Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series

[1]  Matthew C. Hansen.  Global Forest Watch , 2016 .

[2]  Jill L. Caviglia-Harris,et al.  Detecting and interpreting secondary forest on an old Amazonian frontier , 2015 .

[3]  M. Herold,et al.  Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia , 2015 .

[4]  John F. Mustard,et al.  Forest cover change in Miombo Woodlands: modeling land cover of African dry tropical forests with linear spectral mixture analysis , 2015 .

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

[6]  Jan Verbesselt,et al.  Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia , 2015 .

[7]  Rasmus Fensholt,et al.  Mapping dynamics of deforestation and forest degradation in tropical forests using radar satellite data , 2015 .

[8]  Stephen V. Stehman,et al.  National satellite-based humid tropical forest change assessment in Peru in support of REDD+ implementation , 2014 .

[9]  A. Chavez.  Landscape dynamics of Amazonian deforestation between 1986 and 2007 in southeastern Peru: policy drivers and road implications , 2014 .

[10]  Richard M. Lucas,et al.  Land Use and Land Cover Change Dynamics across the Brazilian Amazon: Insights from Extensive Time-Series Analysis of Remote Sensing Data , 2014, PloS one.

[11]  Scott L. Powell,et al.  Bringing an ecological view of change to Landsat‐based remote sensing , 2014 .

[12]  P. Hostert,et al.  Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites , 2014 .

[13]  Ryan J. Frazier,et al.  Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics , 2014 .

[14]  Valerie A. Thomas,et al.  On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Christopher S. R. Neigh,et al.  An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data , 2014, Remote. Sens..

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

[17]  K. Vogt,et al.  Assessing the influence of land-cover change and conflicting land-use authorizations on ecosystem conversion on the forest frontier of Madre de Dios, Peru , 2014 .

[18]  D. King,et al.  Mapping forest growth and decline in a temperate mixed forest using temporal trend analysis of Landsat imagery, 1987–2010 , 2014 .

[19]  Michael A. Wulder,et al.  Interpretation of forest disturbance using a time series of Landsat imagery and canopy structure from airborne lidar , 2014 .

[20]  Dirk Pflugmacher,et al.  Monitoring coniferous forest biomass change using a Landsat trajectory-based approach , 2013 .

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

[22]  Sandra A. Brown,et al.  What Does Zero Deforestation Mean? , 2013, Science.

[23]  G. Asner,et al.  Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring , 2013, Proceedings of the National Academy of Sciences.

[24]  M. Herold,et al.  REDD+ readiness: early insights on monitoring, reporting and verification systems of project developers , 2013 .

[25]  E. Chidumayo.  Forest degradation and recovery in a miombo woodland landscape in Zambia: 22 years of observations on permanent sample plots , 2013 .

[26]  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 .

[27]  E. Lindquist,et al.  Multiple remote sensing data sources for REDD+ monitoring , 2012 .

[28]  J. Rogan,et al.  Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula, Mexico , 2012 .

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

[30]  Kristine N. Stewart,et al.  Averting biodiversity collapse in tropical forest protected areas , 2012, Nature.

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

[32]  C. Woodcock,et al.  Continuous monitoring of forest disturbance using all available Landsat imagery , 2012 .

[33]  W. Cohen,et al.  Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan , 2012 .

[34]  W. Cohen,et al.  Using Landsat-derived disturbance history (1972-2010) to predict current forest structure , 2012 .

[35]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[36]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[37]  J. Ronald Eastman,et al.  A Contextual Mann‐Kendall Approach for the Assessment of Trend Significance in Image Time Series , 2011, Trans. GIS.

[38]  S. Goetz,et al.  Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change , 2011 .

[39]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[40]  Marc J. Ventresca,et al.  Regime-building for REDD+: Evidence from a cluster of local initiatives in south-eastern Peru , 2011 .

[41]  Warren B. Cohen,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation , 2010 .

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

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

[44]  Douglas W. Yu,et al.  Trouble in Paradise: Indigenous Populations, Anthropological Policies, and Biodiversity Conservation in Manu National Park, Peru , 2010 .

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

[46]  Esteve Corbera,et al.  Reducing greenhouse gas emissions from deforestation and forest degradation in developing countries: revisiting the assumptions , 2010 .

[47]  S. D. Zen,et al.  Ranching and the new global range: Amazônia in the 21st century , 2009 .

[48]  Stephen G. Perz,et al.  The Fragmentation of Space in the Amazon Basin: Emergent Road Networks , 2008 .

[49]  R. Betts,et al.  Climate Change, Deforestation, and the Fate of the Amazon , 2008, Science.

[50]  J. Canadell,et al.  Tropical Forests and Climate Policy , 2007, Science.

[51]  T. Feldpausch,et al.  Secondary forest growth deviation from chronosequence predictions in central Amazonia , 2007 .

[52]  Robert E. Wolfe,et al.  A Landsat surface reflectance dataset for North America, 1990-2000 , 2006, IEEE Geoscience and Remote Sensing Letters.

[53]  Simone R. Freitas,et al.  Relationships between forest structure and vegetation indices in Atlantic Rainforest , 2005 .

[54]  W. Cohen,et al.  Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection , 2005 .

[55]  R. Fernandes,et al.  Parametric (modified least squares) and non-parametric (Theil–Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors , 2005 .

[56]  Suming Jin,et al.  Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances , 2005 .

[57]  J. Wickham,et al.  Thematic accuracy of the 1992 National Land-Cover Data for the western United States , 2004 .

[58]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[59]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[60]  S. Sader,et al.  Detection of forest harvest type using multiple dates of Landsat TM imagery , 2002 .

[61]  Achim Zeileis,et al.  Strucchange: An R package for testing for structural change in linear regression models , 2002 .

[62]  D. Nepstad,et al.  Road paving, fire regime feedbacks, and the future of Amazon forests , 2001 .

[63]  Kurt Hornik,et al.  MONITORING STRUCTURAL CHANGES WITH THE GENERALIZED FLUCTUATION TEST , 2000, Econometric Theory.

[64]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[65]  Philip Lewis,et al.  Investigation of the Utility of Spectral Vegetation Indices for Determining Information on Coniferous Forests , 1998 .

[66]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[67]  Alexander Strickland Pfaff Talikoff.  What Drives Deforestation in the Brazilian Amazon? Evidence from Satellite and Socioeconomic Data , 1997 .

[68]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[69]  K. Hornik,et al.  Mosum Tests for Parameter Constancy , 1995 .

[70]  C. Field,et al.  Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types , 1995 .

[71]  M. Fiorella,et al.  Determining successional stage of temperate coniferous forests with Landsat satellite data , 1993 .

[72]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[73]  Eric P. Crist,et al.  A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[74]  P. Sen.  Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[75]  M. Herold,et al.  Fusing Landsat and SAR time series to detect deforestation in the tropics , 2015 .

[76]  Nora L. Álvarez-Berríos,et al.  Global demand for gold is another threat for tropical forests , 2015 .

[77]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[78]  Scott L. Powell,et al.  Observation of Trends in Biomass Loss as a Result of Disturbance in the Conterminous U.S.: 1986–2004 , 2013, Ecosystems.

[79]  M. Herold,et al.  The science of measuring, reporting and verification (MRV). , 2013 .

[80]  M. Herold,et al.  Monitoring, reporting and verification for national REDD + programmes: two proposals , 2011 .

[81]  M. Hansen,et al.  International Journal of Applied Earth Observation and Geoinformation: Time-Series Analysis of Multi-Resolution Optical Imagery for Quantifying Forest Cover Loss in Sumatra and Kalimantan, Indonesia , 2011 .

[82]  Kurt Hornik,et al.  Monitoring Structural Change in Dynamic Econometric Models , 2002 .

[83]  P. Perron,et al.  Computation and Analysis of Multiple Structural-Change Models , 1998 .

[84]  R. J. Kauth,et al.  The Tasseled Cap de-mystified , 1986 .