Monitoring of Changes in Land Use/Land Cover in Syria from 2010 to 2018 Using Multitemporal Landsat Imagery and GIS

Understanding the effects of socio-ecological shocks on land use/land cover (LULC) change is essential for developing land management strategies and for reducing adverse environmental pressures. Our study examines the impacts of the armed conflict in Syria, which began in mid-2011, and the related social and economic crisis on LULC between 2010 and 2018. We used remote sensing for change detection by applying a supervised maximum likelihood classification to Landsat images of the three target years 2010, 2014, and 2018. Based on the computed extent of our LULC classes and accuracy assessment, we calculated area-adjusted estimates and 95% confidence intervals. Our classification achieved an overall accuracy of 86.4%. Compared to 2010, we found an increase in spatial extent for bare areas (40,011 km2), forests (2576 km2), and urban and peri-urban areas (3560 km2), whereas rangelands (37,005 km2) and cultivated areas (9425 km2) decreased by 2018. It is not possible to determine whether the changes in LULC in Syria will be permanent or temporary. Natural conditions such as climate fluctuations had an impact on the uses of the natural environment and cultivated areas during the study period, especially in regions suffering from water stress. Although seasonal precipitation patterns and temperature affect LULC change, however, we could not identify a prevailing climate trend towards more drought-prone conditions. Our analysis focuses on (potential) direct and indirect implications of the Syrian conflict on LULC change, which most notably occurred between 2014 and 2018. Conflict-related main drivers were human activities and demographic changes, which are mainly attributable to large-scale population displacement, military operations, concomitant socio-economic status, and control of local resources. As the study provides quantitative and qualitative information on the dynamics of LULC changes in Syria, it may serve as a framework for further relevant conflict-related research and support planning, management practices, and sustainable development.

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Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification

Remote Sensing ◽

10.3390/rs11141719 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1719 ◽

Cited By ~ 7

Author(s):

Jiaxin Mi ◽

Yongjun Yang ◽

Shaoliang Zhang ◽

Shi An ◽

Huping Hou ◽

...

Keyword(s):

Land Use ◽

Random Forest ◽

Land Cover ◽

Large Scale ◽

Underground Mining ◽

Mining Area ◽

Random Forest Classifier ◽

Land Use Land Cover ◽

Lulc Change ◽

Mining Areas

Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas.

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An Assessment of Changes the Land Use Land Cover and Impact of the It Park at Talwade Pune On the Nearby Villages

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst207526 ◽

2020 ◽

pp. 172-179

Author(s):

Ujjwala Khare ◽

Prajakta Thakur

Keyword(s):

Land Use ◽

Land Cover ◽

Rapid Growth ◽

Urban Areas ◽

Satellite Images ◽

Ground Truth ◽

Land Use Land Cover ◽

Landsat Images ◽

Tertiary Sector ◽

Land Cover Maps

The expansion of urban areas is common in metropolitan cities in India. Pune also has experienced rapid growth in the fringe areas of the city. This is mainly on account of the development of the Information Technology (IT) Parks. These IT Parks have been established in different parts of Pune city. They include Hinjewadi, Kharadi, Talwade and others like the IT parks in Magarpatta area. The IT part at Talwade is located to close to Pune Nashik Highway has had an impact on the villages located around it. The surrounding area includes the villages of Talwade, Chikhli, Nighoje, Mahalunge, Khalumbre and Sudumbre. The changes in the land use that have occurred in areas surrounding Talwade IT parks during the last three decades have been studied by analyzing the LANDSAT images of different time periods. The satellite images of the 1992, 2001 and 2011 were analyzed to detect the temporal changes in the land use and land cover. This paper attempts to study the changes in land use / land cover which has taken place in these villages in the last two decades. Such a study can be done effectively with the help of remote sensing and GIS techniques. The tertiary sector has experienced a rapid growth especially during the last decade near the IT Park. The occupation structure of these villages is also related to the changes due to the development of the IT Park. The land use of study area has been analysed using the ground truth applied to the satellite images at decadal interval. Using the digital image processing techniques, the satellite images were then classified and land use / land cover maps were derived. The results show that the area under built-up land has increased by around 14 per cent in the last 20 years. On the contrary, the land under agriculture, barren, pasture has decreased significantly.

