Whether human-induced activities could change the gradient pattern of coastal land use along the sea-land direction: a case study in Manila Bay, Philippines

Zhi Ding; Fenzhen Su; Yanan Chen; Ying Liu; Xue Feng; Wenqiu Hu; Fengqin Yan; He Li; Pujia Yu; Xuguang Tang

doi:10.1007/s13131-022-2026-0

Volume 42 Issue 2

Feb. 2023

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Article Navigation > Acta Oceanologica Sinica > 2023 > 42(2): 163-174

Zhi Ding, Fenzhen Su, Yanan Chen, Ying Liu, Xue Feng, Wenqiu Hu, Fengqin Yan, He Li, Pujia Yu, Xuguang Tang. Whether human-induced activities could change the gradient pattern of coastal land use along the sea-land direction: a case study in Manila Bay, Philippines[J]. Acta Oceanologica Sinica, 2023, 42(2): 163-174. doi: 10.1007/s13131-022-2026-0

Citation:

Zhi Ding, Fenzhen Su, Yanan Chen, Ying Liu, Xue Feng, Wenqiu Hu, Fengqin Yan, He Li, Pujia Yu, Xuguang Tang. Whether human-induced activities could change the gradient pattern of coastal land use along the sea-land direction: a case study in Manila Bay, Philippines[J]. Acta Oceanologica Sinica, 2023, 42(2): 163-174. doi: 10.1007/s13131-022-2026-0

Citation:

Zhi Ding, Fenzhen Su, Yanan Chen, Ying Liu, Xue Feng, Wenqiu Hu, Fengqin Yan, He Li, Pujia Yu, Xuguang Tang. Whether human-induced activities could change the gradient pattern of coastal land use along the sea-land direction: a case study in Manila Bay, Philippines[J]. Acta Oceanologica Sinica, 2023, 42(2): 163-174. doi: 10.1007/s13131-022-2026-0

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Whether human-induced activities could change the gradient pattern of coastal land use along the sea-land direction: a case study in Manila Bay, Philippines

doi: 10.1007/s13131-022-2026-0

1.
Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Science, Southwest University, Chongqing 400715, China
2.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3.
Key Laboratory of Reservoir Aquatic Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China

Funds: The Innovation Academy of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences under contract No. ISEE2020YB06; the National Natural Science Foundation of China under contract Nos 41830648 and 41801353; the Chongqing Postdoctoral Innovation Fund under contract No. cstc2020jcyj-bshX0103; the Grant from the State Key Laboratory of Resources and Environmental Information System; the Open Project Programme of the Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions under contract No. GTYR201906; the Open Project Programme of Chongqing Key Laboratory of Karst Environment under contract No. Cqk201903.

More Information

Corresponding author: sufz@lreis.ac.cn
Received Date: 2021-10-26
Accepted Date: 2022-04-18

Available Online: 2022-11-23

Publish Date: 2023-02-25

Abstract

Abstract

Increasing intense human activities have largely changed the coastal landscape and caused many environmental issues. However, whether human-induced activities could change the coastal land use gradient pattern, an important coastal zonal characteristic along the sea–land direction, remains unclear. Manila Bay was selected as the study area in this work. According to the distance of the land use and land cover (LULC) to the coastline, we clustered the typical coastal land use sequence patterns (CLUSPs) along the sea–land direction between 1988 and 2016 in Manila Bay and found the following. (1) Four typical CLUSPs, including the natural CLUSP dominated by forest land and grassland, the agricultural CLUSP dominated by dry farm and paddy field, the urbanised CLUSP dominated by construction land and the fishery CLUSP dominated by fishing farm, were mined in 1988. Three typical CLUSPs (a natural CLUSP, an intermediate CLUSP between the agricultural and urbanised CLUSPs, and a fishery CLUSP) were mined in 2016. (2) Affected by the dominant LULC, these typical CLUSPs showed a regular spatial pattern along the sea–land direction. For example, the typical natural CLUSP showed a landward pattern due to the long distance between the forest land and grassland and the coastline. (3) However, influenced by urban and aquaculture expansion, the land intensification of the CLUSP exhibited an obvious increase and caused the decrease of the CLUSP diversity from 1988 to 2016. The increase in the area of LULC coverage showed no obvious correlation with its distance from the coastline (DFC), but the net increase rate of LULC coverage had a significant negative correlation with the DFC. Therefore, human-induced activities have a large impact on the gradient pattern of coastal land use along the sea–land direction.
- coastal zone,
- land use gradient,
- sea–land,
- human activities,
- Manila Bay

