Geomatics and Environmental Engineering, vol. 15, no. 4

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Vol. 15 No. 4 (2021): Geomatics and Environmental Engineering

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Submitted
Mar 10, 2021
Accepted
Jun 28, 2021
Published
Sep 17, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Green Space Assessment and Management in Biscay Province, Spain using Remote Sensing Technology

https://doi.org/10.7494/geom.2021.15.4.21
Esther O. Makinde
DeustoTech, Faculty of Engineering, University of Deusto, Bilbao, Spain; Faculty of Engineering, University of Lagos, Lagos, Nigeria
https://orcid.org/0000-0001-5857-1349
Cristina M. Andonegui
DeustoTech, Faculty of Engineering, University of Deusto, Bilbao, Spain
https://orcid.org/0000-0002-3919-2738
Ainhoa A. Vicario
DeustoTech, Faculty of Engineering , University of Deusto, Bilbao, Spain
https://orcid.org/0000-0001-9738-4191

Corresponding Author(s) : Esther O. Makinde

eomakinde@unilag.edu.ng

Geomatics and Environmental Engineering, Vol. 15 No. 4 (2021): Geomatics and Environmental Engineering

Abstract

Our ecosystem, particularly forest lands, contains huge amounts of carbon storage in the world today. This study estimated the above ground biomass and carbon stock in the green space of Bilbao Spain using remote sensing technology. Landsat ETM+ and OLI satellite images for year 1999, 2009 and 2019 were used to assess its land use land cover (LULC), change detection, spectral indices and model biomass based on linear regression. The result of the LULC showed that there was an increase in forest vegetation by 12.5% from 1999 to 2009 and a further increase by 2.3% in 2019. However, plantation cover had decreased by 3.5% from 1999‑2009; while wetlands had also decreased by 9% within the same period. There was, however, an increase in plantation cover from 2009 to 2019 by 2.1% but a further decrease in wetlands of 4.3%. Further results revealed a positive correlation across the three decades between the widely used Normalized Differential Vegetation Index (NDVI) with other spectral indices such as Enhance Vegetation Index (EVI) and Normalized Differential Moisture Index (NDMI) for biomass were: for 1999 EVI (R2 = 0.1826), NDMI (R2 = 0.0117), for 2009 EVI (R2 = 0.2192), NDMI (R2 = 0.3322), for 2019
EVI (R2 = 0.1258), NDMI (R2 = 0.8148). A reduction in the total carbon stock from 14,221.94 megatons in 1999 to 10,342.44 megatons 2019 was observed. This study concluded that there has been a reduction in the amount of carbon which the Biscay Forest can sequester.

