The production of 3D models of urban areas, using aerial photographs, is of great benefit to companies and small engineering offices. But the major problem is the high cost of Digital Photogrammetry Workstations (DPWS) that are currently used for the
production of this kind of models. In addition, the use of these workstations requires long experience and good knowledge in photogrammetry. In this paper, we propose an alternative solution for 3D modeling of urban areas from a stereoscopic pair of aerial photos, a low cost close range photogrammetry software and the applications of 3D modeling available in some Geographic Information System (GIS) platforms. The close range photogrammetry software is a low coast system, compared to DPWS, and it doesn’t require any spatial background in photogrammetry. This software is used to extract the heights of elements that exist in the study area. GIS is used to produce the 2D map from the aerial photo. This map and the height data are used later to produce the 3D model of the study area.
This study aims is to analyze the effect of spatial accuracy of the control points on the
images geometric correction accuracy, and this is done by applying tests on the same
image (IKONOS), where polynomial transformations were applied using sets
of control
points, each with absolute accuracy different from the other. These points were
extrapolated from a 1/1000 topographic map and from a georeferenced MOMS satellite
image with geometric accuracy of 2m and measured by GPS. The study showed that it is
possible to obtain the most accurate geometric correction by using control points with
absolute accuracy close to the spatial resolution of the image. It also showed that the use of
more precise control points would not ameliorate the accuracy of the geometric correction,
because the measurement of these points on the image is limited by its spatial resolution.
In this study, drought in the eastern region of Syria (Hasake, Rakka,
DerAzzor, Bokmal & Kameshli) has been investigated using SPI, NDVI
indices.
We used a set of data containing precipitation data for period from 1975 to 2010 to calculate Standardized Precipitation Index SPI, and MODIS time series images in April for period from 2000 to 2010 to calculate the Normalized Difference Vegetation Index NDVI.