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References

Ai Bin, Ma Chunlei, Zhao Jun, et al. 2020. The impact of rapid urban expansion on coastal mangroves: a case study in Guangdong Province, China. Frontiers of Earth Science, 14: 37–49. doi: 10.1007/s11707-019-0768-6

Al Abid F B. 2014. A novel approach for PAM clustering method. International Journal of Computer Applications, 86(17): 1–5. doi: 10.5120/15074-3039

Bindra K, Mishra A, Suryakant. 2019. Effective data clustering algorithms. In: Ray K, Sharma T K, Rawat S, et al., eds. Soft Computing: Theories and Applications. Singapore: Springer, 419–432

Cárdenas J A R, Torres G P. 2021. Geospatial data mining techniques survey. In: Oliva D, Houssein E H, Hinojosa S, eds. Metaheuristics in Machine Learning: Theory and Applications. Cham: Springer, 635–643

Chen Yewang, Hu Xiaoliang, Fan Wentao, et al. 2020. Fast density peak clustering for large scale data based on kNN. Knowledge-Based Systems, 187: 104824. doi: 10.1016/j.knosys.2019.06.032

Dado J M, Narisma G T. 2022. The effect of urban expansion in Metro Manila on the southwest monsoon rainfall. Asia-Pacific Journal of Atmospheric Sciences, 58(1): 1–12. doi: 10.1007/s13143-019-00140-x

Di Xianghong, Hou Xiyong, Wang Yuandong, et al. 2015. Spatial-temporal characteristics of land use intensity of coastal zone in China during 2000–2010. Chinese Geographical Science, 25(1): 51–61. doi: 10.1007/s11769-014-0707-0

Ding Zhi, Liao Xiaohan, Su Fenzhen, et al. 2017. Mining coastal land use sequential pattern and its land use associations based on association rule mining. Remote Sensing, 9(2): 116. doi: 10.3390/rs9020116

Ding Zhi, Su Fenzhen, Zhang Junjue, et al. 2019. Clustering coastal land use sequence patterns along the sea–land direction: a case study in the coastal zone of Bohai Bay and the Yellow River Delta, China. Remote Sensing, 11(17): 2024. doi: 10.3390/rs11172024

Disegna M, D’Urso P, Durante F. 2017. Copula-based fuzzy clustering of spatial time series. Spatial Statistics, 21: 209–225. doi: 10.1016/j.spasta.2017.07.002

Du Mingjing, Ding Shifei, Jia Hongjie. 2016. Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowledge-Based Systems, 99: 135–145. doi: 10.1016/j.knosys.2016.02.001

Feng Yu, Sun Tao, Zhu Meisha, et al. 2018. Salt marsh vegetation distribution patterns along groundwater table and salinity gradients in Yellow River Estuary under the influence of land reclamation. Ecological Indicators, 92: 82–90. doi: 10.1016/j.ecolind.2017.09.027

Garcia K B, Malabrigo Jr P L, Gevaña D T. 2014. Philippines’ mangrove ecosystem: status, threats and conservation. In: Faridah-Hanum I, Latiff A, Hakeem K R, et al., eds. Mangrove Ecosystems of Asia. New York: Springer, 81–94

Grachev A A, Leo L S, Fernando H J S, et al. 2018. Air–sea/land interaction in the coastal zone. Boundary-Layer Meteorology, 167(2): 181–210. doi: 10.1007/s10546-017-0326-2

Hadley D. 2009. Land use and the coastal zone. Land Use Policy, 26 (Suppl 1): S198–S203

Hahus I, Migliaccio K, Douglas-Mankin K, et al. 2018. Using cluster analysis to compartmentalize a large managed wetland based on physical, biological, and climatic geospatial attributes. Environmental Management, 62(3): 571–583. doi: 10.1007/s00267-018-1050-5

