Spatiotemporal pattern analysis of land use in Jiangsu Province based on long-term time series remote sensing images

Zhendong  Ji; Lingzhi  Yin; Jinhong  Wang

doi:10.6180/jase.202601_29(1).0001

Spatiotemporal pattern analysis of land use in Jiangsu Province based on long-term time series remote sensing images

Computer Science and Information Engineering

Zhendong Ji¹, Lingzhi Yin¹This email address is being protected from spambots. You need JavaScript enabled to view it., and Jinhong Wang²

¹College of computer science and technology, Zhejiang Sci-Tech University, Hangzhou, China

²Zhejiang academy of surveying and mapping, Hangzhou, China

Received: October 26, 2024
Accepted: March 23, 2025
Publication Date: April 30, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202601_29(1).0001

Studying spatiotemporal patterns of land use is crucial for optimal land resource allocation and sustainable development. This study utilizes the Google Earth Engine (GEE) platform and long-term remote sensing imagery data, selecting Jiangsu Province as a case study area. Principal Component Analysis (PCA) was applied to reduce feature dimensionality, and the Random Forest classification algorithm was optimized with Bayesian Optimization and Tree-structured Parzen Estimators (TPE) for improved performance. The classification achieved an overall accuracy of 92% and a Kappa coefficient of 0.89. Spatiotemporal clustering was conducted at the optimal scale, determined using landscape pattern indices, to analyze the land use evolution from 2000 to 2020. The study results indicate that: (1) PCA effectively reduced feature redundancy, enabling a more robust classification process, while Bayesian optimization improved the model’s predictive performance. (2) Cropland area continuously declined, built-up land expanded significantly, waterbody areas decreased slightly, and forest coverage remained stable. The main transitions occurred between built-up land and cropland, as well as between waterbodies and both cropland and built-up land. (3) From 2000 to 2010, rapid urbanization led to substantial expansion of built-up land, particularly in coastal areas, south of the Yangtze River, and northern cities, causing significant cropland loss and ecological degradation. Post-2010, land use policies helped curb cropland loss. These findings offer valuable insights into land use patterns in Jiangsu, supporting effective land resource management and planning.

Keywords: Landuse classification; Long-term time series; Spatiotemporal pattern evolution; Google Earth Engine

