Prediction of Ecosystem Service Function of Grain for Green Project Based on Ensemble Learning

Huijie Li,Xiang Niu,Bing Wang

doi:10.3390/f12050537

Abstract

The Grain for Green Project (GGP) was implemented over 20 years ago as one of six major forestry projects in China, and its scope of implementation is still expanding. However, it is still unclear how many ecosystem services (ESs) the project will produce in the future. The GGP’s large-scale ecological monitoring officially started in 2015 and there is a lack of early monitoring data, making it challenging to predict the future ecological benefits. Therefore, this paper proposes a method to predict future ESs by using ecological monitoring data. First, a new ensemble learning system, auto-XGBoost-ET-DT, is developed based on ensemble learning theory. Using the GGP’s known ESs in 2015, 2017, and 2019, the missing ESs of the past decade have been evaluated via reverse regression. Data from 2020 to 2022 within a convolution neural network and the coupling coordination degree model have been used to analyze the coupling between the prediction results and economic development. The results show that the growth distributions of ESs in three years were as follows: soil consolidation, 3.70–6.34%; forest nutrient retention, 2.72–.71%; water conservation, 2.52–6.09%; carbon fixation and oxygen release, 3.00–4.64%; and dust retention, 3.82–5.75%. The coupling coordination degree of the ESs and economic development has been improved in 97% of counties in 2020 compared with 2019. The results verify a feasible ES prediction method and provide a basis for the progressive implementation of the GGP.

Highlights

It has been widely recognized that forests can provide ecosystem services (ESs)
Looks atservice only quality calculated should be noted that this prediction does not consider the increase of the Grain for Green Project (GGP)’s vegetation the data of the GGP ecosystem service quality from 2020 to 2022 are shown in Tab restoration resource area, and takes the vegetation restoration resource area in 2019 as should the be criterion
The results show that the ecosystem service function will 9113.42 continue to increase with25,997.2 the continued50.54 implementation of the GGP

Summary

Introduction

It has been widely recognized that forests can provide ecosystem services (ESs). Much research has been produced on the quantitative evaluation of ESs, which is mainly divided into two methods: value quantity evaluation and quality evaluation. The most representative method of value quantity is the market value method proposed by Costanza [1,2]. On this basis, some researchers have put forward the equivalent factor method [3]. The method of matter quality is closer to the process of material circulation and energy exchange within an ecosystem, and the evaluation results are more favorable for ecosystem-related research. This method primarily focuses on the energy method [4] and the model method [5,6]

Methods

Results

Conclusion