1. Introduction
Land cover and land use in different ecological regionalization contexts influence ecosystem processes at a large scale on the global scale, and a number of Earth system models depend on accurate land cover information. The study of the land cover landscape patterns and their continuous transformation is an effective means of revealing regional ecological conditions and spatial changes. Land cover is also an important factor reflecting the surface solar radiation energy [
1], and its spatial pattern distribution and dynamic change are closely related to species diversity, agricultural development, and ecological protection, among others. It is crucial in the material cycle, the energy cycle, and the sustainability of surface resources [
2,
3,
4,
5,
6].
The rapid advances in remote sensing technology have promoted the use of aerospace data for land surface mapping. At present, a large number of land cover product data are now publicly available [
7], for instance, the International Geosphere-Biosphere Programme Data and Information System Cover (IGBP DISCover) data [
8], Global Land Cover 2000 (GLC2000) data [
9], and GlobeLand30 data [
10]. The production from these land cover data supports data for global and regional ecosystem inversion, land-surface process simulation, agriculture, and other related studies [
11,
12,
13,
14]. Nevertheless, because of the diversity of sources of data and methodologies of classification, it is difficult to keep these remote sensing from multiple sources of land cover data consistent [
15,
16,
17]. The differences among multiple sources of remote sensing data are mainly reflected in the number and spatial morphological distribution of surface cover, exposing users to unpredictable uncertainty when using specific land cover data to fulfill their business needs, especially the medium- and high-resolution land cover data released publicly [
18,
19]. However, how to select appropriate land cover data and to what extent the selected data can meet the research needs of users are challenges faced by land cover users.
The performed studies at home and abroad have evaluated and analyzed the free remote sensing land cover data [
20,
21]. For instance, Giri et al. [
22] used common methods of area comparison and spatial consistency analysis to make a full-scale comparative analysis of GLC2000 and MODIS land cover products with coarse resolution. The experiments revealed that the spatial patterns for the two land cover data were less consistent under the fine type, and the percentage consistency between the two data sets also varied greatly between biomes. Giri’s comparative analytical offers insights for both data producers and users. Yang et al. [
23] assessed and analyzed seven land cover products (IGBP DISCover, UMD, GLC, MCD12Q1, GLCNMO, CCI-LC, and GlobeLand30) by using area comparison, spatial pattern comparison, and accuracy evaluation based on verification points. The results show that the GLC 2000 and CCI-LC 2000 have a high spatial agreement, with a percentage of 53.8% of the agreement region. In addition, the accuracy of the seven kinds of data was higher in the homogenous region and lower in other areas. Chen et al. [
24] evaluated and analyzed four global land cover data (MODIS, GlobCover2009, FROM-GC, and GlobeLand30) for cropland types. The experimental results indicate that the total accuracy of the four data types are between 61.26% and 80.63%, and the classification accuracy of the cropland type in GlobeLand30 is the highest. Chen’s research can provide a reference value for building a fusion of cropland classification datasets. Kang et al. [
25] assessed the consistency of three 30 m spatial resolution land cover data from northern Laos based on the landscape index method. The experimental results show that the three data differ significantly in terms of landscape indices and spatial pattern distribution. Taking the Arctic as the experimental area, Liang et al. [
26] assessed data from four global land cover products (CCI-LC, GLCNMO, MODIS, and GlobeLand30) covering the Arctic region and experimentally found that MODIS data had the lowest overall accuracy of 29.5%. Existing research on the assessment and analysis of various land cover products provides a useful reference value for land cover producers and users.
However, there are few landscape pattern evaluations of high-resolution land cover data in various ecological zones [
27]. In fact, the mapping accuracy of some complex landscape cover types is low. The ecological geographic area is in accordance with the natural geographical variation of the division or merger of different levels of regional systems; therefore, from the perspective of ecological geographical division to evaluate multi-source remote sensing land cover data analysis, in terms of ecosystem service benefits of land use, biodiversity and the regional ecological environment effects of research is very important. However, through the literature review, there has been no evaluation study on the consistency of landscape patterns of global 10 m land cover data in different ecological zones. However, through the literature review [
28,
29,
30], there are few studies on the consistent estimation and analysis of landscape patterns of high-resolution (10 m) global land cover data under different ecological zones.
