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Spatio-temporal land use/cover change dynamics in Hangzhou Bay， China， using long-term Landsat time series and GEE platform

  • role： First author第一作者
  • Affiliation：

    College of Marine Science and Technology, Zhejiang Ocean University, Zhoushan 316022, China

  • Email：liangjintao@zjou.edu.cn
  • Introduction：梁锦涛，研究方向为海岸带环境遥感。E-mail： liangjintao@zjou.edu.cn
LIANG Jintao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

  • Email：ayang198206@163.com
  • Introduction：陈超，研究方向为海岸带环境遥感。E-mail： ayang198206@163.com
CHEN Chao2*,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

SUN Weiwei3,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

YANG Gang3,  
  • Affiliation：

    School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022, China

    Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan 316022, China

LIU Zhisong45,  
  • Affiliation：

    Zhejiang Province Environment Monitoring Centre (Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control), Hangzhou 310012, China

ZHANG Zili6

Resümee

Land Use/Cover Change (LUCC) is generally defined as the use of land by humans, which is the direct result of the interaction between humans and nature, and reflects the basic process of the interaction between the Earth’‍s environmental system and human production systems. A bay is an area where land and water interact, with a relatively fragile ecosystem and easily damaged resources and environment. Large-scale long-time series and high-precision LUCC mapping is the basis for territorial spatial planning and environmental protection in bay regions. Random forest algorithms received considerable attention in recent years owing to their high interpretability and reliability in handling complex data. However, room for improvement exists in optimizing the performance of random forest algorithms in processing long-time series datasets. Most existing mapping methods are aimed at original remote sensing images, and fully tapping and jointly utilizing the information potential of the feature space and transformation space are difficult, resulting in the poor application effect of traditional methods in bay areas with high surface heterogeneity. The combined application of the remote sensing spectral index can effectively increase the separability of object categories in bay areas, and principal component transformation can effectively eliminate correlations between features and achieve data compression and image enhancement. Based on Landsat long-term satellite and Google Earth Engine images of the Hangzhou Bay area, this study proposes a random forest remote sensing image classification method that integrates the remote sensing spectral index and principal component transformation and analyzes LUCC mapping spatiotemporal patterns from 1985 to 2020 (at 5-year intervals). Results show that (1) the random forest algorithm, integrating the remote sensing spectral index and principal component transformation, can accurately extract Hangzhou Bay LUCC information, and the average overall accuracy and kappa coefficient of the eight time phases are 92.83% and 0.9108, respectively. (2) During the study period, the construction land area (278.26 km2 to 2984.76 km2, with an average annual increase of 77.33 km2), water area (509.32 km2 to 680.21 km2, with an average annual increase of 4.88 km2), and bare land area (768.99 km2 to 1078.13 km2, with an average annual increase of 8.83 km2) showed an increasing trend, whereas the wood land area (2159.49 km2 to 1881.52 km2, with an annual average decrease of 7.94 km2), cultivated field area (6998.45 km2 to 4800.59 km2, with an annual average decrease of 62.80 km2), and tidal-flat area (181.65 km2 to 161.50 km2, with an annual average decrease of 0.58 km2) showed a decreasing trend. (3) During the study period, the cultivated field area was the main transfer source, whose total area proportion decreased from 64.23% to 41.43%. The transfer out of the cultivated field area was mainly to construction land (2268.05 km2) and bare land (630.20 km2), and the transfer in of the cultivated field area was mainly to water body (376.22 km2) and forest land (352.22 km2). This study provides data support for the scientific management of land resources in the Hangzhou Bay, and the obtained LUCC dataset is of considerable significance to the sustainable development of the region.

Schlüsselwort

Hangzhou Bay;land use/cover change;Google Earth Engine;Landsat;spatio-temporal characteristics

