Damage and recovery assessment of the Philippines' mangroves following Super Typhoon Haiyan

Marine Pollution Bulletin 109 (2016) 734–743

Contents lists available at ScienceDirect

Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

Damage and recovery assessment of the Philippines' mangroves following Super Typhoon Haiyan Jordan Long a,⁎,2, Chandra Giri b, Jurgenne Primavera c, Mandar Trivedi d a

InuTeq 1, Sioux Falls, SD 57198, USA U.S. Geological Survey EROS Center, Sioux Falls, SD 57198, USA c Zoological Society of London, 48 Burgos Street, La Paz, 5000 Iloilo City, Philippines d Zoological Society of London, Regent's Park, London, NW1 4RY, United Kingdom b

a r t i c l e

i n f o

Article history: Received 20 November 2015 Received in revised form 15 June 2016 Accepted 22 June 2016 Available online 7 July 2016 Keywords: Philippines mangrove Landsat eMODIS NDVI Super Typhoon Haiyan Mangrove disturbance

a b s t r a c t We quantified mangrove disturbance resulting from Super Typhoon Haiyan using a remote sensing approach. Mangrove areas were mapped prior to Haiyan using 30 m Landsat imagery and a supervised decision-tree classification. A time sequence of 250 m eMODIS data was used to monitor mangrove condition prior to, and following, Haiyan. Based on differences in eMODIS NDVI observations before and after the storm, we classified mangrove into three damage level categories: minimal, moderate, or severe. Mangrove damage in terms of extent and severity was greatest where Haiyan first made landfall on Eastern Samar and Western Samar provinces and lessened westward corresponding with decreasing storm intensity as Haiyan tracked from east to west across the Visayas region of the Philippines. However, within 18 months following Haiyan, mangrove areas classified as severely, moderately, and minimally damaged decreased by 90%, 81%, and 57%, respectively, indicating mangroves resilience to powerful typhoons. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Mangroves are highly productive coastal ecosystems found within the intertidal zones of the tropics and subtropics ranging from 35° N to 40° S latitude (Giri et al., 2011b). Mangroves provide numerous ecosystem goods and services that support human livelihoods and wellbeing, and are critically important to nature and society (Alongi, 2002; Duke et al., 2007). Mangroves have traditionally been used by coastal populations for construction materials, food, fuel, and medicine (Walters, 2005). In addition to providing suitable breeding and feeding grounds for numerous aquatic and avian species, undisturbed mangrove ecosystems provide coastal protection from erosion, tropical cyclones, and tsunamis (Alongi, 2008; Chmura et al., 2003; Lee et al., 2014); are closely coupled to neighboring ecosystems (e.g., sea grasses and coral reefs) (Mumby et al., 2004); and store carbon (McLeod et al., 2011; Siikamäki et al., 2012; Twilley et al., 1992). Despite the well-understood importance of mangroves, this biome has become among the most threatened in the past half century (Van Lavieren et al., 2012). Globally, pressures from land use competition

⁎ Corresponding author. E-mail address: [email protected] (J. Long). Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. 2 Work performed under U.S. Geological Survey contract G13PC00028. 1

http://dx.doi.org/10.1016/j.marpolbul.2016.06.080 0025-326X/© 2016 Elsevier Ltd. All rights reserved.

have resulted in extensive deforestation and degradation. Brackishwater aquaculture, agriculture, salt production, infrastructure development, and forest extraction are among the leading drivers of mangrove loss, worldwide (Food and Agriculture Organization of the United Nations, 2007; Primavera, 1995; Primavera, 2005a). Natural disturbances such as high water surge, tropical cyclones, tsunamis, and wave action have also contributed to mangrove loss (Cornforth et al., 2013; Giri et al., 2008; Paling et al., 2008); however, mangroves are generally more resilient to natural perturbations than to human-induced disturbances (Jimenez et al., 1985). Typhoon Haiyan, known as “Super Typhoon Yolanda” in the Philippines, made landfall in Eastern Samar, Philippines, on November 8, 2013. According to the Saffir-Simpson hurricane wind scale (SSHWS), Haiyan was rated as a category 5 hurricane with maximum sustained winds of over 251 km/h and wind gusts greater than 300 km/h (Fig. 1). The inordinately powerful typhoon damaged 1.1 million housing structures, displaced 4.1 million people, and caused over 6000 deaths (National Disaster Risk Reduction and Management Council, 2013; United States Agency for International Development, 2014). In the aftermath of Typhoon Haiyan, the Philippines' Environment Secretary announced plans for mangrove replanting and rehabilitation throughout the Philippines to provide bio-protection from future natural disasters similar to Haiyan (Department of Environment and Natural Resources, 2013). Effective mangrove restoration requires precise information on the geographic distribution as well as extent and severity of

J. Long et al. / Marine Pollution Bulletin 109 (2016) 734–743

735

Fig. 1. Typhoon Haiyan path and category in the Philippines (Hurrevac, 2014).

