Remote Sensing Based Conservation Effectiveness Evaluation of Mangrove Reserves in China

Author:

Liu Xiaoliang,Yang XiaomeiORCID,Zhang TaoORCID,Wang ZhihuaORCID,Zhang JunyaoORCID,Liu Yueming,Liu Bin

Abstract

In recent decades, the mangrove area in China has changed dramatically, and governments have established multiple mangrove protected areas at various levels. However, we know little about the effectiveness of conservation on mangroves on a national scale. In this study, we constructed an evaluation index system for landscape health and proposed a landscape health composite index (LHCI) to characterize the landscape health status of mangroves. Based on the distribution dataset of mangrove forests mangrove in the recent 40 years, we evaluated the conservation effectiveness of mangrove reserves in China from a perspective of landscape health. The dynamics of mangrove areas show that the mangrove area in 83% of the reserves increased after the establishment of reserves. Additionally, the increase in mangrove area in provincial-level, municipal-level, and county-level reserves was higher than that in national-level reserves, and the most significant increase in mangrove area was in Guangxi, followed by Fujian and Hong Kong. The evaluation results show that mangrove reserves have achieved outstanding conservation effectiveness in China, with 43% of the reserves significantly improving the landscape health status of mangroves and 35% of the reserves maintaining good condition. The reserves in Guangxi, Guangdong, and Fujian Provinces showed more significant protective effects. Specifically, the most effective reserves protecting mangroves were the Qi’ao Island reserve, Maowei Gulf reserve, and Enping reserve. This study may provide references for formulating a rapid evaluation method of conservation effectiveness based on remote sensing and promote the scientific management of protected areas and the ecological restoration of mangroves in China.

Funder

National Key Research and Development Program of China

CAS Earth Big Data Science Project of China

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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