scholarly journals Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

2017 ◽  
Vol 9 (8) ◽  
pp. 804 ◽  
Author(s):  
Biao Wang ◽  
Jaewan Choi ◽  
Seokeun Choi ◽  
Soungki Lee ◽  
Penghai Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Image Fusion ◽  
Land Cover Change ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Land Cover Change Detection

scholarly journals Application of GIS and Remote Sensing to Analyse Land Use Land Cover Change Detection and Vegetation Dynamics Using Multi-temporal Satellite Images; The Case of Mariamdehan Kebele, Tigray Region, Northern Ethiopia

2019 ◽  
pp. 1-11
Author(s):  
Esayas Meresa ◽  
Yikunoamlak Gebrewhid
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Natural Resources ◽  
Change Detection ◽  
Land Cover Change ◽  
Vegetation Dynamics ◽  
Satellite Images ◽  
Land Use Land Cover ◽  
Multi Temporal ◽  
Land Cover Change Detection

Detecting Land use and land cover change and vegetation condition has become a central component in current strategies for managing and monitoring of environmental changes caused by anthropogenic activities. To come up with such decisions, geoinformatics technology is providing new tools to conduct vegetation and land use land cover change detection analysis for managing and wise utilisation of natural resources as well as to provide information for policymakers in a given study area. This study examines the use of geoinformatics technology to analyse land use land cover (LULC) change and vegetation dynamics using multi-temporal satellite images for the maryamdehan kebele in the years 1984, 2005 and 2015. Both primary and secondary data were used from different sources. Satellite images of the year 1984, 2005 and 2015 were downloaded from the govis.usgs.gov website and ground control points (GCP) data were collected by handheld GPS for supervised image classification in Erdas imagine and ArcGIS environment. The findings show that six main land use land cover classes were detected and vegetation values were also computed in each period.  As a result, the total area of the kebele was 3646.49 hectare, from which in 1984 forest area (40.691%), grassland (26.15%) and farmland (10.81%) were dominant classes and in 2005 settlement (52.41%), forest area (25.04%) & farmland (11.71%) and in 2015, 35.14% was covered by forest land, 30.04% by Settlement, and 14.74% by farmland. Water resource decreases from 9.3% to 0.64% in 2015 and the bare land also changes from 3.18% to 0.903% because of urban expansion and agricultural activities in the kebele. In addition, the vegetation condition looks like a sinusoidal trend from the year 1984 up to 2015 because of climate change and human interventions in the kebele. To conclude that detecting LULC change and analysis of vegetation dynamics plays a great role in land use management and wise utilisation of natural resources by applying Geoinformatics tools in the kebele and it provides information for the policymakers to prepared future plan and for sustainable development.

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