The International Arab Journal of Information Technology (IAJIT)


On Satellite Imagery of Land Cover Classification for Agricultural Development

Distribution of chronological land cover modifications has attained a vibrant concern in contemporary sustainability research. Information delivered by satellite remote sensing imagery plays momentous role in enumerating and discovering the expected land cover for vegetation. Fuzzy clustering has been found successful in implementing a significant number of optimization problems associated with machine learning due to its fractional membership degrees in several neighbouring constellations. This research establishes a framework on land cover classification for agricultural development. The approach is focused on object-oriented classification and is organized with a Fuzzy c-means clustering over segmentation on CIE L*a*b* colour scheme which provides analysis of vegetation coverage and enhances land planning for sustainable developments. This research investigates the land cover variations of the eastern province of Saudi Arabia throughout an elongated span of period from 1984 to 2018 to recognize the possible roles of the land cover alterations on farming. The Landsat satellite imagery and Geographical Information System (GIS), in tandem with Google Earth chronological imagery are employed for land use variation analysis. Experimental results exhibit a reasonable spread in the cultivated zones and reveal that this Colour Segmented Fuzzy Clustering (CSFC) strategy achieves better than the relevant counterpart approaches considering classification accuracy.

[1] Aljoufie M., Zuidgeest M., Brussel M., and Maarseveen M., “Spatial-Temporal Analysis of Urban Growth and Transportation in Jeddah City, Saudi Arabia,” Cities, vol. 31, pp. 57-68, 2013.

[2] Almadini A. and Hassaballa A., “Depicting Changes in Land Surface Cover at Al-Hassa Oasis of Saudi Arabia using Remote Sensing and GIS Techniques,” PloS ONE, vol. 14, no. 11, pp. 0 10 20 30 40 50 60 70 80 90 100 CSFCGATSGAPSOSVMSAReliefF Accuracy (%) Different Approaches BuildingsGroundsVegetationSand DunesWater Bodies On Satellite Imagery of Land Cover Classification for Agricultural Development 17 1-19, 2019.

[3] Alqurashi A. and Kumar L., “Land Use and Land Cover Change Detection in the Saudi Arabian Desert Cities of Makkah and Al-Taif Using Satellite Data,” Advanced Remote Sensing, vol. 3, no. 3, pp. 106-119, 2014.

[4] Alzahrani A., Bhuiyan M., and Akhter F., “Detecting COVID-19 Pneumonia over Fuzzy Image Enhancement on Computed Tomography Images,” Computational and Mathematical Methods in Medicine, vol. 2022, no. 1043299, pp. 1-12, 2022.

[5] Al-Gaadi K., Samdani M., and Patil V., “Assessment of Temporal Land Cover Changes in Saudi Arabia using Remotely Sensed Data,” Middle-East Journal of Scientific Research, vol. 9, no. 6, pp. 711-717, 2011.

[6] Al-Harbi K., “Monitoring of Agricultural Area Trend in Tabuk Region-Saudi Arabia using Landsat TM and SPOT Data,” The Egyptian Journal of Remote Sensing and Space Science, vol. 13, no. 1, pp. 37-42, 2010.

[7] Amini S., Saber M., Rabiei-Dastjerdi H., and Homayouni S., “Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series,” Remote Sensing, vol. 14, no. 11, pp. 2654, 2022.

[8] Badr E. and Al-Naeem A., “Assessment of Drinking Water Purification Plant Efficiency in Al-Hassa, Eastern Region of Saudi Arabia,” Sustainability, vol. 13, no. 11, pp. 1-17, 2021.

[9] Bhuiyan A. and Khan A., “Image Quality Assessment Employing RMS Contrast and Histogram Similarity,” The International Arab Journal of Information Technology, vol. 15, no. 6, pp. 983-989, 2018.

[10] Cox E., Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration, New Delhi, 2015.

[11] Fan H., Wei Q., and Shui D., “The Method of Extracting Land Classification Information by HRI-RefineNET Model,” IEEE Access, vol. 8, pp. 599-610, 2020.

[12] Häme T., Sirro L., Kilpi J., Seitsonen L., Andersson K., and Melkas T., “A Hierarchical Clustering Method for Land Cover Change Detection and Identification” Remote Sensing, vol. 12, no. 1751, pp. 1-22, 2020.

[13] Hemati M., Hasanlou M., Mahdianpari M., and Mohammadimanesh F., “A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth,” Remote Sensing, vol. 13, no. 2869, pp. 1-18, 2021.

[14] Khurana M. and Saxena V., “Green Cover Change Detection using a Modified Adaptive Ensemble of Extreme Learning Machines for North-Western India,” Journal of King Saud University-Computer and Information Sciences, vol. 33, no. 10, pp. 1265-1273, 2021.

[15] Lima R. and Marfurt K., “Convolutional Neural Network for Remote-sensing Scene Classification: Transfer Learning Analysis,” Remote Sensing, vol. 12, pp. 1-21, 2019.

