Combining global and local information for the segmentation of MR images of the brain

Magnetic resonance imaging can provide high resolution volumetric images of the brain with exceptional soft tissue contrast. These factors allow the complex structure of the brain to be clearly visualised. This has lead to the development of quantitative methods to analyse neuroanatomical structures. In turn, this has promoted the use of computational methods to automate and improve these techniques. This thesis investigates methods to accurately segment MRI images of the brain. The use of global and local image information is considered, where global information includes image intensity distributions, means and variances and local information is based on the relationship between spatially neighbouring voxels. Methods are explored that aim to improve the classification and segmentation of MR images of the brain by combining these elements. Some common artefacts exist in MR brain images that can be seriously detrimental to image analysis methods. Methods to correct for these artifacts are assessed by exploring their effect, first with some well established classification methods and then with methods that combine global information with local information in the form of a Markov random field model. Another characteristic of MR images is the partial volume effect that occurs where signals from different tissues become mixed over the finite volume of a voxel. This effect is demonstrated and quantified using a simulation. Analysis methods that address these issues are tested on simulated and real MR images. They are also applied to study the structure of the temporal lobes in a group of patients with temporal lobe epilepsy. The results emphasise the benefits and limitations of applying these methods to a problem of this nature. The work in this thesis demonstrates the advantages of using global and local information together in the segmentation of MR brain images and proposes a generalised framework that allows this information to be combined in a flexible way

Wetland ecotones: testing remote sensing techniques to map ecotones in a Fynbos embedded wetland