damage. Time-series remotely sensed measurements coupled with in situ observations are needed to monitor mangrove damage and recovery at a large spatial scale. Typically, studies of storm impacts on mangroves have used either remote sensing (Wang and D'Sa, 2009; Wang, 2012) or re-surveys of forest plots (Kauffman and Cole, 2010; Smith et al., 2009); however, neither on their own provide a complete assessment. Therefore, the aim of this study is to use remote sensing, coupled with ground observations, to map and quantify change in mangrove condition resulting from Typhoon Haiyan and monitor the subsequent temporal pattern of recovery. The data and information from this work are useful for developing rehabilitation and conservation strategies and providing baseline information for future monitoring. 2. Materials and methods 2.1. Study area The Republic of the Philippines is an archipelago of 7107 islands located off the southeastern coast of Asia. With an extensive coastline comprising numerous low-wave energy intertidal bay areas and a tropical climate, the Philippines provides ideal environmental conditions for mangrove growth. In 2010, the total mangrove area of the Philippines

was approximately 240,000 ha, with the greatest extent located in the provinces of Palawan, Sulu, and Siargao Island, Surigao del Norte (Long et al., 2013). Mangrove biological diversity is relatively high with 35 true mangrove species (Food and Agriculture Organization of the United Nations, 2007); however, mangrove diversity is threatened and declining throughout the Philippines (Garcia et al., 2014; Richards and Friess, 2016). The Philippines' total mangrove area decreased nearly 11% from 1990 to 2010 (Long et al., 2013; Long and Giri, 2011). The principal threats are anthropogenic, with aquaculture and forest extraction among the leading drivers of loss in recent decades (Primavera and Esteban, 2008). Within the next century, mangrove loss and degradation is expected to continue as human populations increase and concentrate in coastal regions. The impacts of climate change, sea level rise, and increased storm strength and frequency also contribute to expected mangrove loss in the Philippines (Gilman et al., 2008). The Philippines has one of the highest disaster risk rankings in the world due to its constant exposure to droughts, earthquakes, floods, landslides, typhoons, and volcanic eruptions (UNU-EHS, 2014). Typhoons are the most frequently occurring natural disaster in the Philippines, historically impacting the greatest number of people and causing the greatest amount of property damage (Bankoff, 2012). On average, 6.5 tropical typhoons make landfall in the Philippines per year (Joint

736

J. Long et al. / Marine Pollution Bulletin 109 (2016) 734–743

Typhoon Warning Center, 2008). The northern Luzon and central Visayas regions are the most frequently impacted during the wet season from June to November, while typhoons impacting the southern Mindanao region are less frequent. Mangroves in Luzon and Visayas make up 68% of the Philippines total mangrove area, and are exposed to recurrent disturbances such as high winds and tropical storms (Long et al., 2013). Tropical cyclones potentially have long-term influences on the distribution and composition of mangroves. Wind effects can range from minor defoliation to catastrophic uprooting of entire mangrove stands, while prolonged hydro-period and increased sediment deposition resulting from storm surge can result in mortality from suffocation (Smith et al., 2009). Frequently disturbed mangroves are typically young successional forests and have a lower biomass than mature old growth mangroves, but still provide important ecosystem goods and services (Twilley et al., 1992). 2.2. Landsat mangrove classification Landsat 30 m resolution satellite imagery was used to map mangrove areas in the storm-affected region prior to Typhoon Haiyan (Fig. 2). Typhoon Haiyan transected 5 Landsat path/row footprints (Fig. 3). Images from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) sensors captured

within 8 months prior to Haiyan's passing were selected from the U.S. Geological Survey Earth Resources Observation and Science (USGS EROS) Center archive through the Global Visualizing Viewer (GloVis) (U.S. Geological Survey, 2016b) for 5 Landsat footprints (Table 1). Once Landsat imagery were visually selected, surface reflectance Landsat imagery were downloaded from the USGS EROS Science Processing Architecture (ESPA) on demand interface (U.S. Geological Survey, 2016d). Surface reflectance data provided via ESPA are generated from the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). The LEDAPS software applies the 6S radiative transfer model (Kotchenova et al., 2006) to Level-1 Landsat data products. Landsat sensors have a relatively low temporal resolution with a combined revisit time every 8 days, so regions of the world that are persistently cloudy such as the Philippines, particularly during the rainy season (i.e., June through November), can be difficult to monitor with single-date Landsat imagery. Therefore, several Landsat images were required per Landsat path/row footprint for complete mangrove classification because of partial cloud cover. Landsat image pre-processing included stacking, masking cloudy pixels and cloud filling, and Normalized Difference Vegetation Index (NDVI) transformation (Fig. 2). Cloudy pixels were identified using the Fmask algorithm (Zhu and Woodcock, 2012) and filled using a Spectral Similarity Grouping (SSG) approach with secondary Landsat imagery (Jin et al., 2013).

Fig. 2. Methods workflow for mapping impact of Typhoon Haiyan on the Philippines' mangrove forests.

J. Long et al. / Marine Pollution Bulletin 109 (2016) 734–743

737

Fig. 3. Landsat path/row footprints over Typhoon Haiyan eye transect.

Mangrove areas were mapped using a supervised decision-tree classification applied to a multilayer data stack. The mapped region of interest included approximately 17.5% (42,098 ha) of the Philippines' total mangrove area (Fig. 3). Several studies have obtained high mangrove land cover classification accuracy using a supervised decision-tree approach applied to Landsat imagery and vegetation indices (Giri et al., 2011a; Heumann, 2011b; Long et al., 2013; Zhang, 2011). For example, a 2010 national mangrove land cover map for the Philippines used a decision-tree classification approach and has a reported overall accuracy of 93% (Long et al., 2013). Training data of known land cover samples for three target classes: i) mangrove ii) water and iii) terrestrial nonmangrove, were collected from very high resolution imagery and Landsat imagery. Very high resolution satellite data with a spatial resolution of 5 m or less were downloaded from the USGS EROS Hazards Data Distribution System (HDDS) Explorer (U.S. Geological Survey, 2016c) and the National Geospatial Agency's (NGA) Web-based Access and Retrieval Portal (WARP) (National Geospatial-Intelligence Agency, 2014). Very-high resolution data sensors included WorldView-2, IKONOS-2, and QuickBird-2. The decision-tree ‘rule set’ was generated using See5 commercial software (Rulequest Research, 2011). Independent variables in the multilayer stack included Landsat spectral bands 1, 2, 3, 4, 5, and 7 for ETM+ sensor and 2, 3, 4, 5, 6, and 7 for OLI sensor,

Landsat path/row

Satellite sensor

Date acquired

P112 R52 P113 R52

OLI OLI OLI OLI OLI ETM+ OLI ETM+ OLI OLI ETM+ ETM+ ETM+

4/13/2013 4/29/2013 5/22/2013 7/25/2013 8/10/2013 4/3/2013 9/2/2013 8/16/2013 7/7/2013 9/9/2013 3/16/2013 4/1/2013 5/19/2013

P116 R52

2.3. Mangrove classification accuracy assessment An accuracy assessment was performed using a total of 250 random points: 50 points for mangrove class, 100 points for terrestrial non-

Table 1 Landsat imagery acquired for mangrove mapping analysis.

