Next Article in Journal
Assessment of Safe and Sustainable Operation for Freight Transportation Company Based on Tire Set Configurations Used in Its Trucks’ Fleet
Previous Article in Journal
Assessment of Debris Flow Triggering Rainfall Using Parameter-Elevation Relationships on an Independent Slope Model
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 4
10.3390/su17041501
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effect of Topographic Factors on Ecological Environment Quality in the Red Soil Region of Southern China: A Case from Changting County

by
Junming Chen
1,
Guangfa Lin
2 and
Zhibiao Chen
2,*
1
College of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China
2
Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou 350117, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(4), 1501; https://doi.org/10.3390/su17041501
Submission received: 15 January 2025 / Revised: 8 February 2025 / Accepted: 10 February 2025 / Published: 12 February 2025
Browse Figures
Review Reports Versions Notes

Abstract

:
The evaluation of ecological environment quality (EEQ) is an important method to determine regional eco-environment status, and topography, as one of the key factors affecting eco-environment, has an impact on the EEQ by influencing hydrothermal conditions. However, research on the effect of topography on the EEQ still needs to be strengthened, especially in the red soil region of southern China. Therefore, based on the evaluation of the EEQ for Changting County using the remote sensing ecological index (RSEI) combined with Landsat images from 2000 to 2019, the effects of topography on the EEQ were analyzed further. The main findings indicated, firstly, that the average values of topographic factors increased as the EEQ grade raised; secondly, the distribution of the EEQ gradually moved to the lower terrain factor categories as the EEQ grade declined for each study period on the whole; thirdly, the coupling effect of any two topographic factors on the EEQ was greater than the effect of a single topographic factor, and the coupling effect of the aspect with the elevation and topographic position index (TPI) on the EEQ was the most prominent. The main findings of the research can enhance the understanding of the variability of the EEQ and the effects of topography on the EEQ.

1. Introduction

Ecological Environment Quality (EEQ) is a measure of how well the eco-environment supports the sustainable development of humankind, drawing on ecological theory [1], and the evaluation of the EEQ can not only identify the eco-environmental conditions quantitatively but also indirectly reflect the value of ecosystem services and products provided [2,3]. At present, many methods and theories have been proposed by scholars to evaluate the eco-environment status of different spatial–temporal scales, such as the comprehensive index method [4,5,6], ecological footprint method [7,8], and artificial neural network [9,10], among which the index system method is the most widely used. Notably, the remote sensing ecological index (RSEI) stands out as a prominent comprehensive indicator system due to the advantages of data visualization, real-time data acquisition extensive area coverage, and periodic and repeated observation compared with the ecological index (EI) and ecological quality index (EQI) proposed by the Ministry of Ecology and Environment of the People’s Republic of China, which is built on the basis of NDVI [11], WET [12,13], NDBSI, and LST [14,15] indexes, integrated with Principal Component Analysis (PCA) [16] and is widely applied in evaluating the EEQ across various spatial–temporal scales, including counties [17], provinces [18], cities or urban agglomeration [19,20], counties [21,22], basins [23,24], natural reserves [25], and so on.
The evaluation outcomes from the RSEI can not only provide direct insight into the spatial distribution of the EEQ across different grades but also offer an indirect reflection on spatial–temporal variation trends, spatial aggregation characteristics, and relationships with the indicators, which can be generally achieved by performing Global Moran’s I, Local Moran’s I, the Geodetector model, etc. [16,26,27]. In particular, the analysis of the primary driving factors revealed the explanatory powers and coupling effects of the affecting factors on the EEQ, such as precipitation [28], land surface temperature, elevation and slope [29], land use [30], GDP, and population [26,31], among others, indicating the effects of factors on the eco-environment are interactive and cumulative. In fact, the surface hydrothermal conditions are one of the most fundamental factors affecting eco-environmental conditions. Topography, in turn, has the most direct impact on the distribution of the hydrothermal conditions by changing surface illumination conditions across different terrains [32], which subsequently affects vegetation coverage [33], soil moisture [34], land surface temperature (LST) [35], and the intensity of human activities [36,37], among other factors.
Generally, the higher the elevation, the steeper the slope; when there are fewer human activities and when the vegetation coverage is greater, the ecological environment status improves, and conversely, in flatter areas with more human activities and less vegetation coverage, the ecological environment status tends to decline [38]. Likewise, the ecological environmental conditions at the shady slopes tend to be better than those of the sunny slopes due to weaker transpiration and higher vegetation coverage [34,38]. To sum up, topography can indirectly influence the EEQ by affecting the hydrothermal conditions, and it is worth discussing the effect of different terrain features on the EEQ, but there is still relatively little research on this topic.
In previous research [39], Changting County, as a typical zone of soil erosion within the red soil region of southern China and a key rare earth industry base [40], was selected as the study area, and its EEQ from 1995 to 2019 was evaluated using the RSEI based on the selected evaluation indicators—NDVI, WET, NDBSI, and LST—integrating with the PCA method, and the spatial–temporal distribution changing characteristics of the EEQ were revealed by employing Global Moran’s I, Standard Deviational Ellipse, and Kernel Density Estimation, showing first a declining trend from 1995 to 2000 and then increasing until 2019; the overall level of the EEQ was good, the majority of the study area was classified into the moderate-to-good grades; there was also a clustered spatial distribution with a strong positive correlation and a directional distribution of southwest–northeast direction for the EEQ during the four periods (1995, 2000, 2010, and 2019).
Due to the strongly acidic nature of red soil, characterized by low fertility, poor water retention, a heavy and sticky texture, and high concentrations of iron and aluminum oxides and the uncontrolled development [40], Changting County has become one of the most severely affected by soil erosion, resulting in eco-environment degradation and an undulating terrain. Therefore, based on previous research, the evaluation outcomes of the EEQ from 2000 to 2019 were resused for revealing the relationship with the selected topographic factors, including elevation, slope, aspect, relief amplitude (RA), and topographic position index (TPI). This study aims to address the following scientific questions: (1) what the changing trend of topographic factors across the EEQ grades was; (2) what the distribution characteristics of each grade EEQ across different categories of topographic factors were; and (3) how the explanatory powers and coupling effects of the topographic factors on the EEQ were. The results of this study could help researchers enhance their understanding of the effect of topography on the EEQ and provide a reference for formulating regional eco-environment protection and restoration strategies.

