Nonpoint Pollution Source-Sink Landscape Pattern Change Analysis in a Coastal River Basin in Southeast China

Int J Environ Res Public Health. 2018 Sep 26;15(10):2115. doi: 10.3390/ijerph15102115.

Abstract

Analyzing the spatiotemporal characteristics of source-sink landscape pattern change in river basins is crucial for managing and controlling nonpoint source pollution. This study investigated the landscape pattern changes in Jiulong River basin from 1990 to 2015. A random forest classifier combined with texture and spectral information was applied to interpret the multi-temporal Landsat images. Landscape metrics were calculated to quantify the landscape at the patch level. Transition matrixes were derived for analyzing the conversion among different landscape types. It is notable that the largest values of the number of patches and patch density of residential land appeared in 2005, indicating the highest degree of fragmentation over this time period. The percentage of landscape for forestland was always higher than 71%, and the percentage of residential land increased from 7.42% to 14.55% during the last three decades, while unused land decreased from 5.3% to 2.8%. The downward trend of DO and the upward trend of NH₃-N and TP indicate the deterioration of water quality during 2005⁻2015. The quantitative monitoring data of water quality indicators in Hua'an and Xiamen sites in Jiulong River basin are shown. The percentage of landscape of cultivated land increased during 2005⁻2010, which was consistent with the change tendency of NH₃-N. Transition matrixes showed that the main changes occurred when forestland and unused land were transformed to residential land and cultivated land over the last three decades. Analysis results demonstrated a higher extent of landscape fragmentation and an unsustainable transition among source-sink landscapes.

Keywords: change; landscape metrics; landscape pattern; random forest; source-sink; transition matrix.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture
  • China
  • Environmental Monitoring
  • Forests*
  • Geographic Mapping
  • Housing
  • Non-Point Source Pollution*
  • Rivers*
  • Satellite Imagery
  • Urbanization / trends*
  • Water Quality*