Pre-stratified modelling plus residuals kriging reduces the uncertainty of aboveground biomass estimation and spatial distribution in heterogeneous savannas and forest environments

[1]  Fausto W. Acerbi-Junior,et al.  Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment , 2019, Int. J. Appl. Earth Obs. Geoinformation.

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

[3]  C. Lovelock,et al.  Modelling above ground biomass accumulation of mangrove plantations in Vietnam , 2019, Forest Ecology and Management.

[4]  Fausto W. Acerbi-Junior,et al.  Reducing the effects of vegetation phenology on change detection in tropical seasonal biomes , 2018, GIScience & Remote Sensing.

[5]  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.

[6]  Inácio T. Bueno,et al.  Water availability drives gradients of tree diversity, structure and functional traits in the Atlantic–Cerrado–Caatinga transition, Brazil , 2018, Journal of Plant Ecology.

[7]  E. Berenguer,et al.  The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest , 2018, Forests.

[8]  A. L. Souza,et al.  INFLUENCE OF INTERSPECIFIC VARIATION ON TREE CARBON STOCK OF A BRAZILIAN CERRADO , 2018 .

[9]  Inácio T. Bueno,et al.  Using Spatial Features to Reduce the Impact of Seasonality for Detecting Tropical Forest Changes from Landsat Time Series , 2018, Remote. Sens..

[10]  P. Leitão,et al.  Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna , 2018, Carbon Balance and Management.

[11]  Lijuan Liu,et al.  Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region , 2018, Remote. Sens..

[12]  Nicholas C. Coops,et al.  Updating stand-level forest inventories using airborne laser scanning and Landsat time series data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[13]  G. Asner,et al.  An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR , 2018 .

[14]  Joanne C. White,et al.  Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series , 2018 .

[15]  Ram K. Deo,et al.  Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA , 2018 .

[16]  R. Silva,et al.  CLIMATE CHANGE IN THE TRIÂNGULO MINEIRO REGION – BRAZILl , 2017 .

[17]  D. W. MacFarlane,et al.  Carbon stock classification for tropical forests in Brazil: Understanding the effect of stand and climate variables , 2017 .

[18]  Onisimo Mutanga,et al.  Remote Sensing of Above-Ground Biomass , 2017, Remote. Sens..

[19]  Kyle G. Dexter,et al.  Seasonal drought limits tree species across the Neotropics , 2017 .

[20]  Luis Santamaría,et al.  Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology , 2017, Remote. Sens..

[21]  J. Scolforo,et al.  Tree dominance and diversity in Minas Gerais, Brazil , 2017, Biodiversity and Conservation.

[22]  Q. Gao,et al.  Spatiotemporal patterns of vegetation phenology change and relationships with climate in the two transects of East China , 2017 .

[23]  Anthony G. Vorster,et al.  A survival guide to Landsat preprocessing. , 2017, Ecology.

[24]  Heterogeneity and assessment uncertainties in forest characteristics and biomass carbon stocks: Important considerations for climate mitigation policies , 2016 .

[25]  Lijuan Liu,et al.  Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[26]  Patrick Hostert,et al.  Mapping Brazilian savanna vegetation gradients with Landsat time series , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[27]  Y. Malhi,et al.  Many shades of green: the dynamic tropical forest–savannah transition zones , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.

[28]  Henrique Ferraco Scolforo,et al.  Spatial interpolators for improving the mapping of carbon stock of the arboreal vegetation in Brazilian biomes of Atlantic forest and Savanna , 2016 .

[29]  Heiko Balzter,et al.  Magnitude, spatial distribution and uncertainty of forest biomass stocks in Mexico , 2016 .

[30]  Ho Tong Minh Dinh,et al.  Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

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

[32]  Guangxing Wang,et al.  Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation , 2016, Remote. Sens..

[33]  Xinkai Zhu,et al.  Estimation of biomass in wheat using random forest regression algorithm and remote sensing data , 2016 .

[34]  Ghislain Vieilledent,et al.  Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar , 2016 .

[35]  Lijuan Liu,et al.  A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems , 2016, Int. J. Digit. Earth.

[36]  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 .

[37]  Liviu Theodor Ene,et al.  Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania , 2015, Carbon Balance and Management.

[38]  Koreen Millard,et al.  On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping , 2015, Remote. Sens..

[39]  Shilong Piao,et al.  MODIS Based Estimation of Forest Aboveground Biomass in China , 2015, PloS one.

[40]  Henrique Ferraco Scolforo,et al.  Spatial Distribution of Aboveground Carbon Stock of the Arboreal Vegetation in Brazilian Biomes of Savanna, Atlantic Forest and Semi-Arid Woodland , 2015, PloS one.

