Object-Based Classification of Vegetation at Stordalen Mire near Abisko by using High-Resolution Aerial Imagery

The focus of this work is to investigate and apply the remote sensing method of object-based image analysis (OBIA) for vegetation classification of a permafrost underlain peatland in sub-arctic Sweden, by using aerial imagery of high resolution. Since the northern landscapes are an important source of naturally stored CH4 and CO2, their contribution to the global carbon cycle is a focus in research about climate change and the global methane exchange. Climate change affects permafrost soils by future increases in the mean temperature and precipitation. It further influences the depth of the frozen layer, and the thickness of the active layer above permafrost increases. This complex relationship results into a changing future landscape distribution of the vegetation at permafrost peatlands. The change has an effect on the exchange of CH4 in particular permafrost areas. For that reason, knowledge about the vegetation distribution of plant communities is interesting for ecological studies. In this work, the observed area is Stordalen mire, which is situated in Swedish Lapland. At this peatland, a landscape change is currently visible as it occurs as variations in the vegetation pattern above the permafrost and by an obvious permafrost thaw. A number of studies focus on the mire and the place has a long history of research in climate change. So far, there is no detailed vegetation map of Stordalen available, indicating the relative spatial distribution of vegetation. Therefore, the main aim is to use a suitable technique to derive a detailed vegetation map by supervised classification. To carry out the information needed, digital aerial photography of high spatial resolution was used. The extraction of thematic information from that data was done by a combination of OBIA methods. Remote sensing has the capability to explore distant regions, and the usage of digital aerial imagery of high resolution allowed to captures the small size of structures of vegetation. It was possible to identify single plant communities from the data, and this information was taken out as vegetation classes. The presented work includes the preprocessing of the data, the segmentation of image objects, establishment of classification controls, set up of training areas, and the image classification to the vegetation map, finally with an evaluation of the results. The resulted map is a contribution to apply OBIA as a method for landscape analysis in future research about northern peatlands. Key words: Physical Geography and Ecosystem Analysis, Object-Based Image Analysis, OBIA, Vegetation Classification, Permafrost, Arctic Peatland, Remote Sensing, Aerial Photography, Environmental Monitoring, Landscape Analysis Advisor: Andreas Persson Master degree project 30 credits in Geomatics, 2014 Department of Physical Geography and Ecosystems Science, Lund University Student thesis series INES nr 323Northern peatlands play an important role in the observation of global climate variations. Permafrost in the ground is melting slowly, because the earth becomes warmer. This tends to result in increasing methane gas emissions from the ground. The impact of degradation is visible in a changing land cover distribution at the peatlands, because the landscape faces a change into moister conditions. Understanding the relation to climate conditions of the future is a challenge. No accurate information about the particular vegetation of peatlands exists. More knowledge about the vegetation cover would help to understand the effects from loosing permafrost in peatlands. It is also a useful source for a long- term observation of landscape changes. In our work, the objective was to derive a map that shows a detailed vegetation distribution of a permafrost underlain area. This was done by a classification of an aerial photography. Because Swedish Lapland is far from populated places, remote sensing is a welcome method to observe the ground vegetation from above. This is commonly done by interpreting photos taken by an aircraft. Our method involves an approach of object- based image analysis. We classify the information from an aircraft-taken image in a meaningful context into groups of vegetation covering the land surface. We produce a detailed vegetation map that helps to explore the vegetation and to observe changes. Background Remains from the last ice age characterize the subarctic landscape of Swedish Lapland. Large areas are still underlain by a constantly frozen ground, permafrost. The regions of permafrost exhibit wetlands that are often peatlands. We know that the permafrost ground layer is sensitive to changes in local climates. An obvious effect, the trend to warmer temperatures and to more rainfall, is that the landscape changes slowly by that. Landscape transformation is highly visible by ongoing ground degradation. This causes an increase in the disturbance of wetlands. The plant cover is affected, as vegetation pattern is connected to the conditions in the ground. In subarctic regions, the plant species distribution covering a peatland is defined by water access. The production of methane, a greenhouse gas, is also connected to water accessibility. Water covered permafrost drives the release of methane to the atmosphere. A better knowledge about ongoing processes within the peatland ecosystems and permafrost disappearance is important. Study Site and Data Northernmost Sweden, Lapland, is a region where peatlands are commonly found. We use an aerial photography that shows the area of a peatland that is underlain by permafrost. The digital image was taken during the growing season. Our study site, Stordalen, is a peatland close to the Abisko Scientific Research Station near lake Torneträsk. Stordalen is a peatland which has a frozen permafrost ground at different stages of degradation. Permafrost is already melting in some places, which suits the area for a closer observation. The used image has a high pixel resolution of 8 cm, which is in higher resolution than other available data of the area. Such high resolution enables to identify a high level of details of the vegetation cover. Image Classification A classification based on aerial imagery is a common technique. Traditional methods are based on a classification of every single pixel the image contains, and hereby considering the spectral information only. But this approach may lead to some problems, as one plant growing naturally consists of more than one pixel in a high- resolution image, as ours. The imagination that most plants covering the ground, e.g. patches of mosses, are naturally larger than 8 cm helps to understand this. We used the approach of an object-based interpretation, which enables a deeper influence on the classification, on the other hand. By the help of parameters and rule setting, object-based allows to capture typical pixel values that form a plant. Our method of object creation enables to include structures of different and very small sizes, so the plant cover is described following the natural shape. Main plant species are collected in vegetation groups that describe the type of plants by their growth form. Such a group is based on common characteristics. This classification method proposes the comparability of the vegetation classes. Using objects also enables new possibilities to analyze the vegetation. Results and Conclusions A map showing the distribution of vegetation at Stordalen peatland was obtained from the image. The work included tests of different parameters, the adjustment of the object size, shape and location. Typically, a vegetation object was identified by the spectral response of the plant surface in the image. A visual inspection, based on field knowledge, helped to determine the best parameters. The evaluation was done by comparing information from a field survey to the classification results. It was possible to produce a map that contains vegetation classes that follow the natural growth. Therefore, the combination of an object-based image analysis with high resolution data is of use to map the vegetation of a peatland. More knowledge about the actual vegetation, to evaluate the classification result, could improve future map results. Key words: Physical Geography and Ecosystem Analysis, Object-Based Image Analysis, OBIA, Vegetation Classification, Permafrost, Arctic Peatland, Remote Sensing, Aerial Photography, Environmental Monitoring, Landscape Analysis Original title: Marco Giljum (2014) Object-Based Classification of Vegetation at Stordalen Mire near Abisko by using High-Resolution Aerial Imagery Advisor: Andreas Persson Master degree project 30 credits in Geomatics, 2014 Department of Physical Geography and Ecosystems Science, Lund University Student thesis series INES nr 32

