Comparison of satellite imagery from LISS-III/Resourcesat-1 and TM/Landsat 5 to estimate stand-level timber volume

Authors

  • Elias Fernando Berra Universidade Federal do Rio Grande do Sul http://orcid.org/0000-0002-0220-5048
  • Denise Cybis Fontana Universidade Federal do Rio Grande do Sul
  • Tatiana Mora Kuplich Instituto Nacional de Pesquisas Espaciais, Centro Regional Sul de Pesquisas Espaciais

DOI:

https://doi.org/10.4336/2016.pfb.36.88.1134

Keywords:

Pinus elliottii, Reflectance, Forest inventory

Abstract

After Landsat 5 activities were discontinued, sensors on board ResourceSat-1 satellite have been pointed as an option for Landsat series. The aim of this study is to estimate timber volume from a slash pine (Pinus elliottii Engelm.) stand using images from both LISS-III/ResourceSat-1 and TM/Landsat 5 sensors, cross comparing their performances. Reflectance values from the four spectral bands considered equivalent for both sensors were compared regarding sensitivity to changes in timber volume. Trends were similar, with direct relationship in the near-infrared bands and inverse relationships in the visible and mid-infrared bands. Significant differences were only found in the equivalent band of green. Multiple linear regressions were used to select spectral bands that would better explain variations in timber volume. The best fit equations for each sensor were inverted to generate maps of timber volume, estimates which were compared at pixel and stand level. None of the scales showed significant differences between estimates generated from the two sensors. We concluded that LISS-III and TM have generally very similar performance for monitoring timber volume, and LISS-III could therefore be potentially used as a complement or substitute to Landsat series.

Downloads

Download data is not yet available.

Author Biographies

Elias Fernando Berra, Universidade Federal do Rio Grande do Sul

http://lattes.cnpq.br/5392383672857589

Denise Cybis Fontana, Universidade Federal do Rio Grande do Sul

http://lattes.cnpq.br/0938505876010695

Tatiana Mora Kuplich, Instituto Nacional de Pesquisas Espaciais, Centro Regional Sul de Pesquisas Espaciais

http://lattes.cnpq.br/8997858562195060

References

Anderson, J. H. et al. Intercalibration and Evaluation of ResourceSat-1 and Landsat-5 NDVI. Canadian Journal of Remote Sensing, v. 37, n. 2, p. 213-219, 2011. DOI: 10.5589/m11-032.

Ardo, J. Volume quantification of coniferous forest compartments using spectral radiance recorded by Landsat Thematic Mapper. International Journal of Remote Sensing, v. 13, n. 9, p. 1779-1786, 1992. DOI: 10.1080/01431169208904227.

Baskent, E. Z. et al. The forest management planning system of Turkey: constructive criticism towards the sustainable management of forest ecosystems. International Forestry Review, v. 7, n. 3, p. 208-217, 2005.

Berra, E. F. et al. Comparação da reflectância espectral e do IVDN dos sensores LISS-III/RESOURCESAT-1 e TM/LANDSAT 5 em povoamento florestal. Revista Brasileira de Cartografia, v. 1, n. 66/2, p. 393-406, 2014.

Berra, E. F. et al. Estimativa do volume total de madeira em espécies de eucalipto a partir de imagens de satélite Landsat. Ciência Florestal, v. 22, n. 4, p. 853-864, 2012. DOI: 10.5902/198050987566.

Boyd, D. S. & Danson, F. M. Satellite remote sensing of forest resources: three decades of research development. Progress in Physical Geography, v. 29, n. 1, p. 1-26, 2005. DOI: 10.1191/0309133305pp432ra.

Canavesi, V. et al. Estimativa de volume de madeira em plantios de Eucalyptus spp. utilizando dados hiperespectrais e dados topográficos. Revista Árvore, v. 34, n. 3, p. 539-549, 2010. DOI: 10.1590/s0100-67622010000300018.

Centro Estadual de Meteorologia (Rio Grande do Sul). Atlas climático do Rio Grande do Sul. Porto Alegre, 2011. Available at: < http://www.cemet.rs.gov.br/area/7/Atlas_Clim%C3%A1tico>. Acess onar.ch 5, 2012.

Chander, G. & Stensaas, G. L. Evaluation of Candidate Landsat Data Gap Sensors. In: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2008 : IGARSS 2008. Boston, Massachusetts. Proceedings... Piscataway: IEEE, 2008. p. IV1376-IV 1379.

