Identification of areas with high aboveground biomass and high species richness of the native forest at northeastern Uruguay
DOI:
https://doi.org/10.4995/raet.2024.20272Keywords:
carbon stocks, biodiversity conservation, Sentinel-2, L-band SAR, texture analysisAbstract
The native forests of Uruguay provide important ecosystem services. Despite this, there are few maps with the spatial distribution of vegetation attributes in the country. The objective of this study was to obtain maps with the spatial distribution of aboveground biomass and species richness that show areas with high concentrations of both variables, essential for climate change mitigation and biodiversity conservation. The study area includes the Gondwanan Sedimentary Basin ecoregion. Generalized Linear Models were used to estimate aboveground biomass and tree species richness, where the response variables were calculated using field data from the National Forest Inventory. Whereas, the predictor variables were obtained with spectral and texture information derived from Sentinel-2, and ALOS PALSAR; as well as environmental, topography and climate data. The biomass estimation model presented an explained deviance (D2) of 0,25, while in the species richness model, the D2 was 0,19. To evaluate both models, cross-validations were carried out, obtaining an R2 of 0.25 for aboveground biomass and 0,19 for species richness, with a relative mean square error of 45,8 % and 32,5 % respectively. The bivariate map with the joint distribution of species richness and aboveground biomass shows that there is a positive correlation between both variables in 63,8 % of the native forest area of the ecoregion. The results of this work could be used for the maintenance of carbon stocks and for the conservation of biodiversity.
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References
Andres-Mauricio, J., Valdez-Lazalde, J.R., George-Chacón, S.P., Hernández-Stefanoni, J.L. 2021. Mapping structural attributes of tropical dry forests by combining Synthetic Aperture Radar and highresolution satellite imagery data. Applied Vegetation Science, 24(2), 1-16. https://doi.org/10.1111/avsc.12580
Bordin, K.M., Esquivel-Muelbert, A., Bergamin, R.S., Klipel, J., Picolotto, R.C., Frangipani, M.A., Zanini, K.J., Cianciaruso, M.V., Jarenkow, J.A., Jurinitz, C.F., Molz, M., Higuchi, P., Silva, A.C. da, Müller, S.C. 2021. Climate and large-sized trees, but not diversity, drive above-ground biomass in subtropical forests. Forest Ecology and Management, 490, 119-126. https://doi.org/10.1016/j.foreco.2021.119126
Brazeiro, A., Achkar, M., Bartesaghi, L., Ceroni, M., Aldabe, J., Carreira, S., Duarte, Alejandro-González, Enrique Haretche, F., Loureiro, M., Martínez-Lanfranco, J. 2015. Mapeo de la biodiversidad de Uruguay. En A. Brazeiro (Ed.), Eco-Regiones de Uruguay: Biodiversidad, Presiones y Conservación. Aportes a la Estrategia (1a ed., 22-31).
Campbell, J., Wynne, R. 2011. Remote sensing. The Guilford Press.
Cayuela, L. 2015. Modelos lineales generalizados (GLM). En Curso Regresion II (1-31). Aréa de Biodiversidad y Conservación, Universidad Rey Juan Carlos.
Cayuela, L., Benayas, J.M.R., Justel, A., Salas-Rey, J. 2006. Modelling tree diversity in a highly fragmented tropical montane landscape. Global Ecology and Biogeography, 15(0), 602-613. https://doi.org/10.1111/j.1466-8238.2006.00255.x
Chave, J., Condit, R., Aguilar, S., Hernandez, A., Lao, S., Perez, R. 2004. Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 359(1443), 409-420. https://doi.org/10.1098/rstb.2003.1425
Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M.S., Delitti, W.B.C., Duque, A., Eid, T., Fearnside, P.M., Goodman, R.C., Henry, M., Martínez-Yrízar, A., Mugasha, W.A., Muller-Landau, H.C., Mencuccini, M., Nelson, B.W., Ngomanda, A., Nogueira, E.M., Ortiz-Malavassi, E., … Vieilledent, G. 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, 20(10), 3177-3190. https://doi.org/10.1111/gcb.12629
Colwell, R. 2022. EstimateS: Statistical estimation of species richness and shared species from samples. Version 9.1. https://www.robertkcolwell.org/pages/estimates
DGF-MGAP. 2018. Cartografía Forestal Nacional 2018. 1-15.
