Optimization of sample size for forest inventories using random sampling in a tropical dry forest of Manabí, Ecuador
DOI:
https://doi.org/10.33936/la_tecnica.v16i2.8381Keywords:
forestry, trees, plots, estimation, calculation.Abstract
Optimizing sample size in forest inventories is key to generating reliable information that supports the management and conservation of vulnerable ecosystems such as tropical dry forests. In this context, the present study aimed to optimize sample size for forest inventories using simple random sampling in a tropical dry forest in Manabí, Ecuador. The research was conducted in 20 ha of main forest belonging to the Andil community, where a simple random sampling design was applied with nine rectangular plots of 20 × 25 m, equivalent to 500 m² each, for a total sampled area of 0.45 ha. In each plot, all individuals with a DBH ≥ 10 cm were measured, recording diameter, total height, basal area, and volume. Variance, standard deviation, coefficient of variation, standard error, and 95% confidence limits were calculated and processed using RStudio software. The results recorded 203 individuals distributed among 17 species, with Guazuma ulmifolia, Senna mollissima, and Cochlospermum vitifolium being dominant. Additionally, a total volume of 62.37 m³ was estimated, with an average of 6.93 m³ per plot, a coefficient of variation of 31.22%, and a calculated sample size of 8.82 plots, rounded to 9. It is concluded that simple random sampling allowed for a technically sufficient sample size to generally characterize the forest stand, although the heterogeneity of the forest suggests increasing the number of plots if greater precision is desired.
Downloads
References
Alberdi, I., Adame, P., Aulló, I., Hernández, L., Montes, F., Rubio, Á., Oliveira, N., Cañellas, I., Ramírez, C., Veloso, J. y Vergara, G. (2023). Estimación e interpretación de indicadores de biodiversidad forestal considerando la información de los inventarios forestales nacionales. Agencia Española de Cooperación Internacional para el Desarrollo (AECID). https://bibliotecadigital.infor.cl/handle/20.500.12220/32696
Angelis, L. and Stamatellos, G. (2004). Multiple objective optimization of sampling designs for forest inventories using random search algorithms. Computers and Electronics in Agriculture, 42(3), 129-148. https://doi.org/10.1016/s0168-1699(03)00121-2
Bravo, D., Ganchozo, M., Mero, O., Pinargote, J. y Cabrera. C. (2024). Análisis estructural de la vegetación arbórea en la finca “El Despeño” de la Comunidad Balsa Tumbada Adentro, Junín, Manabí, Ecuador. Revista de Investigación Talentos, 11(1), 47-63. https://doi.org/10.33789/talentos.11.1.196
Bouasria, A., Bouslihim, Y., Gupta, S., Taghizadeh, R. and Hengl, T. (2023). Predictive performance of machine learning model with varying sampling designs, sample sizes, and spatial extents. Ecological Informatics, 78, 102294. https://doi.org/10.1016/j.ecoinf.2023.102294
Brück, S., Medina, B. and Moraes, M. (2023). The Ecuadorian paramo in danger: What we know and what might be learned from northern wetlands. Global Ecology and Conservation, 47, e02639. https://doi.org/10.1016/j.gecco.2023.e02639
Cabrera, C., Sornoza, L., Cantos, C., Pionce, G. y Ganchozo, M. (2020). Análisis de la regeneración natural de cinco especies forestales de la finca Ándil UNESUM. Revista Perspectivas Rurales, 18(36), 101-123. http://doi.org/10.15359/prne.18-36.5
Cedeño, A. (2023). Variabilidad climática en el cantón Jipijapa, provincia de Manabí [Tesis de Ingeniero Ambiental, Universidad Estatal del Sur de Manabí] https://repositorio.unesum.edu.ec/bitstream/53000/5876/1/Cede%C3%B1o%20Toala%20Alan%20Jair.pdf
De Sousa, C., Silva, J., Ferreira, R., Da Silva, D., Da Silva, E. and Lima, R. (2023). Size of sampling units aproppriate for management level forest inventory in Eastern Amazon, Brazil. Revista em Agronegócio e Meio Ambiente, 16(2), e10938. https://doi.org/10.17765/2176-9168.2023v16n2e10938
Durán, N., González, D., Martínez, J. y Prado, M. (2025). Modelado espacial de Guazuma ulmifolia Lam. ante el cambio climático en México. Revista Mexicana de Ciencias Forestales, 16(87), 153-175. https://doi.org/10.29298/rmcf.v16i87.1510
Goodbody, T. R. H., Coops, N., Senf, C. and Seidl, R. (2023). Airborne laser scanning to optimize the sampling efficiency of a forest management inventory in South-Eastern Germany. Ecological Indicators, 157, 111281. https://doi.org/10.1016/j.ecolind.2023.111281
Hassan, S., Irfan, M., Binte, A., Ahmad, I. y Ali, S. (2021). Evaluación de la deforestación en bosques subtropicales utilizando datos de terrenos espacio-temporales. Revista Cubana de Ciencias Forestales 9(2), 205-225. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid= S2310-34692021000200205&lng=es&tlng=es.
