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Article

Spatial Scale Effect on Fractional Vegetation Coverage Changes and Driving Factors in the Henan Section of the Yellow River Basin

by
Rongxi Wang
1,
Hongtao Wang
1,*,
Cheng Wang
1,2,
Jingjing Duan
1 and
Shuting Zhang
1
1
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454150, China
2
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(14), 2575; https://doi.org/10.3390/rs16142575
Submission received: 5 June 2024 / Revised: 10 July 2024 / Accepted: 11 July 2024 / Published: 13 July 2024
(This article belongs to the Special Issue Monitoring Vegetation Response Based on Remote Sensing and Climate Data (Second Edition))
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Abstract

:
Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m ( q = 0.48).

1. Introduction

Vegetation is a fundamental component of terrestrial ecosystems [1], significantly influencing carbon balances [2], soil integrity [3], and water conservation [4], while also contributing to ecosystem stability on both regional and global scales. FVC (Fractional Vegetation Coverage) is an important index for indicating ecological status [5,6,7], which is a measure of plant community structure that expresses the proportion of an observation area occupied by the vertical projection of vegetation [8]. With the growth of the social economy and population, determining how climate and anthropogenic activity affect FVC has received widespread attention [9,10]. Establishing a comprehensive FVC change monitoring system can promote the attainment of the SDGs (Sustainable Development Goals). Therefore, accurately detecting the dynamic change in FVC and quantifying the main driving factors that affect FVC have become critical in recent research.
Henan Province is a significant region that is marked by a crucial ecological security barrier in ecosystem service [11]. In recent years, under the influence of climate and anthropogenic activities, the development of the Henan section of the Yellow River Basin has faced some ecological problems related to vegetation, such as soil erosion [12], severe flooding [13], and extreme weather events [14]. Vegetation is crucial in soil conservation and climate regulation, directly influencing surface temperature, water cycling, and climate patterns. By analyzing the changes and the driving factors of vegetation, areas where vegetation degradation can be identified, and environmental protection measures will be subsequently implemented to mitigate land degradation. Analyzing the spatial and temporal changes and driving factors of vegetation cover at different spatial scales is essential for the vegetation protection of the Henan section of the Yellow River Basin.
In recent decades, vegetation parameter estimation has primarily relied on ground and remote sensing measurements [15]. The ground measurements had obvious scale limitations, besides being time-consuming and labor-intensive [16]. Large-scale and long-term continuous data could be easily obtained through remote sensing measurements [17]. Many indices such as the EVI (Enhanced Vegetation Index) [18], the RVI (Ration Vegetation Index) [19], the MSAVI (Modified Soil Adjusted Vegetation Index) [20], and the SAVI (Soil Adjusted Vegetation Index) [21] have been used to describe vegetation conditions when utilizing remote sensing measurements [22,23]. The NDVI (Normalized Difference Vegetation Index) has been the most commonly used among dozens of vegetation indices [24,25,26]. Healthy vegetation absorbs most of the visible light (mainly blue and red) for photosynthesis while reflecting most of the near-infrared light. The NDVI has been calculated by comparing the near-infrared light and red light reflected from the surface. However, the NDVI is limited by atmospheric noise, soil background, red light saturation, etc., leading to phenomena such as oversaturation in areas of higher vegetation and sensitivity to soil in areas with low vegetation cover, which can confound measurements [27,28]. The calculation of FVC is independent of vegetation growth conditions, which has made FVC more robust to seasonal and climatic variations. Due to its consideration of the mixed situation within the pixel, FVC could better reflect the degree of vegetation coverage. Compared to the NDVI, FVC has provided a more accurate estimation in areas of high vegetation density, less affected by shadows and atmospheric noise [29,30]. Consequently, FVC was often used for the accurate monitoring of vegetation changes.
