Philippine Journal of Science
152 (5): 1753-1767, October 2023
ISSN 0031 – 7683
Date Received: 20 Feb 2023

Land Use and Land Cover (LULC) Assessment within
the Batanes Protected Landscapes and Seascapes

Nova D. Doyog1*, Roscinto Ian C. Lumbres1, Lynn J. Talkasen2,
and Deign Frolley C. Soriano3

1College of Forestry/Center for Geoinformatics, Benguet State University,
La Trinidad, Benguet 2601 Philippines
2College of Agriculture/Center for Geoinformatics, Benguet State University,
La Trinidad, Benguet 2601 Philippines
3Department of Biology, College of Science, University of the Philippines Baguio,
Governor Pack Road, Baguio City 2600 Philippines

*Corresponding author: n.doyog@bsu.edu.ph

[Download]
Doyog N et al. 2023. Land Use and Land Cover (LULC) Assessment within the
Batanes Protected Landscapes and Seascapes. Philipp J Sci 152(5): 1753–1767.
https://doi.org/10.56899/152.05.18

 

 

ABSTRACT

Declared protected areas have ecologically important landscapes that must be conserved and protected. Status of protected areas could be monitored through land use and land cover (LULC) assessments. LULC offers baseline data for integrated land use planning and improvement of existing policies are therefore necessary to be conducted. This study was conducted to monitor the existing LULC of six islands within the Batanes Protected Landscapes and Seascapes (BPLS) through a machine learning (ML)-based random forest (RF) classifier using multi-sourced data such as Landsat imageries’ surface reflectance (SR), Landsat-derived land surface temperature (LST), and global ecosystem dynamic investigation (GEDI)-derived height (Ht) metrics and to determine the effects of the LST and Ht metrics to LULC classification. Four layer stacked images with different features were analyzed – including SR, SR-LST, SR-Ht, and SR-LST-Ht. The result of the LULC classification showed an accuracy based on Macro F1-score and Kappa (K) of 0.81 and 0.83, 0.83 and 0.86, 0.86 and 0.89, and 0.93 and 0.94, for SR, SR-LST, SR-Ht, and SR-LST-Ht, respectively. When compared to the existing global-scale LULC, this study has higher accuracy than the GLAD and ESRI products, which have Macro F1-scores and K-values of 0.73 and 0.71, and 0.59 and 0.64, respectively. To conclude, the inclusion of LST and Ht information in addition to SR data in LULC classification can improve the accuracy by up to 12% and 11% based on Macro F1-score and K, respectively. The result of this study can serve as a reference for achieving improved and reliable LULC information that is necessary for monitoring fluctuations of the global earth’s resources and comprehensive LULC planning. In addition, the technique used in this study can serve as a reference in generating reliable LULC information that can aid in the sustainable implementation of policies, rules, and regulations intended for declared protected areas like BPLS.