scholarly journals Evaluation of the status of land use/land cover change using remote sensing and GIS in Jewha Watershed, Northeastern Ethiopia

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Dereje Gebrie Habte ◽  
Satishkumar Belliethathan ◽  
Tenalem Ayenew

AbstractEvaluation of land use/land cover (LULC) status of watersheds is vital to environmental management. This study was carried out in Jewha watershed, which is found in the upper Awash River basin of central Ethiopia. The total catchment area is 502 km2. All climatic zones of Ethiopia, including lowland arid (‘Kola’), midland semi-arid (‘Woinadega’), humid highland (Dega) and afro alpine (‘Wurch’) can be found in the watershed. The study focused on LULC classification and change detection using GIS and remote sensing techniques by analyzing satellite images. The data preprocessing and post-process was done using multi-temporal spectral satellite data. The images were used to evaluate the temporal trends of the LULC class by considering the years 1984, 1995, 2005 and 2015. Accuracy assessment and change detection of the classification were undertaken by accounting these four years images. The land use types in the study area were categorized into six classes: natural forest, plantation forest, cultivated land, shrub land, grass land and bare land. The result shows the cover classes which has high environmental role such as forest and shrub has decreased dramatically through time with cultivated land increasing during the same period in the watershed. The forest cover in 1984 was about 6.5% of the total catchment area, and it had decreased to 4.2% in 2015. In contrast, cultivated land increased from 38.7% in 1984 to 51% in 2015. Shrub land decreased from 28 to 18% in the same period. Bare land increased due to high gully formation in the catchment. In 1984, it was 1.8% which turned to 0.6% in 1995 then increased in 2015 to 2.7%. Plantation forest was not detected in 1984. In 1995, it covers 1.5% which turned to be the same in 2015. The study clearly demonstrated that there are significant changes of land use and land cover in the catchment. The findings will allow making informed decision which will allow better land use management and environmental conservation interventions.

2017 ◽  
Vol 06 (02) ◽  
Author(s):  
Remote sensing ◽  
Land cover change ◽  
Urban areas ◽  
Change detection

2016 ◽  
Vol 3 (7) ◽  
pp. 141-144
Author(s):  
Ashish Bhandari ◽  
◽  
Nitin Bela ◽  
Nitin Mishra ◽  
Sakshi Gupta

Author(s):  
Swapnali Barman ◽  
Jaivir Tyagi ◽  
Waikhom Rahul Singh

Using remote sensing and GIS technique, we analyse the change detection of different land use/land cover (LULC) types that has taken place in Puthimari river basin during a two-decade period from 1999 to 2019. Supervised classification method with maximum likelihood algorithm have been applied to prepare the LULC maps. The LULC change detection has been performed employing a post-classification detection method. Puthimari is a north bank sub-catchment of River Brahmaputra, the northern part of which falls in Bhutan and the rest falls in the Assam state of India. The primary LULC types of the basin are, dense vegetation which is predominant in the upper catchment, crop land and rural settlement. Thus, five different classes have been considered for the analysis, viz., dense vegetation, water bodies, silted water, cropland and rural settlement. The results showed that the rural settlement and water bodies in the basin increased by 42.70% and 30.31% from 1999 to 2019. However, dense vegetation, silted water and cropland decreased by 9.24%, 27.47% and 28.10% during these two decades.


2019 ◽  
Vol 4 (6) ◽  
pp. 84-89 ◽  
Author(s):  
Aniekan Effiong Eyoh ◽  
Akwaowo Ekpa

The research was aim at assessing the change in the Built-up Index of Uyo metropolis and its environs from 1986 to 2018, using remote sensing data. To achieve this, a quantitative analysis of changes in land use/land cover within the study area was undertaken using remote sensing dataset of Landsat TM, ETM+ and OLI sensor images of 1986, 2000 and 2018 respectively. Supervised classification, using the maximum likelihood algorithm, was used to classify the study area into four major land use/land cover types; built-up land, bare land/agricultural land, primary swamp vegetation and secondary vegetation. Image processing was carried out using ERDAS IMAGINE and ArcGIS software. The Normalised Difference Built-up Index (NDBI) was calculated to obtain the built-up index for the study area in 1986, 2000 and 2018 as -0.20 to +0.45, -0.13 to +0.55 and -0.19 to +0.63 respectively. The result of the quantitative analysis of changes in land use/land cover indicated that Built-up Land had been on a constant and steady positive growth from 6.76% in 1986 to 11.29% in 2000 and 44.04% in 2018.


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