11,011 research outputs found
GIS Characterization of Beaver Watershed
Beaver Reservoir watershed is located in Northwest Arkansas including portions of Madison, Washington, Benton, Carroll, Franklin and Crawford counties. This watershed is important to the Northwest Arkansas region because it supplies most of the drinking water for the major towns and cities, and several rural water systems. The watershed consists of 308,971 ha with elevations ranging from approximately 341 m to 731 m above mean sea level. It includes the Springfield Plateau and the Boston Mountains provinces within the Ozark Plateau physiographic region. There are approximately 581 km of streams, 532 km of shore line, and 3712 km of roads in the watershed most of which are city streets and rural roads. The soils in the watershed vary extensively and are quite complex due to the differences in parent material, topography and time. Most parent material of the soils in the Springfield Plateau is limestone, whereas in the Boston Mountains the dominant parent material is sandstone and shale. The differences in soils have led to the differences in landuse and land cover. The near surface geology in the watershed is also divided by physiographic provinces. Most of the Springfield Plateau surface geology is limestone, whereas the Boston Mountains are primarily sandstone and shale. Spatial details of the streams, roads, soils and geology attributes in the watershed are presented in this report. The GIS database and characterization of the watershed offers an excellent beginning to future research and modeling of various water quality parameters in this and other watersheds
FLIAT, an object-relational GIS tool for flood impact assessment in Flanders, Belgium
Floods can cause damage to transportation and energy infrastructure, disrupt the delivery of services, and take a toll on public health, sometimes even causing significant loss of life. Although scientists widely stress the compelling need for resilience against extreme events under a changing climate, tools for dealing with expected hazards lag behind. Not only does the socio-economic, ecologic and cultural impact of floods need to be considered, but the potential disruption of a society with regard to priority adaptation guidelines, measures, and policy recommendations need to be considered as well. The main downfall of current impact assessment tools is the raster approach that cannot effectively handle multiple metadata of vital infrastructures, crucial buildings, and vulnerable land use (among other challenges). We have developed a powerful cross-platform flood impact assessment tool (FLIAT) that uses a vector approach linked to a relational database using open source program languages, which can perform parallel computation. As a result, FLIAT can manage multiple detailed datasets, whereby there is no loss of geometrical information. This paper describes the development of FLIAT and the performance of this tool
Expansion of China's Cities and Agricultural Production
In China, there is a growing debate on the role of cultivated land conversion on food security. This paper examines the changes of the area of cultivated land and its potential agricultural productivity in China using satellite images. We find that between 1986 and 2000, China recorded a net increase of cultivated land (+1.9%), which almost offset the decrease in average potential productivity, or bioproductivity (-2.2%). Therefore, we conclude that conversion of cultivated land did not hurt China's national food security. We also show that more recent change in cultivated area also should have little adverse effect on food security.Land Economics/Use,
Rapid methods of landslide hazard mapping : Fiji case study
A landslide hazard probability map can help planners (1) prepare for, and/or mitigate against,
the effects of landsliding on communities and infrastructure, and (2) avoid or minimise the
risks associated with new developments. The aims of the project were to establish, by means
of studies in a few test areas, a generic method by which remote sensing and data analysis
using a geographic information system (GIS) could provide a provisional landslide hazard
zonation map. The provision of basic hazard information is an underpinning theme of the
UNâs International Decade for Natural Disaster Reduction (IDNDR). It is an essential
requirement for disaster preparedness and mitigation planning. This report forms part of BGS
project 92/7 (R5554) âRapid assessment of landslip hazardsâ Carried out under the ODA/BGS
Technology Development and Research Programme as part of the British Governmentâs
provision of aid to developing countries. It provides a detailed technical account of work
undertaken in a test area in Viti Levu in collaboration with Fiji Mineral Resources
Department. The study represents a demonstration of a methodology that is applicable to
many developing countries.
