scholarly journals Assessment of human impacts on landuse and vegetation cover changes in Mubi region, Adamawa state, Nigeria; remote sensing and GIS approach

2010 ◽  
Vol 8 (2) ◽  
Author(s):  
I Mayomi
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Human Impacts ◽  
Remote Sensing And Gis

scholarly journals Detection of Groundwater in Iraq-Wasit Governorate Using Remote Sensing and GIS

2021 ◽  
Vol 2114 (1) ◽  
pp. 012091
Author(s):  
Ali Sadeq Bahet ◽  
Mutasim Ibrahim Malik
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Water ◽  
Vegetation Cover ◽  
Ground Cover ◽  
Underground Water ◽  
Remote Sensing And Gis ◽  
Moisture Index ◽  
Water Index ◽  
Soil Moisture Index

Abstract The groundwater in Iraq has been studied for the need for it due to the shortage of surface water levels.The vegetation cover index, the soil moisture index, and the surface water index were used to detect the presence of groundwater in Wasit Governorate, Iraq. Those indicators that appear on the ground cover and indicate the presence of groundwater in the study area were compared with the coordinates of wells underground water. The results were identical with information obtained from the Ministry of Water Resources.

scholarly journals Assessment of Vegetation Cover Degradation Using Remote Sensing and GIS Techniques along Sudanese Red Sea Coast (Suakin to Ashad)

2016 ◽  
Vol 8 (1) ◽  
pp. 55
Author(s):  
Atiyat Abdalla Fadoul Nuri ◽  
Amna Ahmed Hamid ◽  
El Abbas Doka M. Ali ◽  
Eltegani Mohamed Salih
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Cover ◽  
Field Survey ◽  
Remote Sensing And Gis ◽  
Sparse Vegetation ◽  
Dense Vegetation ◽  
Red Sea Coast ◽  
Vegetation Species ◽  
Sea Coast

This study aimed to assess the vegetation cover degradation in the Sudanese Red Sea coast (from Suakin to Ashad) after the drought during the period from 2000 - 2011. Remote Sensing and GIS techniques were used beside field survey to conduct the study. Moderate Resolution Imaging Spectrometer (MODIS) terra 2000 -2001, 2005-2006 and 2010-2011 time-Series images mainly the 16 days Normalized Difference Vegetation Index (NDVI) product and Enhanced Thematic Mapper plus (ETM+) images dated 2005 and 2010 were used. Unsupervised classification methods were used to detect vegetation cover of the study area. Based on field survey investigations, beside the data collected on the study area and image interpretation, it was evident that season 2005-2006 and season 2006-2010 are good seasons in the vegetation cover compared to season 2000-2001. Five land cover classes were detected; wet land, bare land and three classes of vegetation cover (dense vegetation, moderately dense vegetation and sparse vegetation cover). Spectral signatures of the three dominant land cover vegetation species were detected.  Areas of the three classes of vegetation cover area (dense vegetation, moderately dense vegetation and sparse vegetation cover) were calculated per km2. The study concluded that MODIS could be used as a cost effective tool in assessing land cover changes and monitoring vegetation cover degradation.As well, it could also be used to detect fairly the different vegetation species in arid and semiarid regions.

Characterization and Evaluation of Soils of Singanallur Watershed Using Remote Sensing and GIS

2017 ◽  
Vol 8 (1) ◽  
pp. 51-56
Author(s):  
R. Vasundhara ◽  
◽  
S. Dharumarajan ◽  
Rajendra Hegde ◽  
S. Srinivas ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis
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