1. Introduction
Surface Ecological Status (SES) reflects the structure and function of an ecosystem. SES is influenced by surface biophysical, biochemical, and biological properties [
1,
2]. SES has wide applicability e.g., in ecological and environmental assessments, including ecosystem management and life quality evaluations [
2,
3]. SES and its spatial variations are influenced by natural and anthropogenic factors [
4,
5] e.g., in urban areas. Increased human activity is one of the most important anthropogenic factors affecting the Urban Surface Ecological Status (USES) and its changes [
5,
6,
7]. Given the high concentration of human activity in urban environments, assessing and modeling USES is crucial for urban environmental management and planning, informing decision-makers and the public about ecosystem services, and sustainability assessment in support of achieving sustainable development goals such as sustainable cities and communities [
8].
In previous studies, spectral indices derived from satellite imagery have been widely used to model SES [
1,
5,
6,
7,
9,
10,
11]. These indices include Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Normalized Difference Built-up Index (NDBI), Normalized Difference Soil Index (NDSI), Normalized Difference Water Index (NDWI), and Land Surface Temperature (LST) [
4,
5,
6,
10,
11,
12,
13]. The advantages of remote sensing (RS) data, which can provide observations over a large area and a long period of time, have been extended to SES modeling on a local, regional, and global scale [
14,
15,
16,
17]. However, the complexity in the relationship between SES and biophysical and environmental factors makes it difficult to quantify SES based on a single spectral index [
4,
5,
6]. Aggregated remote sensing indices have shown more advantages than a single index in modeling SES [
4,
18]. An integrated Remote Sensing-based Ecological Index (RSEI) was developed for the rapid assessment of SES, using satellite data [
6]. The advantages of RSEI can be summarized as (a) scalable, (b) visualizable, (c) comparable at different scales, and (d) customizable to minimize error or variation caused by other properties in the weight definitions [
4,
5,
6]. Despite these valuable benefits, the RSEI was developed solely by using spectral indices related to land surface components and surface climate. The use of index-based built-up areas in Hu and Xu (2018) [
6] and subsequent studies showed that the index cannot address the issue of bare land and sparsely vegetated areas, due to spectral confusion with the built-up areas.
Impervious Surface Cover (ISC) is one of the most important factors in distinguishing the characteristics of different types of land use and land cover in urban environments and is responsible for changing the characteristics of surface greenness, moisture, dryness, and heat [
19]. ISC has a clear physical meaning in land surface composition, suitable for comparative urban analysis [
20,
21,
22]. Hence, the inclusion of ISC can potentially increase the accuracy of modeling the USES. Based on the Vegetation-Impervious surface-Soil (V-I-S) model [
23], the percentage of each of the three fractions of impervious, vegetation, and soil covers in a pixel indicates the difference in the surface characteristics of different urban land cover/uses. This model assumes that land cover in urban environments is a linear combination of three components.
The objective of this study is to present a new analytical framework for assessing the Remotely Sensed Urban Surface Ecological index (RSUSEI) by integration of surface greenness, moisture, dryness, heat, and imperviousness using Principal Components Analysis (PCA). Based on the V-I-S model, this study intends to assess USES and compare six cities of the U.S.A including Minneapolis, Dallas, Phoenix, Los Angeles, Chicago, and Seattle which have distinct geographical, geological, climatic, environmental, and surface biophysical conditions.
2. Study Area
The new analytical framework for assessing the SES is tested in urban environments comprising the cities of Minneapolis, Dallas, Phoenix, Los Angeles, Chicago, and Seattle (
Figure 1). To select these cities, various criteria such as (1) geographical conditions, (2) climatic conditions, (3) surface characteristics, (4) density of population, and (5) physical size of the cities were considered. These cities possess different geographical, climatic, environmental, and biophysical conditions. Based on the Köppen climate classification, the selected cities have various climate types: humid continental (Dfa—Minneapolis, Chicago), tropical and subtropical desert (Bwh—Phoenix), dry-summer subtropical (Csa—Los Angeles; Csb—Seattle), or humid subtropical (Cfa—Dallas). Thus, the spatial variability of the surface cover and biophysical characteristics of these cities are different and heterogeneous.