Fassnacht, F.E., Poblete-Olivares, J., Rivero, L., Lopatin, J., Ceballos-Comisso, A., Galleguillos, M. 2021. Using Sentinel-2 and canopy height models to derive a landscape-level biomass map covering multiple vegetation types. International Journal of Applied Earth Observation and Geoinformation, 94(2020), 102236. https://doi.org/10.1016/j.jag.2020.102236
Frazer, G.W., Magnussen, S., Wulder, M.A., & Niemann, K.O. 2011. Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sensing of Environment, 115(2), 636-649. https://doi.org/10.1016/j.rse.2010.10.008
Gao, S., Zhong, R., Yan, K., Ma, X., Chen, X., Pu, J.,... & Myneni, R.B. 2023. Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations. Remote Sensing of Environment, 295, 113665. https://doi.org/10.1016/j.rse.2023.113665
Ghosh, S.M., Behera, M.D. 2018. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography, 96(May), 29-40. https://doi.org/10.1016/j.apgeog.2018.05.011
Goh, J.Y., Miettinen, J., Chia, A.S., Liew, S.C., Kwoh, L.K. 2011. Integrating ALOS PALSAR and spot satellite imagery for tropical forest biomass estimation. 32nd Asian Conference on Remote Sensing 2011, ACRS 2011, 2, 906-911.
Haralick, R.M., Dinstein, I., Shanmugam, K. 1973. Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), 610-621. https://doi.org/10.1109/TSMC.1973.4309314
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-Basto, 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 and Management, 15(1), 1-17. https://doi.org/10.1186/s13021-020-00151-6
Hernández-Stefanoni, J.L., Castillo-Santiago, M., Andres-Mauricio, J., Portillo-Quintero, C.A., Tun-Dzul, F., Dupuy, J.M. 2021. Carbon stocks, species diversity and their spatial relationships in the yucatán peninsula, mexico. Remote Sensing, 13(16), 1-18. https://doi.org/10.3390/rs13163179
Hidasi-Neto, J. 2022. Package ' bivariatemaps ' (1.0). https://cran.r-project.org/web/packages/bivariatemaps/index.html
Hijmans, A.R.J., Etten, J. Van, Hijmans, M.R.J. 2013. Package ' raster '. pp. 195.
INUMET. 2020. Clasificación climática | Inumet. https://www.inumet.gub.uy/clima/estadisticasclimatologicas/clasificacion-climatica.
JAXA. 2019. Global 25m Resolution PALSAR-2 / PALSAR Mosaic and Forest / Non-Forest Map (FNF). Japan Aerospace Exploration Agency. Earth Observation Research Center.
Loreau, M., Hector, A. 2001. Partitioning selection and complementarity in biodiversity experiments. Nature, 412(6842), 72-76. https://doi.org/10.1038/35083573
Lecina-Diaz, J., Alvarez, A., Regos, A., Drapeau, P., Paquette, A., Messier, C., Retana, J. 2018. The positive carbon stocks-biodiversity relationship in forests: co-occurrence and drivers across five subclimates. Ecological Applications, 28(6), 1481-1493. https://doi.org/10.1002/eap.1749
Legendre, P., Legendre, L. 2012. Numerical ecology. Developments in environmental modeling (Vol. 24). Elservier.