Keywords

biomass computation carbon stock land use land cover Landsat remote sensing
Makinde, E. O., Andonegui, C. M., & Vicario, A. A. (2021). Green Space Assessment and Management in Biscay Province, Spain using Remote Sensing Technology. Geomatics and Environmental Engineering, 15(4), 21–43. https://doi.org/10.7494/geom.2021.15.4.21
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References
  1. Pravalie R.: Major Perturbations in the Earth’s Forest Ecosystems. Possible Implications for Global Warming. Earth Science Reviews, vol. 185, 2018, pp. 544–571. https://doi.org/10.1016/j.earscirev.2018.06.010.
  2. Bellassen V., Luyssaert S.: Carbon Sequestration: Managing Forests in Uncertain Times. Nature, vol. 506, 2014, pp. 153–155. https://doi.org/10.1038/506153a.
  3. Tian H., Lu C., Ciais P., Michalak A.M., Canadell J.G., Saikawa E., Huntzinger D.N., Gurney K.R., Sitch S., Zhang B., Yang J.: The terrestrial biosphere as a net source of greenhouse gases to the atmosphere. Nature, vol. 531, 2016, pp. 225–228. https://doi.org/10.1038/nature16946.
  4. Imran A.B., Ahmed S.: Potential of Landsat-8 Spectral Indices to Estimate Forest Biomass. International Journal of Human Capital in Urban Management, vol. 3(4), 2018, pp. 303–314.
  5. UNFCCC: Working Group on further commitments for Annex I Parties under the Kyoto Protocol. United Nations Framework Convention on Climate Change. Bonn, Germany, 2011.
  6. Raufer R., Coussy P., Freeman C., Iyer S.: Emissions trading. [in:] Chen W.Y., Suzuki T., Lackner M. (eds.), Handbook of Climate Change Mitigation and Adap- tation, Springer, Cham 2017, pp. 257–312.
  7. Federici S., Vitullo M., Tulipano S., De Lauretis R., Seufert G.: An Approach to Estimate Carbon Stocks Change in Forest Carbon Pools under the UNFCCC: The Italian Case. iForest – Biogeosciences and Forestry, vol. 1(2), 2008, pp. 86–95. https://doi.org/10.3832/ifor0457-0010086.
  8. De Alegría I.M., Del Río M.B.: Carbon Markets: Linking the International Emission Trading under the United Nations Framework Convention on Climate Change (UNFCCC) and the European Union emission trading scheme (EU ETS). [in:] Chen W.Y., Suzuki T., Lackner M. (eds.), Handbook of Climate Change Mitigation and Adaptation, Springer, Cham 2017, pp. 313–339.
  9. Nonini L., Fiala M.: Estimation of Carbon Storage of Forest Biomass for Voluntary Carbon Markets: Preliminary Results. Journal of Forestry Research, vol. 32, 2021, pp. 329–338. https://doi.org/10.1007/s11676-019-01074-w.
  10. The United Nations Framework Convention on Climate Change). United Nations, 1992. https://unfccc.int/resource/docs/convkp/conveng.pdf [access: 17.06.2020].
  11. Alberdi I., Vallejo R., Álvarez-González J.G., Condés S., González-Ferreiro E., Guerrero S., Hernández L., Martínez-Jáuregui M., Montes F., Oliveira N.: The Multi-objective Spanish National Forest Inventory. Forest System, vol. 26(2), 2017, e04S. https://doi.org/10.5424/fs/2017262-10577.
  12. MAPAMA: Anuario de estadística forestal. Ministerio de Agricultura. Alimentación y Medio Ambiente, Spain 2011.
  13. Biscay. [in:] Wikipedia. https://en.wikipedia.org/wiki/Biscay [access: 23.05.2020].
  14. Mateos E., Ormaetxea L.: Spatial Distribution of Biomass and Woody Litter for Bio-Energy in Biscay Spain. Forests, vol. 9(5), 2018, 253. https://doi.org/10.3390/f9050253.
  15. Wulder M.A.: Optical Remote-Sensing Techniques for the Assessment of Forest Inventory and Biophysical Parameters. Progress in Physical Geography: Earth and Environment, vol. 22(4), 1998, pp. 449–476. https://doi.org/10.1177/030913339802200402.
  16. Gomez C., Alejandro P., Hermosilla T., Montes F., Pascual C., Ruiz L., Alvarez-Taboada F., Tanase M., Valbuena R.: Remote Sensing for the Spanish Forests in the 21st Century: A Review of Advances, Needs and Opportunities. Forest Systems, vol. 28(1), 2019. https://doi.org/10.5424/fs/2019281-14221.
  17. Cohen W.B., Goward S.N.: Landsat’s Role in Ecological Applications of Remote Sensing. BioScience, vol. 54(6), 2004, pp. 535–545. https://doi.org/10.1641/0006-3568(2004)054[0535:LRIEAO]2.0.CO;2.
  18. Cohen W.B., Maiersperger T., Spies T., Oetter D.: Modelling Forest Cover Attributes as Continuous Variables in a Regional Context with Thematic Mapper Data. International Journal of Remote Sensing, vol. 22(12), 2001, pp. 2279–2310. https://doi.org/10.1080/01431160121472.
  19. Dymond C., Mladenoff D., Radeloff V.: Phenological Differences in Tasseled Cap Indices improve Deciduous Forest Classification. Remote Sensing of Environment, vol. 80, 2002, pp. 460–472. https://doi.org/10.1016/S0034-4257(01)00324-8.
  20. Cortés G., Girotto M., Margulis S.A.: Analysis of sub-pixel snow and ice extent over the extratropical Andes using spectral unmixing of historical Landsat imagery. Remote Sensing of Environment, vol. 141, 2014, pp. 64–78. https://doi.org/10.1016/j.rse.2013.10.023.
  21. Makinde E.O., Womiloju A.A., Ogundeko M.O.: The Geospatial Modelling of Carbon Sequestration in Oluwa Forest, Ondo State, Nigeria. European Journal of Remote Sensing, vol. 50(1), 2017, pp. 397–413. https://doi.org/10.1080/22797254.2017.1341819.
