Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models

Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing

(2023) Zheyuan Zhang et al.   Chinese Geographical Science

A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing

(2023) Yali Zhang et al.   GIScience & Remote Sensing

Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China

(2023) Linjing Zhang et al.   International Journal of Digital Earth

Contrasting photoprotective responses of forest trees revealed using PRI light responses sampled with airborne imaging spectrometry

(2023) John A. Gamon et al.   NEW PHYTOLOGIST

Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data

(2023) Qiuli Yang et al.   GIScience & Remote Sensing

Temporal Changes in Mediterranean Pine Forest Biomass Using Synergy Models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors

(2023) Edward A. Velasco Pereira et al.   Remote Sensing

Classification of Zambian grasslands using random forest feature importance selection during the optimal phenological period

(2022) Yifan Zhao et al.   ECOLOGICAL INDICATORS

Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis

(2022) Haifa Tamiminia et al.   Geocarto International

Machine learning models for estimating above ground biomass of fast growing trees

(2022) Warakhom Wongchai et al.   EXPERT SYSTEMS WITH APPLICATIONS

A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform—A Case Study of Yucatán Peninsula, Mexico

(2022) Xingguang Yan et al.   Remote Sensing

Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region, Türkiye

(2022) Sinan Bulut   Ecological Informatics

Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam

(2020) Tien Dat Pham et al.   Remote Sensing

Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

(2020) Haifa Tamiminia et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms

(2020) Yingchang Li et al.   Scientific Reports

New forest biomass carbon stock estimates in Northeast Asia based on multisource data

(2020) Weixue Luo et al.   GLOBAL CHANGE BIOLOGY

A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland

(2020) M. Mahdianpari et al.   GIScience & Remote Sensing

An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products

(2020) Yuzhen Zhang et al.   Remote Sensing

A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling

(2019) Jaime Lynn Speiser et al.   EXPERT SYSTEMS WITH APPLICATIONS

Forest biomass retrieval approaches from earth observation in different biomes

(2019) Pedro Rodríguez-Veiga et al.   International Journal of Applied Earth Observation and Geoinformation

Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery

(2019) Dezhi Wang et al.   International Journal of Applied Earth Observation and Geoinformation

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

(2018) Sasan Vafaei et al.   Remote Sensing

A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

(2018) Zelong Yang et al.   International Journal of Digital Earth

Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery

(2018) Lin Chen et al.   Forests

Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass

(2017) Zhenfeng Shao et al.   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Google Earth Engine: Planetary-scale geospatial analysis for everyone

(2017) Noel Gorelick et al.   REMOTE SENSING OF ENVIRONMENT

Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass

(2016) Suman Sinha et al.   Journal of Earth System Science

Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data

(2016) Yanjun Su et al.   REMOTE SENSING OF ENVIRONMENT

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(2016)   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

(2014) Dengsheng Lu et al.   International Journal of Digital Earth

Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass

(2013) Olivier W. Tsui et al.   REMOTE SENSING OF ENVIRONMENT

Lidar sampling for large-area forest characterization: A review

(2012) Michael A. Wulder et al.   REMOTE SENSING OF ENVIRONMENT

Retrieval of tropical forest biomass information from ALOS PALSAR data

(2012) M. Mahmudur Rahman et al.   Geocarto International

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

(2012) Cristina Gómez et al.   Remote Sensing

Forest biomass mapping from lidar and radar synergies

(2011) Guoqing Sun et al.   REMOTE SENSING OF ENVIRONMENT

The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle

(2011) T. Le Toan et al.   REMOTE SENSING OF ENVIRONMENT

Support vector machines in remote sensing: A review

(2010) Giorgos Mountrakis et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

(2009) Bjoern H Menze et al.   BMC BIOINFORMATICS