基于改进像元三分模型的植被覆盖度提取及时空变化分析

doi:10.11707/j.1001-7488.LYKX20230469

摘要/Abstract

摘要：

目的: 引入DBSCAN聚类算法和QuickHull凸包检测算法，提出一种自适应端元特征值提取（AEEE）算法，解决像元三分模型中纯净像元指数结合二维散点图（PPI-2DSP）算法依赖人工圈选特征像元候选区的问题，利用AEEE算法评估神木市的光合植被覆盖度（f_PV）、非光合植被覆盖度（f_NPV）和裸土覆盖度（f_BS），并分析其时空变化，验证算法的有效性，为该地区生态环境评价和植被覆盖度变化规律研究提供参考。方法: 以Landsat系列卫星遥感影像为数据源，首先对遥感数据进行预处理，然后计算像元的归一化植被指数（NDVI）和干枯燃料指数（DFI），通过以下4个步骤获取特征像元候选区：1）应用随机采样模块减少数据规模；2）采用DBSCAN算法聚类，去除离群数据，得到最大簇；3）利用QuickHull算法计算凸包，构建特征三角形边界；4）计算由凸包点集中3个点所构成的最大面积三角形顶点，分别以3个顶点为中心，取端点阈值（$\theta $）范围内区域作为特征像元候选区。预处理的影像经最小噪声分离变换减少计算量后，采用PPI算法计算纯净像元指数，利用特征像元候选区提取纯净像元指数大于5的纯净像元，将这些像元的NDVI和DFI算术平均值作为端元特征值代入像元三分模型计算f_PV、f_NPV和f_BS，并分析其时空变化。结果: AEEE算法提取的神木市2000—2022年端元特征值与PPI-2DSP算法选取的端元特征值相近，相对误差平均值约7.35%；将其应用于像元三分模型，估算得到神木市的f_PV和f_NPV，与传统方法相比年平均误差分别为4.79%和5.05%，符合精度要求。在时间层面上，2000—2022年神木市的f_PV和f_NPV总体呈波动增长趋势，分别以年平均0.52%和0.22%的速率增长；在空间层面上，2000—2022年神木市的f_PV和f_NPV呈东南增长快速、西北增长缓慢的趋势，其中f_PV主要以增加（39.8%）和基本不变（28.0%）2种变化强度等级为主。结论: AEEE算法适用于对光合植被、非光合植被和裸土3种端元特征值的自适应提取，能够解决PPI-2DSP算法依赖人工圈选特征像元候选区的问题。

关键词: 像元三分模型, 植被覆盖度, Landsat, 自适应端元特征值提取算法, 陕西省神木市

Abstract:

Objective: By introducing the DBSCAN clustering algorithm and QuickHull convex hull detection algorithm, an adaptive endmember eigenvalue extraction (AEEE) algorithm is proposed to address the issue of manual selection of feature pixel candidate areas in the pixel ternary model combined with the pure pixel index-2D scatter plot (PPI-2DSP) algorithm. The AEEE algorithm is utilized to assess the photosynthetic vegetation coverage (f_PV), non-photosynthetic vegetation coverage (f_NPV), and bare soil coverage (f_BS) in Shenmu city and analyze their spatiotemporal variations. The effectiveness of the algorithm is verified, providing reference for the evaluation of the ecological environment and the study of vegetation fractional cover change patterns in the region. Method: Using Landsat series satellite remote sensing images as the data source, the remote sensing data is preprocessed first. Then, the normalized difference vegetation index (NDVI) and dry fuel index (DFI) of pixels are calculated. Feature pixel candidate areas are obtained through the following 4 steps: 1) Reducing the data volume using a random sampling module; 2) Clustering with the DBSCAN algorithm to remove outlier data and identify the largest cluster; 3) Computing the convex hull using the QuickHull algorithm to construct the boundaries of feature triangles; 4) Calculating the vertices of the largest area triangle formed by three points within the convex hull point set. Regions within a specified range centered at the three vertices are selected as feature pixel candidate areas with a vertex threshold ($\theta $). After reducing the computational complexity through the Minimum Noise Fraction transformation of preprocessed images, the pure pixel index is calculated using the PPI algorithm. Pure pixels with a pure pixel index greater than 5 are extracted using feature pixel candidate areas. The arithmetic mean values of the NDVI and DFI for these pixels are used as endmember eigenvalues into the pixel ternary model to calculate f_PV, f_NPV, and f_BS, followed by an analysis of their spatiotemporal variations. Result: The eigenvalues calculated by the AEEE algorithm for Shenmu city from 2000 to 2022 are close to those selected by the PPI-2DSP algorithm, with an average relative error of approximately 7.35%. When applied to the NDVI-DFI model, the estimation of f_PV and f_NPV for Shenmu city using AEEE exhibits errors of 4.79% and 5.05%, respectively, compared to the traditional method, meeting accuracy requirements. On a temporal scale, from 2000 to 2022, f_PV an f_NPV in Shenmu city showed an overall fluctuating growth trend, increasing at average annual rates of 0.52% and 0.22%, respectively. On a spatial scale, from 2000 to 2022, f_PV and f_NPV in Shenmu city exhibited a trend of rapid growth in the southeast and slower growth in the northwest, with f_PV primarily characterized by two main change intensities: increase (39.8%) and basically unchanged (28.0%). Conclusion: The AEEE algorithm is suitable for the adaptive extraction of endmember eigenvalues for photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil (BS). It addresses the issue of the PPI-2DSP algorithm relying on manual selection of feature pixel candidate regions.

Key words: normalized difference vegetation index (NDVI) and dry fuel index (DFI) model, vegetation fractional cover, Landsat, adaptive endmember eigenvalue extraction (AEEE) algorithm, Shenmu city of Shaanxi Province

中图分类号:

TP751.1
Q948

张凡,仇天昊,李欣悦,张姝茵,徐超,谢治国. 基于改进像元三分模型的植被覆盖度提取及时空变化分析[J]. 林业科学, 2024, 60(12): 13-26.

Fan Zhang,Tianhao Qiu,Xinyue Li,Shuyin Zhang,Chao Xu,Zhiguo Xie. Vegetation Fractional Cover Extraction and Spatiotemporal Variation Analysis Based on Improved Normalized Difference Vegetation Index (NDVI) and Dry Fuel Index (DFI) Model[J]. Scientia Silvae Sinicae, 2024, 60(12): 13-26.

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影像名称 Image name	年份 Year	选取原因 Reason for Selection
Landsat 5/TM	2000—2011	同期可选产品有Landsat 5和Landsat 7，但2003年起Landsat 7数据存在异常，故选择Landsat 5 Concurrently available products include Landsat 5 and Landsat 7, but Landsat 7 data has been abnormal since 2003, thus Landsat 5 was selected.
Landsat 7/ETM+	2012	Landsat系列影像只有Landsat 7有2012年影像数据，使用landsat_gapfill工具进行条带错误修复 Only Landsat 7 in the Landsat series has image data from 2012. The landsat_gapfill tool was used to repair stripe errors.
Landsat 8/OLI	2013—2022	2013年发射的新卫星，配备更先进的传感器，数据质量更好 A new satellite launched in 2013, equipped with more advanced sensors, providing better data quality.

$\eta $	$\varepsilon $	min_pts	$\theta $	相对误差 Relative error(%)	$\eta $	$\varepsilon $	min_pts	$\theta $	相对误差 Relative error(%)	$\eta $	$\varepsilon $	min_pts	$\theta $	相对误差 Relative error(%)
0.01	0.014	4	0.04	10.96	0.01	0.018	4	0.04	9.37	0.02	0.016	4	0.04	17.34
0.01	0.014	4	0.05	11.50	0.01	0.018	4	0.05	9.39	0.02	0.016	4	0.05	13.67
0.01	0.014	5	0.04	10.07	0.01	0.018	5	0.04	8.55	0.02	0.016	5	0.04	14.21
0.01	0.014	5	0.05	11.58	0.01	0.018	5	0.05	8.16	0.02	0.016	5	0.05	11.15
0.01	0.014	6	0.04	10.61	0.01	0.018	6	0.04	7.65	0.02	0.016	6	0.04	11.74
0.01	0.014	6	0.05	12.40	0.01	0.018	6	0.05	7.98	0.02	0.016	6	0.05	9.78
0.01	0.016	4	0.04	9.37	0.02	0.014	4	0.04	11.95	0.02	0.018	4	0.04	24.68
0.01	0.016	4	0.05	8.63	0.02	0.014	4	0.05	10.34	0.02	0.018	4	0.05	20.07
0.01	0.016	5	0.04	7.72	0.02	0.014	5	0.04	9.40	0.02	0.018	5	0.04	19.96
0.01	0.016	5	0.05	7.35	0.02	0.014	5	0.05	8.75	0.02	0.018	5	0.05	15.94
0.01	0.016	6	0.04	8.98	0.02	0.014	6	0.04	9.37	0.02	0.018	6	0.04	15.57
0.01	0.016	6	0.05	10.18	0.02	0.014	6	0.05	9.34	0.02	0.018	6	0.05	12.72

特征值类型 Type of eigenvalue	PPI-2DSP 算法提取 Extraction using the PPI-2DSP	AEEE算法提取 Extraction using the AEEE	相对误差 Relative error (%)
BS端元的NDVI特征值 NDVI eigenvalue of the BS endmember	0.070	0.073	9.56
PV端元的NDVI特征值 NDVI eigenvalue of the PV endmember	0.354	0.363	4.42
NPV端元的NDVI特征值 NDVI eigenvalue of the NPV endmember	0.124	0.129	9.59
BS端元的DFI特征值 DFI eigenvalue of the BS endmember	2.660	3.079	14.62
PV端元的DFI特征值 DFI eigenvalue of the PV endmember	10.856	10.867	2.06
NPV端元的DFI特征值 DFI eigenvalue of the NPV endmember	12.221	11.818	3.86

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