Thesis (MA)--Stellenbosch University, 2021.ENGLISH ABSTRACT: Various researchers starting as early as 1903, have developed many definitions of an ecotone (Clements 1905; Livingston 1903; Odum & Barrett 1971). The definition by Holland (1988) described ecotones as zones of transition between adjacent ecological systems, having a set of unique characteristics defined by space and time scales, and by the strength of interactions between adjacent ecological systems (Holland 1988). This definition paves the way for research that may exemplify various aspects of landscape ecology and spatial heterogeneity. Although a niche of high scientific interest, ecotonal research is very understudied, especially research on using Remote Sensing to identify and map fine-scale wetland ecotones. A bibliometric analysis and literature review showed that limited research has been conducted on wetland ecotones in southern Africa, however with sufficient literature covered on wetland delineation, classification, and mapping. Wetlands which are highly dynamic and considered moving entities in a landscape due to their varying hydroperiods, are especially challenging to map. Two main experiments were carried out both of which used Machine Learning (ML) algorithms namely Random Forest (RF) and the naïve Bayes classifier. The aim of the first experiment was to review and test remote sensing techniques to accurately identify and map distinct vegetation communities within the Du Toits River wetland, Western Cape South Africa. The second experiment was then to use probabilistic classification measures to map and characterize the ecotones prevailing in a fynbos embedded wetland ecosystem. The study used freely available satellite imagery namely Landsat 8 Surface Reflectance Tier 1, and Sentinel-2 MSI: MultiSpectral Instrument, Level-2A, obtained from the United States Geological Survey (USGS) through open-source resources such as Google Earth Engine (GEE). This research suggests that Random Forest (RF) classifier showed great potential in accurately mapping landcover, specifically four distinct and dominant vegetation types within the wetland namely Prionium serratum, Psoralea pinnata (referred to as palmiet wetland vegetation), a condensed group of Pteridium aquilinum, Restio paniculatus and Merxmuellera cincta (referred to as Sclerophyllous Wetland Vegetation), and Temporary Wetland Fynbos. RF results showed little spectral confusion between classes and produced moderate to high overall accuracies for classifications run through both the winter and summer seasons. The efficacy of using the fuzzy logic i.e. supervised probabilistic measures to identify and map ecotones in a spatially heterogenous landscape was showcased. Probabilistic mapping and fuzzy graphs showed complex and diverse ecotones within the wetland. It was evident that clear ecotones in the form of rapid and sharp high probabilities of one vegetation type intersected and replaced another. These ecotones may provide useful information about wetland ecosystem functioning and how vegetation zones may contribute to wetland ecosystem services (e.g. flood attenuation and carbon storage). Using a per-pixel based approach to map ecotones is highly useful as ecotones are more complex in reality and mapping them as single vector lines is not useful nor accurate. Although this study aimed to identify and map fine-scale wetland ecotones, further research using even finer scale data and in-depth field analysis that specifically focuses on the identified and mapped ecotonal areas will be significant.AFRIKAANSE OPSOMMING: Verskeie navorsers wat so vroeg as 1903 begin het, het baie definisies van 'n ekotoon ontwikkel (Clements 1905; Livingston 1903; Odum & Barrett 1971). Die definisie deur Holland (1988) het ekotone beskryf as sones van oorgang tussen aangrensende ekologiese sisteme, met 'n stel unieke eienskappe wat gedefinieer word deur ruimte en tydskale, en deur die sterkte van interaksies tussen aangrensende ekologiese sisteme (Holland 1988). Hierdie definisie baan die weg vir navorsing wat verskeie aspekte van landskapekologie en ruimtelike heterogeniteit kan illustreer. Alhoewel 'n nis van hoë wetenskaplike belang is, word ekotonale navorsing baie onderbestudeer, veral navorsing oor die gebruik van Afstandswaarneming om fynskaalse vleiland-ekotone te identifiseer en te karteer. 'n Bibliometriese analise en literatuuroorsig het getoon dat beperkte navorsing oor vleiland-ekotone in Suider-Afrika gedoen is, maar met voldoende literatuur gedek oor vleilandafbakening, klassifikasie en kartering. Vleilande wat hoogs dinamies is en beskou word as bewegende entiteite in 'n landskap as gevolg van hul wisselende hidroperiodes, is veral uitdagend om te karteer. Twee hoofeksperimente is uitgevoer wat albei Masjienleer (ML) algoritmes gebruik het, naamlik Random Forest (RF) en die naïewe Bayes klassifiseerder. Die doel van die eerste eksperiment was om afstandswaarnemingstegnieke te hersien en te toets om afsonderlike plantegroeigemeenskappe in die Du Toitsrivier-vleiland, Wes-Kaap Suid-Afrika akkuraat te identifiseer en te karteer. Die tweede eksperiment was dan om waarskynlikheidsklassifikasiemaatreëls te gebruik om die ekotone wat in 'n fynbos ingebedde vleiland-ekosisteem heers te karteer en te karakteriseer. Die studie het vrylik beskikbare satellietbeelde gebruik, naamlik Landsat 8 Surface Reflectance Tier 1, en Sentinel-2 MSI: MultiSpectral Instrument, Level-2A, verkry van die Verenigde State se Geologiese Opname deur middel van oopbronbronne soos Google Earth Engine (GEE). Hierdie navorsing dui daarop dat Random Forest (RF) klassifiseerder groot potensiaal getoon het in die akkurate kartering van landbedekking, spesifiek vier duidelike en dominante plantegroeitipes binne die vleiland, naamlik Prionium serratum, Psoralea pinnata (na verwys as palmiet-vleilandplantegroei), 'n gekondenseerde groep Pteridium aquilinum, Restio paniculatus en Merxmuellera cincta (na verwys as sklerofilagtige vleilandplantegroei), en Tydelike Vleilandfynbos. RF resultate het min spektrale verwarring tussen klasse getoon en matige tot hoë algehele akkuraatheid getoon vir klassifikasies wat deur beide die winter en somerseisoene loop. Die doeltreffendheid van die gebruik van die fuzzy logika d.w.s. toesighoudende waarskynlikheidsmaatreëls om ekotone in 'n ruimtelik heterogene landskap te identifiseer en te karteer, is ten toon gestel. Probabilistiese kartering en fuzzy grafieke het komplekse en diverse ekotone binne die vleiland getoon. Dit was duidelik dat duidelike ekotone in die vorm van vinnige en skerp hoë waarskynlikhede van een plantegroeitipe gekruis en 'n ander vervang het. Hierdie ekotone kan nuttige inligting verskaf oor vleiland-ekosisteemfunksionering en hoe plantegroeisones kan bydra tot vleiland-ekosisteemdienste (bv. vloeddemping en koolstofberging). Die gebruik van 'n per-pixel-gebaseerde benadering om ekotone te karteer is baie nuttig aangesien ekotone in werklikheid meer kompleks is en om dit as enkelvektorlyne te karteer is nie nuttig of akkuraat nie. Alhoewel hierdie studie daarop gemik was om fynskaalse vleiland-ekotone te identifiseer en te karteer, sal verdere navorsing deur gebruik te maak van selfs fyner skaaldata en meer in-diepte veldanalise wat spesifiek op hierdie geïdentifiseerde en gekarteerde ekotonale gebiede fokus, betekenisvol wees.Master

