A Model-Based Volume Estimator that Accounts for Both Land Cover Misclassification and Model Prediction Uncertainty

[1]  R. Valentini,et al.  Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels , 2015 .

[2]  R. Royall,et al.  Robust Estimation in Finite Populations I , 1973 .

[3]  J. Greenberg,et al.  How much can natural resource inventory benefit from finer resolution auxiliary data , 2018 .

[4]  Victor F. Rodriguez-Galiano,et al.  Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models , 2014, Int. J. Digit. Earth.

[5]  Terje Gobakken,et al.  Inference for lidar-assisted estimation of forest growing stock volume , 2013 .

[6]  Christiane Schmullius,et al.  Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico , 2018, Carbon Balance and Management.

[7]  V. Rodriguez-Galiano,et al.  Land cover change analysis of a Mediterranean area in Spain using different sources of data: Multi-seasonal Landsat images, land surface temperature, digital terrain models and texture , 2012 .

[8]  Christophe Sannier,et al.  Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories , 2020, Remote. Sens..

[9]  M. Nilsson,et al.  Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors , 2020, Forest Ecosystems.

[10]  D. Pitt,et al.  Parametric vs. nonparametric LiDAR models for operational forest inventory in boreal Ontario , 2013 .

[11]  Ronald E. McRoberts,et al.  Propagating uncertainty through individual tree volume model predictions to large-area volume estimates , 2015, Annals of Forest Science.

[12]  Asghar Fallah,et al.  Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms , 2012 .

[13]  Ronald E. McRoberts,et al.  Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators , 2019, Remote. Sens..

[14]  M. L. Guillén-Climent,et al.  High resolution forest inventory of pure and mixed stands at regional level combining National Forest Inventory field plots, Landsat, and low density lidar , 2018 .

[15]  Ashton M. Shortridge,et al.  MODIS land cover uncertainty in regional climate simulations , 2017, Climate Dynamics.

[16]  Juan de la Riva,et al.  Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data , 2019, Remote. Sens..

[17]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[18]  Iciar Alberdi,et al.  O Inventário Florestal Nacional Espanhol: história, desenvolvimento, desafios e perspectivas , 2017 .

[19]  Gherardo Chirici,et al.  Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data , 2011 .

[20]  Roberto Vallejo,et al.  The multi-objective Spanish National Forest Inventory , 2017 .

[21]  G. Vieilledent,et al.  Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier , 2013 .

[22]  Emmanuel Flachaire,et al.  Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap , 2005, Comput. Stat. Data Anal..

[23]  Terje Gobakken,et al.  Estimation for inaccessible, non-sampled forest areas using model-based inference and remotely sensed auxiliary information , 2014 .

[24]  T. Hilker,et al.  Comparison of uncertainty in per unit area estimates of aboveground biomass for two selected model sets , 2015 .

[25]  Göran Ståhl,et al.  Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation , 2016, Forest Ecosystems.

[26]  E. Næsset,et al.  Methods for variable selection in LiDAR-assisted forest inventories , 2017 .

[27]  R. Valbuena,et al.  Accuracy and precision of GPS receivers under forest canopies in a mountainous environment. , 2010 .

[28]  Gherardo Chirici,et al.  Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[29]  Ronald E. McRoberts,et al.  Updating national forest inventory estimates of growing stock volume using hybrid inference , 2017 .

[30]  Klaus von Gadow,et al.  National Forest Inventory and forest observational studies in Spain: Applications to forest modeling , 2014 .

[31]  J Carpenter,et al.  Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. , 2000, Statistics in medicine.

[32]  Erik Næsset,et al.  Advances and emerging issues in national forest inventories , 2010 .

[33]  M. Holopainen,et al.  Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information , 2015 .

[34]  M. H. Hansen.  An Evaluation of Model-Dependent and Probability-Sampling Inferences in Sample Surveys , 1983 .

[35]  Regina Y. Liu.  Bootstrap Procedures under some Non-I.I.D. Models , 1988 .

[36]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[37]  Dimitris G. Stavrakoudis,et al.  Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping , 2014, Remote. Sens..

[38]  R. McRoberts,et al.  A general method for assessing the effects of uncertainty in individual-tree volume model predictions on large-area volume estimates with a subtropical forest illustration , 2015 .

[39]  R. Astrup,et al.  Small area estimation of forest attributes in the Norwegian National Forest Inventory , 2012, European Journal of Forest Research.

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

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

[42]  Joanne C. White,et al.  Remote Sensing Technologies for Enhancing Forest Inventories: A Review , 2016 .

[43]  P. Diaconis,et al.  Computer-Intensive Methods in Statistics , 1983 .

[44]  Erik Næsset,et al.  Using remotely sensed data to construct and assess forest attribute maps and related spatial products , 2010 .

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

[46]  J. Greenberg,et al.  Effects of temporally external auxiliary data on model-based inference , 2017 .

[47]  Gherardo Chirici,et al.  Combination of optical and LiDAR satellite imagery with forest inventory data to improve wall-to-wall assessment of growing stock in Italy , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[48]  D. Freedman.  Bootstrapping Regression Models , 1981 .

[49]  Liviu Theodor Ene,et al.  Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space , 2014 .

[50]  Göran Ståhl,et al.  Hybrid estimators for mean aboveground carbon per unit area , 2016 .

[51]  Terje Gobakken,et al.  The effects of field plot size on model-assisted estimation of aboveground biomass change using multitemporal interferometric SAR and airborne laser scanning data , 2015 .

[52]  Ronald E. McRoberts,et al.  Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data , 2019, Remote. Sens..

[53]  A. Lanz,et al.  The role of European National Forest Inventories for international forestry reporting , 2016, Annals of Forest Science.

[54]  R. McRoberts,et al.  Remote sensing support for national forest inventories , 2007 .

[55]  Göran Ståhl,et al.  Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications , 2018, Canadian Journal of Forest Research.

[56]  Adriano Mancini,et al.  Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series , 2020, Remote. Sens..