Monitoring of Land Use and Land Cover Changes in Mt. Pulag National Park Using Landsat and Sentinel Imageries

Abstract

The degree of land use and land cover (LULC) conversion of Mt. Pulag National Park (MPNP), Benguet Province, Philippines, from 1990–2020 was assessed to provide valuable information for land resource management strategies. The increasing demand for vegetable farming threatens the park with further degradation. The magnitude of changes in the LULC of MPNP was determined using Landsat 5 for 1990–2010 and Sentinel-2 for 2020. The FLAASH (Fast Line-ofsight Atmospheric Analysis of Hypercubes) algorithm was performed to enhance the reflectance of the satellite images before data analysis. The maximum likelihood classification (MLC) was used to classify the MPNP into five LULC changes: agricultural lands, barren lands, built-up areas, forest lands, and grasslands with areal percentage cover of 15.88, 0.26, 1.34, 80.51, and 2.01%, respectively, in 2020, and overall accuracy of 90.58%. The LULC percentage of change from 1990–2020 showed that agricultural areas increased by 8.96%, barren lands decreased by 2.14%, built-up areas increased by 1.02%, forest lands decreased by 8.35%, and grasslands increased by 0.51%. The results showed that the increasing area of human-induced LULC class such as the agriculture lands and built-up areas is the primary cause of forest land loss. However, the barren lands undergoing natural regeneration contribute to the gain of the forest area. Therefore, the LULC conversion pattern serves as a warning that the park will continue to lose a significant portion of the forest due to land conversion if no intervention is done.

Drivers and impacts of land use and land cover changes on ecosystem services provided by the watersheds in the Philippines: a systematic literature review

“…(2022), Doyog et al . (2021), Garcia and Galang (2021), Janiola and Puno (2018) and Jardeleza et al . (2019).…”

“…These sources range from different government entities such as local, municipal, national, and international level. Some authors obtained data on soil, slope, and land uses from the local municipal office (Garcia 2016), the National Mapping and Resource Information Authority (NAMRIA) (Almarines et al 2022, Arceo et al 2018, Encisa-Garcia et al 2020, Magpantay et al 2019, Ramirez et al 2019), while others were able to download at online sources such as the United States Geological Survey (USGS) Earth Explorer Webpage (Briones et al 2016, Doyog et al 2021, Dumago et al 2018, Janiola and Puno 2018, Palao et al 2013, Peñaranda et al 2015, Santillan et al 2019), and USGS-EROS Global Visualization Viewer website (Abino et al 2015). Images that are obtained by Landsat 4, Landsat 5 Thematic Mapper, Landsat 8 Operational Land Imager (OLI), and Landsat 7 ETM+ with 30-m spatial resolutions were used in generating land cover data.…”

“…In data pre-processing, the generated satellite images are subjected to further adjustment and refinement to ensure significant results are derived from subsequent analyses to be conducted. This step is a pre-requisite in modelling studies which use satellite images especially when comparing images taken in different periods (Doyog et al 2021). Included in image pre-processing are atmospheric correction, geometric correction and calibration, cloud-masking, noise reduction, haze reduction and histogram matching (Briones et al 2016).…”

“…Atmospheric correction is carried out to eliminate the effects of various noises resulting from sensor and satellite errors, differences in brightness and other atmospheric disturbances (Vicente-Serrano et al 2008). This is carried out by several authors using the remote sensing software ENVI (Dumago et al 2018, Janiola and Puno 2018, Peñaranda et al 2015), and FLAASH model approach (Doyog et al 2021, Janiola and Puno 2018). Cloud-masking is employed by simply excluding pixels with clouds in the analyses which is done through density slicing, a technique that isolates pixels containing clouds from the rest of the pixels (Peñaranda et al 2015, Santillan et al 2019).…”

“…These studies aim to understand how changes in LULC influence soil erosion processes, sediment transport, compaction and infiltration, and the landscape's capacity to retain sediments. Examples of studies in this theme include those conducted by Abino et al (2015), Almarines et al (2022), Doyog et al (2021), Garcia and Galang (2021), Janiola and Puno (2018) and Jardeleza et al (2019). Groundwater recharge and aquifer storage, as an ES category, have relatively fewer studies (mentioned in eight articles).…”