Floresta e Ambiente
Floresta e Ambiente
Original Article Forest Management

Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data

Anny Francielly Ataide Gonçalves; Márcia Rodrigues de Moura Fernandes; Jeferson Pereira Martins Silva; Gilson Fernandes da Silva; André Quintão de Almeida; Natielle Gomes Cordeiro; Lucas Duarte Caldas da Silva; José Roberto Soares Scolforo

Downloads: 0
Views: 924


ABSTRACT: The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted based on the remote sensing data, taking into consideration the individual bands and vegetation index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508 and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the combined MSI and SRTM data as predictors. The volume estimation using spectral data showed satisfactory results, highlighting the importance of topography in the prediction of the volume of wood for the area under investigation.


atlantic forest, remote sensing, forest inventory, measurement


Alba E, Mello EP, Marchesan J, Silva EA, Tramontina J, Pereira RS. Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management. Pesquisa Agropecuária Brasileira 2017; 52(11): 1072-1079. http://dx.doi.org/10.1590/s0100-204x2017001100013.

Almeida AQ, Mello AA, Dória AL No, Ferra RC. Relações empíricas entre características dendrométricas da Caatinga Brasileira e dados TM Landsat 5. Pesquisa Agropecuária Brasileira 2014; 49(4): 306-315. http://dx.doi.org/10.1590/S0100-204X2014000400009.

Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek G. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift 2013; 22(6): 711-728. http://dx.doi.org/10.1127/0941-2948/2013/0507.

Andrade DF, Gama JRV, Melo LO, Ruschel AR. Inventário florestal de grandes áreas na Floresta Nacional do Tapajós, Pará, Amazônia, Brasil. Biota Amazônia 2015; 5(1): 109-115. http://dx.doi.org/10.18561/2179-5746/biotaamazonia.v5n1p109-115.

Archanjo KMPA, Silva GF, Chichorro JF, Soares CPB. Estrutura do componente arbóreo da reserva particular do patrimônio natural cafundó, Cachoeiro de Itapemirim, Espírito Santo, Brasil. Floresta 2012; 42(1): 145-160. http://dx.doi.org/10.5380/rf.v42i1.26311.

Barros BSX, Guerra SPS, Barros ZX, Catita CMS, Fernandes JCC. Uso de imagens de satélite para cálculo de volume em floresta de eucalipto no Município de Botucatu/SP. Energia na Agricultura 2015; 30(1): 60-67. http://dx.doi.org/10.17224/EnergAgric.2015v30n1p60-67.

Berra EF, Brandelero C, Pereira RS, Sebem E, Goergen LCG, Benedetti ACP et al. Estimativa do volume total de madeira em espécies de eucalipto a partir de imagens de satélite landsat. Ciência Florestal 2012; 22(4): 853-864. http://dx.doi.org/10.5902/198050987566.

Bispo PC. Dados geomorfométricos como subsídio ao mapeamento da vegetação. [dissertação]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais; 2007.

Bispo PC. Efeitos de geomorfometria na caracterização florístico-estrutural da floresta tropical na região de tapajós com dados SRTM e PALSAR [tese]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais; 2012.

Bispo PC, Santos JR, Valeriano MM, Graça PMLA, Balzter H, França H et al. Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo’s region, Brazilian Amazon. PLoS One 2016; 11(4): 1-13. http://dx.doi.org/10.1371/journal.pone.0152009.

Bispo PC, Valeriano MM, Kuplich TM. Variáveis geomorfométricas locais e sua relação com a vegetação da região do interflúvio Madeira-Purus (AM-RO). Acta Amazonica 2009; 39(1): 81-90. http://dx.doi.org/10.1590/S0044-59672009000100008.

Cabo C, Ordónez C, López-Sánchez CA, Armesto J. Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation 2018; 69: 164-174. http://dx.doi.org/10.1016/j.jag.2018.01.011.

Canavesi V, Ponzoni FJ, Valeriano MM. Estimativa de volume de madeira em plantios de Eucalyptus spp. utilizando dados hiperespectrais e dados topográficos. Revista Árvore 2010; 34(3): 539-549. http://dx.doi.org/10.1590/S0100-67622010000300018.

Castillo JAA, Apan AA, Maraseni TN, Salmo SG 3rd. 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 2017; 134: 70-85. http://dx.doi.org/10.1016/j.isprsjprs.2017.10.016.