Forest cover and its change in Unguja Island, Zanzibar

Tropical forests are sources of many ecosystem services, but these forests are vanishing rapidly. The situation is severe in Sub-Saharan Africa and especially in Tanzania. The causes of change are multidimensional and strongly interdependent, and only understanding them comprehensively helps to change the ongoing unsustainable trends of forest decline. Ongoing forest changes, their spatiality and connection to humans and environment can be studied with the methods of Land Change Science. The knowledge produced with these methods helps to make arguments about the actors, actions and causes that are behind the forest decline. In this study of Unguja Island in Zanzibar the focus is in the current forest cover and its changes between 1996 and 2009. The cover and changes are measured with often used remote sensing methods of automated land cover classification and post-classification comparison from medium resolution satellite images. Kernel Density Estimation is used to determine the clusters of change, sub-area –analysis provides information about the differences between regions, while distance and regression analyses connect changes to environmental factors. These analyses do not only explain the happened changes, but also allow building quantitative and spatial future scenarios. Similar study has not been made for Unguja and therefore it provides new information, which is beneficial for the whole society. The results show that 572 km2 of Unguja is still forested, but 0,82–1,19% of these forests are disappearing annually. Besides deforestation also vertical degradation and spatial changes are significant problems. Deforestation is most severe in the communal indigenous forests, but also agroforests are decreasing. Spatially deforestation concentrates to the areas close to the coastline, population and Zanzibar Town. Biophysical factors on the other hand do not seem to influence the ongoing deforestation process. If the current trend continues there should be approximately 485 km2 of forests remaining in 2025. Solutions to these deforestation problems should be looked from sustainable land use management, surveying and protection of the forests in risk areas and spatially targeted self-sustainable tree planting schemes.Siirretty Doriast

Quelques extensions des level sets et des graph cuts et leurs applications à la segmentation d'images et de vidéos

Image processing techniques are now widely spread out over a large quantity of domains: like medical imaging, movies post-production, games... Automatic detection and extraction of regions of interest inside an image, a volume or a video is challenging problem since it is a starting point for many applications in image processing. However many techniques were developed during the last years and the state of the art methods suffer from some drawbacks: The Level Sets method only provides a local minimum while the Graph Cuts method comes from Combinatorial Community and could take advantage of the specificity of image processing problems. In this thesis, we propose two extensions of the previously cited methods in order to soften or remove these drawbacks. We first discuss the existing methods and show how they are related to the segmentation problem through an energy formulation. Then we introduce stochastic perturbations to the Level Sets method and we build a more generic framework: the Stochastic Level Sets (SLS). Later we provide a direct application of the SLS to image segmentation that provides a better minimization of energies. Basically, it allows the contours to escape from local minimum. Then we propose a new formulation of an existing algorithm of Graph Cuts in order to introduce some interesting concept for image processing community: like initialization of the algorithm for speed improvement. We also provide a new approach for layer extraction from video sequence that retrieves both visible and hidden layers in it.Les techniques de traitement d'image sont maintenant largement répandues dans une grande quantité de domaines: comme l'imagerie médicale, la post-production de films, les jeux... La détection et l'extraction automatique de régions d'intérêt à l'intérieur d'une image, d'un volume ou d'une vidéo est réel challenge puisqu'il représente un point de départ pour un grand nombre d'applications en traitement d'image. Cependant beaucoup de techniques développées pendant ces dernières années et les méthodes de l'état de l'art souffrent de quelques inconvénients: la méthode des ensembles de niveaux fournit seulement un minimum local tandis que la méthode de coupes de graphe vient de la communauté combinatoire et pourrait tirer profit de la spécificité des problèmes de traitement d'image. Dans cette thèse, nous proposons deux prolongements des méthodes précédemment citées afin de réduire ou enlever ces inconvénients. Nous discutons d'abord les méthodes existantes et montrons comment elles sont liées au problème de segmentation via une formulation énergétique. Nous présentons ensuite des perturbations stochastiques a la méthode des ensembles de niveaux et nous établissons un cadre plus générique: les ensembles de niveaux stochastiques (SLS). Plus tard nous fournissons une application directe du SLS à la segmentation d'image et montrons qu'elle fournit une meilleure minimisation des énergies. Fondamentalement, il permet aux contours de s'échapper des minima locaux. Nous proposons ensuite une nouvelle formulation d'un algorithme existant des coupes de graphe afin d'introduire de nouveaux concepts intéressant pour la communauté de traitement d'image: comme l'initialisation de l'algorithme pour l'amélioration de vitesse. Nous fournissons également une nouvelle approche pour l'extraction de couches d'une vidéo par segmentation du mouvement et qui extrait à la fois les couches visibles et cachées présentes