Chander, G. et al. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, v. 113, n. 5, p. 893-903, 2009. DOI: 10.1016/j.rse.2009.01.007.

Chen, X. et al. Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images. International Journal of Remote Sensing, v. 34, n. 7, p. 2432-2453, 2013. DOI: 10.1080/01431161.2012.743690.

Chiavetta, U. et al. Estimation of forest attributes by integration of inventory and remotely sensed data in Alto Molise. Rivista Italiana Di Telerilevamento, v. 40, n. 1, p. 89-106, 2008. DOI: 10.5721/ItJRS20084018.

Chubey, M. S. et al. Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing, v. 72, n. 4, p. 383-394, 2006.

Danson, F. M. & Curran, P. J. Factors affecting the remotely sensed response of coniferous forest plantations. Remote Sensing of Environment, v. 43, n. 1, p. 55-65, 1993. DOI: 10.1016/0034-425, 1999. 412p.

Foody, G. M. et al. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, v. 85, n. 4, p. 463-474, 2003. DOI: 10.1016/S0034-4257(03)00039-7.

Franklin, J. Thematic mapper analysis of coniferous forest structure and composition. International Journal of Remote Sensing, v. 7, n. 10, p. 1287-1301, 1986. DOI: 10.1080/01431168608948931.

Gates, D. M. Physical and physiological properties of plants. In: REMOTE sensing with special reference to agriculture and forestry: with special reference to agriculture and forestry. Washington, DC: National Academy of Sciences, 1970. p.164-223.

Gausman, H. W. et al. Reflectance of cotton leaves and their structure. Remote Sensing of Environment, v. 1, n. 1, p. 19-22, 1969. DOI: 10.1016/S0034-4257(69)90055-8.

Gill, T. K. et al. Comparing bright-target surface spectral-reflectance estimates obtained from IRS P6 LISS III to those obtained from Landsat 5 TM and Landsat 7 ETM+. Remote Sensing Letters, v. 3, n. 2, p. 121-130, 2012. DOI: 10.1080/01431161.2010.543180.

Goward, S. N. et al. Complementarity of ResourceSat-1 AWiFS and Landsat TM/ETM+ sensors. Remote Sensing of Environment, v.123, p. 41-56, 2012. DOI: 10.1016/j.rse.2012.03.002.

Goward, S. N. et al. Moderate spatial resolution optical sensors. In: Warner, T. A. et al. (Ed.). The SAGE handbook of remote sensing. London: SAGE Publications, 2009. p. 123-138.

Goward, S. et al. The Future of Landsat-Class Remote Sensing. In: Ramachandran, B. et al. (Ed.). Land remote sensing and global environmental change. New York: Springer, 2011. p.807-834. (Remote sensing and digital image processing, 11).

Gunlu, A. et al. Prediction of some stand parameters using pansharpened IKONOS satellite image. European Journal of Remote Sensing, v. 47, p. 329-342, 2014. DOI: 10.5721/EuJRS20144720.

Markham, B. L. et al. Landsat-7 ETM+ on-orbit reflective-band radiometric stability and absolute calibration. Geoscience and Remote Sensing, IEEE Transactions on, v. 42, n. 12, p. 2810-2820, 2004. DOI: 10.1109/TGRS.2004.836389.

McDonald, A. J. et al. Investigation of the utility of spectral vegetation indices for determining information on coniferous forests. Remote Sensing of Environment, v. 66, n. 3, p. 250-272, 1998. DOI: 10.1016/s0034-4257(98)00057-1.

Montgomery, D. C. et al. Introduction to linear regression analysis. 4th ed. New York: Hoboken, N.J. : Wiley-Interscience, c2006, 2006. 612 ISBN 0470542810.

Myers, V. I. et al. Soil, water, and plant relations. In: REMOTE sensing with special reference to agriculture and forestry. Washington, DC: National Academy of Sciences, 1970. p.164-223. Nasa. Landsat science. 2014. Available at: <http://landsat.gsfc.nasa.gov/>. Acess on: April 21, 2014.

Orue, N. E. Estimativa de volume de povoamentos de Pinus spp. utilizando dados do satélite Landsat 7. 2002. Dissertação (Mestrado em Ciências Florestais) - Universidade Federal do Paraná, Curitiba.

Puhr, C. B. & Donoghue, D. N. M. Remote sensing of upland conifer plantations using Landsat TM data: a case study from Galloway, south-west Scotland. International Journal of Remote Sensing, v. 21, n. 4, p. 633-646, 2000. DOI: 10.1080/014311600210470.

