The geometric correction and registration of airborne line-scanned imagery for temporal thermal studies

This thesis begins by providing a review of techniques for interpreting the thermal response at the earth's surface acquired using remote sensing technology. Historic limitations in the precision with which imagery acquired from airborne platforms can be geometrically corrected and co-registered has meant that relatively little work has been carried out examining the diurnal variation of surface temperature over wide regions. Although emerging remote sensing systems provide the potential to register temporal image data within satisfactory levels of accuracy, this technology is still not widely available and does not address the issue of historic data sets which cannot be rectified using conventional parametric approaches. In overcoming these problems, the second part of this thesis describes the development of an alternative approach for rectifying airborne line-scanned imagery. The underlying assumption that scan lines within the imagery are straight greatly reduces the number of ground control points required to describe the image geometry. Furthermore, the use of pattern matching procedures to identify geometric disparities between raw line-scanned imagery and corresponding aerial photography enables the correction procedure to be almost fully automated. By reconstructing the raw image data on a truly line-by-line basis, it is possible to register the airborne line-scanned imagery to the aerial photography with an average accuracy of better than one pixel. Providing corresponding aerial photography is available, this approach can be applied in the absence of platform altitude information allowing multi-temporal data sets to be corrected and registered.

The application of remote sensing to the management of urban wildlife habitats

The project set out with two main aims. The first aim was to determine whether large scale multispectral aerial photography could be used to successfully survey and monitor urban wildlife habitats. The second objective was to investigate whether this data source could be used to predict population numbers of selected species expected to be found in a particular habitat type. Panchromatic, colour and colour infra-red, 1:2500 scale aerial photographs, taken in 1981 and 1984, were used. For the orderly extraction of information from the imagery, an urban wildlife habitat classification was devised. This was based on classifications already in use in urban environments by the Nature Conservancy Council. Pilot tests identified that the colour infra-red imagery provided the most accurate results about urban wildlife habitats in the study area of the Blackbrook Valley, Dudley. Both the 1981 and 1984 colour infra-red photographs were analysed and information was obtained about the type, extent and distribution of habitats. In order to investigate whether large scale aerial photographs could be used to predict likely animal population numbers in urban environments, it was decided to limit the investigation to the possible prediction of bird population numbers in Saltwells Local Nature Reserve. A good deal of research has already been completed into the development of models to predict breeding bird population numbers in woodland habitats. These models were analysed to determine whether they could be used successfully with data extracted from the aerial photographs. The projects concluded that 1:2500 scale colour infra-red photographs can provide very useful and very detailed information about the wildlife habitats in an urban area. Such imagery can also provide habitat area data to be used with population predictive models of woodland breeding birds. Using the aerial photographs, further investigations into the relationship between area of habitat and the breeding of individual bird species were inconclusive and need further research.

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

2003 Habitat Maps derived from Grey Nurse Shark (GNS) Project of Wolf Rock, Double Island Point, Queensland, Australia

Surveying habitats critical to the survival of grey nurse sharks in South-East Queensland has mapped critical habitats, gathered species inventories and developed protocols for ecological monitoring of critical habitats in southern Queensland. This information has assisted stakeholders with habitat definition and effective management. In 2002 members of UniDive applied successfully for World Wide Fund for Nature, Threatened Species Network funds to map the critical Grey Nurse Shark Habitats in south east Queensland. UniDive members used the funding to survey, from the boats of local dive operators, Wolf Rock at Double Island Point, Gotham, Cherub's Cave, Henderson's Rock and China Wall at North Moreton and Flat Rock at Point Look Out during 2002 and 2003. These sites are situated along the south east Queensland coast and are known to be key Grey Nurse Shark aggregation sites. During the project UniDive members were trained in mapping and survey techniques that include identification of fish, invertebrates and substrate types. Training was conducted by experts from the University of Queensland (Centre of Marine Studies, Biophysical Remote Sensing) and the Queensland Parks and Wildlife Service who are also UniDive members. The monitoring methods (see methods) are based upon results of the UniDive Coastcare project from 2002, the international established Reef Check program and research conducted by Biophysical Remote Sensing and the Centre of Marine Studies. Habitats were mapped using a combination of towed GPS photo transects, aerial photography, bathymetry surveys and expert knowledge. This data provides georeferenced information regarding the major features of each of Sites mapped including Wolf Rock

