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Volume 29 Issue 3
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CONG Pifu, CHEN Kexin, QU Limei, HAN Jianbo. Dynamic Changes in the Wetland Landscape Pattern of the Yellow River Delta from 1976 to 2016 Based on Satellite Data[J]. Chinese Geographical Science, 2019, 20(3): 372-381. doi: 10.1007/s11769-019-1039-x
Citation: CONG Pifu, CHEN Kexin, QU Limei, HAN Jianbo. Dynamic Changes in the Wetland Landscape Pattern of the Yellow River Delta from 1976 to 2016 Based on Satellite Data[J]. Chinese Geographical Science, 2019, 20(3): 372-381. doi: 10.1007/s11769-019-1039-x
shu

Dynamic Changes in the Wetland Landscape Pattern of the Yellow River Delta from 1976 to 2016 Based on Satellite Data

doi: 10.1007/s11769-019-1039-x
  • 1.

    National Marine Environmental Monitoring Center, Dalian 116023, China;

  • 2.

    School of Urban and Environmental Science, Liaoning Normal University, Dalian 116029, China

Funds:  Under the auspices of National Key Research & Development Program of China (No. 2017YFC0505901, 2017YFA 0604904)
More Information
  • Corresponding author: CONG Pifu. E-mail:congpifu@163.com,pfcong@nmemc.org.cn
  • Received Date: 2018-10-08
  • Publish Date: 2019-06-01
  • The Yellow River Delta wetland is the youngest wetland ecosystem in China's warm temperate zone. To better understand how its landscape pattern has changed over time and the underlying factors responsible, this study analyzed the dynamic changes of wetlands using five Landsat series of images, namely MSS (Mulri Spectral Scanner), TM (Thematic Mapper), and OLI (Operational Land Imager) sensors in 1976, 1986, 1996, 2006, and 2016. Object-oriented classification and the combination of spatial and spectral features and both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), as well as brightness characteristic indices, were used to classify the images in eCognition software. Landscape pattern changes in the Yellow River Delta over the past 40 years were then delineated using transition matrix and landscape index methods. Results show that:1) from 1976 to 2016, the total area of wetlands in the study area decreased from 2594.76 to 2491.79 km2, while that of natural wetlands decreased by 954.03 km2 whereas human-made wetlands increased by 851.06 km2. 2) The transformation of natural wetlands was extensive:31.34% of those covered by Suaeda heteropteras were transformed into reservoirs and ponds, and 24.71% with Phragmites australis coverage were transformed into dry farmland. Some human-made wetlands were transformed into non-wetlands types:1.55% of reservoirs and ponds became construction land, and likewise 21.27% were transformed into dry farmland. 3) From 1976 to 2016, as the intensity of human activities increased, the number of landscape types in the study area continuously increased. Patches were scattered and more fragmented. The whole landscape became more complex. In short, over the past 40 years, the wetlands of the Yellow River Delta have been degraded, with the area of natural wetlands substantially reduced. Human activities were the dominant forces driving these changes in the Yellow River Delta.
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Dynamic Changes in the Wetland Landscape Pattern of the Yellow River Delta from 1976 to 2016 Based on Satellite Data

doi: 10.1007/s11769-019-1039-x
  • 1. National Marine Environmental Monitoring Center, Dalian 116023, China;
  • 2. School of Urban and Environmental Science, Liaoning Normal University, Dalian 116029, China
Funds:  Under the auspices of National Key Research & Development Program of China (No. 2017YFC0505901, 2017YFA 0604904)
    Corresponding author: CONG Pifu. E-mail:congpifu@163.com,pfcong@nmemc.org.cn
  • Received Date: 2018-10-08
  • Publish Date: 2019-06-01

Abstract: The Yellow River Delta wetland is the youngest wetland ecosystem in China's warm temperate zone. To better understand how its landscape pattern has changed over time and the underlying factors responsible, this study analyzed the dynamic changes of wetlands using five Landsat series of images, namely MSS (Mulri Spectral Scanner), TM (Thematic Mapper), and OLI (Operational Land Imager) sensors in 1976, 1986, 1996, 2006, and 2016. Object-oriented classification and the combination of spatial and spectral features and both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), as well as brightness characteristic indices, were used to classify the images in eCognition software. Landscape pattern changes in the Yellow River Delta over the past 40 years were then delineated using transition matrix and landscape index methods. Results show that:1) from 1976 to 2016, the total area of wetlands in the study area decreased from 2594.76 to 2491.79 km2, while that of natural wetlands decreased by 954.03 km2 whereas human-made wetlands increased by 851.06 km2. 2) The transformation of natural wetlands was extensive:31.34% of those covered by Suaeda heteropteras were transformed into reservoirs and ponds, and 24.71% with Phragmites australis coverage were transformed into dry farmland. Some human-made wetlands were transformed into non-wetlands types:1.55% of reservoirs and ponds became construction land, and likewise 21.27% were transformed into dry farmland. 3) From 1976 to 2016, as the intensity of human activities increased, the number of landscape types in the study area continuously increased. Patches were scattered and more fragmented. The whole landscape became more complex. In short, over the past 40 years, the wetlands of the Yellow River Delta have been degraded, with the area of natural wetlands substantially reduced. Human activities were the dominant forces driving these changes in the Yellow River Delta.

CONG Pifu, CHEN Kexin, QU Limei, HAN Jianbo. Dynamic Changes in the Wetland Landscape Pattern of the Yellow River Delta from 1976 to 2016 Based on Satellite Data[J]. Chinese Geographical Science, 2019, 20(3): 372-381. doi: 10.1007/s11769-019-1039-x
Citation: CONG Pifu, CHEN Kexin, QU Limei, HAN Jianbo. Dynamic Changes in the Wetland Landscape Pattern of the Yellow River Delta from 1976 to 2016 Based on Satellite Data[J]. Chinese Geographical Science, 2019, 20(3): 372-381. doi: 10.1007/s11769-019-1039-x
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