# Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data

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^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. The basic landforms are low mountains and hills [45,46]. The highest point, Toutuoling, is approximately 448.9 m above sea level [44]. The main soil type in the study region is yellow-brown, and the climate is categorized as the north subtropical monsoon, with a mean annual temperature and precipitation of 15.7 °C and 1000 mm, respectively [46,47]. This region is a transitional zone from north to south, such that the tree species composition is highly heterogeneous [45]. It is composed of a top stratum dominated by Pinus and partial Quercus, an intermediate stratum dominated by shrubs, and basal coverage with herbaceous plants [45,46]. The dominant tree species are coniferous forests, including Pinus massoniana and Pinus thunbergii, and broadleaf forests, including Quercus acutissima, Quercus variabilis, Cyclobalanopsis glauca, Liquidambar formosana, Pistacia chinensis, Acacia trees, Campthecaacminata, and Elm trees [44,45,46,47]. There are also a small number of evergreen trees, Dalbergia hupeana, and Holly [44,45]. The multiple species, heterogeneous characteristics, and seasonal variation in this mixed conifer-broadleaf forest renders it as an ideal case study to analyze the canopy structural composition and features. The research was aimed at evergreen coniferous and deciduous broad-leaved forests.

#### 2.2. Field Measurements

#### 2.2.1. Field Experiment Design

^{2}was selected. A total of forty-five sample plots with sizes of 30 × 30 m, corresponding to different conifer-broadleaf ratios, were preliminarily established in January 2015, and each plot was completely covered with the pixel range of 16 m spatial resolution of GF-1 satellite images. These plots were distributed based on the forest conditions, terrain, and accessibility for measurements. Finally, only thirty-two sample plots were continuously observed at fixed points in the year 2015 to obtain a complete dataset, owing to weather conditions, environmental changes at the sampling sites, and limited manpower and material resources. During actual observations, five sample points were arranged in each plot along with two diagonal tangents; red ropes were tied to the trees as markers to facilitate repeated follow-up observations at fixed points.

#### 2.2.2. LAI Observations

#### 2.2.3. Conifer-Broadleaf Ratio Observations

#### 2.2.4. Model Parameter Input Measurements

#### 2.3. Satellite Image Information and Pre-Processing

#### 2.4. Methodology

- (a)
- Analyzing model parameter sensitivity and determining the value range of the parameter inputs according to the field measurements of the LAI time-series data, sample plot inventory, and other reference materials.
- (b)
- Simulating the visible and near-infrared bi-directional spectral reflectance of coniferous and broad-leaved forest canopies using INFORM and calculating the NDVI time-series under different conifer-broadleaf ratios based on the principles of linear mixing.
- (c)
- Calculation of the Euclidean distance and spectral angle distance between any two NDVI time-series curves of different conifer-broadleaf ratios, analyzing the separability and determining the typical separable ratios.
- (d)
- Constructing a GF-1 NDVI time-series dataset, from which curves with typical separable ratios were extracted as prior sample data.
- (e)
- Using the semi-supervised K-means clustering method, obtain the conifer-broadleaf ratio of the study area at the pixel level based on the GF-I NDVI time-series data and evaluate the overall estimation accuracy.

#### 2.4.1. Model Description

#### 2.4.2. NDVI Time-Series

#### 2.4.3. Similarity Measures for Time-Series

#### 2.4.4. Semi-Supervised K-Means Clustering of NDVI Time-Series

- Step 1.
- Define the number of clusters, k, and calculated the initial cluster centers, ${\mu}_{i}$.
- Step 2.
- Calculate the ED of objects from ${\mu}_{i}$.
- Step 3.
- Obtain the closest cluster to the object and assign the object to that cluster.
- Step 4.
- Recalculate the cluster means and update the cluster centers.
- Step 5.
- Recalculate the ED of the objects from the updated cluster centers.
- Step 6.
- Repeat steps 2–5 until the cluster means do not update.

## 3. Results

#### 3.1. Sensitivity Analysis and Determination of Model Parameters in Different Growth Periods

_{s}, standing degree (SD), CD, solar zenith angle (SZA), and observed zenith angle (OZA) all had a significant influence on the canopy spectral characteristics. The SD and CD remained constant during the annual growth cycle, regardless of seasonal variations. The understory leaf area index (LAIU) only had an influence when the forest did not achieve full canopy cover at the beginning of growth. Other parameters, such as relative azimuth angle (RA), ALA, H, and scattering radiation ratio (FDR), had a negligible influence. Among all of the model input parameters, the continuously changing LAI was the most important factor affecting the canopy spectral characteristics.

#### 3.2. NDVI Time-Series of Different Conifer-Broadleaf Ratios

#### 3.3. Calculation of ED and SA Distance

#### 3.4. NDVI Time-Series of Typical Conifer-Broadleaf Ratios

#### 3.5. Conifer-Broadleaf Ratio Estimation Based on Time-Series

^{2}) and the corresponding region was mainly distributed in the middle and northern areas of Purple Mountain. The area that the pure broad-leaved forest occupied was 5.47 km

^{2}, which was mainly distributed in peripheral regions at the foot of Purple Mountain. The areas of broadleaf-conifer ratio ranges “~50% conifer” and “~75% conifer” were approximately equal, 3.42 km

^{2}and 3.36 km

^{2}, respectively, and the corresponding regions were scattered in the southeast and around the foot of the mountain. The area of pure coniferous forest was the smallest, only 0.48 km

^{2}, and mainly distributed in the vicinity of Linggu Temple and at the top of the north slope of the mountain.

## 4. Discussion

#### 4.1. Parameter Sensitivity Analysis

#### 4.2. The NDVI Time-Series

#### 4.3. Conifer-Broadleaf Ratios

## 5. Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Location map of the study area with distribution of the field sample points. The false-color composite of GF-1 WFV imagery was taken on 12 March 2015 (R: near-infrared, G: red, B: green).

**Figure 2.**The coniferous forest, mixed broadleaf-conifer forest, and broadleaved forest of the study area.

**Figure 4.**NDVI time-series of different conifer-broadleaf ratios (DOY represents the day of the year).

**Figure 5.**Difference analysis of the NDVI time-series for different conifer-broadleaf ratios based on Euclidean distance (ED) (Z0:100 represents pure broadleaf, Z05:95 represents 5% conifer and 95% broadleaf, Z100:0 represents pure conifer).

**Figure 6.**Difference analysis of the NDVI time-series for different conifer-broadleaf ratios based on spectral angle (SA) distance (Z0:100 represents pure broadleaf, Z05:95 represents 5% conifer and 95% broadleaf, …, Z100:0 represents pure conifer).

**Figure 7.**NDVI time series of the conifer-broadleaf ratios with high separability (DOY represents the day of the year).

**Figure 8.**GF-1 NDVI time-series of different conifer-broadleaf ratios: (

**a**) broadleaf, (

**b**) 25% conifer, (

**c**) 50% conifer, (

**d**) 75% conifer, (

**e**) conifer, and (

**f**) mean. (DOY represents the day of the year, YF1 represents the sample plot with the number 1, …, YF29 represents the sample plot with the number 29).

**Figure 10.**The influence of crown parameter LAI on NDVI based on simulation data using the INFORM model.

Date | 08/01/2015 | 08/04/2015 | 26/04/2015 | 12/05/2015 | 31/05/2015 | 09/07/2015 | 15/08/2015 | 10/09/2015 | 28/09/2015 | 16/10/2015 | 01/11/2015 | 16/11/2015 | 11/12/2015 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

DOY | 18 | 98 | 116 | 132 | 151 | 190 | 227 | 253 | 271 | 289 | 305 | 320 | 345 |

**Table 2.**Technical specifications of the GF-1 wide field-of-view (WFV) cameras (Available on the website of China Center For Resources Satellite Data and Application, http://www.cresda.com/CN/Satellite/3076.shtml, accessed on 16 May 2014).

Launch date | 26/04/2013 | |

Payloads | WFV | |

Spatial resolution (m) | 16 | |

Wavelength (nm) | Panchromatic: 450–890 nm | |

Multispectral | Blue: 450–520 Green: 520–590 Red: 630–690 Near-infrared: 770–890 | |

Swath width (km) | 800 (4 cameras combined) | |

Average orbit height (km) | 645 | |

Repetition cycle (day) | 4 | |

Orbit inclination/Local time of descending node | 98°/11:10 am | |

Orbit type | Sun-synchronous | |

Designed lifetime (year) | 5–8 |

Number | Image Acquisition Time | Sensor | Forest Growth Stage |
---|---|---|---|

1 | 01/01/2015 | WFV2 | dormancy |

2 | 17/01/2015 | WFV1 | dormancy |

3 | 19/02/2015 | WFV1 | germination |

4 | 12/03/2015 | WFV3 | frondescence |

5 | 14/04/2015 | WFV3 | frondescence |

6 | 22/04/2015 | WFV2 | frondescence |

7 | 26/04/2015 | WFV2 | frondescence |

8 | 12/05/2015 | WFV1 | frondescence |

9 | 21/05/2015 | WFV4 | frondescence |

10 | 14/06/2015 | WFV2 | leaf peak |

11 | 02/08/2015 | WFV4 | leaf peak |

12 | 02/10/2015 | WFV1 | leaf fall |

13 | 15/10/2015 | WFV1 | leaf fall |

14 | 01/11/2015 | WFV3 | leaf fall |

15 | 03/12/2015 | WFV2 | leaf fall |

**Table 4.**Input parameters for different growing seasons to analyze the influence of leaf and canopy parameters on the spectrum based on simulation data using the PROSPECT + INFORM model.

DOY | 18 | 98 | 116 | 132 | 151 | 190 | 227 | 253 | 271 | 289 | 305 | 320 | 345 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Parameter | ||||||||||||||

Cab | 2 | 6 | 17 | 45 | 70 | 85 | 75 | 40 | 25 | 10 | 8 | 5 | 4 | |

Car | 4 | 4 | 6 | 7 | 8 | 10 | 10 | 12 | 15 | 18 | 19 | 19 | 18 | |

Cw | 0.005 | 0.007 | 0.01 | 0.01 | 0.02 | 0.04 | 0.03 | 0.02 | 0.015 | 0.01 | 0.008 | 0.007 | 0.006 | |

Cm | 0.002 | 0.002 | 0.003 | 0.004 | 0.004 | 0.004 | 0.005 | 0.005 | 0.006 | 0.007 | 0.008 | 0.008 | 0.01 | |

N | 1.5 | |||||||||||||

Cbrown | 0 | |||||||||||||

LAI | 0 | 1–1.5 | 1.5–2.5 | 4–5.5 | 6.5–10 | 6.5–9 | 6.5–8 | 5.5–7.5 | 4–5.5 | 3.5–5 | 2.5–3.5 | 1.5–2.5 | 1–2 | |

ALA | 40 | 70 | 70 | 60 | 50 | 40 | 25 | 25 | 25 | 25 | 40 | 40 | 40 | |

SZA | 35 | 60 | 68 | 71 | 76 | 75 | 62 | 56 | 54 | 50 | 43 | 40 | 35 | |

RA | 59 | 126 | 47 | 39 | 137 | 52 | 52 | 52 | 60 | 63 | 106 | 52 | 69 | |

OZA | 18 | 5 | 3 | 20 | 15 | 28 | 25 | 24 | 24 | 7 | 16 | 15 | 13 | |

LAIU | 0.1–0.5 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | |

SD | 400 | |||||||||||||

H | 15 | |||||||||||||

CD | 5 | |||||||||||||

FDR | 0.34 |

**Table 5.**Input parameters for different growing seasons to analyze the influence of leaf and canopy parameters on the spectrum based on simulation data using the LIBERTY + INFORM model.

DOY | 18 | 98 | 116 | 132 | 151 | 190 | 227 | 253 | 271 | 289 | 305 | 320 | 345 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Parameters | ||||||||||||||

LT | 1.6 | 1.2 | 1.4 | 1.5 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | |

Cab | 40 | 45 | 50 | 55 | 60 | 70 | 90 | 75 | 70 | 65 | 60 | 55 | 50 | |

Cw | 55 | 60 | 65 | 70 | 75 | 90 | 100 | 100 | 90 | 80 | 70 | 60 | 55 | |

LC | 70 | 45 | 45 | 45 | 50 | 55 | 60 | 60 | 60 | 60 | 65 | 70 | 70 | |

N | 1 | 1 | 1 | 1 | 1.05 | 1.15 | 1.15 | 1.15 | 1.15 | 1.15 | 1.15 | 1 | 1 | |

CD | 45 | |||||||||||||

IAS | 0.028 | |||||||||||||

B | 0.0006 | |||||||||||||

AA | 2 | |||||||||||||

LAI | 2–4.5 | 2.5–4.5 | 3–4.5 | 3–5 | 3–5 | 3–5 | 3.5–5 | 3–5 | 3–5 | 3–5 | 3–5 | 3–5 | 2.5–4.5 | |

LAIU | 0.1 | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.8 | 0.5 | 0.1 | |

ALA | 40 | 70 | 65 | 55 | 45 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | |

SZA | 35 | 60 | 68 | 71 | 76 | 75 | 62 | 56 | 54 | 50 | 43 | 40 | 35 | |

RA | 59 | 126 | 47 | 39 | 137 | 52 | 52 | 52 | 60 | 63 | 106 | 52 | 69 | |

OZA | 18 | 5 | 3 | 20 | 15 | 28 | 25 | 24 | 24 | 7 | 16 | 15 | 13 | |

SD | 600 | |||||||||||||

H | 20 | |||||||||||||

CD | 4 | |||||||||||||

FDR | 0.34 |

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## Share and Cite

**MDPI and ACS Style**

Yang, R.; Wang, L.; Tian, Q.; Xu, N.; Yang, Y.
Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data. *Remote Sens.* **2021**, *13*, 4426.
https://doi.org/10.3390/rs13214426

**AMA Style**

Yang R, Wang L, Tian Q, Xu N, Yang Y.
Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data. *Remote Sensing*. 2021; 13(21):4426.
https://doi.org/10.3390/rs13214426

**Chicago/Turabian Style**

Yang, Ranran, Lei Wang, Qingjiu Tian, Nianxu Xu, and Yanjun Yang.
2021. "Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data" *Remote Sensing* 13, no. 21: 4426.
https://doi.org/10.3390/rs13214426