Ripple, W. J. et al. A preliminary comparison of landsat thematic mapper and SPOT-l HRV multispectral data for estimating coniferous forest volume. International Journal of Remote Sensing, v. 12, n. 9, p. 1971-1977, 1991.

Shimabukuro, Y. E. & Smith, A. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. Geoscience and Remote Sensing, IEEE Transactions on, v. 29, n. 1, p. 16-20, 1991. DOI: 10.1109/36.103288.

SISTEMA Brasileiro de Classificação de Solos. Brasília, DF: Embrapa Produção de Informação; Rio de Janeiro: Embrapa Solos, 1999. 412 p.

Spanner, M. A. et al. Remote sensing of temperate coniferous forest leaf area index The influence of canopy closure, understory vegetation and background reflectance. International Journal of Remote Sensing, v. 11, n. 1, p. 95-111, 1990. DOI: 10.1080/01431169008955002.

Teillet, P. M. & Ren, X. Spectral band difference effects on vegetation indices derived from multiple satellite sensor data. Canadian Journal of Remote Sensing, v. 34, n. 3, p. 159-173, 2008.

Trotter, C. M. et al. Estimation of timber volume in a coniferous plantation forest using Landsat TM. International Journal of Remote Sensing, v. 18, n. 10, p. 2209-2223, 1997. DOI: 10.1080/014311697217846.

Watzlawick, L. F. et al. Estimativa de biomassa e carbono em floresta com araucaria utilizando imagens do satélite Ikonos II. Ciência Florestal, v. 19, n. 2, p. 169-181, 2009. DOI: 10.5902/19805098408.

Weber, E. et al. Adaptação do modelo digital de elevação do SRTM para o sistema de referência oficial brasileiro e recorte por unidade da federação. Porto Alegre: UFRGS Centro de Ecologia, 2004. Available at: <http://www.ecologia.ufrgs.br/labgeo/>. Acess on: 10 Mar. 2013.

Williams, D. L. et al. Landsat: yesterday, today, and tomorrow. Photogrammetric Engineering and Remote Sensing, v. 72, n. 10, p. 1171-1178, 2006. DOI: 10.14358/PERS.72.10.1171.

Woolley, J. T. Reflectance and transmittance of light by leaves. Plant physiology, v. 47, n. 5, p. 656-662, 1971. DOI: 10.1104/pp.47.5.656.

Wulder, M. A. et al. Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, v. 112, n. 3, p. 955-969, 2008. DOI: 10.1016/j.rse.2007.07.004.

Xavier, A. C. Estimativa de propriedades biofísicas de plantações de eucaliptos a partir de dados Landsat-TM. 1998. 116 f. Dissertação (Mestrado em Sensoriamento Remoto) - Instituto Nacional de Pesquisas Espaciais, São José dos Campos.

Xiaolin, Z. & Desheng, L. MAP-MRF Approach to Landsat ETM+ SLC-Off Image Classification. Geoscience and Remote Sensing, IEEE Transactions on, v. 52, n. 2, p. 1131-1141, 2014. DOI: 10.1109/TGRS.2013.2247612.

Zakaria, H. E. A. Integration of Remote Sensing and GIS in Studying Vegetation Trends and Conditions in the Gum Arabic Belt in North Kordofan, Sudan. 2010. 146 f. (Doctor of Natural Science) - Institute of Photogrammetry and Remote Sensing, Technical University of Dresden, Dresden, Germany.

Zimble, D. A. et al. Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sensing of Environment, v. 87, n. 2-3, p. 171-182, 2003. DOI: 10.1016/s0034-4257(03)00139-1.

Downloads

Published

2016-12-30

How to Cite

BERRA, Elias Fernando; FONTANA, Denise Cybis; KUPLICH, Tatiana Mora. Comparison of satellite imagery from LISS-III/Resourcesat-1 and TM/Landsat 5 to estimate stand-level timber volume. Pesquisa Florestal Brasileira, [S. l.], v. 36, n. 88, p. 363–373, 2016. DOI: 10.4336/2016.pfb.36.88.1134. Disponível em: https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1134. Acesso em: 18 may. 2024.

Issue

Section

Articles

Most read articles by the same author(s)

Similar Articles

<< < 3 4 5 6 7 8 9 10 11 12 > >> 

You may also start an advanced similarity search for this article.