The underlying principle is that relationships between past landsliding events, interpreted
from remote sensing, and factors such as the geology, relief, soils etc provide the basis for
modelling where future landslides are most likely to occur. This is achieved using a GIS by
âweightingâ each class of each variable (e.g. each lithology âclassâ of the variable âgeologyâ)
according to the proportion of landslides occurring within it compared to the regional
average. Combinations of variables, produced by summing the weights in individual classes,
provide âmodelsâ of landslide probability. The approach is empirical but has the advantage
of potentially being able to provide regional scale hazard maps over large areas quickly and
cheaply; this is unlikely to be achieved using conventional ground-based geotechnical
methods.
In Fiji, landslides are usually triggered by intense rain storms commonly associated with
tropical cyclones. However, the regional distribution of landslides has not been mapped nor
is it known how far geology and landscape influence the location and severity of landsliding
events. The report discusses the remote sensing and GIS methodology, and describes the
results of the pilot study over an area of 713 km2 in south east Viti Levu. The landslide
model uses geology, elevation, slope angle, slope aspect, soil type, and forest cover as
inputs. The resulting provisional landslide hazard zonation map, divided into high, medium
and low zones of landslide hazard probability, suggests that whilst rainfall is the immediate
cause, others controls do exert a significant influence. It is recommended that consideration
be given in Fiji to implementing the techniques as part of a national strategic plan for
landslide hazard zonation mapping
Computer supported estimation of input data for transportation models
Control and management of transportation systems frequently rely on optimization or simulation methods based on a suitable model. Such a model uses optimization or simulation procedures and correct input data. The input data define transportation infrastructure and transportation flows. Data acquisition is a costly process and so an efficient approach is highly desirable. The infrastructure can be recognized from drawn maps using segmentation, thinning and vectorization. The accurate definition of network topology and nodes position is the crucial part of the
process. Transportation flows can be analyzed as vehicleâs behavior based on video sequences of typical traffic situations. Resulting information consists of vehicle position, actual speed and acceleration along the road section. Data for individual vehicles are statistically processed and standard vehicle characteristics can be recommended for vehicle generator in simulation models
A Knowledge-Based Approach to Raster-Vector Conversion of Large Scale Topographic Maps
Paper-based raster maps are primarily for human consumption, and their interpretation always requires some level of human expertese. Todays computer services in geoinformatics usually require vectorized topographic maps. The usual method of the conversion has been an error-prone, manual process. In this article, the possibilities, methods and difficulties of the conversion are discussed. The results described here are partially implemented in the IRIS project, but further work remains. This emphasizes the tools of digital image processing and knowledge-based approach. The system in development separates the recognition of point-like, line-like and surface-like objects, and the most successful approach appears to be the
recognition of these objects in a reversed order with respect to their printing. During the recongition of surfaces, homogeneous and textured surfaces must be distinguished. The most diverse and complicated group constitute the line-like objects. The IRIS project realises a moderate, but significant step towards the automatization of map recognition process, bearing in mind that full automatization is unlikely. It is reasonable to assume that human experts will always be required for high quality interpretation, but it is an exciting challenge to
decrease the burden of manual work
Seafloor Segmentation Based on Bathymetric Measurements from Multibeam Echosounders Data
Bathymetric data depicts the geomorphology of the seabottom and allows characterization of spatial distributions of apparent benthic habitats. The variability of seafloor topography can be defined as a texture. This prompts for the application of well developed image processing techniques for automatic delineation of regions with clucially different physiographic characteristics. In the present paper histograms of biologically motivated invariant image attributes are used for characterization of local geomorphological feahires. This technique can be naturally applied in a range of spatial scales. Local feature vectors are then submitted to a procedure which divides the set into a number of clusters each representing a distinct type of the seafloor. Prior knowledge about benthic habitat locations allows the use of supervised classification, by training a Suppolt Vector Machine on a chosen data set, and then applying the developed model to a full set. The classification method is shown to perform well on the multibeam echosounder (MBES) data from Piscataqua River, New Hampshire, USA
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