scholarly journals Effects of land use/land cover and climatic change on the ecosystem of North Ningxia, China

2020 ◽  
Vol 13 (20) ◽  
Author(s):  
Hazem T. Abd El-Hamid ◽  
Wei Caiyong ◽  
Mohammed A. Hafiz ◽  
Elhadi K. Mustafa
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Climatic Change ◽  
Industrial Development ◽  
Environmental Changes ◽  
Yellow River ◽  
The Yellow River ◽  
Land Use Land Cover ◽  
Effective Strategies ◽  
Total Percentage

AbstractLand use/land cover change (LUCC) and climate changes are responsible for degradation of any ecosystem in arid and semi-arid regions. Studying the ecological variations is particularly essential for any type of sustainable development, in which LUCC considers as one of the chief inputs. The main objective is to evaluate the impacts of LUCC and climatic changes on the Ecosystem Vulnerability (ESV) using remote sensing and some statistical models around the Yellow River, Ningxia, China. Eleven classes of LUCC were identified during 1995 and 2019: village land, bare land, grassland, industrial land, irrigated land, swamp land, tidal flat, transportation land, urban land, water bodies, and water channels. Grassland may be decreased annually with percentage − 5.873% due to some human activities and environmental changes in climate from one season to another. About 24.23 km2 and 24.86 km2 was converted from grassland to industrial lands and irrigated lands, respectively. ESV has been calculated using LULC, DEM, slope, soil, and geology. About 45% and 60% of 1995 and 2019, respectively, undergone moderate vulnerability. The annual rate of ESVI decreased in low and reasonable but it was increased in moderate, high, and extreme showing – 4.166% as a total percentage of annual vulnerability. High vulnerability area needs proper management. Majority of vegetation area is located in zone under the moderate vulnerability zone; in contrast, grasslands were subjected to high vulnerability. Areas around the Yellow River were subjected to drought and flooding due to climatic change affecting negatively on the production of crops. Also, the desert lands of the study area have been turned to agriculture according to statistical model. Population growth, industrial development, and governmental policies for ecosystem protection were responsible for major changes. This study is more beneficial for decision-making in eco-environmental protecting and planning. Results of this study could help planners in formulating effective strategies for better management of ecosystem.

The change of land cover and land use and its impact factors in upriver key regions of the Yellow River

2009 ◽  
Vol 30 (5) ◽  
pp. 1251-1265 ◽  
Author(s):  
Lixin Dong ◽  
Wenke Wang ◽  
Mingguo Ma ◽  
Jinling Kong ◽  
Frank Veroustraete
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Yellow River ◽  
Impact Factors ◽  
The Yellow River

scholarly journals Land use Land Cover Mapping using Modified Ant Colony Optimization Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 100-106
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Environmental Changes ◽  
Optimization Technique ◽  
Unsupervised Classification ◽  
Ant Colony ◽  
Landsat 8 ◽  
Land Use Land Cover

Land use Land cover classification is an important aspect for managing natural resources and monitoring environmental changes. Urban expansion becomes one of the major challenges for the administrator. The LANDSAT 8 images are processed using the open source GRASS (Geographic Resource Analysis Support System). Unsupervised classification technique based on Ant Colony Optimization (ACO) algorithm has been modified and proposed as Modified Ant Colony Optimization (MACO) for LULC classification. In order to improve the classification accuracy of the proposed algorithm, we have combined spatial, spectral and texture features to extract more information of homogeneous land surface. The classification accuracy of the proposed algorithm has been compared with other unsupervised classification methods such as k-means, ISODATA and ACO algorithms. The overall classification accuracy of proposed unsupervised MACO algorithm has been increased by 11.24 %, 8.24% for open space and water bodies class, respectively as compared to ACO algorithm.

scholarly journals LAND USE/LAND COVER CHANGE DETECTION USING MULTI–TEMPORAL SATELLITE DATASET: A CASE STUDY IN ISTANBUL NEW AIRPORT

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 17-22 ◽  
Author(s):  
D. Akyürek ◽  
Ö. Koç ◽  
E. M. Akbaba ◽  
F. Sunar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Environmental Changes ◽  
Satellite Image ◽  
The Black Sea ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Environmental Consequences ◽  
Transportation Capacity ◽  
Sentinel 2A