P114 R52 P115 R52

a 30 m Digital Elevation Model (DEM) derived from Shuttle Radar Topography Mission (SRTM) (Farr et al., 2007), a Slope index generated from the 30 m DEM, NDVI index, and a 30 m 2010 mangrove land cover map consisting of three thematic classes: i) mangrove ii) water and iii) terrestrial non-mangrove (Long et al., 2013). Band ratios, such as NDVI, potentially increase spectral separability of mangrove from non-mangrove vegetation cover (Heumann, 2011a) and DEMs potentially increase separability of low-lying mangrove from upland forest (Jones et al., 2014; Lee et al., 2004). Following classification, we performed a chi-squared test to determine which variables the decisiontree classifier used the most and found that the Landsat surface reflectance bands were the highest used variables, followed by elevation data layers, the 2010 mangrove land cover map, and NDVI (Table 2). It is likely that NDVI values of mangrove were similar to other land cover types (e.g., upland forest and agriculture areas) and were ineffective to the decision-tree classifier in this case study. Additionally, the thematic mangrove classification independent variable was not highly utilized by the classifier likely because of differences in dependent class labels and the independent class labels, most notably in the terrestrial non-mangrove and water classes.

Table 2 Rank order of independent variables used by decision-tree classifier. Rank

Variable

1 2 3 4 5 6 7 8 9 10

ETM+ Band 1 ETM+ Band 3 ETM+ Band 2 ETM+ Band 7 ETM+ Band 5 ETM+ Band 4 SRTM DEM DEM Slope Mangrove 2010 NDVI

OLI Band 2 OLI Band 4 OLI Band 3 OLI Band 7 OLI Band 6 OLI Band 5

738

J. Long et al. / Marine Pollution Bulletin 109 (2016) 734–743

Table 3 Error matrix for 2013 mangrove land cover classification. Reference Predicted Mangrove Land Water Total Producer's % Overall

Mangrove

Land

Water

Total

User's %

45 5 0 50 90

1 99 0 100 99

0 0 100 100 100

46 104 100

97 95 100

97

mangrove class, and 100 points for water class. The randomly generated points were overlaid and compared with high resolution imagery from Google Earth Pro™. High resolution imagery used in assessment were captured on 2/29/2012, 5/23/2012, 3/31/2013, 4/30/2013, 5/17/2013, 6/30/2013, and 8/26/2013. An error matrix was constructed to cross tabulate the observed data with the reference data (Table 3) (Congalton, 1991). The accuracy assessment indicates high classification accuracy, with an overall accuracy of 97%, a producer accuracy of 90% and a user accuracy of 97% for mangrove cover (Table 3). The majority of misclassification errors occurred where mangrove areas were incorrectly mapped as terrestrial non-mangrove (i.e., land). 2.4. Time-series eMODIS Expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) data were used to monitor and quantify changes in mangrove condition resulting from Haiyan. The eMODIS collection is derived from 250 m MODIS data (Jenkerson et al., 2010). MODIS provides surface reflectance data with a higher temporal resolution than Landsat, revisiting once to twice per day, while Landsat combined sensor (ETM+ and OLI) revisit time is every 8 days. The higher temporal resolution of MODIS is particularly advantageous in obtaining timely information and quality cloud-free observations in tropical regions like the Philippines, which is not possible with Landsat data. Moderate resolution eMODIS data were downloaded through EarthExplorer (U.S. Geological Survey, 2016a). The eMODIS datasets include acquisition, quality, and NDVI information at 250 m spatial resolution. NDVI is one of the most widely applied proxies used to measure and monitor plant growth, vegetation cover, and biomass production and has been extensively used for vegetation monitoring, crop yield assessment, and drought detection (Ill et al., 1997; Peters et al., 2002; Pettorelli, 2013; Tucker, 1979). NDVI exploits the contrasting characteristics of the red and near-infrared (NIR) spectral bands and is calculated using the following formula:NDVI = (NIR − RED)/(NIR + RED). Values for NDVI range from −1.0 to 1.0, where densely vegetated areas (e.g., closed canopy tropical forest) generally yield high NDVI values (0.6 to 0.9), sparsely vegetated areas (e.g., open shrub and grasslands) yield moderate values (0.2 to 0.3), and non-vegetated surfaces (e.g., bare rock, water, and snow) yield lower NDVI values (0.1 and lower) (U.S. Geological Survey, 2015). Dense and healthy mangrove forests typically have NDVI values greater than 0.7 year round (Fig. 4). NDVI of mangrove forests remains high year round because they are largely evergreen plants. Several studies have employed repeated measures of NDVI to monitor mangrove vegetation response from varying disturbances (Giri et al., 2011a; Satyanarayana et al., 2011), but few have applied this approach to monitor mangrove disturbance from tropical cyclones (Wang, 2012). The eMODIS products available for our study region included 10-day composited data sets. NDVI can be sensitive to atmospheric scattering and absorption, as well as variations in the illumination and viewing conditions (Kerekes, 1994); however, the eMODIS composite selection process minimizes these artifacts by filtering through input surface reflectance data and flagging poor quality pixels with negative values, clouds, snow cover, low view angles, or low sun angle and labeling