Chrysafis I, Mallinis G, Siachalou S, Patias P. Assessing the relationships between growing stock volume and sentinel-2 imagery in a mediterranean forest ecosystem. Remote Sensing Letters 2017; 8(6): 508-517. http://dx.doi.org/10.1080/2150704X.2017.1295479.

Fassnacht FE, Latifi H, Hartig F. Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR. Remote Sensing of Environment 2018; 213: 115-128. http://dx.doi.org/10.1016/j.rse.2018.05.007.

Fernández-Manso A, Fernández-Manso O, Quintano C. Sentinel-2A red-edge spectral indices suitability for discriminating burn severity. International Journal of Applied Earth Observation and Geoinformation 2016; 50: 170-175. http://dx.doi.org/10.1016/j.jag.2016.03.005.

Fridman J, Holm S, Nilsson M, Nilsson P, Ringvall AH, Stahl G. Adapting National Forest Inventories to changing requirements - the case of the Swedish National Forest Inventory at the turn of the 20th century. Silva Fennica 2014; 48(3): 1-29. http://dx.doi.org/10.14214/sf.1095.

Hall RJ, Skakun RS, Arsenault EJ, Case BS. Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume. Forest Ecology and Management 2006; 225(1-3): 378-390. http://dx.doi.org/10.1016/j.foreco.2006.01.014.

Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988; 25(3): 295-309. http://dx.doi.org/10.1016/0034-4257(88)90106-X.

Hyyppä J, Hyyppä H, Inkinen M, Engdahl M, Linko S, Zhu YH. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management 2000; 128(1-2): 109-120. http://dx.doi.org/10.1016/S0378-1127(99)00278-9.

Immitzer M, Vuolo F, Atzberger C. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing 2016; 8(3): 1-27. http://dx.doi.org/10.3390/rs8030166.

Instituto Brasileiro de Geografia e Estatística – IBGE. Projeto RADAM. de Janeiro: IBGE; 1987.

Instituto Brasileiro de Geografia e Estatística – IBGE. Manual técnico da vegetação brasileira: sistema fitogeográfico, inventário das formações florestais e campestres, técnicas e manejo de coleções botânicas, procedimentos para mapeamentos . 2. ed. Rio de Janeiro: IBGE; 2012.

Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK et al. The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing 1998; 36(4): 1228-1249. http://dx.doi.org/10.1109/36.701075.

Jordan CF. Derivation of leaf-area index from quality of light on the forest floor. Ecological Society of America 1969; 50(4): 663-666. http://dx.doi.org/10.2307/1936256.

Knapp N, Fischer R, Huth A. Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states. Remote Sensing of Environment 2018; 205: 199-209. http://dx.doi.org/10.1016/j.rse.2017.11.018.

Korhonen L, Hadi, Packalen P, Rautiainen M. Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sensing of Environment 2017; 195: 259-274. http://dx.doi.org/10.1016/j.rse.2017.03.021.

Laurin GV, Puletti N, Hawthome W, Liesenberg V, Corona P, Papale D et al. Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data. Remote Sensing of Environment 2016; 176: 163-176. http://dx.doi.org/10.1016/j.rse.2016.01.017.

Maack J, Lingenfelder M, Weinacker H, Koch B. Modelling the standing timber volume of Baden-Württemberg - A large-scale approach using a fusion of Landsat, airborne LiDAR and National Forest Inventory data. International Journal of Applied Earth Observation and Geoinformation 2016; 49: 107-116. http://dx.doi.org/10.1016/j.jag.2016.02.004.

Magnussen S, Nord-Larsen T, Riis-Nielsen T. Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012–2016). Remote Sensing of Environment 2018; 211: 146-153. http://dx.doi.org/10.1016/j.rse.2018.04.015.

Mäkelä H, Pekkarinen A. Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data. Forest Ecology and Management 2004; 196(2-3): 245-255. http://dx.doi.org/10.1016/j.foreco.2004.02.049.

Matasci G, Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW et al. Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots. Remote Sensing of Environment 2018; 209: 90-106. http://dx.doi.org/10.1016/j.rse.2017.12.020.

Mello JM, Diniz FS, Oliveira AD, Scolforo JRS, Acerbi FW Jr, Thiersch CR. Métodos de amostragem e geoestatística para estimativa do número de fustes e volume em plantios de Eucalyptus grandis. Floresta 2009; 39(1): 157-166. http://dx.doi.org/10.5380/rf.v39i1.13735.

