Developing a Random Forest Algorithm for MODIS Global Burned Area Classification
Published 2017 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Developing a Random Forest Algorithm for MODIS Global Burned Area Classification
Authors
Keywords
-
Journal
Remote Sensing
Volume 9, Issue 11, Pages 1193
Publisher
MDPI AG
Online
2017-11-22
DOI
10.3390/rs9111193
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Stratification and sample allocation for reference burned area data
- (2017) Marc Padilla et al. REMOTE SENSING OF ENVIRONMENT
- A new global burned area product for climate assessment of fire impacts
- (2016) Emilio Chuvieco et al. GLOBAL ECOLOGY AND BIOGEOGRAPHY
- Random forest in remote sensing: A review of applications and future directions
- (2016) Mariana Belgiu et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas
- (2016) Charlotte Pelletier et al. REMOTE SENSING OF ENVIRONMENT
- Vegetation Dynamics in the Upper Guinean Forest Region of West Africa from 2001 to 2015
- (2016) Zhihua Liu et al. Remote Sensing
- Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin
- (2015) Andrew Mellor et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation
- (2015) Marc Padilla et al. REMOTE SENSING OF ENVIRONMENT
- MODIS–Landsat fusion for large area 30m burned area mapping
- (2015) Luigi Boschetti et al. REMOTE SENSING OF ENVIRONMENT
- Global burned area mapping from ENVISAT-MERIS and MODIS active fire data
- (2015) Itziar Alonso-Canas et al. REMOTE SENSING OF ENVIRONMENT
- An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms
- (2015) René Colditz Remote Sensing
- Global fire size distribution is driven by human impact and climate
- (2014) Stijn Hantson et al. GLOBAL ECOLOGY AND BIOGEOGRAPHY
- Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling
- (2014) Marc Padilla et al. REMOTE SENSING OF ENVIRONMENT
- BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data
- (2014) Aitor Bastarrika et al. Remote Sensing
- The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables
- (2013) R. Hollmann et al. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
- Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments
- (2013) Florent Mouillot et al. International Journal of Applied Earth Observation and Geoinformation
- Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries
- (2013) Carmen Quintano et al. REMOTE SENSING OF ENVIRONMENT
- Dynamic biomass burning emission factors and their impact on atmospheric CO mixing ratios
- (2013) T. T. van Leeuwen et al. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
- Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4)
- (2013) Louis Giglio et al. Journal of Geophysical Research-Biogeosciences
- Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest
- (2012) Sandra Oliveira et al. FOREST ECOLOGY AND MANAGEMENT
- High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm
- (2012) Onisimo Mutanga et al. International Journal of Applied Earth Observation and Geoinformation
- Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data
- (2012) Giorgos Mallinis et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment
- (2012) L. Naidoo et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- A method for extracting burned areas from Landsat TM/ETM+ images by soft aggregation of multiple Spectral Indices and a region growing algorithm
- (2012) D. Stroppiana et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests
- (2012) Yufang Jin et al. JOURNAL OF GEOPHYSICAL RESEARCH
- Evaluation of random forest method for agricultural crop classification
- (2012) Asli Ozdarici Ok et al. European Journal of Remote Sensing
- Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data
- (2012) Markus Immitzer et al. Remote Sensing
- Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery
- (2011) George H. Mitri et al. International Journal of Applied Earth Observation and Geoinformation
- An assessment of the effectiveness of a random forest classifier for land-cover classification
- (2011) V.F. Rodriguez-Galiano et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors
- (2011) Aitor Bastarrika et al. REMOTE SENSING OF ENVIRONMENT
- Global and regional analysis of climate and human drivers of wildfire
- (2011) Andrew Aldersley et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Variable selection using random forests
- (2010) Robin Genuer et al. PATTERN RECOGNITION LETTERS
- A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America
- (2010) Matthew L. Clark et al. REMOTE SENSING OF ENVIRONMENT
- A predictive model of burn severity based on 20-year satellite-inferred burn severity data in a large southwestern US wilderness area
- (2009) Zachary A. Holden et al. FOREST ECOLOGY AND MANAGEMENT
- What limits fire? An examination of drivers of burnt area in Southern Africa
- (2009) SALLY ARCHIBALD et al. GLOBAL CHANGE BIOLOGY
- Comparison of L3JRC and MODIS global burned area products from 2000 to 2007
- (2009) Di Chang et al. JOURNAL OF GEOPHYSICAL RESEARCH
- Implementation of National Fire Plan treatments near the wildland-urban interface in the western United States
- (2009) T. Schoennagel et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
- (2009) Mark A. Friedl et al. REMOTE SENSING OF ENVIRONMENT
- A new, global, multi-annual (2000–2007) burnt area product at 1 km resolution
- (2008) Kevin Tansey et al. GEOPHYSICAL RESEARCH LETTERS
- Large fires as agents of ecological diversity in the North American boreal forest
- (2008) Philip J. Burton et al. INTERNATIONAL JOURNAL OF WILDLAND FIRE
- Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
- (2008) Jonathan Cheung-Wai Chan et al. REMOTE SENSING OF ENVIRONMENT
- An active-fire based burned area mapping algorithm for the MODIS sensor
- (2008) Louis Giglio et al. REMOTE SENSING OF ENVIRONMENT
- Empirical characterization of random forest variable importance measures
- (2007) Kellie J. Archer et al. COMPUTATIONAL STATISTICS & DATA ANALYSIS
- A VARI-based relative greenness from MODIS data for computing the Fire Potential Index
- (2007) P. Schneider et al. REMOTE SENSING OF ENVIRONMENT
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now