<p><strong>Abstract.</strong> In recent years, especially in metropolitan cities such as Istanbul, the emerging needs of the increasing population and demand for better air transportation capacity have led to big environmental changes. One of them is originated due to the construction of the new airport (Istanbul Grand Airport &amp;ndash; IGA), located on the Black Sea coast on the European side of Turkey and expected as “The biggest hub in Europe” by the early 2020s. The construction has five phases and first construction phase is scheduled to finish up by the end of 2018. With an advanced space technologies including remote sensing, environmental consequences due to Land Use/Land Cover changes (LULC) can be monitored and determined efficiently. The aim of this paper is to analyse LULC changes especially in the forest areas and water bodies by using two different satellite image dataset. In this context, supervised classification method and different spectral indices are applied to both Landsat-8 (2013&amp;ndash;2017) and Sentinel 2A (2015&amp;ndash;2017) image datasets to demonstrate the total and annual changes during the construction of the first phase. The efficiency of two datasets is outlined by comparison of the output thematic map accuracies.</p>

scholarly journals Analysis of the Future Land Use Land Cover Changes in the Gaborone Dam Catchment Using CA-Markov Model: Implications on Water Resources

2021 ◽  
Vol 13 (13) ◽  
pp. 2427
Author(s):  
Botlhe Matlhodi ◽  
Piet K. Kenabatho ◽  
Bhagabat P. Parida ◽  
Joyce G. Maphanyane
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Markov Model ◽  
Water Resource ◽  
Water Body ◽  
Environmental Changes ◽  
Resource Planning ◽  
Catchment Management ◽  
Land Use Land Cover ◽  
Lulc Changes

Land use/land cover (LULC) changes have been observed in the Gaborone dam catchment since the 1980s. A comprehensive analysis of future LULC changes is therefore necessary for the purposes of future land use and water resource planning and management. Recent advances in geospatial modelling techniques and the availability of remotely sensed data have become central to the monitoring and assessment of both past and future environmental changes. This study employed the cellular automata and Markov chain (CA-Markov) model combinations to simulate future LULC changes in the Gaborone dam catchment. Classified Landsat images from 1984, 1995, 2005 and 2015 were used to simulate the likely LULCs in 2015 and 2035. Model validation compared the simulated and observed LULCs of 2015 and showed a high level of agreement with Kappa variation estimates of Kno (0.82), Kloc (0.82) and Kstandard (0.76). Simulation results indicated a projected increase of 26.09%, 65.65% and 55.78% in cropland, built-up and bare land categories between 2015 and 2035, respectively. Reductions of 16.03%, 28.76% and 21.89% in areal coverage are expected for shrubland, tree savanna and water body categories, respectively. An increase in built-up and cropland areas is anticipated in order to meet the population’s demand for residential, industry and food production, which should be taken into consideration in future plans for the sustainability of the catchment. In addition, this may lead to water quality and quantity (both surface and groundwater) deterioration in the catchment. Moreover, water body reductions may contribute to water shortages and exacerbate droughts in an already water-stressed catchment. The loss of vegetal cover and an increase in built-up areas may result in increased runoff incidents, leading to flash floods. The output of the study provides useful information for land use planners and water resource managers to make better decisions in improving future land use policies and formulating catchment management strategies within the framework of sustainable land use planning and water resource management.

scholarly journals Spatial change detections and inventory of wetlands in Yenagoa Urban Area: Bayelsa State, Nigeria

2021 ◽  
Vol 6 (6) ◽  
pp. 230-240
Author(s):  
Eze Promise I ◽  
Elemuwa IC ◽  
Lawrence Hart
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Area ◽  
Urban Expansion ◽  
Rate Of Change ◽  
Percentage Change ◽  
Annual Rate ◽  
Land Use Land Cover ◽  
Total Percentage