Lopatin, J., Dolos, K., Hernández, H.J., Galleguillos, M., Fassnacht, F.E. 2016. Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sensing of Environment, 173, 200-210. https://doi.org/10.1016/j.rse.2015.11.029
Lu, D. 2005. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing, 26(12), 2509-2525. https://doi.org/10.1080/01431160500142145
Mace M.G., Norris, Ken, H., Fitter, A. 2012. Biodiversity and ecosystem services: a multilayered relationship. Trends in Ecology & Evolution, 27, 19-26. https://doi.org/10.1016/j.tree.2011.08.006
Mitchard, E.T.A., Saatchi, S.S., Woodhouse, I.H., Nangendo, G., Ribeiro, N.S., Williams, M., Ryan, C.M., Lewis, S.L., Feldpausch, T.R., Meir, P. 2009. Using satellite radar backscatter to predict above-ground woody biomass: A consistent relationship across four different African landscapes. Geophysical Research Letters, 36(23). https://doi.org/10.1029/2009GL040692
MGAP. 2020. Modelo digital de Terreno. https://www.gub.uy/ministerio-ganaderia-agricultura-pesca/tramites-y-servicios/servicios/modelo-digitalterreno (Acceso 6 diciembre de 2023).
Mutanga, O., Masenyama, A., & Sibanda, M. 2023. Spectral saturation in the remote sensing of highdensity vegetation traits: A systematic review of progress, challenges, and prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 297-309. https://doi.org/10.1016/j.isprsjprs.2023.03.010
Poorter, L., Bongers, F., Aide, T.M., Almeyda Zambrano, A.M., Balvanera, P., Becknell, J.M.,... & Rozendaal, D.M. 2016. Biomass resilience of Neotropical secondary forests. Nature, 530(7589), 211-214. https://doi.org/10.1038/nature16512
QGIS.org. 2021. QGIS Geographic Information System (3.16). QGIS Association. http://www.qgis.org
R Core Team. 2021. R: A Language and Environment for Statistical Computing (R version 4.1.2). vers. https://www.r-project.org/
Réjou-Méchain, M., Barbier, N., Couteron, P., Ploton, P., Vincent, G., Herold, M., Mermoz, S., Saatchi, S., Chave, J., de Boissieu, F., Féret, J.B., Takoudjou, S.M., Pélissier, R. 2019. Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them. En Surveys in Geophysics, 40(4). Springer Netherlands. https://doi.org/10.1007/s10712-019-09532-0
Rocchini, D., Balkenhol, N., Carter, G.A., Foody, G.M., Gillespie, T.W., He, K.S., Kark, S., Levin, N., Lucas, K., Luoto, M., Nagendra, H., Oldeland, J., Ricotta, C., Southworth, J., Neteler, M. 2010. Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges. Ecological Informatics, 5(5), 318-329. https://doi.org/10.1016/j.ecoinf.2010.06.001
Rodríguez-Veiga, P., Quegan, S., Carreiras, J., Persson, H.J., Fransson, J.E.S., Hoscilo, A., Ziółkowski, D., Stereńczak, K., Lohberger, S., Stängel, M., Berninger, A., Siegert, F., Avitabile, V., Herold, M., Mermoz, S., Bouvet, A., Le Toan, T., Carvalhais, N., Santoro, M., … Balzter, H. 2019. Forest biomass retrieval approaches from earth observation in different biomes. International Journal of Applied Earth Observation and Geoinformation, 77, 53-68. https://doi.org/10.1016/j.jag.2018.12.008
Roman, A., Ursu, T.-M. 2016. Multispectral Satellite Imagery and Airborne Laser Scanning Techniques for the Detection of Archaeological Vegetation Marks. Landscape Archaeology on the Northern Frontier of the Roman Empire at Porolissum, December, pp. 141-152.
Rozendaal, D.M., Bongers, F., Aide, T.M., Alvarez-Dávila, E., Ascarrunz, N., Balvanera, P.,... & Poorter, L. 2019. Biodiversity recovery of Neotropical secondary forests. Science advances, 5(3), eaau3114. https://doi.org/10.1126/sciadv.aau3114
Saatchi, S. 2019. SAR Methods for Mapping and Monitoring Forest Biomass. En E. Flores-Anderson, Africa; Herndon, Kelsey; Bahadur, Rajesh; Cherrington (Ed.), The Synthetic Aperture Radar (SAR) Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass (1a ed., pp. 207-254).
Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, M., Mitchard, E.T.A., Salas, W., Zutta, B.R., Buermann, W., Lewis, S.L., Hagen, S., Petrova, S., White, L., Silman, M., Morel, A. 2011. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences of the United States of America, 108(24), 9899-9904. https://doi.org/10.1073/pnas.1019576108
Shimada, M., Ohtaki, T. 2010. Generating Large-Scale High-Quality SAR Mosaic Datasets: Application to PALSAR Data for Global Monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(4), 637-656. https://doi.org/10.1109/JSTARS.2010.2077619
Solberg, S., Hansen, E.H., Gobakken, T., Naessset, E., & Zahabu, E. 2017. Biomass and InSAR height relationship in a dense tropical forest. Remote Sensing of Environment, 192, 166-175. https://doi.org/10.1016/j.rse.2017.02.010
Soto-Navarro, C., Ravilious, C., Arnell, A., De Lamo, X., Harfoot, M., Hill, S.L.L.,... Kapos, V. 2020. Mapping co-benefits for carbon storage and biodiversity to inform conservation policy and action. Philosophical Transactions of the Royal Society B, 375(1794), 20190128. https://doi.org/10.1098/rstb.2019.0128
Tovar-Blanco, A.L., Lizarazo-Salcedo, I.A., Rodríguez-Eraso, N. 2020. Estimación de biomasa aérea de Eucalyptus grandis y Pinus spp. usando imágenes Sentinel1A y Sentinel2A en Colombia. Colombia forestal, 23(1), 117-141. https://doi.org/10.14483/2256201X.14854
Warren, S.D., Alt, M., Olson, K.D., Irl, S.D.H., Steinbauer, M.J., Jentsch, A. 2014. The relationship between the spectral diversity of satellite imagery, habitat heterogeneity, and plant species richness. Ecological Informatics, 24, 160-168. https://doi.org/10.1016/j.ecoinf.2014.08.006
Wing, M.G., Frank, J. 2011. An examination of five identical mapping-grade global positioning system receivers in two forest settings. Western Journal of Applied Forestry, 26(3), 119-125. https://doi.org/10.1093/wjaf/26.3.119
Xu, L., Saatchi, S.S., Shapiro, A., Meyer, V., Ferraz, A., Yang, Y.,... Ebuta, D. 2017. Spatial distribution of carbon stored in forests of the Democratic Republic of Congo. Scientific Reports, 7(1), 15030. https://doi.org/10.1038/s41598-017-15050-z
Zanne, A.E., Lopez-Gonzalez, G., Coomes, D.A., Ilic, J., Jansen, S., Lewis, S.L.,... Chave, J. 2020. Data from: Towards a worldwide wood economics spectrum. https://doi.org/10.5061/dryad.234
Zhang, Y., Chen, H.Y.H., Taylor, A.R. 2017. Positive species diversity and above-ground biomass relationships are ubiquitous across forest strata despite interference from overstorey trees. Functional Ecology, 31(2), 419-426. https://doi.org/10.1111/1365-2435.12699
Zhao, P., Lu, D., Wang, G., Liu, L., Li, D., Zhu, J., Yu, S. 2016. Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data. International Journal of Applied Earth Observation and Geoinformation, 53, 1-15. https://doi.org/10.1016/j.jag.2016.08.007
Zheng, W. 2020. Estimate of vegetation production of terrestrial ecosystem. En J. Liang, S. and Wang (Ed.), Advanced remote sensing (2a ed., 581-609). Elservier. https://doi.org/10.1016/B978-0-12-815826-5.00015-5
Zuur, A.F., Hilbe, J.M., Ieno, E.N. 2013. A Beginner's Guide to GLM and GLMM with R: A Frequentist and Bayesian Perspective for Ecologists. Highland Statistics Limited.
Zvoleff, A. 2020. Package 'glcm'. https://cran.r-project.org/web/packages/glcm/index.html
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