References
Barreras, A., Alanís De La Rosa, J. A., Cuenca Lara, R. A., Moreno
García, C., Godínez Valdivia, C. I., Delgado Caballero, C. E., Soriano
Luna, M. D. L. Á., George, S. P., Aldrete Leal, M. I., Medina Casillas,
S. L., Romero Correa, J., Villela Gaytán, S. A., & Guevara, M. (2022).National Forest and Soils Inventory of Mexico 2009-2014 [Data
set]. Environmental Data Initiative.
https://doi.org/10.6073/PASTA/4620375AEA631AB6A09CB573C7BF8AFF
Barreras, A., & Guevara, M. (2022). Nationwide geospatial dataset
of environmental covariates at 1km resolution in Mexico [Data set].
Zenodo. https://doi.org/10.5281/zenodo.7130164
Breiman, L. (2001). Random Forests. Machine Learning ,45 (1), 5–32. https://doi.org/10.1023/A:1010933404324
Brenning, A. (2012). Spatial cross-validation and bootstrap for the
assessment of prediction rules in remote sensing: The R package
sperrorest. 2012 IEEE International Geoscience and Remote Sensing
Symposium , 5372–5375. https://doi.org/10.1109/IGARSS.2012.6352393
Chopping, M., Moisen, G. G., Su, L., Laliberte, A., Rango, A.,
Martonchik, J. V., & Peters, D. P. C. (2008). Large area mapping of
southwestern forest crown cover, canopy height, and biomass using the
NASA Multiangle Imaging Spectro-Radiometer. Remote Sensing of
Environment , 112 (5), 2051–2063.
https://doi.org/10.1016/j.rse.2007.07.024
CONABIO, C. N. P. E. C. Y. U. D. L. B. (1998). La diversidad
biológica de México: Estudio de País 1998 (pp. 238–283).
CONAFOR, C. N. F. (2017). Inventario Nacional Forestal y de
Suelos, Informe de Resultados 2009-2014 .
Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D.
S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli,
G., Tuanmu, M.-N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani,
R., Green, S., Bruce, G., Williams, S. J., … Bradford, M. A.
(2015). Mapping tree density at a global scale. Nature ,525 (7568), 201–205. https://doi.org/10.1038/nature14967
Davies, M. M., & van der Laan, M. J. (n.d.). Optimal Spatial
Prediction Using Ensemble Machine Learning . Retrieved March 25, 2022,
from https://www.degruyter.com/document/doi/10.1515/ijb-2014-0060/html
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., &
Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial
analysis for everyone. Remote Sensing of Environment , 202 ,
18–27. https://doi.org/10.1016/j.rse.2017.06.031
Haakana, H., Heikkinen, J., Katila, M., & Kangas, A. (2019). Efficiency
of post-stratification for a large-scale forest inventory—Case Finnish
NFI. Annals of Forest Science , 76 (1), 1–15.
https://doi.org/10.1007/s13595-018-0795-6
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S.
A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T.
R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend,
J. R. G. (2013). High-resolution global maps of 21st-century forest
cover change. Science (New York, N.Y.) , 342 (6160),
850–853. https://doi.org/10.1126/science.1244693
Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E.
B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M.,
Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A.,
Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., & Zhu, Z.
(2018). Mapping forest change using stacked generalization: An ensemble
approach. Remote Sensing of Environment , 204 , 717–728.
https://doi.org/10.1016/j.rse.2017.09.029
Hengl, T., Miller, M. A. E., Križan, J., Shepherd, K. D., Sila, A.,
Kilibarda, M., Antonijević, O., Glušica, L., Dobermann, A., Haefele, S.
M., McGrath, S. P., Acquah, G. E., Collinson, J., Parente, L.,
Sheykhmousa, M., Saito, K., Johnson, J.-M., Chamberlin, J., Silatsa, F.
B. T., … Crouch, J. (2021). African soil properties and nutrients
mapped at 30 m spatial resolution using two-scale ensemble machine
learning. Scientific Reports , 11 (1), 6130.
https://doi.org/10.1038/s41598-021-85639-y
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., & Gräler,
B. (2018). Random forest as a generic framework for predictive modeling
of spatial and spatio-temporal variables. PeerJ , 6 , e5518.
https://doi.org/10.7717/peerj.5518
Hijmans. (2005). Very high resolution interpolated climate
surfaces for global land areas . International Journal of Climatology -
Wiley Online Library.
https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.1276
Holloway, J., & Mengersen, K. (2018). Statistical Machine Learning
Methods and Remote Sensing for Sustainable Development Goals: A Review.Remote Sensing , 10 (9), 1365.
https://doi.org/10.3390/rs10091365
Hulley, G. C., & Hook, S. J. (2008). A new methodology for cloud
detection and classification with ASTER data. Geophysical Research
Letters , 35 (16). https://doi.org/10.1029/2008GL034644
Hulley, G. C., & Hook, S. J. (2009). The North American ASTER Land
Surface Emissivity Database (NAALSED) Version 2.0. Remote Sensing
of Environment , 113 (9), 1967–1975.
https://doi.org/10.1016/j.rse.2009.05.005
Hulley, G. C., & Hook, S. J. (2011). Generating Consistent Land Surface
Temperature and Emissivity Products Between ASTER and MODIS Data for
Earth Science Research. IEEE Transactions on Geoscience and Remote
Sensing , 49 (4), 1304–1315.
https://doi.org/10.1109/TGRS.2010.2063034
Hulley, G. C., Hook, S. J., Abbott, E., Malakar, N., Islam, T., &
Abrams, M. (2015). The ASTER Global Emissivity Dataset (ASTER GED):
Mapping Earth’s emissivity at 100 meter spatial scale. Geophysical
Research Letters , 42 (19), 7966–7976.
https://doi.org/10.1002/2015GL065564
Hulley, G. C., Hook, S. J., & Baldridge, A. M. (2009). Validation of
the North American ASTER Land Surface Emissivity Database (NAALSED)
version 2.0 using pseudo-invariant sand dune sites. Remote Sensing
of Environment , 113 (10), 2224–2233.
https://doi.org/10.1016/j.rse.2009.06.005
Hulley, G. C., Hughes, C. G., & Hook, S. J. (2012). Quantifying
uncertainties in land surface temperature and emissivity retrievals from
ASTER and MODIS thermal infrared data. Journal of Geophysical
Research: Atmospheres , 117 (D23).
https://doi.org/10.1029/2012JD018506
Humagain, K., Portillo-Quintero, C., Cox, R. D., & Cain, J. W. (2017).
Mapping Tree Density in Forests of the Southwestern USA Using Landsat 8
Data. Forests , 8 (8), 287. https://doi.org/10.3390/f8080287
INEGI, I. N. de E. y. (2017). Mapas de Uso de suelo y vegetación .
Instituto Nacional de Estadística y Geografía. INEGI.
https://www.inegi.org.mx/temas/usosuelo/
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013a).
Introduction. In G. James, D. Witten, T. Hastie, & R. Tibshirani
(Eds.), An Introduction to Statistical Learning: With Applications
in R (pp. 1–14). Springer.
https://doi.org/10.1007/978-1-4614-7138-7_1
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013b).
Statistical Learning. In G. James, D. Witten, T. Hastie, & R.
Tibshirani (Eds.), An Introduction to Statistical Learning: With
Applications in R (pp. 15–57). Springer.
https://doi.org/10.1007/978-1-4614-7138-7_2
Khaledian, Y., & Miller, B. A. (2020). Selecting appropriate machine
learning methods for digital soil mapping. Applied Mathematical
Modelling , 81 , 401–418.
https://doi.org/10.1016/j.apm.2019.12.016
Leathwick, J. R., & Austin, M. P. (2001). Competitive Interactions
Between Tree Species in New Zealand’s Old-Growth Indigenous Forests.Ecology , 82 (9), 2560–2573.
https://doi.org/10.1890/0012-9658(2001)082[2560:CIBTSI]2.0.CO;2
Li, W., Niu, Z., Shang, R., Qin, Y., Wang, L., & Chen, H. (2020).
High-resolution mapping of forest canopy height using machine learning
by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8
data. International Journal of Applied Earth Observation and
Geoinformation , 92 , 102163.
https://doi.org/10.1016/j.jag.2020.102163
McRoberts, R. E., Holden, G. R., Nelson, M. D., Liknes, G. C., &
Gormanson, D. D. (2005). Using satellite imagery as ancillary data for
increasing the precision of estimates for the Forest Inventory and
Analysis program of the USDA Forest Service. Canadian Journal of
Forest Research , 35 (12), 2968–2980.
https://doi.org/10.1139/x05-222
Mitchell, A. L., Rosenqvist, A., & Mora, B. (2017). Current remote
sensing approaches to monitoring forest degradation in support of
countries measurement, reporting and verification (MRV) systems for
REDD+. Carbon Balance and Management , 12 (1), 9.
https://doi.org/10.1186/s13021-017-0078-9
Møller, A. B., Beucher, A. M., Pouladi, N., & Greve, M. H. (2020).
Oblique geographic coordinates as covariates for digital soil mapping.SOIL , 6 (2), 269–289.
https://doi.org/10.5194/soil-6-269-2020
Myneni, Ranga, Knyazikhin, Yuri, & Park, Taejin. (2015).MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN
Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC.
https://doi.org/10.5067/MODIS/MOD15A2H.006
NASA JPL. (2014). ASTER Global Emissivity Dataset, 100-meter,
HDF5 [Data set]. NASA EOSDIS Land Processes DAAC.
https://doi.org/10.5067/COMMUNITY/ASTER_GED/AG100.003
Ohmann, J. L., Gregory, M. J., & Roberts, H. M. (2014). Scale
considerations for integrating forest inventory plot data and satellite
image data for regional forest mapping. Remote Sensing of
Environment , 151 , 3–15.
https://doi.org/10.1016/j.rse.2013.08.048
Pirotti, F. (2010). Assessing a Template Matching Approach for Tree
Height and Position Extraction from Lidar-Derived Canopy Height Models
of Pinus Pinaster Stands. Forests , 1 (4), 194–208.
https://doi.org/10.3390/f1040194
Polley, E., & Laan, M. van der. (2010). Super Learner In Prediction.U.C. Berkeley Division of Biostatistics Working Paper Series .
https://biostats.bepress.com/ucbbiostat/paper266
Qiao, H., Soberón, J., & Townsend Peterson, A. (n.d.). No silver
bullets in correlative ecological niche modelling: Insights from testing
among many potential algorithms for niche
estimation—Qiao—2015—Methods in Ecology and Evolution—Wiley
Online Library . Retrieved September 30, 2022, from
https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12397
Román-Dañobeytia, F. J., Levy-Tacher, S. I., Macario-Mendoza, P., &
Zúñiga-Morales, J. (2014). Redefining Secondary Forests in the Mexican
Forest Code: Implications for Management, Restoration, and Conservation.Forests , 5 (5), 978–991. https://doi.org/10.3390/f5050978
RStudio Team. (2021). RStudio: Integrated Development Environment
for R (1.4.1717). RStudio, PBC. http://www.rstudio.com/
Saarela, S., Wästlund, A., Holmström, E., Mensah, A. A., Holm, S.,
Nilsson, M., Fridman, J., & Ståhl, G. (2020). Mapping aboveground
biomass and its prediction uncertainty using LiDAR and field data,
accounting for tree-level allometric and LiDAR model errors.Forest Ecosystems , 7 (1), 43.
https://doi.org/10.1186/s40663-020-00245-0
Schumacher, J., Hauglin, M., Astrup, R., & Breidenbach, J. (2020).