[1] J. Liu, Z. Zhang, S. Zhang, C. Yan, S. Wu, R. Li, W. Kuang, W. Shi, L. Huang, J. Ning, et al., (2020) “Innovation and development of remote sensing-based land use change studies based on Shupeng Chen’s academic thoughts" J. Geo-Inf. Sci 22(4): 680–687. DOI: 10.12082/dqxxkx.2020.200052.
[2] J. Cui, M. Zhu, Y. Liang, G. Qin, J. Li, and Y. Liu, (2022) “Land use/land cover change and their driving factors in the Yellow River Basin of Shandong Province based on google earth Engine from 2000 to 2020" ISPRS International Journal of Geo-Information 11(3): 163. DOI: 10.3390/ijgi11030163.
[3] J. Cui, W. Ji, P. Wang, M. Zhu, and Y. Liu, (2023) “Spatial–temporal changes in land use and their driving forces in the circum-Bohai coastal zone of China from 2000 to 2020" Remote Sensing 15(9): 2372. DOI: 10.3390/rs15092372.
[4] S. Tang, L. Song, S. Wan, Y. Wang, Y. Jiang, and J. Liao, (2022) “Long-time-series evolution and ecological effects of coastline length in coastal zone: a case study of the Circum-Bohai coastal zone, China" Land 11(8): 1291. DOI: 10.3390/land11081291.
[5] J.Liu,H.Kang,W.Tao,H.Li,D.He,L.Ma,H.Tang,S. Wu, K. Yang, and X. Li, (2023) “A spatial distribution Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil" Science of The Total Environment 859: 160112. DOI: 10.1016/j.scitotenv.2022.160112.
[6] M.A.MoharramandD.M.Sundaram,(2023) “Land use and land cover classification with hyperspectral data: Acomprehensive review of methods, challenges and future directions" Neurocomputing 536: 90–113. DOI: 10.1016/j.neucom.2023.03.025.
[7] Y. Hu, A. Raza, N. R. Syed, S. Acharki, R. L. Ray, S. Hussain, H. Dehghanisanij, M. Zubair, and A. El beltagi, (2023) “Land use/land cover change detection and NDVI estimation in Pakistan’s Southern Punjab Province" Sustainability 15(4): 3572. DOI: 10.3390/su15043572.
[8] S. Aldiansyah and R. A. Saputra, (2023) “Comparison of machine learning algorithms for land use and land cover analysis using Google Earth engine (Case study: Wangguwatershed)" International Journal of Remote Sensing and Earth Sciences (IJReSES) 19(2): 197 210. DOI: 10.30536/j.ijreses.2022.v19.a3803.
[9] M.Asif, J. H. Kazmi, A. Tariq, N. Zhao, R. Guluzade, W. Soufan, K. F. Almutairi, A. E. Sabagh, and M. Aslam, (2023) “Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest" Geocarto International 38(1): 2210532. DOI: 10.1080/10106049.2023.2210532.
[10] A. M. Abdi, (2020) “Land cover and land use classifi cation performance of machine learning algorithms in a boreal landscape using Sentinel-2 data" GIScience & Remote Sensing 57(1): 1–20. DOI: 10.1080/15481603.2019.1650447.
[11] D.Chen,Y.Wang,Z.Shen,J.Liao,J.Chen,andS.Sun, (2022) “Long time-series mapping and change detection of coastal zone land use based on google earth engine and multi-source data fusion" Remote Sensing 14(1): 1. DOI: 10.3390/rs14010001.
[12] F. Grings, E. Roitberg, and V. Barraza, (2019) “EVI time-series breakpoint detection using convolutional networks for online deforestation monitoring in Chacoforest" IEEE Transactions on Geoscience and Remote Sensing 58(2): 1303–1312. DOI: 10.1109/TGRS.2019.2945719.
[13] Y. Jin, X. Liu, J. Yao, X. Zhang, and H. Zhang, (2020) “Mapping the annual dynamics of cultivated land in typical area of the Middle-lower Yangtze plain using long time-series of Landsat images based on Google Earth Engine" International Journal of Remote Sensing 41(4): 1625–1644. DOI: 10.1080/01431161.2019.1673917.
[14] J. Verbesselt, R. Hyndman, G. Newnham, and D. Cul venor, (2010) “Detecting trend and seasonal changes in satellite image time series" Remote sensing of Environ ment 114(1): 106–115. DOI: 10.1016/j.rse.2009.08.014.
[15] Y. Ye, Y. Wang, J. Liao, J. Chen, Y. Zou, Y. Liu, and C. Feng, (2022) “Spatiotemporal pattern analysis of land use functions in contiguous coastal cities based on long term time series remote sensing data: a case study of Bohai Sea Region, China" Remote Sensing 14(15): 3518. DOI: 10.3390/rs14153518.
[16] X. Cao, J. Han, and X. Li, (2023) “Analysis of the im pact of land use change on grain production in Jiangsu Province, China" Land 13(1): 20. DOI: 10.3390/land13010020.
