The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data

[1]  R. Keane,et al.  Estimating historical range and variation of landscape patch dynamics: limitations of the simulation approach , 2002 .

[2]  R. O'Neill,et al.  The value of the world's ecosystem services and natural capital , 1997, Nature.

[3]  Jialin Li,et al.  Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China , 2018, Remote. Sens..

[4]  Qin Ma,et al.  Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Scott J. Goetz,et al.  Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data , 2016 .

[6]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[7]  Monica G. Turner,et al.  Predicting across scales: Theory development and testing , 1989, Landscape Ecology.

[8]  Qinghua Guo,et al.  Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery , 2017, Int. J. Digit. Earth.

[9]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[10]  Jason Matthiopoulos,et al.  Generalized functional responses for species distributions. , 2011, Ecology.

[11]  Tapash Das,et al.  Climatic Correlates of Tree Mortality in Water- and Energy-Limited Forests , 2013, PloS one.

[12]  A. Baccini,et al.  Mapping forest canopy height globally with spaceborne lidar , 2011 .

[13]  Aaron Moody,et al.  Scale-dependent errors in the estimation of land-cover proportions. Implications for global land-cover datasets , 1994 .

[14]  R. Hall,et al.  Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume , 2006 .

[15]  Shengli Tao,et al.  Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data , 2015 .

[16]  D. Roberts,et al.  Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape , 2004 .

[17]  M. Goyal,et al.  Assessment of ecosystem resilience to hydroclimatic disturbances in India , 2018, Global change biology.

[18]  Gang Chen,et al.  When Big Data are Too Much: Effects of LiDAR Returns and Point Density on Estimation of Forest Biomass , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  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 .

[20]  Q. Guo,et al.  Global patterns of woody residence time and its influence on model simulation of aboveground biomass , 2017 .

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

[22]  H. Balzter,et al.  Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry , 2007 .

[23]  Rowena Lohman,et al.  Forest Canopy Heights in the Pacific Northwest Based on InSAR Phase Discontinuities across Short Spatial Scales , 2014, Remote. Sens..

[24]  Q. Guo,et al.  Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods , 2010 .

[25]  A. Perera,et al.  Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation , 2004, Landscape Ecology.

[26]  Jason Matthiopoulos,et al.  Quantifying the effect of habitat availability on species distributions. , 2013, The Journal of animal ecology.

[27]  E. Næsset,et al.  Estimating tree heights and number of stems in young forest stands using airborne laser scanner data , 2001 .

[28]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

[29]  A. Western,et al.  Predicting groundwater recharge for varying land cover and climate conditions – a global meta-study , 2017 .

[30]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[31]  Se-Ran Hwang,et al.  Current Status of Tree Height Estimation from Airborne LiDAR Data , 2011 .

[32]  G. Foody,et al.  Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions , 2012 .

[33]  A. Huete,et al.  MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data , 1999 .

[34]  D. Donoghue,et al.  Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests , 2006 .

[35]  R. Houghton,et al.  Aboveground Forest Biomass and the Global Carbon Balance , 2005 .

[36]  Igor V. Tetko,et al.  Neural network studies, 1. Comparison of overfitting and overtraining , 1995, J. Chem. Inf. Comput. Sci..

[37]  G. Hay,et al.  A Support Vector Regression Approach to Estimate Forest Biophysical Parameters at the Object Level Using Airborne Lidar Transects and QuickBird Data , 2011 .

[38]  Yanjun Su,et al.  Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas , 2016 .

[39]  A. Gitelson,et al.  Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices , 2004 .

[40]  Ronggao Liu,et al.  A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height , 2016 .

[41]  R. O'Neill,et al.  Effects of changing spatial scale on the analysis of landscape pattern , 1989, Landscape Ecology.

[42]  M. Lefsky.  A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System , 2010 .

[43]  Henrique M. Pereira,et al.  Resistance to wildfire and early regeneration in natural broadleaved forest and pine plantation , 2010 .

[44]  Xiaolin Zhu,et al.  Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series , 2015 .

[45]  David Saah,et al.  Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass , 2012 .

[46]  Jianguo Wu,et al.  Effects of thematic resolution on landscape pattern analysis , 2007, Landscape Ecology.

[47]  Damien Sulla-Menashe,et al.  A Global Land Cover Climatology Using MODIS Data , 2014 .

[48]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[49]  S. Ganguly,et al.  Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data , 2014 .

[50]  Lars Arge,et al.  Regional-scale mapping of tree cover, height and main phenological tree types using airborne laser scanning data , 2014 .

[51]  Michael A. Lefsky,et al.  Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms , 2007 .

[52]  Jin Liu,et al.  Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data , 2016, Remote. Sens..

[53]  Marek K. Jakubowski,et al.  Tradeoffs between lidar pulse density and forest measurement accuracy , 2013 .

[54]  G. Foody,et al.  Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions , 2003 .

[55]  Kunwar K. Singh,et al.  Uncertainties in mapping forest carbon in urban ecosystems. , 2017, Journal of environmental management.

[56]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[57]  Li An,et al.  Modeling the spatio-temporal dynamics and interactions of households, landscapes, and giant panda habitat. , 2005 .