TEST BINARY IMPORT

Der Müller und die fünf Räuber, Überfall²³

Caracterização 3D de movimentos de massa em terrenos com permafrost nas Ilhas Shetland do Sul (1956 - 2010) através de detecção remota, na banda do visível

Dissertação de Mestrado, Geomática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

An obligate beach bird selects sub-, inter- and supra-tidal habitat elements

Few habitat models are available for widespread, obligate, high-energy sandy shore vertebrates, such as the Eastern Hooded Plover Thinornis cucullatus cucullatus. We examined habitat attributes which determined the difference between sites where plovers breed and randomly-selected absence sites (determined from long-term systematic monitoring). A variety of habitat variables were derived from aerial photography and bathymetric and terrestrial Light Detection And Ranging (LiDAR) data. Logistic regression against eight candidate variables, in a model selection framework, revealed considerable support for four variables with respect to explaining the presence of breeding territories. In particular, the amount of unvegetated dune and foredune which was unvegetated, and the amount of intertidal and sub-tidal reef were positively associated with the presence of breeding territories. Thus, plovers apparently select certain habitat in which to breed, involving sub-tidal, intertidal and supra-tidal habitat elements. The model also helps explain the virtual absence of breeding plovers from long sections of superficially suitable habitat, such as the fourth longest continuous beach in the world.

USO Y COBERTURA DE LA TIERRA EN COSTA RICA PARA 1992: UNA APLICACIÓN DE TELEDETECCIÓN ESPACIAL Y SISTEMAS DE INFORMACION GEOGRÁFICA

En el presente artículo se describe el procedimiento utilizado para actualizar el mapa de uso y cobertura de la tierra de Costa Rica para el año 1992. En el proceso se integró la cartografía análoga existente para 1985 a escala 1:200.000 e imágenes digitales del Mapeador Temático de LANDSAT para los años 1991 y 1993. Para comprobar la exactitud de la clasificación se usaron 1.372 puntos obtenidos de fotos aéreas de 1992 y trabajo de campo empleando un Sistema de Posicionamiento Global (SPG). Los resultados obtenidos indican que existe un 46.6% del país bajo pastos, un 32,9 bajo bosques y un 8,5% bajo uso agrícola. Un 7,8% del área se incluyó en una categoría denominada <<no clasificada, usos mezclados, deforestada>>. La exactitud global de la clasificación fue de un 74%; con una confusión entre pasto y bosque de un 19%.  SUMMARY The objective of this paper is to describe the process used by the authors to update the preliminary 1985 land use-land cover map of Costa Rica. Paper maps of 1985 at scale 1:200.000 we digitized, rasterized and use to label the output of a nonsupervised classification carried out using 1991-93 digital data from LANDSAT 5 (Thematic Mapper). Aerial photography and field work aided by a Global Positional System (GPS) was used to gather ground-truth data. A total of 1372 stratified points were used to test the accuracy of the final map. Our results showed that 46,6% of the country is under pasture, 32,9% under forest and 8.5% under agriculture. The nonclassified areas were lumped into one category that accounted for 7,8% of the country. Global accuracy of the classification was 74% with the confusion between forest and pasture accounting for 19% of this error.

Air photography Samoylov Island

For the qualitative description of surface properties like vegetation cover or land-water-ratio of Samoylov Island as well as for the evaluation of fetch homogeneity considerations of the eddy covariance measurements and for the up-scaling of chamber flux measurements, a detailed surface classification of the island at the sub-polygonal scale is necessary. However, up to know only grey-scale Corona satellite images from the 1960s with a resolution of 2 x 2 m and recent multi-spectral LandSat images with a resolution of 30 x 30 m were available for this region. Both are not useable for the desired classification because of missing spectral information and inadequate resolution, respectively. During the Lena 2003 expedition, a survey of the island by air photography was carried out in order to obtain images for surface classification. The photographs were taken from a helicopter on 10.07.2002, using a Canon EOS100 reflex camera, a Soligor 19-23 mm lens and colour slide film. The height from which the photographs were taken was approximately 600 meters. Due to limited flight time, not all the area of the island could be photographed and some regions could only be photographed with a slanted view. As a result, the images are of a varying quality and resolution. In Potsdam, after processing the films were scanned using a Nikon LS-2000 scanner at maximal resolution setting. This resulted in a ground resolution of the scanned images of approximately 0.3x0.3 m. The images were subsequently geo-referenced using the ENVI software and a referenced Corona image dating from 18.07.1964 (Spott, 2003). Geo-referencing was only possible for the Holocene river terrace areas; the floodplain regions in the western part of the island could not be referenced due to the lack of ground reference points. In Figure 3.7-1, the aerial view of Samoylov Island composed of the geo-referenced images is shown. Further work is necessary for the classification and interpretation of the images. If possible, air photography surveys will be carried out during future expeditions in order to determine changes in surface pattern and composition.