Hirschberg D S. 1977. Algorithms for the longest common subsequence problem. Journal of the ACM, 24(4): 664–675. doi: 10.1145/322033.322044

Hou Xiyong, Xu Xinliang. 2011. Spatial patterns of land use in coastal zones of China in the early 21st century. Geographical Research (in Chinese), 30(8): 1370–1379

Hu Luojia, Li Wenyu, Xu Bing. 2018. Monitoring mangrove forest change in China from 1990 to 2015 using Landsat-derived spectral-temporal variability metrics. International Journal of Applied Earth Observation and Geoinformation, 73: 88–98. doi: 10.1016/j.jag.2018.04.001

Hu Wenjia, Wang Yuyu, Zhang Dian, et al. 2020. Mapping the potential of mangrove forest restoration based on species distribution models: A case study in China. Science of The Total Environment, 748: 142321. doi: 10.1016/j.scitotenv.2020.142321

Huang Baoying, Li Zhijian, Dong Chengcheng, et al. 2021. The effects of urbanization on vegetation conditions in coastal zone of China. Progress in Physical Geography: Earth and Environment, 45(4): 564–579. doi: 10.1177/0309133320979501

Huang Chong, Zhang Chenchen, Liu Qingsheng, et al. 2020. Land reclamation and risk assessment in the coastal zone of China from 2000 to 2010. Regional Studies in Marine Science, 39: 101422. doi: 10.1016/j.rsma.2020.101422

Islam M M, Shamsuddoha M. 2018. Coastal and marine conservation strategy for Bangladesh in the context of achieving blue growth and sustainable development goals (SDGs). Environmental Science & Policy, 87: 45–54

Kumar P, Wasan S K. 2011. Comparative study of k-means, pam and rough k-means algorithms using cancer datasets. In: Proceedings of CSIT: 2009 International Symposium on Computing, Communication, and Control (ISCCC 2009). Singapore: IACSIT Press, 136–140

Li Huiying, Man Weidong, Li Xiaoyan, et al. 2017. Remote sensing investigation of anthropogenic land cover expansion in the low-elevation coastal zone of Liaoning Province, China. Ocean & Coastal Management, 148: 245–259

Li Qian, Yu Yang, Jiang Xiaoqian, et al. 2019. Multifactor-based environmental risk assessment for sustainable land-use planning in Shenzhen, China. Science of The Total Environment, 657: 1051–1063. doi: 10.1016/j.scitotenv.2018.12.118

Li Yuan, Zhang Haibo, Li Qingbo, et al. 2016. Characteristics of residual organochlorine pesticides in soils under different land-use types on a coastal plain of the Yellow River Delta. Environmental Geochemistry and Health, 38(2): 535–547. doi: 10.1007/s10653-015-9738-4

Liu Yubin, Hou Xiyong, Li Xiaowei, et al. 2020. Assessing and predicting changes in ecosystem service values based on land use/cover change in the Bohai Rim coastal zone. Ecological Indicators, 111: 106004. doi: 10.1016/j.ecolind.2019.106004

Marani M, Da Lio C, D’Alpaos A. 2013. Vegetation engineers marsh morphology through multiple competing stable states. Proceedings of the National Academy of Sciences of the United States of America, 110(9): 3259–3263. doi: 10.1073/pnas.1218327110

Martinez M L, Silva R, Lithgow D, et al. 2017. Human impact on coastal resilience along the coast of Veracruz, Mexico. Journal of Coastal Research, 77(sp1): 143–153

Mialhe F, Gunnell Y, Mering C, et al. 2016. The development of aquaculture on the northern coast of Manila Bay (Philippines): an analysis of long-term land-use changes and their causes. Journal of Land Use Science, 11(2): 236–256. doi: 10.1080/1747423X.2015.1057245

Mishra B K, Mebeelo K, Chakraborty S, et al. 2021. Implications of urban expansion on land use and land cover: towards sustainable development of Mega Manila, Philippines. GeoJournal, 86(2): 927–942. doi: 10.1007/s10708-019-10105-2