Heberle, T., Denega, L., Stepka, T. F., Nicoletti, M. F., Galéski, G. and de Jesus, L. dos S. (2022). Effectiveness of sampling methods for thinned and non-thinned Pinus taeda L. plantations in the mountain region of Santa Catarina, Brazil. Floresta, 52(4), 541-551. https://doi.org/10.5380/rf.v52i4.85374
Hernández, J., García, X., Pérez, R., González, A. y Martínez L. (2020). Inventario y mapeo de variables forestales mediante sensores remotos en el estado de Quintana Roo, México. Madera y Bosques, 26(1), e2611884. https://doi.org/10.21829/myb.2020.2611884
Hetzer, J., Huth, A., Wiegand, T., Dobner, H. and Fischer, R. (2019). An analysis of forest biomass sampling strategies across scales. Biogeosciences, 17, 1673-1683. https://doi.org/10.5194/bg-17-1673-2020
Iheaturu, C., Wingate, V., Akinyemi, F. and Speranza, C. (2025). An integrated object-based sampling approach for validating non-contiguous forest cover maps in fragmented tropical landscapes. Int. J. Appl. Earth Obs. Geoinformation, 139, 104497. https://doi.org/10.1016/j.jag.2025.104497
Indacochea, B. y García, R. (2024). Inventario forestal con enfoque bioético para la protección sostenible de recursos en la Finca Experimental Andil de Jipijapa. Perfiles, 31(1), 61-73. https://doi.org/10.47187/perf.v1i31.274
Kauai, F., Corte, A., Cysneiros, V., Pelissari, A., & Sanquetta, C. (2019). Evaluation of forest inventory processes in a forest under concession in the southwestern Brazilian Amazon. Acta Amazonica, 49(2), 91-96. https://doi.org/10.1590/1809-4392201801331
Kománek, M., Knott, R., Kadavý, J. and Kneifl, M. (2024). Is the Concentric Plot Design Reliable for Estimating Structural Parameters of Forest Stands?, Forests, 15(12), 2246. https://doi.org/10.3390/f15122246
Laly, G., Atindogbé, G., Akpo, A. H., Dotchamou, T., Kassa, B. D. and Fonton, N. H. (2025). Optimal tree sampling for ecosystem-specific biomass allometry modeling in Congo Basin forests. International Journal of Biological and Chemical Sciences, 18(6), 2205-2220. https://doi.org/10.4314/ijbcs.v18i6.12
Li, C., Yu, Z., Dai, H., Zhou, X. and Zhou, M. (2023). Effect of sample size on the estimation of forest inventory attributes using airborne LiDAR data in large-scale subtropical areas. Annals of Forest Science, 80, 1-15. https://doi.org/10.1186/s13595-023-01209-4
Lozano, L. y Bonilla, J. (2022). Factor de forma para árboles del Bosque Seco Tropical (bs-T) en el norte del Departamento del Tolima - Colombia. Revista Temas Agrarios, 27(2), 344-353. https://doi.org/10.21897/rta.v27i2.3136
Mansingh, A., Pradhan, A., Sahoo, S., Cherwa, S., Mishra, B., Rath, L., Ekka, N. and Panda, B. (2025). Tree diversity, population structure, biomass accumulation, and carbon stock dynamics in tropical dry deciduous forests of Eastern India. BMC Ecology and Evolution, 25. https://doi.org/10.1186/s12862-025-02385-9
Mexudhan, Lal, J. and Patil, G. (2024). Assessment of tree species diversity, biomass, C and N storage in two sites of dry tropical forest of Chhattisgarh, India. International Journal of Economic Plants, 11, 180-187. https://doi.org/10.23910/2/2024.5263
Mena-Mosquera, V.E., Andrade C., H. J. y Torres-Torres, J. J. (2020). Composición florística, estructura y diversidad del bosque pluvial tropical de la subcuenca del río Munguidó, Quibdó, Chocó, Colombia. Entramado, 16(1), 204-215. DOI 10.18041/1900-3803/entramado.1.6109.
