Download PDFOpen PDF in browser

Land Use/Land Cover Changes Monitoring and Analysis of Dubai Emirate, UAE Using Multi-Temporal Remote Sensing Data

9 pagesPublished: September 20, 2018

Abstract

Dubai is a rapidly urbanizing emirate with land development succeeding at a fast pace. The present study aims to develop a low-cost classifier based on the spectral angle mapper (SAM) and image difference (ID) algorithms. The proposed approach was developed in order to improve Land use/ Land cover (LULC) classification maps for the purpose of monitoring and analysing LULC change during the period from 2000 to 2015 for the Emirate of Dubai. The approach starts by collecting 320 training samples from high resolution images such as QuickBird with a spatial resolution of 60 cm followed by applying a 3×3 spatial convulsion filter, majority/ minority analysis, sieving classes and clump map of the produced LULC maps. After that, the accuracy of the maps were assigned using confusion matrix. The accuracy assessment demonstrated that the targeted 2000, 2005,2010 and 2015 LULC maps have 88.125%, 89.069%, 90.122% and 96.096% accuracy, respectively. The results exhibited that the built-up areas increased by 233.72 km2 (5.81%) from 2000 to 2005 and keeps to increase even up and till the present time. The results also showed that the changes in the periods 2000-2005 and 2010-2015 confirmed that net vegetation area loses were more obvious from 2005 to 2005 than from 2010 to 2015, reducing from 47.618 km2 to 40,820 km2, respectively. This study is of great help to urban planners and decision makers.

Keyphrases: dubai, remote sensing, uae, water resources

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1435-1443.

BibTeX entry
@inproceedings{HIC2018:Land_Use/Land_Cover_Changes,
  author    = {Mohamed Mostafa Mohamed and Samy Elmahdy},
  title     = {Land Use/Land Cover Changes Monitoring and Analysis of Dubai Emirate, UAE Using Multi-Temporal Remote Sensing Data},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/z1zL},
  doi       = {10.29007/jvz3},
  pages     = {1435-1443},
  year      = {2018}}
Download PDFOpen PDF in browser