Moschetto F A, Ribeiro R B, De Freitas D M. 2021. Urban expansion, regeneration and socioenvironmental vulnerability in a mangrove ecosystem at the southeast coastal of São Paulo, Brazil. Ocean & Coastal Management, 200: 105418

Nor A N M, Corstanje R, Harris J A, et al. 2017. Impact of rapid urban expansion on green space structure. Ecological Indicators, 81: 274–284. doi: 10.1016/j.ecolind.2017.05.031

Politi E, Paterson S K, Scarrott R, et al. 2019. Earth observation applications for coastal sustainability: potential and challenges for implementation. Anthropocene Coasts, 2(1): 306–329. doi: 10.1139/anc-2018-0015

Ray G C. 1991. Coastal-zone biodiversity patterns. BioScience, 41(7): 490–498. doi: 10.2307/1311807

Ray G C, Hayden B P. 1992. Coastal zone ecotones. In: Hansen A J, Castri F, eds. Landscape Boundaries. New York: Springer, 403–420

Rodriguez A, Laio A. 2014. Clustering by fast search and find of density peaks. Science, 344(6191): 1492–1496. doi: 10.1126/science.1242072

Sajjad M, Li Yangfan, Tang Zhenghong, et al. 2018. Assessing hazard vulnerability, habitat conservation, and restoration for the enhancement of mainland China’s coastal resilience. Earth’s Future, 6(3): 326–338. doi: 10.1002/2017EF000676

Sheik M, Chandrasekar N. 2011. A shoreline change analysis along the coast between Kanyakumari and Tuticorin, India, using digital shoreline analysis system. Geo-spatial Information Science, 14(4): 282–293. doi: 10.1007/s11806-011-0551-7

Shi Lifeng, Liu Fang, Zhang Zengxiang, et al. 2015. Spatial differences of coastal urban expansion in China from 1970s to 2013. Chinese Geographical Science, 25(4): 389–403. doi: 10.1007/s11769-015-0765-y

Tseng Kuo-Tseng, Chan De-Sheng, Yang Chang-Biau, et al. 2018. Efficient merged longest common subsequence algorithms for similar sequences. Theoretical Computer Science, 708: 75–90. doi: 10.1016/j.tcs.2017.10.027

Vallejo Jr B M, Aloy A B, Ocampo M, et al. 2019. Manila bay ecology and associated invasive species. In: Makowski C, Finkl C W, eds. Impacts of Invasive Species on Coastal Environments. Cham: Springer, 145–169

Wang Wenyue, Zhang Junjue, Su Fenzhen. 2018. An index-based spatial evaluation model of exploitative intensity: A case study of coastal zone in Vietnam. Journal of Geographical Sciences, 28(3): 291–305. doi: 10.1007/s11442-018-1473-1

Xin Pei, Gibbes B, Li Ling, et al. 2010. Soil saturation index of salt marshes subjected to spring-neap tides: A new variable for describing marsh soil aeration condition. Hydrological Processes, 24(18): 2564–2577. doi: 10.1002/hyp.7670

Xu Xiao, Ding Shifei, Shi Zhongzhi. 2018. An improved density peaks clustering algorithm with fast finding cluster centers. Knowledge-Based Systems, 158: 65–74. doi: 10.1016/j.knosys.2018.05.034

Xu Yan, Pu Lijie, Zhang Runsen, et al. 2012. Spatial-temporal dynamics of land use and land cover change in the coastal zone of Jiangsu Province. Resources and Environment in the Yangtze Basin (in Chinese), 21(5): 565–571

Xu Dongkuan, Tian Yingjie. 2015. A comprehensive survey of clustering algorithms. Annals of Data Science, 2(2): 165–193. doi: 10.1007/s40745-015-0040-1

Zhang Feng, Sun Xiao, Zhou Yan, et al. 2017. Ecosystem health assessment in coastal waters by considering spatio-temporal variations with intense anthropogenic disturbance. Environmental Modelling & Software, 96: 128–139

Zhou Yunkai, Ning Lixin, Bai Xiuling. 2018. Spatial and temporal changes of human disturbances and their effects on landscape patterns in the Jiangsu coastal zone, China. Ecological Indicators, 93: 111–122. doi: 10.1016/j.ecolind.2018.04.076

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