Morris, J., Fuente, F., Morris, S., Li, K., Benítez, M. and Perfecto, I. (2026). Agricultural intensification associated with significant reduction in epiphyte diversity on coffee plants. Agriculture, Ecosystems and Environment, 401, 110249. https://doi.org/10.1016/j.agee.2026.110249
Mosquera, J. V., Auguste, G., Wong, D., Turner, A. W., Hodonsky, C. J., Alvarez-Yela, A. C., Song, Y., Cheng, Q., Lino Cardenas, C. L., Theofilatos, K., Bos, M., Kavousi, M., Peyser, P. A., Mayr, M., Kovacic, J. C., Björkegren, J. L. M., Malhotra, R., Stukenberg, P. T., Finn, A. V., van der Laan, S. W., Zang, C., Sheffield, N. C. and Miller, C. L. (2023). Integrative single-cell meta-analysis reveals disease-relevant vascular cell states and markers in human atherosclerosis. Cell Reports, 42(11), 113380. https://doi.org/10.1016/j.celrep.2023.113380
O´Brien, F. y Cabrera, R. (2024). Especies florísticas y actividades antrópicas en el bosque seco tropical en Cantagallo del cantón Jipijapa. MQRInvestigar, 8(1), 5376-5387. https://doi.org/10.56048/MQR20225.8.1.2024.5376-5387
Palma, D. y Sánchez, R. (2023). Uso y manejo del suelo. Vol 3. En: Hacia un conocimiento global y multidisciplinario del recurso suelo. Sociedad Mexicana de las Ciencias del Suelo. https://www.smcsmx.org/files/2023/LIBRO_3_2023.pdf
Papa, D., Almeida, D., Silva, C., Figueiredo, E., Stark, S., Valbuena, R., Rodríguez, L. and Oliveira, M. (2020). Evaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring. Forest Ecology and Management, 457, 117634. https://doi.org/10.1016/j.foreco.2019.117634
Pohjankukka, J., Tuominen, S. and Heikkonen, J. (2022). Bayesian approach for optimizing forest inventory survey sampling with remote sensing data. Forests, 13(10), 1692. https://doi.org/10.3390/f13101692
Pretzsch, H. (2020). The course of tree growth. Theory and reality. Forest Ecology and Management, 478, 118508. https://doi.org/10.1016/j.foreco.2020.118508
Rebolledo López, D. C., y Lores Ochoa, D. C. (2021). Evaluación espaciotemporal de la cobertura vegetal del parque nacional Henri Pittier, Venezuela. GeoFocus, Revista Internacional de Ciencia y Tecnología de la Información Geográfica, 28, 25-58. http://dx.doi.org/10.21138/GF.742
Rodrígues Pinto, L. O., Rodrigo de Souza, C., Terra, M., Mello, J., Calegário, N. and Acerbi, F. (2021). Optimal plot size for carbon-diversity sampling in tropical vegetation. Forest Ecology and Management, 482, 118778. https://doi.org/10.1016/j.foreco.2020.118778
Scheeres, J., De Jong, J., Brede, B., Brancalion, P., Noth, E., Almeyda, A., Bastos, E., Silva, C., Valbuena, R., Molin, P., Stark, S., Ribeiro, R., Brossi, G., Faria, A., Torres, C. and Alves, D. (2023). Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR. Remote Sensing of Environment, 290, 113533. https://doi.org/10.1016/j.rse.2023.113533
Schillaci, M. A. and Schillaci, M. E. (2022). Estimating the population variance, standard deviation, and coefficient of variation: Sample size and accuracy. Journal of Human Evolution, 171, 103230. https://doi.org/10.1016/j.jhevol.2022.103230
Shu, Q., Xi, L., Wang, K., Xie, F., Pang, Y. and Song, H. (2022). Optimization of samples for remote sensing estimation of forest aboveground biomass at the regional scale. Remote Sens., 14(17), 4187. https://doi.org/10.3390/rs14174187
Silva, E., Guadalupe, J., Aguirre, O., Treviño, E., Corral, J. y Manzanilla, G. (2024). Diversidad y estructura de especies arbóreas en tres tipos de vegetación forestal al sur de Durango, México. Polibotánica, 58(29), 103-118. https://doi.org/10.18387/polibotanica.58.7
West, P. W. (2017). Population structure and correlation between auxiliary and target variables may affect precision of estimates in forest inventory. Communications in Statistics - Simulation and Computation, 46(6), 4951-4965. https://doi.org/10.1080/03610918.2016.1139128
Wu, H., Xu, H., Tian, X., Zhang, W. and Lu, C. (2023). Multistage sampling and optimization for forest volume inventory based on spatial autocorrelation analysis. Forests, 14(2), 250. https://doi.org/10.3390/f14020250
Zhao, N., Prieur, J., Liu, Y., Kneeshaw, D., Morasse, E., Paquette, A., Zinszer, K., Dupras, J., Villeneuve, P., Rainham, D., Lavigne, E., Chen, H., Van, M., Viamo, T. and Smargiassi, A. (2021). Tree characteristics and environmental noise in complex urban settings – A case study from Montreal, Canada. Environmental Research, 202, 111887. https://doi.org/10.1016/j.envres.2021.111887
Zhou, X., Hu, C., & Wang, Z. (2023). Distribution of biomass and carbon content in estimation of carbon density for typical forests. Global Ecology and Conservation 48, e02707. https://doi.org/10.1016/j.gecco.2023.e02707
Zhu, Y., Xin, L., Cz, C., Mei, Z. and Lc, G. (2021). Effect of field sample size on large-scale subtropic forest inventory attribute estimation based on airborne laser scanner data. Preprints. https://doi.org/10.20944/preprints202106.0530.v1
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Nestor Leopoldo Tarazona Meza, Gabriela Delgado Macias, Marcos Vinicio Loor Alcívar, 2Sandra Grace Pincay Sánchez, Maverick Ángel Magallan Rodríguez

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
https://orcid.org/0000-0002-2145-6475