References

  1. 1.
    Abdi H and Williams L J. 2010. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4): 433-459
  2. 2.
    Akar Ö and Güngör O. 2012. Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation, 1(2): 105-112
  3. 3.
    Bangira T, Alfieri S M, Menenti M and Van Niekerk A. 2019. Comparing thresholding with machine learning classifiers for mapping complex water. Remote Sensing, 11(11): 1351
  4. 4.
    Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32
  5. 5.
    Chan J C W and Paelinckx D. 2008. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment, 112(6): 2999-3011
  6. 6.
    Chen C, Chen H X, Chen D, Zhang Z L, Zhang X F, Zhuang Y, Zhu Y L, Chen J Y and Zheng H. 2021. Coastline extraction and spatial-temporal variations using remote sensing technology in Zhoushan Islands. Remote Sensing for Land and Resources, 33(2): 141-152
  7. 7.
    Chen C, Fu J Q, Sui X X, Lu X and Tan A H. 2018. Construction and application of knowledge decision tree after a disaster for water body information extraction from remote sensing images. Journal of Remote Sensing, 22(5): 792-801
  8. 8.
    Chen G, Zhong C R, Li M Y, Yu Z, Liu X Y and Jia M M. 2022. Disturbance of mangrove forests in Guangxi Beilun Estuary during 1990-2020. Journal of Remote Sensing, 26(6): 1112-1120
  9. 9.
    Chen H X, Chen C, Zhang Z L, Wang L Y and Liang J T. 2022. A remote sensing information extraction method for intertidal zones based on Google Earth Engine. Remote Sensing for Natural Resources, 34(4): 60-67
  10. 10.
    Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, Peng S, Han G, Zhang H W, He C Y, Wu H and Lu M. 2014. Concepts and key techniques for 30 m global land cover mapping. Acta Geodaetica et Cartographica Sinica, 43(6): 551-557
  11. 11.
    Chen Q, Mao Y and Morrison A M. 2021. The influence of land use evolution on the visitor economy in Wuhan from the perspective of ecological service value. Land, 11(1): 1
  12. 12.
    Cheng L N, Zhong C R, Li X Y, Jia M M, Wang Z M and Mao D H. 2022. Rapid and automatic classification of intertidal wetlands based on intensive time series Sentinel-2 images and Google Earth Engine. National Remote Sensing Bulletin, 26(2): 348-357
  13. 13.
    Cutler D R, Edwards T C, Beard K H, Cutler A, Hess K T, Gibson J and Lawler J J. 2007. Random forests for classification in ecology. Ecology, 88(11): 2783-2792
  14. 14.
    Ghorbanian A, Kakooei M, Amani M, Mahdavi S, Mohammadzadeh A and Hasanlou M. 2020. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 276-288
  15. 15.
    Jia M M, Wang Z M, Mao D H, Huang C L and Lu C Y. 2021. Spatial-temporal changes of China’s mangrove forests over the past 50 years: an analysis towards the Sustainable Development Goals (SDGs). Chinese Science Bulletin, 66(30): 3886-3901
  16. 16.
    Khwarahm N R, Qader S, Ararat K and Fadhil Al-Quraishi A M. 2021. Predicting and mapping land cover/land use changes in Erbil/Iraq using CA-Markov synergy model. Earth Science Informatics, 14(1): 393-406
  17. 17.
    Kiggundu N, Anaba L A, Banadda N, Wanyama J and Kabenge I. 2018. Assessing land use and land cover changes in the Murchison Bay catchment of Lake Victoria basin in Uganda. Journal of Sustainable Development, 11(1): 44
  18. 18.
    Li D Q, Lu D S, Wu M, Shao X X and Wei J H. 2017. Examining land cover and greenness dynamics in Hangzhou Bay in 1985-2016 using Landsat time-series data. Remote Sensing, 10(2): 32
  19. 19.
    Li J L and Wang L J. 2020. Spatial and temporal evolutions of the major bays in the East China Sea under the influence of reclamation. Acta Geographica Sinica, 75(1): 126-142
  20. 20.
    Li N, Lu D S, Wu M, Zhang Y L and Lu L Y. 2018. Coastal wetland classification with multiseasonal high-spatial resolution satellite imagery. International Journal of Remote Sensing, 39(23): 8963-8983
  21. 21.
    Li Q Q, Lu Y, Hu S B, Hu Z W, Li H Z, Liu P, Shi T Z, Wang C S, Wang J J and Wu G F. 2016. Review of remotely sensed geo-environmental monitoring of coastal zones. Journal of Remote Sensing, 20(5): 1216-1229
  22. 22.
    Liu X P, Liang X, Li X, Xu X C, Ou J P, Chen Y M, Li S Y, Wang S J and Pei F S. 2017. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168: 94-116