Therefore, the three global land cover product data, FROM-GLC, ESA, and ESRI, were selected as the study data for this paper, using Sichuan Province, China as the study area. The consistency of landscape patterns under various ecological zones was evaluated by a process of spatial overlay, landscape index, and accuracy of the three independent validation samples, and the factors affecting data inconsistency were discussed. The experimental results can provide a reference value for the research of ecological environment monitoring, ecosystem service function, and land resources sustainable development.
5. Discussion
5.1. Analysis of the Impact of Land Cover Landscape Patterns on Research under Ecological Zoning
The land has the characteristics of an ecological environment, such as soil, hydrology, climate, vegetation, and topography. It is a complex of nature and social economy, an important production factor, and a key resource for human survival and development [
46,
47]. According to
Figure 2 of this paper, the land cover types of Sichuan Province are mainly grassland, forest, and cropland, and other types are supplemented. Vegetation types such as grassland, forest, and cropland are very important to the ecological environment and the ecosystem’s service value. The spatial distribution of landscape patterns for grassland, forest, and farmland types in the FROM-GLC, ESA, and ESRI data is shown in
Figure 11. It can be found that there are differences in landscape patterns of grassland, forest, and cropland in the three data. For example, the coverage area of the grassland type in ESRI data is small, mainly because the grassland type is determined as the shrubland type in remote sensing recognition of this data. Therefore, the mapping accuracy of vegetation types will significantly influence the results of studies related to ecological zoning (such as the impact of ecosystem service value studies) and even lead to wrong conclusions. In addition, vegetation, as the mainstay of land ecosystems, plays an irreplaceable role in recycling global material and energy, and plays a clear role in reducing atmospheric greenhouse gas concentrations and regulating the global carbon balance [
48]. Therefore, the production precision of FROM-GLC, ESA, and ESRI data makes further efforts to improve, and thus to supply, a reference for ecological environment monitoring, land ecological security assessment, and the influence of surface change on vegetation carbon stocks in various ecological zones.
5.2. Discussion of Difference Factors of Multi-Source Remote Sensing Land Cover Data
The landscape patterns of the three data differ to some extent, which is due to the influence of the classification methods, classification systems, and surface complexity used in the generation of the surface cover data [
49].
Classification systems are crucial in land cover mapping. The classification system established by land cover producers when producing global-scale land cover data is based primarily on global-scale land information characteristics [
50]. This study found that one of the reasons for the large differences in landscape patterns between the three data is the different extraction of some vegetation, such as the type of shrub by the three data, which is inextricably linked to the influence of semantic similarity between the different vegetation types. Therefore, to enhance the precision of future surface cover data acquisition, it is necessary to carefully take into account the clear definition of some vegetation types in the classification system, such as the determination of vegetation cover and tree height element values.
Different classification methods were used to produce FROM-GLC, ESA, and ESRI data. In the production of FROM-GLC data, it is assumed that the training samples of 2015 will be used for the classification of this data under the condition that the land surface type change from 2015 to 2017 is less than 5% [
31]. However, this assumption may lead to errors in the mapping accuracy of some seasonal features. When producing ESA land cover data, the L2A product’s scene classification layer was used to eliminate the effect of clouds and shadows on mapping accuracy in the Sentinel-2 data. A 10-day median composite is then calculated from the wavelength time series of data to take away other noise. However, this method of pre-processing ESA data may affect the physical information of the cropland type and thus the precision of the cropland type. Additionally, topographic data were introduced in the cartographic mapping of the three surface cover data. However, there are other data (e.g., vegetation health measurement data) [
51] that can reflect the characteristics of seasonal ground features and can be considered in the subsequent mapping to assist in the identification of vegetation (such as grassland), in order to increase the mapping precision of these easily confused vegetation categories.
Furthermore, the number and quality of the validation points obtained for this study covering the region of study also affected the experimental results of this paper. The Geo-wiki and GLCVSS validation samples cover a small number of areas of study compared to the GLCVSS validation samples. Additionally, the accuracy of these two third-party validation samples was not considered in the absolute accuracy evaluation.
