase. The findings of the research is supported by forest transition model, which emphasizes increase of awareness of forests' role in providing ecosystem services, such as habitats for pollinators, water harvesting and storage at the same time when economic reasons in making land-use choices between cropland and woodland, and governmental legislation supports trees on farms.

Mosquitoes are vectors for numerous pathogens, which are collectively responsible for millions of human deaths each year. As such, it is vital to be able to accurately predict their distributions, particularly in areas where species composition is unknown. Species distribution modeling was used to determine the relationship between environmental, anthropogenic and distance factors on the occurrence of two mosquito genera, Culex Linnaeus and Stegomyia Theobald (syn. Aedes), in the Taita Hills, southeastern Kenya. This study aims to test whether any of the statistical prediction models produced by the Biomod2 package in R can reliably estimate the distributions of mosquitoes in these genera in the Taita Hills; and to examine which factors best explain their presence. Mosquito collections were acquired from 122 locations between January–March 2016 along transects throughout the Taita Hills. Environmental-, anthropogenic- and distance-based geospatial data were acquired from the Taita Hills geo-database, satellite- and aerial imagery and processed in GIS software. The Biomod2 package in R, intended for ensemble forecasting of species distributions, was used to generate predictive models. Slope, human population density, normalized difference vegetation index, distance to roads and elevation best estimated Culex distributions by a generalized additive model with an area under the curve (AUC) value of 0.791. Mean radiation, human population density, normalized difference vegetation index, distance to roads and mean temperature resulted in the highest AUC (0.708) value in a random forest model for Stegomyia distributions. We conclude that in the process towards more detailed species-level maps, with our study results, general assumptions can be made about the distribution areas of Culex and Stegomyia mosquitoes in the Taita Hills and the factors which influence their distribution.

There is a growing demand for geospatial technologies and skills in Kenya due to on-going devolution of government to the county level, development of GIS-based National Land Management Information System, and digitalization of information and maps to databases. Furthermore, adaptation of agricultural production to the impacts of climate change, and its transition towards climate-smart landscape approach require support from geospatial technologies to stakeholders to sustainably manage land use interactions, such as soil, water and nutrients along with agro-forestry, livestock, husbandry, and forest and grassland utilization at landscape level. We developed a simple and visual Multifunctional Agricultural Landscape Mosaic (MALM) Story Map and Web Application to support this transition and adoption of open access geospatial technology among the universities, government organizations and NGOs in Kenya. The thematic content of the web application was designed to support climate change adaptation action planning in the target area with a focus on water resources, conservation agriculture, agro-forestry for the smallholder farms, and insect pest management. This chapter describes the emerging challenges of advancing geospatial technologies in Kenya, presents the results of a feasibility study of MALM and discusses its potential in supporting spatial planning and decision-making in climate change adaptation in the Taita Hills, southeast Kenya.

The numbers of reported human tick-borne encephalitis (TBE) cases in Europe have increased in several endemic regions (including Finland) in recent decades, indicative of an increasing threat to public health. As such, it is important to identify the regions at risk and the most influential factors associated with TBE distributions, particularly in understudied regions. This study aimed to identify the risk areas of TBE transmission in two different datasets based on human TBE disease cases from 2007 to 2011 (n = 86) and 2012-2017 (n = 244). We also examined which factors best explain the presence of human TBE cases. We used ensemble modelling to determine the relationship of TBE occurrence with environmental, ecological, and anthropogenic factors in Finland. Geospatial data including these variables were acquired from several open data sources and satellite and aerial imagery and, were processed in GIS software. Biomod2, an ensemble platform designed for species dis-tribution modelling, was used to generate ensemble models in R. The proportion of built-up areas, field, forest, and snow-covered land in November, people working in the primary sector, human population density, mean precipitation in April and July, and densities of European hares, white-tailed deer, and raccoon dogs best es-timated distribution of human TBE disease cases in the two datasets. Random forest and generalized boosted regression models performed with a very good to excellent predictive power (ROC = 0.89-0.96) in both time periods. Based on the predictive maps, high-risk areas for TBE transmission were located in the coastal regions in Southern and Western Finland (including the angstrom land Islands), several municipalities in Central and Eastern Finland, and coastal municipalities in Southern Lapland. To explore potential changes in TBE distributions in future climate, we used bioclimatic factors with current and future climate forecast data to reveal possible future hotspot areas. Based on the future forecasts, a slightly wider geographical extent of TBE risk was introduced in the angstrom land Islands and Southern, Western and Northern Finland, even though the risk itself was not increased. Our results are the first steps towards TBE-risk area mapping in current and future climate in Finland.

