Monitoring and Landscape Dynamic Analysis of Alpine Wetland Area Based on Multiple Algorithms: A Case Study of Zoige Plateau.

Wenlong Li,Gaofeng Zhu,Pengfei Xue,Yapeng Cao,Chenli Liu,Hepiao Yan

doi:10.3390/s20247315

Abstract

As an important part of the wetland ecosystem, alpine wetland is not only one of the most important ecological water conservation areas in the Qinghai–Tibet Plateau region, but is also an effective regulator of the local climate. In this study, using three machine learning algorithms to extract wetland, we employ the landscape ecological index to quantitatively analyze the evolution of landscape patterns and grey correlation to analyze the driving factors of Zoige wetland landscape pattern change from 1995 to 2020. The following results were obtained. (1) The random forest algorithm (RF) performs best when dealing with high-dimensional data, and the accuracy of the decision tree algorithm (DT) is better. The performance of the RF and DT is better than that of the support vector machine algorithm. (2) The alpine wetland in the study area was degraded from 1995 to 2015, whereas wetland area began to increase after 2015. (3) The results of landscape analysis show the decrease in wetland area from 1995 to 2005 was mainly due to the fragmentation of larger patches into many small patches and loss of the original small patches, while the 2005 to 2015 decrease was caused by the loss of many middle patches and the decrease in large patches from the edge to the middle. The 2015 to 2020 increase is due to an increase in the number of smaller patches and recovery of original wetland area. (4) The grey correlation degree further shows that precipitation and evaporation are the main factors leading to the change in the landscape pattern of Zoige alpine wetland. The results are of great significance to the long-term monitoring of the Zoige wetland ecosystem.

Highlights

Wetland provides many important environmental services, including flood storage, drought control, regional climate regulation, erosion control, degradation of environmental pollutants, biodiversity, and habitat [1,2,3,4,5,6]
After studying the landscape pattern changes of wetland separately, we noticed the ratio of wetland to other landscapes has been changing dynamically, and we used the results of six phases of classification to make a transfer matrix in order to further explore the transformation relationship between wetland and other features
The results show that random forest algorithm (RF) and decision tree algorithm (DT) have higher user accuracy, producer accuracy, overall accuracy, and Kappa coefficient, and their performance is better than that of the support vector machine (SVM)

Summary

Introduction

Wetland provides many important environmental services, including flood storage, drought control, regional climate regulation, erosion control, degradation of environmental pollutants, biodiversity, and habitat [1,2,3,4,5,6]. Plateau is an important natural resource for the headwaters of the Yangtze and Yellow Rivers [7,8], the first and second largest rivers in China, respectively, and is one of the most important living environments for local Tibetans. Serious degradation of wetland has been threatening the sustainable development of the regional economy and ecosystem. There is an urgent need for advanced technologies and methods to monitor local alpine wetland [11] in order to provide a scientific basis for the protection and management of regional wetland resources.

Methods

Results

Discussion

Conclusion