Miguel EP, Rezende AV, Leal FA, Matricardi EAT, Vale AT, Pereira RS. Redes neurais artificiais para a modelagem do volume de madeira e biomassa do cerradão com dados de satélite. Pesquisa Agropecuária Brasileira 2015; 50(9): 829-839. http://dx.doi.org/10.1590/S0100-204X2015000900012.

Mohammadi J, Shataee Joibary S, Yaghmaee F, Mahiny AS. Modelling forest stand volume and tree density using landsat ETM+ data. International Journal of Remote Sensing 2010; 31(11): 2959-2975. http://dx.doi.org/10.1080/01431160903140811.

Mura M, Bottalico F, Giannetti F, Bertani R, Giannini R, Mancini M et al. Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems. International Journal of Applied Earth Observation and Geoinformation 2018; 66: 126-134. http://dx.doi.org/10.1016/j.jag.2017.11.013.

Pandit S, Tsuyuki S, Dube T. Estimating above-ground biomass in sub-tropical buffer zone community forests, Nepal, using Sentinel 2 data. Remote Sensing 2018; 10(4): 1-18. http://dx.doi.org/10.3390/rs10040601.

Plowright AA, Coops NC, Chance CM, Sheppard SRJ, Aven NW. Multi-scale analysis of relationship between imperviousness and urban tree height using airborne remote sensing. Remote Sensing of Environment 2017; 194: 391-400. http://dx.doi.org/10.1016/j.rse.2017.03.045.

Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S. A modified soil adjusted vegetation index. Remote Sensing of Enviroment 1994;48: 119-126.

Rajashekar G, Fararoda R, Reddy RS, Jha CS, Ganeshaiah KN, Singh JS et al. Spatial distribution of forest biomass carbon (Above and below ground) in Indian forests. Ecological Indicators 2018; 85: 742-752. http://dx.doi.org/10.1016/j.ecolind.2017.11.024.

Rouse JW, Hass RH, Schell JA, Deering DW. Monitoring vegetation systems in the great plains with ERTS. In: Third earth resources technology satellite (ERTS) symposium ; 1974; Greenbelt. Washington: NASA; 1973. p. 301-317.

Saarela S, Grafstrom A, Stahl G, Kangas A, Holopaonen M, Tuominen S et al. Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information. Remote Sensing of Environment 2015; 158: 431-440. http://dx.doi.org/10.1016/j.rse.2014.11.020.

Santos MM, Machado IES, Carvalho EV, Viola MR, Giongo M. Estimativa de parâmetros florestais em área de Cerrado a partir de imagens do sensor Oli Landsat 8. Floresta 2017; 47(1): 75-83. http://dx.doi.org/10.5380/rf.v47i1.47988.

Silva EM, Santana AC. Modelos de regressão para estimação do volume de árvores comerciais, em florestas de Paragominas. Revista Ceres 2014; 61(5): 631-636. http://dx.doi.org/10.1590/0034-737X201461050005.

Takagi K, Yone Y, Takahashi H, Sakai R, Hojyo H, Kamiura T et al. Forest biomass and volume estimation using airborne LiDAR in a cool-temperate forest of northern Hokkaido, Japan. Ecological Informatics 2015; 26: 54-60. http://dx.doi.org/10.1016/j.ecoinf.2015.01.005.

Varvia P, Rautiainen M, Seppänen A. Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data. Journal of Quantitative Spectroscopy & Radiative Transfer 2018; 208: 19-28. http://dx.doi.org/10.1016/j.jqsrt.2018.01.008.

Vibrans AC, Sevgnani L, Lingner DV, Gasper AL, Sabbagh S. Inventário florístico florestal de Santa Catarina (IFFSC): aspectos metodológicos e operacionais. Pesquisa Florestal Brasileira 2010; 30(64): 291-302. http://dx.doi.org/10.4336/2010.pfb.30.64.291.

Wang R, Chen JM, Liu Z, Arain A. Evaluation of seasonal variations of remotely sensed leaf area index over five evergreen coniferous forests. ISPRS Journal of Photogrammetry and Remote Sensing 2017; 130: 187-201. http://dx.doi.org/10.1016/j.isprsjprs.2017.05.017.

Watzlawick LF, Kirchner FF, Sanquetta CR. Estimativa de biomassa e carbono em floresta com araucaria utilizando imagens do satélite IKONOS II. Ciência Florestal 2009; 19(2): 169-181. http://dx.doi.org/10.5902/19805098408.

5c94dec00e8825e564122984 floram Articles
Links & Downloads


Share this page
Page Sections