Human-wildlife conflict (HWC) is a growing concern for local communities living in the vicinity of protected areas. These conflicts commonly take place as attack by wild animals and crop-raiding events, among other forms. We studied crop-raiding patterns by non-human primates in forest-agricultural landscape mosaic in the Taita Hills, southeast Kenya. The study applies both qualitative and quantitative methods. Semi-structured questionnaire was used in the primary data collection from the households, and statistical tests were performed. We used applied geospatial methods to reveal spatial patterns of crop-raiding by primates and preventive actions by farmers. The results indicate most of the farms experienced crop-raiding on a weekly basis. Blue monkey (Cercopithecus mitis) was the worst crop-raiding species and could be found in habitats covered by different land use/land cover types. Vervet monkey (Chlorocebus pygerythrus) and galagos crop-raided farms in areas with abundant tree canopy cover. Only few baboons (Papio cynocephalus) were reported to raid crops in the area. Results also show that the closer a farm is to the forest boundary and the less neighbouring farms there are between the farm and the forest, the more vulnerable it is for crop-raiding by blue monkeys, but not by any other studied primate species. The study could not show that a specific type of food crop in a farm or type of land use/land cover inside the wildlife corridor between the farmland and the forest boundary explain households' vulnerability to crop-raiding by primates. Preventive actions against crop-raiding by farmers where taken all around the studied area in various forms. Most of the studied households rely on subsistence farming as their main livelihood and therefore crop-raiding by primates is a serious threat to their food security in the area.

Geographic information system (GIS) based methodologies are widely used to various problems. However, its potential for application to strategic maritime search and rescue (SAR) planning remains largely unexplored. To investigate the applicability of GIS-based tools to this problem, this paper presents an approach to evaluate accessibility and response times in a sea area. Such information aids to objectify the response effectiveness of a SAR system, which is important for rational resource allocation. The presented methodology accounts for the main characteristics of maritime response, namely spatial accessibility, capabilities of search and rescue units (SRUs) and prevailing wave conditions, which affect the attainable SRU speeds. An application to the Finnish areas of the Gulf of Finland is shown. Despite the existence of some difficulties with currently available tools (e.g. accurate and user-friendly spatial wave models and challenges with using raster-based methods in topologically complex areas) and limitations in knowledge (e.g. the SRU capabilities in actual operations), the results indicate that the methodology provides good opportunities for enhancing maritime decision making.

Persistence of forest-dependent species in fragmented landscapes strongly relies on sufficient dispersal between patches, making it important to understand how animal movements are affected by the intervening matrix. Movements can be influenced through selection or avoidance of land cover based on their perceived suitability for foraging or providing cover. The composition and configuration of the matrix will, therefore, most likely be an important factor to consider when estimating connectivity between patches. To address this, we performed translocation experiments to understand how forest birds used different land cover types in a fine-grained matrix of a fragmented Afromontane biodiversity hotspot (Taita Hills, Kenya). Our results revealed that use of land cover types for both the forest specialist Cabanis’s greenbul Phyllastrephus cabanisi and for the forest generalist white-starred robin Pogonocichla stellata was disproportional to their availability. However, this effect was influenced by matrix configuration; in patchy matrices, land cover selection was more pronounced compared with more uniform matrices, especially for the forest specialist. At the scale of movement steps, risk avoidance seemed to be a strong factor in the route decisions for both species. Observed steps contained on average lower proportions of open land cover and did less frequently intersect built-up areas than expected. P. stellata did not differentiate between the alternative land cover types, whereas P. cabanisi preferred steps that contained more indigenous forest. The observed negative relationship between degree of forest dependency and matrix permeability implies that for members of the Taita bird community, which are even more dependent on intact forest habitat (i.e. the critically endangered Taita thrush Turdus helleri), current permeability of the matrix may be even lower. Matrix restoration to improve connectivity may, therefore, be a crucial instrument for the long-term survival of forest-dependent species in these fragmented Afromontane landscapes.