[17] G. Vanum, (2012) “Impact of urbanization and land use changes on climate" International Journal of Physical and Social Sciences 2(9): 414–432.
[18] G. Shi, P. Ye, L. Ding, A. Quinones, Y. Li, and N. Jiang, (2019) “Spatio-temporal patterns of land use and cover change from 1990 to 2010: A case study of Jiangsu Province, China" International Journal of Environ mental Research and Public Health 16(6): 907. DOI: 10.3390/ijerph16060907.
[19] X. Du, X. Jin, X. Yang, X. Yang, and Y. Zhou, (2014) “Spatial pattern of land use change and its driving force in Jiangsu Province" International journal of environ mental research and public health 11(3): 3215–3232. DOI: 10.3390/ijerph110303215.
[20] L. Yang, J. Zhang, E. Gong, M. Liu, J. Ren, and Y. Wang, (2022) “Analysis of spatio-temporal land-use pat terns and the driving forces in Xi’an City using GEE and multi-source data" Trans. Chin. Soc. Agric. Eng 38: 279–288. DOI: 10.11975/j.issn.1002-6819.2022.02.031.
[21] D. Ming, T. Zhou, M. Wang, and T. Tan, (2016) “Land cover classification using random forest with genetic algorithm-based parameter optimization" Journal of Ap plied Remote Sensing 10(3): 035021–035021. DOI: 10.1117/1.JRS.10.035021.
[22] W. Zhang, C. Wu, H. Zhong, Y. Li, and L. Wang, (2021) “Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization" Geoscience Frontiers 12(1): 469–477. DOI: 10.1016/j.gsf.2020.03.007.
[23] C.-C. Feng and D. M. Flewelling, (2004) “Assessment of semantic similarity between land use/land cover classification systems" Computers, Environment and Ur ban Systems 28(3): 229–246. DOI: 10.1016/S0198 9715(03)00020-6.
[24] N.Wentao,S. Qinghui, X. Zhenzhen, and S. Wenwen, (2023) “Evaluation of the land use benefit of rapidly expanding cities based on coupling coordination and a trans fer matrix" Journal of Resources and Ecology 14(3): 542–555. DOI: 10.5814/j.issn.1674-764x.2023.03.010.
[25] L. Rui and Z. Daolin, (2010) “Methods for detecting land use changes based on the land use transition matrix" Resources Science 32(8): 1544–1550.
[26] A. N. Jaber and S. U. Rehman, (2020) “FCM–SVM based intrusion detection system for cloud computing environment" Cluster Computing 23(4): 3221–3231. DOI: 10.1007/s10586-020-03082-6.
[27] F. Fu, S. Deng, D. Wu, W. Liu, and Z. Bai, (2022) “Research on the spatiotemporal evolution of land use landscape pattern in a county area based on CA-Markov model" Sustainable Cities and Society 80: 103760. DOI: 10.1016/j.scs.2022.103760.
[28] Y.Zhou, M.Chen, Z.Tang, andZ.Mei,(2021)“Urbanization, land use change, and carbon emissions: Quantitative assessments for city-level carbon emissions in Beijing Tianjin-Hebei region" Sustainable Cities and Society 66: 102701. DOI: 10.1016/j.scs.2020.102701.
[29] W. Chen, J. Zeng, and N. Li, (2021) “Change in land use structure due to urbanisation in China" Journal of Cleaner Production 321: 128986. DOI: 10.1016/j.jclepro.2021.128986.
[30] X.Gao, J. Shen, W. He, X. Zhao, Z. Li, W. Hu, J. Wang, Y. Ren, and X. Zhang, (2021) “Spatial-temporal analysis of ecosystem services value and research on ecological compensation in Taihu Lake Basin of Jiangsu Province in China from 2005 to 2018" Journal of Cleaner Production 317: 128241. DOI: 10.1016/j.jclepro.2021.128241.
[31] S. Jiang, J. Meng, L. Zhu, and H. Cheng, (2021) “Spatial-temporal pattern of land use conflict in China and its multilevel driving mechanisms" Science of the Total Environment 801: 149697. DOI: 10.1016/j.scitotenv.2021.149697.
[32] B.-Y. Yuan, J.-H. Gao, Y. Chi, B. Zha, and Z.-H. Gong, (2022) “Cross-scale spatiotemporal characteristics of land scape ecological conditions index in coastal zone of Jiangsu Province, China during 1990-2020" Ying yong sheng taixue bao= The journal of applied ecology 33(2): 489–499. DOI: 10.13287/j.1001-9332.202202.016.
[33] Y.Jian, Y.Chen, J. Gong,C.Wenli,andK.Chen,(2022) “Application of spatiotemporal pattern mining methods for land use transition research: A case study of Baiyun District in Guangzhou" Frontiers in Environmental Science 10: 893217. DOI: 10.3389/fenvs.2022.893217.