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Forest Complexity in the Green Tonality of Satellite Images

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Abstract

Forest complexity is associated with biodiversity and tells us information about the ecosystem health. A healthy forest must be in a scale-invariant state of balance between robustness and adaptability, reflected in the tonalities present on its vegetation. Remote imaging can be used to determine forest complexity based on the scale-invariance of green tones in the images. Here is proposed a simple technique to monitor changes on the forest using statistical moments and spectral analysis of the green tones on the satellite images.

Keywords

  • Forest complexity
  • Ecosystem health
  • Scale invariance

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References

  1. Laurance, W.F., Camargo, J.L., Fearnside, P.M., Lovejoy, T.E., Williamson, G.B., Mesquita, R.C., et al.: An Amazonian rainforest and its fragments as a laboratory of global change. Biol. Rev. 93(1), 223–247 (2018). https://doi.org/10.1111/brv.12343

    CrossRef  Google Scholar 

  2. Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G., et al.: Forest disturbances under climate change. Nat. Clim. Change 7(6), 395 (2017). https://doi.org/10.1038/nclimate3303

    CrossRef  ADS  Google Scholar 

  3. Reyer, C.P., Brouwers, N., Rammig, A., Brook, B.W., Epila, J., Grant, R.F., et al.: Forest resilience and tipping points at different spatio-temporal scales: approaches and challenges. J. Ecol. 103(1), 5–15 (2015). https://doi.org/10.1111/1365-2745.12337

    CrossRef  Google Scholar 

  4. Lausch, A., Erasmi, S., King, D.J., Magdon, P., Heurich, M.: Understanding forest health with remote sensing-Part II—A review of approaches and data models. Remote Sens. 9, 129 (2017). https://doi.org/10.3390/rs9020129

    CrossRef  ADS  Google Scholar 

  5. Banskota, A., Kayastha, N., Falkowski, M.J., Wulder, M.A., Froese, R.E., White, J.C.: Forest monitoring using landsat time series data: a review. Can. J. Remote Sens. 40, 362–384 (2014). https://doi.org/10.1080/07038992.2014.987376

    CrossRef  ADS  Google Scholar 

  6. Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10, 989–1003 (1989). https://doi.org/10.1080/01431168908903939

    CrossRef  ADS  Google Scholar 

  7. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E.: Digital change detection methods in ecosystem monitoring: a review. Int. J. Remote Sens. 25, 1565–1596 (2004). https://doi.org/10.1080/0143116031000101675

    CrossRef  ADS  Google Scholar 

  8. Kennedy, R.E., Andrefouet, S., Cohen, W.B., Gomez, C., Griffiths, P., Hais, M., et al.: Bringing an ecological view of change to Landsat-based remote sensing. Front. Ecol. Environ. 12, 339–346 (2014). https://doi.org/10.1890/130066

    CrossRef  Google Scholar 

  9. Zhu, Z.: Change detection using Landsat time series: a review of frequencies, pre-processing, algorithms, and applications. ISPRS J. Photogram. Remote Sens. 130, 370–384 (2017). https://doi.org/10.1016/j.isprsjprs.2017.06.013

    CrossRef  ADS  Google Scholar 

  10. Woodcock, C.: Free access to Landsat imagery teach by the book science education. Science 320, 1011–1012 (2008). https://doi.org/10.1126/science.320.5879.1011a

    CrossRef  Google Scholar 

  11. Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., et al.: The global Landsat archive: status, consolidation, and direction. Remote Sens. Environ. 185, 271–283 (2016). https://doi.org/10.1016/j.rse.2015.11.032

    CrossRef  ADS  Google Scholar 

  12. White, J.C., Wulder, M.A., Vastaranta, M., Coops, N.C., Pitt, D., Woods, M.: The utility of image-based point clouds for forest inventory: a comparison with airborne laser scanning. Forests 4, 518–536 (2013). https://doi.org/10.3390/f4030518

    CrossRef  Google Scholar 

  13. Hartig, T., Mitchell, R., De Vries, S., Frumkin, H.: Nature and health. Ann. Rev. Public Health 35, 207–228 (2014). https://doi.org/10.1146/annurev-publhealth-032013-182443

    CrossRef  Google Scholar 

  14. Rivera, A.L., Estañol, B., Toledo, J.C., Fossion, R., Frank, A.: Looking for biomarkers in physiological time series. In: Quiroz, L.O., Antonio, O.R. (eds.) Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues. Springer, Cham (2018)

    Google Scholar 

  15. Rivera, A.L., Estañol, B., Fossion, R., Toledo-Roy, J.C., Callejas-Rojas, R.C., Gien, J.A., et al.: Loss of breathing modulation of heart rate variability in patients with recent and long-standing Diabetes Mellitus Type II. PLoS ONE 11(11), e0165904 (2016). https://doi.org/10.1371/journal.pone.0165904

    CrossRef  Google Scholar 

  16. Rivera, A.L., Estañol, B., Sentíes-Madrid, H., Fossion, R., Toledo-Roy, J.C., Mendoza-Temis, J., et al.: Heart rate and systolic blood pressure variability in the time domain in patients with recent and long-standing Diabetes Mellitus. PLoS ONE 11(2), e0148378 (2016). https://doi.org/10.1371/journal.pone.0148378

    CrossRef  Google Scholar 

  17. Hughes, M.J., Hayes, D.J.: Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing. Remote Sens. 6, 4907–4926 (2014). https://doi.org/10.3390/rs6064907

    CrossRef  ADS  Google Scholar 

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Acknowledgments

Partial Financial support for this work was provided by UNAM through grants DGAPA-PAPIIT IN106215, IV100116, and by CONACyT Frontera grants FC-2016-1/2277.

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Correspondence to Juan Antonio López-Rivera .

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López-Rivera, J.A., Rivera, A.L., Frank, A. (2018). Forest Complexity in the Green Tonality of Satellite Images. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_19

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