Lars Harrie

Investigating relationships between Landsat ETM+ sensor data and leaf area index in a boreal conifer forest

The aim of this paper is to investigate the feasibility of using Landsat ETM+ data for the determination of leaf area index (LAI). The investigation is prompted by the need for obtaining spatially distributed data on LAI to be used as input for carbon modelling of northern boreal forests. Detailed field data have been collected in a coniferous forest area in Uppland, central Sweden, dominated by Norway spruce and Scots pine. A forest canopy reflectance model (Kuusk and Nilson, 2000) has been used to simulate stand reflectances in the Landsat ETM+ wavelength bands as a means of investigating the theoretical reflectance response to LAI changes. The analysis shows that the response to changes in LAI is strongest in the visible wavelength bands, particularly Channel 3, whereas only weak response is noted in the NIR band and for some vegetation indices [simple ratio (SR) and NDVI]. Modelled reflectances are influenced by various other factors, particularly ground reflectance and leaf biochemical properties. Observed reflectances from the Landsat ETM+ sensor have been compared with reflectance modelling results and with field-based LAI estimates. The results indicate that LAI estimation using inverse canopy reflectance modelling may be difficult, given the large number of input parameters required and the current level of uncertainty in these parameters. Statistical relationships between LAI and observed ETM+ reflectances are strongest in ETM+ Channel 7.

The Constraint Method for Solving Spatial Conflicts in Cartographic Generalization

New methods are needed to automate cartographic generalization. This article presents a method for automating the displacement of vector data, called the constraint method. The framework of the method consists of five displacement behavior types. Each cartographic object type belongs to one of these displacement behavior types, which determines the properties of the object type in the displacement process. The displacement behavior types are specified by a set of rules called constraints. The constraint method works as follows. First, it is determined which objects should participate in the displacement process. Then, for each object a set of constraints is set up, depending on its displacement behaviour type. All the constraints build up an overdetermined equation system. This equation system gives how much each object must be moved and/or distorted to solve the spatial conflict. The paper concludes with two applications.

Higher-degree reference field in the generalized Stokes-Helmert scheme for geoid computation

In this paper we formulate two corrections that have to be applied to the higher-degree reference spheroid if one wants to use it in conjunction with the Stokes-Helmert scheme for geoid determination. We show that in a precise geoid determination one has to apply the correction for the residual topographical potential and the correction for the earth ellipticity. Both these corrections may reach several decimetres; we show how their magnitudes vary within Canada and we give their global ranges.

Selection of Streets from a Network Using Self-Organizing Maps

We propose a novel approach to selection of important streets from a network, based on the technique of a self-organizing map (SOM), an artificial neural network algorithm for data clustering and visualization. Using the SOM training process, the approach derives a set of neurons by considering multiple attributes including topological, geometric and semantic properties of streets. The set of neurons constitutes a SOM, with which each neuron corresponds to a set of streets with similar properties. Our approach creates an exploratory linkage between the SOM and a street network, thus providing a visual tool to cluster streets interactively. The approach is validated with a case study applied to the street network in Munich, Germany.

Simultaneous Graphic Generalization of Vector Data Sets

Manual cartographic generalization is a simultaneous process. However, most automatic approaches so far have been sequential; generalization operators are applied one at a time in a certain order. This has been the case both for model generalization (generalization of the conceptual model) and graphic generalization. Our research seeks to demonstrate that the graphic part of cartographic generalization can be formulated as an optimization problem and accordingly be solved in a single step. This paper deals with several issues regarding this optimization approach. Firstly, a set of appropriate analytical constraints for the generalization process is given, as well as rules for when to apply these constraints. In our approach, we are limited to formulating these constraints on point locations. Secondly, least-squares adjustment is proposed to find the optimal solution according to the constraints. Finally, the conjugate-gradients method is recommended for solving the normal equations. A prototype system for simultaneous graphic generalization has been implemented in C++, which communicates with a commercial map production system. Results from three tests of the prototype system are included in the paper.

Are associations between socio-economic characteristics and exposure to air pollution a question of study area size? An example from Scania, Sweden

BackgroundNumerous studies have shown that exposure to air pollutants in the area of residence and the socio-economic status of an individual may be related. Therefore, when conducting an epidemiological study on the health effect of air pollution, socio-economy may act as a confounding factor. In this paper we examine to what extent socio-economic status and concentrations of NO2 in the county/region of Scania, southern Sweden, are associated and if such associations between these factors differ when studying them at county or city level. To perform this study we used high-resolution census data and modelled the annual exposure to NO2 using an emission database, a dispersion modelling program and a geographical information system (GIS).ResultsThe results from this study confirm that socio-economic status and the levels of NO2 in the area of residence are associated in some cities. The associations vary considerably between cities within the same county (Scania). Even for cities of similar sizes and population bases the associations observed are different. Studying the cities together or separately yields contradictory results, especially when education is used as a socio-economic indicator.ConclusionFour conclusions have been drawn from the results of this study. 1) Adjusting for socio-economy is important when investigating the health effects of air pollution. 2) The county of Scania seems to be heterogeneous regarding the association between air pollution and socio-economy. 3) The relationship between air pollution and socio-economy differs in the five cities included in our study, depending on whether they are analysed separately or together. It is therefore inadvisable to determine and analyse associations between socio-economy and exposure to air pollutants on county level. This study indicates that the size and choice of study area is of great importance. 4) The selection of socio-economic indices (in this study: country of birth and education level) is important.

