• DocumentCode
    124473
  • Title

    Remote sensing monitoring of wind-preventing and sand-fixing effects of coastal protection forests: A case study in Haitan Island, Fujian, China

  • Author

    Xianli Peng ; Feng Ding ; Wenfeng Wu ; Xin Zhang

  • Author_Institution
    Key Lab. of Humid Subtropical Eco-Geogr. Process, Fujian Normal Univ., Fuzhou, China
  • fYear
    2014
  • fDate
    11-14 June 2014
  • Firstpage
    57
  • Lastpage
    61
  • Abstract
    Forests play an important role in protecting the coastal environment by slowing down the wind speed and fixing the soil. With the aid of Remote Sensing technology, the monitoring and analysis of the wind-preventing and sand-fixing effects of the coastal protection forests can provide useful and near real-time information for better forest construction and management in such areas. One major issue in previous studies was the somewhat subjective selection of the indicators when establishing the models for evaluating the wind-preventing and sand-fixing effects of the protection forests, which made the research results less convincing and less reliable. Moreover, the analysis of the relationships between the indicators and effects of the evaluation models was also seldom reported. To overcome the aforementioned weakness of previous research, this present paper, taking Haitan Island, China, as study area, presented a new and detailed procedure for indicator selection for coastal protection forests´ wind-preventing and sand-fixing effects evaluation and monitoring. Data used for the present study included: one Landsat 7 ETM+ image and one Landsat 8 OLI image, a DEM image with a spatial resolution of 5 meters and the meteorological data of the study area. According to a comprehensive literature survey, six most applicable indicators for the study area, namely, Climate Erosion Force (CE), Bare Sand Area Ratio (BS), Soil Adjust Vegetation Index (SAVI), Surface Roughness (SR), Soil Moisture (SM), and Soil Erodibility (SE) were selected. The Principal Component Analysis (PCA) and the Stepwise Regression Analysis (SRA) were applied to determine the weights for the six different indicators and to construct the evaluation model. By using the established evaluation model, the wind-preventing and sand-fixing effects of the coastal protection forests of the study area in 2002 and 2013 were computed, compared and analyzed.
  • Keywords
    climatology; erosion; principal component analysis; regression analysis; remote sensing; sand; soil; vegetation mapping; wind; AD 2002 to 2013; BS; CE; China; DEM image; Fujian; Haitan Island case study; Landsat 7 ETM+ image; Landsat 8 OLI image; PCA; SE; SM; SRA; bare sand area ratio; climate erosion force; coastal environment protection; coastal protection forest sand-fixing effect evaluation; coastal protection forest sand-fixing effect monitoring; coastal protection forest wind-preventing effect evaluation; coastal protection forest wind-preventing effect monitoring; comprehensive literature survey; established evaluation model; evaluation model; evaluation model effect; forest construction; forest management; meteorological data; principal component analysis; real-time information; remote sensing monitoring; remote sensing technology; soil erodibility; soil fixing slowing down; soil moisture; spatial resolution; stepwise regression analysis; subjective indicator selection; wind speed slowing down; Accuracy; Earth; Mathematical model; Principal component analysis; Remote sensing; Sea measurements; Soil; Coastal protection forest; Remote Sensing monitoring; evaluation; model; wind-preventing and sand-fixing effects;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Earth Observation and Remote Sensing Applications (EORSA), 2014 3rd International Workshop on
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4799-5757-6
  • Type

    conf

  • DOI
    10.1109/EORSA.2014.6927849
  • Filename
    6927849