Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.

Digital elevation model error in terrain analysis

Digital elevation models (DEMs) have been an important topic in geography and surveying sciences for decades due to their geomorphological importance as the reference surface for gravita-tion-driven material flow, as well as the wide range of uses and applications. When DEM is used in terrain analysis, for example in automatic drainage basin delineation, errors of the model collect in the analysis results. Investigation of this phenomenon is known as error propagation analysis, which has a direct influence on the decision-making process based on interpretations and applications of terrain analysis. Additionally, it may have an indirect influence on data acquisition and the DEM generation. The focus of the thesis was on the fine toposcale DEMs, which are typically represented in a 5-50m grid and used in the application scale 1:10 000-1:50 000. The thesis presents a three-step framework for investigating error propagation in DEM-based terrain analysis. The framework includes methods for visualising the morphological gross errors of DEMs, exploring the statistical and spatial characteristics of the DEM error, making analytical and simulation-based error propagation analysis and interpreting the error propagation analysis results. The DEM error model was built using geostatistical methods. The results show that appropriate and exhaustive reporting of various aspects of fine toposcale DEM error is a complex task. This is due to the high number of outliers in the error distribution and morphological gross errors, which are detectable with presented visualisation methods. In ad-dition, the use of global characterisation of DEM error is a gross generalisation of reality due to the small extent of the areas in which the decision of stationarity is not violated. This was shown using exhaustive high-quality reference DEM based on airborne laser scanning and local semivariogram analysis. The error propagation analysis revealed that, as expected, an increase in the DEM vertical error will increase the error in surface derivatives. However, contrary to expectations, the spatial au-tocorrelation of the model appears to have varying effects on the error propagation analysis depend-ing on the application. The use of a spatially uncorrelated DEM error model has been considered as a 'worst-case scenario', but this opinion is now challenged because none of the DEM derivatives investigated in the study had maximum variation with spatially uncorrelated random error. Sig-nificant performance improvement was achieved in simulation-based error propagation analysis by applying process convolution in generating realisations of the DEM error model. In addition, typology of uncertainty in drainage basin delineations is presented.

Geospatial environmental data modelling applications using remote sensing, GIS and spatial statistics

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.

Where and how to conserve: Extending the scope of spatial reserve network design

Ongoing habitat loss and fragmentation threaten much of the biodiversity that we know today. As such, conservation efforts are required if we want to protect biodiversity. Conservation budgets are typically tight, making the cost-effective selection of protected areas difficult. Therefore, reserve design methods have been developed to identify sets of sites, that together represent the species of conservation interest in a cost-effective manner. To be able to select reserve networks, data on species distributions is needed. Such data is often incomplete, but species habitat distribution models (SHDMs) can be used to link the occurrence of the species at the surveyed sites to the environmental conditions at these locations (e.g. climatic, vegetation and soil conditions). The probability of the species occurring at unvisited location is next predicted by the model, based on the environmental conditions of those sites. The spatial configuration of reserve networks is important, because habitat loss around reserves can influence the persistence of species inside the network. Since species differ in their requirements for network configuration, the spatial cohesion of networks needs to be species-specific. A way to account for species-specific requirements is to use spatial variables in SHDMs. Spatial SHDMs allow the evaluation of the effect of reserve network configuration on the probability of occurrence of the species inside the network. Even though reserves are important for conservation, they are not the only option available to conservation planners. To enhance or maintain habitat quality, restoration or maintenance measures are sometimes required. As a result, the number of conservation options per site increases. Currently available reserve selection tools do however not offer the ability to handle multiple, alternative options per site. This thesis extends the existing methodology for reserve design, by offering methods to identify cost-effective conservation planning solutions when multiple, alternative conservation options are available per site. Although restoration and maintenance measures are beneficial to certain species, they can be harmful to other species with different requirements. This introduces trade-offs between species when identifying which conservation action is best applied to which site. The thesis describes how the strength of such trade-offs can be identified, which is useful for assessing consequences of conservation decisions regarding species priorities and budget. Furthermore, the results of the thesis indicate that spatial SHDMs can be successfully used to account for species-specific requirements for spatial cohesion - in the reserve selection (single-option) context as well as in the multi-option context. Accounting for the spatial requirements of multiple species and allowing for several conservation options is however complicated, due to trade-offs in species requirements. It is also shown that spatial SHDMs can be successfully used for gaining information on factors that drive a species spatial distribution. Such information is valuable to conservation planning, as better knowledge on species requirements facilitates the design of networks for species persistence. This methods and results described in this thesis aim to improve species probabilities of persistence, by taking better account of species habitat and spatial requirements. Many real-world conservation planning problems are characterised by a variety of conservation options related to protection, restoration and maintenance of habitat. Planning tools therefore need to be able to incorporate multiple conservation options per site, in order to continue the search for cost-effective conservation planning solutions. Simultaneously, the spatial requirements of species need to be considered. The methods described in this thesis offer a starting point for combining these two relevant aspects of conservation planning.