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Drivers and Dynamics of Land Use Land Cover in Phewa Watershed, Kaski, Nepal

Journal of Forest and Natural Resource Management ◽

10.3126/jfnrm.v2i1.40218 ◽

2020 ◽

Vol 2 (1) ◽

pp. 19-36

Author(s):

Sudip Raj Regmi ◽

Mahendra Singh Thapa ◽

Raju Raj Regmi

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Areas ◽

Road Construction ◽

Rate Of Change ◽

Lake Area ◽

Land Use Land Cover ◽

Lulc Change ◽

Barren Land ◽

Area Change

Geospatial tools play an important role in monitoring Land Use Land Cover (LULC) dynamics. This study assessed the extent of LULC changes during 2003, 2010 and 2018 using temporal satellite imageries, computed the rate of change in area of Phewa Lake and explored the drivers of LULC change and lake area change in Phewa watershed. It used Landsat Imageries for 2003, 2010 and 2018 and carried out purposive household survey (N=60), key informant survey (N=5), focus group discussion (N=4) and direct field observation to explore the drivers of LULC change and lake area change. It generated LULC maps by using supervised classification and computed LULC change by applying post classification change detection technique. On screen digitization was done to find the area of Phewa Lake during 2010 and 2018. Agricultural land and urban areas were found to have increased by 11.63% and 1.46% respectively while forest area, barren land and water bodies were found to have decreased by 9.21%, 3.56% and 0.5% respectively between 2003 and 2010. Forest area, urban areas and barren land were found to have increased by 5.9%, 3.28% and 5.02% respectively while agricultural landand water bodies were observed to have decreased by 7.83% and 0.16% respectively between 2010 and 2018. During 2010-2018, rate of change in lake area was found to have decreased by 0.61% with periodic annual decrement by 2.59 ha. The drivers responsible for LULC change were alternative form of energy, community forestry, promotion of private forestry, migration for foreign employment, inadequate market price of agricultural products, road construction, soil erosion and population pressure. Lake area was found to have decreased due to sedimentation, encroachment and road construction. Further study is important to know the exact contributions of these drivers of LULC change and lake area change for the sustainability of Phewa watershed.

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Stormwater runoff quality in correlation to land use and land cover development in Yongin, South Korea

Water Science & Technology ◽

10.2166/wst.2014.207 ◽

2014 ◽

Vol 70 (2) ◽

pp. 218-225 ◽

Cited By ~ 8

Author(s):

M. A. Paule ◽

S. A. Memon ◽

B.-Y. Lee ◽

S. R. Umer ◽

C.-H. Lee

Keyword(s):

Land Use ◽

Correlation Analysis ◽

Land Cover ◽

Urban Areas ◽

Management Practices ◽

Stormwater Runoff ◽

Stormwater Quality ◽

Lulc Change ◽

Bare Land ◽

Runoff Quality

Stormwater runoff quality is sensitive to land use and land cover (LULC) change. It is difficult to understand their relationship in predicting the pollution potential and developing watershed management practices to eliminate or reduce the pollution risk. In this study, the relationship between LULC change and stormwater runoff quality in two separate monitoring sites comprising a construction area (Site 1) and mixed land use (Site 2) was analyzed using geographic information system (GIS), event mean concentration (EMC), and correlation analysis. It was detected that bare land area increased, while other land use areas such as agriculture, commercial, forest, grassland, parking lot, residential, and road reduced. Based on the analyses performed, high maximum range and average EMCs were found in Site 2 for most of the water pollutants. Also, urban areas and increased conversion of LULC into bare land corresponded to degradation of stormwater quality. Correlation analysis between LULC and stormwater quality showed the influence of different factors such as farming practices, geographical location, and amount of precipitation, vegetation loss, and anthropogenic activities in monitoring sites. This research found that GIS application was an efficient tool for monthly monitoring, validation and statistical analysis of LULC change in the study area.

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Impact of Land Use/Land Cover Change on Hydrological Components in Chongwe River Catchment

Sustainability ◽

10.3390/su11226415 ◽

2019 ◽

Vol 11 (22) ◽

pp. 6415 ◽

Cited By ~ 4

Author(s):

Tena ◽

Mwaanga ◽

Nguvulu

Keyword(s):

Land Use ◽

Land Cover ◽

Landsat Imagery ◽

Irrigated Agriculture ◽

Catchment Management ◽

Land Use Land Cover ◽

Weap Model ◽

Lulc Change ◽

River Catchment ◽

The Impact

Chongwe River Catchment, a sub-catchment of the Zambezi River Basin, has been experiencing changes in land use/land cover (LULC) and in its hydrology. This study aims to assess the impact of LULC changes on the catchment’s hydrological components such as streamflow, evapotranspiration and water abstractions. LULC change data, detected from the 1984, 1994, 2014 and 2017 USGS Landsat imagery using a maximum likelihood supervised classifier, were integrated into the WEAP Model along with soil, slope and hydro–climate data. The results showed that between 1984 and 2017 built-up area increased by 382.77% at 6.97 km2/year, irrigated agriculture increased by 745.62% at 1.70 km2/year, rainfed farms/ranch/grassland increased by 14.67% at 14.53 km2/year, forest land decreased by 41.11% at 22.33 km2/year and waterbodies decreased by 73.95% at 0.87 km2/year. Streamflow increased at a rate of 0.13 Mm3 per annum in the wet seasons and showed a high variation with flow volume of 79.68 Mm3 in February and 1.01 Mm3 in September. Annual actual evapotranspiration decreased from 840.6 mm to 796.3 mm while annual water abstraction increased from 8.94 mm to 23.2 mm from the year 1984 to 2017. The pattern of LULC change between 1984 and 2017 has negatively impacted the hydrology of the Chongwe River Catchment. From these findings, an integrated catchment management and protection approach is proposed to mitigate the negative impacts of LULC dynamics on hydrological components in the Chongwe River Catchment.

