Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests

[1]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[2]  Jeffrey A. Cardille,et al.  Multi-sensor detection of forest-cover change across 45 years in Mato Grosso, Brazil , 2020 .

[3]  Nikos Koutsias,et al.  Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review , 2017, Current Forestry Reports.

[4]  S. Frolking,et al.  Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure , 2009 .

[5]  Marie-Josée Fortin,et al.  High-resolution global maps of 21st-century annual forest loss: Independent accuracy assessment and application in a temperate forest region of Atlantic Canada , 2017 .

[6]  Valerie A. Thomas,et al.  Fitting the Multitemporal Curve: A Fourier Series Approach to the Missing Data Problem in Remote Sensing Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Lian Pin Koh,et al.  Free and open-access satellite data are key to biodiversity conservation , 2015 .

[8]  Evan B. Brooks,et al.  How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms , 2017 .

[9]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[10]  Xiaomei Zhang,et al.  30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine , 2018, Remote. Sens..

[11]  Joanne C. White,et al.  Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots , 2018, Remote Sensing of Environment.

[12]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[13]  Zhiqiang Yang,et al.  Implementation of the LandTrendr Algorithm on Google Earth Engine , 2018, Remote. Sens..

[14]  Katharine R. E. Sims,et al.  Reductions in deforestation and poverty from decentralized forest management in Nepal , 2019, Nature Sustainability.

[15]  S. Stehman,et al.  Stratification and sample allocation for reference burned area data , 2017 .

[16]  Katsuto Shimizu,et al.  A comprehensive evaluation of disturbance agent classification approaches: Strengths of ensemble classification, multiple indices, spatio-temporal variables, and direct prediction , 2019 .

[17]  David A. Seal,et al.  The Shuttle Radar Topography Mission , 2007 .

[18]  Michael A. Wulder,et al.  Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm , 2015 .

[19]  Belinda A. Margono,et al.  Primary forest cover loss in Indonesia over 2000–2012 , 2014 .

[20]  Paulo J. Murillo-Sandoval,et al.  Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series , 2018 .

[21]  Rob J Hyndman,et al.  Phenological change detection while accounting for abrupt and gradual trends in satellite image time series , 2010 .

[22]  Zhiqiang Yang,et al.  Continuous monitoring of land disturbance based on Landsat time series , 2020, Remote Sensing of Environment.

[23]  Tetsuji Ota,et al.  Assessing the Importance of Tree Cover Threshold for Forest Cover Mapping Derived from Global Forest Cover in Myanmar , 2019, Forests.

[24]  Joanne C. White,et al.  Large Area Mapping of Annual Land Cover Dynamics Using Multitemporal Change Detection and Classification of Landsat Time Series Data , 2015 .

[25]  Eliakim Hamunyela,et al.  Space-time detection of deforestation, forest degradation and regeneration in montane forests of Eastern Tanzania , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[26]  Christophe Sannier,et al.  Methods for evaluating the utilities of local and global maps for increasing the precision of estimates of subtropical forest area , 2016 .

[27]  Nandin-Erdene Tsendbazar,et al.  Global data and tools for local forest cover loss and REDD+ performance assessment: Accuracy, uncertainty, complementarity and impact , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[28]  Robert E. Wolfe,et al.  Automated registration and orthorectification package for Landsat and Landsat-like data processing , 2009 .

[29]  S. Stehman.  Estimating area from an accuracy assessment error matrix , 2013 .

[30]  Zhe Zhu,et al.  Current status of Landsat program, science, and applications , 2019, Remote Sensing of Environment.

[31]  Masoud Mahdianpari,et al.  Google Earth Engine for geo-big data applications: A meta-analysis and systematic review , 2020 .

[32]  Joanne C. White,et al.  Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada , 2016 .

[33]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[34]  Louis-Vincent Fichet,et al.  Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon , 2014 .

[35]  M. Herold,et al.  Near real-time disturbance detection using satellite image time series , 2012 .

