Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data

[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.