Empirical modeling potential transfer of land cover change pa city with neural network algorithms

Authors

Abstract:

Land-use change is one of the most important challenges of land-use planning that lies with planners, decision-makers and policymakers and has a direct impact on many issues, such as economic growth and the quality of the environment. The present study examines the land use change trends in Behbahan city for 2014 and 2028 using LCM in the GIS environment. Analysis and visibility of user variations, carried out in two periods of Landsat satellite images of 2000 (ETM + sensor) and 2014 (OLI sensors), and land cover maps for each year. The transmission potential modeling was performed by using the multi-layer perceptron artificial neural network algorithm using six independent variables and the distribution of changes in user usage were calculated by Markov chain method. The results of the prediction showed that the most reduction in the changes is the degradation of the rangelands and the greatest increase in the area of agricultural use. According to the horizontal tabulation results of the 2028 map, it can be stated that from the total area of the area 28336.22 hectares of land were unchanged and 33223.78 hectares of land use change. Also Rangeland and forest degradation during this time period can be a danger to urban planners and natural resources.   #s3gt_translate_tooltip_mini { display: none !important; }

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

Modeling Land-use and Land-cover Change

Models are used in a variety of fields, including land change science, to better understand the dynamics of systems, to develop hypotheses that can be tested empirically, and to make predictions and/or evaluate scenarios for use in assessment activities. Modeling is an important component of each of the three foci outlined in the science plan of the Land-use and -cover change (LUCC) project (Tu...

full text

Potential climate forcing of land use and land cover change

Pressure on land resources is expected to increase as global population continues to climb and the world becomes more affluent, swelling the demand for food. Changing climate may exert additional pressures on natural lands as present-day productive regions may shift, or soil quality may degrade, and the recent rise in demand for biofuels increases competition with edible crops for arable land. ...

full text

Land cover/use change modeling with CLUE-S

Introduction and model structure The Conversion of Land Use and its Effects modelling framework (CLUE) (Veldkamp and Fresco, 1996; Verburg et al., 1999) was developed to simulate land use change using empirically quantified relations between land use and its driving factors in combination with dynamic modelling of competition between land use types. The modelling approach has been modified and ...

full text

Impact of urban land cover change on land surface temperature

The rapid growth in urban population is seen to create a need for the development of more urban infrastructures. In order to meet this need, natural surfaces such as vegetation are been replaced with non-vegetated surfaces such as asphalt and bricks which has the ability to absorb heat and release it later. This change in land cover is seen to increase the land surface temperature. Previous stu...

full text

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 18  issue 50

pages  219- 234

publication date 2018-06

By following a journal you will be notified via email when a new issue of this journal is published.

Keywords

No Keywords

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023