Application of Satellite Images and Artificial Intelligence to Monitor Land Cover Changes in Hanoi Area During 2013-2023 Period
Main Article Content
Abstract
Artificial intelligence (AI) and remote sensing technology have now increasingly improved their efficiency and reliability in monitoring the changes in land cover. With the amendment of the Vietnamese Law on Land in 2013 and the administrative boundary expansion of Hanoi, Hanoi experiences significant changes in land use and land cover for the last ten years. To monitor the actual land use changes in the area, this study used the Random Forest (RF) machine learning algorithm to classify the basic land covers, monitor, and analyze the spatial variation of land use and land cover in the 2013 to 2023 period. The study findings indicate a relatively high rate of expansion of construction zone area and a decrease in land cover related to water bodies and vegetated area. Water bodies decrease by an average of 0.8% annually, whereas the construction zone area increased by 7% of the total area.
Keywords:
Monitoring the change, Land cover, Google Earth Engine, Random Forest algorithms.
References
[1] L. J. M. Breim, Random Forests, Kluwer Academic Publishers Manufactured in the Netherlands,
Vol. 45, Iss. 5, 2001.
[2] N. T. P. Thi et al., Applying Random Forest Approach in Forecasting Flash Flood Susceptibility Area in Lao Cai Region, Journal of Mining and Earth Sciences, Vol. 61, Iss. 5, 2020, pp. 30-42.
[3] L. H. Trinh et al., Assessment and Prediction of Urban Land Use Changes of Hanoi City Using Remote Sensing and GIS Technique, Natural Sciences and Technolog Vol. 14, 2017,
pp. 176-187.
[4] T. N. Nguyen et al., Applcation of Remote Sensing and GIS in Studying Land Cover Changes in Thanh Tri District, Hanoi City in The Period 2001-2008, Doctoral Dissertation, 2011.
[5] N. T. Luyen, Urban Land Cover Classification with Venus Satellite Images for Hanoi, Vietnam, Doctoral Dissertation, 2019.
[6] G. Noel et al, Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone, Remote Sensing of Environment, Vol. 202, 2017, pp. 18-27.
[7] A. S. Mishra, Google Earth Engine: A Review of Its Applications and Future Directions, Journal of the Indian Society of Remote Sensing, Vol. 49,
Iss. 1, 2021, pp. 107-124.
[8] Hanoi Portal, An Overview of the Geography of Hanoi City, https://hanoi.gov.vn/diachihanoi/-/hn/ RtLibd2X8kEn/1001/124742/gioi-thieu-tong-quan-va-khai-quat-ve-ia-li-thanh-pho-ha-noi.html#:~:text=Th%E1%BB%A7%20%C4%91%C3%B4%20H%C3%A0%20N%E1%BB%99i%20sau,x%C3%A3%2C%20ph%C6%B0%E1%BB%9Dng%2C%20th%E1%BB%8B%20tr%E1%BA%A, 2023 (accessed on: April 10th , 2023).
[9] G. Robin et al., Random Forests for Big Data, Big Data Research, Vol. 9, 2017, pp. 28-46.
[10] B. Leo, Random Forests, Machine Learning,
Vol. 45, 2001, pp. 5-32.
[11] H. T. Kam, Random Decision Forests, Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 1995, pp. 278-282.
[12] S. Deliang et al., A Random Forest Model of Landslide Susceptibility Mapping Based on Hyperparameter Optimization Using Bayes Algorithm, Geomorphology, Vol. 362, 2020,
pp. 107201.
[13] L. Amalia et al., The Impact of Class Imbalance in Classification Performance Metrics Based on the Binary Confusion Matrix, Pattern Recognition, Vol. 91, 2019, pp. 216-231,
[14] C. Alexis et al., Spatial Analysis of Remote Sensing Image Classification Accuracy, Remote Sensing of Environment, Vol. 127, 2012,
pp. 237-246.
[15] V. Sofia et al., Confusion Matrix-Based Feature Selection, Vol. 120, 2011, pp. 710.
[16] P. Uday et al., Google Earth Engine Based Three Decadal Landsat Imagery Analysis for Mapping of Mangrove Forests and Its Surroundings in the Trat Province of Thai Land, 2018.
