Use of Remote Sensing and GIS to Assess the Effects of Agricultural Land Production Capacity on Spatial Extension of Construction Land in Chengdu

2012 ◽  
Author(s):  
Pan Hongyi ◽  
He Wei
Keyword(s):  
Remote Sensing ◽  
Agricultural Land ◽  
Production Capacity ◽  
Remote Sensing And Gis ◽  
Construction Land ◽  
Spatial Extension

scholarly journals REMOTE SENSING AND GIS FOR LAND COVER CLASSIFICATION AND CHANGE DETECTION IN GAZIPUR SADAR, BANGLADESH DURING 1973-2017

2018 ◽  
Vol 5 (2) ◽  
pp. 215
Author(s):  
Md Arafat Hassan ◽  
Rakibul Islam ◽  
Rehnuma Mahjabin
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Remote Sensing And Gis ◽  
Vegetation Coverage ◽  
Accurate Information ◽  
Sal Forest ◽  
Shed Light

This paper has been developed to capture the land coverage change in Gazipur Sadar Upazila with the help of remote sensing data of 44 years from 1973 to 2017. After acquiring the study area image of 1973, 1991, 2006 and 2017 supervised classification method has been used to get the accurate information from the satellite image and the whole outcome has been transformed into measurable unit (sq km) and graphs. The accuracy of land coverage was ranged from 85% to 89%. The outcome says that the acceleration of economic growth and pressure of huge population took a heavy toll on the vegetation coverage which decreased -199.7%. People are destroying vegetation coverage for building up settlements and infrastructure. In the year 2017, the map shows that the built-up area increased 312.9% for industry, settlement and agricultural purpose. Moreover agricultural land also drops down from 42% to 32%.  The rapid rate of decreasing vegetation coverage and small amount of existing vegetation coverage only 57 sq km (in 2017) is a red alert for the region. The Sal forest and other special flora species of that region is valuable resource for environment. This paper shed light on the fact that it is urgent to protect vegetation coverage so it will help the authority to make good policies and use other techniques to save vegetation coverage.

scholarly journals Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework

2019 ◽  
Vol 8 (10) ◽  
pp. 454 ◽  
Author(s):  
Junfeng Kang ◽  
Lei Fang ◽  
Shuang Li ◽  
Xiangrong Wang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Cellular Automata ◽  
Markov Model ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Remote Sensing Images ◽  
Mapreduce Framework ◽  
Construction Land ◽  
Model Based

The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land.

A Remote Sensing and GIS Integrated Study on Assessment of Change in Land Use and Loss of Agricultural Land due to Urbanization – A Case Study of Nashik City

2021 ◽  
Vol 9 (8) ◽  
pp. 3055-3063
Author(s):  
Dipti Bakare
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Rapid Change ◽  
Remote Sensing And Gis ◽  
Suitability Analysis ◽  
The World

Abstract: Urbanization may be a process having a serious impact ashore use characteristics. Basically, as an impression of urbanization, the world is observed with rapid change within the land use character of agricultural land. Generally, the agricultural land is employed for various development activities like industrial establishments, residential colonies and other urban infrastructure during the method of urbanization. it's necessary to possess a periodical assessment of land use change for the developing populated area , which helps to make a decision the longer term expansion strategies for the world. Nashik city is located in the state of Maharashtra in the western part of India. It is one of the most dynamic cities of India with a rapid growth rate due to migration from various parts of Maharashtra. The Nashik city is presently spread over an area of 264.15 sq. km. with a periodical increase in municipal corporation boundary during the last few decades. As a result of urbanization and expansion of municipal corporation limits, the city has undergone drastic changes in land use character. In this study, land-use change is quantified for the existing six zones of Nashik city during the last 30 years using remote sensing and GIS. The study has analysed the relationship between urban expansion and the loss of agricultural land because of an increase in a built-up area and other land use. The study present excellent scenario for land use change during the year 1991, 2001, 2011 and 2020. This can surely guide the development strategies for the study area of Nashik. Also the study can be extended for conducting a suitability analysis to assess future change of land use based on various criteria. Keywords: Land use, Remote sensing, GIS, Supervised classification, Urbanization, Agricultural land loss

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