Monitoring land use/land cover change using multi-temporal Landsat satellite images in an arid environment: a case study of El-Arish, Egypt

2013 ◽  
Vol 7 (5) ◽  
pp. 1671-1681 ◽  
Author(s):  
Nasem Badreldin ◽  
Rudi Goossens
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Satellite Images ◽  
Arid Environment ◽  
Land Use Land Cover ◽  
Multi Temporal ◽  
Landsat Satellite

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.

Spatio-temporal analysis of land use/land cover change and its effects on soil erosion (Case study in the Oplenac wine-producing area, Serbia)

2018 ◽  
Vol 190 (11) ◽  
Author(s):  
Veljko Perović ◽  
Darko Jakšić ◽  
Darko Jaramaz ◽  
Nikola Koković ◽  
Dragan Čakmak ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Land Cover Change ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Spatio Temporal

scholarly journals Land Cover Change Assessment of Vaal Harts Irrigation Scheme using Multi-temporal Satellite Sata

2013 ◽  
Vol 39 (4) ◽  
pp. 59-70 ◽  
Author(s):  
Fredrick Ao Otieno ◽  
Olumuyiwa I Ojo ◽  
George M. Ochieng
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Program ◽  
Land Use Land Cover ◽  
Irrigation Scheme ◽  
Multi Temporal

Abstract Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

scholarly journals Observing land-use/land-cover dynamics in semi-arid environment: Evidence from Damaturu Town and its surrounding lands, Yobe State, Nigeria

2020 ◽  
Vol 4 (2) ◽  
pp. 55-78
Author(s):  
Modibbo Babagana-Kyari ◽  
Babagana Boso
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Arid Environment ◽  
Land Use Land Cover ◽  
Temporal Aspects ◽  
Erdas Imagine ◽  
Annual Change Rate ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Supervised Image Classification

The fragile Sudano-Sahelian ecological zone of Nigeria has been classified as a hotspot of land cover change (LCC) that has been suffering from serious anthropogenic and biophysical stresses. Damaturu, being the fastest growing town situated in the region happened to be a victim of this negative development. The purpose of this study is to remotely observe and assess the prevailing land-use/land-cover (LULC) dynamics of Damaturu town and its delicate surrounding lands from the year 1987-2017 study periods. To achieve this, a supervised image classification technique with Maximum Likelihood Classifier (MLC) algorithm was used in ERDAS Imagine version 15 software to classify the three epochs multi-temporal and multi-spectral Landsat imageries (TM 1987, ETM+7 2000 and OLI 2017). The classified LULC maps and their resulting statistics were then used to assess the spatio-temporal aspects of the observed changes by placing the results within the wider context of previous related literature and evidences. Findings revealed that the built-up area has been expanding since 1987 with an annual change rate of 4.5% between 1987-2000, and 5.3% during 2000-2017 respectively. The growth of the town is being accompanied by massive farmlands expansion and vegetal cover (trees and shrubs) lost making the surrounding arable lands seriously disturbed. Thus, if the observed trends continue, the entire studied region will be subjected to severe environmental hazard such as desertification. Overall, the study provides valuable information required for sustainable  environmental management.

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