The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery

Abstract

The Philippines is rich in mangrove forests, containing 50% of the total mangrove species of the world. However, the vast mangrove areas of the country have declined to about half of its cover in the past century. In the 1970s, action was taken to protect the remaining mangrove forests under a government initiative, recognizing the ecological benefits mangrove forests can bring. Here, we examine two mangrove areas in the Philippines—Coron in Palawan and Balangiga-Lawaan in Eastern Samar over a 30-year period. Sets of Landsat images from 1987 to 2016 were classified and spatially analyzed using four landscape metrics. Additional analyses of the mangrove areas’ spatiotemporal dynamics were conducted. The impact of typhoon landfall on the mangrove areas was also analyzed in a qualitative manner. Spatiotemporal changes indicate that both the Coron and Balangiga-Lawaan mangrove forests, though declared as protected areas, are still suffering from mangrove area loss. Mangrove areal shrinkage and expansion can be attributed to both typhoon occurrence and management practices. Overall, our study reveals which mangrove forests need more responsive action, and provides a different perspective in understanding the spatiotemporal dynamics of these mangrove areas.

A Preliminary Survey of Beach Forest Species in Guiuan Eastern Samar, Philippines: An Ethnobotanical Study

“…Furthermore, the vulnerability of the Philippines to natural disasters such as earthquakes and tsunamis being along the Pacific Ring of Fire, and typhoons and storm surges being a tropical cyclone gateway from the Pacific Ocean, puts more pressure to the weakening defensive stance offered by the mangrove forest as the coasts the first line of defense. Mangrove cover in some towns in the province of Eastern Samar, Philippines has been in the decline since 1987-2016, which are accounted for the human activities in the area while aggravated by more frequent tropical cyclones [8]. The town of Balankayan alone had experienced a massive mangrove forest damage to 86 percent tree density and 68 percent basal area, brought about by the 2013 Supertyphoon Haiyan [9].…”

Multitemporal Land Use and Land Cover Classification from Time-Series Landsat Datasets Using Harmonic Analysis with a Minimum Spectral Distance Algorithm

“…Mi et al [14] applied an RF classifier with time-series Landsat datasets (1987-2017) to detect the LULC changes in a mining area, achieving an average OA of about 84%. Buitre et al [13] applied a support vector machine with time-series Landsat datasets (1987-2016) to classify mangroves, non-mangroves, seawater, and clouds in the Philippines and attained an average OA of about 84%.…”

“…Accelerated urban growth and LULC changes exert pressure on the natural environment and human welfare and have become a global concern [8]. Several studies on LULC changes and their impacts have been conducted worldwide from multiple dimensions using satellite remote sensing and GIS technology [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. All of these studies require time-series datasets that are mostly derived from Earth observation satellites to classify multitemporal LULC maps.…”

Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

“…Remote sensing data has proven to be a good tool for mapping and monitoring mangrove changes over a regional scale as it has low-cost and is not time-consuming (Boardman and Kruse, 1994;Guild et al, 2004;Ghanavati et al, 2008;Mondal et al, 2018Mondal et al, , 2019Elmahdy et al, 2019Elmahdy et al, , 2020aSpruce et al, 2020). The multitemporal Landsat images have been widely used for mapping and monitoring mangrove changes due to their suitable of spatial and temporal resolutions, as well as their easy availability and accessibility (Birth and McVey, 1968;Edwards et al, 2007;Klemas, 2009;Vo et al, 2013;Nguyen et al, 2013;Kanniah et al, 2015;Chen et al, 2017;Elmahdy and Mohamed, 2018;Buitre et al, 2019).…”