Accurately exploring the driving factors of FVC is essential for designating vegetation conservation plans [31]. Multiple techniques have been used to analyze factors influencing FVC, including correlation analysis [32], partial correlation analysis [33], geographically weighted regression [34], multiple logistic regression [35], and residual analysis [36]. The analysis methods mentioned above have been effective in identifying the impact of individual driving factors but are inadequate in revealing the interactive influences among several factors [37]. The results of these methods may be affected by multicollinearity. GD can accurately evaluate the individual impact of each factor on the spatial distribution of FVC changes by eliminating the multicollinearity among independent variables. As a statistical tool for analyzing geographic phenomena, the GD (Geographic Detector) is often used to accurately identify and analyze the spatial heterogeneity as well as the driving factors behind various geographical phenomena [38,39].
Generally, driving factors of vegetation changes can be categorized into natural factors (e.g., temperature, rainfall, and sunshine duration) and anthropogenic activities (night light, land use, etc.). The exploration of which factors influence FVC changes has received widespread attention. For instance, the driving factors of the TNSF (Three-North Shelter Forest) Region of China were analyzed based on 5000 m spatial scale data [40]. The study results indicated that climate change and anthropogenic activity contributed to a significant increase in FVC in 15.58% and 46.81% of the TNSF region, respectively. Tao et al. [41] found that temperature and soil could satisfactorily account for vegetation changes at a spatial scale of 1000 m. Huang et al. [42] found that elevation and slope significantly influence FVC through their research using DEM data with a spatial scale of 300 m. The driving factors with the strongest explanatory power for FVC vary across different studies, which include rainfall, elevation, slope, etc. Among these studies, the primary driving factors of FVC were different at various scales and study areas. By analyzing the drivers of FVC change in the Yellow River Basin, Wang et al. [43] found that the natural environmental factor was the key factor for vegetation change at the 1000 m scale, but anthropogenic activities became the most important factor when the spatial scale was 0.5° [44]. A similar phenomenon could be found in studies on the regions of Northwest China [45,46,47]. Generally, most of the existing studies were based on single-scale analyses, and the spatial scale effect of FVC needs to be emphasized to explore the driving factors of vegetation changes.
In this study, we analyze the scale effect on FVC changes and Driving Factors in the Henan Section of the Yellow River Basin by using multi-source remote sensing data. The objectives of the study are to (1) answer the basic characteristics of interannual FVC succession in the Henan section of the Yellow River basin from 2014 to 2022 at different spatial scales, (2) analyze the synergistic effects of multiple factors affecting FVC changes and further identify the key factors, and (3) explore the impact of spatial scale on the driving factors of FVC and provide a new perspective on the dynamic monitoring and the conservation of vegetation. Our research can provide more scientific and specific guidance for ecological protection and promote the coupled development of regional ecology and economy.

