[
Adiguzel-Zengin, A.C., Zengin, G., Kilicarislan-Ozkan, C., Dandar, U., Kilic, E., 2017. Characterization and application of Acacia nilotica L. as an alternative vegetable tanning agent for leather processing. Fresenius Environmental Bulletin, 26 (12): 7319–7326.
]Search in Google Scholar
[
Ajbilou, R., Marañón, T., Arroyo, J., 2003. Distribución de clases diamétricas y conservación en el norte de Marruecos [Diameter class distribution and conservation in northern Morocco]. Investigación Agraria: Sistemas y Recursos Forestales, 12 (2): 111–123.
]Search in Google Scholar
[
Askar, A., Nuthammachot, N., Phairuang, W., Wicaksono, P., Sayektiningsih, T., 2018. Estimating aboveground biomass on private forest using Sentinel-2 Imagery. Journal of Sensors, 2018: Article ID 6745629, 11 p. https://doi.org/10.1155/2018/674562910.1155/2018/6745629
]Search in Google Scholar
[
Assmann, E., 1970. The principles of forest yield study. Oxford: Pergamon Press. 504 p.
]Search in Google Scholar
[
Bedón, P.P., Pinto, A.A., 2007. Evaluación de técnicas de detección de cambios del uso de la tierra a través del análisis multitemporal de imágenes satelitales en el Cantón Daule [Evaluation of techniques to detect changes in land use through multitemporal analysis of satelite images established for the area of the district Daule]. [cit. 2021-06-24].www.repositorio.espe.edu.ec/bitstream/21000/514/2/T-ESPE033066-A.pdf
]Search in Google Scholar
[
Born, D.J., Chojnacky, D.C., 1985. Woodland tree volume estimation: A visual segmentation technique. Research Paper INT-344. Ogden, Utah: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 16 p. https://doi.org/10.5962/bhl.title.6907410.5962/bhl.title.69074
]Search in Google Scholar
[
Borreli, P., Oliva, G., 2001. Efectos de los animales sobre los pastizales [Effects of animals on grasslands]. In Borreli, P., Oliva, G. (eds). Ganadería sustentable en la Patagonia Austral. Santiago, Chile: INTA, p. 99–128.
]Search in Google Scholar
[
Chavez, P.S., 1996. Image-based atmospheric corrections - revisited and improved photogrammetric engineering and remote sensing. Photogrammetric Engineering and Remote Sensing, 62: 1025–1036.
]Search in Google Scholar
[
Comisión Nacional Forestal, 2015. Inventario Nacional Forestal y de Suelos. Procedimientos de muestreo [National Forest and Soil Inventory. Sampling procedures]. Guadalajara, Jal., México: CONAFOR.
]Search in Google Scholar
[
De Lima, R.B., Bufalino, L., Alves Júnior, F.T., Da Silva, J.A.A., Ferreira, R.L.C., 2017. Diameter distribution in a Brazilian tropical dry forest domain: Predictions for the stand and species. Anais da Academia Brasileira de Ciências, 89 (2): 1189–1203. [cit. 2021-06-02]. https://www.redalyc.org/articulo.oa?id=32751197036.10.1590/0001-376520172016033128640356
]Search in Google Scholar
[
Deka, J., Tripathi, O.P., Khan, M.L., 2012. Implementation of forest canopy density model to monitor tropical deforestation. Journal of Indian Society of Remote Sensing, 41: 469–475. https://doi.org/10.1007/s12524-012-0224-510.1007/s12524-012-0224-5
]Search in Google Scholar
[
Delgadillo-Puga, C., Cuchillo-Hilario, M., Navarro-Ocaña, A., Medina-Campos, O.N., Nieto-Camachoa, Ramírez-Apan, T., López-Tecpoyotl, Z.G., Díaz-Martínez, M., Álvarez-Izazaga, M.A., Cruz-Martínez, Y.R., Sánchez-Quezada, V., Gómez-Franciscoe, Torre- Villalvazo, I., Furuzawa-Carballedaj, Camacho-Coronam, R., Pedraza-Chaverri, J., 2018. Phenolic compounds in organic and aqueous extracts from Acacia farnesiana pods analyzed by ULPS-ESI-Q-oa/TOF-MS. In vitro antioxidant activity and anti-inflammatory response in CD-1 mice. Molecules, 23: 2386. https://doi.org/10.3390/molecules2309238610.3390/molecules23092386622538530231503
]Search in Google Scholar
[
Delgado, D., Jorge, J., Groffman, P., Nearing, M., Goddard, T., Reicosky, D., Lal, R., Kitchen, N., Rice, C., Towery, D., Salon, P., 2011. Conservation practices to mitigate and adapt to climate change. Journal of Soil and Water Conservation, 66 (4): 118A–129A. https://doi.org/10.2489/jswc.66.4.118A10.2489/jswc.66.4.118A
]Search in Google Scholar
[
Deng, J., Huang, Y., Chen, B., Tong, C., Liu, P., Wang, H., Hong, Y., 2019. A methodology to monitor urban expansion and green space change using a time series of multi-sensor SPOT and Sentinel-2A images. Remote Sensing, 11 (10): 1230. https://doi.org/10.3390/rs1110123010.3390/rs11101230
]Search in Google Scholar
[
Duncanson, L., Armston, J., Disney, M., Avitabile, V., Barbier, N., Calders, K., Carter, S., Chave, J., Herold, M., Macbean, N., Mcroberts, R., Minor, D., Paul, K., Réjou-Méchain, M., Roxburgh, S., Williams, M., Albinet, C., Baker, T., Bartholomeus, H., Bastin, J.F., Coomes, D., Crowther, T., Davies, S., de Bruin, S., De Kauwe, M., Domke, G., Falkowski, M., Fatoyinbo, L., Goetz, S., Jantz, P., Jonckheere, I., Jucker, T., Kay, H., Kellner, J., Labriere, N., Lucas, R., Morsdorf, F., Phillips, O.L., Quegan, S., Saatchi, S., Schaaf, C., Schepaschenko, D., Scipal, K., Stovall, A., Thiel, C., Wulder, M.A., Camacho, F., Nickeson, J., Roman, M., Margolis, H., 2020. Global aboveground biomass product validation best practices protocol. Version 1.0. In Duncanson, L., Disney, M., Armston, J., Minor, D., Camacho, F., Nickeson, J. (eds). Best practice protocol for satellite derived land product validation. Land Product Validation Subgroup (Working Group on Calibration and Validation, Committee on Earth Observation Satellites). 222 p. DOI: 10.5067/doc/ceoswgcv/lpv/agb.001
]Search in Google Scholar
[
Esa, 2016. European Spatial Agency. Data access, Annual report 2016. [cit. 2021-05-31]. https://sentinels.copernicus.eu/documents/247904/0/Sentinel-Data-Access-Annual-Report-2016/1de5e2b3-c108-4c6f-9240-1b8ac9539e33
]Search in Google Scholar
[
García, E., 1998. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. Climatología. [National Commission for the Knowledge and Use of Biodiversity. Climatology]. [cit. 2021-07-02]. http://www.conabio.gob.mx/informacion/gis/
]Search in Google Scholar
[
Gebeyew, K., Beriso, K., Mohamed, A., Silassie, G., Melaku, S., Worku, S., 2015. Review on the nutritive value of some selected Acacia species for livestock production in dryland areas. Journal of Advances in Dairy Research, 3: 139. DOI: 10.4172/2329-888X.100013910.4172/2329-888X.1000139
]Search in Google Scholar
[
Gitelson, A.A., Merzlak, M.N., Grits, Y., 1996. Novel algorithms for remote sensing of chlorophyll content in higher plant leaves. Papers in Natural Resources, 238. https://doi.org/10.1109/IGARSS.1996.51698510.1109/IGARSS.1996.516985
]Search in Google Scholar
[
Gitelson, A.A., Viña, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G., Leavitt, B., 2003. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30 (5): 1248. https://doi.org/10.1029/2002GL01645010.1029/2002GL016450
]Search in Google Scholar
[
Gitelson, A.A., Viña, A., Ciganda, V., Rundquist, D.C., Arkebauer, T.J., 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: L08403. https://doi.org/10.1029/2005GL02268810.1029/2005GL022688
]Search in Google Scholar
[
Gómez-Acevedo, S.L., Tapia-Pastrana, F., 2003. Estudio genecológico en Prosopis laevigata, Acacia farnesiana y Acacia schaffneri (Leguminosae) [Genecological study in Prosopis laevigata, Acacia farnesiana and Acacia schaffneri (Leguminosae)]. Darwiniana, 41 (1-4): 47–54 https://www.redalyc.org/articulo.oa?id=66941406
]Search in Google Scholar
[
Gómez, C., White, J.C., Wulder, M.A., 2016. Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 116: 55–72. https://doi.org/10.1016/j.isprsjprs.2016.03.00810.1016/j.isprsjprs.2016.03.008
]Search in Google Scholar
[
Huang, C., Ye, X., Deng, C., Zhang, Z., Wan, Z., 2016. Mapping above-ground biomass by integrating optical and SAR imagery: A case study of Xixi National Wetland Park, China. Remote Sensing, 8 (8): 647.10.3390/rs8080647
]Search in Google Scholar
[
Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25: 295–309. https://doi.org/10.1016/0034-4257(88)90106-X10.1016/0034-4257(88)90106-X
]Search in Google Scholar
[
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83: 195–213. https://doi.org/10.1016/S0034-4257(02)00096-210.1016/S0034-4257(02)00096-2
]Search in Google Scholar
[
Inegi, 2016. Carta de uso de suelo y vegetación escala 1:250,000, serie VI [Land use and vegetation chart scale 1: 250,000, series VI]. [cit. 2021-06-17]. http://www.conabio.gob.mx/informacion/gis/
]Search in Google Scholar
[
Issa, S., Dahy, B., Ksiksi, T., Saleous, N., 2020. A review of terrestrial carbon assessment methods using geo-spatial technologies with emphasis on arid lands. Remote Sensing, 12 (12): 2008. https://doi.org/10.3390/rs1212200810.3390/rs12122008
]Search in Google Scholar
[
Kasaye, M., Abebe, G., Abiyu, A., Wondie, M., Belay, B., 2020. Selection of different trees/shrubs species for rehabilitation of degraded lands in Wag-lasta area, Northeastern Ethiopia. Journal of Forest Research, 9: 231. DOI: 10. 35248/2168-9776.20.9.231
]Search in Google Scholar
[
Landeros-Márquez, O., Trejo-Calzada, R., Reveles-Hernández, M., Valdez-Cepeda, R.D., Arreola-Ávila, J.G., Pedroza-Sandoval, S., Ruíz-Torres, J., 2011. Uso potencial del huizache (Acacia farnesiana L. Will) en la fitorremediación de suelos contaminados con plomo [Potential use of huizache (Acacia farnesiana L. Will) in the phytoremediation of lead-contaminated soils]. Revista Chapingo Serie Ciencias Forestales y del Ambiente, 17, Special issue: 11–20. http://www.scielo.org.mx/pdf/rcscfa/v17nspe/v17nspea3.pdf10.5154/r.rchscfa.2010.08.059
]Search in Google Scholar
[
Lin, H.Y., Chang, T.C., Chang, S.T., 2018. A review of antioxidant and pharmacological properties of phenolic compounds in Acacia confusa. Journal of Traditional and Complementary Medicine, 8 (4): 443–450. https://doi.org/10.1016/j.jtcme.2018.05.00210.1016/j.jtcme.2018.05.002617426330302324
]Search in Google Scholar
[
López-Calderón, M.J., Estrada-Ávalos, J., Rodríguez-Moreno, V.M., Mauricio-Ruvalcaba, J.E., Martínez-Sifuentes, A.R., Delgado-Ramírez, G., Miguel-Valle, E., 2020. Estimation of total nitrogen content in forage maize (Zea mays L.) using spectral indices: Analysis by random forest. Agriculture, 10 (10): 451. https://doi.org/10.3390/agriculture1010045110.3390/agriculture10100451
]Search in Google Scholar
[
López-López, M.A., 2005. Un procedimiento alternativo al tradicional para la medición de alturas con clinómetro [An alternative to the traditional procedure for measuring heights with a clinometer]. Madera y Bosques, 11 (2): 69–77.10.21829/myb.2005.1121257
]Search in Google Scholar
[
López-Sánchez, C.L., Bolívar-Cimé, B., Aparicio-Rentería, A., Viveros-Viveros, H., 2020. Population structure of Alnus jorullensis, a species used as firewood by five rural communities in a natural protected area of Mexico. Botanical Sciences, 98 (2): 238–247. https://doi.org/10.17129/botsci.239210.17129/botsci.2392
]Search in Google Scholar
[
Machuca-Velasco, R., Borja-Delarosa, A., Corona-Ambriz, A., Zaragoza-Hernández, I., Arreola-Avila, J.G., Jiménez-Machorro, J., 2017. Xilotecnia of the wood of Acacia schaffneri from the state of Hidalgo, Mexico. Maderas, Ciencia y Tecnología, 19 (3): 293–308. DOI: 10.4067/S0718-221X201700500002510.4067/S0718-221X2017005000025
]Search in Google Scholar
[
Nugroho-Marsoem, S., Irawati, D., 2016. Basic properties of Acacia mangium and Acacia auriculiformis as a heating fuel. In Advances of science and technology for society. Proceedings of the 1st international conference on science and technology 2015. ICST-2015, 11-13 November 2015, Yogyakarta, Indonesia. AIP Conference Proceedings, 1755. Melville, N.Y.: Institute of Physics, 130007-1–130007-7. https://doi.org/10.1063/1.495855110.1063/1.4958551
]Search in Google Scholar
[