2. Materials and Methods

2.1. Introduction to Study Area

Changting County, situated in the western part of Fujian Province, China (25.31°~26.03° N and 116.01°~116.66° E), spans an area of 3104.16 square kilometers, as shown in Figure 1. This region is typically characterized by a subtropical humid monsoon climate, with an annual average temperature of around 18.3 °C, an annual average precipitation of approximately 1700 mm, and an annual average frost-free period of about 260 days. The topography is predominantly hilly, which accounts for approximately 71% of the total area, and is characterized by a forest coverage rate of 74% [41]. Historically, due to long-term irrational exploitation, Changting County was among the most severely affected areas by soil erosion. However, over the past two decades (from 2000 to 2019), the implementation of soil erosion control measures such as ecological forestry and grassland planting, low-efficiency forest transformation, fruit cultivation, orchard renovation, and terracing has significantly improved the ecological environment.

2.2. Methodology

2.2.1. Data Sources and Processing

(1)
The administrative boundary vector data for Changting County, situated within Longyan City of Fujian Province, was generously provided by the Fujian Provincial Natural Resources and Geographical Information Center located in Fuzhou City, Fujian Province, China, which was used for masking DEM.
(2)
The evaluation results of the EEQ for the study area from 2000 to 2019 were achieved using RSEI integrating with Landsat ETM+, TM, and OLI/TIR images, respectively, which was detailed in the previous research [39], and each grade EEQ was converted to points whose terrain characteristics were determined using the Extract Multi Values to Points tool in ArcGIS Pro 3.2, which was used for calculating the average value of each topographic factor for each grade EEQ to reveal the changing trends of topography across different EEQ grades.
(3)
A digital elevation model (DEM) with a 30 m grid resolution was downloaded from the Geospatial Data Cloud Website (http://www.gscloud.cn/). This DEM was employed for extracting topographic factors using ArcGIS Pro 3.2, including slope, aspect, relief amplitude (RA), and topographic position index (TPI).
(4)
The study area was divided into 0.33 × 0.33 km2 grids to generate a total of 28,420 grid points using the Fishnet tool in ArcGIS Pro 3.2, and the terrain characteristics of each grid point were still determined using the Extract Multi Values to Points tool in ArcGIS Pro 3.2, which was used for calculating the percentage of the grid points falling in each of the topographic factors categorization for each EEQ grade and the explanatory powers for the EEQ combining with Geodetector model to reveal the terrain distribution characteristics of the EEQ and the primary affecting factors for topography on the EEQ.
(5)
Figure 2 provides a detailed description of the data processing procedures and purposes.

2.2.2. Selection of Topographic Factors

Topography stands out as a critical factor influencing the eco-environment. Drawing from the existing studies [42,43,44], elevation, slope, aspect, relief amplitude (RA), and topographic position index (TPI) factors were selected to investigate the relationship with the EEQ. Among which, RA factor can be calculated using Equation (1) [44].
R A = E m a x − E m i n
where Emax and Emin are the maximum and the minimum elevations within a specified neighborhood, respectively, which was achieved using the Focal Statistics Tool in ArcGIS Pro 3.2, with a rectangular neighborhood defined by a width and height of 3 cells. Compared with the RA factor, the TPI factor is usually used to delineate the microscopic terrain undulation and geomorphology, which can be calculated using Equation (2) [45].
T P I = ln ⁡ E E 0 + 1 × S S 0 + 1
where E and S are the elevation and slope, respectively, and E0 and S0 are the average values of the elevation and slope for the study area. Moreover, these topographic factors were extracted from DEM with a 30 m resolution, which were classified into various categories through the ArcGIS Pro 3.2 platform using diverse classification methods, including Equal Interval, Natural Breaks (Jenks), and Manual methods (Table 1).

2.2.3. Geodetector Model

The Geodetector model is often used to reveal the driving force behind geographical processes or phenomena, including factor detectors, interactive detectors, risk detectors, and ecological detectors [33]. In this study, we assumed the RSEI values of the study area from 2000 to 2019 as the dependent variable, the selected five topographic factors as independent variables, and employed the factor detector and interactive detector to explore the effect of each topographic factor and the coupling effect of any two topographic factors on the EEQ of the study area, respectively. And the effect of each factor can be calculated using Equation (3) [46].
q = 1 − ∑ h = 1 L N h σ h 2 N σ 2 = 1 − S S W S S T
where q is the explanatory power of each topographic factor on the EEQ or evaluation indicators, ranging from 0 to 1, and the greater the q value, the stronger the effect of the topographic factor; on the contrary, the smaller the q value, the weaker the effect; L is the number of the classifications for each topographic factor; N and Nh represent the number of all units and the number of units located in the class h, respectively; σ2 and σh2 represent the variance of the RSEI value and the variance of the RSEI value in the class h, respectively; SSW is the sum of variances of units in each class, and SST is the total sum of variance of all units [46]. Similarly, the coupling effect of any two topographic factors is also calculated using Equation (3) based on overlapping the two topographic factors as independent variables, which is used to compare with the explanatory power (q) of single topographic factors to confirm that the coupling effect is enhanced or weakened [33].

