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Forestland Rehabilitation Impact Indicators and Spatiotemporal Visualization

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Bulanad, J.D. and Tumibay, G.M., 2021. Forestland Rehabilitation Impact Indicators and Spatiotemporal Visualization. United International Journal for Research & Technology (UIJRT), 2(4), pp.44-47.

Abstract

A keynote from the United Nations (UN) during the International Day of Forests, 2019, stated that a country’s economic growth and social development is impacted by how rich and wide forests are growing in that place. Forests plays a significant role in the livelihood of many people and forest rehabilitation is said to be one of the best solutions to sustain the livelihood of people. Deforestation is one of the Global Forests issues that concerns the United Nations (UN) for several decades. This paper addresses the factors and predictors that affects the forestland rehabilitation significantly. And with the aid of spatiotemporal visualization, rehabilitated forestlands were easily identified and species of seedlings and trees can easily be located. For the National Greening Program of the country, the result of this paper may serve as their basis as to which factors should be considered when rehabilitation of the forestland is concerned.

Keywords: Forestland Rehabilitation, Impact Predictors, Spatiotemporal Visualization, National Greening Program.

References

  1. Ferreira, K. R., De Oliveira, A. G., Monteiro, A. M. V., & De Almeida, D. B. F. C. (2015). Temporal GIS and spatiotemporal data sources. Proceedings of the Brazilian Symposium on GeoInformatics, 2015-Novem(April 2017), 1–13.
  2. Ferreira, K. R., Oliveira, A. G. De, Miguel, A., Monteiro, V., & Almeida, D. B. F. C. De. (2016). TEMPORAL GIS AND SPATIOTEMPORAL DATA SOURCES SIG Temporal e Fonte de Dados Espaço-temporais. (2014), 1191–1202.
  3. Amini Parsa, V., Yavari, A., & Nejadi, A. (2016). Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran. Modeling Earth Systems and Environment, 2(4), 1–13. https://doi.org/10.1007/s40808-016-0227-2
  4. Lindstrom, J., Szpiro, A., Sampson, P. D., Bergen, S., & Sheppard, L. (2013). SpatioTemporal : An R Package for Spatio-Temporal Modelling of Air-Pollution. CRAN Vignettes, 1–34.
  5. Kuzniar, K., & Zajac, M. (2015). Some methods of pre-processing input data for neural networks. Computer Assisted Methods in Engineering and Science, 22, 141–151. Retrieved from http://cames.ippt.gov.pl/index.php/cames/article/view/33
  6. Kukkonen, M., & Käyhkö, N. (2014). Spatio-temporal analysis of forest changes in contrasting land use regimes of Zanzibar, Tanzania. Applied Geography, 55, 193–202. https://doi.org/10.1016/j.apgeog.2014.09.013
  7. König, C., Weigelt, P., Schrader, J., Taylor, A., Kattge, J., & Kreft, H. (2019). Biodiversity data integration—the significance of data resolution and domain. PLoS Biology, Vol. 17. https://doi.org/10.1371/journal.pbio.3000183
  8. UN Sustainable Goal Development 2018 Report. (2019).
  9. Yu, G., Yang, R., Wei, Y., Yu, D., Zhai, W., Cai, J., … Qin, J. (2018). Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005-2016. BMC Infectious Diseases, 18(1), 1–13. https://doi.org/10.1186/s12879-018-3240-4

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