Water resources and land use are closely linked with each other and with regional climate, assembling a very complex system. The understanding of the interconnecting relations involved in this system is an essential step for elaborating public policies that can effectively lead to the sustainable use of water resources. In this study, an integrated modelling framework was assembled in order to investigate potential impacts of agricultural expansion and climate changes on Irrigation Water Requirements (IWR) in the Taita Hills, Kenya. The framework comprised a land use change simulation model, a reference evapotranspiration model and synthetic precipitation datasets generated through a Monte Carlo simulation. In order to generate plausible climate change scenarios, outputs from General Climate Models were used as reference to perturbing the Monte Carlo simulations. The results indicate that throughout the next 20 years the low availability of arable lands in the hills will drive agricultural expansion to areas with higher IWR in the foothills. If current trends persist, agricultural areas will occupy roughly 60% of the study area by 2030. This expansion will increase by approximately 40% the annual water volume necessary for irrigation. Climate change may slightly decrease crops’ IWR in April and November by 2030, while in May a small increase will likely be observed. The integrated assessment of these environmental changes allowed a clear identification of priority regions for land use allocation policies and water resources management.

The Taita Hills form the northernmost part of the Eastern Arc Mountains of Kenya and Tanzania, is one of the world's most important regions for biological conservation. Due to the expansion of agricultural activities during the last centuries, currently only 1% of the original vegetation remains preserved in the Taita Hills. These landscape changes, together with potential increases in rainfall volumes caused by climate change, offer a great risk for soil conservation. The present research aims to evaluate how future changes in climate and land use can alter, in time and space, the variables inherent to a widely used soil erosion model, and to assess the impacts of these changes for soil conservation. A modelling framework was assembled by integrating a landscape dynamic model, a soil erosion model and synthetic precipitation datasets generated through a Monte Carlo simulation. The results indicate that, if the current trends persist, agricultural areas will occupy roughly 60% of the study area by 2030. Although these land use changes will certainly increase soil erosion figures, new croplands will likely take place predominantly in the lowlands, which comprises areas with lower soil erosion potential. By the year 2030, rainfall erosivity is likely to increase during April and November, while a slight decrease tendency is observed during March and May. An integrated assessment of these environmental changes, performed using the modelling framework, allows a clear distinction of priority regions for soil conservation policies during the next 20 years.

The Taita Hills are the northernmost part of the Eastern Arc Mountains of Kenya and Tanzania, which is one of the most important regions for biological conservation in the world. The indigenous cloud forests in this area have suffered substantial degradation for several centuries due to agricultural expansion. In the Taita Hills, currently only 1% of the original forested area remains preserved. In order to create effective policies to preserve the natural resources and biodiversity of the Eastern Arc Mountains it is crucial to understand the causes and interactions involved in the landscape changes in the most degraded areas. The research presented here aimed to understand the role of landscape attributes and infrastructure components as driving forces of agricultural expansion in the Taita Hills. Geospatial technology tools and a landscape dynamic simulation model were integrated to identify and evaluate the driving forces of agricultural expansion and simulate future landscape scenarios. The results indicate that, if current trends persist, agricultural areas will occupy roughly 60% of the study area by 2030. Agricultural expansion will likely take place predominantly in lowlands and foothills throughout the next 20 years, increasing the spatial dependence on distance to rivers and other water bodies. The main factors driving the spatial distribution of new croplands were the distance to markets, proximity to already established agricultural areas and distance to roads. Other driving forces of the agricultural expansion, as well as their implications for natural resources conservation, are discussed. Further studies are necessary to integrate the effects of population pressure and climate change on the sustainability and characteristics of local agricultural systems.

We studied changes in area and species composition of six indigenous forest fragments in the Taita Hills, Kenya using 1955 and 1995 aerial photography with 2004 airborne digital camera mosaics. The study area is part of Eastern Arc Mountains, a global biodiversity hot spot that boasts an outstanding diversity of flora and fauna and a high level of endemism. While a total of 260 ha (50%) of indigenous tropical cloud forest was lost to agriculture and bushland between 1955 and 2004, large-scale planting of exotic pines, eucalyptus, grevillea, black wattle and cypress on barren land during the same period resulted in a balanced total forest area. In the Taita Hills, like in other Afrotropical forests, indigenous forest loss may adversely affect ecosystem services.