[41]  Florian Hartig,et al.  Stratified aboveground forest biomass estimation by remote sensing data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[42]  Rosana Cristina Grecchi,et al.  Land cover changes in the Brazilian Cerrado and Caatinga biomes from 1990 to 2010 based on a systematic remote sensing sampling approach , 2015 .

[43]  Bo Wu,et al.  Estimating spatiotemporal patterns of aboveground biomass using Landsat TM and MODIS images in the Mu Us Sandy Land, China , 2015 .

[44]  Sandra Díaz,et al.  Does functional trait diversity predict above‐ground biomass and productivity of tropical forests? Testing three alternative hypotheses , 2015 .

[45]  Zhi-Wei Liu,et al.  Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach , 2015 .

[46]  L. Aragão,et al.  A large‐scale field assessment of carbon stocks in human‐modified tropical forests , 2014, Global change biology.

[47]  O. Phillips,et al.  Structural, physiognomic and above-ground biomass variation in savanna–forest transition zones on three continents – how different are co-occurring savanna and forest formations? , 2014 .

[48]  Fei Deng,et al.  Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests , 2013 .

[49]  F. Breidt,et al.  Nonparametric endogenous post-stratification estimation , 2013 .

[50]  Barbara Koch,et al.  Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands , 2012 .

[51]  Jungho Im,et al.  Forest biomass estimation from airborne LiDAR data using machine learning approaches , 2012 .

[52]  K. Mckinley,et al.  Fuels or microclimate? Understanding the drivers of fire feedbacks at savanna–forest boundaries , 2012 .

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

[54]  Vivien Rossi,et al.  Water Availability Is the Main Climate Driver of Neotropical Tree Growth , 2012, PloS one.

[55]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

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

[57]  S. Lamsal,et al.  Spatial variation and prediction of forest biomass in a heterogeneous landscape , 2012, Journal of Forestry Research.

[58]  C. Mello,et al.  Multivariate models for annual rainfall erosivity in Brazil , 2013 .

[59]  S. Higgins,et al.  When is a ‘forest’ a savanna, and why does it matter? , 2011 .

[60]  G. Fernandes,et al.  Caatinga: The Scientific Negligence Experienced by a Dry Tropical Forest , 2011 .

[61]  Carlos Pedro Boechat Soares,et al.  Above- and belowground biomass in a Brazilian Cerrado , 2011 .

[62]  L. Blanc,et al.  Disentangling stand and environmental correlates of aboveground biomass in Amazonian forests , 2011 .

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

[64]  S. Vincenzi,et al.  Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy , 2011 .

[65]  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.

[66]  L. Blanc,et al.  Contrasting above‐ground biomass balance in a Neotropical rain forest , 2010 .

[67]  Onisimo Mutanga,et al.  A comparison of regression tree ensembles: Predicting Sirex noctilio induced water stress in Pinus patula forests of KwaZulu-Natal, South Africa , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[68]  Michael A. Wulder,et al.  Integration of GLAS and Landsat TM data for aboveground biomass estimation , 2010 .

[69]  Yadvinder Malhi,et al.  Regional and large-scale patterns in Amazon forest structure and function are mediated by variations in soil physical and chemical properties , 2009 .

[70]  M. Heurich,et al.  Estimation of forestry stand parameters using laser scanning data in temperate, structurally rich natural European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests , 2008 .

[71]  J. Barlow,et al.  The cost-effectiveness of biodiversity surveys in tropical forests. , 2008, Ecology letters.

[72]  J. Slik,et al.  Soil nutrients affect spatial patterns of aboveground biomass and emergent tree density in southwestern Borneo , 2008, Oecologia.

[73]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[74]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[75]  Kanehiro Kitayama,et al.  Natural Resource Ecology and Management 1-2006 Temperature Influences Carbon Accumulation in Moist Tropical Forests , 2017 .

[76]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[77]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[78]  D. A. King,et al.  Soil‐related performance variation and distributions of tree species in a Bornean rain forest , 2005 .

[79]  D. Lu.  Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon , 2005 .

[80]  M. Batistella,et al.  Exploring TM Image Texture and its Relationships with Biomass Estimation in Rondônia, Brazilian Amazon. , 2005 .

[81]  W. Cohen,et al.  Landsat's Role in Ecological Applications of Remote Sensing , 2004 .

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

[83]  Joanna Isobel House,et al.  Conundrums in mixed woody–herbaceous plant systems , 2003 .

[84]  Denis Larocque,et al.  An empirical comparison of ensemble methods based on classification trees , 2003 .

[85]  Giles M. Foody,et al.  Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development , 2003 .

[86]  F. Adesina,et al.  VEGETATION PATTERNS ALONG THE FOREST‐SAVANNA BOUNDARY IN NIGERIA , 1988 .