Fig. 4. Phenology of mangrove area located in Palawan, Philippines (12°16′10.38″N, 119° 54′48.28″E). Monthly NDVI observations derived from 250 m 16-day MODIS NDVI products. NDVI remained between 7.5 and 9.5 during the 2-year observation period (2011–2012).

these pixels in a quality band (Jenkerson et al., 2010). We further preprocessed the standard 10-day eMODIS data to generate NDVI time series composites prior to, and three time periods following, Typhoon Haiyan: 6 months prior (April 15, 2013–November 4, 2013) (B); 1 to 6 months after (November 21, 2013–May 26, 2014) (A1); 7 to 12 months after (June 5, 2014–November 6, 2014) (A2); and 12 to 18 months after (November 6, 2014 – May6, 2014) (A3). Our eMODIS pre-processing algorithm took raw 10-day eMODIS data and performed data stacking, poor quality pixel removal based on the image quality data provided, and calculated the median NDVI value per pixel to produce a smoothed cloud-free NDVI composite for each of the three time periods. Only data with known high quality indicated in the quality layer were analyzed. Next, the 30 m mangrove map derived from Landsat imagery was resampled to 250 m resolution using a nearest neighbor resampling method and applied to mask areas where only mangrove are located for all eMODIS time series composites (B, A1, A2, and A3). The differences of B and A1, B and A2, and B and A3 NDVI composites were calculated to create NDVI change maps of mangrove areas for the three time periods following Haiyan. Finally, we classified mangrove change into three damage level categories. Mangrove areas where NDVI decreased by 0.2 to 0.3, 0.3 to 0.5, and 0.5 or greater were classified into Minimal (i.e., mostly intact, minor defoliation), Moderate (i.e., branches broken, tree trunks partially broken, but still standing and mostly defoliated), or Severe damage (i.e., trees uprooted or trunks broken at base and completely defoliated) level categories, respectively.

2.5. Ground referencing Mangrove change maps were quality checked with independently collected field photos and field notes. Two field teams conducted field observations of mangrove damage from Haiyan in Eastern Samar and Leyte over a total of 7 days in January and March of 2014. With coordination by the Zoological Society of London (ZSL), the teams comprised 17 participants from non-governmental organizations (NGOs), universities, and the Philippine government. The locations surveyed included Quinapondan, Guiuan (Bagongbanwa Island), Salcedo (Maliwaliw Island and Abejao), General MacArthur, Hernani, and Lawaan in Eastern Samar, and Ormoc City, Palompon, Isabel, Merida, Carigara, Palo, and Tacloban City in Leyte. Mangrove damage and recovery potential were assessed for 39 plots using two methods. First, counts of live seedlings, saplings, and trees were made in 10 × 10 m or 4 × 4 m plots. Second, the degree of defoliation and other damage of trees was classified as minimally damaged (i.e., intact), partially damaged (i.e., branches broken, tree trunks partially broken but still standing and with or without sprouts), or totally damaged (i.e., trees uprooted or trunks broken at the base without sprouts). Mangrove damage maps produced from this study were quality checked with ground observations by overlaying georeferenced ground observations points over mangrove damage

J. Long et al. / Marine Pollution Bulletin 109 (2016) 734–743

maps and comparing field note damage level classifications and georeferenced photos with mapped damage level category. 3. Results We found general patterns of mangrove damage with the greatest decreases in NDVI values, indicating increasing severity of mangrove disturbance, occurring in proximity to Typhoon Haiyan's eye transect. According to Chen et al. (Chen et al., 2013), most tropical storms cause the greatest damage severity on the right and front side of the eye track, where wind and wave stress can be up to 25% greater than on the left. Consistent with most tropical storms (Dahal et al., 2014), the greatest extent of moderately and severely damaged mangrove occurred on the right side (i.e., north side of Haiyan transect) of the storm eye track and generally decreased with increasing distance from the eye path (Fig. 5); however, non-linear spatial patterns of mangrove damage were observed in some areas. For example, mangroves in Southwestern Matarinao Bay were less-severely damaged compared with mangroves further north from the eye transect in Northern Matarinao Bay and near Hernani (Fig. 6). This non-linear damage likely resulted from variations in storm surge intensity, bathymetry and topography, wind speed and direction, and type and condition of the existing mangrove vegetation. Additionally, damaged mangrove extent ranging from 0 to 10 km north of Haiyan was relatively low because mangrove extent is relatively low in this region (i.e. less than 1500 ha.). Overall, mangrove damage was greatest in extent and severity 20 to 30 km north of Hiayan's eye path where Haiyan first made landfall in the eastern Philippines and lessened corresponding with decreasing storm intensity as Haiyan tracked from east to west (Fig. 5). Total mangrove area initially impacted (i.e., mangrove experiencing NDVI decreases greater than or equal to 0.2 during time period A1) by Haiyan was 8568 ha, about 3.5% of the Philippines' total mangrove area or 20.4% of the region of interest's total mangrove area. Nearly 870 ha of mangrove were severely damaged, 1820 ha were moderately damaged, and 5900 ha were minimally damaged during this time period (Fig. 7).

