Densification of spatially-sparse legacy soil data at a national scale: a digital mapping approach

Digital soil mapping (DSM) is a viable approach to providing spatial soil information but its adoption at the national scale, especially in sub-Saharan Africa, is limited by low spread of data. Therefore, the focus of this thesis is on optimizing DSM techniques for densification of sparse legacy soil data using Nigeria as a case study. First, the robustness of Random Forest model (RFM) was tested in predicting soil particle-size fractions as a compositional data using additive log-ratio technique. Results indicated good prediction accuracy with RFM while soils are largely coarse-textured especially in the northern region. Second, soil organic carbon (SOC) and bulk density (BD) were predicted from which SOC density and stock were calculated. These were overlaid with land use/land cover (LULC), agro-ecological zone (AEZ) and soil maps to quantify the carbon sequestration of soils and their variation across different AEZs. Results showed that 6.5 Pg C with an average of 71.60 Mg C ha–1 abound in the top 1 m soil depth. Furthermore, to improve the performance of BD and effective cation exchange capacity (ECEC) pedotransfer functions (PTFs), the inclusion of environmental data was explored using multiple linear regression (MLR) and RFM. Results showed an increase in performance of PTFs with the use of soil and environmental data. Finally, the application of Choquet fuzzy integral (CI) technique in irrigation suitability assessment was assessed. This was achieved through multi-criteria analysis of soil, climatic, landscape and socio-economic indices. Results showed that CI is a better aggregation operator compared to weighted mean technique. A total of 3.34 x 106 ha is suitable for surface irrigation in Nigeria while major limitations are due to topographic and soil attributes. Research findings will provide quantitative basis for framing appropriate policies on sustainable food production and environmental management, especially in resource-poor countries of the world

Final Report of the DAUFIN project

DAUFIN = Data Assimulation within Unifying Framework for Improved river basiN modeling (EC 5th framework Project

Computationally efficient simulation in urban mechanised tunnelling based on multi-level BIM models

The design of complex underground infrastructure projects involves various empirical, analytical or numerical models with different levels of complexity. The use of simulation models in current state-of-the-art tunnel design process can be cumbersome when significant manual, time-consuming preparation, analysis and excessive computing resources are required. This paper addresses the challenges connected with minimising the user workload and computational time, as well as enabling real-time computations during the construction. To ensure a seamless workflow during design and to minimise the computation time of the analysis, we propose a novel concept for BIM-based numerical simulations, enabling the modelling of the tunnel advance on different levels of detail in terms of geometrical representation, material modelling and modelling of the advancement process. To ensure computational efficiency, the simulation software has been developed with special emphasis on efficient implementation, including parallelisation strategies on shared and distributed memory systems. For real-time on-demand calculations, simulation based meta models are integrated into the software platform. The components of the BIM-based multi-level simulation concept are described and evaluated in detail by means of representative numerical examples

Geostatistical modeling of geochemical variables in 3D

Geostatistical mapping of soil properties in 3D refers to the application of geostatistical methods to the soil data in order to produce maps of soil properties at different depths. Through two separate studies, this thesis elaborates on two different approaches for 3D soil mapping. At first, the well established Spline-Than-Krige approach for the mapping of soil pollutants atmospherically deposited from the copper smelting plant, was used. In the absence of the monitoring data, which can be used for a detailed characterization of the plume spreading process, this study was confined to the consideration of terrain exposure to explain spatial trend in arsenic distribution at different depths. This study aims to explore the extent to which the commonly available information, such as the prevailing wind direction, or the location of the source of pollution, in combination with the digital terrain model, can be used to quantify the terrain exposure, and hence to improve the spatial prediction of the arsenic concentration at several soil depths. Next, the innovative geostatistical approach to 3D mapping of soil properties, based on soil profile data, was proposed. It provides the semi-automatic way for 3D modeling of soil variables, prediction over the regular grids (rasters) and also the evaluation of prediction accuracy. Methodologically, this approach operates within the 3D regression kriging framework. 3D trend model is conceptualized as hierarchical or non-hierarchical linear interaction model. This means that the model includes the interactions between the spatial covariates and depth in the hiearchial or non-hierarchial manner. The trend modeling is based on the application of the penalized regression technique, lasso. The lasso uses a specific regularization penalty in a fitting procedure to enable the efficient parameter estimation and variable selection (including interaction terms) at the same time...Geostatistiˇcko kartiranje zemljišta u 3D odnosi se na primenu geostatistiˇckih metoda na zemljišnim podacima u cilju izrade karata zemljišnih karakteristika jednog podruˇcja, koje se odnose na razliˇcite dubine zemljišta. U okviru dve nezavisne studije, ova doktorska disertacija razmatra dva razliˇcita pristupa geostatistiˇckog modeliranja zemljišta u 3D. U okviru prve studije, "Spline-Than-Krige" metod je koriš´cen za kartiranje koncentracije arsena u zemljištu, u blizini Rudarsko-topioniˇcarskog basena Bor, na tri razliˇcite dubine (0-5 cm, 5-15 cm i 15-30 cm). Dugogodišnje emitovanje nepreˇciš´cenih materija iz topionice rudnika u atmosferu, dovelo je do zagadjenja zemljišta u okolini, taloženjem štetnih materija nošenih vetrom. U odsustvu podataka kojima bi se detaljnije mogao opisati proces raspršivanja štetnih materija, ova studija se ograniˇcila na analizu izloženosti terena uticaju vetra, a time i procesu zagad¯enja. Predstavljen je inovativan pristup kvantifikaciji izloženosti terena izvoru zagad¯enja. Na osnovu opšte dostupnih podataka, kreirano je nekoliko parametara kojima se kvantifikuje geometrijska i topografska izloženost svake tacˇke terena izvoru zagad¯enja. Tako kreirani parametri, iskorišc´eni su za opisivanje prostornog trenda koncentracije arsena na tri razliˇcite dubine. Definisani trendovi, koriš´ceni su u okviru regresionog kriginga, za prostornu predikciju. Na taj naˇcin pokušalo se odgovoriti na pitanje, u kojoj meri, opšte dostupni podaci, kao što su pravac dominantnog vetra ili poznavanje taˇcne lokacije izvora zagadjenja u kombinaciji sa digitalnim modelom terena, mogu biti iskoriš´ceni da bi se unapredila preciznost prostorne predikcije zemljišnih zagadjivaˇca, kako na površinskim slojevima tako i na ve´cim dubinama..

