Construction and Application of an Ecological Quality Evaluation System Based on a PIE-Engine ^†

Abstract

Ecosystem services, including climate regulation and biodiversity maintenance, are vital for human well-being and sustainable development. The ecological quality evaluation system, based on the three dimensions of ecological function, ecological stability, and ecological stress, was established using the Pixel Information Expert Engine (PIE-Engine) and Moderate Resolution Imaging Spectroradiometer (MODIS) products to assess the ecological quality of the Taihu Basin from 2001 to 2020. The findings reveal that (1) the average Ecological Function Index (EFI) of the Taihu Basin showed a trend of initially decreasing and then increasing, with significant spatial differences. The highest EFI was observed in the western and southwestern regions of the Taihu Basin, which are mainly covered by forest and grassland, while the relatively lower EFI was found in the densely urbanized northeastern part of the basin. (2) The average Ecological Stability Index (ESI) of the Taihu Basin showed a similar trend to the EFI, with the rate of increase higher than the rate of decrease. The ESI was higher in the southwestern part, while in the southeastern and western parts of cropland and wetlands, the ESI was relatively low. (3) The Ecological Threat Index (ETI) of the Taihu Basin showed a fluctuating decrease followed by an increase, with the rate of increase higher than the rate of decrease. The reduction in grassland and the expansion of urban space are the main factors contributing to the increase in ecological stress. The research results of this paper will provide an important reference value for the coordinated and sustainable development of the economy and ecosystem in the Taihu Basin.

1. Introduction

Ecosystem quality refers to the health and overall condition of the whole ecosystem or its components within a certain time and space range [1]. Good ecosystem quality is a prerequisite for socio-economic development and a fundamental condition for human survival. Currently, the global ecological environment is facing serious challenges such as climate change, biodiversity loss, land degradation, and desertification. In the context of international ecological environmental protection, the evaluation of ecosystem quality is essential for monitoring ecosystem conditions and managing the ecological environment. Promoting ecosystem quality evaluation and clarifying trends in ecological changes are crucial for effective ecosystem management, protection, and economic development.

Many studies on the assessment of ecological quality have been conducted. Initially, various indices were established to assess ecological quality, such as the Normalized Difference Vegetation Index (NDVI) and the Leaf Area Index (LAI). However, most of them only analyzed specific aspects of the ecological environment and cannot reflect the overall ecosystem situation. Comprehensive models have been developed to address this issue, such as the Pressure–State–Response (PSR) model proposed by the United Nations departments [2], which reflects the conceptual framework of sustainable development with clear causal links, and the Vigor–Organization–Resilience (VOR) model proposed by Costanza [3], which fully reflects the function of ecosystems and has been widely used in different regions. With the development of remote sensing technology, the Remote Sensing Ecological Index (RSEI) has been developed based on quantitative remote sensing parameters, offering faster computation speeds and easier data acquisition [4]. However, most current ecological quality evaluation systems have complex structures and lack multi-angle analysis, making them inaccessible and limited in macro-scale evaluation and analysis. To address these problems, a comprehensive ecological quality index system was established based on the three dimensions of Function–Stability–Threat, leveraging the advantages of quantitative remote sensing data [5]. The use of the evaluation system can assess ecological quality from three perspectives: ecological function, ecological stability, and ecological threat, providing a rapid, comprehensive, and objective method for ecosystem quality detection and evaluation.

The Pixel Information Expert Engine (PIE-Engine), a one-stop real-time Earth science big data computing platform, leverages cloud computing, Internet of Things, big data, artificial intelligence, and other technologies to build a parallel and efficient underlying architecture based on cloud-native technology, providing a convenient and powerful platform for remote sensing data processing. The platform hosts a large number of remote sensing data sets and enables online on-demand real-time computing. By utilizing back-end cloud resources, it can quickly batch process extensive remote sensing images, offering advantages such as low operational difficulty, stable performance, and high efficiency [6]. In this paper, the PIE-Engine was used to observe and evaluate the ecological quality of the Taihu Basin based on the Function–Stability–Threat ecological quality evaluation model. It will provide theoretical support for the governance and protection of the ecosystem in the Taihu Basin.

2. Materials

2.1. Study Area

The Taihu Basin is located in the Yangtze River Delta region in eastern China (30°28′–32°15′ N, 119°11′–121°51′ E), covering an area of 36,900 km². It spans the southern part of Jiangsu Province, the cities of Jiaxing and Huzhou, part of Hangzhou in Zhejiang Province, and most of Shanghai (Figure 1). The average elevation of the basin is 34.4 m, with elevations ranging from −4 to 1574 m. The terrain is high in the west and low in the east. The Taihu Basin is predominantly plains, with plains accounting for two-thirds, water areas for one-sixth, and hills and mountains for the remaining one-sixth. Located in the mid-latitude region, it belongs to the subtropical monsoon climate zone. The Taihu Basin is one of the most densely populated and economically developed areas in China. Rapid urbanization poses a serious threat to regional ecological security and sustainable social and economic development.

