Estimating deciduous broadleaf forest gross primary productivity by remote sensing data using a random forest regression model

2019 ◽  
Vol 13 (03) ◽  
pp. 1
Author(s):  
Yue Chen ◽  
Wei Shen ◽  
Shuai Gao ◽  
Kun Zhang ◽  
Jian Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Regression Model ◽  
Primary Productivity ◽  
Remote Sensing Data ◽  
Gross Primary Productivity ◽  
Random Forest Regression ◽  
Broadleaf Forest ◽  
Sensing Data ◽  
Deciduous Broadleaf Forest

scholarly journals Estimation of biomass in wheat using random forest regression algorithm and remote sensing data

2016 ◽  
Vol 4 (3) ◽  
pp. 212-219 ◽  
Author(s):  
Li'ai Wang ◽  
Xudong Zhou ◽  
Xinkai Zhu ◽  
Zhaodi Dong ◽  
Wenshan Guo
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Random Forest Regression ◽  
Sensing Data

scholarly journals STUDY ON REMOTE SENSING MONITORING MODEL OF AGRICULTURAL DROUGHT BASED ON RANDOM FOREST DEVIATION CORRECTION

2021 ◽  
pp. 413-422
Author(s):  
Shao Li ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Data Sources ◽  
Drought Monitoring ◽  
Monitoring Methods ◽  
Large Area ◽  
Sensing Data ◽  
Monitoring Model

Using remote sensing data to monitor large area drought is one of the important methods of drought monitoring at present. However, the traditional remote sensing drought monitoring methods mainly focus on monitoring single drought response factors such as soil moisture or vegetation status, and the research on comprehensive multi-factor drought monitoring is limited. In order to improve the ability to resist drought events, this paper takes Henan Province of China as an example, takes multi-source remote sensing data as data sources, considers various disaster-causing factors, adopts random forest method to model, and explores the method of regional remote sensing comprehensive drought monitoring using various remote sensing data sources. Compared with neural network, classification regression tree and linear regression, the performance of random forest is more stable and tolerant to noise and outliers. In order to provide a new method for comprehensive assessment of regional drought, a comprehensive drought monitoring model was established based on multi-source remote sensing data, which comprehensively considered the drought factors such as soil water stress, vegetation growth status and meteorological precipitation profit and loss in the process of drought occurrence and development.

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