Recognizing land cover spatial heterogeneity is crucial for ecological process modeling, spatial pattern understanding, and environmental change analysis [1
]. Land cover heterogeneity is a key concept of land system science, a discipline that has long focused on regional structure and patterns. The heterogeneity of land cover can be quantitatively described in different forms, such as fragmentation [5
], diversity [6
], connectivity [7
], and complexity [8
]. Recently, some studies have focused on land cover heterogeneity in terms of land surface parameterization and land cover classification quality [9
]. The need for consistent and accurate information on land cover heterogeneity to support large-scale geospatial applications has been increasingly acknowledged and emphasized [11
]. Therefore, it is necessary to extract standardized land cover heterogeneity information at fine resolution and large scales to meet the requirements of scientists and policy-makers.
One of the priorities in the quantitative assessment of land cover heterogeneity is the development and the utilization of various landscape metrics, which are generally particularly well suited for use at the regional scale. In practice, different landscape metrics broadly fall into one of two categories, non-spatial and spatial, which are widely used to quantify heterogeneity composition and configuration, respectively [12
]. For instance, Riitters uses a landscape mosaic metric to identify the United States land cover heterogeneity composition to support the Forest Service Renewable Resources Planning Act Assessment [13
]. The EEA (European Environment Agency) is using an effective mesh size metric to quantify the degree of European land cover fragmentation for various human activity planning applications and the sustainable conservation of nature [14
]. Although landscape metrics are helpful for extracting regional land cover heterogeneity information, measuring land cover heterogeneity at large scales is still a difficult task. This is due to several factors, including the correlation and the robustness of different metrics and the comprehensive evaluation of multiple metrics [15
]. Further, choosing the appropriate index and constructing a complete quantization scheme for measuring large-scale land cover heterogeneity remains a challenge.
Entropy is another concept that has been introduced to measure land cover heterogeneity. It has long been considered as an excellent tool to estimate the complexity of a system [16
]. Shannon’s entropy-based indices have been widely used to quantify landscape heterogeneity in space and time [20
]. In particular, Shannon’s diversity index (SHDI) was one of the most common indicators [23
]. However, the Shannon’s-based entropy lacks the configurational information due to the fact that it only considers one-dimensional information, thus it cannot effectively describe land cover heterogeneity [25
]. Although several former attempts have been made to extend the Shannon entropy for measuring the spatial complexity, these methods based on the distance were relatively complex and varied [26
]. Recently, two classes methods have been developed to apply Boltzmann entropy in landscape ecology. Cushman proposed the first-ever idea that a landscape mosaic can be computed using the Boltzmann entropy, which adopted the total edge (TE) of a landscape mosaic to measure macrostate [31
]. The efficiency problem limited its application in real landscapes due to the large number of possibilities [33
]. Another method to compute Boltzmann entropy of a landscape gradients was proposed by Gao, adopting a hierarchical perspective to define macrostate and microstate [34
]. The configurational entropy calculated by Boltzmann entropy can distinguish different land cover patterns and provide additional perspectives to understand the relationship of complexity and entropy. The characteristic of Boltzmann entropy is the capability to capture the composition and the configurational information of a system, which appears to be more suitable for landscape quantification [35
]. A general and effective method for both landscape mosaic and landscape gradients based on Boltzmann entropy would be desirable for development.
Land cover heterogeneity can be characterized by relatively few components, and each one can be quantified by suitable independent metrics [36
]. Two main components were recognized: a more heterogeneous land cover structure, which is an area with various proportions of different cover types, called compositional heterogeneity, and a more complex spatial structure called configurational heterogeneity [37
]. In ecogeographic studies, the separation evaluation of land cover heterogeneity components, especially diversity and fragmentation, are fundamental in biodiversity and environmental research at different time scales. However, as the purpose of this study is to characterize heterogeneity and identify the heterogeneity distribution, we do not consider multiple landscape metrics to construct indicator sets; instead, we adopt an adaptive method to combine land cover composition and land cover configuration for comprehensive heterogeneity information. As the utilization of various metrics may possibly yield redundant information, limiting our approach to two metrics ensures the most effective interpretation of both structure and patterns. In other words, the more complex the calculations are, the more difficult the interpretation of the land cover heterogeneity is [38
In this study, a readily applicable measure is proposed to address the lack of a consistent and standardized framework for heterogeneity information extraction at large spatial scales. Specifically, information-theoretical metrics were employed for fusing a consistent indicator, the Land Cover Complexity Index (LCCI), for large-scale land cover heterogeneity quantification at 1 km resolution. The main objectives of this study are as follows: (1) to describe a methodology suitable for quantifying the characteristics of large-scale land cover heterogeneity; (2) to build a database of continent land cover heterogeneity for large-scale geospatial sampling and ecological assessment; and (3) to discover the heterogeneous distribution characteristics of the different continents.
This paper is organized as follows. In Section 2
, we illustrate the inconsistency problems in large-scale quantifications of land cover heterogeneity and give a solution. Section 3
introduces the key concepts for the construction of methodology in this paper. In Section 4
, we present the quantification results and compare LCCI with different single metric approaches using path analysis. Section 5
provides a summary of our results and discussion and includes our conclusions.