3. Results

3.1. Changing Trend of Topographic Factors for the EEQ

Figure 3 and Table 2 illustrate the trend for the average values of topographic factors—elevation, slope, RA, and TPI—corresponding to the EEQ grades from 2000 to 2019. In 2000, a distinct pattern was identified: regions with higher EEQ grades corresponded to areas with greater average values of topographic factors, suggesting that human activities were predominantly concentrated in regions with gentler terrain, while steeper terrains were less conducive to economic and social activities.
Notably, in 2010, the average values of topographic factors across the EEQ grades exhibited a decline followed by an increase. Over the decade from 2000 to 2010, the EEQ of the study area saw significant improvements, with many areas originally classified as worse, poor, moderate, or excellent transitioning to the good grade. Despite these improvements, a few areas with the poor EEQ remained in steep terrain, possibly due to the difficulty of ecological restoration or a shift in human activities to these regions. The trends in the average values of topographic factors from the poor to the excellent grade in 2010 were almost consistent with those observed in 2000. Similarly, the trend in the average values of topographic factors across the EEQ grades in 2019 was almost identical to that in 2000. Collectively, from 2000 to 2019, the variation patterns of the mean values of topographic factors showed a sustained increasing trend as the EEQ grade increased.

3.2. Terrain Distribution Characteristics of Different Grades EEQ

Additionally, we delved into the spatial distribution of each grade EEQ across various topographic factor categories, as illustrated in Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8
(1)
Distribution Characteristics of the EEQ at the Elevation Factor
As depicted in Figure 4, in 2000, the EEQ in the excellent grade was predominantly found at altitudes between 655 and 1000 m above sea level. This distribution was attributed to the higher vegetation cover and reduced human activities in these high-altitude areas. Over the period from 2000 to 2019, as the overall EEQ level of the study area saw continuous improvement [39], the excellent grade began to encompass regions at lower altitudes, specifically between 212 and 655 m. From 2000 to 2019, the EEQ in the good grade was relatively evenly distributed across the altitude range of 212 to 1000 m, indirectly reflecting the good ecological conditions prevalent in most parts of the study area [39]. Significantly, the EEQ, ranging from the moderate to the worse grade, was predominantly distributed at altitudes between 212 and 385 m, attributed to the increased human activities in these areas, presenting a pattern where higher elevations were associated with smaller areas, which were not suitable for human activities.
Sustainability 17 01501 g004
Figure 4. Effect of the elevation factor on the EEQ for each grade from 2000 to 2019.
Figure 4. Effect of the elevation factor on the EEQ for each grade from 2000 to 2019.
Sustainability 17 01501 g004
(2)
Distribution Characteristics of the EEQ at the Slope Factor
Figure 5 demonstrates that the EEQ in the excellent and good grades was mainly found on the slopes ranging from 5° to 35°. This distribution was primarily attributed to the higher vegetation coverage and the relative scarcity of human activities within these slope ranges, and when the slope exceeded 35°, the areas began to decrease gradually, indicating as the slope increases, not only do human activities become impractical, but the conditions also become increasingly unsuitable for vegetation growth. Notably, areas with a moderate and poor EEQ were distributed in the regions with slopes of less than 15°, while areas with worse EEQ were located in the regions with slopes of less than 2°, indicating human activities primarily occurred in the terrain with gentle slopes (less than 15°) and areas of the slopes with less than 5° exhibited a significant increase compared with those in the good and excellent grades. Furthermore, as the slope increased, the intensity of human activities correspondingly decreased, the vegetation coverage increased, and areas of the poor and worse grade EEQ decreased.
Sustainability 17 01501 g005
Figure 5. Effect of the slope factor on the EEQ for each grade from 2000 to 2019.
Figure 5. Effect of the slope factor on the EEQ for each grade from 2000 to 2019.
Sustainability 17 01501 g005
(3)
Distribution Characteristics of the EEQ at the Aspect Factor
We categorized slopes with azimuth angles between 112.5° and 292.5° as sunny slopes, and those with azimuth angles between 337.5 and 22.5° and between 292.5 and 337.5°, as well as between 22.5 and 112.5°, as shady slopes. There is a significant difference in hydrothermal conditions between sunny and shady slopes, primarily attributed to variations in received solar radiation. Shady slopes, receiving less solar radiation and exhibiting weaker transpiration, have better moisture conditions, which are conducive to vegetation growth. Consequently, vegetation coverage on sunny slopes is typically lower than on shady slopes. As depicted in Figure 6, from 2000 to 2019, the EEQ of the excellent grade was predominantly found in shady slope areas. However, due to the EEQ in the good and moderate grades covering the majority of the study area [39], the distributions of these grades across sunny and shady slopes were nearly even from 2000 to 2019. Notably, there was a significant increase in the areas of the EEQ distributed in flat terrain (azimuth angle of −1) from the moderate to the worse grade, compared to the good and excellent grades. This indicated that human activities were primarily concentrated on flat terrain.
Sustainability 17 01501 g006
Figure 6. Effect of the aspect factor on the EEQ for each grade from 2000 to 2019.
Figure 6. Effect of the aspect factor on the EEQ for each grade from 2000 to 2019.
Sustainability 17 01501 g006
(4)
Distribution Characteristics of the EEQ at the RA Factor
From Figure 7, it was observed that the trend of the excellent and good grades from 2000 to 2019 was nearly identical, exhibiting a pattern of initial increase followed by a decline. The distributions of the excellent and good grades EEQ for the RA factor of 4.438–14.698 m were more prominent because of fewer economic and social activities, greater vegetation coverage, and better soil moisture conditions. Starting with the interval of 4.438–9.151 m, as the RA factor value increased, there was a general downward trend, suggesting that higher RA values were less favorable for soil moisture retention and vegetation growth. Similarly, in the moderate, poor, and worse grades, there was a noticeable increase in the area of EEQ distributed in the interval of less than 4.438 m from 2000 to 2019. This also indicates that higher RA values are less conducive to human activities.
Sustainability 17 01501 g007
Figure 7. Effect of the RA factor on the EEQ for each grade from 2000 to 2019.
Figure 7. Effect of the RA factor on the EEQ for each grade from 2000 to 2019.
Sustainability 17 01501 g007
(5)
Distribution Characteristics of the EEQ at the TPI Factor
In Figure 8, in the excellent grade, the trend of the areas for all the categories of the TPI showed a pattern with increasing as the TPI rose. Likewise, the EEQ in the good grade had almost covered all the categories of the TPI from 2000 to 2019. Similarly, it was because the areas of the good grade covered the majority of the study area [39]. The trends of the moderate and poor grades were opposite to that of the excellent grade, and the EEQ in the categories of 1.168–1.497 and 1.498–1.838 were more prominent, while, as previously mentioned, the areas of the EEQ for the worse grade distributed on the TPI of less than 0.796 became very outstanding, except that in 2010. This pattern was attributed to human activities concentrated in the relatively flat terrain, and as the TPI increased, economic development became increasingly unfeasible, human activity intensity gradually decreased, and vegetation coverage gradually increased.
Sustainability 17 01501 g008
Figure 8. Effect of the TPI factor on the EEQ for each grade from 2000 to 2019.
Figure 8. Effect of the TPI factor on the EEQ for each grade from 2000 to 2019.
Sustainability 17 01501 g008
Notably, the terrain distribution characteristics of the worse grade EEQ in 2010 were different from those in 2000 and 2019, which was primarily concentrated in relatively steep terrain regions, including the TPI ranging from 1.498 to 2.983, the slopes ranging from 15° to 25°, and the elevations ranging from 655 to 1000 m. According to the 2010 Government Work Report of Changting County [47], the soil erosion control task of the practical projects for the people assigned by the Fujian Provincial Party Committee and Provincial Government was comprehensively completed, indicating the ecological environment was greatly improved. This was consistent with the changing trend of the EEQ from 2000 to 2010 [39]. However, there were still lots of patches scattered in the regions with poor soil and difficult access, which made the governance challenging. Therefore, the Fujian Provincial Party Committee and Provincial Government have decided to continue the soil erosion control project in Changting County, focusing on the management of mines and gully erosion areas. This also indirectly explains the reasons for the terrain distribution of the worse grade EEQ in 2010.
To sum up, human activities mainly focused on the relatively flat terrain, causing the EEQ of this region to tend to be the worse, poor, and moderate grades. The measures of combining biology and engineering should be performed such as fruit cultivation, orchard renovation, terracing, and so on. However, for the regions with the worse and poor grades located in the relatively steep terrain, it was imperative to minimize human activities and primarily consider natural restoration measures, combined with grassland planting and low-efficiency forest transformation, etc. Naturally, various restoration measures must be complemented by one another in order to more effectively improve the EEQ of the study area.

