Mapping LCLU using python scripting

Land cover land use changes constantly with the time at local, regional, and global scales, therefore, remote sensing provides wide, and broad information for quantifying the location, extent, and variability of change; the reason and processes of change; and the responses to and consequences of cha...

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Veröffentlicht in:Engineering and Technology Journal Jg. 37; H. 4A; S. 140 - 147
Hauptverfasser: Jasim, Adi Z., Sadiq, Nur E., Hassun, Khalid Ibrahim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Baghdad, Iraq University of Technology 25.04.2019
Unviversity of Technology- Iraq
Schlagworte:
artificial neural network
land cover
land use
support vector machine
استخدام الأراضي
الخوارزميات
الشبكات العصبية
الغطاء الأرضي
ISSN:1681-6900, 2412-0758
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Beschreibung
Zusammenfassung:Land cover land use changes constantly with the time at local, regional, and global scales, therefore, remote sensing provides wide, and broad information for quantifying the location, extent, and variability of change; the reason and processes of change; and the responses to and consequences of change. And considering to the importance of mapping of (LCLU). For that reason this study will focus on the problems arising from the traditional classification (LCLU) that based on spatial resolution only which leads to prediction a thematic map with noisy classes, and using a new method that depend on spectral and spatial resolution to produce an acceptable classification and producing a thematic map with an acceptable database by using artificial neural network (ANN) and python in additional to other program. In this study the methods of classification were studied through using two images for the same study area , rapid eye image which has three spectral bands with high spatial resolution(5m) and Landsat 8 image (high spectral resolution with eight bands), also several programs like ENVI version 5.1, Arc GIS version 10.3, Python 3, and GPS. The result for this research was sensuousness as geometrics accuracy accepted in map production
ISSN:1681-6900
2412-0758
DOI:10.30684/etj.37.4A.5