739

The majority of severely and moderately damaged mangrove was identified in Eastern Samar, Western Samar, and Leyte provinces, including mangrove areas along Matarinao Bay north to Hernani and from Guiuan west to Balangiga (Fig. 6). As Haiyan tracked east to west it impacted mangroves in Leyte province, from Ormoc City west to Isabel, and north to Tabango. Haiyan crossed the northern tip of Cebu, causing moderate damage to mangroves. Haiyan also caused moderate damage to mangroves in northern Iloilo, northern Capiz, and northern Aklan, although damage was minimal in extent because mangroves in this region are highly fragmented from aquaculture development and mangrove area is relatively small. Haiyan's intensity lessened to a category 4 before passing over northern Palawan (Fig. 1); minimal and moderate mangrove damage was detected here, but was negligible in terms of area compared with mangroves in the Eastern Samar, Western Samar, and Leyte provinces. Severely, moderately, and minimally damaged mangrove respectively decreased by 90%, 81%, and 57% within 18 months following Haiyan (Fig. 7). Very high R-squared values near 1.0 indicate mangrove damage recovered near linearly for all damage level (Fig. 7). Based on linear extrapolation, if the remaining damaged mangrove continues to recover at constant rates, minimally damaged mangrove could potentially recover completely within 12 months following time period A3 and moderately and severely damaged mangrove could potentially recover completely within 6 months following time period A3; although, Wang (Wang, 2012) reported that mangrove recovery following a category 5 hurricane disturbance in southern Florida did not recover linearly and recovery was greatest in the first year, slowed in the second year, and was minimal in the third year. A complete statistical validation was not performed on the mangrove damage maps because of inadequate reference data across the study area; however, georeferenced field photos collected during independent field surveys following Haiyan indicate our results are consistent with on-the-ground mangrove condition observations (Primavera et al., 2016). Fig. 8 (A) illustrates a mangrove area on Anahaw Island, Eastern Samar, in Matarinao Bay where our change analysis indicated mangrove damage was minimal during time period A1. Ground observations showed mangrove cover remained intact and the field team measured minimal observable defoliation or blowdown. Fig. 8 (B) illustrates a mangrove area on Tubabao Island, Guiuan, in the Leyte Gulf that we classified as moderately damaged (NDVI decreased by 0.4) during time period A1; consistent with field observations, Haiyan caused moderate damage in this area. Fig. 8 (C) shows a mangrove area on Tubabao Island, Guiuan, in the Leyte Gulf that we classified as severely damaged; ground observation were consistent with damage maps indicating severe damage to mangrove. Such field surveys found that the majority severely damaged mangroves were plantations of Rhizophora while most natural mangrove stands were recovering from minimal and moderate damage (Primavera et al., 2016). 4. Discussion

Fig. 5. Mangrove damage observed at 1 degree longitude intervals (A) and 10 km intervals north and south of Haiyan's eye path (B) during time period A1.

Tropical storms are a frequently occurring phenomenon in the Philippines and greatly influence mangrove forest dynamics. Similar to most mangroves worldwide, mangroves in the Philippines are mostly located in remote and inaccessible regions, making it difficult, if not impossible, to conduct a comprehensive impact assessment from field surveys alone. Remote sensing offers consistent, timely, and reliable measurements that allow disturbance monitoring across large areas. The results from this study indicate that a hybrid remote sensing approach using a combination of medium-resolution Landsat and time-series eMODIS data is effective for identifying storm damage severity and recovery response of mangrove ecosystems. These findings are supported by ground-referencing using field surveys at impacted sites. Our analysis found that nearly 3.5% of the Philippines' total mangrove area was initially impacted by Haiyan. There was a high degree of spatial variation in mangrove damage with damage found to be greatest in extent

740

J. Long et al. / Marine Pollution Bulletin 109 (2016) 734–743

Fig. 6. Mangrove damage across the Philippines. Mangrove damage was greatest in severity and extent in Eastern Samar where Haiyan made first landfall in the Philippines.

and severity in the Eastern Samar, Western Samar, and Leyte provinces where Haiyan initially made landfall. Of the total impacted mangrove area, approximately 868 ha of mangrove experienced severe damage, with Eastern Samar experiencing the majority of severe and moderate mangrove damage. Despite the initial damage caused to 3.5% of the Philippines' total mangrove area, the majority of damaged mangrove recovered within 18 months following Super Typhoon Haiyan and, in the absence of further typhoons or other perturbations. These findings demonstrate the value of remote sensing as an ongoing monitoring tool to assess damage and document recovery, and also indicate that some of the initial damage was likely due to defoliation rather than mortality. Additionally, ground referencing surveys provide an important validation of the true status of mangroves post-typhoon and need to be integrated into

Fig. 7. Total mangrove area impacted by Typhoon Haiyan. Time periods after Haiyan: 1– 6 months (A1), 7–12 month (A2), and 13–18 months (A3).

any remote sensing methodology. These findings have implications for government policy. The immediate response of the Philippine government to Haiyan was to allocate approximately 350 million Philippine pesos (US $7.4 million dollars) to re-plant mangroves in thousands of hectares along hundreds of kilometers of coastline (Department of Environment and Natural Resources, 2013). The present results suggest that mangrove recovery should be surveyed as late as 1.5 years (18 months) following a category 5 typhoon to determine if mangrove areas require rehabilitation. This study suggests that natural recovery and regeneration would be a more economically and ecologically viable strategy. The high mangrove recovery rates we observed in a relatively short amount of time (1.5 years) further demonstrate the resilience of mangroves to inordinately powerful tropical cyclones. These findings are consistent with several studies affirming mangroves resilience and high rate of recovery shortly following natural perturbations across the globe (Alongi, 2008; Aung et al., 2013; Ayyappan et al., 2016; Herbert et al., 1999; Wang, 2012). The resilience of the Philippines' mangroves, and hence their ability to act as an important buffer for coastal communities, could be enhanced through domestic policies and measures to reduce recent rates of loss (i.e., 10.5% total mangrove loss from 1990 to 2010) primarily resulting from anthropogenic activities (Long et al., 2013). Dating back to the 1970s, the Philippine greenbelt laws require mangrove buffer zones of 50–100 m facing open seas and 20–50 m along riverbanks (e.g., Presidential Decree (PD) 705 of 1975, PD 953 of 1976, PD 1067, MNR Administrative Order (AO) 42 of 1986, DENR AO 76 of 1987), but these laws have been observed more in the breach than in the compliance (Primavera, 2000). In the wake of the 2004 tsunami, Primavera (Primavera, 2005b) called for their implementation by planting mangrove greenbelts. Nearly a decade later with the added urgency from the damage to life and property caused by Typhoon Haiyan, this advocacy has resulted in the filing of the National Coastal Greenbelt Bill in the Philippine Senate (National Coastal Greenbelt Act of 2014, 2014), that mandates a 100 m wide band of mangrove vegetation. Remote sensing observations, as applied in this study, can be a viable approach to documenting how effectively this legislation