  23. 23.
    Luo J C, Hu X D, Wu T J, Liu W, Xia L G, Yang H P, Sun Y W, Xu N, Zhang X, Shen Z F and Zhou N. 2021. Research on intelligent calculation model and method of precision land use/cover change information driven by high-resolution remote sensing. National Remote Sensing Bulletin, 25(7): 1351-1373
  24. 24.
    Lyons M B, Phinn S R and Roelfsema C M. 2012. Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia. ISPRS Journal of Photogrammetry and Remote Sensing, 71: 34-46
  25. 25.
    Mateo-García G, Gómez-Chova L, Amorós-López J, Muñoz-Marí J and Camps-Valls G. 2018. Multitemporal cloud masking in the Google Earth Engine. Remote Sensing, 10(7): 1079
  26. 26.
    Ning X G, Chang W T, Wang H, Zhang H C and Zhu Q D. 2022. Extraction of marsh wetland in Heilongjiang Basin based on GEE and multi-source remote sensing data. National Remote Sensing Bulletin, 26(2): 386-396
  27. 27.
    Peng J T, Sun W W, Wei T H and Fan W Q. 2020. A modified correlation alignment algorithm for the domain adaptation of GF-5 hyperspectral image. Journal of Remote Sensing, 24(4): 417-426
  28. 28.
    Rawat J S and Kumar M. 2015. Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1): 77-84
  29. 29.
    Regasa M S, Nones M and Adeba D. 2021. A review on land use and land cover change in Ethiopian basins. Land, 10(6): 585
  30. 30.
    Rodrigues S W P and Souza-Filho P W M. 2011. Use of multi-sensor data to identify and map tropical coastal wetlands in the Amazon of Northern Brazil. Wetlands, 31(1): 11-23
  31. 31.
    Sun W W, Yang G, Chen C, Chang M H, Huang K, Meng X Z and Liu L Y. 2020. Development status and literature analysis of China’s earth observation remote sensing satellites. Journal of Remote Sensing, 24(5): 479-510
  32. 32.
    Sun W W, Zhang D F, Yang G and Li W Y. 2018. Band selection for hyperspectral imagery based on weighted probabilistic archetypal analysis. Journal of Remote Sensing, 22(1): 110-118
  33. 33.
    Sun Z, Luo J H, Yang J Z C, Yu Q Y, Zhang L, Xue K and Lu L R. 2020. Nation-scale mapping of coastal aquaculture ponds with sentinel-1 SAR data using Google Earth Engine. Remote Sensing, 12(18): 3086
  34. 34.
    Teluguntla P, Thenkabail P S, Oliphant A, Xiong J, Gumma M K, Congalton R G, Yadav K and Huete A. 2018. A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 325-340
  35. 35.
    Tian P, Li J L, Cao L D, Pu R L, Gong H B, Liu Y C, Zhang H T and Chen H L. 2021. Impacts of reclamation derived land use changes on ecosystem services in a typical gulf of eastern China: a case study of Hangzhou bay. Ecological Indicators, 132: 108259
  36. 36.
    Tu C R, Li P, Li Z H, Wang H J, Yin S W, Li D H, Zhu Q T, Chang M X, Liu J and Wang G Y. 2021. Synergetic classification of coastal wetlands over the Yellow River Delta with GF-3 full-polarization SAR and Zhuhai-1 OHS hyperspectral remote sensing. Remote Sensing, 13(21): 4444
  37. 37.
    Wang C, Jia M M, Chen N C and Wang W. 2018. Long-term surface water dynamics analysis based on Landsat imagery and the Google Earth Engine platform: a case study in the middle Yangtze River Basin. Remote Sensing, 10(10): 1635
  38. 38.
    Wang X, Liu Y L, Ling F, Liu Y F and Fang F G. 2017. Spatio-temporal change detection of Ningbo coastline using Landsat time-series images during 1976-2015. ISPRS International Journal of Geo-Information, 6(3): 68
  39. 39.
    Wingate V R, Phinn S R, Kuhn N, Bloemertz L and Dhanjal-Adams K L. 2016. Mapping decadal land cover changes in the woodlands of north eastern Namibia from 1975 to 2014 using the Landsat satellite archived data. Remote Sensing, 8(8): 681
  40. 40.
    Wu Y Q and Liu Z L. 2019. Research progress on methods of automatic coastline extraction based on remote sensing images. Journal of Remote Sensing, 23(4): 582-602
  41. 41.
    Zhao Y H, An R, Xiong N X, Ou D Y and Jiang C F. 2021. Spatio-temporal land-use/land-cover change dynamics in coastal plains in Hangzhou Bay Area, China from 2009 to 2020 using Google Earth Engine. Land, 10(11): 1149
  42. 42.
    Zurqani H A, Post C J, Mikhailova E A, Schlautman M A and Sharp J L. 2018. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 69: 175-185

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