Procedures for the analysis and use of multiple view angle image data

It is recognised that the majority of vegetative cover types have anisotropic reflectance characteristics that are largely a function of their canopy geometry. Several studies have made attempts at formulating methods for the use of data remotely sensed from off-nadir directions. The best of these methods attempt to utilise the "extra" information implicitly contained in off-nadir image datasets. In this study, an attempt is made to extract information concerning agro-physical parameters of a number of vegetative cover types using imagery acquired by an airborne sensor, the Daedalus Airborne Thematic Mapper (ATM). It is also recognised in the literature that the nature of spatial variance in images is related to the size and distribution of the objects in the scene and the sampling characteristics of the sensor. In previous work this relationship has been explored by examining scenes using images of varying spatial resolutions, using a number of measurements of spatial variance. The underlying trend of these measurements is then used to interpret the nature of the objects in the scene. No previous work exists which attempts to utilise the change in variance of images acquired at different off-nadir view angles. In this study, the understanding of this relationship is developed by examining the change in variance of a number of vegetative cover types from multiple view angle image datasets. The geometry of the ATM sensor is derived to allow an understanding of the sampling characteristics of the instrument. Two important geometric factors are established: first, the area of the ground resolution element increases with view angle, which effectively reduces spatial resolution at off-nadir angles; and second, overlap between adjacent ground resolution elements increases with view angle, increasing the spatial auto-correlation between these samples. The effects of illumination, atmosphere and topography can all influence variance in an image. A parametric procedure for normalising multiple view angle (and therefore multitemporal) datasets for these factors is developed, based upon the production of reflectance images using a sky radiance model of the spectral and spatial distributions of irradiance, ground measurements of irradiance, and a digital terrain model of the study site. Finally, it is shown that image variance is likely to decrease at off-nadir view angles, the magnitude of this decrease being related to the sensor geometry and (more importantly) the geometry of the canopy. By a simple statistical analytical procedure it is possible to construct broad classes within which the nature of the canopy can be classified

Mapping three-dimensional geological features from remotely-sensed images and digital elevation models.

Accurate mapping of geological structures is important in numerous applications, ranging from mineral exploration through to hydrogeological modelling. Remotely sensed data can provide synoptic views of study areas enabling mapping of geological units within the area. Structural information may be derived from such data using standard manual photo-geologic interpretation techniques, although these are often inaccurate and incomplete. The aim of this thesis is, therefore, to compile a suite of automated and interactive computer-based analysis routines, designed to help a the user map geological structure. These are examined and integrated in the context of an expert system. The data used in this study include Digital Elevation Model (DEM) and Airborne Thematic Mapper images, both with a spatial resolution of 5m, for a 5 x 5 km area surrounding Llyn Cow lyd, Snowdonia, North Wales. The geology of this area comprises folded and faulted Ordo vician sediments intruded throughout by dolerite sills, providing a stringent test for the automated and semi-automated procedures. The DEM is used to highlight geomorphological features which may represent surface expressions of the sub-surface geology. The DEM is created from digitized contours, for which kriging is found to provide the best interpolation routine, based on a number of quantitative measures. Lambertian shading and the creation of slope and change of slope datasets are shown to provide the most successful enhancement of DEMs, in terms of highlighting a range of key geomorphological features. The digital image data are used to identify rock outcrops as well as lithologically controlled features in the land cover. To this end, a series of standard spectral enhancements of the images is examined. In this respect, the least correlated 3 band composite and a principal component composite are shown to give the best visual discrimination of geological and vegetation cover types. Automatic edge detection (followed by line thinning and extraction) and manual interpretation techniques are used to identify a set of 'geological primitives' (linear or arc features representing lithological boundaries) within these data. Inclusion of the DEM data provides the three-dimensional co-ordinates of these primitives enabling a least-squares fit to be employed to calculate dip and strike values, based, initially, on the assumption of a simple, linearly dipping structural model. A very large number of scene 'primitives' is identified using these procedures, only some of which have geological significance. Knowledge-based rules are therefore used to identify the relevant. For example, rules are developed to identify lake edges, forest boundaries, forest tracks, rock-vegetation boundaries, and areas of geomorphological interest. Confidence in the geological significance of some of the geological primitives is increased where they are found independently in both the DEM and remotely sensed data. The dip and strike values derived in this way are compared to information taken from the published geological map for this area, as well as measurements taken in the field. Many results are shown to correspond closely to those taken from the map and in the field, with an error of < 1°. These data and rules are incorporated into an expert system which, initially, produces a simple model of the geological structure. The system also provides a graphical user interface for manual control and interpretation, where necessary. Although the system currently only allows a relatively simple structural model (linearly dipping with faulting), in the future it will be possible to extend the system to model more complex features, such as anticlines, synclines, thrusts, nappes, and igneous intrusions