5. Summary and Conclusions
Usually, land cover heterogeneity appears to be captured easily by landscape metrics. Numerous indices have been used to quantify land cover heterogeneity by describing features such as density, texture, size, and area. However, choosing an appropriate method for robust quantification on a global scale is still challenging because no single index can adequately take into account the whole spectrum of spatial characteristics [36
]. In this study, a consistent indicator for large-scale land cover heterogeneity quantification was developed based on information theory. This measure effectively extracts more comprehensive information to distinguish the spatial variation of the land cover distribution at the continental level. Our experimental results suggest that the LCCI, a standardized and harmonized indicator, may be a good candidate parameter for large-scale geospatial sampling considerations of heterogeneity features [49
]. One advantage of the LCCI is its consistent information theory framework because it eliminates the need to standardize, whereas landscape metrics are characterized by multiple value ranges and strong correlations between each other that necessitate the elimination of redundancy [52
]. Furthermore, the moderate resolution of the land cover dataset can capture features at any scale, provided they are greater than 30 m, for analyses, and the temporal updateability and easy accessibility of the data should promote land cover heterogeneity data applications in environmental conservation.
Another important advantage of the entropy-based LCCI is that it extracts more abundant heterogeneity information compared with single landscape metrics by utilizing a fusion approach, thus successfully capturing information closer to the true heterogeneity of the surface. This is especially important for landscape ecology research. Within a sampling unit, the same land cover configuration may have higher LCCI values, identifying richer land cover type distributions and more complex arrangements. In such regions, edge effects may lead to unstable habitats. A recent study indicated that heterogeneous land cover mosaics may be represented as separate classes [4
]. By measuring the heterogeneity using the LCCI, similar land cover patterns can be identified that offer valuable information to relevant developers.
A thorough evaluation and comparison for all quantification indices is beyond the purpose of this study. Instead, the SHDI index was chosen as a representative basic composition metric for validating the LCCI because of its computational simplicity and ease of interpretation. The results show that the SHDI does not always express the compositional complexity of the 1 km× 1 km units. This is because it extracts not only the diversity of land cover types but also the evenness of the distributions. Therefore, small numbers of classes with even distributions may have high SHDI values, even though those regions are in actuality not high diversity regions. As a relative indicator for the assessment of heterogeneity change in the same region in different periods, it is still excellent. The diversity of land cover, however, should be fully explored through explicit classification of mosaic types in the future. For quantifying the configuration heterogeneity, the ED index is the most suitable when compared with the patch-based metrics, because it is easily computable, which is a big advantage. However, the ED index overlooks patch information, which results in underestimation of the heterogeneity. It is noteworthy that, although the LCCI is positively correlated with ED, PD, SHDI, and SPILT metrics, as shown by path analysis, the meanings of LCCI are different from them due to the fact that it quantifies land cover heterogeneity by incorporation of composition and configuration simultaneously. Because the LCCI provides comprehensive land cover heterogeneity information and thus more closely captures the actual degree of heterogeneity, we predict that the LCCI heterogeneity information can resolve fine-grained land cover variations.
Understanding the key role of the spatial scale is essential in geography analysis [53
]. No optimal measurement scale exists because land cover patterns are naturally scale-dependent [13
]. The scale should be selected based on at least one principle—the scale should be large enough to stand for one unit of landscape and reflect the heterogeneity features [54
]. Previous studies have shown that a scale of 1 km2
is useful for studying land cover heterogeneity at country and continental scales [55
]. At the continental scale, we chose 1 km × 1 km square cells for this study, because this unit size is typical for the representation of local landscapes, supporting the subsequent analysis at the national level by aggregating the available metrics. In addition, the resolution of the selected size (1 km) allows for easy resampling of socioeconomic data (1 km) for future heterogeneity change and associated driving force research.
The GlobeLand30 dataset for the year 2010 was used for extracting the African heterogeneity characteristics. However, the accuracy of the GlobeLand30 leads to uncertainties of the heterogeneity characteristics, and, hence, misclassification of land cover data is unavoidable. Heterogeneity data extracted directly from remote sensing data may be a satisfying solution for research that requires high precision heterogeneity data. A recent study improved the land cover mapping accuracy by clustering the heterogeneity types of land cover, which helped to improve the classification accuracy of remote sensing-based land cover mapping [57
]. In this study, we extracted heterogeneity information for spatial variation analysis. For environmental monitoring, obtaining the heterogeneity of each class is essential, and its success depends on the classification accuracy.
Overall, the LCCI is a novel indicator that can provide detailed information on land cover heterogeneity to support regional planning and ecological assessment. By integrating the occurrence of land cover differences between neighboring grids and information theory, we (i) propose the LCCI, a consistent scheme for the quantification of land cover heterogeneity and (ii) build a database of continent-scale land cover heterogeneity-elemental data for sustainable development monitoring and geographical analysis. Further, the performance of selected metrics at both regional and continental scales was evaluated, and the LCCI was found to enhance the robustness of land cover pattern characterization and distinction by combining both composition and configuration information. Meanwhile, our results show an improved accuracy compared with single metric approaches. We expect that our work will contribute to large-scale environmental sustainability monitoring and conservation planning by providing more direct data. Future work will attempt to apply our entropy-based index to the extraction of homogeneous land cover regions at multiple scales, which will simplify spatial statistics, increase their efficacy, and improve the meaningful to analyze.