您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China

文献类型： 外文期刊

作者： Li Zhen-wang 1 ; Xin Xiao-ping 1 ; Tang Huan 1 ; Yang Fan 1 ; Chen Bao-rui 1 ; Zhang Bao-hui 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Natl Hulunber Grassland Ecosyst Observat & Res St, Beijing 100081, Peoples R China

关键词： leaf area index;Random Forests grassland;remote sensing;Hulunber

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）

ISSN： 2095-3119

年卷期： 2017 年 16 卷 2 期

页码：

收录情况： SCI

摘要： Leaf area index (LAI) is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance. The accurate retrieval of LAI is important when modeling biophysical processes of vegetation and the productivity of earth systems. The Random Forests (RF) method aggregates an ensemble of decision trees to improve the prediction accuracy and demonstrates a more robust capacity than other regression methods. This study evaluated the RF method for predicting grassland LAI using ground measurements and remote sensing data. Parameter optimization and variable reduction were conducted before model prediction. Two variable reduction methods were examined: the Variable Importance Value method and the principal component analysis (PCA) method. Finally, the sensitivity of RF to highly correlated variables was tested. The results showed that the RF parameters have a small effect on the performance of RF, and a satisfactory prediction was acquired with a root mean square error (RMSE) of 0.1956. The two variable reduction methods for RF prediction produced different results; variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction, whereas variable reduction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information. After removing highly correlated variables, the relative variable importance remained steady, and the use of variables selected based on the best-performing vegetation indices performed better than the variables with all vegetation indices or those selected based on the most important one. The results in this study demonstrate the practical and powerful ability of the RF method in predicting grassland LAI, which can also be applied to the estimation of other vegetation traits as an alternative to conventional empirical regression models and the selection of relevant variables used in ecological models.

  • 相关文献

[1]Application of Crop Model Data Assimilation With a Particle Filter for Estimating Regional Winter Wheat Yields. Jiang, Zhiwei,Chen, Jin,Jiang, Zhiwei,Chen, Zhongxin,Liu, Jia,Ren, Jianqiang,Li, Zongnan,Sun, Liang,Li, He. 2014

[2]Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods. Li, Zhenwang,Tang, Huan,Yang, Fan,Chen, Baorui,Wang, Xu,Xin, Xiaoping,Wang, Jianghao,Ge, Yong,Huang, Chengquan. 2016

[3]The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation. Jiang, Zhiwei,Chen, Jin,Jiang, Zhiwei,Chen, Zhongxin,Ren, Jianqiang,Li, Zongnan,Sun, Liang,Jiang, Zhiwei,Chen, Zhongxin,Ren, Jianqiang,Li, Zongnan,Sun, Liang. 2014

[4]VALIDATION OF MODIS FAPAR PRODUCTS IN HULUNBER GRASSLAND OF CHINA. Li, Gang,Xin, Xiaoping,Zhang, Hongbin,Li, Gang,Xin, Xiaoping,Zhang, Hongbin,Wang, Daolong,Zhang, Hua,Xin, Xiaoping,Zhang, Hongbin,Liu, Shimin. 2010

[5]Effects of cattle grazing on small mammal communities in the Hulunber meadow steppe. Cao, Chan,Shuai, Ling-Ying,Song, Yan-Ling,Zeng, Zhi-Gao,Cao, Chan,Xin, Xiao-Ping,Liu, Zhi-Tao. 2016

[6]Assessment of the MODIS LAI Product Using Ground Measurement Data and HJ-1A/1B Imagery in the Meadow Steppe of Hulunber, China. Li, Zhenwang,Tang, Huan,Xin, Xiaoping,Zhang, Baohui,Wang, Dongliang. 2014

[7]Measurement of surface albedo and its environmental effects over a temperate desert steppe. Fulin Yang,Jing Zhou. 2013

[8]Measurement of surface albedo and its environmental effects over a temperate desert steppe. Fulin Yang,Jing Zhou. 2013

[9]Identification of plant configurations maximizing radiation capture in relay strip cotton using a functional-structural plant model. Lili Mao,Lizhen Zhang,Jochem B. Evers,Michael Henke,Wopke van der Werf,Shaodong Liu,Siping Zhang,Xinhua Zhao,Baomin Wang,Zhaohu Li.

[10]Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation. Li He,Jiang Zhi-wei,Chen Zhong-xin,Ren Jian-qiang,Liu Bin,Hasituyu,Jiang Zhi-wei. 2017

[11]Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat. Li He,Chen Zhong-xin,Jiang Zhi-wei,Wu Wen-bin,Ren Jian-qiang,Liu Bin,Hasi, Tuya,Jiang Zhi-wei,Hasi, Tuya. 2017

[12]Analysis of Electric-Energy Utilization Efficiency in a Plant Factory with Artificial Light for Lettuce Production. Tong, Y.,Yang, Q.,Shimamura, S.. 2014

[13]Utilizing the MODIS-derived leaf area index to investigate the impact of vegetation processes on hydrological simulation of macroscale catchment. Zhao, H. G.. 2018

[14]Simulating maize (Zea mays L.) growth and yield, soil nitrogen concentration, and soil water content for a long-term cropping experiment in Ontario, Canada. Liu, S.,Liu, S.,Yang, J. Y.,Drury, C. F.,Reynolds, W. D.,Liu, H. L.. 2014

[15]Estimation of Overstory and Understory Leaf Area Index by Combining Hyperion and Panchromatic QuickBird Data Using Neural Network Method. Huang, Jianxi,Su, Wei,Zeng, Yuan,Wu, Wenbin,Mao, Kebiao,Xu, Jingyu.

[16]Estimating the crop leaf area index using hyperspectral remote sensing. Liu Ke,Zhou Qing-bo,Wu Wen-bin,Tang Hua-jun,Liu Ke,Zhou Qing-bo,Wu Wen-bin,Tang Hua-jun,Wu Wen-bin,Xia Tian.

[17]Establishment of The Crop Growth and Nitrogen Nutrition State Model Using Spectral Parameters Canopy Cover. Tao Zhi-qiang,Bagum, Shamim Ara,Ma Wei,Zhou Bao-yuan,Fu Jin-dong,Cui Ri-xian,Sun Xue-fang,Zhao Ming. 2016

[18]REGIONAL YIELD PREDICTION OF WINTER WHEAT BASED ON RETRIEVAL OF LEAF AREA INDEX BY REMOTE SENSING TECHNOLOGY. Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Yang, Xiaomei,Liu, Xingren. 2009

[19]Nitrogen accumulation, remobilization and partitioning in rice (Oryza sativa L.) under an improved irrigation practice. Lin, XQ,Zhou, WJ,Zhu, DF,Chen, HZ,Zhang, YP. 2006

[20]Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Huang, Jianxi,Ma, Hongyuan,Li, Xinlu,Zhang, Xiaodong,Huang, Jianxi,Sedano, Fernando,Liang, Shunlin,Huang, Yanbo,Liang, Shunlin,Tian, Liyan,Fan, Jinlong,Wu, Wenbin.

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台