Cambios en la producción primaria bruta (GPP) de la vegetación naturalen la Comunidad Valenciana (2001-2018)

  1. Martínez, Beatriz 1
  2. Sánchez-Ruiz, Sergio 1
  3. Campos-Taberner, Manuel 1
  4. García-Haro, Francisco Javier 1
  5. Gilabert, María Amparo 1
  1. 1 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

Journal:
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Year of publication: 2023

Issue: 61

Pages: 15-27

Type: Article

DOI: 10.4995/RAET.2023.18659 DIALNET GOOGLE SCHOLAR

More publications in: Revista de teledetección: Revista de la Asociación Española de Teledetección

Sustainable development goals

Abstract

This work analyzes the vegetation changes in the Comunidad Valenciana observed during the period 2001-2018, using the daily GPP (Gross Primary Production) time series at 1-km spatial resolution derived from Earth observation-based (EO) data. The GPP time series have been obtained from EO-based data (e.g., MODIS/Terra-Aqua and SEVIRI/MSG) and meteorological (e.g., precipitation and temperature) data using the light use efficiency model proposed by Monteith. The carbon fluxes detection has been performed by means of a multi-resolution analysis (MRA) based on the wavelet transform (WT). This analysis allows to decomposing the signal into different temporal resolution components. The interanual trend determines the vegetation change, positive (greening) or negative (browning) of vegetation photosynthetic activity over long-term scales. The negative long-term changes observed in natural vegetation reveal the presence of areas characterized by high degradated conditions. This is the case of Natural Pack of ‘Serra d’ Espadà’ in Castellon province, which is also controlled by a local ecosystem conservation program. To identify more precisely these areas, the areas affected by abrupt changes (associated to forest fires) in which vegetation has not been yet recovered have been removed. In this case, the results show a good agreement with the official burnt areas from the local government.