Bridging structure and function in semi-arid ecosystems by integrating remote sensing and ground based measurements

The Southwestern US is projected to continue the current significant warming trend, with increased variability in the timing and magnitude of rainfall events. The effects of these changes in climate are already evident in the form of multi-year droughts which have resulted in the widespread mortality of woody vegetation across the region. Therefore, the need to monitor and model forest mortality and carbon dynamics at the landscape and regional scale is an essential component of regional and global climate mitigation strategies, and critical if we are to understand how the imminent state transitions taking place in forests globally will affect climate forcing and feedbacks. Remote sensing offers the only solution to multitemporal regional observation, yet many challenges exist with employing modern remote sensing solutions in highly stressed vegetation characteristic of semi-arid biomes, making one of the most expansive biomes on the globe also one of the most difficult to accu- rately monitor and model. The goal of this research was to investigate how changes in the structure of semi-arid woodlands following forest mortality impacts ecosystem function, and to determine how this question can be addressed using remotely sensed data sets. I focused primarily on Pinus edulis and Juniperous monosperma (piñon-juniper) woodlands, and took advantage of an existing manipulation experiment where mortality was imposed on all of the large piñon (¡ 7 cm dbh) in a 4 ha PJ woodland in 2009 and the ecosystem functional responses have been quantified using eddy covariance. A nearby intact PJ woodland, also instrumented with eddy covariance, was used as a control for this experiment. I tested the ability of high resolution remote sensing data to mechanistically describe the patterns in overstory mortality and understory green-up in this manipulated woodland by comparing it to the intact woodland, and observed the heterogeneous response of the understory as a function of cover type. I also investigated the relationship between changes in soil water content and the greenness of the canopy, noting that in the disturbed woodland, I observed a decoupling between how the canopy was measured remotely (e.g., via vegetation indices, VI) and photosynthesis. This is significant in that it potentially represents a significant source of error in using existing light use efficiency models of carbon uptake in these disturbed woodlands. This research also suggested that leveraging remote sensing data which measures in the red-edge portion of reflected light can provide increased sensitivity to the low leaf area, ephemeral pulses of greenup that were identified in the disturbed woodland, post-canopy mortality. Given these findings, I developed a hierarchy of simple linear models to test how well vegetation indices acquired through different spatial resolution sensors (Land- sat and RapidEye) were able to predict carbon uptake in both intact and disturbed piñon-juniper woodlands. The vegetation indices used were a moisture sensitive VI, and a red-edge leveraging VI from these sensors, and I compared estimates of carbon uptake derived from these models to the Gross Primary Productivity estimated from tower-based eddy covariance at both the manipulated and intact piñon-juniper sites. I determined that the red-edge VI and the moisture sensitive VI both constrained uncertainty associated with carbon uptake, but that the variability in satellite view angle from scene to scene can impose a significant amount of noise in sparse canopy ecosystems. Finally, given the extent and prevalence of J. monosperma across the region, and its complex growth morphology, I tested the ability of aerial lidar to quantify the biomass of juniper. In this simplified case study, I developed a method- ology to relate the volume of canopy measured via lidar to the equivalent stem area at the root crown. By working in a single species ecosystem, I circumvented many challenges associated with driving allometries remotely, but also present a work-flow that I intend to adapt to more complex systems, namely piñon-juniper woodlands. Together, this work describes and addresses existing challenges with respect to us- ing remote sensing to understand both the structure and function of piñon-juniper woodlands, and how it changes in response to widespread piñon mortality. It provides several new techniques to mitigate the difficulties associated with monitoring mortality / recovery dynamics, predicting canopy function, and determining ecosystem state parameters in these complex, sensitive biomes

Remote Sensing in Applications of Geoinformation

Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis