The meteorological data such as rainfall and temperatures, covering the period between 1979 and 2008, has been analyzed. The data were simulated using the geographic information systems (GIS) and computer software "MATLAB". The output results were converted into geographical maps. Three parameters were analyzed: annual mean maximum temperature, annual mean minimum temperature, and mean annual rainfall during the period . The analyzed results were also used to forecast for the period (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018).The results show that no change has occurred in the mean annual rainfall in both northern and eastern part, while it has increased in the central region of Jordan. Although local temperatures fluctuate naturally, but over the past 50 years, the mean local temperature in Jordan has increased rapidly since 1992 by 1.5-2 • C.It is noticed from the data that the change in both maximum and minimum temperatures has clearly begun after 1991, in which this phenomenon may give an indication of changing point in climate of Jordan. As for prediction is concern, the show continuous increase in both maximum and minimum temperatures in the eastern, northern and southern regions of Jordan.The application of GIS in this study was successfully used to analyze the data and to produce 'easy to use' maps to understand the impact of global warming. This application is the first in terms of its applicability in Jordan. The authors believe that the results of this study will be of great help to the decision makers in the field of environment in Jordan.
Amman, the capital city of Jordan, faces urbanization challenges and lacks reliable data for urban planning. This study is aimed at assessing, monitoring, and mapping urban land cover using multitemporal Landsat satellite images. Four different land use/cover maps were produced; periods of over ten years between 1987 and 2017 (i.e., in 1987, 1997, 2007, and 2017) were used to evaluate and analyze urban expansion visually and quantitatively. Supervised classification technique followed by the post classification comparison change detection approach was used to analyze images. Over the past three decades, the urban area has increased rapidly in Amman. It increased by 90.78 km2, from 149.08 km2 in 1987 to 237.86 km2 in 2017, with an average annual rate of increase of 2.03%. Urban area increases were significantly higher in the first 10 years of the study period (i.e., from 1987 to 1997), during which the average annual rate of increase reached 3.33%, while it was 2.04% for the last two decades of the study period (i.e., from 1997 to 2017). Urban growth in Amman generally occurred along transport routes away from the core of Amman, and as a result, this growth led to the expansion of urban areas into other types of land use/cover classes, particularly vegetation areas. The spatial analysis of urban expansion and trends of urban growth in Amman could provide the required input data for the urban modeling of the city.
A Japanese Earth Resources Satellite (JERS)-1 L-band synthetic aperture radar (SAR) dataset was used for estimating topsoil thickness, of different types, in arid and semi-arid areas in north-eastern Jordan. In this research, the relationship between remotely sensed data, backscattering coefficient and the thickness of topsoil was investigated. Based on the dielectric constant properties of the topsoil samples, the relationship between the backscattering coefficient and the topsoil thickness was obtained by developing a multilayer modelling analysis. Using this model, the topsoil thickness had been estimated by means of the derived backscattering coefficients from JERS-1 SAR image. The estimated thickness values of the different topsoil types were found to be comparable with field ground data. The estimated minimum thickness for hardpan topsoil is 55 cm; Qaa topsoil, 74 cm; and topsoil of herbaceous area, 46 cm, while the estimated maximum thickness is more than 98 cm, more than 100 cm, and 82 cm, respectively. Ground data, on the other hand, revealed the minimum thickness for hardpan topsoil to be 50 cm; Qaa topsoil, 70 cm; and topsoil of herbaceous area, 40 cm, while the maximum thickness is more than 120 cm, more than 100 cm, and 80 cm, respectively.
A subset of each of the Landsat (TM) and (ETM +) images acquired in August 1987, and August 2001, respectively, were used for mapping land degradation and change detection purposes in the central parts of Jordan. The two multi-temporal images were geometrically and radiometrically calibrated to each other and used as input to an automatic change detection procedure. Color composites were generated for analyses using the TM bands-2,-3 ,-4 and-2,-4,-7. To map changes that had occurred between the two dates six spectral bands of both TM and ETM+ digital data (with the thermal bands being excluded) were individually used as input for supervised classification purpose. This paper describes a suite of techniques that have been used to develop an operational approach, which will ensure high accuracy and compatibly. Monitoring of the land degradation, particularly in vegetation coverage, had been done using NDVI image differencing. The histogram of difference image shows that unchanged pixels were centered around the mean, the changed pixels were located in the tail regions on either side. The difference image indicated that significant negative changes in land use/cover have occurred between 1987 and 2001. Change detection results of central Jordan revealed that the decline of cultivated areas and cropland/grassland areas is clearly the result of accelerated expansion through the process of urbanization, which has negative effects on both agricultural lands and water basins, and is therefore strictly land degradation. 1. Introduction Satellite remote sensing is a useful technique to map land use/cover, where this technique offers unparalleled technique to monitor urban, green and non-green lands changes at regional, global and local scales from space. Changes in the land cover cannot be understood without a better knowledge of the land use changes that drive them and their links to human causes. The linkage between the human and the biophysical causes or drivers to land management and land cover are not sufficiently understood. Remote sensing offers new dimensions to the study of urban morphology from the broad scale of distinguishing urban areas from their surroundings, to local scales within cities, characterizing biophysical properties with space borne and airborne sensors provides objective estimates of the composition and structure of cities and their environs. According to United Nation Convention to Combat Desertification2) ; land degradation has been defined as : •g Reduction or loss in arid, semiarid and dry sub humid areas, of the biological or economic productivity and complexity of rainfed cropland, or range, pasture, forest and woodlands resulting from land uses or from a process or combination of processes, including processes arising from human activities and habitation patterns, such as : soil erosion caused by wind and/or water ; deterioration of the physical, chemical and biological or economic prop
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