Tree species inventory and mapping are important for the management and conservation of forests. Especially in tropical forests, field based inventories are very tedious and time consuming. Therefore, the crown-level spectral data collected by the high spatial resolution airborne imaging spectroscopy provides promising possibilities for improving the accuracy and efficiency of tree species inventory and mapping. In this study, the feasibility of AISA Eagle VNIR data for spectral discrimination of indigenous and exotic tree species in the Ngangao forest in the Taita Hills in south-eastern Kenya was examined. The airborne AISA Eagle VNIR data (400-876 nm, bandwidth approximately 4.6 nm) was acquired in January 2013. The data was georeferenced and atmospherically corrected with a final spatial resolution of 1 m. The field data consisted of 152 samples from 10 species (six indigenous and four exotic species), which were mapped both in the field and from the AISA images. Stepwise Discriminant Analysis was used for tree species classification using three sets of inputs: (1) all narrowbands, (2) a combination of narrowbands and selected vegetation indices (VIs), and (3) simulated blue, green, red and NIR broadbands. According to the results, both the narrowbands and VIs provided a cross-validated overall accuracy of 77.0%. The simulated broadbands provided considerably lower overall accuracy of 38.2%, which emphasizes the utility of hyperspectral data in tropical tree species discrimination. High overall accuracy (92.8%) was attained when separating only exotic and indigenous species.

This study draws together information on several aspects of species diversity in Scopulini (Lepidoptera: Geometridae, Sterrhinae) on a world-wide scale. Eight variables, describing for example rates of species description, synonymy rates, and geographical distribution of the Scopulini and its constituent genera, were coded for all putatively valid species of Scopulini and were incorporated into a computerized database. The dynamics of species descriptions per decade shows that it peaked around 1900, remained high until the 1940s, and the cumulative curve is yet to reach an asymptote. Both absolute and cumulative curves of species descriptions per decade have taken a variety of forms in the different biogeographical regions. Amongst biogeographical regions the number of synonyms per species is broadly related, that is, the more valid names, the more synonyms. The distributions of the total number of synonyms associated with valid species names and the numbers of authors describing different numbers of species are both right-skewed. Based on the distribution of the type localities of the putatively valid species, the Scopulini are a cosmopolitan group. The biogeographical regions where most species have been described from were found to be the African, especially sub-Saharan areas, and the Orient, whereas the low number of described species of the Neotropics is noticeable. The number of described species in relation to latitude follows roughly the shape of a normal distribution, being highest at low latitudes with a peak in temperate zones in the Northern hemisphere, and decreasing towards higher latitudes. The underlying causes for patterns of species descriptions are discussed, and the results of our analyses are in part used to estimate actual numbers of the Scopulini species occurring in each geographical area.

Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600–2200 m. The field measurements were carried out in 215 plots in 2013–2015 and ALS flights conducted in 2014–2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R2 = 0.88, RMSE = 52.9 Mg ha−1). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region.

Tree species regeneration determines future forest structure and composition, but is often severely hampered in small forest relicts. To study succession, long-term field observations or simulation models are used but data, knowledge or resources to run such models are often scarce in tropical areas. We propose and implement a species accounting equation, which includes the co-occurring events extinction, colonization and recruitment and which can be solved by using data from a single inventory. We solved this species accounting equation for the 12 remaining Afromontane cloud forest relicts in Taita Hills, Kenya by comparing the tree species present among the seedling, sapling and mature tree layer in 82 plots. A simultaneous ordination of the seedling, sapling and mature tree layer data revealed that potential species extinctions, colonizations and recruitments may induce future species shifts. On landscape level, the potential extinction debt amounted to 9% (7 species) of the regional species pool. On forest relict level, the smallest relicts harbored an important proportion of the tree species diversity in the regeneration layer. The average potential recruitment credit, defined as species only present as seedling or sapling, was 3 and 6 species for large and small forest relicts, while the average potential extinction debt was 12 and 4 species, respectively. In total, both large and small relicts are expected to lose approximately 20% of their current local tree species pool. The species accounting equations provide a time and resource effective tool and give an improved understanding of the conservation status and possible future succession dynamics of forest relicts, which can be particularly useful in a context of participatory monitoring.