2. Study Area and Data Sources

2.1. Study Area

The Henan section of the Yellow River Basin is located between 33°66′N and 36°11′N latitude and 110°36′E and 116°10′E longitude, as shown in Figure 1. The geographical area of the study area is 36,617 km2, which constitutes 22% of the total area of Henan Province and constitutes 4.5% of the total area of the Yellow River Basin. The topography of this region is low in the east and high in the west. The northeast is a vast plain, and the west is mountainous, characterized by uneven rainfall, diverse land use, and frequent meteorological disasters. The area has a temperate climate that is humid subtropical to the south of the Yellow River and bordering on humid continental to the north. It is characterized by hot, humid summers and cool to cold, windy, dry winters. The area receives an annual rainfall of 700 mm to 900 mm, with an average temperature of 13.5 °C to 15.4 °C.

2.2. Data Sources

In this study, FVC and driving factor data for the growing seasons from the years 2014 to 2022 were selected. Driving factor data were divided into three categories: climate factors (evapotranspiration, rainfall, and temperature), topography factors (aspect, elevation, and slope), and anthropogenic activities (night light). The details of the data are listed in Table 1.

3. Methodology

In this study, based on the spatial resolution of the data source and the commonly used spatial resolution for FVC driving factor analysis in existing references, five different scales (300 m, 500 m, 1000 m, 2000 m, and 4000 m) were selected for the experiment. The experiment had three main steps: (1) FVC and driving factors data acquisition; (2) analysis of spatial and temporal changes in FVC at multi-spatial scales by using linear regression and CV (Coefficient of Variation), and the driving factor datasets at different spatial scales were obtained by using IDW (Inverse Distance Weighting); and (3) using a GD to explore the impact of spatial scale on the driving factors of FVC. The process schematic diagram of the overall workflow is shown in Figure 2.
Data acquisition was mainly performed through the Google Earth Engine platform, the calculation of vegetation cover change trends and the acquisition of degree spatial resolution data were completed in ArcGIS software (10.8), and the driving factor analysis was conducted using the GD in R (4.3.3 version) language.

3.1. Dimidiate Pixel Model

The Dimidiate Pixel model is a regular method for calculating FVC, which maximizes the weakening of the impact of vegetation type, atmosphere, soil background, etc., on vegetation information [48,49]. Therefore, the Dimidiate pixel model is used to calculate FVC in this study, and the formula is shown in Equation (1).
f v = N D V I − N D V I 0 N D V I V − N D V I 0
where N D V I 0 is the N D V I value of non-vegetated area and N D V I V is the N D V I of a pure vegetation pixel.
To better reflect the diversity of surface cover types and avoid the impact of extreme values, according to the definition of the pixel dichotomy model, N D V I 0 and N D V I V are determined by the upper and lower N D V I thresholds according to the confidence level α = 0.05. Specifically, the cumulative 5% value can exclude the low-end extreme values, which may be caused by image quality issues (such as clouds, shadows, etc.) or atypical surface cover types (such as water bodies). Similarly, the cumulative 95% value can exclude the high-end extreme values, which may be caused by atypical vegetation cover types (such as treetop canopies). In this study, the FVC was categorized into 5 classes based on the equidistant classification method, as shown in Table 2.

3.2. Linear Regression

Linear regression is widely used in the trend analysis of long-term series data due to its algorithmic efficiency and effectiveness. Linear regression is employed to analyze the long-term series data of FVC trends in this study. The FVC change trend slop obtained through linear regression can be calculated using Equation (2).
s l o p = n ∑ i = 1 n i ⋅ F V C i − ∑ i = 1 n i ⋅ ∑ i = 1 n F V C i n ∑ i = 1 n i 2 − ( ∑ i = 1 n i ) 2
where n is the length of the research time series, i is the time series, and F V C i is the vegetation coverage of the i-th year.

3.3. Coefficient of Variation

The coefficient of variation is a dimensionless quantity, so it is often used to compare multiple sets of data with different units or different means. Using CV can describe the uneven distribution of vegetation and reflect the fluctuation of vegetation coverage over time. The CV can be obtained from Equation (3).
C v = ∑ i = 1 n ( F V C i − F V C a v g ) 2 n − 1 F V C a v g
where n is the length of the time series, i is the year, F V C a v g is the average FVC of n years, and F V C i is the FVC value of the i-th year.

3.4. Detection of Key Driving Factors

Spatial heterogeneity refers to the geographical phenomenon where the variance between land types is greater than the variance within land type. The GD is based on spatial heterogeneity and quantitatively and qualitatively detects the spatial heterogeneity between independent and dependent variables of the same geographical phenomenon in a region [38], thereby revealing the driving forces that form the spatial heterogeneity of geographical phenomena. We used factor tests to distinguish the drivers of vegetation degradation, restoration, and global area differences in the study region. Factor detection quantifies the explanatory power with a q value that ranges from 0 to 1. The higher the q value, the stronger the factor’s ability to explain the spatial differentiation of the dependent variable [50]. The mathematical expression for factor detection is shown in Equation (4).
q = 1 − ∑ h = 1 L N h σ h 2 N σ 2
where q is the explanatory power of the factors on the dependent variable; h = 1, …; L is the strata (the classification or partitioning of variable X or Y); N h and N are the number of units in layer h and the whole area, respectively; σ h 2 and σ 2 are the variances of the Y values in layer h and the whole area, respectively. The GD can be used for interaction detection but also for determining the explanatory power of individual factors. The types of interaction of the two dependent variables and the judgment criteria are shown in Table 3.

4. Results

In this study, we describe the experimental results in two parts: the first part focuses on the spatial and temporal changes in FVC at multiple spatial resolutions and the second part focuses on how the explanatory power of driving factor changes at different spatial scales.

4.1. Spatial and Temporal Change Characteristics of FVC at Different Spatial Scales

4.1.1. Temporal Change Characteristics of FVC at Multi-Spatial Scales

In this study, we used linear regression to analyze the trends of FVC at five spatial scales (300 m, 500 m, 1000 m, 2000 m, and 40,000 m) in the Henan section of the Yellow River Basin for the period between 2014 and 2022 (Figure 3). The average FVC increased from 0.510, 0.511, 0.509, 0.508, and 0.509 to 0.534, 0.537, 0.535, 0.535, and 0.535 at five spatial scales, respectively. FVC at all spatial scales showed an increasing trend, with an average annual growth rate of 0.0033. FVC increased by an average of 0.55% per year, and the average value of FVC gradually stabilized above and below 0.53.
To effectively identify the significant features of FVC change, FVC data from the years 2014, 2016, 2018, 2020, and 2022 were selected for mapping. The statistical results of different FVC classes are shown in Figure 4, based on the FVC classification scheme mentioned above (Table 2).
From Figure 4, it can be seen that the proportions of low and relatively lower classes of FVC showed a declining trend, and the proportions of high and relatively higher classes of FVC exhibited a growing trend. The proportion of the medium class of FVC remained relatively stable.

4.1.2. Spatial Change Characteristics of FVC at Multi-Spatial Scales

To effectively identify the significant features of FVC distribution, FVC data from the years 2014, 2016, 2018, 2020, and 2022 were selected for mapping. The spatial distribution of FVC at different spatial scales is shown in Figure 5.
As can be seen from Figure 5, the distribution at the five spatial scales was generally similar. Areas with high and relatively high vegetation coverage were mainly distributed on the east and west sides, areas with medium vegetation coverage were scattered, and areas with low and relatively low vegetation coverage were primarily located in the middle.
To observe the distribution of slope more intuitively, the slope was classified into different levels. The threshold values of slop were determined by visually interpreting the remote sensing images and considering the distribution of the experimental results. The classification of slop is shown in Table 4.
The results of linear regression at five spatial scales are shown in Figure 6. The areas showing vegetation improvement were larger than those exhibiting vegetation degradation. The improved areas were mostly concentrated in the west, while the degraded areas were concentrated in the east. There was a slight degradation phenomenon in some areas in the middle. The areas of severe degradation were mainly distributed around the cities in the east. The areas of vegetation recovery were 37.34% and the degradation areas were 26.51% of the total study area, based on the averaged results of five spatial scales.
Combined with the specific situation of the research area in this study, C v was divided into five levels, as shown in Table 5.
The results of variation coefficients of FVC at five spatial scales are shown in Figure 7. The fluctuation in vegetation change was relatively stable in most areas. Areas with low, relatively low, and medium fluctuation were widely distributed in the western mountainous areas and the eastern farmland areas. Areas with relatively high and high fluctuations were mainly located in the central and eastern parts. In the central part, degradation was mostly concentrated in large blocks, while in the eastern part, degraded areas were mostly scattered.
In this study, FVC at five different spatial scales was calculated to analyze the spatial and temporal characteristics. Over the past decade, vegetation in the Henan section of the Yellow River Basin has improved gradually. The areas of high and relatively high FVC increased, and the areas of low and relatively low FVC decreased. The conclusions mentioned above were consistent with previous studies on the long-term spatiotemporal changes in vegetation coverage in both the Yellow River basin and the Henan section of the Yellow River basin [51,52,53].

4.2. Driving Factors of FVC Changes at Different Spatial Scales

4.2.1. Independent Effects of Factors on FVC Changes at Multi-Spatial Scales

A GD was used to quantitatively analyze the driving factors of FVC changes at different spatial scales in this study. The driving factors need to be reclassified into equal interval categories for analysis using GD. This experiment divided all the driving factors into eight equal interval categories. This study, using a GD, indicates that all factors significantly influence the spatial variation of FVC at different scales (p < 0.01), with each factor having distinct values. The specific single-factor explanatory power (q) results are shown in Figure 8.
From Figure 8, we can see that the same factor has different q values at different spatial scales. The q value of evapotranspiration is always the biggest across all the spatial scales. The q value of temperature is always second at all the spatial scales. In descending order of explanatory power after the summation of multiple spatial scales, the factors are evapotranspiration, temperature, rainfall, night light, elevation, slope, and aspect.
As the spatial scale decreases, the q value of topography factors (elevation, slope, and aspect) exhibits a noticeable increasing trend. The q value of aspect is 0.22 at the 300 m scale, but the value decreases sharply to 0.02 when the spatial scale is 4000 m. The other driving factors (evapotranspiration, temperature, night light, and rainfall) showed the opposite trend.

4.2.2. Interactive Effects of Factors on FVC Changes at Multi-Spatial Scales

The results of the interaction relationship between driving factors of FVC changes are shown in Figure 9.
The majority of factor interactions present a dual-factor enhancement and nonlinear enhancement relationship. All results of interaction detection are dual-factor enhancement at a spatial scale of 300 m. Nonlinear enhancement results of interaction detection mainly occurred when aspect and slope interacted with other factors at the spatial scales of 2000 m and 4000 m.

5. Discussion

5.1. Effect of Anthropogenic Activities on the Spatio-Temporal Characteristics of FVC

With a direct correlation existing between the intensity and spatial distribution of nocturnal artificial illumination and the scale of human activities, night light data have been widely recognized as a reliable proxy for anthropogenic activities [54]. For instance, regions with high luminosity typically correspond to urban or industrialized areas, indicative of elevated levels of human activity. Conversely, areas with low light emissions are usually representative of rural or natural landscapes with less anthropogenic influence [55]. Numerous studies have capitalized on nighttime light data to estimate economic productivity, quantify the degree of urbanization, delineate population density, and even monitor the repercussions of warfare and natural calamities [56]. In this study, nighttime lighting, as an indicator of human activity, is used to investigate the impact of anthropogenic activities on FVC.
The areas of vegetation improvement in the Henan section of the Yellow River Basin are concentrated in the west. The areas of improvement are mountainous regions, with vegetation types consisting of tree communities, shrub communities, and grass communities. Combined with the implementation of ecological engineering construction policies such as closing mountains for afforestation and returning farmland to forests and grasslands in China in recent years [57,58,59], it was found that anthropogenic activities played a positive role in the improvement of vegetation.
Vegetation degradation areas are mostly distributed in the central and eastern parts. These areas of degradation are concentrated in open and low-elevation regions, where multiple coal mines and large-scale water conservancy hub projects, in addition to urban clusters and farmland, are located. The areas mentioned above showed obvious traces of urban expansion [60,61]. According to the night light data and the satellite image data, traces of urban expansion can also be detected in satellite imagery from 2014 and 2022 (Figure 10). Especially in the areas of urban expansion, FVC shows a significant downward trend. In the areas of vegetation degradation, it was found that anthropogenic activities in these areas have a negative impact on FVC. Cheng et al. found a significant negative correlation (with a correlation coefficient exceeding −0.78) between vegetation cover and nighttime light brightness in the Chengdu area [62]. This conclusion also verifies our results.

5.2. Analysis of the Spatial Scale Effect on Topography Factors

The spatial scale impacts the factors driving FVC, which is particularly noticeable in topography factors (aspect, elevation, and slope). The explanatory power of topography factors gradually decreases as the spatial scale increases, as shown in Table 6. From Table 6, we can see that when the spatial scale increases from 300 m to 4000 m, the q value of elevation decreases from 0.241 to 0.099.
In this study, driving factors were reclassified into eight equal interval categories for analysis using a GD. The percentages of elevation in each category were calculated to determine the cause of the differing explanatory power of elevation factors at different spatial scales (Figure 11). From Figure 11, we can see that the proportion of pixels with elevation under 329 m at the 300 m spatial scale was 5% higher than those at the 4000 m spatial scale. The percentage of the same elevation category varies at different spatial scales, and the number of image elements at larger spatial scales is much smaller than that at smaller spatial scales. As spatial scale decreases, extreme elevation points and small topography features are erased, leading to a lack of topography details. The loss of these details may be the reason for the decreased explanatory power of topography factors. A similar conclusion can be found for the other two factors of topography (aspect and slope).
In addition, we can see that the distribution of FVC varies in low-elevation areas from the spatial distribution of FVC (Figure 5) and elevation in the Henan section of the Yellow River basin (Figure 1). The topography changes are tiny in the low-elevation areas, which means that there are many different FVC values within a small elevation range. The differences will be used when GD calculates the explanatory power of the factors. However, the difference will be diminished when the spatial scale is increased. This decrease leads to a decrease in the q value of the elevation factor.

5.3. Impact of Climate Factors at Different Spatial Scales

The explanatory power of the climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) gradually decreases as the spatial scale decreases, as shown in Table 7.
Based on the phenomenon of explanatory power varying with spatial scale, the driving factors with large-scale continuity (e.g., rainfall, temperature) exhibit stronger explanatory power at larger spatial scales. The existence of significant temperature and rainfall differences in some parts of the Henan section of the Yellow River Basin also indirectly verifies the conclusion mentioned above [63,64,65].
The situation is the opposite for large-scale continuous driving factors compared to topography factors. The driving factors with large-scale continuity are affected by some errors (noise, data errors, etc.) at smaller spatial scales (e.g., 300 m and 500 m). These errors can lead to a decrease in the explanatory power of the driving factors.

6. Conclusions

In this study, multi-source data have been used for the analysis of FVC changes and driving factors at different scales in the Henan section of the Yellow River Basin from 2014 to 2022. The main findings are as follows.
  • The FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend.
  • The scale has a significant impact on the explanatory power of the driving factors of FVC. As the spatial scale increased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased.
  • The explanatory power of evapotranspiration was always the biggest across all spatial scales. The q values of evapotranspiration peak notably at a spatial scale of 1000 m ( q = 0.48), and the q values of climate factors begin to stabilize when the spatial scale is reduced to 1000 m.
Due to the limitations of the GD model, studies at higher spatial scales could not be carried out for the time being. In addition, rainfall data at a high spatial scale were not obtained. The impact of the data obtained through inverse distance-weighted interpolation on the experimental results has yet to be investigated.

Author Contributions

Conceptualization, R.W. and H.W.; methodology, R.W. and H.W.; software, S.Z.; validation, R.W.; formal analysis, H.W. and J.D.; investigation, R.W.; resources, J.D; data curation, S.Z.; writing—original draft preparation, R.W.; writing—review and editing, R.W., J.D. and S.Z.; visualization, R.W.; funding acquisition, H.W. and C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the State Key Project of the National Natural Science Foundation of China—Key projects of joint fund for regional innovation and development [grant number U22A20566], the National Natural Science Foundation of China [grant number 42071405], and the Fundamental Research Funds for the Universities of Henan Province [grant number NSFRF220203].

Data Availability Statement

No applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of the study area.
Figure 1. Location of the study area.
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Figure 2. Schematic diagram of the overall workflow.
Figure 2. Schematic diagram of the overall workflow.
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Figure 3. The temporal trends of FVC in Henan section of the Yellow River basin at different spatial scales, 2014–2022. (a) The temporal trends of FVC at 300 m scale; (b) the temporal trends of FVC at 500 m scale; (c) the temporal trends of FVC at 1000 m scale; (d) the temporal trends of FVC at 2000 m scale; (e) the temporal trends of FVC at 4000 m scale; (f) the temporal trends of the average FVC over five spatial scales.
Figure 3. The temporal trends of FVC in Henan section of the Yellow River basin at different spatial scales, 2014–2022. (a) The temporal trends of FVC at 300 m scale; (b) the temporal trends of FVC at 500 m scale; (c) the temporal trends of FVC at 1000 m scale; (d) the temporal trends of FVC at 2000 m scale; (e) the temporal trends of FVC at 4000 m scale; (f) the temporal trends of the average FVC over five spatial scales.
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Figure 4. Area percentage of different classes of FVC in Henan section of the Yellow River basin at five spatial scales, 2014–2022.
Figure 4. Area percentage of different classes of FVC in Henan section of the Yellow River basin at five spatial scales, 2014–2022.
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Figure 5. Spatial distribution of FVC in Henan section of the Yellow River basin at five spatial scales, 2014–2022.
Figure 5. Spatial distribution of FVC in Henan section of the Yellow River basin at five spatial scales, 2014–2022.
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Figure 6. Spatial distribution of FVC Change trends in Henan section of the Yellow River basin at five spatial scales, 2014–2022.
Figure 6. Spatial distribution of FVC Change trends in Henan section of the Yellow River basin at five spatial scales, 2014–2022.
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Figure 7. Spatial distribution in variation coefficients of FVC in Henan section of the Yellow River basin at five spatial scales, 2014–2022.
Figure 7. Spatial distribution in variation coefficients of FVC in Henan section of the Yellow River basin at five spatial scales, 2014–2022.
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Figure 8. The explanatory power of different driving factors in the Henan section of the Yellow River basin at five spatial scales.
Figure 8. The explanatory power of different driving factors in the Henan section of the Yellow River basin at five spatial scales.
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Figure 9. Interaction detection between various factors at different spatial scales.
Figure 9. Interaction detection between various factors at different spatial scales.
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Figure 10. Satellite images of the same region at different times. (a) Satellite imagery for June 2014; (b) satellite imagery for June 2022.
Figure 10. Satellite images of the same region at different times. (a) Satellite imagery for June 2014; (b) satellite imagery for June 2022.
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Figure 11. The explanatory power of topography factors at five spatial scales.
Figure 11. The explanatory power of topography factors at five spatial scales.
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Table 1. Data sources.
Table 1. Data sources.
Data NameData ProductsUnitResolutionData Sources
FVCLandsat-8 Surface Reflectance Tier 1
(Dimidiate Pixel Model)		-30 mGoogle Earth Engine
(https://earthengine.google.com/, accessed on 10 December 2023)
AspectNASADEM°30 mEARTHDATA
(http://www.earthdata.nasa.gov/, accessed on 10 December 2023)
ElevationNASADEMm30 mEARTHDATA
(http://www.earthdata.nasa.gov/, accessed on 10 December 2023)
EvapotranspirationLandsat-8 Surface Reflectance Tier 1
(Surface Energy Balance Algorithm for Land Model)		mm100 mGoogle Earth Engine
(https://earthengine.google.com/, accessed on 10 December 2023)
NightlightsSVDNB-500 mEarth Observation Group
(https://eogdata.mines.edu/, accessed on 10 December 2023)
RainfallChina Meteorological
Dataset		mm1 kmResource and Environmental Science Data Platform (https://www.resdc.cn/, accessed on 10 December 2023)
SlopeNASADEM◦30 mEARTHDATA
(http://www.earthdata.nasa.gov/, accessed on 10 December 2023)
TemperatureLandsat-8 Surface Reflectance Tier 1°C30 mGoogle Earth Engine
(https://earthengine.google.com/, accessed on 10 December 2023)
Table 2. Vegetation cover class classification standards.
Table 2. Vegetation cover class classification standards.
Vegetation CoverClassification Criteria
Low0 ≤ FVC < 0.2
Relatively Low0.2 ≤ FVC < 0.4
Medium0.4 ≤ FVC < 0.6
Relatively High0.6 ≤ FVC < 0.8
High0.8 ≤ FVC < 1
Table 3. Types of interaction between two covariates.
Table 3. Types of interaction between two covariates.
InteractionCriterion of Interval
Nonlinear Weakeningq(X1∩X2) < Min [q(X1), q(X2)]
Single-factor Nonlinear WeakeningMin[q(X1), q(X2)] < q(X1∩X2) < Max [q(X1), q(X2)]
Dual-factor Enhancementq(X1∩X2) > Max [q(X1), q(X2)]
Independentq(X1∩X2) = q(X1) + q(X2)
Nonlinear Enhancementq(X1∩X2) > q(X1) + q(X2)
Table 4. The slop class classification standard.
Table 4. The slop class classification standard.
ClassClassification Criteria
Significant Degradationslop < −0.1
Slight Degradation−0.1 ≤ slop < −0.05
Essentially Stable−0.05 ≤ slop < 0.05
Slight Improvement0.05 ≤ slop < 0.1
Significant Improvementslop > 0.1
Table 5. The C v classification standard.
Table 5. The C v classification standard.
ClassClassification Criteria
Low variation 0   <   C v ≤ 0.10
Relatively low variation 0.10   <   C v ≤ 0.20
Medium variation 0.20   <   C v ≤ 0.30
Relatively high variation 0.30   <   C v ≤ 0.40
High variation C v > 0.40
Table 6. The explanatory power of topography factors at five spatial scales.
Table 6. The explanatory power of topography factors at five spatial scales.
Scale (m)AspectElevationSlopeTemperature
3000.1920.2410.2170.253
5000.1460.2030.1160.295
10000.0980.1190.0760.327
20000.0120.1070.0550.336
40000.0180.0990.0180.330
Table 7. The explanatory power of the climate factors and anthropogenic activity at five spatial scales.
Table 7. The explanatory power of the climate factors and anthropogenic activity at five spatial scales.
Scale (m)EvapotranspirationRainfallTemperatureNightlights
3000.4290.2140.2530.231
5000.4420.2550.2950.266
10000.4760.3110.3270.304
20000.4610.3250.3360.311
40000.4500.3200.3300.318
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Wang, R.; Wang, H.; Wang, C.; Duan, J.; Zhang, S. Spatial Scale Effect on Fractional Vegetation Coverage Changes and Driving Factors in the Henan Section of the Yellow River Basin. Remote Sens. 2024, 16, 2575. https://doi.org/10.3390/rs16142575

AMA Style

Wang R, Wang H, Wang C, Duan J, Zhang S. Spatial Scale Effect on Fractional Vegetation Coverage Changes and Driving Factors in the Henan Section of the Yellow River Basin. Remote Sensing. 2024; 16(14):2575. https://doi.org/10.3390/rs16142575

Chicago/Turabian Style

Wang, Rongxi, Hongtao Wang, Cheng Wang, Jingjing Duan, and Shuting Zhang. 2024. "Spatial Scale Effect on Fractional Vegetation Coverage Changes and Driving Factors in the Henan Section of the Yellow River Basin" Remote Sensing 16, no. 14: 2575. https://doi.org/10.3390/rs16142575

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