A Hybrid Approach of Combining Random Forest with Texture Analysis and VDVI for Desert Vegetation Mapping Based on UAV RGB Data

Huoyan Zhou,Jinping Guo,Yuancai Lei,Liyong Fu,Ram P Sharma

doi:10.3390/rs13101891

Abstract

Desert vegetation is an important part of arid and semi-arid areas, which plays an important role in preventing wind and fixing sand, conserving water and soil, maintaining the balanced ecosystem. Therefore, mapping the vegetation accurately is necessary to conserve rare desert plants in the fragile ecosystems that are easily damaged and slow to recover. In mapping desert vegetation, there are some weaknesses by using traditional digital classification algorithms from high resolution data. The traditional approach is to use spectral features alone, without spatial information. With the rapid development of drones, cost-effective visible light data is easily available, and the data would be non-spectral but with spatial information. In this study, a method of mapping the desert rare vegetation was developed based on the pixel classifiers and use of Random Forest (RF) algorithm with the feature of VDVI and texture. The results indicated the accuracy of mapping the desert rare vegetation were different with different methods and the accuracy of the method proposed was higher than the traditional method. The most commonly used decision rule in the traditional method, named Maximum Likelihood classifier, produced overall accuracy (76.69%). The inclusion of texture and VDVI features with RGB (Red Green Blue) data could increase the separability, thus improved the precision. The overall accuracy could be up to 84.19%, and the Kappa index with 79.96%. From the perspective of features, VDVI is less important than texture features. The texture features appeared more important than spectral features in desert vegetation mapping. The RF method with the RGB+VDVI+TEXTURE would be better method for desert vegetation mapping compared with the common method. This study is the first attempt of classifying the desert vegetation based on the RGB data, which will help to inform management and conservation of Ulan Buh desert vegetation.

Highlights

Desertification is a global environmental problem [1], which affects 14% of the world's land area and nearly 1 billion people, and it continues to expand at a rate of 57,000 km2 per year
The result of different methods indicate that trees mainly distribute in three parts of the plot, as shown in Figures 8–10, and the map of desert vegetation shows with more details and accuracy than the Maximum likelihood classification (MLC) method
When comparing with the field survey, Random Forest (RF) method with the RGB+visible light difference vegetation index (VDVI)+TEXTURE will be the good approach for desert vegetation mapping

Summary

Introduction

Desertification is a global environmental problem [1], which affects 14% of the world's land area and nearly 1 billion people, and it continues to expand at a rate of 57,000 km per year. The monitoring and evaluation of desertification has always been a hot topic in the world and it is an important way to prevent and control desertification effectively [4]. Desertification study has become a hot issue in multi-disciplinary study, and rapid and accurate acquisition of desert vegetation information is the foundation and key link of desertification study. Remote sensing has been widely used in the monitoring and evaluation of land desertification due to its advantages of wide observation range, large amount of information, fast updating of data, and high accuracy

Methods

Results

Discussion

Conclusion