Funding information

Funders

Bibliographic References

  • Alcaraz-Segura, D., Liras, E., Tabik, S., Paruelo, J., Cabello, J. 2010. Evaluating the Consistency of the 1982-1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II. Sensors, 10, 1291-1314. https://doi.org/10.3390/s100201291
  • Alsamamra, H., Ruiz-Arias, J.A., Pozo-Vázquez, D., Tovar-Pescador, J. 2009._A comparative study of ordinary and residual kriging techniques for mapping global solar radiation over southern Spain, Agricultural and Forest Meteorology, 149(8), 1343-1357. https://doi.org/10.1016/j.agrformet.2009.03.005
  • Azzali, A., Menenti, M. 2000. Mapping vegetation-soil complexes in southern Africa using temporal Fourier analysis of NOAA AVHRR NDVI data. International Journal of Remote Sensing, 21, 973−996. https://doi.org/10.1080/014311600210380
  • Ben Abbes, A., Bounouh, O., Farah, I.R., de Jong, R., Martínez, B. 2018. Comparative study of three satellite image time-series decomposition methods for vegetation change detection. European Journal of Remote Sensing, 51(1), 607-615. https://doi.org/10.1080/22797254.2018.1465360
  • Berdugo, M., Delgado-Baquerizo, M., Soliveres, S., Hernández-Clemente, R., Zhao, Y., Gaitán, J.J., Gross, N., Saiz, H., Maire, V., Lehman, A., Rillig, M.C., Solé, R.V., Maestre, F.T. 2020. Global ecosystem thresholds driven by aridity. Science. 367, 787-790. https://doi.org/10.1126/science.aay5958
  • CGLOPS1, 2018. Copernicus Global Land Operations "Vegetation and Energy" Product User Manual for Dry Matter Productivity (DMP) and Gross Dry Matter Productivity (GDMP). Collection 1 km, version 2- CGLOPS1_PUM_DMP1km-V2, February 2018, 47 pp.
  • Chapin III, F.S., Matson, P.A., Mooney, H.A. 2002. Principles of Terrestrial Ecosystem Ecology. Springer-Verlag, New York. https://doi.org/10.1007/b97397
  • de Beurs, K.M., Henebry, G.M. 2005. A statistical framework for the analysis of long image time series. International Journal of Remote Sensing, 26, 1551−1573. https://doi.org/10.1080/01431160512331326657
  • de Jong, R. de Bruin, S. de Wit, A. Schaepman, M.E.Dent, D.L. 2011. Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115(2), 692-702. https://doi.org/10.1016/j.rse.2010.10.011
  • Furon, A. C., Wagner-Riddle, C., Smith, C. R., Warland, J. S. 2008. Wavelet analysis of wintertime and spring thaw CO2 and N2O fluxes from agricultural fields. Agricultural and Forest Meteorology, 148, 1305−1317. https://doi.org/10.1016/j.agrformet.2008.03.006
  • Gilabert, M.A., Moreno, A., Maselli, F., Martínez, B., Chiesi, M., Sánchez-Ruiz, S., García-Haro, F.J., Pérez-Hoyos, A., Campos-Taberner, M., PérezPriego, O., Serrano-Ortiz, P., Carrara, A. 2015. Daily GPP estimates in Mediterranean ecosystems by combining remote sensing and meteorological data. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 184-197. https://doi.org/10.1016/j.isprsjprs.2015.01.017
  • Giner, C., Martínez, B., Gilabert, M.A., Alcaraz-Segura, D. 2012. Tendencias en el verdor de la vegetación y en la producción primaria bruta de las áreas forestales en la España peninsular (2000-2009). Revista de Teledetección, 38, 51-64. Disponible en: http://www.aet.org.es/?q=revista38-7
  • Heinsch, F.A., Maosheng, Z., Running, S.W., Kimball, J.S., Nemani, R.R., Davis, K.J., et al., 2006. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Transaction on Geoscience and Remote Sensing, 44(7), 1908-1925. https://doi.org/10.1109/TGRS.2005.853936
  • Huang, S., Tang, L., Hupy, J., Wang, Y., Shao, G. 2020. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forest Research, 32, 1-6. https://doi.org/10.1007/s11676-020-01155-1
  • Jamali, S., Jönsson, P., Eklundh, L., Ardö, J., Seaquist, J. 2015. Detecting changes in vegetation trends using time series segmentation. Remote Sensing of Environment, 156, 182-195. https://doi.org/10.1016/j.rse.2014.09.010
  • Jones, L.A., Kimball, J.S., Reichle, R.H., Madani, N., Glassy, J., Ardizzone, J.V., et al. 2017. The SMAP level 4 carbon product for monitoring ecosystem land-atmosphere CO2 exchange. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6517- 6532. https://doi.org/10.1109/TGRS.2017.2729343
  • Kimball, J.S., Jones, L.A., Zhang, K., Heinsch, F.A., McDonald, K.C., Oechel, W.C. 2009. A satellite approach to estimate land-atmosphere CO2 exchange for boreal and arctic biomes using MODIS and AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 47(2), 569-587. https://doi.org/10.1109/TGRS.2008.2003248