2.2. Construction of the Remote Sensing Index of Ecological Quality

Ecosystem quality reflects the condition of the whole or part of the ecosystem within a certain time and space, which is reflected in the ecosystem function, ecosystem stability, external disturbances, and impacts on human survival and sustainable socioeconomic development. The ecological quality index constructed in this paper consists of three main components: the Ecological Function Index (EFI), which reflects the status of regional ecological function; the Ecological Stability Index (ESI), which reflects the changes in ecosystem; and the Ecological Threat Index (ETI), which indicates the pressure caused by human activities and serves as an early warning indicator.

2.2.1. Ecological Function Index

Currently, several common indicators have been used to reflect vegetation growth status in remote sensing, such as the Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR), Net Primary Productivity (NPP), and Gross Primary Production (GPP). Considering the physical significance and accessibility of these indicators, the annual average values of FVC, LAI, and GPP were selected to represent vegetation growth in horizontal and vertical directions and the strength of photosynthesis. The calculation formula was detailed in Equation (1) [7].

$$EFI={\displaystyle \frac{1}{3}}{\displaystyle \frac{FVC}{{FVC}_{ref}}}+{\displaystyle \frac{1}{3}}{\displaystyle \frac{LAI}{{LAI}_{ref}}}+{\displaystyle \frac{1}{3}}{\displaystyle \frac{GPP}{{GPP}_{ref}}}$$

(1)

EFI is the Ecosystem Function Index. FVC, LAI, and GPP are, respectively, the annual average FVC, LAI, and GPP. FVC_ref, LAI_ref, and GPP_ref serve as the reference values for each ecosystem index, with the reference value defined as the top 5% of the corresponding index. FVC was obtained from the China regional fractional vegetation cover dataset available on the PIE-Engine, LAI was sourced from the MOD15A2H dataset, and GPP was extracted from the MOD17A2H dataset.

2.2.2. Ecological Stability Index

The ecosystem stability can be expressed by the deviation from the average value of an ecosystem indicator, GPP was chosen as the evaluation indicator for ESI calculation, and the formula was detailed in Equation (2).

$$ESI=1-A\times \sqrt{{\left(GPP-{GPP}_{ref}\right)}^2}$$

(2)

ESI is the Ecosystem Stability Index. A is the normalization coefficient. GPP is the annual average GPP. In this study, GPP_ref is the mean value of GPP from 2001 to 2020.

2.2.3. Ecological Threat Index

Different land use types have different disturbances to the ecosystem, so land cover data obtained by remote sensing are used to calculate the ETI, and the formula was detailed in Equation (3).

$$ETI={\displaystyle \frac{{\sum }_{i=1}^n{A}_i{P}_i}{TA}}$$

(3)

$ETI$ is the Ecological Threat Index.$n$ is the number of land use type. $A_i$ is the area of land use type i, $P_i$ is the coefficient of land use type i, and $TA$ represents the total area. The ecological threat coefficient for land use was determined by referencing the Lohani list method, the Leopold matrix method, and the Delphi method [7]. The coefficients obtained by these methods were averaged to establish the ecological threat coefficients corresponding to different land use types.

3. Results and Discussion

3.1. Spatiotemporal Variation in EFI in the Taihu Basin

Between 2001 and 2020, the overall average EFI of the Taihu Basin fluctuated, with an average value of 0.45 (Figure 2a), showing a trend characterized by a decrease followed by an increase. Specifically, the average EFI of the Taihu Basin was 0.464 in 2001, which declined to 0.428 in 2011 and then rose to 0.457 in 2020. The average EFI value of the Taihu Basin excluding impervious surface and water body fluctuated around 0.46 from 2001 to 2011 (Figure 2b), showing a slight downward trend and an obvious upward trend from 2012 to 2022. The result indicated that during the initial decade, urbanization in the Taihu Basin led to an expansion of impervious surfaces, thereby causing a decline in the overall ecological function. During the second decade, the measures of ecological restoration and ecological protection were obviously effective.

