基于Sentinel-2的绿洲-荒漠过渡带植被地上生物量估算
Estimation of aboveground vegetation biomass in oasis-desert transition zone based on Sentinel-2
刘书田 1王雪梅 2赵枫2
作者信息
- 1. 新疆师范大学地理科学与旅游学院,乌鲁木齐 830054
- 2. 新疆师范大学地理科学与旅游学院,乌鲁木齐 830054;新疆维吾尔自治区重点实验室"新疆干旱区湖泊环境与资源实验室",乌鲁木齐 830054
- 折叠
摘要
开展绿洲-荒漠过渡带植被地上生物量监测是植被生长状况评价与荒漠化监测的重要手段.文中利用Sentinel-2影像数据构建了地上生物量估算模型,比较了统计模型和两种机器学习算法模型的性能,并对渭干河-库车河绿洲的绿洲-荒漠过渡带的植被地上生物量进行了估算.结果显示,在统计模型中,红边三角植被指数(RTVI)与地上生物量的非线性模型拟合效果最好,且相关最显著.在机器学习算法中,随机森林模型优于支持向量机回归模型.通过验证发现,RTVI非线性估测模型和随机森林模型具有较好的外推能力.在绿洲-荒漠过渡带植被地上生物量的反演中,随机森林模型表现出较高的精度,验证集R2为0.65,RMSE和MAE分别为255.08g·m-2和192.93g·m-2.相较其他模型,随机森林模型可以在小样本情况下更精确,对科学监测绿洲-荒漠过渡带植被地上生物量和维护绿洲的稳定发展提供依据.
Abstract
Monitoring the above-ground biomass of vegetation in the oasis-desert transition zone is an important means to evaluate vegetation growth and monitor the desertification.In this study,Sentinel-2 image data was used to construct an above-ground biomass estimation model.The performance of the statistical model and two machine learning algorithm models were compared,and the above-ground biomass of vegetation in the oasis-desert transition zone of the Weigan-Kuqa River oasis was estimated.The results showed that,among the statistical models,RTVI has the best fitting effect and the most significant correlation with the nonlinear model of above-ground biomass.In machine learning algorithms,the random forest model is superior to the support vector machine regression model.The results show that the RTVI nonlinear estimation model and the random forest model have better extrapolation abilities.In the inversion of above-ground biomass of the oasis-desert transition zone,the random forest model achieves higher accuracy,the verification set R2 is 0.65,RMSE and MAE are 255.08g·m-2 and 192.93g·m-2,respectively.Compared with other models,the random forest model can be more accurate in the case of small samples,and provide a basis for scientific monitoring of the aboveground biomass of vegetation in the oasis-desert transition zone and maintaining the stable development of the oasis.
关键词
地上生物量估算/植被指数/机器学习算法/Sentinel-2/绿洲-荒漠过渡带Key words
estimation of aboveground biomass/vegetation index/machine learning algorithm/Sentinel-2/oasis-desert transition zone引用本文复制引用
基金项目
新疆维吾尔自治区自然科学基金项目(2023D01A44)
国家自然科学基金项目(41561051)
大学生创新创业训练计划项目(S202210762007)
出版年
2024
国家科技期刊平台
NSTL
万方数据