Biomass and carbon estimation with remote sensing tools in tropical dry forests of Tolima, Colombia

Authors

DOI:

https://doi.org/10.4995/raet.2023.19242

Keywords:

Sentinel-2A, climate change, vegetation index, allometric models, carbon stocks

Abstract

Forests store a large amount of carbon in biomass, which constitutes an option for climate change mitigation. This research focused on the estimation of aboveground biomass and carbon using remote sensing and mathematical modeling tools in dry forests of the Centro Universitario Regional del Norte (CURDN) of the University of Tolima: gallery and riparian forest (152.2 ha) and secondary or transitional vegetation (329.1 ha). Fifty-nine temporary sampling plots were established and the aboveground biomass and carbon were estimated by measuring trees and using allometric models and a carbon fraction of 0.47. Four vegetation indexes (NDVI, EVI, SAVI and OSAVI) were estimated from two Sentinel 2A satellite images from rainy and dry season. The NDVI from the rainy season showed the best R2 (0.87), which allowed the development of a model for estimation of aboveground biomass. Biomass and carbon distribution mapping was generated in the study area, yielding an average value of 95.1 and 44.1 t/ha of aboveground biomass and carbon, respectively. These results made it possible to spatialize the biomass content and carbon sinks within the CURDN and serve as a first step to manage the territory and establish mechanisms for the preservation of the bs-T in the department of Tolima.

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Author Biographies

Carlos E. Mejía, University of Tolima

Ingeniero Forestal, MSc

Hernán J. Andrade, University of Tolima

Grupo de Investigación PROECUT, Departamento de Ciencias Forestales, Facultad de Ingeniería Forestal

Milena Segura, University of Tolima

Grupo de Investigación PROECUT, Departamento de Ciencias Forestales, Facultad de Ingeniería Forestal

References

Álvarez, E., Duque, A., Saldarriaga, J., Cabrera, K., del Valle, I., Lema, A., Moreno, F., Orrego, S., Rodríguez, L., 2012. Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Colombia. Forest Ecology and management, 267, 297-308. https://doi.org/10.1016/j.foreco.2011.12.013

Álvarez-Dávila, E., Cayuela, L., González-Caro, S., Aldana, A. M., Stevenson, P. R., Phillips, O., Cogollo, Á., Peñuela, M., von Hildebrand, P., Jiménez, E., Melo, O., Londoño, A., Mendoza, I., Velásquez, O., Fernández, F., Serna, M., Velázquez, C., Benítez, D., Rey-Benayas, J. M., 2017. Forest biomass density across large climate gradients in northern South America is related to water availability but not with temperature. PloS one, 12(3), e0171072. https://doi.org/10.1371/journal.pone.0171072

Andrade, H.J., Segura, M.A., Forero, L.A. (2014). Desarrollo de modelos alométricos para volumen de madera, biomasa y carbono en especies leñosas perennes: conceptos básicos, métodos y procedimientos. Sello Editorial Universidad del Tolima, Ibagué, Colombia. 48 p.

Aubry-Kientz, M., Rossi, V., Wagner, F., Hérault, B., 2015. Identifying climatic drivers of tropical forest dynamics. Biogeosciences, 12(19), 5583-5596. https://doi.org/10.5194/bg-12-5583-2015

Bhatti, S., Ahmad, S. R., Asif, M., Farooqi, I. U. H., 2022. Estimation of aboveground carbon stock using Sentinel-2A data and Random Forest algorithm in scrub forests of the Salt Range, Pakistan. Forestry, 96(1), 104-120. https://doi.org/10.1093/forestry/cpac036

Cáceres, J., Martín, M., Salas, J., 2015. Análisis temporal de biomasa y stocks de carbono en un ecosistema de dehesa mediante imágenes Landsat, y su relación con factores climáticos. Ciencias Espaciales, 8(1), 190-211. https://doi.org/10.5377/ce.v8i1.2049

Cárdenas, L., 2012. Biomasa y depósitos de carbono en bosques en regeneración del Ecoparque Bataclán (Cali, Colombia) [tesis de pregrado, Universidad del Valle]. Repositorio Univalle. https://n9.cl/atpyd

Castillo, J. A. A., Apan, A. A., Maraseni, T. N., Salmo, S. G., 2017. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 70-85. https://doi.org/10.1016/j.isprsjprs.2017.10.016

Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Folster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J. P., Nelson B. W., Ogawa, H., Puig, H., Riéra, B., Yamakura, T., 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145, 87-99. https://doi.org/10.1007/s00442-005-0100-x

Chavez, P. S., 1996. Image-based atmospheric corrections - Revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025-1036.

Chuvieco, E., 2002. Teledetección ambiental. La observación de la Tierra desde el Espacio. Barcelona: Ariel S. A.

