As the largest body of terrestrial ecosystems, forests play a dominant role in global ecosystem services [1
], such as maintaining biodiversity [2
], carbon storage [3
], water conservation [4
], and climate regulation [5
]. Forest loss is followed by losses of ecosystem services. Although the global deforestation rate has slowed, it remains high in many countries and regions [6
]. Reducing emissions from deforestation and forest degradation (REDD) is considered to be an important, cost-effective way to achieve large-scale emission reductions [7
]. Since the end of the 20th century, the central government of China has implemented a set of forest restoration projects, including the Grain for Green (GFG) Project, the Natural Forest Protection Project, and the Three North Shelterbelt Project [9
]. The Loess Plateau, an ecotone with a prototypically fragmented landscape, was selected as a pilot region for the GFG project and has also been an important region of interest for other projects. However, the current status (spatial extent and distribution) of the forests on the Loess Plateau (LP) continues to be uncertain, which hinders our ability to gauge the effectiveness of China’s ecological restoration projects and forest management practices. Therefore, an accurate evaluation of the existing forest products within a baseline year is important for characterizing the forest extent and distribution on the LP.
Remote sensing has become the primary approach for regional and global forest surveys [11
]. Due to limitations in the availability of remote sensing data and the technological incapacity to acquire and process large amounts of remote sensing data prior to 2010, the existing global and regional forest maps were generally based on data obtained using sensors with coarse resolutions [13
]. For example, these coarse platforms include the IGBP DISCOVER land cover data based on the Advanced Very High Resolution Radiometer (AVHRR) [14
], the UMD Land Cover Data Set based on the AVHRR [15
], the GLC2000 data based on the SPOT-VGT [16
], the GlobCover data based on the MEdium Resolution Imaging Spectrometer (MERIS) [17
], and the MCD12Q1 data based on the Moderate Resolution Imaging Spectroradiometer (MODIS) [18
Since 2008, with the free release of all the Landsat archive data by United States Geological Survey (USGS), land cover and forest mapping efforts based on Landsat data have increased rapidly. These products include the global land cover data product (GlobeLand30) from the China National Geomatics Center of China (NGCC) [19
] and the Finer Resolution Observation and Monitoring-Global Land Cover (FROM-GLC) dataset from the Tsinghua University [20
]. Utilizing the Google Earth Engine (GEE) computing platform, Hansen et al. [11
] used all the available Landsat 7 data for global forest dynamic monitoring. Based on this approach, the World Resources Institute (WRI) developed the Global Forest Watch program (http://www.globalforestwatch.org/
). The University of Maryland’s Global Land Cover Facility Data Center (GLCF) also produced a global, 30-m resolution product named Landsat Tree Cover Continuous Fields (VCF) [21
]. In addition to Landsat data, the ALOS/PALSAR data has also been widely used in recent years after the Japan Aerospace Exploration Agency (JAXA) released global forest maps in 2007, 2008, 2009, 2010, and 2015 at 25 m and 50 m resolutions [22
]. In addition to the global scale products, several national scale forest mapping efforts have been made in China. Through the integration of PALSAR and MODIS/Landsat data, Qin et al. [13
] generated a new forest map of China. Another land cover and land use product widely used in China is the NLCD-China from the Chinese Academy of Sciences [23
], which has produced several epochs of datasets for 1990, 1995, 2000, 2005, 2010, and 2015. The ChinaCover dataset is another important land cover mapping product, which was generated by the 10-year Environmental Monitoring Program in China [24
Sexton et al. [25
] found that the global forest maps with coarse resolutions showed a large discrepancy. However, the performance and consistency of the aforementioned eight medium resolution forest maps in China are still unclear. Previous studies conducted inter-comparisons among some of the products; for example, Wang et al. [26
] found substantial differences among the ChinaCover, FAO FRA, WRI, and NLCD-China data on forest area in China. The reasons for these uncertainties could be complicated, due to different factors, including data types, algorithms, validation methods, forest definitions, etc. [13
]. It is of great significance to thoroughly evaluate the accuracies of the existing medium resolution forest maps and examine the uncertainties of existing Landsat- and PALSAR-based forest mapping approaches.
Based on the above concerns, we collected, evaluated, and analyzed the uncertainty of the eight latest 30–50 m resolution forest products for 2010 on the Loess Plateau. This study attempts to answer three scientific questions: (1) What are the differences in the accuracies of a variety of medium resolution forest products on the Loess Plateau? (2) Are there any differences in the forest area estimates according to the various products? and (3) Is the spatial consistency of the eight forest maps showing a certain regulation along with different topographic gradients? The uncertainty analysis of the eight forest products not only provides a clear picture of forest distribution on the Loess Plateau, which will help us to understand the effectiveness of the existing ecological restoration projects, but also provides implications for future forest monitoring efforts based on remote sensing.
With the rapid development of remote sensing technology in recent years, especially the open access of Landsat and PALSAR data, meaningful breakthroughs have been made in the field of forest mapping. A variety of medium resolution forest maps at the regional and global scales have emerged. In this study, we collected all the medium resolution forest cover maps in a typical forest restoration region, the Loess Plateau, from 2010. The eight forest maps, including GlobeLand30, FROM-GLC, Hansen, ChinaCover, NLCD-China, GLCF VCF, OU-FDL, and JAXA, were generated by Landsat and/or PALSAR images. We evaluated and ranked all the eight forest maps based on the ground truth samples derived from a stratified sampling approach, which utilized very high resolution images from Google Earth and field photos from the Global Geo-referenced Field Photo Library. We found that for the Loess Plateau, (1) the six Landsat-based forest maps showed large variances in accuracy, (2) the 2010 GlobeLand30 forest map had the highest accuracy (0.97 ± 0.002), and (3) the PALSAR-based forest maps (OU-FDL, JAXA) generally had a relatively high accuracy (0.95 ± 0.003). Forest area estimates based on these various forest products were different, and ranged from 7.627 ± 0.077 to 10.196 ± 0.1 m ha. With the GlobeLand30 forest map as a reference, we found the county-level forest areas of OU-FDL and JAXA forest maps had high correlation with that of GlobeLand30. These high correlations indicated that the radar data is reliable in the classification of forested areas. This study also suggested that the integration of the latest optical and radar data for more accurate forest mapping is a necessity for future research. The spatial consistency of the forest maps increased along with the increasing elevation until 2000 m asl. and then decreased with higher elevation, while the forest maps showed higher consistency as slope increased. These analyses can guide the direction of future forest mapping efforts by considering topographical factors.