3.3. Primary Driving Factors of Topography for the EEQ

The explanatory power (q-values) of each topographic factor on the EEQ of the study area from 2000 to 2019 can be explored using the factor detector model (Table 3). The factors with the greatest effect on the EEQ are elevation, TPI, and TPI in the four periods (2000, 2010, and 2019), respectively. It follows that the relationship between topographic factors and the EEQ is not static. Since 1995, the industrial development of the study area has been attached importance, and the urbanization process has sped up, causing the dramatic changes of the Earth’s surface superimposed on different topographic factors to affect the EEQ. With the support of various soil and water conservation measures and eco-environment protection policies, the EEQ of the study area was improved from 2000 to 2010, and the main topographic factor affecting the EEQ was changed from elevation to TPI, and then the EEQ became gradually stable from 2010 to 2019 as well as there is no change about the main topographic factor affecting the EEQ.
Table 4 and Figure 9 show that the coupling effect of two factors among the topographic factors is greater than that of any single factor on the EEQ. The two factors with the maximum q-values are elevation and aspect, aspect and TPI, and aspect and TPI for the three periods (2000, 2010, and 2019), respectively. And we can find that the change trend of the combination of two topographic factors with the maximum q-values are consistent with ones of any single topographic factor with the maximum q-values on the EEQ (2000–2019) further. Similarly, the same change trend had appeared from 2010 to 2019. To sum up, these results are a good reminder that it is necessary to pay more attention to the effect of the TPI on the eco-environment when making eco-environment protection and restoration measures.

4. Discussion

4.1. Rationality of Topographic Factor Classifications

In this research, we primarily employed three methods for the reclassification of topographic factors, including Natural Breaks (Jenks), Equal Interval, and Manual methods. Among these, the classifications of the aspect factor were generated using the Equal Interval method. However, the Equal Interval method is not applicable to other topographic factors because the classification outcomes fail to consider how data are distributed along the number line. In addition, the Natural Breaks method is good for mapping data values that are not evenly distributed. Therefore, this method was applied to the classifications of the elevation, RA, and TPI factors, and the classifications of the elevation factor were also manually fine-tuned, combining personal experiences and local topography and geomorphology. Notably, the slope factor was categorized according to the slope grading method established by the Geomorphological Survey and Mapping Committee of the International Geographical Union. In fact, the categories of topographic factors were rarely discussed in some existing studies, which was derived from personal experiences or software analysis results in general [32,36,37].
The classifications of topographic factors can affect directly the analysis results. If the classifications of topographic factors are more detailed, the analysis results reflected are more refined; however, there is a possibility of redundant results. Conversely, if the classifications are too coarse, it may fail to reflect the truth. Moreover, different terrains and landforms require different classification criteria. Therefore, the discussion on the classifications of topographic factors will be worthy and crucial in the next step.

