Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data
暂无分享,去创建一个
Efraín Duarte | Juan A. Barrera | Francis Dube | Fabio Casco | Alexander J. Hernández | Erick Zagal | F. Dube | E. Zagal | J. Barrera | Efraín Duarte | Fabio Casco
[1] I. Thompson,et al. An Operational framework for defining and monitoring forest degradation , 2013 .
[2] K. Orvis,et al. Modern pollen spectra from the highlands of the Cordillera Central, Dominican Republic , 2005 .
[3] Michael A. Wulder,et al. Historical forest biomass dynamics modelled with Landsat spectral trajectories , 2014 .
[4] C. Woodcock,et al. Continuous change detection and classification of land cover using all available Landsat data , 2014 .
[5] J. Chambers,et al. Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. , 2007, Trends in ecology & evolution.
[6] Zhe Zhu,et al. Cloud detection algorithm comparison and validation for operational Landsat data products , 2017 .
[7] José A. Sobrino,et al. Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco , 2000 .
[8] Michael Dixon,et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .
[9] Robert C. Balling,et al. Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover , 2018 .
[10] M. Herold,et al. Institutional effectiveness of REDD+ MRV: Countries progress in implementing technical guidelines and good governance requirements , 2016 .
[11] R. Ponce-Hernandez,et al. Assessing and Monitoring Forest Degradation in a Deciduous Tropical Forest in Mexico via Remote Sensing Indicators , 2017 .
[12] Sylvain Arlot,et al. A survey of cross-validation procedures for model selection , 2009, 0907.4728.
[13] C. Justice,et al. High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.
[14] D. Lobell,et al. Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring , 2017 .
[15] J. Townshend,et al. Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .
[16] Sandra A. Brown,et al. Greenhouse gas emissions from tropical forest degradation: an underestimated source , 2017, Carbon Balance and Management.
[17] Neil Flood,et al. Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median) , 2013, Remote. Sens..
[18] R. B. Jackson,et al. CO 2 emissions from forest loss , 2009 .
[19] G. Domke,et al. Contemporary forest carbon dynamics in the northern U.S. associated with land cover changes , 2020 .
[20] M. Claverie,et al. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.
[21] W. Salas,et al. Baseline Map of Carbon Emissions from Deforestation in Tropical Regions , 2012, Science.
[22] M. Herold,et al. An assessment of deforestation and forest degradation drivers in developing countries , 2012 .
[23] Paolo Gamba,et al. Scaling up to National/Regional Urban Extent Mapping Using Landsat Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[24] David A. Seal,et al. The Shuttle Radar Topography Mission , 2007 .
[25] Zhe Zhu,et al. Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .
[26] Masoud Mahdianpari,et al. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review , 2020 .
[27] Martha C. Anderson,et al. Free Access to Landsat Imagery , 2008, Science.
[28] Jinwei Dong,et al. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. , 2016, Remote sensing of environment.
[29] David Saah,et al. Collect Earth: An online tool for systematic reference data collection in land cover and use applications , 2019, Environ. Model. Softw..
[30] C. Woodcock,et al. Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis , 2020, Remote Sensing of Environment.
[31] S. Goetz,et al. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+ , 2015 .
[32] F. Gao,et al. Improved forest change detection with terrain illumination corrected Landsat images , 2013 .
[33] Jennifer Burney,et al. High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão , 2017, Remote. Sens..
[34] Lars Laestadius,et al. When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration , 2016, Ambio.
[35] Curtis E. Woodcock,et al. Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series , 2017 .
[36] Alan H. Strahler,et al. Global land cover mapping from MODIS: algorithms and early results , 2002 .
[37] Dar A. Roberts,et al. Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon , 2013, Remote. Sens..
[38] A. Huete. A soil-adjusted vegetation index (SAVI) , 1988 .
[39] Yuqi Bai,et al. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine , 2017 .
[40] Stefano Ricci,et al. Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation , 2016, Remote. Sens..
[41] N. Clinton,et al. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform , 2017 .
[42] E. Lindquist,et al. Multiple remote sensing data sources for REDD+ monitoring , 2012 .
[43] M. Herold,et al. A review of methods to measure and monitor historical carbon emissions from forest degradation , 2011 .
[44] C. Woodcock,et al. Continuous monitoring of forest disturbance using all available Landsat imagery , 2012 .
[45] Curtis E. Woodcock,et al. Near real-time monitoring of tropical forest disturbance: New algorithms and assessment framework , 2019, Remote Sensing of Environment.
[46] Sandra A. Brown. Measuring, monitoring, and verification of carbon benefits for forest–based projects , 2002, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[47] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[48] D. Roberts,et al. Combining spectral and spatial information to map canopy damage from selective logging and forest fires , 2005 .
[49] Michael A. Wulder,et al. Landsat continuity: Issues and opportunities for land cover monitoring , 2008 .
[50] Curtis E. Woodcock,et al. Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors , 2001 .
[51] Giles M. Foody,et al. Good practices for estimating area and assessing accuracy of land change , 2014 .
[52] C. Field,et al. Canopy near-infrared reflectance and terrestrial photosynthesis , 2017, Science Advances.
[53] D. Roberts,et al. Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models , 2003 .
[54] A. Huete,et al. A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .
[55] Michael Schultz,et al. Performance of vegetation indices from Landsat time series in deforestation monitoring , 2016, Int. J. Appl. Earth Obs. Geoinformation.