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Land-Use/Land-Cover Change Analysis and Urban Growth Modelling in the Greater Accra Metropolitan Area (GAMA), Ghana

Urban Science ◽

10.3390/urbansci3010026 ◽

2019 ◽

Vol 3 (1) ◽

pp. 26 ◽

Cited By ~ 18

Author(s):

Bright Addae ◽

Natascha Oppelt

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Urban Growth ◽

Urban Areas ◽

City Planning ◽

Land Use Land Cover ◽

Vegetative Cover ◽

Landsat Images ◽

Urban Extent

A rapid increase in the world’s population over the last century has triggered the transformation of the earth surface, especially in urban areas, where more than half of the global population live. Ghana is no exception and a high population growth rate, coupled with economic development over the last three decades, has transformed the Greater Accra region into a hotspot for massive urban growth. The urban extent of the region has expanded extensively, mainly at the expense of the vegetative cover in the region. Although urbanization presents several opportunities, the environmental and social problems cannot be underestimated. Therefore, the need to estimate the rate and extent of land use/land cover changes in the region and the main drivers of these changes is imperative. Geographic Information Systems (GIS) and remote sensing techniques provide effective tools in studying and monitoring land-use/land-cover change over space and time. A post classification change detection of multiple Landsat images was conducted to map and analyse the extent and rate of land use/land cover change in the region between 1991 and 2015. Subsequently, the urban extent of the region was forecasted for the year 2025 using the Markov Chain and the Multi-Layer Perceptron neural network, together with drivers representing proximity, biophysical, and socio-economic variables. The results from the research revealed that built-up areas increased by 277% over the 24-year study period. However, forest areas experienced massive reduction, diminishing from 34% in 1991 to 6.5% in 2015. The 2025 projected land use map revealed that the urban extent will massively increase to cover 70% of the study area, as compared to 44% in 2015. The urban extent is also anticipated to spill into the adjoining districts mainly on the western and eastern sides of the region. The success of this research in generating a future land-use map for 2025, together with the other significant findings, demonstrates the usefulness of spatial models as tools for sustainable city planning and environmental management, especially for urban planners in developing countries.

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Remote Sensing-Based Quantification of the Relationships between Land Use Land Cover Changes and Surface Temperature over the Lower Himalayan Region

Sustainability ◽

10.3390/su11195492 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5492 ◽

Cited By ~ 8

Author(s):

Ullah ◽

Tahir ◽

Akbar ◽

Hassan ◽

Dewan ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface ◽

Urban Areas ◽

Linear Regression Analysis ◽

Support Vector ◽

Land Use Land Cover ◽

Landsat Images ◽

Total Study Area

Population growth and population inflow from other regions has caused urbanization which altered land use land cover (LULC) in the lower Himalayan regions of Pakistan. This LULC change increased the land surface temperature (LST) in the region. LULC and LST changes were assessed for the period of 1990–2017 using Landsat data and the support vector machine (SVM) method. A combined cellular automata and artificial neural network (CA-ANN) prediction model was used for simulation of LULC changes for the period of 2032 and 2047 using transition potential matrix obtained from the data years of 2002 and 2017. The accuracy of the CA-ANN model was validated using simulated and classified images of 2017 with correctness value of 70% using validation modules in QGIS. The thermal bands of Landsat images from the years 1990, 2002 and 2017 were used for LST derivation. LST acquired for this period was then modeled for 2032 and 2047 using urban indices (UI) and linear regression analysis. The SVM land cover classification results showed a 5.75% and 4.22% increase in built-up area and bare soil respectively, while vegetation declined by 9.88% during 1990–2017. The results of LST for LULC classes showed that the built-up area had the highest mean LST as compared to other classes. The future projection of LULC and LST showed that the built-up area may increase by 12.48% and 14.65% in 2032 and 2047, respectively, of the total LULC area which was ~11% in 2017. Similarly, the area with temperature above 30 °C could be 44.01% and 58.02% in 2032 and 2047, respectively, of the total study area which was 18.64% in 2017. This study identified major challenges for urban planners to mitigate the urban heat island (UHI) phenomenon. In order to address the UHI in the study area, an urban planner might focus on urban plantation and decentralization of urban areas.

