Research on Estimation of Coniferous Forest Volume in Longnan County Based on Landsat 8 and PALSAR-2 Images

doi:10.13466/j.cnki.lyzygl.2021.01.010

Abstract

Abstract:

Forest stand volume estimation is an important research field of forestry remote sensing.Factors such as cloud and fog weather and spectral saturation have restricted the accuracy of optical remote sensing image estimation.Synthetic Aperture Radar(SAR) images have the characteristics of strong penetrability and are less likely to be affected by cloud and fog,which make up for the deficiencies of optical remote sensing.This study uses the coniferous forest in Longnan county,Jiangxi Province as the study area,combines Landsat 8 and PALSAR-2 dual-polarization SAR image data to extract a total of 245 remote sensing factors such as spectral information,vegetation index,texture information and backscattering coefficient based on remote sensing data preprocessing.Based on the Pearson correlation coefficient method and the multiple stepwise regression method,65 remote sensing factors are selected for the estimation of stand stock.Taking forest stand canopy closure as a stratification factor,the study adopts five models of linear,KNN,support vector machine(SVM),multi-perceptron(MLP) and random forest(RF) to estimate the forest stand volume,and tests the accuracy of the estimated results.The experimental results show that:1) Compared with using the spectrum and texture information of Landsat8 alone,the backscatter information of PALSAR-2 based on the canopy closure classification and fusion significantly improves the inversion accuracy of accumulation 2) In low canopy closure forest stand,the linear model has the highest accuracy(rRMSE=21.16%),in medium canopy closed forest stand,the MLP model has the best estimation effect(rRMSE=30.61%),in high canopy closure forest stand,the MLP model has the best estimation effect(rRMSE=27.53%).Based on the backscattering coefficient of PALSAR-2,the canopy closure stratification can effectively improve the inversion accuracy of the medium and high accumulation areas.

Key words: canopy density classification, PALSAR-2, stand volume, multi-perceptron model, coniferous forest

CLC Number:

S758
S771.8

LUO Kaijian, XV Xiaodong, LONG Jiangping, XV Congrong, LIN Hui, HE Xiaofeng. Research on Estimation of Coniferous Forest Volume in Longnan County Based on Landsat 8 and PALSAR-2 Images[J]. FOREST RESOURCES WANAGEMENT, 2021, 0(1): 69-76.

Figures/Tables 10

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变量名	变量形式
单波段(7个)	Band1			Band2		Band3		Band4		Band5	Band6		Band7
植被指数 (32个)	ARVI			B		W		G		EVI	II		NDVI
	PVI			SARV		MVI5		MVI7		ND43	ND67		ND563
	VI3			B_ALBEDO		G_ALBEDO		R_ALBEDO		N_ALBEDO	R1		R3
	R2			DVI(24)		DVI(34)		DVI(54)		RVI(24)	RVI(34)		RVI(54)
	ALBEDO			VIS234		VIS2345		R4
图像变换特征 (26个)	KT1			KT2		KT3		PC1_A		PC1_B	PC1_P		PC2_A
	PC2_B			PC2_P		PC3_A		PC3_B		PC3_P	MNF_B1		MNF_B2
	MNF_B3			MNF_B4		MNF_B5		MNF_B6		MNF_B7	PCA_B1		PCA_B2
	PCA_B3			PCA_B4		PCA_B5		PCA_B6		PCA_B7
纹理特征(168个)	窗口3×3,5×5,7×7
平均值	MEAN1			MEAN2		MEAN3		MEAN4		MEAN5	MEAN6		MEAN7
方差	VAR1			VAR2		VAR3		VAR14		VAR5	VAR6		VAR7
协同性	HOM1			HOM2		HOM3		HOM4		HOM5	HOM6		HOM7
对比度	CON1			CON2		CON3		CON4		CON5	CON6		CON7
相异性	DIS1			DIS2		DIS3		DIS4		DIS5	DIS6		DIS7
熵	ENT1			ENT2		ENT3		ENT4		ENT5	ENT6		ENT7
变量名		变量形式
二阶矩		SM1	SM2		SM3		SM4		SM5			SM6		SM7
相关性		COR1	COR2		COR3		COR4		COR5			COR6		COR7
SAR因子 (12)		HH	HV		HH/(HV+HH)		HV/(HV+HH)		HH/HV			HV/HH
SAR因子 (12)		HH-HV	HV-HH		HV×HH		HV+HH		(HH-HV)/(HV+HH)			(HV-HH)/(HV+HH)

数据源	郁闭度
Landsat 8	不分级	Band6	3_b3_sm	3_b4_cor	3_b6_cor	5_b2_hom	5_b5_hom	5_b5_sm	7_b3_ent	7_b3_mean
	低	5_b4_cor	3_b3_sm	3_b1_con	7_b6_sm	3_b2_con	3_b7_con
	中	Band3	3_b3_cor	3_b2_hom	3_b7_cor	3_b1_sm	5_b7_sm	PC3_A
	高	3_ent_rvi	3_b6_cor	3_b7_sm	N_Albedo	7_b1_var	3_b1_cor	7_b5_var	pca_b2
Landsat 8+ Alos-palsar 2	不分级	Band6	3_b3_sm	3_b4_cor	3_b6_cor	5_b2_hom	5_b5_hom	5_b5_sm	7_b3_ent	7_b3_mean
	低	5_b4_cor	3_b3_sm	3_b1_con	7_b6_sm	3_b2_con	3_b7_con
	中	3_b3_cor	3_b6_cor	3_b2_hom	3_b7_cor	3_b1_sm	hv_hh	Band6	7_b2_cor	5_b1_con	3_b7_hom	5_b1_cor
	高	3_ent_rvi	3_b6_cor	3_b7_sm	N_Albedo	7_b1_var	3_b1_cor	7_b5_var	pca_b2	hh

方法	R²					RMSE					rRMSE/%
方法	线性	KNN	SVM	MLP	RF	线性	KNN	SVM	MLP	RF	线性	KNN	SVM	MLP	RF
L(不分级)	0.56	0.27	0.49	0.57	0.26	34.58	44.80	37.26	34.49	45.10	41.09	53.24	44.27	40.98	53.59
L(低)	0.92	-0.31	-0.09	0.70	0.04	4.43	18.18	16.59	8.74	15.52	21.16	86.87	79.28	41.78	74.18
L(中)	0.68	0.40	0.71	0.71	0.33	24.09	32.88	22.99	22.80	34.87	34.83	47.53	33.23	32.95	50.41
L(高)	0.46	0.15	0.32	0.43	-0.03	33.92	42.60	38.18	34.87	46.94	29.85	37.50	33.61	30.69	41.32
L+P(不分级)	0.56	0.27	0.49	0.57	0.26	34.58	44.80	37.26	34.49	45.10	41.09	53.24	44.27	40.98	53.59
L+P(低)	0.92	-0.31	-0.09	0.70	0.04	4.43	18.18	16.59	8.74	15.52	21.16	86.87	79.28	41.78	74.18
L+P(中)	0.75	0.31	0.64	0.75	0.16	21.27	35.41	25.63	21.18	39.02	30.74	51.19	37.05	30.61	56.40
L+P(高)	0.40	0.13	0.38	0.54	-0.01	35.69	43.13	36.43	31.28	46.34	31.41	37.96	32.06	27.53	40.79