Using a water treatment residual and compost co-amendment as a sustainable soil improvement technology to enhance flood holding capacity

The recycling of clean wastes, such as those from the treatment of drinking water, has gained importance on the environmental agenda due to rising costs of landfill disposal and movement towards a ‘zero’ waste economy. More than one third of the globe’s soils are degraded and as such policies towards determining soil health parameters and reversing destruction of the globe’s most valuable non-renewable source are at the forefront of environmental debate. This thesis questions the opportunity for water treatment residual (WTR) to be used as a beneficial material for the co-amendment of soil with compost to improve the soil’s flood holding capacity (Kerr et al., 2016), which includes functions such as the water holding capacity, hydraulic conductivity, soil structure and shear strength. Currently, water treatment residual is typically sent to landfill for disposal, but this research shows that the reuse of WTR as a co-amendment is able to improve the flood holding capacity of soils. This research crosses the boundary between geotechnical and geoenvironmental and provides a holistic approach to quantifying a soil from both perspectives. Iron based water treatment residual from Northumbrian Water Ltd was used in both laboratory and field trials to establish the effect of single WTR and a compost and WTR co-amendment on the water holding capacity (the gravimetric water content, volumetric water content, volume change of samples i.e. swelling and shrinkage), and the effect of amendment on the erosional resistance, hydraulic conductivity and shear strength compared to a control soil. A series of four trials were conducted to develop and establish a novel method to determine the water holding capacity, supplemented by standard geotechnical methods to determine the flood holding capacity. The use of x-ray computed tomography has provided accompanying information on the morphology of dried WTR and changes in the internal characteristics of amended soil between a dry and wet state. The amendment application rate ranges from 10 – 50%. Experiments have shown that the single amendment of WTR, compared to a control soil, yields significant increases in the hydraulic conductivity (by up to a factor of 28), increases the shear strength of soils at low testing pressure (25 kPa) by 129%, increases the maximum gravimetric water content by up to 13.7%, and improves swelling by up to 12% (but only at the highest amendment rate, 30%), increases the maximum void ratio when saturated by 11%, and reduces shrinkage by maintaining porosity by 14%. However the application of WTR as a single amendment has implications for the chemical health of the soil as it is highly effective at immobilising phosphorous as and such cannot not effectively be used as a soil amendment. The single application of compost yielded significant improvement in the water holding capacity (improving gravimetric water content by up to 34.7%, increasing the sample volume by up to 83.3%, and increased the void ratio by 8.2%), however this application reduces the hydraulic conductivity by up to 84.5% and the shear strength by 3% compared to the control soil. Co-amendment using compost and WTR (in two forms, air dried 80% solids and wet at 20% solids, as produced from water treatment works) improved the flood holding capacity of soils by retaining the structural improvements of amendment using WTR and the water holding capacity improvements of compost. Compared to the control soil, for co-amended soils the gravimetric water content was improved by up to 25%, the volume increased by up to 51.7%, experienced 13% less shrinkage and an 11.5% increase in maximum void ratio. The hydraulic conductivity was also improved by up to 475%, and shear strength was increased at both low and high testing pressures by to 53.8%. Taking into account these effects of co-amendment on essential soil functions that determines a soil’s flood holding capacity (maximum gravimetric water content, volume change, resistance against shrinkage, void ratio (porosity), hydraulic conductivity and shear strength), the economical and environmental sustainability issues, the co-amendment of soil using compost and WTR may provide a solution to both recycling clean waste product and improving the quality of soil

