Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
Authors
Keywords
-
Journal
Scientific Reports
Volume 10, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-06-19
DOI
10.1038/s41598-020-67024-3
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Improving Forest Aboveground Biomass (AGB) Estimation by Incorporating Crown Density and Using Landsat 8 OLI Images of a Subtropical Forest in Western Hunan in Central China
- (2019) Chao Li et al. Forests
- How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset
- (2019) Hristos Tyralis et al. JOURNAL OF HYDROLOGY
- Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms
- (2019) Li et al. Forests
- Annual forest aboveground biomass changes mapped using ICESat/GLAS measurements, historical inventory data, and time-series optical and radar imagery for Guangdong province, China
- (2018) Wenjuan Shen et al. AGRICULTURAL AND FOREST METEOROLOGY
- A novel ensemble method for credit scoring: Adaption of different imbalance ratios
- (2018) Hongliang He et al. EXPERT SYSTEMS WITH APPLICATIONS
- Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting
- (2018) Stefanos Georganos et al. IEEE Geoscience and Remote Sensing Letters
- Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region
- (2018) Yukun Gao et al. Remote Sensing
- Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance
- (2016) Elizabeth A. Freeman et al. CANADIAN JOURNAL OF FOREST RESEARCH
- Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data
- (2016) Lin Cao et al. REMOTE SENSING OF ENVIRONMENT
- Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data
- (2016) Wenjuan Shen et al. Remote Sensing
- Forest aboveground biomass estimation using polarization coherence tomography and PolSAR segmentation
- (2015) Wenmei Li et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- An novel random forests and its application to the classification of mangroves remote sensing image
- (2015) Yan-Min Luo et al. MULTIMEDIA TOOLS AND APPLICATIONS
- Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
- (2015) Iftikhar Ali et al. Remote Sensing
- Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China
- (2014) Songqiu Deng et al. Remote Sensing
- Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions
- (2012) M.E.J. Cutler et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa)
- (2012) João M.B. Carreiras et al. REMOTE SENSING OF ENVIRONMENT
- GMES Sentinel-1 mission
- (2012) Ramon Torres et al. REMOTE SENSING OF ENVIRONMENT
- Support Vector Regression for the Estimation of Forest Stand Parameters Using Airborne Laser Scanning
- (2011) Jean-Matthieu Monnet et al. IEEE Geoscience and Remote Sensing Letters
- Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment
- (2010) Barbara Koch ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina
- (2010) Néstor Ignacio Gasparri et al. JOURNAL OF ARID ENVIRONMENTS
- Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets
- (2010) J. M. Kellndorfer et al. JOURNAL OF GEOPHYSICAL RESEARCH
- Importance of biomass in the global carbon cycle
- (2009) R. A. Houghton et al. JOURNAL OF GEOPHYSICAL RESEARCH
- Estimating Siberian timber volume using MODIS and ICESat/GLAS
- (2009) R. Nelson et al. REMOTE SENSING OF ENVIRONMENT
- A working guide to boosted regression trees
- (2008) J. Elith et al. JOURNAL OF ANIMAL ECOLOGY
- Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information
- (2007) J BLACKARD et al. REMOTE SENSING OF ENVIRONMENT
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search