Investigation and comparison of land use map database in the Urmia lake basin

Document Type : Research Article

Authors

1 Tehran university

2 Department of Renewable Energies and Environment, Faculty of New Sciences and Technologies, University of Tehran

3 University of Tehran

4 Professor, Faculty of Civil Engineering, Sharif university of Technology, Tehran

Abstract

The important role of land use and land cover in the Lake Urmia basin on the water consumption of this area due to better water management in the basin, has made it necessary to have in-depth knowledge of basic information such as land use and land cover. Unfortunately, the available information and statistical sources about LU/LC of basin sometimes are insufficient and contradictory. This study, as one of the important aspects affecting the address of the Urmia Lake issue, has determined the databases that provide land use maps from satellite images, also it examines the accuracy of these global products and compares them with the map which is created by object oriented method with eCognition software. The results of the overall accuracy assessment of the maps illustate that the land use maps extracted from the LCtype and GLCF global products are performing well, and Globecover has provided poor results in this regard. There was the best fit in the results of the MODIS product, so that the MODIS product is not only better in pixel dimensions than most products, but also has the longest land use extraction time in terms of time sequence. The results of this product in the study were in good agreement with the map produced by the object-oriented method, therefore it is recommended to use the MODIS land use product in studies related to the Urmia Lake basin.

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Main Subjects


[1]. Khosroshahi. Detection of changes in the catchment area of lake Urmia from 1998 to 2013. Thirty-second Assembly and the First International Congress of Earth Sciences. 2013. [Persian]
[2]. Fathian F. Investigating the trend of landuse change using remote sensing technology and meterological variables in the Urmia Lake basin. Master Thesis, Department of Water Resources Engineering, Tarbiat Modares University, 2011. [Persian]
[3]. Hua H, Zhao A, Liu Y, Wang S, Yang S. Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO.Remote Sens. 2018, 10, 1846.
[4]. Wulder MA, Hermosilla T, Stinson G, Gougeon FA, White JC, Hill SA, et al. Satellite-based time series land cover and change information to map forest area consistent with national and international reporting requirements. Forestry: 2020; 00, 1–13.
[5]. Sui L, Kang J, Yang X, Wang Z, Wang J. Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning Open Geosciences 2020; 12: 324–341
[6]. De Oliveira Silveira EM, De Menezes MD, Júnior FWA, Terra MCNS, De Mello JM. Assessment of Geostatistical Features for Object-Based Image Classification of Contrasted Landscape Vegetation Cover. Journal of Applied Remote Sensing, 2017. 11(3), 036004.
[7]. Szostak M, HawryƂo P, Piela D. Using of Sentinel-2 images for automation of the forest succession detection. European Journal of Remote Sensing, 2018. 51(1): 142-149.
[8]. Huo LZ, Boschetti L, Sparks AM. Object-Based Classification of Forest Distrubance Types in the Conterminous United States. Journal of Remote Sensing , 2019. 11(5), 477.
[9]. Lima TA, Beuchle R, Langner A, Grecchi RC, Griess VC, Achard F. Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon. Remote sensing, 2019. 11, 1-21.
[10]. Mercier A, Betbeder J, Rumiano F, Baudry J, Gond V, Blanc L, et al. Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest– Agriculture Mosaics in Temperate and Tropical Landscapes. Remote sensing, 2019. 11, 1-20.
[11]. Zhang Y, Zhao H, Ni JA. Comparison of Four Algorithms for Land-Use Classification Based on Landsat 8 OLI Image. Journal of Physics: Conference Series, 2020.
[12]. Hansen M, DeFries R, Townshend JRG, Sohlberg R. Global land cover classification at 1km resolution using a decision tree classifier. International Journal of Remote Sensing. 2000. 21: 1331-1365.
[13]. Bartholome et al.,. GLC 2000 Global Land Cover mapping for the year 2000. European Commission, DG Joint Research Centre, EUR 20524 EN, 2002. pp. 62.
 
[14]. Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH et al. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment. 2002, 83(1-2): 287-302.
[15]. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A et al. High-resolution global maps of 21st-century forest cover change. Science. 2013, 342(6160): 850-853.
[16]. Latham J, Cumani R, Rosati I, Bloise M. Global Land Cover SHARE (GLC-SHARE) database Beta-Release Version 1.0. FAO, Rome. 2014.
[17]. Ellis EC, Ramankutty N. Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment: 2008, Vol. 6, No. 8, pp. 439-447.
[18]. Carroll M, Townshend J, DiMiceli C, Noojipady P, Sohlberg RA. New Global Raster Water Mask at 250 Meter Resolution. International Journal of Digital Earth. 2009, volume 2, number 4.
[19]. Schneider A, Friedl M, Potere D. A new map of global urban extent from MODIS data. Environmental Research Letters. 2009, vol. 4, article 044003.
[20]. Broxton PD, Zeng X, Sulla-Menashe D, Troch PA. A Global Land Cover Climatology Using MODIS Data. J. Appl. Meteor. Climatol. 2014a, 53, 1593,1605. doi: http://dx.doi.org/10.1175/JAMC-D-13-0270.1
[21]. Iran Water Resources Management Company and Ashna Samaneh Technical and Engineering Services Company. Production of GIS based on processing of satellite data and production of digital content (land use changes and land cover of Lake Urmia catchment). 2008.
[22]. Water Research Institue. Research Institue for Water Resources Studies and Research. Investigation of changing environmental conditions in the Lake Urmia catchment (land use). 2013.
Volume 8, Issue 4
April 2022
Pages 891-905
  • Receive Date: 19 February 2021
  • Revise Date: 19 June 2021
  • Accept Date: 19 June 2021
  • First Publish Date: 22 June 2021