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 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 Implementation of CCDC to produce the LCMAP Collection 1.0 annual land surface change product

2021 ◽  
Author(s):  
George Xian ◽  
Kelcy Smith ◽  
Danika Wellington ◽  
Josephine Horton ◽  
Qiang Zhou ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Surface ◽  
The United States ◽  
Land Resource ◽  
Land Change ◽  
Continuous Change ◽  
Ancillary Data ◽  
Surface Change

Abstract. The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985–2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5 %. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30 m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.

Analysis and prediction of land cover changes using the Land Change Modeler ( LCM ) in a semi‐arid river basin, Iran

2021 ◽  
Author(s):  
Sajad Khoshnood Motlagh ◽  
Amir Sadoddin ◽  
Amin Haghnegahdar ◽  
Saman Razavi ◽  
Abdolrassoul Salmanmahiny ◽  
...  
Keyword(s):  
Land Cover ◽  
River Basin ◽  
Land Cover Changes ◽  
Land Change ◽  
Land Change Modeler ◽  
Semi Arid

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 Improvement of the ESA CCI Land cover maps for water balance analysis in tropical regions: A case study in the Muda River Basin, Malaysia

2021 ◽  
Vol 36 ◽  
pp. 100837
Author(s):  
Mou Leong Tan ◽  
Yi Lin Tew ◽  
Kwok Pan Chun ◽  
Narimah Samat ◽  
Shazlyn Milleana Shaharudin ◽  
...  
Keyword(s):  
Land Cover ◽  
Water Balance ◽  
River Basin ◽  
Tropical Regions ◽  
Balance Analysis ◽  
Land Cover Maps
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