In general, the rules for the development of datasets (e.g., classification systems and classification methods) established by different production organizations are a major factor in the variation between products, and this variation leads to significant challenges in the rigorous comparison of maps with each other and the synergistic use of different maps.
5.3. Advantages and Disadvantages of Remote Sensing Technology in Land Cover Data Production
Remote sensing technology has the characteristics of a large coverage area and has free and timely access to data, and has now been used in many fields, such as remote sensing mapping. The development of land cover mapping technology largely depends on the development of remote sensing technology [
52,
53,
54,
55]. Google Earth Engine (GEE) is an online data processing platform for planar geospatial analysis, with the enormous computing power of Google’s servers [
56]. The three kinds of land cover data analyzed in this paper are all based on the GEE cloud computing platform, and remote sensing mapping of land cover is carried out by using Sentinel series satellites with high spatial resolution. This suggests that the computing power provided by cloud computing can well support producing large surface area cover data using high-resolution satellite remote sensing images. Currently, there is a growing trend of research toward surface cover data production using remote sensing and cloud computing services [
57,
58,
59].
With the advent of the Sentinel series of satellites with higher spatial resolutions, the appearance of data processing tools represented by the GEE and the appearance of more advanced machine learning algorithms, the problems related to remote sensing mapping, such as the use of single-phase images or optical data, will be solved. This has created the conditions for a new generation of land cover mapping characterized by higher resolution and accuracy, as well as less human and material costs.
However, there are certain limitations of surface cover data collection, mainly in the following aspects. (1) A common problem with the current generation of global and regional land cover data is the accuracy of land cover data is low in areas with complex surface landscapes. This is mainly due to the complexity of the surface types in these areas, which are difficult to identify accurately with remote sensing techniques. (2) Some vegetation (such as forests) showed serious confusion owing to the resemble spectral characteristics of ground features. (3) When making global surface cover data, features with smaller patches (such as construction, cropland, etc.,) face great challenges due to their large scale. (4) At present, large area ground cover mapping also mainly adopts a supervised classification strategy based on the training sample, the feasibility, efficiency, and precision of mapping which has certain advantages, but the training sample collection is often an extremely time-consuming, especially for large regional or global scale surface cover mapping samples, which collected a huge workload. On the other hand, the category scheme depends directly on the a priori knowledge of the sample collector.
6. Conclusions
Based on three 10 m worldwide land cover data obtained from geodata, the consistency of the three data under various ecological zones was evaluated using the spatial overlay method, a landscape index for the quantitative evaluation of landscape ecological landscape patterns, and a confusion matrix for three interdependent validation points. The results showed that (1) the spatial consistency of FROM-GLC, ESA, and ESRI data was high in the PA0518 and PA1017 ecological zones, and the area of complete consistency was 44,420.9 km2 and 53,368.9 km2, respectively. These areas were mainly forest types. The consistency of spatial pattern distribution of these data was low under the PA0509 and PA0437 ecological divisions, which were mainly dispersed in the mountainous areas in the south of Sichuan Province and the Sichuan basin in the east. (2) From the perspective of landscape ecological evaluation, a certain degree of variation in the landscape pattern of the PD, LSI and AI indices of the three data was found under various ecological zones, indicating a low level of landscape fragmentation, landscape morphological complexity, and connectivity between patches. Therefore, it is necessary to be cautious when carrying out studies such as ecological environment monitoring and land ecological security assessment under ecological zoning based on these data. (3) The evaluation results of independent verification points show that the exactitude of the FROM-GLC, ESA, and ESRI data in the study area was low, with an OA of less than 60%. Hence, the future production quality of high-resolution land cover data in the area needs to be further improved.
Although the multiple sets of publicly available high-resolution global land surface data provide precious basic data for many in academic research, these data differ greatly in the landscape patterns of some vegetation types. Therefore, future mapping should be further aimed at improving the accuracy of these key types, or adopt data fusion methods to integrate land cover products from different data sources into high-precision new land cover products.