**1. Introduction**

Mangrove forests are important intertidal ecosystems that link terrestrial and marine systems [1], protecting land from the impact of storm surges, waves, and the erosion of the shore [2–4]. Mangrove plays a major role in the carbon cycle and helps maintain biodiversity. These forests cover only 2% of the world's coastal areas, ye<sup>t</sup> they provide 5% of the net primary production of global coastal ecosystems [5,6]. While mangrove forests comprise only 0.7% of the area of tropical forests [7], their total carbon density is four times that of other tropical forests in the Indo-Pacific region [8]. Mangrove forests consist of approximately seventy taxonomically diverse tree, shrub, and fern species [9–11]. Moreover, mangrove is an important habit for other organisms [12], such as birds [13] and fish [14], such as mangroves in the Caribbean that have strong e ffect on the community structure of fish living in the coral reef [14].

Currently, mangroves are highly threatened by both climate change and human activities. As a result of global warming, suitable habitats for mangrove in tropical and subtropical areas have expanded poleward, but sea level rise may be a major threat to the mangrove forests as a result of changes in swamp duration, frequency, or salinity [15,16]. During the past century, approximately 35% of the area with mangrove forests has disappeared [17]. There is an annual deforestation rate of 1–3% [1,17–20] as these areas are converted for use in aquaculture or agriculture [21]. The amount of and change in aboveground biomass act as indicators of other ecosystem services, such as biodiversity [22]. For example, studies indicate a degraded mangrove forest in Malaysia can lose half of its aboveground biomass (AGB) when compared to a natural mangrove forest [22]. Consequently, accurate estimates of the global distribution of mangrove aboveground biomass is beneficial for our understanding of the status of mangrove ecosystems under threat from deforestation and degradation.

Field surveys are the most basic and most accurate methods for acquiring mangrove AGB at the local scale [23–28]. However, this method is time-consuming and costly when applied to larger areas while providing only discrete measurements of AGB at specified points [29,30]. Moreover, field surveys in mangrove areas are more difficult than surveys in other terrestrial ecosystems due to the muddy conditions and the peculiar structure of mangroves [9]. There are two additional methods for estimating regional or global mangrove AGB: model-based methods and remote sensing. Model-based methods usually provide mangrove AGB estimations from local to global scales based on a relationship between environmental drivers and mangrove biomass [31–33]. However, model-based methods usually reflect potential biomass distribution, which is often inconsistent with actual distribution. Remote sensing methods provide an indirect approach for obtaining mangrove AGB measurements using regression models built by linking surface measurements with remote sensing data. Development of these remote sensing methods has greatly improved the efficiency and lowered the cost of mapping mangrove AGB at large scales [34,35].

There are three popular remote sensing techniques for estimating mangrove biomass: passive optical remote sensing, radar, and light detection and ranging (LiDAR) [36]. Passive optical remote sensing and radar are the earliest and most frequently used methods for estimating mangrove extent and biomass mapping [35,37,38], since they have the benefit of complete global coverage and the data are easily accessible. However, both passive optical remote sensing and radar suffer from a saturation effect at high biomass levels. Neither of these methods can retrieve complete vertical canopy information because optical remote sensing only acquires canopy surface information and radar has limited penetration ability [39].

An active remote sensing method, light detection and ranging, effectively penetrates the forest canopy and can be used to derive information about forest structure in three dimensions [40,41]. Because of its ability to quantify forest height, AGB, and other structural parameters in a variety of forest environments, LiDAR is a major advance in the field of forestry remote sensing [42,43]. Moreover, LiDAR does not saturate at high biomass [44,45]. Current limitations in temporal and spatial coverage restrict the application of LiDAR at continental to global scales [46,47]. Airborne and spaceborne LiDAR can acquire large scale data, but neither can provide worldwide, continuous LiDAR measurements. The high cost of flight missions limits the use of airborne LiDAR to certain regions. Spaceborne LiDAR such as the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat) have collected global LiDAR measurements, but the low density and discontinuous distribution of the GLAS footprint prevents direct production of continuous global data [48,49].

Recently, studies have demonstrated that using multi-source data can overcome the deficiencies associated with GLAS data [48,49]. Passive optical images along with other continuous variables, such as climate layers and a digital terrain model, can be used to build a regression model with GLAS measurements, allowing us to extrapolate from discrete GLAS pixels into spatially continuous layers [46,47]. This method has been used to estimate forest biomass at the scale of the GLAS footprint through a direct-link method proposed by Baccini et al. [50]. A second method uses airborne LiDAR as

a medium [51], thereby extrapolating from discrete AGB points into full coverage layers. However, airborne LiDAR and plots in areas of mangrove are limited, and it is not possible to combine field measurements with GLAS data. Another method suggested by Su et al. [47] provides wall-to-wall estimates of forest AGB at larger scales. First, continuous remote sensing data are used to extrapolate discrete GLAS parameters into spatially continuous layers. Second, a model is built using surface observations rather than linking plot data directly with GLAS data.