J. Long et al. / Marine Pollution Bulletin 109 (2016) 734–743

741

Fig. 8. (A) Mangrove area on Anahaw Island, Matarinao Bay in Eastern Samar where change analysis indicated minimal mangrove damage (NDVI decreased by 0.1 from B to A1 time periods). Ground observation agreed with damage maps showing minimal damage to mangrove. (B) Mangrove area on Tubabao Island, Guiuan, in Leyte Gulf where change analysis indicated moderate mangrove damage (NDVI decreased by 0.4 from B to A1 time periods). Consistent with field observations, Haiyan caused moderate damage (i.e., branches broken, tree trunks partially broken but still standing and with shoots) in this location. (C) Mangrove area on Tubabao Island, Guiuan, in Leyte Gulf where change analysis indicated severe mangrove damage (NDVI decreased by 0.6 from B to A1 time periods). Ground observation agreed with damage maps showing severe damage to mangrove. (Photos ZSL/Amado Blanco).

is implemented over time to restore coastal mangrove greenbelts in the Philippines. It is vital that mangrove replanting activities are scientifically based to ensure the correct species are planted in the correct locations and with the support of local communities to ensure ongoing maintenance and protection (Primavera et al., 2012). Mangrove replanting schemes are currently dominated by those resulting in mono-type plantations (e.g., planting only Rhizophora) often with low survival rates, or impact on other important habitats such as mudflats and seagrass beds (Alongi, 2002; Erftemeijer and Lewis, 1999; Field, 1999; Walters, 2000). Further research is needed into the relative resilience of these mono-type species mangrove plantations versus natural mixed species stands. Small-scale studies in Panay have shown that severe damage from Typhoon Haiyan was much higher in mono-type Rhizophora plantations than in diverse natural mangrove forests (Primavera et al., 2016). Aung et al. (2013) also observed higher mortality rates (N90%) among monotype Rhizophora stands compared with non-Rhizophora stands (b 20%) following a typhoon disturbance in Myanmar.

The data and findings from this study are useful for developing mangrove rehabilitation and conservation strategies and provide baseline information for future monitoring. Long-term monitoring of mangrove areas impacted by Typhoon Haiyan using ground and remote sensing observations is needed to fully understand the impacts on the ecosystem. Author contributions All listed authors contributed to study design. Authors Long and Giri performed analysis and Long prepared figures and wrote the first draft of the manuscript. Giri, Primavera, and Trivedi revised the draft. Primavera and Filipino colleagues organized the field surveys, with suggestions from Trivedi. All authors read and approved the final version of the manuscript. Conflicts of Interest The authors declare no conflicts of interest.

742

J. Long et al. / Marine Pollution Bulletin 109 (2016) 734–743

Acknowledgments Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Work performed under U.S. Geological Survey contract G13PC00028. ZSL gratefully acknowledge the efforts of the field team including representatives from Guiuan Development Foundation (GDFI), Environmental Leadership and Training Initiative (ELTI), Haribon Foundation, Tambuyog Development Center, Conservation International, University of the Philippines Diliman, University of the Philippines Tacloban College, Ateneo University, La Salle University, and the Department of Environment and Natural Resources (DENR), Region 6. ZSL is also grateful to Christian Aid and the Darwin Initiative for funding the field surveys and Dr. Primavera. We appreciatively acknowledge peer and anonymous reviewers.