The spatial differences of the EFI were significant (Figure 3), with the western and southwestern regions of the Taihu Basin dominated by forest and grassland, exhibiting the highest EFI value and exceeding 0.7 in most areas. The northwestern and southeastern regions dominated by cropland also displayed higher EFI values, with most areas recording EFI values above 0.5. Conversely, the densely urbanized northeastern region along the lake showed lower EFI levels, despite some cropland areas maintaining higher EFI levels. In addition, the narrow wetland area south of Taihu Lake also had lower EFI.

From 2001 to 2011, the EFI decreased in the majority of areas (Figure 4a), except for the forest region in the west and southwest. The decreased areas constituted approximately 73% of the total area. Between 2011 and 2020, only about one-fourth of the entire Taihu Basin experienced a decrease in the EFI (Figure 4b), primarily concentrated in the southeastern part of Taihu and certain cropland areas in the west. Conversely, the EFI increased throughout the rest of the Taihu Basin, with the increment mainly within 0.1.

3.2. Spatiotemporal Variation in ESI in the Taihu Basin

The spatial and temporal variations in the ESI in the Taihu Basin were also obvious, exhibiting an initial decrease followed by an increase. The average value of ESI was 0.687 in 2001, subsequently declining to 0.421 in 2010, marking a decrease of 0.266. By 2020, it had risen to 0.731, indicating an increase of 0.310 compared to 2010. These trends suggested a decline followed by the gradual improvement in the ecological stability of the Taihu Basin from 2001 to 2020, ultimately reaching its highest value in 2020.

Using the distribution in 2020 as an example (Figure 5), the ESI of forest and grassland in the southwestern region exhibited high values. Conversely, in the southeastern, southern, and western regions, dominated by croplands and wetlands, the ESI was relatively low. This indicated that different land use types are the primary factors affecting ecological stability. Consequently, based on the implementation of measures such as “Returning Cropland to Forests”, “Returning Cropland to Grassland”, and “Returning Cropland to Lakes”, promoting integrated protection and restoration efforts for mountains, water body, forests, lakes, grassland, and sands from the perspective of ecosystem integrity, ecosystem stability can be enhanced.

3.3. Change Analysis of ETI in the Taihu Basin

The overall change in the ETI in the Taihu Basin was significant (Figure 6). From 2001 to 2009, it experienced a fluctuating decline, with the ETI at 0.465 in 2001 and reaching its lowest point of 0.434 in 2009, reflecting a decrease of approximately 6.6%. The ETI gradually increased from 2009 to 2020, reaching 0.492 in 2020, which was approximately 12% higher than in 2009, with an increase of less than 0.06. Land use change is the most important threatening factor to the quality of the habitat. From 2001 to 2009, the cropland in the Taihu Basin decreased by around 6700 km², while the grassland increased by approximately 6300 km², and the impervious surface expanded by about 2600 km². The increase in grassland is the main reason for the decrease in ETI during this period. From 2009 to 2020, cropland in the Taihu Basin expanded by approximately 900 km², while the grassland decreased by about 3500 km² and the impervious surface increased by about 2300 km². Therefore, the reduction in grassland and the expansion of urban space emerged as the primary factors contributing to the increase in ETI.

4. Conclusions

Based on the PIE-Engine, the spatial and temporal changes in the ecological quality of the Taihu Basin over the past 20 years from 2001 to 2020 were analyzed in terms of three dimensions: ecological function, ecological stability, and ecological threat. The main conclusions are as follows:

(1)

The overall average EFI of the Taihu Basin exhibited a trend of an initial decrease followed by an increase between 2001 and 2020. Significant spatial differences in EFI were observed, with the highest EFI appearing in the western and southwestern areas of the Taihu basin, characterized by forest and grassland cover, while the densely urbanized northeastern part of the basin exhibited relatively lower EFI values;

(2)

The mean ESI of the Taihu Basin exhibited a trend of initial decrease followed by an increase, with the rate of increase surpassing the rate of decrease. The ESI was higher in the southwest, while it was relatively lower in the southeastern, southern, and western regions;

(3)

The ETI of the Taihu Basin exhibited a fluctuating trend, initially decreasing and then increasing. The rate of increase was higher than the rate of decrease, indicating that the risk of ecosystem quality degradation is still rising. Land use change is the most important threat factor for habitat quality, with grassland reduction and urban expansion being the primary contributors to the increase in the ETI.

Land use and land cover changes caused by rapid economic development and urbanization seriously threaten the ecological quality of the Taihu Basin. Over the past decade, the policy of “Returning Cropland to Forests, Grassland, and Lakes” has contributed significantly to ecological restoration. However, in the process of steady ecological protection and restoration in the future, it remains essential to optimize the industrial structure layout in a coordinated manner, aiming to achieve a balance between a green economy and sustainable development.