Congedo, L. (2016). Semi-automatic classification plugin documentation. Release, 4(0.1), 29. pp 164-166.

de Queiroga Miranda, R., Nóbrega, R. L. B., de Moura, M. S. B., Raghavan, S., Galvíncio, J. D., 2020. Realistic and simplified models of plant and leaf area indices for a seasonally dry tropical forest. International Journal of Applied Earth Observation and Geoinformation, 85, 101992. https://doi.org/10.1016/j.jag.2019.101992

Escandón-Calderón, J., 1999. Evaluación de dos métodos para la estimación de biomasa arbórea a través de datos LANDSAT TM en Jusnajab La Laguna, Chiapas, México: estudio de caso. Investigaciones Geográficas, 40, 71-84. https://doi.org/10.14350/rig.59095

Fremout, T., Cobián-De Vinatea, J., Thomas, E., Huaman-Zambrano, W., Salazar-Villegas, M., Limache-de la Fuente, D., Bernardino, P. N., Atkinson, R., Csaplovics, E., Muys, B., 2022. Site-specific scaling of remote sensing-based estimates of woody cover and aboveground biomass for mapping long-term tropical dry forest degradation status. Remote Sensing of Environment, 276, 113040. https://doi.org/10.1016/j.rse.2022.113040

Gonzaga, C., 2014. Aplicación de Índices de Vegetación Derivados de Imágenes Satelitales Landsat 7 ETM + y ASTER para la Caracterización de la Cobertura Vegetal en la Zona Centro de la Provincia De Loja, Ecuador [tesis de maestría, Universidad Nacional de la Plata]. Repositorio UNLP. https://n9.cl/plv4ry

Hammer, Ø., Harper, D.A.T., Ryan, P.D. 2001. Paleontological Statistics Software package for education and data analysis. Paleontologia Electronica, 4(1), 9 pp.

Hantson, S., Chuvieco, E., 2011. Evaluation of different topographic correction methods for landsat imagery. International Journal of Applied Earth Observation and Geoinformation, 13(5), 691-700. https://doi.org/10.1016/j.jag.2011.05.001

Harris, N. L., Gibbs, D. A., Baccini, A., Birdsey, R. A., De Bruin, S., Farina, M., Fatoyinbo, L., Hansen, M. C. Herold, M., Houghton, R., Potapov, P., Suarez, D. R., Roman, S. S., Saatchi, S. S., Slay, C., Turubanova, S. A., Tyukavina, A., 2021. Global maps of twenty-first century forest carbon fluxes. Nature Climate Change, 11(3), 234-240. https://doi.org/10.1038/s41558-020-00976-6

Hernández-Stefanoni, J. L., Castillo-Santiago, M. Á., Mas, J. F., Wheeler, C. E., Andres-Mauricio, J., Tun-Dzul, F., George-Chacón, S. P., Reyes-Palomeque, G., Castellanos, B., Vaca, R., Dupuy, J. M., 2020. Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data. Carbon Balance Manage, 15, 1-17. https://doi.org/10.1186/s13021-020-00151-6

Holdridge, L. R., 1979. Life zone ecology. Costa Rica: Tropical Science Center.

Huang, C. Y., Durán, S. M., Hu, K. T., Li, H. J., Swenson, N. G., Enquist, B. J., 2021. Remotely sensed assessment of increasing chronic and episodic drought effects on a Costa Rican tropical dry forest. Ecosphere, 12(11), e03824. https://doi.org/10.1002/ecs2.3824

Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295-309. https://doi.org/10.1016/0034-4257(88)90106-X

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(1-2), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2

IDEAM., 2010. Leyenda nacional de coberturas de la tierra. Metodología Corine Land Cover adaptada para Colombia, Escala 1:100.000. Bogotá: Instituto de Hidrología, Meteorología y Estudios Ambientales. https://n9.cl/uyh4a

IPCC., 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Hayama, Japan: The Intergovernmental Panel on Climate Change (IPCC). https://n9.cl/cwz4xr

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/rs12122008

Khan, M. R., Khan, I. A., Baig, M. H. A., Liu, Z. J., Ashraf, M. I., 2020. Exploring the potential of Sentinel-2A satellite data for aboveground biomass estimation in fragmented Himalayan subtropical pine forest. Journal of Mountain Science, 17(12), 2880-2896. https://doi.org/10.1007/s11629-019-5968-8

Leal, J., Pérez, U., Ortiz, N. E., 2017. Incidencia del cambio de las coberturas vegetales en la distribución espacial de los deslizamientos en la cuenca del río Combeima (Ibagué - Tolima, Colombia). En: Semana Geomática Internacional. Bogotá, Colombia, Agosto. https://doi.org/10.13140/RG.2.2.29189.86247

