# Monitoring Damage Caused by Pantana phyllostachysae Chao to Moso Bamboo Forests Using Sentinel-1 and Sentinel-2 Images

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Data Used

#### 2.2.1. Field Observations

#### 2.2.2. Remote Sensing Data Used

^{o}) through radiometric correction. The speckle of the corrected images was reduced using the refined Lee filter (window size: 7 × 7 pixels). The geometric distortion of the images was corrected using the range Doppler terrain correction method. The spatial resolution of the images was resampled to 10 m during this step. Finally, logarithmic transformation was performed to covert the unitless backscatter coefficient into decibels (dB) [22].

_{Feb}(images from February), SC2020

_{Apr}(images from April), and SC2020

_{Oct}(images from October) for facilitating description. Additionally, polygons with 2 × 2 pixels centered on the GPS observation of each plot were sketched to retrieve data used to construct the models.

#### 2.3. Mapping the Distribution of Bamboo Forests

_{re}) were calculated for mapping bamboo distribution [24,25]. Extreme gradient boosting (i.e., XGBoost, see Section 2.5.1 for detailed description) was applied to generate the distribution map of bamboo forests (Figure A1). Assessed using collected samples, the classification accuracy reached 90.88% (Table A1).

#### 2.4. Feature Selection

#### 2.5. Development of Severity Identification Model

#### 2.5.1. Model Establishment and Optimization

#### 2.5.2. Design of Model Scenario

_{Oct}. Double-time observation features were calculated according to the differences in remote sensing signals between post- (i.e., SC2020

_{Oct}) and pre-disturbance periods (i.e., SC2020

_{Feb}or SC2020

_{Apr}). Finally, a total of 9 scenarios were designed and evaluated (Table 2).

#### 2.5.3. Accuracy Evaluation

## 3. Results

#### 3.1. Optical and SAR Signals of Bamboo Forests with Different Damage Severities

_{740}and red-edge

_{783}. The spectral differences among damaged groups were most evident in the near-infrared region. The reflectance dropped notably in this wavelength with the increase in damage severity. The reflectance in the shortwave infrared wavelength is normally linked with the moisture content of the canopy, i.e., a higher moisture content leads to a lower reflectance in this region and vice versa. However, the observational data showed that there was no distinct spectrum variation in damaged samples in the two shortwave infrared bands. On- and off-year samples exhibited a visible spectral difference in each damaged group. The reflectance of off-year samples was relatively lower than that of on-year samples, except for the moderately damaged group.

^{o}vv and σ

^{o}vh of bamboo forests decreased noticeably at the initial stage of PPC damage, especially for the on-year samples. However, the signals had no visible changes when the damage severity was further increased. It is noteworthy that the σ

^{o}vv and σ

^{o}vh values of damaged off-year samples were higher than those of on-year samples, which differed from the changing pattern of the spectrum.

#### 3.2. Model Performance

#### 3.2.1. Classification Results

_{Feb}and Double-on

_{Apr}models performed relatively poorly in identifying mildly damaged samples, i.e., their PA and UA values were below 80%. For off-year samples, the Single-off model performed better than the double-time observation feature-based models, with an OA value higher by about 10%. The PA and UA values of the Single-off model were about 90% for healthy and severely damaged samples. The PA and UA values for mildly damaged samples were 82.76% and 84.21%, respectively. However, this model performed relatively poorly for moderately damaged samples. When on- and off-year samples were combined, the performances of the double-time observation feature-based models were poorer than that of the Single-BF model, owing to their failure to properly identify damaged off-year samples.

#### 3.2.2. Contribution of SAR Features to Classification Model

_{spec}, Table A3) to quantitatively evaluate the role of SAR features in identifying PPC damage by comparing its performance with that of optical and SAR feature-based models (i.e., Single-on and Single-off).

^{o}vh in Single-on; σ

^{o}vv and PIR in Single-off) were relatively discrete. The one-way ANOVA method was used to assess the ability of SAR features to discriminate PPC damage severity. The p-value of the one-way ANOVA indicates the significance of differences between different health level groups. As shown in Table 4, the differences in SAR features between various damage groups were not always statistically significant. The differences between mildly and moderately, mildly and severely, and moderately and severely damaged samples were insignificant.