Evaluation and modelling of the spatial and temporal variability of particulate matter in urban areas

This thesis contains three subject areas concerning particulate matter in urban area air quality: 1) Analysis of the measured concentrations of particulate matter mass concentrations in the Helsinki Metropolitan Area (HMA) in different locations in relation to traffic sources, and at different times of year and day. 2) The evolution of traffic exhaust originated particulate matter number concentrations and sizes in local street scale are studied by a combination of a dispersion model and an aerosol process model. 3) Some situations of high particulate matter concentrations are analysed with regard to their meteorological origins, especially temperature inversion situations, in the HMA and three other European cities. The prediction of the occurrence of meteorological conditions conducive to elevated particulate matter concentrations in the studied cities is examined. The performance of current numerical weather forecasting models in the case of air pollution episode situations is considered. The study of the ambient measurements revealed clear diurnal variation of the PM10 concentrations in the HMA measurement sites, irrespective of the year and the season of the year. The diurnal variation of local vehicular traffic flows seemed to have no substantial correlation with the PM2.5 concentrations, indicating that the PM10 concentrations were originated mainly from local vehicular traffic (direct emissions and suspension), while the PM2.5 concentrations were mostly of regionally and long-range transported origin. The modelling study of traffic exhaust dispersion and transformation showed that the number concentrations of particles originating from street traffic exhaust undergo a substantial change during the first tens of seconds after being emitted from the vehicle tailpipe. The dilution process was shown to dominate total number concentrations. Minimal effect of both condensation and coagulation was seen in the Aitken mode number concentrations. The included air pollution episodes were chosen on the basis of occurrence in either winter or spring, and having at least partly local origin. In the HMA, air pollution episodes were shown to be linked to predominantly stable atmospheric conditions with high atmospheric pressure and low wind speeds in conjunction with relatively low ambient temperatures. For the other European cities studied, the best meteorological predictors for the elevated concentrations of PM10 were shown to be temporal (hourly) evolutions of temperature inversions, stable atmospheric stability and in some cases, wind speed. Concerning the weather prediction during particulate matter related air pollution episodes, the use of the studied models were found to overpredict pollutant dispersion, leading to underprediction of pollutant concentration levels.

Topics in Spatial Econometrics - With Applications to House Prices

Topics in Spatial Econometrics — With Applications to House Prices Spatial effects in data occur when geographical closeness of observations influences the relation between the observations. When two points on a map are close to each other, the observed values on a variable at those points tend to be similar. The further away the two points are from each other, the less similar the observed values tend to be. Recent technical developments, geographical information systems (GIS) and global positioning systems (GPS) have brought about a renewed interest in spatial matters. For instance, it is possible to observe the exact location of an observation and combine it with other characteristics. Spatial econometrics integrates spatial aspects into econometric models and analysis. The thesis concentrates mainly on methodological issues, but the findings are illustrated by empirical studies on house price data. The thesis consists of an introductory chapter and four essays. The introductory chapter presents an overview of topics and problems in spatial econometrics. It discusses spatial effects, spatial weights matrices, especially k-nearest neighbours weights matrices, and various spatial econometric models, as well as estimation methods and inference. Further, the problem of omitted variables, a few computational and empirical aspects, the bootstrap procedure and the spatial J-test are presented. In addition, a discussion on hedonic house price models is included. In the first essay a comparison is made between spatial econometrics and time series analysis. By restricting the attention to unilateral spatial autoregressive processes, it is shown that a unilateral spatial autoregression, which enjoys similar properties as an autoregression with time series, can be defined. By an empirical study on house price data the second essay shows that it is possible to form coordinate-based, spatially autoregressive variables, which are at least to some extent able to replace the spatial structure in a spatial econometric model. In the third essay a strategy for specifying a k-nearest neighbours weights matrix by applying the spatial J-test is suggested, studied and demonstrated. In the final fourth essay the properties of the asymptotic spatial J-test are further examined. A simulation study shows that the spatial J-test can be used for distinguishing between general spatial models with different k-nearest neighbours weights matrices. A bootstrap spatial J-test is suggested to correct the size of the asymptotic test in small samples.

