Spatiotemporal Data Analysis and Forecasting Model for Forestland Rehabilitation

  • Jehan D. Bulanadi School of Computing, Holy Angel University
  • Gilbert M. Tumibay DIT Program, Graduate School, Angeles University Foundation
  • Mary Ann F. Quioc Mabalacat City College


Purpose – Deforestation is one of the Global Forests issues that concern the United Nations (UN) for several decades and it thus leads to a vision of increasing the forestland area by 2030 that is the same size as South Africa. With this concern, spatiotemporal data analysis had been an effective way to visualize and represent the area that have been damaged and affected with the integration of the use of Geographical Information System. The National Greening Program (NGP) of the Philippines is in charge of the rehabilitation of unproductive, denuded and degraded forestlands in every province.

Method – Using the spatiotemporal data in the form of shapefiles, predictors that could contribute on how the forestland may be rehabilitated were analysed and foreseen. Also, with the analysis stage of Artificial Neural Network (ANN) with Back Propagation, a forecasting model was identified.

Result – It has been determined that with the combination of ANN and Spatiotemporal visualization, possible additional increase in the size of the rehabilitated forestland and its representation can be done efficiently.

Conclusion – Thus, the finding may be used as a helpful way for the NGP for forestland rehabilitation and reforestation strategic planning and resource management.

Practical Implications – A dynamic and interactive web application may be implemented to monitor implementation of the program. Furthermore, public awareness may be initiated about the importance of forestland.

How to Cite
BULANADI, Jehan D.; TUMIBAY, Gilbert M.; QUIOC, Mary Ann F.. Spatiotemporal Data Analysis and Forecasting Model for Forestland Rehabilitation. International Journal of Computing Sciences Research, [S.l.], v. 3, n. 4, p. 229-245, jan. 2020. ISSN 2546-115X. Available at: <//>. Date accessed: 28 nov. 2020.