[17] K. Karren, G. Lightbody, R. Yacamini, Power System Harmonic Analysis Using the Kalman Filter, in 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No. 03CH37491), Vol. 2, 2003, pp. 752-757.
[18] L. V. Hao, L. T. P. Mi, Application of Remote Sensing and GIS to Monitor the Urbanization Process in Ho Chi Minh City in The Period 1989–2019, Journal of Hydrometeorology, Vol. 720, 2020, pp. 48-59.
[19] Department of Water Resouces Management, Symptoms of Ground Water Degradation in Hanoi City and Causes, http://dwrm.gov.vn/index.php?language=vi&nv=news&op=Khoa-hoc-Cong-nghe/cac-bieu-hien-suy-thoai-nuoc-duoi-dat-vung-thanh-pho-ha-noi-va-cac-nguyen-nhan-3036, 2023 (accessed on: April 10th, 2023).
[20] E. F. Lambin, H. J. Geist, E. Lepers, Dynamics of Land-use and Land-cover Change in Tropical Regions, Annual Review of Environment and Resources, Vol. 28, Iss. 1, 2003, pp. 205-241.
[21] T. Billie, F. L. Eric, A. Reenberg, The Emergence of Land Change Science for Global Environmental Change and Sustainability, Proceedings of the National Academy of Sciences, Vol. 104, Iss. 52, 2007, pp. 20666-20671.
[22] N. T. Xuan, D. T. B. Hoa, Application of Remote Sensing and GIS for Analysis of Wetland for Building Multi-Benefit Solution to Sustainable Uses of Wetland in Quang Yen Commune, Quang Ninh Province, Journal of Geodesy and Cartography, Vol. 22, 2014, pp. 24-31.
[23] M. K. Donald, Mathematical Contributions to the Theory of Evolution, VII, on the Correlation of Characters not Quantitatively Measurable, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 187, 1986, pp. 253.
[24] P. M. Hai et al., Impact of Urban Expansion to Cultural Heritage: the Case Study of the Complex of Hue Monu-ments, Journal of Geodesy and Cartography, Vol. 40, 2019, pp. 42-49.
[25] C. Jacob, A Coefficient of Agreement for Nominal Scales, Educational and Psychological Measurement, Vol. 20, No. 1, 1960, pp. 37-46.
[26] M. W. P. David, Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation, Journal of Machine Learning Technologies, Vol. 2, Iss. 1, 2011,
pp. 37-63.
[27] F. Bin Siddique et al., Impact of Image Quality on Object Detection Performance: A Study on Influence of JPEG Compression, Noise, and Resolution, IEEE Transactions on Image Processing, 2019.
[28] K. Nørvåg et al., The Importance of Data Quality for Classification Performance in Machine Learning, Journal of Intelligent Learning Systems and Applications, 2016.
Vol. 45, Iss. 5, 2001.
[2] N. T. P. Thi et al., Applying Random Forest Approach in Forecasting Flash Flood Susceptibility Area in Lao Cai Region, Journal of Mining and Earth Sciences, Vol. 61, Iss. 5, 2020, pp. 30-42.
[3] L. H. Trinh et al., Assessment and Prediction of Urban Land Use Changes of Hanoi City Using Remote Sensing and GIS Technique, Natural Sciences and Technolog Vol. 14, 2017,
pp. 176-187.
[4] T. N. Nguyen et al., Applcation of Remote Sensing and GIS in Studying Land Cover Changes in Thanh Tri District, Hanoi City in The Period 2001-2008, Doctoral Dissertation, 2011.
[5] N. T. Luyen, Urban Land Cover Classification with Venus Satellite Images for Hanoi, Vietnam, Doctoral Dissertation, 2019.
[6] G. Noel et al, Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone, Remote Sensing of Environment, Vol. 202, 2017, pp. 18-27.
[7] A. S. Mishra, Google Earth Engine: A Review of Its Applications and Future Directions, Journal of the Indian Society of Remote Sensing, Vol. 49,
Iss. 1, 2021, pp. 107-124.
[8] Hanoi Portal, An Overview of the Geography of Hanoi City, https://hanoi.gov.vn/diachihanoi/-/hn/ RtLibd2X8kEn/1001/124742/gioi-thieu-tong-quan-va-khai-quat-ve-ia-li-thanh-pho-ha-noi.html#:~:text=Th%E1%BB%A7%20%C4%91%C3%B4%20H%C3%A0%20N%E1%BB%99i%20sau,x%C3%A3%2C%20ph%C6%B0%E1%BB%9Dng%2C%20th%E1%BB%8B%20tr%E1%BA%A, 2023 (accessed on: April 10th , 2023).
