Prediction of Land Cover Change Using Markov and Cellular Automata Models: Case of Al-Ain,UAE, 1992-2030   总被引：1，自引：0，他引：1  

M. M. Yagoub  Abdallah Ahmad Al Bizreh 《Journal of the Indian Society of Remote Sensing》2014,42(3):665-671

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012. Land Use and Land Cover maps for the study area were derived from 1992, 2006, and 2012 Landsat satellite images (TM, ETM⁺). The models achieved an overall accuracy of approximately 80 %. A Markov model was applied for 2006 and 2012 to predict land cover in 2030. The results conformed to the general trend of the Al Ain Master Plan 2030. This study demonstrates that remote sensing, with the availability of free Landsat data, is a viable technology that could be used to help in the prediction process especially in developing countries, where data availability is a problem.  相似文献   

Markov CA,Multi Regression,and Multiple Decision Making for Modeling Historical Changes in Kirkuk City,Iraq     

Najat Qader Omar  Mohd Sanusi S. Ahamad  Wan Muhd Aminuddin Wan Hussin  Narimah Samat  Siti Zubaidah Binti Ahmad 《Journal of the Indian Society of Remote Sensing》2014,42(1):165-178

The cellular automata (CA) model is an important tool in land use change studies. Swift increases in population and long-term expectations of rapid urbanization have led to extensive land use change, and normal living conditions have affected the natural resources of the land. This paper highlights and analyzes the historical urban changes in Kirkuk City, Iraq, considering repeated changes undergone by the state such change as government infrastructures, wars, and economic blockade. In this paper, an integrated model, built-in multi regression model, and multi-criteria evaluation were considered to improve the representation of CA transition rules. Environmental and socioeconomic factors were used to produce Suitable Maps (SMs). These SMs were practicalities to create factor layers and weight usage, rating method process for variance expert decision-making groups, and geographic information systems for the periods 1984, 1990, 2000, and 2010. The roots of the equation (R²) values are compared and these values are chosen to produce a good model of suitable maps. The approach used in this study provides a mechanism for monitoring suitability maps in Kirkuk. Furthermore, the model Markov CA is implemented and evaluated. The results indicate that the model, its related concepts performs sufficiency  相似文献   

Gravitational clustering of galaxies in an expanding universe     

Naseer Iqbal  Farooq Ahmad  M. S. Khan 《Journal of Astrophysics and Astronomy》2006,27(4):373-379

We inquire the phenomena of clustering of galaxies in an expanding universe from a theoretical point of view on the basis of thermodynamics and correlation functions. The partial differential equation is developed both for the point mass and extended mass structures of a two-point correlation function by using thermodynamic equations in combination with the equation of state taking gravitational interaction between particles into consideration. The unique solution physically satisfies a set of boundary conditions for correlated systems and provides a new insight into the gravitational clustering problem.  相似文献   

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan     

Ishfaq Ahmad  Umer Saeed  Muhammad Fahad  Asmat Ullah  M. Habib ur Rahman  Ashfaq Ahmad  Jasmeet Judge 《Journal of the Indian Society of Remote Sensing》2018,46(10):1701-1711

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.  相似文献   

Dispersive thermohaline convection near salt domes: a case at Napoleonville Dome,southeast Louisiana,USA     

Zahra Jamshidzadeh  Frank T. -C. Tsai  Hasan Ghasemzadeh  Seyed Ahmad Mirbagheri  Majid Tavangari Barzi  Jeffrey S. Hanor 《Hydrogeology Journal》2015,23(5):983-998

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Estimation of rainfall threshold and its use in landslide hazard mapping of Kuala Lumpur metropolitan and surrounding areas     

Omar F. Althuwaynee  Biswajeet Pradhan  Noordin Ahmad 《Landslides》2015,12(5):861-875

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Peculiar motions of galaxy clusters: correlation function approach     

Naseer Iqbal  Tabasum Masood  Mubashir Hamid  Naveel Ahmad  Bari Maqbool 《Astrophysics and Space Science》2014,353(2):621-624

The correlation function theory on the basis of prescribed boundary conditions provides a deeper understanding in studying the dynamical parameters of galaxy clusters. The approach approximates that the moderate dense systems discussed by a two point correlation function is helpful for describing the dynamical nature of galaxy clusters. The projected theory of two point correlation function for point mass and extended mass structures can be used an alternative tool in measuring the average peculiar motion and temperature profile of galaxy clusters.  相似文献   

VHF scintillations as a diagnostic tool for the study of ionospheric irregularities     

Altaf Ahmad  M. M. Ahmad  B. M. Pathan 《Earth, Moon, and Planets》1994,65(3):247-268

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Coronal holes: Mass loss driven by magnetic reconnection     

D. J. Mullan  I. A. Ahmad 《Solar physics》1982,75(1-2):347-350

We propose that bubbles of matter ejected from magnetic reconnection sites in polar plumes drive the solar wind in coronal holes.  相似文献   

Geostatistical analysis of soil moisture measurements and remotely sensed data at different spatial scales   总被引：1，自引：0，他引：1  

Shakil?Ahmad?RomshooEmail author 《Environmental Geology》2004,45(3):339-349

The European remote sensing satellite (ERS-2) synthetic aperture radar (SAR) data was used for temporal monitoring of soil moisture at Sukhothai, Thailand. Higher correlations were found between the observed soil moisture and the radar backscattering coefficient. The soil moisture distribution shows great variation in space and time due to its stochastic nature. In order to obtain a better understanding of the nature and causes of spatial variation of soil moisture, the extensive soil moisture measurements observed in Thailand and also remotely sensed ERS-2 SAR data were used for geostatistical analysis. The observed soil moisture shows seasonal variations with mean varying from 3.33 %v/v (dry season) to 33.44 %v/v (wet season). The spatial geostatistical structure also shows clear seasonal variations in the geostatistical characteristics such as range and sill. The sills vary from 1.00 (%v/v)² for the driest day to 107.57 (%v/v)² for one of the wet days. The range or the correlation lengths varies between 46.5 and 149.8 m for the wettest and driest periods. The nugget effect does not show strong seasonal pattern or trend but the dry periods usually have a smaller nugget effect than the wet periods. The spherical variogram model fits the sample variograms very well in the case of soil moisture observations while the exponential model fits those of the remotely sensed data. The ranges observed from the observed soil moisture data and remotely sensed data at the same resolution are very similar. Resolution degradation affects the geostatistical structure of the data by reducing the sills, and increasing the ranges.  相似文献