Inter-Comparison of Four Models for Detecting Forest Fire Disturbance from MOD13A2 Time Series
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Inter-Comparison of Four Models for Detecting Forest Fire Disturbance from MOD13A2 Time Series
Authors
Keywords
-
Journal
Remote Sensing
Volume 14, Issue 6, Pages 1446
Publisher
MDPI AG
Online
2022-03-21
DOI
10.3390/rs14061446
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Forest Disturbance Detection with Seasonal and Trend Model Components and Machine Learning Algorithms
- (2022) Jonathan V. Solórzano et al. Remote Sensing
- An autoencoder-based model for forest disturbance detection using Landsat time series data
- (2021) Gaoxiang Zhou et al. International Journal of Digital Earth
- Vegetation structure drives forest phenological recovery after hurricane
- (2021) Yuan Gong et al. SCIENCE OF THE TOTAL ENVIRONMENT
- How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest
- (2021) Yan Gao et al. Remote Sensing
- BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis
- (2021) Dainius Masiliūnas et al. Remote Sensing
- Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland
- (2020) Ling Wu et al. Remote Sensing
- Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing
- (2020) Theodomir Mugiraneza et al. Remote Sensing
- A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection
- (2020) Su Ye et al. REMOTE SENSING OF ENVIRONMENT
- Detecting Spatiotemporal Changes in Vegetation with the BFAST Model in the Qilian Mountain Region during 2000–2017
- (2019) Liying Geng et al. Remote Sensing
- Continuous monitoring of land disturbance based on Landsat time series
- (2019) Zhe Zhu et al. REMOTE SENSING OF ENVIRONMENT
- Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery
- (2019) Lihong Zhu et al. Remote Sensing
- Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico
- (2019) Yan Gao et al. Geocarto International
- A time-series classification approach based on change detection for rapid land cover mapping
- (2019) Jining Yan et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data
- (2019) Joshua Lizundia-Loiola et al. REMOTE SENSING OF ENVIRONMENT
- Predictability of monthly temperature and precipitation using automatic time series forecasting methods
- (2018) Georgia Papacharalampous et al. Acta Geophysica
- A comparison of NDVI and EVI in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas: a case study in Southern China
- (2018) Jizhong Qiu et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada
- (2018) Xiuqin Fang et al. REMOTE SENSING OF ENVIRONMENT
- Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series
- (2018) Paulo Murillo-Sandoval et al. Forests
- Predictability of monthly temperature and precipitation using automatic time series forecasting methods
- (2018) Georgia Papacharalampous et al. Acta Geophysica
- Day-of-week and seasonal patterns of PM2.5 concentrations over the United States: Time-series analyses using the Prophet procedure
- (2018) Naizhuo Zhao et al. ATMOSPHERIC ENVIRONMENT
- Continuous subpixel monitoring of urban impervious surface using Landsat time series
- (2018) Chengbin Deng et al. REMOTE SENSING OF ENVIRONMENT
- Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications
- (2017) Zhe Zhu ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Google Earth Engine: Planetary-scale geospatial analysis for everyone
- (2017) Noel Gorelick et al. REMOTE SENSING OF ENVIRONMENT
- How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?
- (2017) Warren Cohen et al. Forests
- The benefit of synthetically generated RapidEye and Landsat 8 data fusion time series for riparian forest disturbance monitoring
- (2016) Philipp Gärtner et al. REMOTE SENSING OF ENVIRONMENT
- A forest vulnerability index based on drought and high temperatures
- (2016) David Mildrexler et al. REMOTE SENSING OF ENVIRONMENT
- Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series
- (2015) Ben DeVries et al. REMOTE SENSING OF ENVIRONMENT
- Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images
- (2015) Zhe Zhu et al. REMOTE SENSING OF ENVIRONMENT
- Detecting Change Dates from Dense Satellite Time Series Using a Sub-Annual Change Detection Algorithm
- (2015) Shanshan Cai et al. Remote Sensing
- Abrupt spatiotemporal land and water changes and their potential drivers in Poyang Lake, 2000–2012
- (2014) Lifan Chen et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Continuous change detection and classification of land cover using all available Landsat data
- (2014) Zhe Zhu et al. REMOTE SENSING OF ENVIRONMENT
- Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation
- (2013) Damien Sulla-Menashe et al. REMOTE SENSING OF ENVIRONMENT
- Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan
- (2012) Robert E. Kennedy et al. REMOTE SENSING OF ENVIRONMENT
- Near real-time disturbance detection using satellite image time series
- (2012) Jan Verbesselt et al. REMOTE SENSING OF ENVIRONMENT
- Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau
- (2010) Lei Zhong et al. CLIMATIC CHANGE
- Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms
- (2010) Robert E. Kennedy et al. REMOTE SENSING OF ENVIRONMENT
- Phenological change detection while accounting for abrupt and gradual trends in satellite image time series
- (2010) Jan Verbesselt et al. REMOTE SENSING OF ENVIRONMENT
- Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006
- (2009) MICHAEL A. WHITE et al. GLOBAL CHANGE BIOLOGY
- Detecting trend and seasonal changes in satellite image time series
- (2009) Jan Verbesselt et al. REMOTE SENSING OF ENVIRONMENT
- Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains
- (2007) Brian D. Wardlow et al. REMOTE SENSING OF ENVIRONMENT
- Use of a dark object concept and support vector machines to automate forest cover change analysis
- (2007) Chengquan Huang 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 MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now