  • Li, X.B., Chen, Y.H., Fan, Y. Da, Zhang, Y.X. 2003. Detecting inter-annual variations of vegetation growth based on satellite-sensed vegetation index data from 1983 to 1999. International Geoscience and Remote Sensing Symposium (IGARSS), 5(C), 3263-3265.
  • McKee, T.B., Doesken, N.J., Kliest, J. 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference of Applied Climatology, 17-22 January, Anaheim, CA. American Meteorological Society, Boston, MA. 179-184.
  • Martínez, B., Gilabert, M.A. 2009. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sensing of Environment, 113(9), 1823-1842. https://doi.org/10.1016/j.rse.2009.04.016
  • Martínez, B. Gilabert, M.A. García-Haro, F.J. Faye, A. Meliá, J. 2011. Characterizing land condition variability in Ferlo, Senegal (2001-2009) using multi-temporal 1-km Apparent Green Cover (AGC) SPOT Vegetation data. Global and Planetary Change, 76, 152-165. https://doi.org/10.1016/j.gloplacha.2011.01.001
  • Monteith, J.L. 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9, 747-766. https://doi.org/10.2307/2401901
  • Moreno, A., Gilabert, M.A., Martínez, B. 2011. Mapping daily global solar irradiation over Spain: a comparative study of selected approaches. Solar Energy, 85, 2072-2084. https://doi.org/10.1016/j.solener.2011.05.017
  • Percival, D.B., Walden, A.T. (2000). Wavelet methods for time series analysis. Cambridge University Press 594 pp. https://doi.org/10.1017/CBO9780511841040
  • Pérez-Hoyos, A., García-Haro, F.J., San Miguel-Ayanz, J. 2012a. A methodology to generate a synergetic land-cover map by fusion of different land-cover products. International Journal of Applied Earth Observation and Geoinformation, 19, 72-87. https://doi.org/10.1016/j.jag.2012.04.011
  • Pérez-Hoyos, A., García-Haro, F.J., San-MiguelAyanz, J. 2012b. Conventional and fuzzy comparisons of large-scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe. ISPRS Journal of Photogrammetry and Remote Sensing, 74, 185-201. https://doi.org/10.1016/j.isprsjprs.2012.09.006
  • Poyatos, R., Latron, J. Llorens, P. 2003. Land Use and Land Cover Change After Agricultural Abandonment. The Case of a Mediterranean Mountain Area (Catalan Pre-Pyrenees). Mountain Research and Development, 23(4), 362-368. https://doi.org/10.1659/0276-4741(2003)023[0362:LUALCC]2.0.CO;2
  • Rhif, M., Ben Abbes, A., Farah, I.R., Martínez, B., Sang, Y. 2019. Wavelet transform application for/in nonstationary time-series analysis: A review. Applyed Sciences, 9(7), 1345. https://doi.org/10.3390/app9071345
  • Rigina, O., Rasmussen, M.S. 2003. Using trend line and principal component analysis to study vegetation changes in Senegal 1986-1999 from AVHRR NDVI 8 km data. Geografisk Tidsskrift, Danish Journal of Geography, 103(1), 31−42. https://doi.org/10.1080/00167223.2003.10649477
  • Roujean, J.L., Breon, F.M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements, Remote Sensing of Environment, 51(3), 375-384. https://doi.org/10.1016/0034-4257(94)00114-3
  • Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Harlan, J.C. 1974. Monitoring the vernal advancement of retrogradation of natural vegetation, Final Report, Type III, NASA/GSFC, Greenbelt, MD, 371 pp.
  • Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves, M., Hashimoto, H. 2004. Continuous Satellite-Derived Measure of Global Terrestrial Primary Production, BioScience, 54(6), 547-560. https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2
  • Schimel, D. 2010. Drylands in the earth system. Science, 22, 418-419. https://doi.org/10.1126/science.1184946
  • Stöckli, R., Vidale, P.L. 2004. European plant phenology and climate as seen in a 20-year AVHRR landsurface parameter dataset. International Journal of Remote Sensing, 25, 3303−3330. https://doi.org/10.1080/01431160310001618149
  • Tramontana, G., Jung, M., Schwalm, C.R., Ichii, K., Camps-Valls, G., Radulu, B., et al., 2016. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression. Biogeosciences 13, 4291-4313. https://doi.org/10.5194/bg-13-4291-2016
  • Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D. 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1), 106-115. https://doi.org/10.1016/j.rse.2009.08.014
  • Xiao, J. Chevallier, F. Gomez, C. Guanter, L. Hicke, J.A. Huete, A.R. Ichii, K. Ni, W. Pang, Y. Rahman, A.F. et al., 2019. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sensing of Environment, 233, 111383. https://doi.org/10.1016/j.rse.2019.111383
  • Zhao, X., Hu, H., Shen, H., Zhou, D., Zhou, L., Myneni, R.B., Fang, J. 2015 Satellite-indicated longterm vegetation changes and their drivers on the Mongolian Plateau. Landscape Ecology, 30, 1599-611. https://doi.org/10.1007/s10980-014-0095-y