_{spec}was generally poorer than that of the optical and SAR feature-based model (Figure 8). For on-year samples, in comparison with the Single-on model, the Single-on

_{spec}model had slightly higher OA for the training set, but a 1.33% lower OA for the test set. The role of SAR features in distinguishing damaged off-year samples was more evident. The OA values of Single-off were 3.70% and 2.37% higher than those of Single-off

_{spec}for the training and test sets, respectively. SAR features were mainly useful for identifying the healthy and mildly damaged samples, especially the latter. For the mildly damaged samples, the PA and UA of Single-on were 4.44% and 0.85% higher than those of Single-on

_{spec}, respectively. The PA and UA of Single-off were 6.90% and 2.73% higher than those of Single-off

_{spec}, respectively.

#### 3.3. Distribution of PPC Damage

## 4. Discussion

#### 4.1. Significance of SAR Features to PPC Monitoring

#### 4.2. Interference Factors during the PPC Damage Identification

#### 4.3. Limitations and Research Prospects

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Coniferous | Broadleaf | Off-Year Bamboo | On-Year Bamboo | PA (%) | UA (%) | OA (%) | |
---|---|---|---|---|---|---|---|

Coniferous | 191 | 28 | 2 | 2 | 85.65 | 90.09 | 90.88 |

Broadleaf | 17 | 187 | 5 | 5 | 87.38 | 85.39 | |

Off-year bamboo | 4 | 1 | 203 | 10 | 93.12 | 95.31 | |

On-year bamboo | 0 | 3 | 3 | 216 | 97.30 | 92.70 |

Index | Formula |
---|---|

Enhanced Vegetation Index | $\mathrm{EVI}=\frac{2.5\times (\mathrm{b}8-\mathrm{b}4)}{\mathrm{b}8+6\times \mathrm{b}4-7.5\times \mathrm{b}2+1}$ |

Green Ratio Vegetation Index | $\mathrm{GRVI}=\mathrm{b}8/\mathrm{b}3$ |

Modified Non-Linear Index | $\mathrm{MNLI}=\frac{{(\mathrm{b}8}^{2}-\mathrm{b}4)\times 1.5}{{\mathrm{b}8}^{2}+\mathrm{b}4+0.5}$ |

Modified Red-Edge Normalized Difference Vegetation Index | $\mathrm{MRENDVI}=\frac{\mathrm{b}6-\mathrm{b}5}{\mathrm{b}6+\mathrm{b}5-2\times \mathrm{b}1}$ |

Modified Simple Ratio | $\mathrm{MSR}=\frac{\mathrm{b}8/\mathrm{b}4-1}{\sqrt{\mathrm{b}8/\mathrm{b}4}+1}$ |

Modified Triangular Vegetation Index (Improved) | $\mathrm{MTVII}=\frac{1.5\times [1.2\times \left(\mathrm{b}6-\mathrm{b}3\right)-2.5\times (\mathrm{b}4-\mathrm{b}3)]}{\sqrt{{(2\times \mathrm{b}6+1)}^{2}-\left(6\times \mathrm{b}6-5\times \sqrt{\mathrm{b}4}\right)-0.5}}$ |

Non-Linear Index | $\mathrm{NLI}=\frac{{\mathrm{b}8}^{2}-\mathrm{b}4}{{\mathrm{b}8}^{2}+\mathrm{b}4}$ |

Normalized Burn Ratio | $\mathrm{NBR}=\frac{\mathrm{b}8-\mathrm{b}12}{\mathrm{b}8+\mathrm{b}12}$ |

Optimized Soil-Adjusted Vegetation Index | $\mathrm{OSAVI}=\frac{1.5\times (\mathrm{b}8-\mathrm{b}4)}{\mathrm{b}8+\mathrm{b}4+0.16}$ |

Plant Senescence Reflectance Index | $\mathrm{PSRI}=(\mathrm{b}4-\mathrm{b}2)/\mathrm{b}6$ |

Renormalized Difference Vegetation Index | $\mathrm{RDVI}=\frac{\mathrm{b}8-\mathrm{b}4}{\sqrt{\mathrm{b}8+\mathrm{b}4}}$ |