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.Taitavuoret sijaitsevat Kaakkois-Keniassa ja muodostavat pohjoisimman osan Itäisistä Kaarivuorista. Conservation International -järjestön mukaan Itäisten Kaarivuorten alue kuuluu luonnon monimuotoisuuden (biodiversiteetin) kannalta kymmenen tärkeimmän joukkoon maailmassa. Taitavuorilla, kuten monilla muilla kehittyvien maiden alueilla, viime vuosikymmenten aikana väestönkasvu on johtanut merkittäviin maankäytön muutoksiin kuten esimerkiksi kiihtyvään maan heikkenemiseen, erityisesti maaperäeroosion muodossa. Moniaikaiset optisen alueen SPOT-satelliittikuvat tarjoavat arvokasta tietoa ympäristön tilan seurantaan ja ympäristömallinnukseen paikallisella ja alueellisella tasolla. SPOT-satelliittikuva-aineiston hyödyntäminen kvantitatiivisessa kaukokartoituksessa vaatii kuitenkin ilmakehän vaikutuksen poistamista sekä maanpinnan heijastussuhteen määrittämistä. Lisäksi alueilla, joilla maasto on epätasaista, kuten Taitavuorilla, satelliittikuvalle on tehtävä topografinen korjaus, jotta maanpinnan heijastusarvot olisivat vertailukelpoisia koko satelliittikuvan alueella. Maankäytön muutosten monitorointi ja maaperän huononemisen sekä väestön levinneisyyden ja runsauden mallintaminen ovat ratkaisevan tärkeitä kestävälle kehitykselle, luonnonvarojen hallinnalle, biologisen monimuotoisuuden suojelulle ja ilmastonmuutoksen hillitsemiselle ja sen vaikutusten vähentämiselle. Tämän tutkimuksen tarkoituksena oli kehittää ja arvioida tehostettuja prosessointimenetelmiä SPOT-satelliittikuville. Tutkimuksessa kehitettyjä menetelmiä voidaan hyödyntää ympäristön tilan seurannassa ja mallintamisessa kehittyvissä maissa alueilla, joilla täydentävä tutkimusaineisto on puutteellista. Tässä tutkimuksessa Taitavuoret oli varsinainen tutkimusalue, jossa sovellukset kehitettiin ja Helsinki toimi kontrollialueena validoinnissa ja ilmakehäkorjausten hyvyyden arvioinnissa. Tutkimuksessa esitetty ilmakehäkorjaus menetelmä, ns. historical empirical line method (HELM), osoittautui ainoaksi menetelmäksi, jolla maanpinnan heijastussuhteen arvion keskivirhe (RMSE) oli < 0.02 ja suhteellinen tarkkuus < 10%. Yllä mainittu tarkkuustaso on yleisesti hyväksytty vertailuarvo osoittamaan ilmakehäkorjauksen onnistumisen. Monitasoista kuvasegmentointia ja objekti-orientoitunutta mallintamista (MSS/ORM) hyödynnettiin Taitavuorten maankäytön kartoittamisessa SPOT-satelliittikuvalta. Objekti-orientoitunut menetelmä onnistui parantamaan huomattavasti yksi-tasoista maximum-likelihood -luokitusta. Kuvasegmentoinnilla tuotettua Taitavuorten maankäyttöaineistoa käytettiin maaperän huonontumisen mallintamisessa yhdessä alhaisen kustannuksen geospatiaalisten karttatasojen kanssa, jotka kuvaavat mm. Taitavuorten topografiaa, sadantaa ja maaperää. Mallintamisessa arvioitiin potentiaalista maa-aineksen häviämistä ns. USLE-eroosiomallin avulla. Lisäksi Taitavuorten väestön leviämistä ja väestön määrää mallinnettiin SPOT-satelliittikuvalta ja paikkatieto-aineistoista saaduilla geospatiaalisilla muuttujilla. Ennustemallit kalibroitiin käyttäen epälineaarista regressiota. Mallinnuksessa pyrkimyksenä oli sen toistettavuus myös kehittyvissä maissa. Täten mallinnuksessa pyrittiin hyödyntämään alhaisen kustannuksen tai vapaasti saatavilla olevia aineistoja ja ohjelmistoja

Earth Resources: A continuing bibliography with indexes, issue 11, October 1976

This bibliography lists 714 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1976 and September 1976. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis