基于GF-1数据的二阶段遥感特征优选与森林地上生物量反演模型

doi:10.11707/j.1001-7488.LYKX20240485

摘要/Abstract

摘要：

目的: 提出二阶段遥感特征选择方法与多机器学习模型结合的思路，探索适用于国产高分一号（GF-1）数据估测森林地上生物量（AGB）的有效模式，为高分系列卫星数据在林业监测中的应用提供参考。方法: 首先，从GF-1数据中提取波段光谱特征、纹理特征、植被指数等44个特征变量；然后，基于二阶段特征优选策略优选特征，第一阶段采用过滤式法（Relief）、嵌入式法（Lasso、RF）进行初步筛选，第二阶段通过包装式法（RFE）进一步优化特征子集，提升模型估测能力；最后，利用以往森林AGB估测研究中表现较好的K最近邻（KNN）、支持向量回归（SVR）、随机森林（RF）、梯度提升回归树（GBRT）和极限梯度提升树（XGBoost）机器学习模型进行森林AGB估测，并评估不同特征选择方法与模型之间的匹配度及其对森林AGB估测精度的影响，探寻最优的基于GF-1数据估测森林AGB的策略。结果: 1） Relief-RFE方法在XGBoost模型森林AGB反演中取得较优效果（R2 = 0.811，RMSE = 8.45 t·hm^?2，rRMSE = 23.39%）。2）绿光波段纹理特征（mea_b2）能够捕捉到森林地表覆盖物的空间分布信息，如森林的冠层密度、树木分布等空间结构变化信息；蓝光波段光谱特征（b1）对叶绿素和叶色素浓度变化反应敏感，可表征植被健康状况和生长阶段信息，在多种方法的二阶段特征选择中上述2种特征均被选为核心特征用于森林AGB估测。3）特征优选可显著提升模型性能，XGBoost模型的提升效果明显大于KNN、SVR、RF和GBRT模型。结论: 通过对比不同特征选择方法与机器学习模型结合估测森林AGB的效果，验证了二阶段特征优选策略在基于GF-1数据估测森林AGB中的有效性，尤其是将该策略与XGBoost模型结合，能够构建出高精度、高稳健性的森林AGB估测策略。

关键词: GF-1数据, 森林地上生物量, 混合特征选择, 机器学习, 二阶段优化策略

Abstract:

Objective: In this study, a two-stage remote sensing feature selection method combined with multiple machine learning models was proposed to explore an efficient model for estimating forest aboveground biomass (AGB) using domestic GF-1 satellite data, thereby providing scientific reference for the application of GF-1 satellite data in forestry monitoring. Method: A total of 44 remote sensing feature variables, such as spectral features, texture features, and vegetation indices, were first extracted from the GF-1 data. Then a two-stage feature selection strategy was proposed and then applied for feature optimization. In the first stage, the filtered method (Relief) and embedded method (Lasso, RF) were used for initial screening, and in the second stage, the feature subset was further optimized by the wrapper method (RFE) to enhance the model estimation capability. Finally, 5 machine learning models including the K nearest neighbor (KNN), support vector regression (SVR), random forest (RF), gradient boosting (GBRT), gradient boosted regression tree (GBRT) and extreme gradient boosting tree (XGBoost) models were selected and applied for AGB estimation. They were applied for evaluating the match between the different feature selection methods and models and their effects on forest AGB estimation accuracy. According to the results, the optimal strategy was explored for estimating for forest AGB based on GF-1 data. Result: 1) The Relief-RFE method achieved the best results in AGB inversion using the XGBoost model with R2 = 0.811, RMSE = 8.45 t·hm^?2, rRMSE = 23.39%. 2) The green light band texture feature (mea-b2) was able to capture spatial distribution information of forest surface cover, such as canopy density, tree distribution, and other spatial structural changes of the forest; The spectral characteristics of the blue light band (b1) were sensitive to changes in chlorophyll and leaf pigment concentrations, and was able to characterize vegetation health status and growth stage information. In the two-stage feature selection of various methods, both of the above two features were selected as core features for forest AGB estimation. 3) Feature selection significantly improved model performance, with XGBoost showing a more pronounced improvement compared to KNN, SVR, RF, and GBRT. Conclusion: By comparing the application of different feature selection methods combined with machine learning model in forest AGB estimation, the results have demonstrated the effectiveness of the proposed two-stage hybrid feature selection strategy in forest AGB estimation based on GF-1 data, especially combining it with the XGBoost model, a highly accurate and robust forest AGB estimation strategy can be constructed.

Key words: GF-1 data, forest above-ground biomass (AGB), hybrid feature selection, machine learning, two-stage optimization strategy

中图分类号:

S771.8

杨菲菲,张王菲,赵磊,赵含,姬永杰,王梦金. 基于GF-1数据的二阶段遥感特征优选与森林地上生物量反演模型[J]. 林业科学, 2025, 61(4): 9-19.

Feifei Yang,Wangfei Zhang,Lei Zhao,Han Zhao,Yongjie Ji,Mengjin Wang. Two-Stage Remote Sensing Feature Optimization and GF-1 Data-Supported Forest Above-Ground Biomass Inversion[J]. Scientia Silvae Sinicae, 2025, 61(4): 9-19.

图/表 8

表1

图1

表2

GF-1的一阶段特征优选参数和估测精度①"

模型 Model	方法 Method	R²	RMSE/ (t·hm^?2)	rRMSE （%）	特征 Feature
KNN	Relief	0.209	14.85	41.10	b3、DVI、b2、b4、b1、PVI、var_b4、var_b3、con_b4、con_b3、var_b2、con_b2、mea_b3、mea_b4、mea_b2、var_b1、con_b1、ent_b1
	RF	0.320	13.68	37.86	mea_b1、cor_b1、mea_b2、cor_b2、mea_b3、con_b4、NDVI、SR、EVI、b1、b2、b3、b4
	Lasso	0.232	15.13	41.88	var_b1、con_b1、ent_b1、Cor_b1、mea_b2、dis_b2、ent_b2、var_b3、con_b3、mea_b4、var_b4、con_b4、dis_b4、ent_b4、Cor_b4、PVI、DVI、EVI、b1、b2、b3、b4
SVR	Relief	0.262	11.87	32.85	b3、DVI、b2、b4、b1、PVI、var_b4、var_b3、con_b4、con_b3、var_b2、con_b2、mea_b3、mea_b4、mea_b2、var_b1、con_b1、ent_b1
	RF	0.321	10.89	30.14	mea_b1、cor_b1、mea_b2、cor_b2、mea_b3、hom_b4、NDVI、SAVI、SR、 EVI、MSAVI、b1、b2、b3、b4
	Lasso	0.386	9.82	27.18	var_b1、con_b1、ent_b1、Cor_b1、mea_b2、dis_b2、ent_b2、var_b3、con_b3、mea_b4、var_b4、con_b4、dis_b4、ent_b4、Cor_b4、PVI、DVI、EVI、b1、b2、b3、b4
RF	Relief	0.220	18.80	52.03	b3、DVI、b2、b4、b1、PVI、var_b4、var_b3、con_b4、con_b3、var_b2、con_b2、mea_b3、mea_b4、mea_b2、var_b1、con_b1、ent_b1
	RF	0.404	14.07	38.94	b3、DVI、b2、b4、b1、PVI、var_b4、var_b3、con_b4、con_b3、var_b2、con_b2、mea_b3、mea_b4、mea_b2、var_b1、con_b1、ent_b1
	Lasso	0.267	15.50	42.90	b3、DVI、b2、b4、b1、PVI、var_b4、var_b3、con_b4、con_b3、var_b2、con_b2、mea_b3、mea_b4、mea_b2、var_b1、con_b1、ent_b1
GBRT	Relief	0.135	15.21	42.10	mea_b1、mea_b2、cor_b2 、mea_b3、sec_b3、con_b4、NDVI、SR、 VARI、EVI、b1、b2、b3、b4
	RF	0.119	15.60	43.18	mea_b1、cor_b1、mea_b2、cor_b2、mea_b3、mea_b4、con_b4、NDVI、SR、EVI、b1、b2、b3
	Lasso	0.132	15.23	42.15	mea_b1、cor_b1、mea_b2、cor_b2、mea_b3、con_b4、SR、VARI、EVI、 MSAVI、b1、b2、b3、b4
XGBoost	Relief	0.296	10.81	29.92	var_b1、con_b1、ent_b1、Cor_b1、mea_b2、dis_b2、ent_b2、var_b3、con_b3、mea_b4、var_b4、con_b4、dis_b4、ent_b4、Cor_b4、PVI、DVI、EVI、b1、b2、b3、b4
	RF	0.416	9.90	27.40	var_b1、con_b1、ent_b1、Cor_b1、mea_b2、dis_b2、ent_b2、var_b3、con_b3、mea_b4、var_b4、con_b4、dis_b4、ent_b4、Cor_b4、PVI、DVI、EVI、b1、b2、b3、b4
	Lasso	0.444	9.90	27.40	var_b1、con_b1、ent_b1、Cor_b1、mea_b2、dis_b2、ent_b2、var_b3、con_b3、mea_b4、var_b4、con_b4、dis_b4、ent_b4、Cor_b4、PVI、DVI、EVI、b1、b2、b3、b4

表2

图2

图3

表3

图4

图5

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植被指数 Vegetation index	植被指数计算公式 Vegetation index calculation equation	文献 References
NDVI	$ \mathrm{NDVI}=\dfrac{\rho_{\text{NIR}}-\rho_{\text{red}}}{\rho_{\text{NIR}}+\rho_{\text{red}}} $	Rouse et al., 1974
SR	$ \mathrm{SR}=\rho_{\text{red}}/\rho_{\text{NIR}} $	Jordan , 1969
VARI	$ \mathrm{VARI}=\dfrac{\rho_{\text{green }}-\rho_{\text{red}}}{\rho_{\text{green}}+\rho_{\text{red}}-\rho_{\text{blue}}^{ }} $	Gitelson et al., 2002
DVI	$ \mathrm{DVI}=\rho_{\text{NIR}}-\rho_{\text{red}} $	Richardson et al., 1977
PVI	$ {\text{PVI}} = {\text{0}}{\text{.939 }} \times {\rho _{{\text{NIR}}}} - {\text{0}}{\text{.344 }} \times {\rho _{{\text{red}}}} + {\text{0}}{\text{.09}} $	Richardson et al., 1977
SAVI	$ \mathrm{SAVI}=\dfrac{1.5\times(\rho_{\text{NIR}}-\rho_{\text{red}})}{\rho_{\text{NIR}}+\rho_{\text{red}}+\text{0}\text{.5}} $	Huete , 1988
EVI	$ \mathrm{EVI}=\dfrac{2.5\times(\rho_{\text{NIR}}-\rho_{\text{red}})}{1+\rho_{\text{NIR}}+6\times\rho_{\text{red}}-7.5\times\rho_{\text{bule}}} $	Huete et al., 2002
MSAVI	$ \mathrm{MSAVI}=\dfrac{2\rho_{\text{NIR}}+1-\sqrt{(2\rho_{\text{NIR}}+1)^2-8(\rho_{\text{NIR}}-\rho_{\text{red}})}}{2} $	Qi et al., 1994

模型 Model	一阶段方法 First-stage method	二阶段方法 Two-stage method	R²	RMSE/ (t·hm^?2)	rRMSE （%）	特征 Feature
KNN	Relief	RFE	0.474	11.59	32.08	b3、b2、b4、b1、con_b4、con_b3、mea_b3、mea_b4、mea_b2
	RF		0.384	10.87	30.01	mea_b2、con_b4、EVI、b1、b3、b4
	Lasso		0.417	10.63	29.42	con_b1、cor_b1、mea_b2、con_b3、mea_b4、con_b4、EVI、 b1、b2、b3、b4
SVR	Relief		0.301	11.45	31.69	b3、DVI、b2、b4、b1、con_b4、con_b3、mea_b3、mea_b2
	RF		0.473	9.10	25.19	mea_b2、mea_b3、hom_b4、NDVI、EVI、b1、b4
	Lasso		0.493	10.84	30.00	con_b1、cor_b1、mea_b2、con_b3、mea_b4、con_b4、 EVI、b1、b2、b3、b4
RF	Relief		0.593	8.11	22.45	b3、b2、b4、b1、con_b4、con_b3、mea_b3、mea_b2、con_b1
	RF		0.629	7.29	20.18	b3、b2、b4、b1、con_b4、con_b3、mea_b3、mea_b4、mea_b2
	Lasso		0.571	7.34	20.32	b3、b2、b4、b1、PVI、con_b4、con_b3、mea_b3、mea_b2
GBRT	Relief		0.658	11.51	31.86	mea_b2、mea_b3、con_b4、NDVI、EVI、b1、b4
	RF		0.651	7.54	20.87	mea_b2、mea_b4、con_b4、SR、EVI、b1
	Lasso		0.591	9.12	25.24	mea_b2、con_b4、EVI、MSAVI、b1、b3、b4
XGBoost	Relief		0.811	8.45	23.39	mea_b1、cor_b2、var_b3、con_b3、cor_b3、con_b4、 dis_b4、cor_b4、PVI、DVI、b1
	RF		0.708	6.21	17.19	con_b1、cor_b1、mea_b2、con_b3、con_b4、dis_b4、 EVI、b1、b2、b3、b4
	Lasso		0.781	6.21	17.19	con_b1、cor_b1、mea_b2、con_b3、con_b4、dis_b4、 EVI、b1、b2、b3、b4

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