Simple Ratio | $\mathrm{SR}=\mathrm{b}8\mathrm{a}/\mathrm{b}4$ |

Structure Insensitive Pigment Index | $\mathrm{SIPI}=\frac{\mathrm{b}7-\mathrm{b}1}{\mathrm{b}7-\mathrm{b}4}$ |

Visible Atmospherically Resistant Index | $\mathrm{VARI}=\frac{\mathrm{b}3-\mathrm{b}4}{\mathrm{b}3+\mathrm{b}4-\mathrm{b}2}$ |

Atmospherically Resistant Vegetation Index | $\mathrm{ARVI}=\frac{\mathrm{b}8-(2\times \mathrm{b}4-\mathrm{b}2)}{\mathrm{b}8+(2\times \mathrm{b}4-\mathrm{b}2)}$ |

Green Normalized Difference Vegetation | $\mathrm{GNDVI}=\frac{\mathrm{b}7-\mathrm{b}3}{\mathrm{b}7+\mathrm{b}3}$ |

Inverted Red-Edge Chlorophyll Index | $\mathrm{IRECI}=\frac{\mathrm{b}7-\mathrm{b}4}{\mathrm{b}5/\mathrm{b}6}$ |

Modified Chlorophyll Absorption Ratio Index | $\mathrm{MCARI}=[\left(\mathrm{b}5-\mathrm{b}4\right)-0.2\times (\mathrm{b}5-\mathrm{b}3)]\times (\mathrm{b}5/\mathrm{b}4)$ |

Meris Terrestrial Chlorophyll Index | $\mathrm{MTCI}=\frac{\mathrm{b}6-\mathrm{b}5}{\mathrm{b}5-\mathrm{b}4}$ |

Normalized Difference Index | $\mathrm{NDI}45=\frac{\mathrm{b}5-\mathrm{b}4}{\mathrm{b}5+\mathrm{b}4}$ |

Normalized Difference Vegetation Index | $\mathrm{NDVI}=\frac{\mathrm{b}8-\mathrm{b}4}{\mathrm{b}8+\mathrm{b}4}$ |

Normalized Difference Moisture Index | $\mathrm{NDMI}=\frac{\mathrm{b}8\mathrm{a}-\mathrm{b}12}{\mathrm{b}8\mathrm{a}+\mathrm{b}12}$ |

Normalized Difference Water Index | $\mathrm{NDWI}=\frac{\mathrm{b}3-\mathrm{b}8}{\mathrm{b}3+\mathrm{b}8}$ |

Pigment Specific Simple Ratio algorithm | $\mathrm{PSSRA}=\mathrm{b}7/\mathrm{b}4$ |

Red-Edge Inflection Point Index | $\mathrm{REIP}=705+\frac{35\times \left[\right(\mathrm{b}4+\mathrm{b}7)/2-\mathrm{b}5]}{\mathrm{b}6-\mathrm{b}5}$ |

Ratio Vegetation Index | $\mathrm{RVI}=\mathrm{b}8/\mathrm{b}4$ |

Moisture Stress Index | $\mathrm{MSI}=\mathrm{b}12/\mathrm{b}8$ |

Normalized Difference Red-Edge | $\mathrm{NDRE}=\frac{\mathrm{b}6-\mathrm{b}5}{\mathrm{b}6+\mathrm{b}5}$ |

Normalized Multi-band Drought Index | $\mathrm{NMDI}=\frac{\mathrm{b}8\mathrm{a}-(\mathrm{b}11-\mathrm{b}12)}{\mathrm{b}8\mathrm{a}-(\mathrm{b}11+\mathrm{b}12)}$ |

Simple Ratio Water Index | $\mathrm{SRWI}=\mathrm{b}12/\mathrm{b}8\mathrm{a}$ |

Green Chlorophyll Index | ${\mathrm{CI}}_{\mathrm{green}}=\mathrm{b}7/\mathrm{b}3-1$ |

Red-Edge Chlorophyll Index | ${\mathrm{CI}}_{\mathrm{red}-\mathrm{edge}}=\mathrm{b}7/\mathrm{b}5-1$ |

Global Vegetation Moisture Index | $\mathrm{GVMI}=\frac{\left(\mathrm{b}8+0.1\right)-(\mathrm{b}12+0.02)}{\left(\mathrm{b}8+0.1\right)+(\mathrm{b}12+0.02)}$ |

Normalized Difference Vegetation Index Red-Edge | ${\mathrm{NDVI}}_{\mathrm{re}}=\frac{\mathrm{b}8-\mathrm{b}5}{\mathrm{b}8+\mathrm{b}5}$ |

Moisture Adjusted Vegetation Index | $\mathrm{MAVI}=\frac{\mathrm{b}8-\mathrm{b}4}{\mathrm{b}8+\mathrm{b}4+\mathrm{b}12}$ |

Dual Polarization SAR Vegetation Index | $\mathrm{DPSVI}=\frac{\left({\sigma}^{\mathrm{o}}{vv}_{\mathrm{max}}{-\sigma}^{\mathrm{o}}vv\right){+\sigma}^{\mathrm{o}}vh}{\sqrt{2}}\times \frac{{\sigma}^{\mathrm{o}}{vv+\sigma}^{\mathrm{o}}vh}{{\sigma}^{\mathrm{o}}vv}{\times \sigma}^{\mathrm{o}}vh$ |

Polarization Intensity Ratio | ${\mathrm{PIR}=\sigma}^{\mathrm{o}}vh/{\sigma}^{\mathrm{o}}vv$ |

Radar Vegetation Index | ${\mathrm{RVI}}_{\mathrm{SAR}}=\frac{{4\times \sigma}^{\mathrm{o}}vh}{{\sigma}^{\mathrm{o}}{vv+\sigma}^{\mathrm{o}}vh}$ |

Backscattering at Cross-polarization (VH) and Co-polarization (VV) | ${\sigma}^{\mathrm{o}}vh$$,\text{}{\sigma}^{\mathrm{o}}vv$ |

_{705}, red-edge

_{740}, red-edge

_{783}, NIR, NIR

_{narrow}, SWIR-1 and SWIR-2 bands of Sentinel-2 imagery.

Model | Hyper-Parameters | Features | ||||
---|---|---|---|---|---|---|

LR | NE | MD | MCW | GAM | ||

Single-on_{Spec} | 0.24 | 83 | 6 | 1 | 0 | EVI, MRENDVI, MSR, NLI, NDVI, NDMI, NDWI, PSSRA, NDRE, NMDI, SRWI, MAVI |

Single-off_{Spec} | 0.35 | 70 | 6 | 1 | 0 | GRVI, NLI, NBR, SIPI, GNDVI, IRECI, NDVI, NDMI, NDWI, PSSRA, NDRE, GVMI, MAVI |

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**Figure 3.**The spectra and SAR signals of (

**a**) on-year and (

**b**) off-year bamboo forests with different damage severities. b1~b9 represent the coastal, blue, green, red, red-edge

_{705}, red-edge

_{740}, red-edge

_{783}, NIR, NIR

_{narrow}, and water vapor bands of Sentinel-2 imagery, while b11~b12 represent the SWIR-1 and SWIR-2 bands of Sentinel-2 imagery.

**Figure 6.**Comparison of Single-on + Single-off and Single-BF models in identifying samples of different damage severities.

**Figure 8.**Differences in the performance of models constructed using optical features and optical and SAR features. Values shown are the PA and UA of the optical model minus those of the optical and SAR features model.

**Figure 9.**(

**a**) Spatial distribution of PPC damage severity and (

**b**) area proportions of different severity groups in the study area.

**Figure 10.**Changes in areas of different PPC damage severities with terrain conditions in (

**a**) on-year bamboo forest and (

**b**) off-year bamboo forest.

**Figure 12.**(

**a**) The area proportions of bamboo forests with different damage severities output by Single-BF. A comparison of Single-on + Single-off and Single-BF in (

**b**) on-year and (

**c**) off-year areas.

**Figure 13.**Distribution of PPC damage detected using (

**a**) Single-on + Single-off, (

**b**) Double-on

_{Feb}+ Double-off

_{Feb}, and (

**c**) Double-on

_{Apr}+ Double-off

_{Apr}in a typical area (selectively logged). The area proportions of bamboo forests with different damage severities output by (

**d**) Double-on

_{Feb}+ Double-off

_{Feb}and (

**e**) Double-on

_{Apr}+ Double-off

_{Apr}.

