Consistency Analysis of Remote Sensing Land Cover Products in the Tropical Rainforest Climate Region: A Case Study of Indonesia

Abstract

Land cover changes in tropical rainforest climate zones play an important role in global climate change and the functioning of the Earth’s natural system. Existing research on the consistency of different land cover products has mainly focused on administrative divisions (continental or national scales). However, the ongoing production of large regional or global land cover products with higher resolutions requires us to have a better grasp of confusing land types and their geographical locations for different zoning (e.g., geographical zoning) in order to guide the optimization of strategies such as zoning and sample selection in automated land cover classification. Therefore, we selected the GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC2015 global land cover products with a 30-m resolution covering Indonesia, which has a tropical rainforest climate, as a case study, and then analyzed these products in terms of areal consistency, spatial consistency, and accuracy evaluation. The results revealed that (a) all three land cover products revealed that forest is the main land cover type in Indonesia. The area correlation coefficient of any two products is better than 0.89; (b) the areas that are completely consistent among the three products account for 58% of the total area of Indonesia, mainly distributed in the central and northern parts of Kalimantan and Papua, which are dominated by forest land types. The spatial consistency of the three products is low, however, due to the complex surface types and staggered distributions of grassland, shrub, cultivated land, artificial surface, and other land cover types in Java, eastern Sumatra, and the eastern, southern, and northwestern sections of Kalimantan, where the elevation is less than 200 m. Given these results, land cover producers should take heed of the classification accuracy of these areas; (c) the absolute accuracy evaluation demonstrated that the GLC_FCS30-2015 product has the highest overall accuracy (65.59%), followed by the overall accuracy of the GlobeLand30-2010 product (61.65%), while the FROM_GLC2015 exhibits the lowest overall accuracy (57.71%). The mapping accuracy of the three products is higher for forests and artificial surfaces. The cropland mapping accuracy of the GLC_FCS30-2015 product is higher than those of the other two products. The mapping accuracy of all products is low for grassland, shrubland, bareland, and wetland. The classification accuracy of these land cover types requires further improvement and cannot be used directly by land cover users when conducting relevant research in tropical rainforest climate zones, since the utilization of these products could lead to serious errors.

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

“…The area-based and pixel-based consistencies are widely used to assess the agreement between different global products. The area-based analysis mainly focuses on the consistency of the areal proportions of each LC type between the different GLC products [39,41], whereas the pixel-based analysis is concerned with the spatial (dis)agreement for each LC type between the different GLC products [34,41,42]. In this study, both an analysis of the consistency of the current 30 m GLC products and an analysis for different classes (impervious surface, forest, cropland, and water body) of current global 30 m thematic products were performed to give a more comprehensive understanding of the process of global land-cover mapping.…”

“…Then, in order to intuitively illustrate the spatial consistency between the different products, the spatial superposition method [41] was employed to obtain the pixel-by-pixel spatial correspondence for the different products. A maximum value (equal to 3) was assigned for highly consistent pixels when the target land-cover types for the three LC products were exactly the same; a value of 2 was assigned for moderately consistent pixels when only two land-cover types agreed; a minimum value (equal to 1) was assigned for lowconsistency pixels when the three LC products all had different target land-cover types [39]. To obtain more detailed information about the spatial consistency between the different products, the spatial superposition method was also applied to each pair of products.…”

“…Since GlobeLand30 and FROM_GLC were released earlier, numerous validations of these products have been conducted, and the reported accuracies have ranged from 46.0% for Central Asia [46] to 88.9% for the European Union [45] and from 57.71% for Indonesia [39] to 80.36% for Beijing, China [47], for GlobeLand30 and FROM_GLC, respectively. Recently, two sets of reported results for GLC_FCS30-2015 gave an accuracy of 65.59% for Indonesia [39] and 84.33% for the European Union [45].…”

“…Gao et al [45] investigated the performance of the three 30 m GLC products for the European Union and found that GlobeLand30-2010 had the highest overall accuracy of 88:90 ± 0:68%; this was followed by 84:33 ± 0:80% for GLC_FCS30-2015 and 65:31 ± 1:0% for FROM_GLC-2015. Kang et al [39] investigated the performance of the three 30 m GLC products for Indonesia and found that the accuracy of the 30 m GLC products requires further improvement in tropical rainforest regions: the highest overall accuracy was 65.59% for GLC_FCS30-2015. Accuracies of 61.65% and 57.71% were found for GlobeLand30-2010 and FROM_ GLC-2015, respectively.…”

“…According to reports relating to global validation campaigns, there are large differences between the overall accuracies of fine-resolution GLC products: 64.9% for FROM_GLC [12], 80.3% for GlobeLand30 [14], and 82.5% for GLC_FCS30 [16]. In spite of the validation assessments provided by the developers of these products, some third-party researchers have found considerably lower accuracies for some regions when verifying GLC products [39,40].…”