[16] Macarringue L., Bolfe É., and Pereira P., “Developments in Land Use and Land Cover Classification Techniques in Remote Sensing: A Review,” Journal of Geographic Information System, vol. 14, no. 1, pp. 1-28, 2022.

[17] Memon N., Parikh H., Patel S., Patel D., and Patel V., “Automatic Land Cover Classification of Multi-Resolution Dualpol Data using Convolutional Neural Network (CNN) Remote Sensing Applications,” Society and Environment, vol. 22, pp. 1-7, 2021.

[18] Nasiri V., Deljouei A., Moradi F., Sadeghi S., and Borz S., “Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods,” Remote Sensing, vol. 14, no. 1977, pp. 1-18, 2022.

[19] Phiri D., Morgenroth J., and Xu C., “Four Decades of Land Cover and Forest Connectivity Study in Zambia-An Object-Based Image Analysis Approach,” International Journal of Applied Earth Observation and Geo-information, vol. 79, no. 7, pp. 97-109, 2019.

[20] Praticò S., Solano F., Fazio S., and Modica G., “Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time- Series and Input Image Composition Optimisation,” Remote Sensing, vol. 13, no. 4, pp. 586, 2021.

[21] Qian X. and Zhang L., “An Integration Method to Improve the Quality of Global Land Cover,” Advanced Space Research, vol. 69, no. 3, pp. 1427-1438, 2022.

[22] Rahman M., “Land Use and Land Cover Changes and Urban Sprawl in Riyadh, Saudi Arabia: An Analysis using Multi-Temporal Landsat Data and Shannon's Entropy Index,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 41, pp. 1017-1021, 2016.

[23] Rahman M., “Detection of Land Use/Land Cover Changes and Urban Sprawl in Al-Khobar, Saudi Arabia: An Analysis of Multi-Temporal Remote Sensing Data,” ISPRS International Journal of Geo-Information, vol. 5, no. 2, pp. 15, 2016.

[24] Salam A., “Population and Household Census, Kingdom of Saudi Arabia 2010: Facts and Figures,” International Journal of Humanities and Social Science, vol. 3, no. 16, pp. 258-263, 2013. 18 The International Arab Journal of Information Technology, Vol. 20, No. 1, January 2023

[25] Salih A., “Classification and Mapping of Land Cover Types and Attributes in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia using Landsat-7 Data,” Journal of Remote Sensing and GIS, vol. 7, no. 1, pp. 228, 2018.

[26] Sanz W., Saa-Requejo A., Díaz-Ambrona C., Ruiz-Ramos M., Rodríguez A., Iglesias E., Esteve P., Soriano B., and Tarquis A., “Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands,” Remote Sensing, vol. 13, no. 5, pp. 840, 2021.

[27] Szwliski R., Computer Vision: Algorithms and Applications, London, 2022.

[28] Taani A., AlFanatseh A., Taran A., and ALRashid F., “Monitoring Land use Changes in Al-Hofuf City using Geographic Information System (GIS) and Remote Sensing (RS) Techniques,” European Journal of Scientific Research, vol. 146, no. 3, pp. 267-283, 2017.

[29] Van T., Tran N., Bao H., Phuong D., Hoa P., and Han T., “Optical Remote Sensing Method for Detecting Urban Green Space as Indicator Serving City Sustainable Development,” Multidisciplinary Digital Publishing Institute Proceedings, vol. 2, no. 3, pp. 140, 2018.

[30] Xu L., Che Y., Pan J., and Gao A., “Multi- Structure Joint Decision-Making Approach for Land Use Classification of High-Resolution Remote Sensing Images Based on CNNs,” IEEE Access, vol. 8, pp. 42848-42863, 2020.

[31] Yao Y., Yan X., Luo P., Liang Y., Ren S., Hu Y., Han J., and Guan Q., “Classifying Land-Use Patterns by Integrating Time-Series Electricity Data and High-Spatial Resolution Remote Sensing Imagery,” International Journal of Applied Earth Observation and Geo-information, vol. 106, no. 102664, pp. 1-18, 2022.

[32] Youssef A., Abu abdullah M., Pradhan B., and Gaber A., “Agriculture Sprawl Assessment using Multi-Temporal Remote Sensing Images and its Environmental Impact, Al-Jouf, KSA,” Sustainability, vol. 11, no. 15, pp. 4177, 2019. Ali Alzahrani received his B.Eng. degree in Computer Engineering from Umm Al-Qura University, Makkah, Saudi Arabia, and His M.Sc. and Ph.D. in Computer Engineering from the University of Victoria, BC, Canada, in 2015 and 2018, respectively. He is an Assistant Professor at the Department of Computer Engineering, King Faisal University. His research interests include hardware security, encryption processors, image processing, systems-on-chip and so on. Al-Amin Bhuiyan graduated from University of Dhaka, Bangladesh and received his Ph. D from Osaka City University, Japan. He is an Associate Professor at the Department of Computer Engineering, King Faisal University, Saudi Arabia. Dr. Bhuiyan lent his teaching and research experiences at several Universities in Japan, Bangladesh and UK. His research interests include image processing, pattern recognition, computer graphics, neural networks, AI, robotic vision, and so on.