Methods in general model localization

The aim of this study was to evaluate and test methods which could improve local estimates of a general model fitted to a large area. In the first three studies, the intention was to divide the study area into sub-areas that were as homogeneous as possible according to the residuals of the general model, and in the fourth study, the localization was based on the local neighbourhood. According to spatial autocorrelation (SA), points closer together in space are more likely to be similar than those that are farther apart. Local indicators of SA (LISAs) test the similarity of data clusters. A LISA was calculated for every observation in the dataset, and together with the spatial position and residual of the global model, the data were segmented using two different methods: classification and regression trees (CART) and the multiresolution segmentation algorithm (MS) of the eCognition software. The general model was then re-fitted (localized) to the formed sub-areas. In kriging, the SA is modelled with a variogram, and the spatial correlation is a function of the distance (and direction) between the observation and the point of calculation. A general trend is corrected with the residual information of the neighbourhood, whose size is controlled by the number of the nearest neighbours. Nearness is measured as Euclidian distance. With all methods, the root mean square errors (RMSEs) were lower, but with the methods that segmented the study area, the deviance in single localized RMSEs was wide. Therefore, an element capable of controlling the division or localization should be included in the segmentation-localization process. Kriging, on the other hand, provided stable estimates when the number of neighbours was sufficient (over 30), thus offering the best potential for further studies. Even CART could be combined with kriging or non-parametric methods, such as most similar neighbours (MSN).

Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

Abstract. Methane emissions from natural wetlands and rice paddies constitute a large proportion of atmospheric methane, but the magnitude and year-to-year variation of these methane sources is still unpredictable. Here we describe and evaluate the integration of a methane biogeochemical model (CLM4Me; Riley et al., 2011) into the Community Land Model 4.0 (CLM4CN) in order to better explain spatial and temporal variations in methane emissions. We test new functions for soil pH and redox potential that impact microbial methane production in soils. We also constrain aerenchyma in plants in always-inundated areas in order to better represent wetland vegetation. Satellite inundated fraction is explicitly prescribed in the model because there are large differences between simulated fractional inundation and satellite observations. A rice paddy module is also incorporated into the model, where the fraction of land used for rice production is explicitly prescribed. The model is evaluated at the site level with vegetation cover and water table prescribed from measurements. Explicit site level evaluations of simulated methane emissions are quite different than evaluating the grid cell averaged emissions against available measurements. Using a baseline set of parameter values, our model-estimated average global wetland emissions for the period 1993–2004 were 256 Tg CH4 yr−1, and rice paddy emissions in the year 2000 were 42 Tg CH4 yr−1. Tropical wetlands contributed 201 Tg CH4 yr−1, or 78 % of the global wetland flux. Northern latitude (>50 N) systems contributed 12 Tg CH4 yr−1. We expect this latter number may be an underestimate due to the low high-latitude inundated area captured by satellites and unrealistically low high-latitude productivity and soil carbon predicted by CLM4. Sensitivity analysis showed a large range (150–346 Tg CH4 yr−1) in predicted global methane emissions. The large range was sensitive to: (1) the amount of methane transported through aerenchyma, (2) soil pH (± 100 Tg CH4 yr−1), and (3) redox inhibition (± 45 Tg CH4 yr−1).