Liu, Y., Weishu, G., Xing, Y., Hu, X., Gong, J., 2019. Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 2019, 277-289, https://doi.org/10.1016/j.isprsjprs.2019.03.016

Lu, D., Mausel, P., Brondízio, E., Moran, E., 2004. Relationships between forest stand parameters and landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198, 149-167. https://doi.org/10.1016/j.foreco.2004.03.048

Madhab, S., Dev, M., 2018. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest, Applied Geography, 96, 29-40, https://doi.org/10.1016/j.apgeog.2018.05.011

Mani, J. K., Varghese, A. O., 2018. Remote Sensing and GIS in Agriculture and Forest Resource Monitoring. En: Obi, G. P., Singh, S. (eds) Geospatial Technologies in Land Resources Mapping, Monitoring and Management. USA: Geotechnologies and the Environment. https://doi.org/10.1007/978-3-319-78711-4_19

Martínez-Barrón, R. A., Aguirre-Calderón, O. A., Vargas-Larreta, B., Jiménez-Pérez, J., Treviño-Garza, E. J., Yerena-Yamallel, J. I., 2016. Modelación de biomasa y carbono arbóreo aéreo en bosques del estado de Durango. Revista Mexicana de Ciencias Forestales, 7(35), 91-106. https://doi.org/10.29298/rmcf.v7i35.77

Montes-Pulido, C. R., Parrado-Rosselli, Á., Álvarez-Dávila, E., 2017. Tipos funcionales de plantas como estimadores de carbono en bosque seco del Caribe colombiano. Revista Mexicana de Biodiversidad, 88(1), 241-249. https://doi.org/10.1016/j.rmb.2017.01.006

Muller‐Landau, H. C., Cushman, K. C., Arroyo, E. E., Martinez Cano, I., Anderson‐Teixeira, K. J., Backiel, B., 2021. Patterns and mechanisms of spatial variation in tropical forest productivity, woody residence time, and biomass. New Phytologist, 229(6), 3065-3087. https://doi.org/10.1111/nph.17084

Nazarova, T., Martin, P., Giuliani, G., 2020. Monitoring vegetation change in the presence of high cloud cover with Sentinel-2 in a lowland tropical forest region in Brazil. Remote Sensing, 12(11), 1829. https://doi.org/10.3390/rs12111829

Perea-Ardila, M. A., Andrade-Castañeda, H. J., Segura-Madrigal, M. A., 2021. Estimación de biomasa aérea y carbono con Teledetección en bosques alto-Andinos de Boyacá, Colombia. Estudio de caso: Santuario de Fauna y Flora Iguaque. Revista cartográfica, (102), 99-123. https://doi.org/10.35424/rcarto.i102.821

Phillips J.F., Duque A.J., Yepes A.P., Cabrera K.R., García M.C., Navarrete D.A., Álvarez E., Cárdenas D., 2011. Estimación de las reservas actuales (2010) de carbono almacenadas en la biomasa aérea en bosques naturales de Colombia. Estratificación, alometría y métodos análiticos. Bogotá D. C. Instituto de Hidrología, Meteorología, y Estudios Ambientales -IDEAM. https://n9.cl/f98vk

Phillips, J., Duque, A., Scott, C., Wayson, C., Galindo, G., Cabrera, E., Chave, J., Peña, M., Álvarez, E., Cárdenas, D., Duivenvoorden, J., Hildebrand, P., Stevenson, P., Ramírez, S., Yepes, A., 2016. Live aboveground carbon stocks in natural forests of Colombia. Forest Ecology and Management. 374: 119-128. https://doi.org/10.1016/j.foreco.2016.05.009

Pizano, C., García, H., 2014. El bosque seco tropical en Colombia (Vol. 53, Issue 9). Bogotá: Instituto de Recursos Biológicos Alexander von Humboldt (IAvH). https://doi.org/10.1017/CBO9781107415324.004

Poorter, L., Bongers, F., 2006. Leaf traits are good predictors of plant performance across 53 rain forest species. Ecological Society of America, 87(7), 1733-1743. https://doi.org/10.1890/0012-9658(2006)87[1733:LTAGPO]2.0.CO;2

Portillo-Quintero, C. A., Sánchez-Azofeifa, G. A., 2010. Extent and conservation of tropical dry forests in the Americas. Biological Conservation, 143, 143-155. https://doi.org/10.1016/j.biocon.2009.09.020

Pötzschner, F., Baumann, M., Gasparri, N. I., Conti, G., Loto, D., Piquer-Rodríguez, M., Kuemmerle, T., 2022. Ecoregion-wide, multi-sensor biomass mapping highlights a major underestimation of dry forests carbon stocks. Remote sensing of environment, 269, 112849. https://doi.org/10.1016/j.rse.2021.112849