Yenegoa Town has in recent years witnessed rapid City growth and Urban development and much of these developments are unplanned and unregulated. This has seriously impacted on wetlands in several locations of the town as persistent Wetlands reclamations are being witnessed in study area. This prompted the need for the study which is aimed to map wetlands location in Yenagoa’s urban area using GIS and Remote Sensing approach. The study analyzes land use/land cover changes (LULC) using LANDSAT(5) TM, LANDSAT(5) ETM and LANDSAT(7) OLI satellite imageries of 1990, 2000, 2010 and 2020 respectively. Through this study, the pattern of urban expansion for Thirty years were been studied. The satellite imageries covering the area were acquired and analyzed using ArcGIS 10.1 and ENVI 5.0 software. The supervised image classification method was adopted and the classification results were validated using the Kappa Index of Agreement (KIA) yielding an accuracy of 0.69m for year 1990, 0.62m for year 2000, 0.58m for year 2010 and 0.73m for 2020. A total area of 13,741.4 hectares was delineated in the study area which is identified as Yenagoa’s urban area. After processing the imageries, four land use/land cover (LULC) classes where considered, and the results shows that Built-up area continuously increased in land area from 1990 -2020 with total percentage change of 273.31% (4,178.7ha) and total annual rate of change of 25.33. Vegetation have total percentage change of 38.55% (974.34Ha) and total annual rate of change of 3.85, wetland cover loss with total percentage Change of 61.96% (-51,44.99ha) and total annual rate of change of -6.19ha, and the water body have loss of total percentage of -2.16% (-8.05Ha) and total annual rate of change of -0.22ha wetland at Yenegwe loss by Total %change of -29.918% ( -197.95ha), and wetland at Igbogene loss by total percentage change of -36.028% (-358.7ha). The research findings also revealed that the wetlands in Anyama, Swali, Kpansia and Opolo Towns were completely lost from the third Epoch of 2010, this may be as a result of persistence reclamation of wetland in this parts of the study area. The Markov Chain predicted model were utilized for predicting the likely changes in land use land cover for a period of thirty years. The predicted results also indicates that wetland size of 32.47,%, 30.68% and 28.99% may likely be lost by the year 2030, 2040 and 2050 respectively in study area if no action is taking by concerned authorities to forestall the factors responsible for the lost in wetland. The study justified the dynamics of remote sensing and GIS techniques in modeling wetlands changees over these periods, wise use of wetland resources and improvement of institutional arrangement were recommended so that wetland policies can be fully integrated into the planning process across all disciplines.

scholarly journals Land Use and Land Cover Change in the Yellow River Basin from 1980 to 2015 and Its Impact on the Ecosystem Services

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1080
Author(s):  
Bo Liu ◽  
Libo Pan ◽  
Yue Qi ◽  
Xiao Guan ◽  
Junsheng Li
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Yellow River ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Restoration Project ◽  
The Yellow River Basin

Land use and land cover change is an important driving force for changes in ecosystem services. We defined several important human-induced land cover change processes such as Ecological Restoration Project, Cropland Expansion, Land Degradation, and Urbanization by the land use / land cover transition matrix method. We studied human-induced land cover changes in the Yellow River Basin from 1980 to 2015 and evaluated its impact on ecosystem service values by the benefit transfer method and elasticity coefficient. The results show that the cumulative area of human-induced land cover change reaches 65.71 million ha from 1980 to 2015, which is close to the total area of the Yellow River Basin. Before 2000, Ecological Restoration Project was the most important human-induced land cover change process. However, due to the large amount of cropland expansion and land degradation, the area of natural vegetation was reduced and the ecosystem value declined. Since 2000, due to the implementation of the "Grain for Green" program, the natural vegetation of upstream area and midstream area of Yellow River Basin has been significantly improved. This implies that under an appropriate policy framework, a small amount of human-induced land cover change can also improve ecosystem services significantly.

scholarly journals Quantifying the Land Use and Land Cover Changes in the Yellow River Basin while Accounting for Data Errors Based on GlobeLand30 Maps

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Xiaofang Sun ◽  
Guicai Li ◽  
Junbang Wang ◽  
Meng Wang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Yellow River ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Data Errors ◽  
The Yellow River Basin ◽  
Second Period ◽  
Land Changes

Land use and land cover (LULC) change influences many issues such as the climate, ecological environment, and economy. In this study, the LULC transitions in the Yellow River Basin (YRB) were analyzed based on the GlobeLand30 land use data in 2000, 2010, and 2020. The intensity analysis method with hypothetical errors calculation was used, which could explain the deviations from uniform land changes. The strength of the evidence for the deviation was revealed even though the confusion matrixes of the LULC data at each time point for the YRB were unavailable. The results showed that at the interval scale, the land transition rate increased from the first to the second period for all of the upper, middle, and lower reaches. The exchange component was larger than the quantity and shift component, and the gross change was 4.1 times larger than the net change. The size of cultivated land decreased during both intervals. The artificial surfaces gains were active for all three reaches and had strong evidence. A hypothetical error in 93% of the 2000 data and 58% of the 2010 data can explain deviations from uniform transition given woodland gain during 2000–2010 and 2010–2020. Ecological restoration projects such as Grain for Green implemented in 2000 in the upper reaches resulted in the woodland increase.

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