scholarly journals Future land use change simulations for the Lepelle River Basin using Cellular Automata Markov model with Land Change Modeller-generated transition areas

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 796
Author(s):  
Darlington Chineye Ikegwuoha ◽  
Harold Louw Weepener ◽  
Megersa Olumana Dinka
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
River Basin ◽  
Land Resource ◽  
Land Use Land Cover ◽  
Land Change ◽  
Resource Managers ◽  
Global Environmental ◽  
Natural Resource Managers ◽  
Transition Areas

Background: Land use/land cover (LULC), change is one of the major contributors to global environmental and climate variations. The ability to predict future LULC is crucial for environmental engineers, civil engineers, urban designers, and natural resource managers for planning activities. Methods: TerrSet Geospatial Monitoring and Modelling System in conjunction with ArcGIS Pro 2.8 were used to process LULC data for the region of the Lepelle River Basin (LRB) of South Africa. Driver variables such as population density, slope, elevation as well as the Euclidean distances of cities, roads, highways, railroads, parks and restricted areas, towns to the LRB in combination with LULC data were analysed using the Land Change Modeller (LCM) and Cellular-Automata Markov (CAM) model. Results: The results reveal an array of losses (-) and gains (+) for certain LULC classes in the LRB by the year 2040: natural vegetation (+8.5%), plantations (+3.5%), water bodies (-31.6%), bare ground (-8.8%), cultivated land (-29.3%), built-up areas (+10.6%) and mines (+14.4%). Conclusions: The results point to the conversion of land uses from natural to anthropogenic by 2040. These changes also highlight how the potential losses associated with resources such as water will negatively impact society and ecosystem functioning in the LRB by exacerbating water scarcity driven by climate change. This modelling study seeks to provides a decision support system for predicting future land resource utilization in the LRB and perhaps assist for planning purposes.

scholarly journals Future land use change simulations for the Lepelle River Basin using Cellular Automata Markov model with Land Change Modeller-generated transition areas

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 796
Author(s):  
Darlington Chineye Ikegwuoha ◽  
Harold Louw Weepener ◽  
Megersa Olumana Dinka
Keyword(s):  
Cellular Automata ◽  
Land Cover ◽  
River Basin ◽  
Land Resource ◽  
Climate Variations ◽  
Land Change ◽  
Global Environmental ◽  
Euclidean Distances ◽  
Transition Areas ◽  
Cam Model

Background: Land cover/land cover (LULC) change is one of the major contributors to global environmental and climate variations. The ability to predict future LULC is crucial for environmental engineers, civil engineers, urban designers, and natural resources managers for planning activities. Methods: TerrSet Geospatial Monitoring and Modelling System and ArcGIS Pro 2.8 were used to process LULC data for the region of the Lepelle River Basin (LRB) of South Africa. Driver variables such as population density, slope, elevation as well as the Euclidean distances of cities, roads, highways, railroads, parks and restricted areas, towns to the LRB in combination with LULC data were analysed using the Land Change Modeller (LCM) and Cellular-Automata Markov (CAM) model. Results: The results reveal an array of losses (-) and gains (+) for certain LULC classes in the LRB by the year 2040: natural vegetation (+8.5%), plantations (+3.5%), water bodies (-31.6%), bare ground (-8.8%), cultivated land (-29.3%), built-up areas (+10.6%) and mines (+14.4%). Conclusions: The results point to the conversion of land uses from natural to anthropogenic by 2040. These changes also highlight how the potential losses associated with resources such as water that will negatively impact society and ecosystem functioning in the LRB by exacerbating water scarcity driven by climate change. This modelling study provides a decision support system for the establishment of sustainable land resource utilization policies in the LRB.

scholarly journals Land use/ Land cover Change Detection in Auranga River Basin, Jharkhand

