Applied Geomatics最新文献

The research dealt with the application of the spatial spread analysis of the slums housing areas in the city of Zagazig using geographic information systems, and the spatial distribution pattern of the slums and the regions were classified in terms of the area into (large, medium, and small). And it determined the spatial spread of these areas: the central area of the city with a radius of 1.5 km, the next area on the borders of the urban boundary with a distance of about 1.5: 3 km, and the area outside the urban boundaries, including Shaybah and Nakaria, about 3: 4.5 km. The results of the research revealed that small slum areas are the most prevalent in the central region, representing (61.54%) and an area of (13.22%), but large areas represent a total area of (49.11%) and therefore small areas can be removed and large areas developed. With the provision of alternative and safe housing according to a plan and decision of the state. And by defining future development policies and dealing with priorities, priority is given to the closest and largest region of the central region in Zagazig city, according to decisions and studies by the state in the following order: 16, 36, 21, 27, 31, 32, 33, 15, 35, 37, 34, 17, 24, 28, 20, 22, 19, 39, 38, 30, 25, 8, 4, 26, 23, 5, 7, 29, 12, 18, 2, 13, 14, 6, 3, 11, 9, 10, 1.

The Italian official height system is defined through a high precision levelling network established and maintained by the Istituto Geografico Militare - IGM. During the last 20 years, IGM has performed levelling campaigns on almost the whole peninsular area of Italy with the aim of both densifying the existing network and updating the reference heights. This paper reports about the procedure applied to correct the levelling observations for the gravity effects and the assessment on the results. The needed gravity values were predicted from the Italian gravity dataset (IGD), and both from EGM2008 and XGM2019e high resolution global gravity models. A new formulation of the normal correction as well as the standard orthometric correction were applied. The IGD derived corrections proved to be effective by reducing the misclosure error of critical loops below the tolerance level. Gravity data derived from EGM2008 and XGM2019e proved to be too poor for the correction purposes, as it was also confirmed by a comparison against available observed data, with RMS of the differences, in Alpine ares, ranging between 50 and 100 mGal.

One of the basic factors that should be investigated and monitored in the field of urban heat islands is the exploration and detection of their spatiotemporal changes, which have been well addressed in spatial statistics. The current study aimed to detect the spatiotemporal changes of surface urban heat islands (SUHI) in Tehran metropolis during the daytime/nighttime at monthly and seasonal scales and over the warm and cold periods of the year. The consequences of many elements like as daytime/nighttime land surface temperature (LST) extracted by the MODIS/006/MOD11A1 and the NDVI extracted by MODIS/006/MOD13A2 over a 20-year period (2001–2020) were first investigated. Then, the SUHI index was computed for the study area. The correlations between the heat islands and urban land use (traffic, population density, airport, etc.), air pollutants (CO, NO2, SO2, etc.), and NDVI were investigated in the next stage. Finally, Moran’s algorithm was used to measure the spatial autocorrelation, and Gi statistic was used to analyze the cold and warm spots. The results indicated that the LST trend was constant during the daytime/nighttime, and the NDVI also had a slight rising trend. The results of the SUHI maps indicated that the zones with heat islands during the daytime over the seasons’ warm and cold times are located in the south, southeast, and west of the city. During the nighttime, the central zones of the city as well as some parts in the east and southeast have had higher heat islands. The results of the correlation between the heat islands and land use, vegetation, and air pollutants indicated a direct correlation between the heat islands and the airport and industrial land use over time, while it was inversely correlated with other land uses. During the nighttime, all land uses had a direct correlation with the heat islands. Regarding the air pollutants, PM2.5 and PM10 were most correlated with the heat islands during both daytime/nighttime while other pollutants have been inversely correlated. The heat islands and the NDVI were also inversely correlated during both daytime/nighttime. The OLS (ordinary least-squares) model results also indicated that the R² values during the daytime/nighttime were 0.70 and 0.59, respectively, over the cold period of the year, compared to values of 0.69 and 0.68 over the warm period of the year. The results of global Moran’s I and G*i statistics also indicated that the heat islands of the Tehran metropolis had a spatial structure distributed in a cluster in which the southern, western, southwestern, and northern parts had warm spots during the daytime and cold spots during the nighttime. Moreover, the northern and northeastern parts had cold spots during the daytime, and the central and eastern parts had warm spots during the nighttime.

Shoreline changing position along the coast is an immediate and long-term indicator determined by the interplaying driving forces across the dry and wet parts of coastal areas. Extreme waves, such as tsunamis, may result in a remarkable shift of shoreline position and a change of sediment transport regime, thus potentially inducing coastal hazards. This work investigates the multi-temporal changes and development of shorelines at the tsunami-affected coast nearly two decades after the Indian Ocean tsunami on 26 December 2004. Additionally, the dynamic responses of the coast to the man-made coastal structures as a means of protection measures during the observed period are also evaluated. This study uses the US Army’s Digital Shoreline Analysis System (DSAS) extension in the ArcGIS to calculate the multi-temporal shoreline changes and erosion/accretion rates. Multi-temporal shoreline vectors delineated from the LANDSAT satellite images are utilized to calculate the Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR) for the respective short-term and decadal-term shoreline change analysis. The shoreline change rates are examined at the three shoreline segments at Aceh coast, the north tip of Sumatra Island of Indonesia. The results show that Segment A has the highest erosion rate due to the 2004 tsunami (− 395.19 m/year) compared to Segment B (− 26.46 m/year) and Segment C (− 74.26 m/year). The 2004 tsunami has changed the coastal states from erosional coast prior to the tsunami to accretional coast in Segment A and C, and the eastern side of Segment B in almost two decades since the tsunami. Consequently, ignoring such phenomena in designing coastal protection measures may lead to structural failures such the ones identified in the investigated coast. Thus, a thorough investigation of shoreline change is fundamental for coastal managers, particularly in determining appropriate coastal protection measures.

In this research, the band rationing technique was used to expect accurate detection of built-up in a dry period over El-Eulma city (North-East Algeria). In this context, the VNIR Sentinel-2 bands were examined statistically over the study area. Consequently, two bands ratio (BR) which are mainly the blue-near-infrared (B2/B8) and the green-near-infrared (B3/B8), were selected to be used singly as input data, for the binarization process via the use of Otsu method. To evaluate the approach and find the optimal bands ratio for built-up detection in the dry period, the accuracy assessment was done, using the high-resolution Google Earth images as a reference map. Also, the results obtained were compared to the both built-up mapping resulting from the support vector machine (SVM) classification and built-up area index (BAI). The findings showed that the BR (B2/B8) works approximately similar to the SVM classification result. In contrast, the BR (B2/B8) works better than the BR (B3/B8) and BAI index; the overall accuracy (OA) and kappa coefficient of the first BR (B2/B8) are about 92,33% and 80,81%, respectively. In contrast, the (OA) and kappa coefficient of the second BR (B3/B8) are about 90,97% and 76,72% respectively, Meanwhile, the (OA) of the BAI index is about 89.33% and its kappa coefficient is about 74,80%. Therefore, the results present BR (B2/B8) as a simple automatic technique that could be suitable for mapping cities accurately in a dry climate, for better land use planning.