EGU22-7143
https://doi.org/10.5194/egusphere-egu22-7143
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

The division of dry and wet areas of evergreen broad-leaved forest in Sichuan Province

shiqi zhang
shiqi zhang
  • Chengdu University of Technology, College Of Earth Sciences, China (zhangshiqiiiii@163.com)

The topography of Sichuan Province is extremely complex, with a rich variety of vegetation, and its vegetation shows a clear horizontal and vertical distribution structure. The subtropical evergreen broad-leaved forest is the zonal vegetation of Sichuan. In 1980, according to the field survey data, forestry scientists roughly divided the evergreen broad-leaved forest in Sichuan Province into Erlang Mountain, Daxiangling Mountain, Xiaoliang Mountain or Huangmaogeng. It was divided into a dry evergreen broad-leaved forest in the west and a moist evergreen broad-leaved forest in the east. However, there is no quantitative classification of wet and dry evergreen broad-leaved forests in Sichuan. The traditional forest vegetation survey mainly relies on manual field survey, which has a long period, high cost, and consumes a lot of manpower and material resources. Remote sensing technology, with its wide coverage, large amount of information and short update cycle, brings the possibility of rapid and accurate quantitative classification of wet and dry evergreen broad-leaved forests. In this paper, based on the field survey data of evergreen broad-leaved forests in Sichuan Province, we combined NASADEM_HGT elevation data and Landsat8 images to perform SCS+C topographic correction on remote sensing images of the whole region of Sichuan on the Google earth engine cloud computing platform, and also based on the differences in spectral, textural and temporal characteristics between dry and wet evergreen broad-leaved forests. The experimental results were compared with the field survey data and obtained excellent accuracy, and it provides a strong technical support for vegetation mapping and forestry resources investigation and monitoring, and also lays a certain foundation for the classification of complex mountain forest vegetation.

 

How to cite: zhang, S.: The division of dry and wet areas of evergreen broad-leaved forest in Sichuan Province, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7143, https://doi.org/10.5194/egusphere-egu22-7143, 2022.