Machine Learning Based Modeling for Forest Aboveground Biomass Retrieval
Author:
Affiliation:
1. Indian Institute of Remote Sensing,Dehradun,Uttarakhand,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10064385/10064489/10064607.pdf?arnumber=10064607
Reference19 articles.
1. Logistics Distribution Path Optimization Using Support Vector Machine Algorithm under Different Constraints
2. Learning Deep Features for Discriminative Localization
3. POLINSAR Coherence-Based Regression Analysis of Forest Biomass Using RADARSAT-2 Datasets
4. Comparison of three modeling methods for estimating forest biomass using TM, GLAS and field measurement data
5. Prediction of discharge coefficient of combined weir-gate using ANN, ANFIS and SVM
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Forest Aboveground Biomass Estimation Using GEDI Observations Over Indian Forest;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
2. Forest aboveground biomass estimation by GEDI and multi-source EO data fusion over Indian forest;International Journal of Remote Sensing;2024-02-02
3. Total and component forest aboveground biomass inversion via LiDAR-derived features and machine learning algorithms;Frontiers in Plant Science;2023-10-26
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