[9] G. Robin et al., Random Forests for Big Data, Big Data Research, Vol. 9, 2017, pp. 28-46.
[10] B. Leo, Random Forests, Machine Learning,
Vol. 45, 2001, pp. 5-32.
[11] H. T. Kam, Random Decision Forests, Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 1995, pp. 278-282.
[12] S. Deliang et al., A Random Forest Model of Landslide Susceptibility Mapping Based on Hyperparameter Optimization Using Bayes Algorithm, Geomorphology, Vol. 362, 2020,
pp. 107201.
[13] L. Amalia et al., The Impact of Class Imbalance in Classification Performance Metrics Based on the Binary Confusion Matrix, Pattern Recognition, Vol. 91, 2019, pp. 216-231,
[14] C. Alexis et al., Spatial Analysis of Remote Sensing Image Classification Accuracy, Remote Sensing of Environment, Vol. 127, 2012,
pp. 237-246.
[15] V. Sofia et al., Confusion Matrix-Based Feature Selection, Vol. 120, 2011, pp. 710.
[16] P. Uday et al., Google Earth Engine Based Three Decadal Landsat Imagery Analysis for Mapping of Mangrove Forests and Its Surroundings in the Trat Province of Thai Land, 2018.
[17] K. Karren, G. Lightbody, R. Yacamini, Power System Harmonic Analysis Using the Kalman Filter, in 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No. 03CH37491), Vol. 2, 2003, pp. 752-757.
[18] L. V. Hao, L. T. P. Mi, Application of Remote Sensing and GIS to Monitor the Urbanization Process in Ho Chi Minh City in The Period 1989–2019, Journal of Hydrometeorology, Vol. 720, 2020, pp. 48-59.
[19] Department of Water Resouces Management, Symptoms of Ground Water Degradation in Hanoi City and Causes, http://dwrm.gov.vn/index.php?language=vi&nv=news&op=Khoa-hoc-Cong-nghe/cac-bieu-hien-suy-thoai-nuoc-duoi-dat-vung-thanh-pho-ha-noi-va-cac-nguyen-nhan-3036, 2023 (accessed on: April 10th, 2023).
[20] E. F. Lambin, H. J. Geist, E. Lepers, Dynamics of Land-use and Land-cover Change in Tropical Regions, Annual Review of Environment and Resources, Vol. 28, Iss. 1, 2003, pp. 205-241.
[21] T. Billie, F. L. Eric, A. Reenberg, The Emergence of Land Change Science for Global Environmental Change and Sustainability, Proceedings of the National Academy of Sciences, Vol. 104, Iss. 52, 2007, pp. 20666-20671.
[22] N. T. Xuan, D. T. B. Hoa, Application of Remote Sensing and GIS for Analysis of Wetland for Building Multi-Benefit Solution to Sustainable Uses of Wetland in Quang Yen Commune, Quang Ninh Province, Journal of Geodesy and Cartography, Vol. 22, 2014, pp. 24-31.
[23] M. K. Donald, Mathematical Contributions to the Theory of Evolution, VII, on the Correlation of Characters not Quantitatively Measurable, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 187, 1986, pp. 253.
[24] P. M. Hai et al., Impact of Urban Expansion to Cultural Heritage: the Case Study of the Complex of Hue Monu-ments, Journal of Geodesy and Cartography, Vol. 40, 2019, pp. 42-49.
[25] C. Jacob, A Coefficient of Agreement for Nominal Scales, Educational and Psychological Measurement, Vol. 20, No. 1, 1960, pp. 37-46.
[26] M. W. P. David, Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation, Journal of Machine Learning Technologies, Vol. 2, Iss. 1, 2011,
pp. 37-63.
[27] F. Bin Siddique et al., Impact of Image Quality on Object Detection Performance: A Study on Influence of JPEG Compression, Noise, and Resolution, IEEE Transactions on Image Processing, 2019.
[28] K. Nørvåg et al., The Importance of Data Quality for Classification Performance in Machine Learning, Journal of Intelligent Learning Systems and Applications, 2016.