Mapping forest age using National Forest Inventory, airborne laser
scanning, and Sentinel-2 data. Forest Ecosystems , 7 (1),
60. https://doi.org/10.1186/s40663-020-00274-9
Selkowitz, D. J., Green, G., Peterson, B., & Wylie, B. (2012). A
multi-sensor lidar, multi-spectral and multi-angular approach for
mapping canopy height in boreal forest regions. Remote Sensing of
Environment , 121 , 458–471.
https://doi.org/10.1016/j.rse.2012.02.020
Smith, W. B. (2002). Forest inventory and analysis: A national inventory
and monitoring program. Environmental Pollution , 116 ,
S233–S242. https://doi.org/10.1016/S0269-7491(01)00255-X
Soriano-Luna, M. D. los Á., Ángeles-Pérez, G., Guevara, M., Birdsey, R.,
Pan, Y., Vaquera-Huerta, H., Valdez-Lazalde, J. R., Johnson, K. D., &
Vargas, R. (2018). Determinants of Above-Ground Biomass and Its Spatial
Variability in a Temperate Forest Managed for Timber Production.Forests , 9 (8), 490. https://doi.org/10.3390/f9080490
Taghizadeh-Mehrjardi, R., Hamzehpour, N., Hassanzadeh, M., Heung, B.,
Ghebleh Goydaragh, M., Schmidt, K., & Scholten, T. (2021). Enhancing
the accuracy of machine learning models using the super learner
technique in digital soil mapping. Geoderma , 399 , 115108.
https://doi.org/10.1016/j.geoderma.2021.115108
Tomppo, E., Haakana, M., Katila, M., & Peräsaari, J. (2008).Multi-Source National Forest Inventory: Methods and Applications .
Springer Science & Business Media.
Tomppo, E., Olsson, H., Ståhl, G., Nilsson, M., Hagner, O., & Katila,
M. (2008). Combining national forest inventory field plots and remote
sensing data for forest databases. Remote Sensing of Environment ,112 (5), 1982–1999. https://doi.org/10.1016/j.rse.2007.03.032
Tomppo, E., Schadauer, K., McRoberts, R. E., Gschwantner, T., Gabler,
K., & Ståhl, G. (2010). Introduction. In E. Tomppo, T. Gschwantner, M.
Lawrence, & R. E. McRoberts (Eds.), National Forest Inventories:
Pathways for Common Reporting (pp. 1–18). Springer Netherlands.
https://doi.org/10.1007/978-90-481-3233-1_1
van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super
Learner. Statistical Applications in Genetics and Molecular
Biology , 6 (1). https://doi.org/10.2202/1544-6115.1309
Villaseñor, J., & Gual, M. (2014). El bosque mesófilo de montaña
y sus plantas con flores (pp. 221–236).
Viscarra Rossel, R. A., Webster, R., Bui, E. N., & Baldock, J. A.
(2014). Baseline map of organic carbon in Australian soil to support
national carbon accounting and monitoring under climate change.Global Change Biology , 20 (9), 2953–2970.
https://doi.org/10.1111/gcb.12569
Wadoux, A. M. J.-C., Heuvelink, G. B. M., de Bruin, S., & Brus, D. J.
(2021). Spatial cross-validation is not the right way to evaluate map
accuracy. Ecological Modelling , 457 , 109692.
https://doi.org/10.1016/j.ecolmodel.2021.109692
Wadoux, A. M. J.-C., Minasny, B., & McBratney, A. B. (2020). Machine
learning for digital soil mapping: Applications, challenges and
suggested solutions. Earth-Science Reviews , 210 , 103359.
https://doi.org/10.1016/j.earscirev.2020.103359
Wadoux, A. M. J.-C., Walvoort, D. J. J., & Brus, D. J. (2022). An
integrated approach for the evaluation of quantitative soil maps through
Taylor and solar diagrams. Geoderma , 405 , 115332.
https://doi.org/10.1016/j.geoderma.2021.115332
Wang, G., Oyana, T., Zhang, M., Adu-Prah, S., Zeng, S., Lin, H., & Se,
J. (2009). Mapping and spatial uncertainty analysis of forest vegetation
carbon by combining national forest inventory data and satellite images.Forest Ecology and Management , 258 (7), 1275–1283.
https://doi.org/10.1016/j.foreco.2009.06.056