  22. Ren H.Y., Zhuang D.F., Pan J.J., Shi X.Z., Wang H.J.: Hyper-spectral remote sensing to monitor vegetation stress. Journal of Soils and Sediments, vol. 8, 2008, pp. 323–326. https://doi.org/10.1007/s11368-008-0030-4.
  23. Price J.: Calibration of Satellite Radiometers and the Comparison of Vegetation Indices. Remote Sensing of Environment, vol. 21(1), 1987, pp. 15–27. https://doi.org/10.1016/0034-4257(87)90003-4.
  24. Kallel A., Le-Hégarat-Mascle S., Ottlé C., Hubert-Moy L.: Determination of Vegetation Cover Fraction by Inversion of a Four-Parameter Model Based on Isoline Parametrization. Remote Sensing of Environment, vol. 111(4), 2007, pp. 553–566. https://doi.org/10.1016/j.rse.2007.04.006.
  25. Jiang Z., Huete A.R., Didan K., Miura T.: Development of a Two-Band Enhanced Vegetation Index without a Blue Band. Remote Sensing of Environment, vol. 112(10), 2008, pp. 3833–3845. https://doi.org/10.1016/j.rse.2008.06.006.
  26. Makinde E.O.: Spectral indices for detecting change trend in vegetation affected by hydrocarbon spillage. [in:] Environment Science and Engineering V: Selected, Peer Reviewed Papers from the 2015 5th International Conference on Environment Science and Engineering (ICESE 2015), April 24–25, 2015, Istanbul, Turkey, International Proceedings of Chemical, Biology & Environmental Engineering, vol. 83, IACSIT Press, 2015, pp. 97–102.
  27. AEMET: Standard climate values for Bilbao. Bilbao Aeropuerto. http://www.aemet.es/en/serviciosclimaticos/datosclimatologicos/valoresclimatologicos?l=1082&k=pva [access: 17.06.2020].
  28. Omodanisi E.O., Eludoyin A.O., Salami A.T.: Ecological Effects and Perceptions of Victims of Pipeline Explosion in a Developing Country. International Journal of Environmental Science and Technology, vol. 12(5), 2015, pp. 1635–1646. https://doi.org/10.1007/s13762-014-0569-0.
  29. Anderson J.R., Hardy E.E., Roach J.T., Witmer R.E.: A Land Use and Land Cover Classification System for Use with Remote Sensor Data. U.S. Government Printing Office, Washington 1976.
  30. Salami A.T.: Vegetation Dynamics on the Fringes of Lowland Humid Tropical Rainforest of Southwestern-Nigeria: An Assessment of Environmental Changes with Air Photos and Landsat ETM. International Journal of Remote Sensing, vol. 20(6), 1999, pp. 1169–1181. https://doi.org/10.1080/014311699212920.
  31. ERDAS Imagine V9.1. ERDAS, Atlanta 2014.
  32. Omodanisi E.O.: Resultant Land Use and Land Cover Change from Oil Spillage using Remote Sensing and GIS. Research Journal of Applied Sciences, Engineering and Technology, vol. 6(11), 2013, pp. 2032–2040. https://doi.org/10.19026/rjaset.6.3820.
  33. Rouse J.W., Haas R.H., Schell J.A., Deering D.W.: Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA-CR-132982, E73-10693, Texas 1973.
  34. Jackson R.D.: Spectral indices in N-space. Remote Sensing Environment, vol. 13(5), 1983, pp. 409–421. https://doi.org/10.1016/0034-4257(83)90010-X.
  35. Li P., Jiang L., Fen Z.: Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors. Remote Sensing, vol. 6, 2014, pp. 310–329. https://doi.org/10.3390/rs12020291.
  36. Omodanisi E.O., Salami A.T.: An Assessment of the Spectra Characteristics of Vegetation in South Western Nigeria. IERI Procedia, vol. 9, 2014, pp. 26–32.
  37. Zha Yong., Gao Jingqing, Ni S.: Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing, vol. 24, 2003, pp. 583–594. https://doi.org/10.1080/01431160304987.
  38. Hanqiu X.U.: Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water features in Remotely Sensed Imagery. International Journal of Remote Sensing, vol. 27(14), 2006, pp. 3025–3033. https://doi.org/10.1080/01431160600589179.
  39. Huete A.: Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices. Remote Sensing of Environment, vol. 83, 2002, pp. 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2.
  40. Gao B.C.: NDWI: A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment, vol. 58(3), 1996, pp. 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3.
  41. DeLamo X., Ravilious C., Pollini B.: Using Spatial Information to Support Decisions on Safeguards and Multiple Benefits for REDD+. Step-by-Step Tutorial v2.0: Understanding and Comparing Carbon Datasets, using QGIS 2.18. UN-REDD Programme, UNEP World Conservation Monitoring Centre, Cambridge 2019.
  42. Kaufman Y.J., Tanre D.: Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, vol. 30(2), 1992, pp. 261–270. https://doi.org/10.1109/36.134076.
  43. Ponce-Hernandez R.: Assessing carbon stocks and modelling win–win scenarios of carbon sequestration through land-use changes. Food and Agriculture Organization of the United Nations, Rome 2004.
  44. IHOBE: Inventario de Carbono Orgánico en Suelos y Biomasa de la Comunidad Autónoma del País Vasco 2015. http://www.ingurumena.ejgv.euskadi.eus/r4911293/es/contenidos/inventario/carbono_organico/es_pub/adjuntos/carbono_organico.pdf [access: 20.06.2020].
  45. Rouse J., Haas R., Schell J., Deering D.: Monitoring Vegetation Systems in the Great Plains with ERTS. [in:] Third Earth Resources Technology Satellite-1 Symposium. Volume 1: Technical Presentations, section A, NASA, Washington 1973, pp. 309–317.
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