A survey of urban open space using colour infra-red aerial photographs

There has been a dramatic change in the U.K. government policy regarding the establishment of new towns. The emphasis is now on the redevelopment of existing cities rather than on building new ones. This has created an urgent need to carry out detailed surveys and inventories of many aspects of urban land use in metropolitan areas: this study concentrates on just one aspect - urban open space. In the first stage a comparison was made between 1:10,000 scale black and white and 1:10,000 scale colour infra-red aerial photographs, to compare the type and amount of open space information which could be obtained from these two sources. The advantages of using colour infra-red photography were clearly demonstrated in this comparison. The second stage was the use of colour infra-red photography as the sole source of data to survey and map the urban open space of a sample area in Merseyside Metropolitan County. This sample area comprised eleven 1/4km2 squares, on each of which a 20m x 20m grid cell was placed to record, directly from the photography, 625 sets of data. Each set of data recorded the type and amount of open space, its surface cover, maintenance status and management. The data recorded were fed into a computer and a suite of programs was developed to provide output in both computer map and statistical form, for each of the eleven -1/4km2 -sample areas. The third stage involved a comparison of open space data with socio-economic status. Merseyside County Planning Authority had previously conducted a socio-economic survey of the county, and this information was used to identify ' the socio-economic status of the population in the eleven ilkm2 areas of this project. This comparison revealed many interesting and useful relationships between the provision of urban open space and socio-economic status.

Automatic Detection of Marine Mammal from Aerial Imagery

The following technical report describes the approach and algorithm used to detect marine mammals from aerial imagery taken from manned/unmanned platform. The aim is to automate the process of counting the population of dugongs and other mammals. We have developed and algorithm that automatically presents to a user a number of possible candidates of these mammals. We tested the algorithm in two distinct datasets taken from different altitudes. Analysis and discussion is presented in regards with the complexity of the input datasets, the detection performance.

Remediating vernacular creativity : photography and cultural citizenship in the Flickr photosharing network

Read through a focus on the remediation of personal photography in the Flickr photosharing website, in this essay I treat vernacular creativity as a field of cultural practice; one that that does not operate inside the institutions or cultural value systems of high culture or the commercial popular media, and yet draws on and is periodically appropriated by these other systems in dynamic and productive ways. Because of its porosity to commercial culture and art practice, this conceptual model of ‘vernacular creativity’ implies a historicised account of ‘ordinary’ or everyday creative practice that accounts for both continuity and change and avoids creating a nostalgic desire for the recuperation of an authentic folk culture. Moving beyond individual creative practice, the essay concludes by considering the unintended consequences of vernacular creativity practiced in online social networks: in particular, the idea of cultural citizenship.

Enhancing digital road map with lane details extracted from large-scale stereo aerial imagery using object-oriented image analysis

Precise, up-to-date and increasingly detailed road maps are crucial for various advanced road applications, such as lane-level vehicle navigation, and advanced driver assistant systems. With the very high resolution (VHR) imagery from digital airborne sources, it will greatly facilitate the data acquisition, data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lane information from aerial images with employment of the object-oriented image analysis method. Our proposed algorithm starts with constructing the DSM and true orthophotos from the stereo images. The road lane details are detected using an object-oriented rule based image classification approach. Due to the affection of other objects with similar spectral and geometrical attributes, the extracted road lanes are filtered with the road surface obtained by a progressive two-class decision classifier. The generated road network is evaluated using the datasets provided by Queensland department of Main Roads. The evaluation shows completeness values that range between 76% and 98% and correctness values that range between 82% and 97%.

Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.