2.1.

Forest Ecological Zone Map

Since global mapping usually involves a great amount of data and a complicated classifier, areas with similar features are grouped into a single file; the FEZ map classification is one of them.¹⁸ The underlying concept of FEZ delineation is a preclassification division of the landscape into a finite number of units that represent relative homogeneity with respect to landform, soil, vegetation, spectral reflectance, and image footprints at a project scale that is affordable.

The FEZs are based on several existing global maps, starting with the FAO GEZ map, a map designed for reporting forest and forest change statistics in the context of the FRAs.^19,20 The GEZ map is basically a map of natural vegetation types. Based on GEZ map, the type of main forest is extracted, and the other vegetation types are merged, and at the same time the boundary is modified by reference to several global products primarily developed for land cover or forest cover. Forest and nonforest areas were included in order to provide global coverage, and there are 45 FEZs in global region. The 45 FEZs are identified by a code from 1 to 45 (Fig. 1). Classification using FEZs can not only optimize the classifiers, but because the ecological zones are independent, they can also be processed in parallel to reduce processing time.

Fig. 1

FEZ map, spatial extent of 45 FEZs representing different landscapes for global forest mapping, identified by codes from 01 to 45.

2.2.

Multisource Reference Data Integration

A large number of high-quality sample points are an important guarantee for extrapolating forest classification from national to global scale. At the global scale, there are a variety of reference datasets that can be used to support 30-m GLC mapping, such as existing GLC maps at coarser resolution, 30 m or higher resolution of regional land cover data. Online-distributed geospatial datasets and services (such as GEE, Map World, and Open Street Map), as well as land cover-related services (such as Geo-Wiki and Global Crowdsourced data) also provide valuable external and interoperable ancillary sources of information for forests.^21–23

Ancillary data are less uniform than satellite image data, varying in format, accuracy, and spatial resolution. To facilitate the use of such data and their incorporation into classification and validation processes, all the ancillary data were processed and checked carefully. When all the reference data were combined, they were divided into each FEZ and checked by experts for the balance of all forest and nonforest categories. The samples were supplemented by interpretation from high-resolution images, such as Google map. Under the premise of ensuring no less than 1000 sample points per ecological zones, a total of 61,653 points were collected for training datasets and the distribution of the points is shown in Fig. 2.

Fig. 2

Spatial distribution of sample points used for training datasets, which was grouped into 45 FEZs, can be used for every FEZ separately.

2.3.

Forest Cover Mapping via Google Earth Engine

In this study, the definition of forest is the same as used in FRA of FAO, that is, land spanning more than 0.5 ha with trees higher than 5 m and a canopy cover of more than 10%, or trees able to reach these thresholds in situ.²⁴

A FEZ-based classification approach was developed, as shown in Fig. 3, which mainly consists of three steps, data preprocessing, feature selection, and accuracy assessment. This method takes the FEZs as the processing unit and processes the classification of each zone in parallel in GEE and generates GFC products as results. GEE can combine FEZs and other classification techniques well.

Fig. 3

Overall flowchart of forest cover mapping using GEE.

3.1.

Forest Cover Map

With the methodology and reference data described in Sec. 2.3, based on the database and RF classifier algorithm provided on GEE, a 30-m-resolution GFC for 2018 (GFC30,2018), as shown in Fig. 4, is generated, which was projected in a geographical (Lat/Long) projection at 0.00025 deg ($\sim 30\mathrm{m}$) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum. The result consisted of $10\times 10(\mathrm{deg})$ tiles ranges from 180 W to 180 E and 80 N to 60 S, which can be freely downloaded from Ref. 35.

Fig. 4

Global forest and climate zone map.

According to statistical results, the total area of global forest in 2018 is about $3815\times {10}^6\mathrm{ha}$, which makes up $\sim 25.60\%$ of the whole land area (about $15\times {10}^9\mathrm{ha}$). The global forest distribution is shown in Fig. 4. The global forests are unequally distributed, with zonal distribution along latitudes. These major forests include South America, the tropical rainforest area in Southeast Asia and Central Africa, the boreal forest area in Russia and Canada, and the Pacific coast and Atlantic coast. This distribution has certain relevance with the pattern of global climate zones.

The world is divided into four climatic zones, which are tropical, subtropical, temperate, and northern frigid. The distribution of global forest is spatially uneven in different climatic zones. From large to small, the forest area is tropical, northern frigid, temperate, and subtropical, as shown in Table 1 and Fig. 5. Tropic has the greatest forest area of $1.847\times {10}^9\mathrm{ha}$, accounting for 48.40% of the global forest area, which is almost a half of the GFC. While subtropical zone has the smallest forest area of $383\times {10}^6\mathrm{ha}$, accounting for 10.12%.

Table 1

Forest distribution of each climate zone and proportion of GFC.

Fig. 5

Forest distribution of each climate zone and proportion of global forest area.

The regional distribution of global forest has significant relation with temperature and precipitation. Generally, high temperature makes the growth period of vegetation longer, and abundant precipitation can improve the status of vegetation growth.

In the aspects of continental views, the forest distribution in six continents also has big differences (Fig. 6). Table 2 shows the figure of forest cover of each continent and proportion of global forest. From the table, it can be seen that the forest cover in Asia is the largest on Earth, mainly because of the broad north forest in Russia. Owing to its relatively small area, Oceania is the continent with the least forest. And South America has the highest forest coverage rate, mainly because of the Amazon rainforest, the world’s largest and contiguous tropical rainforest.

Fig. 6

Statistic map of GFC in continents.

Table 2

Forest cover of each continent and proportion of global forest.

3.2.

Validation Result

Around the world, 39,900 points created in Sec. 2.3.3 are selected as points for final validation. The distribution of these validation points for GFC Product 2018 is shown in Fig. 7. According to the validation result, the overall accuracy (OA) of GFC30 (2018) is 90.94%.

Fig. 7

Distribution of global validation points.

To better analyze the accuracy of our product, the accuracy of GFC30 across the six continents was also validated (Table 3). Accuracy for Oceania, Europe, and South America was relatively high, namely, 95.59%, 94.25%, and 91.79%, respectively. The main reason is that the remote sensing images for these areas have high cover rate so that there is a large amount of available and high-quality data for these areas. As an example, South America has dense tropical rainforest, high forest cover rate, and less influence from seasonal change. Therefore, with little limitation on temporal phases, the data for these regions have high quality.

Table 3

Preliminary accuracy assessment of forest cover 2018.

However, the accuracy of the data for Africa is not as good as expected. One important reason is that the trees in Savanna, which is the transverse zone of forest and grassland at the north and south of Africa, are distributed unevenly. Thus, the cover rate is difficult to be identified. In addition, Savanna regions have distinct climates for wet and dry seasons, and hence the cover of deciduous forests changes significantly with seasons. Therefore, the selected images may not contain the most accurate data, leading to the difference in the result of analysis and lower accuracy.