2020 ◽  
Vol 66 (1) ◽  
pp. 51-58
Author(s):  
Chnadrakesh Maurya ◽  
◽  
V. N. Sharma ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Supervised Classification ◽  
Land Resource ◽  
Land Use Land Cover ◽  
Ancillary Data ◽  
Classification Technique ◽  
Spatio Temporal ◽  
Cover Pattern

Land use is a man-made dynamic process in which human uses land resource to fulfil their various economic, social and cultural needs and at the same time it also provides a base for development. The proper management needed for sustainable development of land can improve the eco-system and its productivity in a particular geographical region. The present study focuses on spatio-temporal changes in land use and land cover pattern in Auranga river basin of Jharkhand using geospatial approach. Supervised classification technique was applied in this study to detect land use/ land cover changes. The main objective of the study is to analyse temporal change of land use/ land cover pattern during 1996, 2007 and 2018 using various dataset as well as other ancillary data. The result reveales both increase and decrease of the different land use/ land cover classes from 1996 to 2018.

scholarly journals Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

2020 ◽  
Vol 30 (1) ◽  
pp. 273-286
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar
Keyword(s):  
Land Use ◽  
Image Segmentation ◽  
Cellular Automata ◽  
Land Cover ◽  
Satellite Imagery ◽  
Current Model ◽  
Discrete Dynamical System ◽  
Land Use Land Cover ◽  
Data Set ◽  
Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

scholarly journals Effect of Land Use Land Cover and Climate Change on River Flow and Soil Loss in Didessa River Basin, South West Blue Nile, Ethiopia

Hydrology ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 2 ◽  
Author(s):  
Kinati Chimdessa ◽  
Shoeb Quraishi ◽  
Asfaw Kebede ◽  
Tena Alamirew
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Sediment Yield ◽  
Soil Loss ◽  
River Flow ◽  
Land Use Land Cover ◽  
The Impact ◽  
Soil Losses

In the Didessa river basin, which is found in Ethiopia, the human population number is increasing at an alarming rate. The conversion of forests, shrub and grasslands into cropland has increased in parallel with the population increase. The land use/land cover change (LULCC) that has been undertaken in the river basin combined with climate change may have affected the Didessa river flow and soil loss. Therefore, this study was designed to assess the impact of LULCC on the Didessa river flow and soil loss under historical and future climates. Land use/land cover (LULC) of the years 1986, 2001 and 2015 were independently combined with the historical climate to assess their individual impacts on river flow and soil loss. Further, the impact of future climates under Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) scenarios on river flow and soil loss was assessed by combining the pathways with the 2015 LULC. A physically based Soil and Water Assessment Tool (SWAT2012) model in the ArcGIS 10.4.1 interface was used to realize the purpose. Results of the study revealed that LULCC that occurred between 1986 and 2015 resulted in increased average sediment yield by 20.9 t ha−1 yr−1. Climate change under RCP2.6, RCP4.5 and RCP8.5 combined with 2015 LULC increased annual average soil losses by 31.3, 50.9 and 83.5 t ha−1 yr−1 compared with the 2015 LULC under historical climate data. It was also found that 13.4%, 47.1% and 87.0% of the total area may experience high soil loss under RCP2.6, RCP4.5 and RCP8.5, respectively. Annual soil losses of five top-priority sub catchments range from 62.8 to 57.7 per hectare. Nash Stuncliffe Simulation efficiency (NSE) and R2 values during model calibration and validation indicated good agreement between observed and simulated values both for flow and sediment yield.

Evaluation of global land use/land cover products for hydrologic simulation in the Upper Yom River Basin, Thailand

2020 ◽  
Vol 708 ◽  
pp. 135148 ◽  
Author(s):  
Chirayut Chirachawala ◽  
Sangam Shrestha ◽  
Mukand S. Babel ◽  
Salvatore G.P. Virdis ◽  
Supattana Wichakul
Keyword(s):  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Land Use Land Cover ◽  
Hydrologic Simulation ◽  
Global Land
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