The indigenous cloud forests in the Taita Hills have suffered substantial degradation for several centuries due to agricultural expansion. Additionally, climate change imposes an imminent threat for local economy and environmental sustainability. In such circumstances, elaborating tools to conciliate socioeconomic growth and natural resources conservation is an enormous challenge. This chapter describes applications of remote sensing and geographic information systems for assessing land-cover changes in the Taita Hills and its surrounding lowlands. Furthermore, it provides an overall assessment on the consequences of land-cover changes to water resources, biodiversity and livelihoods. The analyses presented in this study were undertaken at multiple spatial scales, using field data, airborne digital images and satellite imagery. Furthermore, a modelling framework was designed to delineate agricultural expansion projections and evaluate the future impacts of agriculture on soil erosion and irrigation water demand.

This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km2 (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km2; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D 2, adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for po...

This thesis presents novel modelling applications for environmental geospatial data using remote sensing, GIS and statistical modelling techniques. The studied themes can be classified into four main themes: (i) to develop advanced geospatial databases. Paper (I) demonstrates the creation of a geospatial database for the Glanville fritillary butterfly (Melitaea cinxia) in the Åland Islands, south-western Finland; (ii) to analyse species diversity and distribution using GIS techniques. Paper (II) presents a diversity and geographical distribution analysis for Scopulini moths at a world-wide scale; (iii) to study spatiotemporal forest cover change. Paper (III) presents a study of exotic and indigenous tree cover change detection in Taita Hills Kenya using airborne imagery and GIS analysis techniques; (iv) to explore predictive modelling techniques using geospatial data. In Paper (IV) human population occurrence and abundance in the Taita Hills highlands was predicted using the generalized additive modelling (GAM) technique. Paper (V) presents techniques to enhance fire prediction and burned area estimation at a regional scale in East Caprivi Namibia. Paper (VI) compares eight state-of-the-art predictive modelling methods to improve fire prediction, burned area estimation and fire risk mapping in East Caprivi Namibia. The results in Paper (I) showed that geospatial data can be managed effectively using advanced relational database management systems. Metapopulation data for Melitaea cinxia butterfly was successfully combined with GPS-delimited habitat patch information and climatic data. Using the geospatial database, spatial analyses were successfully conducted at habitat patch level or at more coarse analysis scales. Moreover, this study showed it appears evident that at a large-scale spatially correlated weather conditions are one of the primary causes of spatially correlated changes in Melitaea cinxia population sizes. In Paper (II) spatiotemporal characteristics of Socupulini moths description, diversity and distribution were analysed at a world-wide scale and for the first time GIS techniques were used for Scopulini moth geographical distribution analysis. This study revealed that Scopulini moths have a cosmopolitan distribution. The majority of the species have been described from the low latitudes, sub-Saharan Africa being the hot spot of species diversity. However, the taxonomical effort has been uneven among biogeographical regions. Paper III showed that forest cover change can be analysed in great detail using modern airborne imagery techniques and historical aerial photographs. However, when spatiotemporal forest cover change is studied care has to be taken in co-registration and image interpretation when historical black and white aerial photography is used. In Paper (IV) human population distribution and abundance could be modelled with fairly good results using geospatial predictors and non-Gaussian predictive modelling techniques. Moreover, land cover layer is not necessary needed as a predictor because first and second-order image texture measurements derived from satellite imagery had more power to explain the variation in dwelling unit occurrence and