4.2. Limitations of Geodetector Model

The Geodetector model is often used to analyze the driving force factors behind geographical phenomena or processes, which can be achieved by analyzing the explanatory power of each affecting factor and the synergistic effect of any two factors [33,48]. In the research, the factor detector and the interaction detector in the Geodetector model were performed to reveal the relationship between the topography and the EEQ of the study area, showing elevation, TPI, and TPI factors had the greatest explanatory power for the EEQ in 2000, 2010, and 2019, respectively, and the superimposed effects of elevation and aspect, aspect and TPI, and aspect and TPI on the EEQ were the greatest from 2000 to 2019. Meanwhile, we also found that the coupling effect of any two factors was greater than the effect of a single factor.
However, there are still some limitations for the Geodetector model. For example, Geodetector requires the independent variables to be categorical so that all the independent variables need to be discretized. But now there is a lack of unified discretization standards. Common discretization methods include the Equal Interval method, the Quantile method, the K-means clustering method, and so on [49]. Different discretization methods may lead to different analysis results so as to affect the comparability and credibility of the results. Moreover, Geodetector uses q-values to measure the explanatory power of factors for dependent variables, but there are currently no clear interpretation standards for q-values [49]. Similarly, although the interaction detector can determine the interaction effects of two factors, it cannot explain the underlying specific mechanisms [49], and it cannot also analyze the coupling effect of three or more factors on the EEQ. Interestingly, Wu P. et al. explored the relationship between land use and topographic factors in Fuzhou City using the Apriori algorithm [50], which can clearly describe the association rules with confidence level and support level between the subcategories of topographic factors and land use. This way, including other machine learning algorithms may help to gain a deep understanding of the relationship between the EEQ and topographic factors.

4.3. Applicability of the Conclusions

According to the above analysis, the effect of topography on the EEQ is still quite evident, mainly manifested in the different distribution of the EEQ across varying topographic characteristics. In general, a distinct pattern emerged: the lower the elevation, the flatter the slope, the smaller the RA and the TPI, the greater the intensity of human activities, the lower the vegetation coverage, the lower the NDVI and WET indices, and the higher the NDBSI and LST indices, resulting in a worse EEQ. Conversely, as the terrain became steeper, the intensity of human activities declined, vegetation coverage increased, the NDVI and WET indicators rose, and the NDBSI and LST indicators fell, leading to improved EEQ. However, the applicability of this finding is worthy of further discussion. Combining with the geographical characteristics of the study area, this conclusion is only valid for the coastal hilly regions of China at present.

5. Conclusions

In this research, we constructed the EEQ evaluation model of the study area based on the RSEI and assessed the EEQ for three periods (2000, 2010, and 2019). Moreover, the effects of different topographic characteristics on the EEQ were analyzed further. The primary conclusions could be summarized as follows:
(1)
In general, across all study periods, the trends in topographic factors exhibited a consistent pattern: the average values rose in tandem with the increase in the EEQ grades.
(2)
Areas in the excellent grade were mainly found at elevations ranging from 385 to 1000 m, on the slopes between 5° and 35°, and on the shady slopes. These areas corresponded to the RA of 4.438–14.698 m and the TPI of 1.168–2.983. The regions in the good grade encompassed nearly all the classifications of the topographic factors. Typically, for the moderate, poor, and worse grades, there was a trend of decreasing areas as topographic factor classification increased. However, in 2010, the worse grade EEQ was observed in the regions with relatively steep terrain compared with those in 2000 and 2019; this may be because there still are some patches scattered in the regions with poor soil and difficult access, which has been difficult to govern since 2010.
(3)
Elevation, TPI, and TPI factors had the greatest explanatory power for the EEQ in 2000, 2010, and 2019, respectively. Simultaneously, the coupling effect of any two topographic factors on the EEQ was greater than that of a single factor, and elevation and aspect, aspect and TPI, and aspect and TPI had the greatest coupling effect on the EEQ for the three periods (2000, 2010, and 2019), respectively, which also better reminds us that it is necessary to pay more attention to the comprehensive impact of topographic factors.

Author Contributions

Methodology, J.C.; software, J.C.; validation, J.C., G.L. and Z.C.; data curation, J.C.; writing—original draft preparation, J.C.; writing—review and editing, G.L. and Z.C.; visualization, J.C.; supervision, G.L.; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 42277013) and the Program for Cultivating Innovative Team, Fujian Normal University (Y0720409B06), China.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Liu, P.; Ren, C.; Wang, Z.; Zhang, B.; Chen, L. Assessment of the eco-enviromental quality in the Nanweng River Nature Reserve, Northeast China by remote sensing. Chin. J. Appl. Ecol. 2018, 29, 3347–3356. [Google Scholar] [CrossRef]
  2. Ministry of Environmental Protection of the People’s Republic of China. Available online: https://www.mee.gov.cn/image20010518/6257.pdf (accessed on 25 December 2024).
  3. Ministry of Ecology and Environment of the People’s Republic of China. Available online: https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/stzl/201503/W020150326489785523925.pdf (accessed on 25 December 2024).
  4. Hu, X.; Xu, H. A New Remote Sensing Index for Assessing the Spatial Heterogeneity in Urban Ecological Quality: A Case from Fuzhou City, China. Ecol. Indic. 2018, 89, 11–21. [Google Scholar] [CrossRef]
  5. Ma, H.; Shi, L. Assessment of Eco-Environmental Quality of Western Taiwan Straits Economic Zone. Env. Monit Assess 2016, 188, 311. [Google Scholar] [CrossRef] [PubMed]
  6. Guo, Z.; Wei, W.; Zhang, X.; Li, Z.; Zhou, J.; Xie, B. Spatial distribution characteristics and influencing factors of eco-environmental quality based on RS and GIS in Shiyang River Basin, China. Chin. J. Appl. Ecol. 2019, 30, 3075–3086. [Google Scholar] [CrossRef]
  7. Liu, T.; Wang, H.; Wang, H.; Xu, H. The Spatiotemporal Evolution of Ecological Security in China Based on the Ecological Footprint Model with Localization of Parameters. Ecol. Indic. 2021, 126, 107636. [Google Scholar] [CrossRef]
  8. Miao, C.; Sun, L.; Yang, L. The Studies of Ecological Environmental Quality Assessment in Anhui Province Based on Ecological Footprint. Ecol. Indic. 2016, 60, 879–883. [Google Scholar] [CrossRef]
  9. Shi, Z.; Li, H. Application of Artificial Neural Network Approach and Remotely Sensed Imagery for Regional Eco-Environmental Quality Evaluation. Env. Monit Assess 2007, 128, 217–229. [Google Scholar] [CrossRef] [PubMed]
  10. Nourani, V.; Foroumandi, E.; Sharghi, E.; Dąbrowska, D. Ecological-Environmental Quality Estimation Using Remote Sensing and Combined Artificial Intelligence Techniques. J. Hydroinform. 2021, 23, 47–65. [Google Scholar] [CrossRef]
  11. Fung, T.; Siu, W. Environmental quality and its changes, an analysis using NDVI. Int. J. Remote Sens. 2000, 21, 1011–1024. [Google Scholar] [CrossRef]
  12. Baig, M.H.A.; Zhang, L.; Shuai, T.; Tong, Q. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sens. Lett. 2014, 5, 423–431. [Google Scholar] [CrossRef]
  13. Crist, E.P. A TM Tasseled Cap equivalent transformation for reflectance factor data—NASA Technical Reports Server (NTRS). Remote Sens. Environ. 1985, 17, 301–306. [Google Scholar] [CrossRef]
  14. Tan, Z.; Xi, S. Analysis on the Evolution of Heat Island Effect and Its Influencing Factors in Ningbo Central City from 1990 to 2015. J. Hebei Univ. Eng. (Nat. Sci. Ed.) 2021, 38, 69–77. [Google Scholar] [CrossRef]
  15. Qin, Z.; Zhang, M.; Arnon, K.; Pedro, B. Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM6 data. Acta Geogr. Sin. 2001, 56, 456–466. [Google Scholar]
  16. Xu, H. A remote sensing urban ecological index and its application. Acta Ecol. Sin. 2013, 33, 7853–7862. [Google Scholar]
  17. Liao, W.; Jiang, W. Evaluation of the Spatiotemporal Variations in the Eco-Environmental Quality in China Based on the Remote Sensing Ecological Index. Remote Sens. 2020, 12, 2462. [Google Scholar] [CrossRef]
  18. Gong, C.; Lyu, F.; Wang, Y. Spatiotemporal Change and Drivers of Ecosystem Quality in the Loess Plateau Based on RSEI: A Case Study of Shanxi, China. Ecol. Indic. 2023, 155, 111060. [Google Scholar] [CrossRef]
  19. Gan, X.; Du, X.; Duan, C.; Peng, L. Evaluation of Ecological Environment Quality and Analysis of Influencing Factors in Wuhan City Based on RSEI. Sustainability 2024, 16, 5809. [Google Scholar] [CrossRef]
  20. Ji, J.; Wang, S.; Zhou, Y.; Liu, W.; Wang, L. Studying the Eco-Environmental Quality Variations of Jing-Jin-Ji Urban Agglomeration and Its Driving Factors in Different Ecosystem Service Regions From 2001 to 2015. IEEE Access 2020, 8, 154940–154952. [Google Scholar] [CrossRef]
  21. Cheng, L.; Wang, Z.; Tian, S.; Liu, Y.; Sun, M.; Yang, Y. Evaluation of eco-environmental quality in Mentougou District of Beijing based on im proved remote sensing ecological index. Chin. J. Ecol. 2021, 40, 1177–1185. [Google Scholar] [CrossRef]
  22. Muhadaisi, A.; Zhang, F.; Liu, K.; Ayinuer, Y. Urban ecological environment evaluation based on Tiangong—2 and Landsat8 data. Remote Sens. Land Resour. 2020, 32, 209–218. [Google Scholar] [CrossRef]
  23. Zhang, J.; Gu, Y.; Shen, J.; Cong, M.; Sun, Y.; He, J.; Yang, L. Analysis of Spatiotemporal Changes and Driving Factors of Ecological Environment Quality in the Yellow River Basin. Environ. Sci. 2024, 46, 956–971. [Google Scholar] [CrossRef]