For the selected cities in the U.S.A, the area and percentage of each land cover are different (
Figure 1 and
Table 1). The highest area of land cover in Minneapolis, Dallas, Phoenix, Los Angeles, Chicago, and Seattle cities is related to “Developed, Open Space”, “Developed, Low Intensity”, “Developed, Low Intensity”, “Developed, Medium Intensity”, “Developed, Low Intensity”, and “Developed, Low Intensity”, respectively. Among the cities, the highest percentage of “Developed, Open Space”, “Developed, Low Intensity”, “Developed, Medium Intensity”, and “Developed, High Intensity” lands is found in Minneapolis (36.0%), Chicago (45.5%), Los Angeles (44.6%), and Los Angeles (16.3%), respectively.
5. Discussion
The SES in urban environments is a function of the surface biophysical, biochemical, and biological properties. Recent studies have used the data of surface greenness, moisture, dryness, and heat for SES modeling [
4,
6,
7]. However, many processes in the urban environments are subject to the impact of surface imperviousness [
23,
44,
45,
46]. ISC has a clear physical meaning in land surface composition, suitable for distinguishing the characteristics of different types of land use and land cover in the urban environments, and is associated with changes in the characteristics of surface greenness, moisture, dryness, and heat [
19,
20,
22,
45]. This study shows that the association degree of imperviousness is higher than surface heat, greenness, dryness, and moisture. Therefore, considering surface imperviousness information in USES modeling is very necessary. Other studies have also shown that surface imperviousness affected the SES [
4,
47,
48]. For many cities around the world, surface imperviousness data are available with functional spatial resolution for urban modeling. In this study, five components including surface greenness, moisture, dryness, heat, and imperviousness are considered for RSUSEI development. Results showed that RSUSEI is highly capable in the modeling of the USES spatial heterogeneity in cities with different geographical, climatic, environmental, and biophysical conditions. This index has a high capacity to differentiate between USES of different land covers. Assessment and modeling of USES are crucial in sustainability assessment in support of achieving sustainable development goals such as sustainable cities and communities [
8]. Hence, RSUSEI can be used for assessing urban sustainability over space and time.
6. Conclusions
In this study, an analytical framework is proposed for assessing the SES in urban environments and tested in six selected cities in the U.S.A, i.e., Minneapolis, Dallas, Phoenix, Los Angeles, Chicago, and Seattle. This analytical framework is centered on a new index, Remotely Sensed Urban Surface Ecological index (RSUSEI), which integrated satellite derived information on the greenness, moisture, dryness, heat, and imperviousness in a city. The results showed that the spatial distribution of USES varied with the cities and land cover types. In general, land covers with low vegetation density and moisture, and high heat, imperviousness, and dryness exhibit high RSUSEI values and poor USES, and vice versa. The USES in arid regions, such as Los Angeles, are found to be worse than the USES in humid regions, such as Seattle. The association degree of ISC is higher than nLST, nNDVI, nNDSI, and nWetness in the RSUSEI modeling. An increase in surface imperviousness reduces surface vegetation density and moisture while increasing surface dryness and heat degree, thereby worsening USES. Our results show that RSUSEI has a high capability in revealing the differences in USES within and between cities with different geographical, climatic, environmental, and surface conditions. Due to the functional spatial resolution and continuity of Landsat imagery, the results of this study can be very useful in USES modeling in urban environments with different biophysical, geographical, and climatic conditions. In addition, the availability of NLCD data products in the U.S.A is highly beneficial for USES assessment, monitoring, and modeling. RSUSEI can be used for assessing urban sustainability over space and time. It is suggested that in future studies, the efficiency of disaggregation models in improving the spatial resolution of USES maps should be considered. It is also useful to compare the performance of different spectral indices in surface imperviousness modeling to assess USES. In addition, RSUSEI can be used as a time series to monitor and model the long-term changes in a region and to quantify the impact of anthropogenic activities on USES.