Journal of Spatial Science, Vol. 54, No. 2
M. Salah, J. Trinder, A. Shaker
“Integration of aerial images and lidar data compensate for the individual weaknesses of each data set when used alone, thus providing more accurate classification of terrain cover, such as buildings, roads and green areas, and advancing the potential for automation of large scale digital mapping and GIS database compilation. This paper presents work on the development of automatic feature extraction from multispectral aerial images and lidar data. A total of 22 feature attributes have been generated from the aerial image and the lidar data which contribute to the detection of the features. The attributes include those derived from the Grey Level Co-occurrence Matrix (GLCM), Normalized Difference Vegetation Indices (NDVI), and standard deviation of elevations and slope. A Self-Organizing Map (SOM) was used for fusing the aerial image, lidar data and the generated attributes for building detection. The classified images were then processed through a series of image processing techniques to separate the detected buildings. Results show that the proposed method can extract buildings accurately. Compared with a building reference map, 95.5 percent of the buildings were detected with a completeness and correctness of 83 percent and 80 percent respectively for buildings around 100m2 in area; these measures increased to 96 percent and 99 percent respectively for buildings around 1100m2 in area. Further, the contributions of lidar and the individual attributes to the quality of the classification results were evaluated.”
Journal of Spatial Science, Vol. 54, No. 2
C. Zhang, C. S. Fraser
“This paper develops an improved approach to digital surface model (DSM) generation from high-resolution satellite imagery (HRSI). The approach centres upon an image matching strategy that integrates feature point, grid point and edge matching algorithms within a coarse-to-fine hierarchical process. The starting point is a knowledge of precise sensor orientation, achieved in this case through bias-compensated rational polynomial coefficients (RPCs), and the DSM is sequentially constructed through a combination of the matching results for feature and grid points, and edges at different image pyramid levels. The approach is designed to produce precise, reliable and very dense DSMs which preserve information on surface discontinuities. Following a brief introduction to sensor orientation modelling, the integrated image matching algorithms and DSM generation stages are described. The proposed approach is then experimentally tested through the generation of a DSM covering the Hobart area from a stereo pair of IKONOS Geo images. The accuracy of the resulting surface model is assessed using both ground checkpoints and a lidar DSM, with the results indicating that for favourable imagery and land cover, a heighting accuracy of 2 – 4 pixels can be readily achieved. This result validates the feasibility of the developed approach for DSM production from HRSI.”
…from The Chetek Alert…
“Mike Steiner, a science teacher in Chetek, was named a 2009 Outstanding Earth Science Teacher by National Association of Geoscience Teachers.
“Steiner started a water quality testing program and, with the help of a social studies teacher, expanded the effort to involve students in GPS and GIS mapping and landscape analysis.”
“We seek a post-doctoral researcher with experience in limnological biogeochemical research to join the Department of Geographical and Earth Sciences’ Earth Surface Dynamics research group. This position is working with Dr. Susan Waldron, Prof. Michael Bird (University of St. Andrews) and Amazonica colleagues, in applying field, laboratory and GIS modelling methods to quantify carbon effluxes from freshwater systems in the Amazon basin.
“Due to the nature of this project and its location please read the job description fully to make sure it is appropriate for you.
“This post has funding until 11.01.2013
“Further information or informal enquiries can be made to Dr. Susan Waldron (Susan.Waldron@ges.gla.ac.uk), Department of Geographical and Earth Sciences, University of Glasgow, G12 8QQ.
“Apply online at www.glasgow.ac.uk/jobs
“If you are unable to apply online please contact us on 0141 330 3898 for an application pack.
“Closing Date: 29 January 2010”
13 to 19 June 2010
Facilitators: Gilberto Camara, Henk Scholten
The week will be organized around two main topics:
- Development of nature-society models using cellular automata and agents
- Mobile GIS and natural disasters modelling and response coordination
Topic (1) will be based on the current work at INPE. It will start with a discussion on how to do social and environmental modelling using cellular automata. Then, participants will develop simple models using the open source TerraME software (www.terrame.org). This will lead to an informed discussion on how such models can be enhanced using agent-based modelling. Topic (2) will be addressed with a mix of talks and hands-on work, based on leading edge industry developments using mobile technologies to interface environmental and social models.
Eyes in the Sky II is a long-term professional development program that prepares high school science teachers to use NASA data and visualizations along with other geospatial information technologies. Throughout the program, teachers and students investigate both global and local environmental issues. The program includes four parts:
- A 12-week online Web course, consisting of three 4-week modules
- A 7-day face-to-face summer workshop held onsite at a NASA research center
- One year of classroom implementation, ending with a virtual student showcase
- An ambassador program for providing professional development for other teachers in participants’ schools or districts.
