The Temporal-Based Forest Disturbance Monitoring Analysis: A Case Study of Nature Reserves of Hainan Island of China From 1987 to 2020

1Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.

Abstract

Forest disturbance monitoring can provide scientific data for the decision making and management of nature reserves. LandTrendr algorithm has been applied to identify forest disturbances on a long-time scale through appropriate segmentation and linear fitting. In this study, 23 nature reserves were detected using LandTrendr during 1987–2020, and the vegetation loss was quantified by years and pixel numbers. The results illustrated that (1) most disturbances occurred in the 1990s and early 21st century. (2) From the spatial distribution of forest loss, the area of forest vegetation disturbance in the coastal zone was larger than the protected area in the internal Hainan Island, the area disturbed in the coastal zone protected area was 97.12 km2, and the area disturbed in the internal area of Hainan Island protected area was 63.02 km2. (3) In terms of different levels of nature reserves, the disturbed area of national nature reserves was 28.39 km2 and the total disturbed area of provincial nature reserves was 131.75 km2. (4) In terms of different types of nature reserves, forest ecological nature reserves had the largest disturbed area of 102.96 km2, followed by marine coastal nature reserves with a disturbed area of 36.99 km2, wildlife nature reserves with a disturbed area of 10.22 km2, and wild plant nature reserves with the smallest disturbed area of 9.96 km2. The results are hoped to provide scientific support and data for the management and planning of nature reserves in Hainan Island.

Cite

CITATION STYLE

APA

Xiao, H., Zhang, X., Yan, M., Zhang, L., Wang, H., Ma, Y., & Liu, J. (2022). The Temporal-Based Forest Disturbance Monitoring Analysis: A Case Study of Nature Reserves of Hainan Island of China From 1987 to 2020. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.891752

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free