abundance. Paper V showed that generalized linear model (GLM) is a suitable technique for fire occurrence prediction and for burned area estimation. GLM based burned area estimations were found to be more superior than the existing MODIS burned area product (MCD45A1). However, spatial autocorrelation of fires has to be taken into account when using the GLM technique for fire occurrence prediction. Paper VI showed that novel statistical predictive modelling techniques can be used to improve fire prediction, burned area estimation and fire risk mapping at a regional scale. However, some noticeable variation between different predictive modelling techniques for fire occurrence prediction and burned area estimation existed. Ihmisen toiminnan seurauksena ympäristön tila on heikentynyt kiihtyvällä vauhdilla. Ilmasto lämpenee, metsähakkuut ja metsäpalot lisääntyvät ja luonnon monimuotoisuus on katoamassa. Ympäristöongelmia ja -uhkia voidaan tutkia ja mallintaa geoinformatiikan menetelmin ja metodein: kaukokartoituksen, paikkatietojärjestelmien (GIS) sekä spatiaalis-tilastollisten ennustemallien avulla. Väitöskirjassa tutkittiin geoinformatiikan menetelmin geospatiaalista aineistoa hyväksi käyttäen: (i) Täpläverkkoperhosen (Melitaea cinxia) esiintymistä Ahvenanmaalla; (ii) Lehtimittarin (Scopulini moths; Lepidoptera: Geometridae, Sterrhinae) esiintymisen spatiaalis-temporaalista levinneisyyttä ja diversiteettiä globaalissa mittakaavassa; (iii) alkuperäismetsien häviämistä ja (iv) asutuksen levinneisyyttä Taita Hills -ylänköalueella Keniassa, sekä (v ja vi) savannipalojen esiintymistä ja paloarpien laajuuden arviointia Itä-Kaprivilla, Namibiassa. Tulokset: (i) Ahvenanmaalla esiintyvälle Täpläverkkoperhoselle luotiin paikkatietokanta, jonka avulla selvitettiin lajin metapopulaation spatiaalis-temporaalisia tekijöitä. Tärkein tulos todisti että paikallisilmaston vaikutus on yksi merkittävimmistä tekijöistä Täpläverkkoperhosen populaatioiden koon vaihtelussa. (ii) Paikkatietomenetelmin pystyttiin selvittämään Lehtimittarin (Scopulini moths; Lepidoptera: Geometridae, Sterrhinae) globaali maantieteellinen levinneisyys ja diversiteetti. Lajia tavataan ympäri maailmaa paitsi arktisilla alueilla. Pääosa lajeista on löydetty troppisilta alueilta. Diversiteetti on erityisen runsasta Saharan eteläpuoleisessa Afrikassa. (iii) Taita Hills ylänköalueella alkuperäismetsät ovat vähentyneet 50% (260 hehtaaria) vuodesta 1955 vuoteen 2004. Kuitenkin metsäpinta-ala oli Taita Hillsin ylänköalueella pienentynyt vain 2% johtuen metsänistutuksista. Maankäytön muutostulkintaan perustuen Taita Hills ylänköalueen alkuperäismetsät ovat pääosin muuttuneet maatalousmaaksi. Alkuperäismetsien tilalle on myös istutettu ns. eksoottisia lajikkeita kuten eukalyptusta, joka on heikentänyt metsien laatua, jolla on haitalliset vaikutukset mm. eliölajien monimuotoisuuteen Taita Hillsin alueella. (iv) Taita Hills ylänköalueen asutuksen levinneisyyttä voidaan mallintaa geospatiaalisilla karttatasoilla ja spatiaalis-tilastollisilla ennustemenetelmillä. Tutkimuksessa ilmeni, että satelliittikuvalta saadut ensimmäisen asteen tilastolliset tekstuuripiirteet ja toisen asteen tilastolliset tekstuuripiirteet, jotka perustuvat ns. Haralickin tekstuuripiirteiden yhtenevyysmatriisiin, olivat parhaita muuttujia selittämään asutuksen levinneisyyttä. Tutkimus paljasti että asutuksen mallintamiseen ei välttämättä tarvita satelliittikuvalta luokiteltua maankäyttökarttatasoa, sillä ensimmäisen ja toisen asteen tilastolliset tekstuuripiirteet olivat parempia selittäviä muuttujia ennustemalleissa. (v) Itä-Kaprivilla spatiaalis-tilastollisella ennustemallilla; yleistetty lineaarinen regressio (generalized linear model, GLM) voidaan arvioida paloalueiden laajuus paikallistasolla tarkemmin kuin käyttämällä olemassa olevaa MODIS satelliittiin perustuvaa (MCD45A1) globaalia paloaluemallia. Spatiaalis-tilastollisissa malleissa on kuitenkin huomioitava palojen spatiaalinen autokorrelaatio kalibrointiprosessissa. (vi) Kahdeksaa eri spatiaalis-tilastollista ennustemallinnusmenetelmää verrattiin palojen esiintymisen ja paloalueiden laajuuden analyysissä Itä-Kaprivilla. GBM (Generalized boosting methods) -menetelmä osoittautui parhaaksi sekä palojen esiintymisen ja paloalueiden laajuuden mallintamisessa. Ennustemalleilla pystyttiin arvioimaan paloalueiden laajuus ja paloriskialueet tarkemmin kuin käyttämällä olemassa olevaa MODIS satelliittiin perustuvaa (MCD45A1) globaalia paloaluemallia.