Qiu, A., Yang, Y., Wang, D., Xu, S., Wang, X., 2020. Exploring parameter selection for carbon monitoring based on Landsat-8 imagery of the aboveground forest biomass on Mount Tai. European Journal of Remote Sensing, 53:sup1, 4-15. https://doi.org/10.1080/22797254.2019.1686717

Restrepo, L., González, J., 2007. From Pearson to Spearman. Revista Colombiana de Ciencias Pecuarias, 20(2), 183-192. https://n9.cl/8cb9

Rodríguez, A., 2015. Estimación de biomasa arbórea por medio de índices de vegetación para el Parque Nacional Natural La Paya [proyecto de especialización, Universidad Militar Nueva Granada]. Repositorio UNIMILITAR. https://n9.cl/qw8f7

Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote sensing of environment, 55(2), 95-107. https://doi.org/10.1016/0034-4257(95)00186-7

Rouse Jr, J.W., Haas, R.H., Deering, D.W., Schell, J.A., & Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. E75-10354).

Schlesinger, W. H., 2000. Biogeoquímica un análisis del Cambio global. España: Ariel España.

Segura, M. A., Andrade, H. J., 2008. ¿Cómo construir modelos alométricos de biomasa o carbono de especies leñosas perennes?. Agroforestería En Las Américas, (46), 89-96. https://n9.cl/key2s

Singh, C., Karan, S. K., Sardar, P., Samadder, S. R., 2022. Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis. Journal of Environmental Management, 308, 114639. https://doi.org/10.1016/j.jenvman.2022.114639

Srinivas, K., Sundarapandian, S., 2018. Biomass and carbon stocks of trees in tropical dry forest of East Godavari region, Andhra Pradesh, India. Geology, Ecology, and Landscapes, 3(2), 114-122. https://doi.org/10.1080/24749508.2018.1522837

Teillet, P. M., Guindon, B., Goodenough, D. G., 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8(2), 84-106. https://doi.org/10.1080/07038992.1982.10855028

Tetemke, B. A., Birhane, E., Rannestad, M. M., Eid, T., 2021. Species diversity and stand structural diversity of woody plants predominantly determine aboveground carbon stock of a dry Afromontane forest in Northern Ethiopia. Forest Ecology and Management, 500, 1-9. https://doi.org/10.1016/j.foreco.2021.119634

Torres, A., Bautista, J., Cárdenas, M., Vargas, J., Londoño, V., Rivera, K., Home, J., Duque, O., Gonzales, A., 2012. Dinámica sucesional de un fragmento de bosque seco tropical del Valle del Cauca, Colombia. Biota Colombiana, 8(2), 66-85.

Tovar, A. (2018). Estimación de biomasa aérea de eucalipto (Eucalyptus grandis) y pino (Pinus spp) en plantaciones forestales comerciales, usando imágenes satelitales Sentinel [tesis de maestría, Universidad Nacional de Colombia]. Repositorio UNAL. https://n9.cl/c3h9y

Universidad del Tolima. Centro Universitario Regional del Norte CURDN. Recuperado Febrero de, 2017, de https://n9.cl/7tp45

Valdivia, J. (2020). Índices de Vegetación para la estimación de Biomasa Arbórea en Sistemas Agrosilvícolas de fincas en Zihuateutla, Puebla, México [tesis de pregrado, Universidad Nacional Agraria de la Selva]. Repositorio UNAS. https://n9.cl/qaqxp

Yepes, A., del Valle, J., Jaramillo, S., Orrego, S., 2010. Recuperación estructural en bosques sucesionales andinos de Porce (Antioquia, Colombia). Revista de Biología Tropical, 58(1), 427-445. https://doi.org/10.15517/rbt.v58i1.5220

Zhang, Y., Chen, H. Y. H., Reich, P. B., 2012. Forest productivity increases with evenness, species richness and trait variation: A global meta-analysis. Journal of Ecology, 100(3), 742-749. https://doi.org/10.1111/j.1365-2745.2011.01944.x

Zhu, X., Liu, D., 2015. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 222-231. https://doi.org/10.1016/j.isprsjprs.2014.08.014

Zhu, Z., Wulder, M. A., Roy, D. P., Woodcock, C. E., Hansen, M. C., Radeloff, V. C., Healey, S., Schaaf, C., Hostert., Strobl, P., Francois, J., Lymburner, L., Pahlevan, N., Scambos, T. A., 2019. Benefits of the free and open Landsat data policy. Remote Sensing of Environment, 224, 382-385. https://doi.org/10.1016/j.rse.2019.02.016

Published

2023-07-28

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Research articles