Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data

Yan Meng,Peili Mao,Mengxiao Wang,Yue Wu,Banghua Cao,Lin Qi,Xidong Cao,Chao Dong

doi:10.3390/f11020130

Abstract

Located in the Mount Tai state-owned forest farm, this study adopted Landsat multispectral remote sensing data in 2000 and 2016 on the GEE (Google Earth Engine) platform and selected four phases of images each year according to the phenological period. By dealing with the current situation map of forestry resources in 2000 and the field survey data in 2016, the samples of tree species distribution in 2000 and 2016 were obtained. On the basis of topographic correction with the empirical rotation model, this study used the random forest (RF) classifier to classify tree species from remote sensing images in 2000 and 2016, achieving high classification accuracy. The results showed that, after 16 years of evolution, the percentage of pine species in the forest decreased from 55.69% to 50.22%, with a percentage decrease as high as 5.47%. The percentage of black locust (Robinia pseudoacacia) increased from 10.15% in 2000 to 13.75% in 2016, with an increase of 3.60%. Quercus also had a positive growth in the area. This result reflected the expansion of black locust.

Highlights

IntroductionThe investigation of tree species distribution is a considerable workload
Tree species classification is an essential task in forest management [1]
The application of remote sensing technology in forest tree species classification is relatively mature, there are few studies on the analysis of forest tree species change by comparing the classification results of two periods

Summary

Introduction

The investigation of tree species distribution is a considerable workload. If it only relies on manual work, it will be time-consuming. As remote sensing technology can promptly collect massive real-time surface information, it was used to classify tree species over the past 40 years [2]. The classifier needs sample data; whether historical data are available or not becomes a key limiting factor. Another important reason is that satellite data, which are widely used at present, may be too recent to study the long-term changes of forest tree species.

Objectives

Methods

Results

Discussion

Conclusion