Grade 9 to 12 science teachers will benefit from this program. Through participating, teachers will:
- Become proficient using NASA data and geospatial analysis tools
- Receive a $1000 stipend for completing the online course and the 7-day summer workshop
- Receive an additional $1000 stipend as compensation for delivering professional development as an Eyes in the Sky II Ambassador
- Equip their students with geospatial technology skills that are in increasing demand in the workplace
- Obtain optional graduate credit through Northern Arizona University.
For more information about the Eyes in the Sky II program, including the online application visit http://serc.carleton.edu/eyesinthesky2/index.html. Applications are due by January 15, 2010. We expect this will be a popular program. As there are a limited number of openings available, first consideration will be given to early applicants. If you have further questions, please contact Carla McAuliffe (Carla_McAuliffe@xxxxxxxx) or Erin Bardar (Erin_Bardar@xxxxxxxx).
“From beetles to barnacles, pikas to pine warblers, many species are already on the move in response to shifting climate regimes. But how fast will they—and their habitats—have to move to keep pace with global climate change over the next century? In a new study, a team of scientists including Dr. Healy Hamilton from the California Academy of Sciences have calculated that on average, ecosystems will need to shift about 0.42 kilometers per year (about a quarter mile per year) to keep pace with changing temperatures across the globe. Mountainous habitats will be able to move more slowly, since a modest move up or down slope can result in a large change in temperature. However, flatter ecosystems, such as flooded grasslands, mangroves, and deserts, will need to move much more rapidly to stay in their comfort zone—sometimes more than a kilometer per year. The team, which also included scientists from the Carnegie Institute of Science, Climate Central, and U.C. Berkeley, will publish their results in the December 24 issue of Nature. ”
International Journal of Public Policy, 2010 – Vol. 5, No.2/3 pp. 237 – 258
Fahui Wang, Lan Luo, Sara McLafferty
“Patients diagnosed with late-stage cancer have lower survival rates than those with early-stage cancer. This paper examines possible associations between several risk factors and late-stage diagnosis for four types of cancer in Illinois: breast cancer, prostate cancer, colorectal cancer, and lung cancer. Potential risk factors are composed of spatial factors and nonspatial factors. The spatial factors include accessibility to primary healthcare and distance or travel time to the nearest cancer screening facility. A set of demographic and socioeconomic variables are consolidated into three nonspatial factors by factor analysis. The Bayesian model with convolution priors is utilised to analyse the relationship between the above risk factors and each type of late-stage cancer while controlling for spatial autocorrelation. The results for breast cancer suggest that people living in neighbourhoods with socioeconomic disadvantages and cultural barriers are more likely to be diagnosed at a late stage. In regard to prostate cancer, people in regions with low socioeconomic status are also more likely to be diagnosed at a late stage. Diagnosis of late-stage colorectal or lung cancer is not significantly associated with any of the abovementioned risk factors. The results have important implications in public policy.”
International Journal of Remote Sensing, Volume 30, Issue 23 2009 , pages 6343 – 6360
Weiqi Zhou; Austin Troy.
“This paper presents the development of a framework for classifying and inventorying Eastern US forestland based on the level of anthropogenic disturbance and fragmentation using high spatial resolution remote sensing data and a multiscale object-based classification system. We implemented the framework using a suburban area in Baltimore County, Maryland, USA as a case study. We developed a three-level hierarchical scheme of image objects. The object-based, multiscale classification and inventory framework provides an effective and flexible way of showing different mixes of human development and forest cover in a hierarchical fashion for human-dominated forest ecosystems. At the finest scale (level 1), the classification nomenclature describes basic land cover feature types, which are divided up into trees and individual features that fragment forests. The overall accuracy of the classification was 91.25%. At level 2, forest patches were delineated and classified into different categories based on the degree of human disturbance. At level 3, major roads were used to segment the study area into larger objects, which were classified on the basis of relative composition and spatial arrangement of forests and fragmenting features. This study provides decision makers, planners and the public with a new methodological framework that can be used to more precisely classify and inventory forest cover. The comparisons of the estimates of forest cover from our analyses with those from the 2001 National Land Cover Dataset (NLCD) show that aggregated figures of forest cover are misleading and that much of what is mapped as forest is highly degraded and is more suburban than natural in its land use.”