Continuous Modeling of 3D Building Rooftops From Airborne LIDAR and Imagery

In recent years, a number of mega-cities have provided 3D photorealistic virtual models to support the decisions making process for maintaining the cities' infrastructure and environment more effectively. 3D virtual city models are static snap-shots of the environment and represent the status quo at the time of their data acquisition. However, cities are dynamic system that continuously change over time. Accordingly, their virtual representation need to be regularly updated in a timely manner to allow for accurate analysis and simulated results that decisions are based upon. The concept of "continuous city modeling" is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. However, developing a universal intelligent machine enabling continuous modeling still remains a challenging task. Therefore, this thesis proposes a novel research framework for continuously reconstructing 3D building rooftops using multi-sensor data. For achieving this goal, we first proposes a 3D building rooftop modeling method using airborne LiDAR data. The main focus is on the implementation of an implicit regularization method which impose a data-driven building regularity to noisy boundaries of roof planes for reconstructing 3D building rooftop models. The implicit regularization process is implemented in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). Secondly, we propose a context-based geometric hashing method to align newly acquired image data with existing building models. The novelty is the use of context features to achieve robust and accurate matching results. Thirdly, the existing building models are refined by newly proposed sequential fusion method. The main advantage of the proposed method is its ability to progressively refine modeling errors frequently observed in LiDAR-driven building models. The refinement process is conducted in the framework of MDL combined with HAT. Markov Chain Monte Carlo (MDMC) coupled with Simulated Annealing (SA) is employed to perform a global optimization. The results demonstrates that the proposed continuous rooftop modeling methods show a promising aspects to support various critical decisions by not only reconstructing 3D rooftop models accurately, but also by updating the models using multi-sensor data

Identifying green spaces in Kuala Lumpur using higher resolution satellite imagery

There is a growing need for municipal councils to map and to monitor the extent and condition of urban green spaces, as one measure of the overall sustainability of a city. In this study, we identify the information about urban green spaces that can be obtained from satellite imagery. This research which is based in the city of Kuala Lumpur tests the ability of IKONOS higher resolution satellite imagery in identifying the different types and the different functions provided by green spaces using both automated methods and manual methods of visual interpretation. Both these methods were found to produce a map of green space for the entire city area that was 70% accurate when validated against ground surveys. In cases where higher resolution satellite imagery exists, we show how it can produce a variety of enhanced information which may enable city planners to monitor green space more regularly and to evaluate consistently which areas of green space within the city ought to be protected in order to maintain its benefits for the city populatio

New techniques for the automatic registration of microwave and optical remotely sensed images

Remote sensing is a remarkable tool for monitoring and mapping the land and ocean surfaces of the Earth. Recently, with the launch of many new Earth observation satellites, there has been an increase in the amount of data that is being acquired, and the potential for mapping is greater than ever before. Furthermore, sensors which are currently operational are acquiring data in many different parts of the electromagnetic spectrum. It has long been known that by combining images that have been acquired at different wavelengths, or at different times, the ability to detect and recognise features on the ground is greatly increased. This thesis investigates the possibilities for automatically combining radar and optical remotely sensed images. The process of combining images, known as data integration, is a two step procedure: geometric integration (image registration) and radiometric integration (data fusion). Data fusion is essentially an automatic procedure, but the problems associated with automatic registration of multisource images have not, in general, been resolved. This thesis proposes a method of automatic image registration based on the extraction and matching of common features which are visible in both images. The first stage of the registration procedure uses patches as the matching primitives in order to determine the approximate alignment of the images. The second stage refines the registration results by matching edge features. Throughout the development of the proposed registration algorithm, reliability, robustness and automation were always considered priorities. Tests with both small images (512x512 pixels) and full scene images showed that the algorithm could successfully register images to an acceptable level of accuracy