Olivares, B., 2014. Aplicación del análisis de Componentes Principales (ACP) en el diagnóstico socioambiental. Caso: sector Campo Alegre, municipio Simón Rodríguez de Anzoátegui [Application of the Principal Component Analysis (APC) in the socio-environmental diagnosis. Case: Campo Alegre sector, Simón Rodríguez de Anzoátegui municipality]. Multiciencias, 14 (4): 364–374. [cit. 2021-06-14]. http://produccioncientificaluz.org/index.php/multiciencias/article/view/19470
]Search in Google Scholar
[
Papaefthimiou, E., Vagias, C., Couladis, M., Tzakou, O., 2017. Study of volatile components of Acacia farnesiana Willd. flowers. Record of Natural Products, 11 (5): 474–478. http://doi.org/10.25135/rnp.60.17.03.01510.25135/rnp.60.17.03.015
]Search in Google Scholar
[
R Core Team, 2015. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. [cit. 2021-06-23]. http://www.R-project.org/
]Search in Google Scholar
[
Riaño-Melo, O., Lizarazo, I., 2017. Estimación del volumen de madera en árboles mediante polinomioúnico de ahusamiento [Estimation of the volume of wood in trees by means of a single taper polynomial]. Colombia Forestal, 20 (1): 55–62.10.14483/udistrital.jour.colomb.for.2017.1.a05
]Search in Google Scholar
[
Romahn-Delavega, C.F., Ramírez-Maldonado, H., Treviño, J.L., 1994. Dendrometría. México: Universidad Autónoma de Chapingo. 354 p.
]Search in Google Scholar
[
Rosenqvist, A., Milne, A., Lucas, R., Imhoff, R., Dobson, C., 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environmental Science & Policy, 6: 441–455. https://doi.org/10.1016/S1462-9011(03)00070-410.1016/S1462-9011(03)00070-4
]Search in Google Scholar
[
Rzedowski, J., Calderón-de Rzedowski, G., 2003. Flora del Bajío y de regiones adyacentes [Flora of the Bajío and adjacent regions]. Xalapa: Instituto de Ecología, A.C. y la Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. 344 p.
]Search in Google Scholar
[
Sina-Conagua, 2021. Regiones hidrológicas. Reporte [Hydrological regions. Reports]. Sistema Nacional de Información del Agua-Comisión Nacional del Agua. México. [cit. 2021-06-08]. http://sina.conagua.gob.mx/sina/index.php?p=32
]Search in Google Scholar
[
Stanturf, J.A., Palik, B.J., Dumroese, R.K., 2014. Contemporary forest restoration: A review emphasizing function. Forest Ecology and Management, 331: 292–323. https://doi.org/10.1016/j.foreco.2014.07.02910.1016/j.foreco.2014.07.029
]Search in Google Scholar
[
Taylor, A.H., Halpern, C.B., 1991. The structure and dynamics of Abies magnifica forests in the southern Cascade Range, USA. Journal of Vegetation Science, 2: 189–200.10.2307/3235951
]Search in Google Scholar
[
Timothy, D., Onisimo, M., Cletah, S., Adelabu, S., Tsitsi, B., 2016. Remote sensing of aboveground forest biomass: A review. Tropical Ecology, 57 (2): 125–132
]Search in Google Scholar
[
Torre-Tojal, L., Bastarrik, A., Barrett, B., Sanchez-Espeso, J.M., Lopez-Guede, J.M., Graña, M., 2019. Prediction of aboveground biomass from low-density LiDAR data: validation over P. radiata data from a region North of Spain. Forests, 10: 819 https://doi.org/10.3390/f1009081910.3390/f10090819
]Search in Google Scholar
[
Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127–150. https://doi.org/10.1016/0034-4257(79)90013-010.1016/0034-4257(79)90013-0
]Search in Google Scholar
[
Valiente-Banuet, A., Casas, A., Alcántara, A., Dávila, P., Flores-Hernández, N., Arizmendi, M.C., Villaseñor, J.L., Ortega-Ramírez, J., 2000. La vegetación del Valle de Tehuacán-Cuicatlán [The vegetation of the Tehuacán-Cuicatlán Valley]. Boletín de la Sociedad Botánica de México, 67: 24–74. https://doi.org/10.17129/botsci.162510.17129/botsci.1625
]Search in Google Scholar
[
Vásquez-Grandón, A., Donoso, P.J., Gerding, V. 2018. Forest degradation: When is a forest degraded? Forests, 9: 726. https://doi.org/10.3390/f911072610.3390/f9110726
]Search in Google Scholar
[
Wani, A.A., Joshi, P.K., Singh, O., 2015. Estimating biomass and carbon mitigation of temperate coniferous forests using spectral modeling and field inventory data. Ecological Informatics, 25, Suppl. C: 63–70.10.1016/j.ecoinf.2014.12.003
]Search in Google Scholar