scholarly journals CELLULAR AUTOMATA (CA) CONTIGUITY FILTERS IMPACTS ON CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS RESULTS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1585-1591
Author(s):  
M. S. Mondal ◽  
N. Sharma ◽  
M. Kappas ◽  
P. K. Garg
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Land Cover Change ◽  
Correlation Coefficient ◽  
Satellite Images ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Land Cover Map ◽  
Liss Iii

Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. Land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.

scholarly journals Land use/land cover change prediction in Dak Nong Province based on remote sensing and Markov Chain Model and Cellular Automata

2018 ◽  
Vol 9 (3) ◽  
pp. 132-140 ◽  
Author(s):  
Thi Thanh Huong Nguyen ◽  
Thi Thuy Phuong Ngo
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Satellite Images ◽  
Residential Area ◽  
Forest Conversion ◽  
Chain Model ◽  
Changes Over Time ◽  
Spot 5 ◽  
Over Time

Land use and land cover changes (LULCC) including deforestation for agricultural land and others are elements that contribute on global environmental change. Therefore, understanding a trend of these changes in the past, current, and future is important for making proper decisions to develop in a sustainable way. This study analyzed land use and land cover (LULC) changes over time for Tuy Duc district belonging to Dak Nong province based on LULC maps classified from a set of multi-date satellite images captured in year 2003, 2006, 2009, and 2013 (SPOT 5 satellite images). The LULC spatio-temporal changes in the area were classified as perennial agriculture, cropland, residential area, grassland, natural forest, plantation and water surface. Based on these changes over time, potential LULC in 2023 was predicted using Cellular Automata (CA)–Markov model. The predicted results of the change in LULC in 2023 reveal that the total area of forest will lose 9,031ha accounting of 50% in total area of the changes. This may be mainly caused by converting forest cover to agriculture (account for 28%), grassland (12%) and residential area (9%). The findings suggest that the forest conversion needs to be controlled and well managed, and a reasonable land use plan should be developed in a harmonization way with forest resources conservation. Thay đổi sử dụng đất và thảm phủ (LULCC) bao gồm cả việc phá rừng để phát triển nông nghiệp và vì các mục đích khác là tác nhân đóng góp vào biến đổi môi trường toàn cầu. Vì vậy hiểu biết về khuynh hướng của sự thay đổi này trong quá khứ, hiện tại và tương lai là quan trọng để đưa ra những quyết định dúng đắn để phát triển bền vững. Nghiên cứu đã phân tích LULCC trong thời gian qua dựa vào các bản đồ sử dụng đất và thảm phủ (LULC) đã được phân loại từ một loạt ảnh vệ tinh đa phổ được thu chụp vào năm 2003, 2006, 2009 (ảnh SPOT 5). Những thay đổi LULC theo thời gian và không gian trong khu vực được phân loại thành đất nông nghiệp với cây dài ngày, cây ngắn ngày, thổ cư, trảng cỏ cây bụi, rừng tự nhiên, rừng trồng và mặt nước. Dựa trên sự thay đổi này theo thời gian, LULC tiềm năng cho năm 2023 đã được dự báo bằng cách sử dụng mô hình CA-Markov. Kết quả dự báo LULCC năm 2023 đã cho thấy tổng diện tích rừng bị mất khoảng 9,031 ha chiếm 50% trong tổng số diện tích thay đổi. Điều này chủ yếu là do chuyển đổi từ rừng tự nhiên sang canh tác nông nghiệp (chiếm 28%), trảng cỏ cây bụi (12%) và khu dân cư (9%). Kết quả cho thấy việc chuyển đổi rừng cần phải được kiểm soát và quản lý tốt và một kế hoạch sử dụng đất hợp lý cần được xây dựng trong sự hài hòa với bảo tồn tài nguyên rừng.

scholarly journals Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)

2020 ◽  
Vol 12 (24) ◽  
pp. 10452
Author(s):  
Auwalu Faisal Koko ◽  
Wu Yue ◽  
Ghali Abdullahi Abubakar ◽  
Roknisadeh Hamed ◽  
Akram Ahmed Noman Alabsi
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Land Cover ◽  
Markov Chain Model ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Lulc Change ◽  
Barren Land ◽  
Spatio Temporal

Monitoring land use/land cover (LULC) change dynamics plays a crucial role in formulating strategies and policies for the effective planning and sustainable development of rapidly growing cities. Therefore, this study sought to integrate the cellular automata and Markov chain model using remotely sensed data and geographical information system (GIS) techniques to monitor, map, and detect the spatio-temporal LULC change in Zaria city, Nigeria. Multi-temporal satellite images of 1990, 2005, and 2020 were pre-processed, geo-referenced, and mapped using the supervised maximum likelihood classification to examine the city’s historical land cover (1990–2020). Subsequently, an integrated cellular automata (CA)–Markov model was utilized to model, validate, and simulate the future LULC scenario using the land change modeler (LCM) of IDRISI-TerrSet software. The change detection results revealed an expansion in built-up areas and vegetation of 65.88% and 28.95%, respectively, resulting in barren land losing 63.06% over the last three decades. The predicted LULC maps of 2035 and 2050 indicate that these patterns of barren land changing into built-up areas and vegetation will continue over the next 30 years due to urban growth, reforestation, and development of agricultural activities. These results establish past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

scholarly journals Assessing Changes of Water Yield in Qinghai Lake Watershed of China

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 11 ◽  
Author(s):  
Xi-hong Lian ◽  
Yuan Qi ◽  
Hong-wei Wang ◽  
Jin-long Zhang ◽  
Rui Yang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Correlation Coefficient ◽  
Qinghai Lake ◽  
Water Yield ◽  
Land Use Land Cover ◽  
Cultivated Land ◽  
Human Welfare ◽  
Unused Land

Water yield is an important ecosystem service, which is directly related to human welfare and affects the sustainable development. Using the integrated valuation of environmental services and tradeoffs model (InVEST model), we simulated the dynamic change of water yield in Qinghai lake watershed, Qinghai, China, and verified the simulation results. This paper emphatically explored how precipitation change and land use/land cover change (LUCC) affected the change of water yield on the spatial and temporal scales. Before 2004, the areas of cultivated land and unused land showed a dramatic increasing tendency, while forestland and water area presented a decreasing trend. After 2004 cultivated land changed slowly, unused land decreased. Grassland revealed a general trend of decline during 1977–2018, while built-up land basically presented a linear increase. The results show that water yield fluctuated and increased during 1977–2018. From 1977 to 2000, the mean water yield in each sub-watershed showed an increasing trend and afterward a decreasing one. After 2000, the sub-watersheds basically showed an increasing tendency. There was a strong correlation, with a correlation coefficient of 0.954 ** (** correlation is significant at the 0.01 level), between precipitation and water yield. Land use/land cover change can change the hydrological state of infiltration, evapotranspiration, and water retention. Meanwhile, the correlation between built-up land and water yield was the highest, with a correlation coefficient of 0.932, followed by forestland, with a correlation coefficient of 0.897. Through the analysis of different scenarios, we found that compared with land use/land cover change, precipitation played a more dominant role in affecting water yield.

Sign in / Sign up

Export Citation Format

Share Document