References National Coastal Greenbelt Act of 2014 (Ed.), 2014. Senate Bill 2179, First ed. Alongi, D.M., 2002. Present state and future of the world's mangrove forests. Environ. Conserv. 29, 331–349. Alongi, D.M., 2008. Mangrove forests: resilience, protection from tsunamis, and responses to global climate change. Estuar. Coast. Shelf Sci. 76, 1–13. Aung, T.T., Mochida, Y., Than, M.M., 2013. Prediction of recovery pathways of cyclone-disturbed mangroves in the mega delta of Myanmar. For. Ecol. Manag. 293, 103–113. Ayyappan, N., Stephen, A., Muthusankar, G., Jeyakumar, S., Presena, J., 2016. Spatio-temporal changes in fringe mangrove extent in Pondicherry, India after two phenomenal perturbations: tsunami and cyclone Thane. Trop. Ecol. 57, 361–368. Bankoff, G., 2012. Cultures of Disaster: Society and Natural Hazard in the Philippines. Routledge. Chen, S.S., Zhao, W., Donelan, M.A., Tolman, H.L., 2013. Directional wind–wave coupling in fully coupled atmosphere–wave–ocean models: results from CBLAST-Hurricane. J. Atmos. Sci. 70, 3198–3215. Chmura, G.L., Anisfeld, S.C., Cahoon, D.R., Lynch, J.C., 2003. Global carbon sequestration in tidal, saline wetland soils. Glob. Biogeochem. Cycles 17, 1111. Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37, 35–46. Cornforth, W., Fatoyinbo, T., Freemantle, T., Pettorelli, N., 2013. Advanced Land Observing Satellite Phased Array Type L-Band SAR (ALOS PALSAR) to inform the conservation of mangroves: sundarbans as a case study. Remote Sens. 5, 224–237. Dahal, D., Liu, S., Oeding, J., 2014. The carbon cycle and hurricanes in the United States between 1900 and 2011. Sci. Rep. 4, 5197. Department of Environment and Natural Resources, 2013. DENR sets aside P347M for coastal forest rehabilitation in Eastern Visayas. http://www.denr.gov.ph/news-andfeatures/latest-news/1604-denr-sets-aside-p347m-for-coastal-forest-rehabilitationin-eastern-visayas.html. Duke, N.C., Meynecke, J.-O., Dittmann, S., Ellison, A.M., Anger, K., Berger, U., Cannicci, S., Diele, K., Ewel, K.C., Field, C.D., 2007. A world without mangroves? Science 317, 41–42. Erftemeijer, P.L., Lewis, R.R., 1999. Planting Mangroves on Intertidal Mudflats: Habitat Restoration or Habitat Conversion, Proceedings of the ECOTONE VIII Seminar Enhancing Coastal Ecosystems Restoration for the 21st Century. Ranong, Thailand, pp. 23–28. Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., 2007. The shuttle radar topography mission. Rev. Geophys. 45. Field, C., 1999. Rehabilitation of mangrove ecosystems: an overview. Mar. Pollut. Bull. 37, 383–392. Food and Agriculture Organization of the United Nations, 2007. The World's Mangroves, 1980–2005: A Thematic Study in the Framework of the Global Forest Resources Assessment 2005. Food and Agriculture Organization of the United Nations, Rome. Garcia, K.B., Malabrigo Jr., P.L., Gevaña, D.T., 2014. Philippines' mangrove ecosystem: status, threats and conservation. In: Latiff, A., Hakeem, K.R., Ozturk, M. (Eds.), FaridahHanum, I. Mangrove Ecosystems of Asia, Springer New York, pp. 81–94. Gilman, E.L., Ellison, J., Duke, N.C., Field, C., 2008. Threats to mangroves from climate change and adaptation options: A review. Aquat. Bot. 89, 237–250. Giri, C., Long, J., Tieszen, L., 2011a. Mapping and monitoring Louisiana's mangroves in the aftermath of the 2010 Gulf of Mexico oil spill. J. Coast. Res. 27, 1059–1064. Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., Masek, J., Duke, N., 2011b. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159. Giri, C., Zhu, Z., Tieszen, L., Singh, A., Gillette, S., Kelmelis, J., 2008. Mangrove forest distributions and dynamics (1975–2005) of the tsunami-affected region of Asia†. J. Biogeogr. 35, 519–528. Herbert, D.A., Fownes, J.H., Vitousek, P.M., 1999. Hurricane damage to a Hawaiian forest: nutrient supply rate affects resistance and resilience. Ecology 80, 908–920. Heumann, B.W., 2011a. An object-based classification of mangroves using a hybrid decision tree—support vector machine approach. Remote Sens. 3, 2440. Heumann, B.W., 2011b. Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Prog. Phys. Geogr. 35, 87–108. Hurrevac, 2014. A storm tracking and decision support tool for government emergency managers, 1.4.2 ed. http://www.hurrevac.com.