Although global mangrove biomass estimates have been generated in the past using climate-based [31,32] and remote sensing [52,53] methods, these results have had little explanatory power or su ffer from signal saturation. Moreover, structural information obtained using LiDAR were not fully utilized in previous e fforts to map global mangrove biomass. The objectives of this study, then, were to estimate global mangrove AGB using ground inventory data, spaceborne LiDAR, and other multi-source data and then to determine if structural information provided by GLAS can improve our understanding of the distribution of mangrove AGB. To meet these objectives, a map of global mangrove AGB map at 250 m has been created and will be disseminated via the internet. This new biomass map provides information about mangrove forests, allowing us to better monitor regional and global biomass trends into the future.

#### **2. Materials and Methods**

The global map of mangrove AGB was generated using field observation data, GLAS data, the enhanced vegetation index (EVI), topographic data, and climate data. The methodology outlined in Figure 1 allowed us to successfully estimate nation-wide forest AGB for China [47] and global forest AGB [46]. A detailed description of each dataset (Table 1) and a brief introduction to the method used to estimate mangrove forest AGB are provided below.

**Figure 1.** The workflow for producing global mangrove aboveground biomass map based on the multisource remote sensing data and ground observation data.


**Table 1.** The variables used in the random forest method to determine GLAS parameters and model mangrove aboveground biomass.


**Table 1.** *Cont.*

#### *2.1. Surface Measurements of Mangrove AGB*

Field data are fundamental for estimating mangrove AGB from remote sensing data. In this study, we obtained 510 plot measurements from previously published articles and free-access mangrove biomass databases, such the Sustainable Wetlands Adaptation and Mitigation Program (https://data. cifor.org/dataverse/swamp) [57,58]. Since these in situ plot measurements were collected from a variety of sources using different protocols, we used three filtering criteria to ensure their quality: (1) the plot has a georeferenced location, (2) the inventory was taken after 2000, and (3) the site was not surveyed using harvesting methods. The geolocation of each individual plot was vital to this study. Using Google Earth, we manually checked each point to determine whether the plot location was in the ocean or on land. Records with the same geolocations were averaged together. In the end, 342 plot samples were retained for use in the mangrove AGB mapping procedures (Figure 2).

**Figure 2.** The collected mangrove plots distribution across the world. The color of each point indicated the value of aboveground biomass.

#### *2.2. Spacebrone LiDAR Data*

The GLAS instrument is the only waveform LiDAR instrument that has provided global coverage, and it was as an important data source for mapping global tree height and forest biomass. The GLAS instrument aboard the NASA (National Aeronautics and Space Administration) ICESat satellite was launched on 12 January 2003. After seven years in orbit and 18 laser-operation campaigns, the ICESat mission ended with the failure of the GLAS instrument. This instrument had three laser sensors, L1–L3, and each sensor used a 1064-nm laser pulse to record surface altimetry at 20 Hz. Each laser pulse had an ~65 m ellipsoidal footprint and was spaced at 170 m along a track with tens of kilometers between tracks [59]. We selected GLAS data from 2004 for use in mapping mangrove AGB since the quantity and quality of these GLAS data are better than those from later operational periods [46,47]. We downloaded three products (GLA01, GLA06, and GLA14) from the National Snow & Ice Data Center (https://nsidc.org/data). These three products were provided in HDF5 (Hierarchical Data Format) and contained full-waveform information (GLA01); geolocation and data quality information (GLA06); and surface elevation information (GLA14). Laser pulses from these products were linked together based on their unique ID and shot time.

Based on previous research [46–49], we applied four filtering criteria to quality control the GLAS data: (1) laser shots taken under cloudy conditions were removed; (2) data with saturation e ffects were removed; (3) the data had high signal to noise ratios (>50); and (4) data was not taken from a location significantly higher (i.e., >100 m) than the land surface elevation as indicated by the Shuttle Radar Topography Mission (SRTM) data. All GLAS data points used in this study were determined to be within Spalding et al.'s mangrove map [19]. The final GLAS dataset contained 13,686 records in areas of mangrove forests. From this dataset, three parameters were derived from the full-waveform information of each pulse (waveform extent, leading edge extent, and trailing edge extent). These GLAS parameters have been proven to be highly correlated with forest biomass, canopy height, canopy height variability, and slope of the terrain [48,60].

## *2.3. EVI Data*

We used the MOD13Q1 Version 6 product to obtain cumulative EVI for 2004. The EVI has improved sensitivity for regions of high biomass as compared with NDVI [56]. MOD13Q1 is a composite 16-day product at a 250-m resolution. The composite algorithm chooses the best available pixel value from all acquisitions within the 16-day period, selecting pixels with low clouds, a low view angle, and the highest EVI value. Cumulative EVI can provide more accurate estimates of AGB when compared with values taken from a single time period [61,62]. Therefore, we calculated cumulative EVI from the sum of all collected MOD13Q1 data, and clipped it using a 100-km coastline bu ffer. These data were used as a predictor in the AGB analysis and mapping procedure.

## *2.4. Climate Data*

In addition to using structure and spectral information from remote sensing data, we included climate data to use in model predictions of mangrove AGB (Table 1). We selected the WorldClim dataset (http://www.worldclim.org), and 50-year (1950–2000) average bioclimatic variables were calculated from monthly temperature and precipitation layers [54]. We selected eight climate variables that can be divided into two categories: precipitation and temperature (Table 1). The climate layers were obtained with a 1-km resolution and then downscaled to 250 m using a bilinear method.