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Impact of Climate and Land Use/Land Cover Change on the Water Resources of a Tropical Inland Valley Catchment in Uganda, East Africa

Climate ◽

10.3390/cli8070083 ◽

2020 ◽

Vol 8 (7) ◽

pp. 83

Author(s):

Geofrey Gabiri ◽

Bernd Diekkrüger ◽

Kristian Näschen ◽

Constanze Leemhuis ◽

Roderick van der Linden ◽

...

Keyword(s):

Climate Change ◽

Land Use ◽

Water Resources ◽

Land Cover ◽

Rainy Season ◽

Hydrological Processes ◽

Land Use Land Cover ◽

Lulc Change ◽

Inland Valley ◽

Headwater Catchment

The impact of climate and land use/land cover (LULC) change continues to threaten water resources availability for the agriculturally used inland valley wetlands and their catchments in East Africa. This study assessed climate and LULC change impacts on the hydrological processes of a tropical headwater inland valley catchment in Uganda. The hydrological model Soil and Water Assessment Tool (SWAT) was applied to analyze climate and LULC change impacts on the hydrological processes. An ensemble of six regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment for two Representative Concentration Pathways (RCPs), RCP4.5 and RCP8.5, were used for climate change assessment for historical (1976–2005) and future climate (2021–2050). Four LULC scenarios defined as exploitation, total conservation, slope conservation, and protection of headwater catchment were considered. The results indicate an increase in precipitation by 7.4% and 21.8% of the annual averages in the future under RCP4.5 and RCP8.5, respectively. Future wet conditions are more pronounced in the short rainy season than in the long rainy season. Flooding intensity is likely to increase during the rainy season with low flows more pronounced in the dry season. Increases in future annual averages of water yield (29.0% and 42.7% under RCP4.5 and RCP8.5, respectively) and surface runoff (37.6% and 51.8% under RCP4.5 and RCP8.5, respectively) relative to the historical simulations are projected. LULC and climate change individually will cause changes in the inland valley hydrological processes, but more pronounced changes are expected if the drivers are combined, although LULC changes will have a dominant influence. Adoption of total conservation, slope conservation and protection of headwater catchment LULC scenarios will significantly reduce climate change impacts on water resources in the inland valley. Thus, if sustainable climate-smart management practices are adopted, the availability of water resources for human consumption and agricultural production will increase.

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Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.8.084596 ◽

2013 ◽

Vol 8 (1) ◽

pp. 084596 ◽

Cited By ~ 16

Author(s):

Zhongchang Sun ◽

Xinwu Li ◽

Wenxue Fu ◽

Yingkui Li ◽

Dongsheng Tang

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Surface Runoff ◽

Urban Areas ◽

Land Use Land Cover ◽

Long Term Effects ◽

Effects Of Land Use ◽

Beijing China

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IDENTIFYING LAND USE AND LAND COVER (LULC) CHANGE FROM 2000 TO 2025 DRIVEN BY TOURISM GROWTH: A STUDY CASE IN BALI

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-1621-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1621-1627

Author(s):

A. B. Rimba ◽

T. Atmaja ◽

G. Mohan ◽

S. K. Chapagain ◽

A. Arumansawang ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Agricultural Land ◽

Growth Direction ◽

Landsat 8 ◽

Open Area ◽

Landsat Images ◽

Lulc Change ◽

Tourism Activity ◽

Accuracy Of Prediction

Abstract. Bali has been open to tourism since the beginning of the 20th century and is known as the first tourist destination in Indonesia. The Denpasar, Badung, Gianyar, and Tabanan (Sarbagita) areas experience the most rapid growth of tourism activity in Bali. This rapid tourism growth has caused land use and land cover (LULC) to change drastically. This study mapped the land-use change in Bali from 2000 to 2025. The land change modeller (LCM) tool in ArcGIS was employed to conduct this analysis. The images were classified into agricultural land, open area, mangrove, vegetation/forest, and built-up area. Some Landsat images in 2000 and 2015 were exploited in predicting the land use and land cover (LULC) change in 2019 and 2025. To measure the accuracy of prediction, Landsat 8 OLI images for 2019 were classified and tested to verify the LULC model for 2019. The Multi-Layer Perceptron (MLP) neural network was trained with two influencing factors: elevation and road network. The result showed that the built-up growth direction expanded from the Denpasar area to the neighbouring areas, and land was converted from agriculture, open area and vegetation/forest to built-up for all observation years. The built-up was predicted growing up to 43 % from 2015 to 2025. This model could support decision-makers in issuing a policy for monitoring LULC since the Kappa coefficients were more than 80% for all models.

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