  24. Wang, S.; Wu, W. Spatiotemporal Variation of Ecological Environmental Quality and Its Response to Different Driving Factors in Jialing River Basin In Recent 31 Years. Res. Soil Water Conserv. 2024, 31, 427–439. [Google Scholar] [CrossRef]
  25. Jing, Y.; Zhang, F.; He, Y.; Kung, H.; Johnson, V.C.; Arikena, M. Assessment of Spatial and Temporal Variation of Ecological Environment Quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China. Ecol. Indic. 2020, 110, 105874. [Google Scholar] [CrossRef]
  26. Yu, H.; Zhang, F.; Ma, H.; Lu, Y. Spatio-temporal Evolution and Driving Factors of Ecological Environment Quality in the Huaihe River Basin Based on RSEI. Environ. Sci. 2024, 45, 4112–4121. [Google Scholar] [CrossRef]
  27. Li, J.; Zhang, Y.; Yu, T.; Yan, R. Assessment of Spatial-Temporal Changes of Ecological Environment Quality and Its Driving Force Analysis in Aksu Region Based on Remote Sensing Ecological Index. J. Soil Water Conserv. 2023, 37, 333–340. [Google Scholar] [CrossRef]
  28. Wang, F.; Li, W.; Lin, Y.; Nan, X.; Hu, Z. Spatiotemporal Pattern and Driving Force Analysis of Ecological Environmental Quality in Typical Ecological Areas of the Yellow River Basin from 1990 to 2020. Environ. Sci. 2023, 44, 2518–2527. [Google Scholar] [CrossRef]
  29. Liu, Y.; Zhou, T.; Yu, W. Analysis of Changes in Ecological Environment Quality and Influencing Factors in Chongqing Based on a Remote-Sensing Ecological Index Mode. Land 2024, 13, 227. [Google Scholar] [CrossRef]
  30. Xiong, Q.; Hong, Q.; Chen, W. Temporal and Spatial Response of Ecological Environmental Quality to Land Use Transfer in Nanling Mountain Region, China Based on RSEI: A Case Study of Longnan City. Land 2024, 13, 675. [Google Scholar] [CrossRef]
  31. Xu, H.; Wang, M.; Shi, T.; Guan, H.; Fang, C.; Lin, Z. Prediction of Ecological Effects of Potential Population and Impervious Surface Increases Using a Remote Sensing Based Ecological Index (RSEI). Ecol. Indic. 2018, 93, 730–740. [Google Scholar] [CrossRef]
  32. Ullah, W.; Ahmad, K.; Ullah, S.; Tahir, A.A.; Javed, M.F.; Nazir, A.; Abbasi, A.M.; Aziz, M.; Mohamed, A. Analysis of the Relationship among Land Surface Temperature (LST), Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with Topographic Elements in the Lower Himalayan Region. Heliyon 2023, 9, e13322. [Google Scholar] [CrossRef] [PubMed]
  33. Shi, S.; Wang, X.; Hu, Z.; Zhao, X.; Zhang, S.; Hou, M.; Zhang, N. Geographic Detector-Based Quantitative Assessment Enhances Attribution Analysis of Climate and Topography Factors to Vegetation Variation for Spatial Heterogeneity and Coupling. Glob. Ecol. Conserv. 2023, 42, e02398. [Google Scholar] [CrossRef]
  34. Cheng, M.; Cao, G.; Zhao, M.; Diao, E.; He, Q.; Gao, S.; Qiu, X.; Cheng, G. Temporal and spatial variation characteristics and influencial factors of soil moisture in the Xiangride-Qaidam River Basin. Arid Zone Res. 2022, 39, 615–624. [Google Scholar] [CrossRef]
  35. Karbalaee, A.R.; Hedjazizadeh, Z.; Masoodian, S.A. Dependency of LSA and LST to Topographic Factors in Iran, Based on Remote Sensing Data. Theor. Appl. Clim. 2023, 153, 709–726. [Google Scholar] [CrossRef]
  36. Xu, X.; Genovese, P.V.; Zhao, Y.; Liu, Y.; Woldesemayat, E.M.; Zoure, A.N. Geographical Distribution Characteristics of Ethnic-Minority Villages in Fujian and Their Relationship with Topographic Factors. Sustainability 2022, 14, 7727. [Google Scholar] [CrossRef]
  37. Xu, J.; Zheng, L.; Ma, R.; Tian, H. Correlation between Distribution of Rural Settlements and Topography in Plateau-Mountain Area: A Study of Yunnan Province, China. Sustainability 2023, 15, 3458. [Google Scholar] [CrossRef]
  38. Shi, S.; Peng, S.; Lin, Z.; Zhu, Z.; Ma, D.; Yin, Y.; Lu, X.; Li, T.; Gong, L. Analysis of Ecological Environment Quality Heterogeneity across Different Landform Types in Myanmar and Its Driving Forces. Ecol. Indic. 2024, 168, 112755. [Google Scholar] [CrossRef]
  39. Chen, J.; Lin, G.; Chen, Z. Evaluation of Spatial–Temporal Variations in Ecological Environment Quality in the Red Soil Region of Southern China: A Case Study of Changting County. Appl. Sci. 2024, 14, 8641. [Google Scholar] [CrossRef]
  40. Deng, C.; Chen, Z.; Chen, H.; Chen, Z. Hyperspectral Estimation Models of Chlorophyll Content for Dicranopteris Dichotoma Leaves at Different Ecological Restoration Stages in the Eroded Red Soil Areas of Southern China. J. Geo-Inf. Sci. 2019, 21, 948–957. [Google Scholar] [CrossRef]
  41. Jiang, T.; Xie, Y.; Chen, Z.; Chen, Z.; Chen, J. Reductions of Water Flow and Sediment Under Different Water Conservation Measures in Changting County of Fujian Province. Bull. Soil Water Conserv. 2018, 38, 35–41. [Google Scholar] [CrossRef]
  42. Zhou, J.; Zhang, B.; Luo, J. Terrain Gradient Differentiation of Ecosystem Service Value and Its Driving Factors in Anning River Basin. J. Hydroecology 2023, 1–9. [Google Scholar] [CrossRef]
  43. Chen, X.; Lin, X.; Mu, F.; Wang, Y.; Cheng, Z.; Jia, L. Temporal and Spatial Evolution of Ecosystem Service Value and Topographic Gradient Effect in Northeast Chongqing. J. Henan Polytech. Univ. (Nat. Sci.) 2023, 42, 92–102. [Google Scholar] [CrossRef]
