Identifying Forest Types and Distribution Patterns in Shennongjia National Park

Abstract

Forests are indispensable ecosystems, providing vital resources and services crucial for human well-being and sustainable development. Remote sensing has emerged as a potent tool for mapping forests across diverse spatial scales. The Shennongjia region stands out globally for its exceptional biodiversity and the presence of rare endangered flora. Despite scholarly attention to the region's forest ecosystem, there remains a gap in detailed and high-resolution assessments of forest type patterns based on remote sensing data, especially following the official establishment of Shennongjia National Park in 2020. This study utilizes multi-resolution and multi-temporal remote sensing data to delineate forest types and distributions within the National Park in 2023. Through the integration of multi-temporal remote sensing data, the Google Earth Engine platform and machine learning techniques, our method achieved an overall accuracy of the six forest types at 86.1%, and the distribution patterns of various forest types generally conform to a natural-law accordance trend with increasing altitude in Shennongjia National Park. We hope that our research results can optimize the workflow for forest type classification, thereby furnishing basic data for national park management.