Modelling the Overall Process of Generalisation

Publisher Summary Research on the automation of cartographic generalization has led to the development of a large number of generalization algorithms. This chapter describes modeling techniques for using these algorithms to form a comprehensive generalization process. Research on the automation of cartographic generalization has led to the development of a large number of generalization algorithms. Important issues include when to use the generalization algorithms and how to trigger and control them. Three main modeling techniques are described: condition-action modeling, human interaction modeling, and constraint-based modeling. In a condition-action modeling process an identification of objects and relationships between objects is first performed. Then, based on the identified conditions, generalization algorithms are triggered. Human interaction modeling is based on the principle that the cognitive workload can be shared between computer and human. The computer typically carries out those tasks that can be sufficiently formalized to be cast into algorithms, while the human assumes responsibility for guiding and controlling the computer software. Finally, in constraint-based modeling the starting point is the requirements (constraints) of the generalized map. An optimization process then finds a generalization solution that satisfies as many of the constraints as is possible. The chapter describes the historical evolution of these modeling techniques as well as their strengths and weaknesses.

A study of spatial resolution in pollution exposure modelling

BackgroundThis study is part of several ongoing projects concerning epidemiological research into the effects on health of exposure to air pollutants in the region of Scania, southern Sweden. The aim is to investigate the optimal spatial resolution, with respect to temporal resolution, for a pollutant database of NOx-values which will be used mainly for epidemiological studies with durations of days, weeks or longer periods. The fact that a pollutant database has a fixed spatial resolution makes the choice critical for the future use of the database.ResultsThe results from the study showed that the accuracy between the modelled concentrations of the reference grid with high spatial resolution (100 m), denoted the fine grid, and the coarser grids (200, 400, 800 and 1600 meters) improved with increasing spatial resolution. When the pollutant values were aggregated in time (from hours to days and weeks) the disagreement between the fine grid and the coarser grids were significantly reduced. The results also illustrate a considerable difference in optimal spatial resolution depending on the characteristic of the study area (rural or urban areas). To estimate the accuracy of the modelled values comparison were made with measured NOx values. The mean difference between the modelled and the measured value were 0.6 μg/m3 and the standard deviation 5.9 μg/m3 for the daily difference.ConclusionThe choice of spatial resolution should not considerably deteriorate the accuracy of the modelled NOx values. Considering the comparison between modelled and measured values we estimate that an error due to coarse resolution greater than 1 μg/m3 is inadvisable if a time resolution of one day is used. Based on the study of different spatial resolutions we conclude that for urban areas a spatial resolution of 200–400 m is suitable; and for rural areas the spatial resolution could be coarser (about 1600 m). This implies that we should develop a pollutant database that allows different spatial resolution for urban and rural areas.

Estimating Flow Distribution over Digital Elevation Models Using a Form-Based Algorithm

Abstract This paper discusses a new approach to estimate flow distribution over a continuous surface. This approach is based on the analysis of topographic form of a surface facet that dictates the flow distribution. In the case of a raster Digital Elevation Model (DEM), the facet consists of a centre cell and its eight neighbouring cells. If the form of the facet is convex, the water flow is divergent; thus the amount of flow is distributed to all cells that have a lower elevation. In the case of a concave or flat surface, the convergent flow is directed to the main drainage direction. Comparison between the results of this algorithm with the traditional ‘eight-move’ algorithm, which is widely used in todays commercial GIS software, indicated that the form-based algorithm yielded a more realistic results in estimating flow accumulation over the land surface, but produced less convincing results in deriving a drainage network.

Detection and typification of linear structures for dynamic visualization of 3D city models

Cluttering is a fundamental problem in 3D city model visualization. In this paper, a novel method for removing cluttering by typification of linear building groups is proposed. This method works. in static as well as dynamic visualization of 3D city models. The method starts by converting building models in higher Levels of Details (LoDs) into LoD1 with ground plan and height. Then the Minimum Spanning Tree (MST) is generated according to the distance between the building ground plans. Based on the MST, linear building groups are detected for typification. The typification level of a building group is determined by its distance to the viewpoint as well as its viewing angle. Next, the selected buildings are removed and the remaining ones are adjusted in each group separately. To preserve the building features and their spatial distribution, Attributed Relational Graph (ARC) and Nested Earth Movers Distance (NEMD) are used to evaluate the difference between the original building objects and the generalized ones. The experimental results indicate that our method can reduce the number of buildings while preserving the visual similarity of the urban areas