  44. Cen, Y.; Zhang, B.; Luo, J.; Deng, Q.; Liu, H.; Wang, L. Influence of Topographic Factors on the Characteristics of Gully Systems in Mountainous Areas of Ningnan Dry-Hot Valley, SW China. Int. J. Environ. Res. Public Health 2022, 19, 8784. [Google Scholar] [CrossRef]
  45. Yu, H.; Zeng, H.; Jiang, Z. Study on Distribution Characteristics of Landscape Elements along the Terrain Gradient. Sci. Geogr. Sin. 2001, 21, 64–69. [Google Scholar] [CrossRef]
  46. Wang, J.; Li, X.; Christakos, G.; Liao, Y.; Zhang, T.; Gu, X.; Zheng, X. Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China. Int. J. Geogr. Inf. Sci. 2010, 24, 107–127. [Google Scholar] [CrossRef]
  47. 2010 Government Work Report of Changting County, Longyan City, Fujian Province. Available online: http://www.changting.gov.cn/xxgk/gzbg/201007/t20100714_822071.htm (accessed on 7 February 2025).
  48. Su, Y.; Zhang, P.; Zhang, X.; Zhao, Y.; Li, L. Ecological environment quality evaluation and driving factor analysis of Wu’an national forest park in Hebei province. J. Hebei Univ. (Nat. Sci. Ed.) 2023, 43, 197–206. [Google Scholar] [CrossRef]
  49. Wang, J.; Xu, C. Geodetector: Principle and Prospective. Acta Geogr. Sin. 2017, 72, 116–134. [Google Scholar] [CrossRef]
  50. Wu, P.; Chen, J.; Chen, S. Applications of Spatial Association Rules in the Study of Relationship between Landuse and Terrain Features. J. Subtrop. Resour. Environ. 2011, 6, 64–69. [Google Scholar]
Sustainability 17 01501 g001
Figure 1. Geographical location of the study area.
Figure 1. Geographical location of the study area.
Sustainability 17 01501 g001
Sustainability 17 01501 g002
Figure 2. Technical flow chart.
Figure 2. Technical flow chart.
Sustainability 17 01501 g002
Sustainability 17 01501 g003
Figure 3. Changing trends of topographic factors for each EEQ grade from 2000 to 2019.
Figure 3. Changing trends of topographic factors for each EEQ grade from 2000 to 2019.
Sustainability 17 01501 g003
Sustainability 17 01501 g009
Figure 9. Coupling effect of any two topographic factors on the EEQ.
Figure 9. Coupling effect of any two topographic factors on the EEQ.
Sustainability 17 01501 g009
Table 1. Classification of topographic factors.
Table 1. Classification of topographic factors.
Topographic FactorsClassificationUnitMethod
Elevation1 (212–385); 2 (385–500); 3 (500–655); 4 (655–1000); 5 (1000–1400)MeterNatural Breaks
(Jenks) + Manual
Slope1 (≤2°); 2 (2–5°); 3 (5–15°); 4 (15–25°); 5 (25–35°); 6 (35–55°);
7 (<70.72°)		DegreeManual
Aspect1 (−1); 2 (0–22.5°, 337.5–360°); 3 (22.5–67.5°); 4 (67.5–112.5°); 5 (112.5–157.5°); 6 (157.5–202.5°); 7 (202.5–247.6°); 8 (247.5–292.5°); 9 (292.5–337.5°); DegreeEqual Interval
Relief Amplitude
(RA)		1 (≤4.437); 2 (4.438–9.151); 3 (9.152–14.698); 4 (14.699–23.017);
5 (23.018–70.715)		MeterNatural Breaks
(Jenks)
Topographic Position Index (TPI)1 (≤0.796); 2 (0.797–1.167); 3 (1.168–1.497); 4 (1.498–1.838);
5 (1.839–2.983)		 Natural Breaks
(Jenks)
Table 2. Average values of topographic factors for each EEQ grade.
Table 2. Average values of topographic factors for each EEQ grade.
YearGradesElevationSlopeRATPI
2000Poor2913.33.360.62
Worse352.25.75.10.84
Moderate468.6128.21.24
Good626.916.69.21.58
Excellent864.1239.32
2010Poor655.216.97.21.62
Worse404.25.84.60.88
Moderate423.38.36.51.03
Good562.715.49.11.48
Excellent674.224.610.31.87
2019Poor410.12.11.620.67
Worse372.13.33.30.73
Moderate392.75.55.30.86
Good536.414.38.71.4
Excellent586.320.610.21.66
Table 3. q-values of topographic factors on the EEQ.
Table 3. q-values of topographic factors on the EEQ.
YearTPIRAAspectSlopeElevation
20000.3930.0790.1640.2430.421
20100.2930.0840.1960.2380.193
20190.2930.0920.2320.2660.129
Table 4. Coupling effect of topographic factors on the EEQ.
Table 4. Coupling effect of topographic factors on the EEQ.
YearTPI and RAAspect and RAAspect and TPISlope and RASlope and TPI
20000.4090.1990.4620.2730.414
20100.3160.2250.3630.2690.296
20190.3250.2550.3480.3020.303
YearSlope and AspectElevation and RAElevation and TPIElevation and AspectElevation and Slope
20000.3160.4480.4970.5170.488
20100.3090.2400.3140.3280.311
20190.3170.2000.2990.3010.304
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, J.; Lin, G.; Chen, Z. Effect of Topographic Factors on Ecological Environment Quality in the Red Soil Region of Southern China: A Case from Changting County. Sustainability 2025, 17, 1501. https://doi.org/10.3390/su17041501

AMA Style

Chen J, Lin G, Chen Z. Effect of Topographic Factors on Ecological Environment Quality in the Red Soil Region of Southern China: A Case from Changting County. Sustainability. 2025; 17(4):1501. https://doi.org/10.3390/su17041501

Chicago/Turabian Style

Chen, Junming, Guangfa Lin, and Zhibiao Chen. 2025. "Effect of Topographic Factors on Ecological Environment Quality in the Red Soil Region of Southern China: A Case from Changting County" Sustainability 17, no. 4: 1501. https://doi.org/10.3390/su17041501

APA Style

Chen, J., Lin, G., & Chen, Z. (2025). Effect of Topographic Factors on Ecological Environment Quality in the Red Soil Region of Southern China: A Case from Changting County. Sustainability, 17(4), 1501. https://doi.org/10.3390/su17041501

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop