MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass
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Yann Nouvellon | Claire Marsden | Guerric Le Maire | Jean-Paul Laclau | Rodrigo Hakamada | G. Maire | Y. Nouvellon | J. Stape | J. Laclau | C. Marsden | C. Grinand | Rodrigo Hakamada | José Luiz Stape | Clovis Grinand
[1] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[2] Jonas Ardö,et al. Volume quantification of coniferous forest compartments using spectral radiance recorded by Landsat Thematic Mapper , 1992 .
[3] Piermaria Corona,et al. Large-scale monitoring of coppice forest clearcuts by multitemporal very high resolution satellite imagery. A case study from central Italy , 2011 .
[4] Michael A. Lefsky,et al. Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity , 2005 .
[5] C. Tucker,et al. Remote Sensing of Total Dry-Matter Accumulation in Winter Wheat , 1981 .
[6] R. Houghton,et al. Mapping Russian forest biomass with data from satellites and forest inventories , 2007 .
[7] Kenneth B. Pierce,et al. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches , 2010 .
[8] Raleigh Nc,et al. Spatial and temporal variability of the carbon budget of tropical eucalyptus plantations assessed using ecosystem modelling and remote- sensing , 2010 .
[9] M. D. Nelson,et al. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information , 2008 .
[10] Martha C. Anderson,et al. Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery , 2004 .
[11] K. Soudani,et al. Modeling annual production and carbon fluxes of a large managed temperate forest using forest inventories, satellite data and field measurements. , 2005, Tree physiology.
[12] Y. Nouvellon,et al. Time Course of Radiation Use Efficiency in a Shortgrass Ecosystem: Consequences for Remotely Sensed Estimation of Primary Production , 2000 .
[13] C. Tucker,et al. North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer , 1985, Vegetatio.
[14] D. Roy,et al. Achieving sub-pixel geolocation accuracy in support of MODIS land science , 2002 .
[15] Alicia K. Birky,et al. NDVI and a simple model of deciduous forest seasonal dynamics , 2001 .
[16] P. Ciais,et al. Potential knowledge gain in large-scale simulations of forest carbon fluxes from remotely sensed biomass and height . , 2011 .
[17] S. Goetz,et al. Reply to Comment on ‘A first map of tropical Africa’s above-ground biomass derived from satellite imagery’ , 2008, Environmental Research Letters.
[18] T. M. Lillesand,et al. Estimating the leaf area index of North Central Wisconsin forests using the landsat thematic mapper , 1997 .
[19] D. Lu. The potential and challenge of remote sensing‐based biomass estimation , 2006 .
[20] W. Verhoef,et al. Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations , 2011 .
[21] F. J. García-Haro,et al. A generalized soil-adjusted vegetation index , 2002 .
[22] M. Battaglia,et al. Photosynthetic responses to overnight frost in Eucalyptus nitens and E. globulus , 2004, Trees.
[23] Michael Battaglia,et al. Managing productivity and drought risk in Eucalyptus globulus plantations in south-western Australia. , 2009 .
[24] A. Beaudoin,et al. A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery , 2007 .
[25] J. Deckers,et al. World Reference Base for Soil Resources , 1998 .
[26] P. Sands,et al. Parameterisation of 3-PG for plantation grown Eucalyptus globulus , 2002 .
[27] S. Goetz,et al. Mapping and monitoring carbon stocks with satellite observations: a comparison of methods , 2009, Carbon balance and management.
[28] Michael Gebreslasie,et al. Predicting forest structural attributes using ancillary data and ASTER satellite data , 2010, Int. J. Appl. Earth Obs. Geoinformation.
[29] J. L. Carvalho,et al. Greenhouse gas mitigation options in Brazil for land-use change, livestock and agriculture , 2010 .
[30] C. Goulding,et al. Estimation of timber volume in a coniferous plantation forest using Landsat TM , 1997 .
[31] M. Schaepman,et al. Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics , 2009 .
[32] H. Rocha,et al. Fluxes of CO2 above a plantation of Eucalyptus in southeast Brazil , 2011 .
[33] R. Fournier,et al. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland , 2006 .
[34] J. Stape,et al. Productivity gains by fertilisation in Eucalyptus urophylla clonal plantations across gradients in site and stand conditions , 2009 .
[35] C. Proisy,et al. Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images , 2007 .
[36] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[37] J. L. Parra,et al. Very high resolution interpolated climate surfaces for global land areas , 2005 .
[38] P. Curran. Multispectral remote sensing of vegetation amount , 1980 .
[39] C. Woodcock,et al. Forest biomass estimation over regional scales using multisource data , 2004 .
[40] Scott L. Powell,et al. Forest Disturbance and North American Carbon Flux , 2008 .
[41] Thomas R. Crow,et al. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA , 2004 .
[42] Ranga B. Myneni,et al. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks , 2003 .
[43] José Leonardo de Moraes Gonçalves,et al. Almost symmetrical vertical growth rates above and below ground in one of the world's most productive forests , 2011 .
[44] J. Nichol,et al. Improved forest biomass estimates using ALOS AVNIR-2 texture indices , 2011 .
[45] J. Tenhunen,et al. On the relationship of NDVI with leaf area index in a deciduous forest site , 2005 .
[46] B. Koch,et al. Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors , 2010 .
[47] Paul E. Gessler,et al. Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments , 2008 .
[48] Danny Lo Seen,et al. Relating MODIS vegetation index time-series with structure, light absorption and stem production of fast-growing Eucalyptus plantations , 2010 .
[49] W. Walker,et al. An empirical InSAR-optical fusion approach to mapping vegetation canopy height , 2007 .
[50] João Roberto dos Santos,et al. Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data , 2010, Remote. Sens..
[51] Y. Takane,et al. Generalized Inverse Matrices , 2011 .
[52] T. Dawson,et al. Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol , 2005 .
[53] Compton J. Tucker,et al. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel - 1980-1984 , 1985 .
[54] Richard A. Fournier,et al. Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS , 2008, Sensors.
[55] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[56] Gretchen G. Moisen,et al. Comparing five modelling techniques for predicting forest characteristics , 2002 .
[57] G. Maire,et al. Spatial and temporal variability of the carbon budget of tropical eucalyptus plantations assessed of tropical eucalyptus plantations assessed using ecosystem modelling and remote-sensing , 2010 .
[58] Alan Grainger,et al. Achieving forest carbon information with higher certainty: A five-part plan , 2010 .
[59] S. Goetz,et al. Remote sensing of net primary production in boreal forest stands , 1996 .