[36]  Giles M. Foody,et al.  Key issues in rigorous accuracy assessment of land cover products , 2019, Remote Sensing of Environment.

[37]  Jeffrey Pickering,et al.  An Assessment of Global Forest Change Datasets for National Forest Monitoring and Reporting , 2020, Remote. Sens..

[38]  Peter T. Wolter,et al.  Landsat remote sensing of forest windfall disturbance , 2014 .

[39]  Tetsuji Ota,et al.  Attribution of Disturbance Agents to Forest Change Using a Landsat Time Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar , 2017 .

[40]  Curtis E. Woodcock,et al.  Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting , 2020 .

[41]  J. Dwyer,et al.  The Landsat Burned Area algorithm and products for the conterminous United States , 2020 .

[42]  Damien Sulla-Menashe,et al.  Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation , 2014 .

[43]  T. Danaher,et al.  A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest , 2020 .

[44]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[45]  A. Huth,et al.  Global patterns of tropical forest fragmentation , 2018, Nature.

[46]  Joanne C. White,et al.  Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics , 2015 .

[47]  Giles M. Foody,et al.  Good practices for estimating area and assessing accuracy of land change , 2014 .

[48]  Michael J. Falkowski,et al.  A review of methods for mapping and prediction of inventory attributes for operational forest management , 2014 .

[49]  M. Falkowski,et al.  Characterizing over Four Decades of Forest Disturbance in Minnesota, USA , 2020, Forests.

[50]  Chengquan Huang,et al.  Forest disturbance across the conterminous United States from 1985-2012: The emerging dominance of forest decline , 2016 .

[51]  M. Hansen,et al.  Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series , 2020 .

[52]  Ronald E. McRoberts,et al.  Mitigating the effects of omission errors on area and area change estimates , 2020 .

[53]  W. Cohen,et al.  Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics , 2014 .

[54]  Michael A. Wulder,et al.  Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia , 2014, Remote. Sens..

[55]  Chris E. Jordan,et al.  Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA , 2015 .

[56]  C. Bradshaw,et al.  Primary forests are irreplaceable for sustaining tropical biodiversity , 2011, Nature.

[57]  Stephen V. Stehman,et al.  Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes , 2014 .

[58]  C. Woodcock,et al.  Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation , 2013 .

[59]  Daniel J. Hayes,et al.  Patch-Based Forest Change Detection from Landsat Time Series , 2017 .

[60]  Edward T. A. Mitchard,et al.  Forest loss maps from regional satellite monitoring systematically underestimate deforestation in two rapidly changing parts of the Amazon , 2017 .

[61]  M. Herold,et al.  Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series , 2015 .

[62]  Zhe Zhu,et al.  Mapping forest change using stacked generalization: An ensemble approach , 2018 .

[63]  Sean P. Healey,et al.  Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance , 2020, Remote. Sens..

[64]  Guy Ziv,et al.  Striking divergences in Earth Observation products may limit their use for REDD+ , 2018, Environmental Research Letters.

[65]  J. Dungan,et al.  US National Maps Attributing Forest Change: 1986–2010 , 2020, Forests.

[66]  Xuesong Zhang,et al.  Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm , 2019, Remote Sensing of Environment.

[67]  James Cajka,et al.  What is Threatening Forests in Protected Areas? A Global Assessment of Deforestation in Protected Areas, 2001–2018 , 2020, Forests.

[68]  Joanne C. White,et al.  Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots , 2018 .

[69]  Xu Wang,et al.  Accuracy assessment of the global forest watch tree cover 2000 in China , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[70]  Mariela Soto-Berelov,et al.  Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[71]  E. Lindquist,et al.  Global forest area disturbance from fire, insect pests, diseases and severe weather events , 2015 .

[72]  Limin Yang,et al.  Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance , 2002 .

[73]  Simon D. Jones,et al.  A spatial and temporal analysis of forest dynamics using Landsat time-series , 2018, Remote Sensing of Environment.

[74]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .