Assessment of Phenological Dynamics of Different Vegetation Types and Their Environmental Drivers with Near-Surface Remote Sensing: A Case Study on the Loess Plateau of China
<p>The study area located at Chunhua County, Shaanxi Province, China, on the southern Loess Plateau. The red triangle represents the position of the flux tower.</p> "> Figure 2
<p>As an example, four images from different dates in 2021, the fields contained in yellow lines represent the region of interest (ROI). The vegetation types within ROI 1, ROI 2, and ROI 3 are scrub, arbor, and grassland, respectively.</p> "> Figure 3
<p>Variations of daily PhenoCam GCC (<b>a</b>), RCC (<b>b</b>), VARI (<b>c</b>), ExG (<b>d</b>), and VCI (<b>e</b>) from ROI1. The red dashed lines and blue dashed lines represent the start of season (SOS) and the end of season (EOS) for each year, respectively. SOS derived from RCC in 2021 cannot be accurately extracted. In addition, SOS and EOS also cannot be extracted in the VARI series.</p> "> Figure 4
<p>Variations of daily PhenoCam GCC (<b>a</b>), RCC (<b>b</b>), VARI (<b>c</b>), ExG (<b>d</b>), and VCI (<b>e</b>) from ROI2. The red dashed lines and blue dashed lines represent the start of season (SOS) and the end of season (EOS) for each year, respectively. For RCC and VARI, we extracted only the phenological metrics derived from RCC for the year 2022.</p> "> Figure 5
<p>Variations of daily PhenoCam GCC (<b>a</b>), RCC (<b>b</b>), VARI (<b>c</b>), ExG (<b>d</b>), and VCI (<b>e</b>) from ROI3. The red dashed lines and blue dashed lines represent the start of season (SOS) and the end of season (EOS) for each year, respectively. For RCC and VARI, we extracted only the phenological metrics for the year 2020. Due to the lack of raw data, we were unable to obtain phenological metrics information for 2022.</p> "> Figure 6
<p>Variations of green chromatic coordinate (GCC) derived from different ROIs, as well as daily precipitation (P) and evapotranspiration (ET) processes. Different colored line segments represent the length of growing season while the numbers indicate the days.</p> "> Figure 7
<p>Effects of environmental factors on GCC derived from ROI1. Effect of (<b>a</b>) net radiation (Rn), (<b>b</b>) sunshine hours (Sunh), (<b>c</b>) wind speed (U), (<b>d</b>) relative humidity (RH), (<b>e</b>) vapor pressure deficit (VPD), (<b>f</b>) soil temperature (Ts), (<b>g</b>) upper soil water content (SWCu), (<b>h</b>) medium soil water content (SWCm), and (<b>i</b>) deep soil water content (SWCd) on the variation of GCC. The <span class="html-italic">y</span>-axis represents the partial effects of each driver. The orange line represents the smoothed fitted curve for explanatory variables. The gray shaded area represents the 95% confidence interval. The numbers in brackets in the <span class="html-italic">y</span>-axis labels are the effective degrees of freedom.</p> "> Figure 8
<p>Effects of environmental factors on GCC derived from ROI2. Effect of (<b>a</b>) net radiation (Rn), (<b>b</b>) sunshine hours (Sunh), (<b>c</b>) wind speed (U), (<b>d</b>) relative humidity (RH), (<b>e</b>) vapor pressure deficit (VPD), (<b>f</b>) soil temperature (Ts), (<b>g</b>) upper soil water content (SWCu), (<b>h</b>) medium soil water content (SWCm), and (<b>i</b>) deep soil water content (SWCd) on the variation of GCC. The <span class="html-italic">y</span>-axis represents the partial effects of each driver. The orange line represents the smoothed fitted curve for explanatory variables. The gray shaded area represents the 95% confidence interval. The numbers in brackets in the <span class="html-italic">y</span>-axis labels are the effective degrees of freedom.</p> "> Figure 9
<p>Effects of environmental factors on GCC derived from ROI3. Effect of (<b>a</b>) net radiation (Rn), (<b>b</b>) sunshine hours (Sunh), (<b>c</b>) wind speed (U), (<b>d</b>) relative humidity (RH), (<b>e</b>) vapor pressure deficit (VPD), (<b>f</b>) soil temperature (Ts), (<b>g</b>) upper soil water content (SWCu), (<b>h</b>) medium soil water content (SWCm), and (<b>i</b>) deep soil water content (SWCd) on the variation of GCC. The <span class="html-italic">y</span>-axis represents the partial effects of each driver. The orange line represents the smoothed fitted curve for explanatory variables. The gray shaded area represents the 95% confidence interval. The numbers in brackets in the <span class="html-italic">y</span>-axis labels are the effective degrees of freedom.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Site Description
2.2. Data Acquisition
2.3. Image Processing and Vegetation Indexes Calculation
2.4. Extraction of Phenological Metrics
2.5. Statistical Analyses
3. Results
3.1. Phenological Dynamics of Different Vegetation Types
3.1.1. Scrub
3.1.2. Arbor Forest
3.1.3. Grassland
3.2. Comparison of the Same Index in Different Vegetation Types
3.3. Relationship between GCC and Explanatory Variables
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Index | Year | SOS | EOS | LOS (Days) |
---|---|---|---|---|
GCC | 2020 | 30 April | 28 October | 181 |
2021 | 8 May | 10 November | 186 | |
2022 | 30 April | 2 November | 186 | |
RCC | 2020 | 29 April | 2 November | 187 |
2021 | - | 9 November | - | |
2022 | 23 April | 13 November | 204 | |
ExG | 2020 | 30 April | 29 October | 182 |
2021 | 8 May | 9 November | 185 | |
2022 | 29 April | 2 November | 187 | |
VCI | 2020 | 30 April | 28 October | 181 |
2021 | 8 May | 9 November | 185 | |
2022 | 30 April | 2 November | 186 |
Index | Year | SOS | EOS | LOS (Days) |
---|---|---|---|---|
GCC | 2020 | 25 April | 13 July | 79 |
2021 | 1 May | 4 October | 156 | |
2022 | 23 April | 3 October | 163 | |
RCC | 2020 | - | - | - |
2021 | - | - | - | |
2022 | 16 March | 14 October | 212 | |
ExG | 2020 | 26 April | 12 July | 77 |
2021 | 1 May | 5 October | 157 | |
2022 | 23 April | 3 October | 163 | |
VCI | 2020 | 25 April | 12 July | 78 |
2021 | 1 May | 3 October | 155 | |
2022 | 23 April | 3 October | 163 |
Index | Year | SOS | EOS | LOS (Days) |
---|---|---|---|---|
GCC | 2020 | 12 June | 30 September | 110 |
2021 | 11 June | 23 October | 134 | |
2022 | - | - | - | |
RCC | 2020 | 17 May | 22 November | 189 |
2021 | - | - | - | |
2022 | - | - | - | |
VARI | 2020 | 16 June | 19 September | 95 |
2021 | - | - | - | |
2022 | - | - | - | |
ExG | 2020 | 14 June | 19 September | 97 |
2021 | 9 June | 23 October | 136 | |
2022 | - | - | - | |
VCI | 2020 | 13 June | 30 September | 109 |
2021 | 12 June | 23 October | 133 | |
2022 | - | - | - |
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Guo, F.; Liu, D.; Mo, S.; Li, Q.; Meng, J.; Huang, Q. Assessment of Phenological Dynamics of Different Vegetation Types and Their Environmental Drivers with Near-Surface Remote Sensing: A Case Study on the Loess Plateau of China. Plants 2024, 13, 1826. https://doi.org/10.3390/plants13131826
Guo F, Liu D, Mo S, Li Q, Meng J, Huang Q. Assessment of Phenological Dynamics of Different Vegetation Types and Their Environmental Drivers with Near-Surface Remote Sensing: A Case Study on the Loess Plateau of China. Plants. 2024; 13(13):1826. https://doi.org/10.3390/plants13131826
Chicago/Turabian StyleGuo, Fengnian, Dengfeng Liu, Shuhong Mo, Qiang Li, Jingjing Meng, and Qiang Huang. 2024. "Assessment of Phenological Dynamics of Different Vegetation Types and Their Environmental Drivers with Near-Surface Remote Sensing: A Case Study on the Loess Plateau of China" Plants 13, no. 13: 1826. https://doi.org/10.3390/plants13131826