结合改进模拟连续变化检测与分类算法的桉树年龄和蓄积量遥感估测

doi:10.11707/j.1001-7488.LYKX20240753

Abstract

Abstract:

Objective: This study aims to enhance the accuracy of eucalyptus stem volume estimation through remote sensing by ameliorating the detection algorithm to acquire precise age variables, thereby mitigating the saturation issue of eucalyptus stem volume. Method: Multi-temporal Landsat-8 and Sentinel-2 data were utilized as the primary data sources. The time series growth curve of eucalyptus was analyzed, and a combination of linear interpolation and dynamic recognition approaches was adopted. The simulated continuous change detection and classification (CCDC) algorithm was employed to identify the felling points, and the least squares method along with significant changes in slope and intercept was used to analyze the felling points and estimate the age of eucalyptus. On this basis, two variable sets were constructed using remote sensing features and eucalyptus age: variable set 1 (comprising band values, vegetation indices, and texture features) and variable set 2 (consisting of variable set 1 and age). Four models, namely multiple linear regression (MLR), k-nearest neighbor (KNN), random forest (RF), and support vector regression (SVR), were utilized to estimate eucalyptus stem volume. Result: The recognition accuracy of the simulated CCDC detection algorithm for 100 samples was 82%, and the distance correlation coefficient between age and stem volume was 0.71, which was significantly higher than that of other remote sensing variables. The results of eucalyptus stem volume estimation indicated that the R² of variable set 1 was 0.40, the RMSE (root mean square error) was 41.11 m³·hm^?2, and the rRMSE (relative root mean square error) was 34%. The R² of variable set 2 was 0.83, the RMSE was 22.08 m³·hm^?2, and the rRMSE was 18%. In variable set 1, the model calculation outcomes were weak, with a low R² value, high RMSE, and high rRMSE. However, with the introduction of the age variable in variable set 2, the model calculation results were notably improved, especially in the SVR model, where the R² increased to 0.83, and the RMSE and rRMSE decreased obviously. The introduction of the age variable enhanced the accuracy of the eucalyptus stem volume estimation model. Conclusion: The improved simulated CCDC change detection method has augmented the accuracy of age variables, and age is significantly correlated with stem volume. The introduction of the age variable has significantly enhanced the estimation performance of the model, with the estimation accuracy increasing by 16 percentage points.

Key words: forestry remote sensing, change detection, stem volume, age, Eucalyptus

CLC Number:

S718.5

Caihong Duan,Hui Lin,Jiangping Long,Peisong Yang,Zilin Ye,Tingchen Zhang,Xunwei Li,Lixin Zhu. Remote Sensing Estimation of Eucalyptus Age and Stem Volume Combining Improved Simulating Continuous Change Detection with Classification Algorithm[J]. Scientia Silvae Sinicae, 2025, 61(4): 46-55.

Figures/Tables 11

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序号 Number	数据源 Data sources	日期 Date	分辨率 Resolution/m	云量 Cloud (%)	序号 Number	数据源 Data sources	日期 Date	分辨率 Resolution/m	云量 Cloud (%)
1	Landsat-8	2013?12	15/30	0	14	Sentinel-2	2018?12	10/20	0
2	Landsat-8	2014?02	15/30	0	15	Sentinel-2	2019?03	10/20	40
3	Landsat-8	2014?06	15/30	40	16	Sentinel-2	2019?09	10/20	0
4	Landsat-8	2015?04	15/30	0	17	Landsat-8	2019?10	15/30	0
5	Landsat-8	2015?11	15/30	10	18	Landsat-8	2019?12	15/30	0
6	Landsat-8	2016?06	15/30	10	19	Sentinel-2	2020?02	10/20	2
7	Landsat-8	2016?10	15/30	0	20	Landsat-8	2020?03	15/30	15
8	Landsat-8	2016?12	15/30	0	21	Landsat-8	2020?05	15/30	0
9	Landsat-8	2017?03	15/30	0	22	Sentinel-2	2020?10	10/20	0
10	Sentinel-2	2017?04	10/20	0	23	Sentinel-2	2020?12	10/20	0
11	Landsat-8	2017?10	15/30	10	24	Sentinel-2	2021?01	10/20	0
12	Landsat-8	2018?02	15/30	5	25	Landsat-8	2021?10	15/30	10
13	Sentinel-2	2018?10	10/20	0	26	Sentinel-2	2021?12	10/20	0

变量集 Variable set	模型 Model	R²	RMSE/ （m³ ·hm^?2）	rRMSE （%）
变量集1（波段值、植被指数和纹理特征） Set 1 (band values, vegetation index, and texture characteristics)	MLR	0.16	48.73	41.12
	KNN	0.32	43.83	37.23
	RF	0.34	43.10	35.35
	SVR	0.40	41.11	34.22
变量集2（波段值、植被指数、纹理特征和年龄） Set 2 (band value, vegetation index, texture characteristics, and age)	MLR	0.59	33.79	28.47
	KNN	0.71	28.33	23.39
	RF	0.73	27.31	22.43
	SVR	0.83	22.08	18.15

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Remote Sensing Estimation of Eucalyptus Age and Stem Volume Combining Improved Simulating Continuous Change Detection with Classification Algorithm

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检测算法 Detection algorithm	样本数量 Number of samples	准确率 Accuracy(%)
基于PELT算法检测 Pruned exact linear Time	100	35
BS算法变点检测 Binary segmentation	100	44
基于窗口的变点检测 Window-based detection	100	66
改进的模拟CCDC Improved simulating CCDC	100	82