Sensors | Observation Time | Tile/Absolute Orbit Numbers |
---|---|---|

Sentinel-2 | 21 February 2020 | T50RNQ, T50RNR, T50RPQ |

16 April 2020 | ||

23 October 2020 | ||

Sentinel-1 | 22 February 2020 | 031, 364 |

22 April 2020 | 032, 239 | |

19 October 2020 | 034, 864 |

Scenario | Model Input | Model Abbreviation |
---|---|---|

Single-time observation (October) | On-year samples | Single-on |

Off-year samples | Single-off | |

Total | Single-BF | |

Double-time observation (October–February) | On-year samples | Double-on_{Feb} |

Off-year samples | Double-off_{Feb} | |

Total | Double-BF_{Feb} | |

Double-time observation (October–April) | On-year samples | Double-on_{Apr} |

Off-year samples | Double-off_{Apr} | |

Total | Double-BF_{Apr} |

Model | Hyper-Parameters | OA (%) | |||||
---|---|---|---|---|---|---|---|

LR | NE | MD | MCW | GAM | Training Set | Test Set | |

Single-on | 0.32 | 73 | 6 | 1 | 0 | 89.29 ± 1.05 | 88.00 |

Single-off | 0.28 | 62 | 6 | 1 | 0.3 | 88.65 ± 1.15 | 85.80 |

Single-BF | 0.3 | 183 | 6 | 1 | 0 | 85.24 ± 1.30 | 82.76 |

Double-on_{Feb} | 0.3 | 109 | 6 | 2 | 0 | 87.22 ± 1.24 | 84.67 |

Double-off_{Feb} | 0.3 | 61 | 6 | 1 | 0 | 80.72 ± 1.97 | 75.15 |

Double-BF_{Feb} | 0.25 | 207 | 6 | 1 | 0 | 81.59 ± 1.46 | 78.06 |

Double-on_{Apr} | 0.3 | 172 | 6 | 1 | 0 | 86.90 ± 1.09 | 83.33 |

Double-off_{Apr} | 0.32 | 89 | 6 | 1 | 0 | 78.36 ± 1.28 | 76.92 |

Double-BF_{Apr} | 0.15 | 101 | 6 | 1 | 0 | 79.08 ± 0.72 | 75.24 |

Compared Groups | σ^{o}vh/Single-On | Σ^{o}vv/Single-Off | PIR/Single-Off |
---|---|---|---|

Healthy–Mildly damaged | 0.000 ** | 0.000 ** | 0.001 ** |

Healthy–Moderately damaged | 0.000 ** | 0.003 ** | 0.302 |

Healthy–Severely damaged | 0.000 ** | 0.000 ** | 0.007 ** |

Mildly damaged–Moderately damaged | 0.121 | 0.429 | 0.242 |

Mildly damaged–Severely damaged | 0.054 | 0.492 | 0.998 |

Moderately damaged–Severely damaged | 0.999 | 0.120 | 0.648 |

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## Share and Cite

**MDPI and ACS Style**

Huang, X.; Zhang, Q.; Hu, L.; Zhu, T.; Zhou, X.; Zhang, Y.; Xu, Z.; Ju, W.
Monitoring Damage Caused by *Pantana phyllostachysae* Chao to Moso Bamboo Forests Using Sentinel-1 and Sentinel-2 Images. *Remote Sens.* **2022**, *14*, 5012.
https://doi.org/10.3390/rs14195012

**AMA Style**

Huang X, Zhang Q, Hu L, Zhu T, Zhou X, Zhang Y, Xu Z, Ju W.
Monitoring Damage Caused by *Pantana phyllostachysae* Chao to Moso Bamboo Forests Using Sentinel-1 and Sentinel-2 Images. *Remote Sensing*. 2022; 14(19):5012.
https://doi.org/10.3390/rs14195012

**Chicago/Turabian Style**

Huang, Xuying, Qi Zhang, Lu Hu, Tingting Zhu, Xin Zhou, Yiwei Zhang, Zhanghua Xu, and Weimin Ju.
2022. "Monitoring Damage Caused by *Pantana phyllostachysae* Chao to Moso Bamboo Forests Using Sentinel-1 and Sentinel-2 Images" *Remote Sensing* 14, no. 19: 5012.
https://doi.org/10.3390/rs14195012