Ill, E.W.R., Chappell, D.K., Baldwin, D.G., 1997. AVHRR imagery used to identify hurricane damage in a forested wetland of Louisiana. Photogramm. Eng. Remote. Sens. 63, 293–297. Jenkerson, C., Maiersperger, T., Schmidt, G., 2010. eMODIS: A User-friendly Data Source, in: Survey, U.S.G. (Ed.). Reston, Virginia, p. 10. Jimenez, J.A., Lugo, A.E., Cintron, G., 1985. Tree mortality in mangrove forests. Biotropica 17, 177–185. Jin, S., Homer, C., Yang, L., Xian, G., Fry, J., Danielson, P., Townsend, P.A., 2013. Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA. Int. J. Remote Sens. 34, 1540–1560. Center, Joint Typhoon Warning, 2008. Appendix B: Characteristics of Tropical Cyclones Affecting the Philippine Islands (Shoemaker 1991). Jones, T.G., Ratsimba, H.R., Ravaoarinorotsihoarana, L., Cripps, G., Bey, A., 2014. Ecological variability and carbon stock estimates of mangrove ecosystems in northwestern Madagascar. Forests 5, 177–205. Kauffman, J.B., Cole, T., 2010. Micronesian mangrove forest structure and tree responses to a severe typhoon. Wetlands 30, 1077–1084. Kerekes, J.P., 1994. NDVI sensitivity to atmospheric water vapor as a function of spectral bandwidth, Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International. IEEE, pp. 1506–1508. Kotchenova, S.Y., Vermote, E.F., Matarrese, R., Klemm Jr., F.J., 2006. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance. Applied optics Vol. 45, pp. 6762–6774. Lee, J., Shuai, Y., Zhu, Q., 2004. Using images combined with DEM in classifying forest vegetations, Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International. IEEE, pp. 2362–2364. Lee, S.Y., Primavera, J.H., Dahdouh-Guebas, F., McKee, K., Bosire, J.O., Cannicci, S., Diele, K., Fromard, F., Koedam, N., Marchand, C., Mendelssohn, I., Mukherjee, N., Record, S., 2014. Ecological role and services of tropical mangrove ecosystems: a reassessment. Glob. Ecol. Biogeogr. 23, 726–743. Long, J., Napton, D., Giri, C., Graesser, J., 2013. A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010. J. Coast. Res. 260–271. Long, J.B., Giri, C., 2011. Mapping the Philippines' mangrove forests using Landsat imagery. Sensors 11, 2972–2981. McLeod, E., Chmura, G.L., Bouillon, S., Salm, R., Björk, M., Duarte, C.M., Lovelock, C.E., Schlesinger, W.H., Silliman, B.R., 2011. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 9, 552–560. Mumby, P.J., Edwards, A.J., Arias-González, J.E., Lindeman, K.C., Blackwell, P.G., Gall, A., Gorczynska, M.I., Harborne, A.R., Pescod, C.L., Renken, H., 2004. Mangroves enhance the biomass of coral reef fish communities in the Caribbean. Nature 427, 533–536. National Disaster Risk Reduction, Management Council, 2013. NDRRMC Update SitRep No. 74 Effects of Typhoon “Yolanda” (Haiyan). http://www.ndrrmc.gov.ph/ attachments/article/1125/updsitrep74.pdf. National Geospatial-Intelligence Agency, 2014. Web-based Access and Retrieval Portal. https://warp.nga.mil. Paling, E.I., Kobryn, H.T., Humphreys, G., 2008. Assessing the extent of mangrove change caused by Cyclone Vance in the eastern Exmouth Gulf, northwestern Australia. Estuar. Coast. Shelf Sci. 77, 603–613. Peters, A.J., Walter-Shea, E.A., Ji, L., Vina, A., Hayes, M., Svoboda, M.D., 2002. Drought monitoring with NDVI-based standardized vegetation index. Photogramm. Eng. Remote. Sens. 68, 71–75. Pettorelli, N., 2013. The Normalized Difference Vegetation Index. Oxford University Press. Primavera, J.H., 1995. Mangroves and Brackishwater Pond Culture in the Philippines, AsiaPacific Symposium on Mangrove Ecosystems. Springer, pp. 303–309. Primavera, J.H., 2000. Development and conservation of Philippine mangroves: institutional issues. Ecol. Econ. 35, 91–106. Primavera, J.H., 2005a. Mangroves, fishponds, and the quest for sustainability. Science 310, 57–59. Primavera, J.H., 2005b. Tsunamis and Mangrove Greenbelts The Philippine Star. Primavera, J.H., Cruz, M.D., Montilijiao, C., Consunji, H., Paz, M.D., Rollon, R., Maranan, K., Samson, M.C., Blanco, A., 2016. Preliminary Assessment of Post-Haiyan Mangrove Damage and Reocvery in Eastern Samar, Central Philippines (in Press). Primavera, J.H., Esteban, J., 2008. A review of mangrove rehabilitation in the Philippines: successes, failures and future prospects. Wetl. Ecol. Manag. 16, 345–358. Primavera, J.H., Savaris, J., Bajoyo, B., Coching, J., Curnick, D., Golbeque, R., Henderin, A.G.J., Joven, R., Loma, R., Koldewey, H., 2012. Manual on Community-Based Mangrove Rehabilitation, Mangrove Manual Series No. 1. Zoological Society of London, United Kingdom, p. 240. Richards, D.R., Friess, D.A., 2016. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. 113, 344–349. Rulequest Research, 2011. See5/c5.0. http://www.rulequest.com/see5-info.html. Satyanarayana, B., Mohamad, K.A., Idris, I.F., Husain, M.-L., Dahdouh-Guebas, F., 2011. Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia. Int. J. Remote Sens. 32, 1635–1650. Siikamäki, J., Sanchirico, J.N., Jardine, S.L., 2012. Global economic potential for reducing carbon dioxide emissions from mangrove loss. Proc. Natl. Acad. Sci. 109, 14369–14374. Smith, T.J., Anderson, G.H., Balentine, K., Tiling, G., Ward, G.A., Whelan, K.R.T., 2009. Cumulative impacts of hurricanes on Florida mangrove ecosystems: sediment deposition, storm surges and vegetation. Wetlands 29, 24–34. Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150.

J. Long et al. / Marine Pollution Bulletin 109 (2016) 734–743 Twilley, R.R., Chen, R.H., Hargis, T., 1992. Carbon sinks in mangroves and their implications to carbon budget of tropical coastal ecosystems. Water Air Soil Pollut. 64, 265–288. U.S. Geological Survey, 2015. Remote Sensing Phenology. http://phenology.cr.usgs.gov/ ndvi_foundation.php. U.S. Geological Survey, 2016a. Earth Explorer. http://earthexplorer.usgs.gov. U.S. Geological Survey, 2016b. Global Visualization Viewer (GloVis). http://glovis.usgs. gov. U.S. Geological Survey, 2016c. Hazards Data Distribution Systems Explorer. http:// hddsexplorer.usgs.gov. U.S. Geological Survey, 2016d. LSDS Science Research and Development. http://espa.cr. usgs.gov. United States Agency for International Development, 2014. Philippines - Typoon Yolanda/ Haiyan, FACT SHEET #22. FISCAL YEAR (FY). Vol. 2014, p. 7. UNU-EHS, 2014. World Risk Report. In: Lars Jeschonnek, M. (Ed.), United Nations University, Institute for Environment and Human Security.

743

Van Lavieren, H., Spalding, M., Alongi, D.M., Kainuma, M., Clüsener-Godt, M., Adeel, Z., 2012. Securing the future of mangroves. Walters, B.B., 2000. Local mangrove planting in the Philippines: are fisherfolk and fishpond owners effective restorationists? Restor. Ecol. 8, 237–246. Walters, B.B., 2005. Patterns of local wood use and cutting of Philippine mangrove forests. Econ. Bot. 59, 66–76. Wang, F., D'Sa, E.J., 2009. Potential of MODIS EVI in identifying hurricane disturbance to coastal vegetation in the northern Gulf of Mexico. Remote Sens. 2, 1–18. Wang, Y., 2012. Detecting Vegetation Recovery Patterns After Hurricanes in South Florida Using NDVI Time Series. Zhang, X.-H., 2011. Identification of Mangrove Using Decision Tree Method, Information and Computing (ICIC). 2011 Fourth International Conference on. IEEE, pp. 130–132. Zhu, Z., Woodcock, C.E., 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83–94.