长白山区4种针叶林有效叶面积指数遥感精细反演及空间分布规律

doi:10.11707/j.1001-7488.LYKX20220545

Abstract

Abstract:

Objective: The aim of this study was to study develop the rapid, accurate and macroscopic methods for obtaining effective leaf area index (LAI_e) of different forest types, and to explore their spatial distribution, so as to provide new ideas for the development of medium and small-scale forest LAI_e remote sensing products, and offer scientific and reliable technological means for precision forestry monitoring and simulation of forest ecosystem carbon and water cycles. Method: Using Changbai Mountain as the research area, this study extracts the spatial distribution of four coniferous forest types (Larix olgensis, Pinus sylvestris var. mongolica, Pinus koraiensis and Picea koraiensis ) based on Sentinel-2A multispectral images through a three-dimensional convolutional neural network. It adopts 2 schemes, differentiated forest types and full sample, to analyze the correlation between field-measured LAI_e and 7 vegetation indices [enhanced vegetation index (EVI), inverted red-edge chlorophyll index (IRECI), modified simple ratio (MSR), normalized difference water index (NDWI), normalized difference vegetation index(NDVI), soil adjusted vegetation index (SAVI), simple ratio (SR)]. The optimal vegetation index corresponding to each tree species was used to construct regression models for LAI_e and vegetation index of differentiated forest type and full sample, and the accuracy performance of differentiated forest type model, full sample model and PROSAIL model in LAI_e inversion was compared based on validation sample data. Subsequently, spatial pattern and change rule of LAI_e of 4 tree species were analyzed by combining geographical factors. Result: 7 vegetation indices in all sample groups were significantly correlated with relative LAI_e values (P<0.01), except for EVI and LAI_e of Pinus koraiensis , SR and LAI of Picea koraiensis, the correlation coefficients were all greater than 0.6, while the correlation between LAI_e and different vegetation indexes was significantly different among groups. LAI_e of Pinus koraiensis, Larix olgensis and Pinus sylvestris var. mongholica had the highest correlation with IRECI, while LAI_e of Picea koraiensis and Pinus sylvestris var. mongholica had the highest correlation with EVI and MSR, respectively. Compared with the full-sample model, the R² value of the 4 different forest types model increased by more than 12.7%, and RMSE decreased by 34.5%. The LAI_e of the 4 forest types in the study area ranged from 0.37 to 5.86, and the average LAI_e from high to low was Pinus koraiensis, Larix olgensis, Pinus sylvestris var. mongholica. and Picea koraiensis. Pinus koraiensis was the most sensitive to changes in altitude, slope and aspect, followed by Picea koraiensis and Pinus sylvestris var. mongholica, while Larix olgensis was the least affected. Conclusion: There were significant differences in the correlation between LAI_e and remote sensing vegetation index among different forest types. The inversion accuracy of LAI_e can be improved by constructing specific regression models for different forest types. The accuracy of the fitted linear model after distinguishing forest types was higher than that of the PROSAIL model and the full-sample model, but in the area with high LAI_e value performed unstable compared with the PROSAIL model. LAI_e of the 4 tree species varied greatly in reflecting the changes of geographical factors. This study can provide a scientific reference for the selection of LAI remote sensing inversion models for coniferous forests in small and medium scales under tree species differentiation.

Key words: effective leaf area index (LAI_e), conifer species, forest type, satellite remote sensing, spatial distribution

CLC Number:

Guangdao Bao,Ting Liu,Zhonghui Zhang,Zhibin Ren,Chang Zhai,Mingming Ding,Xuefei Jiang. Remote Sensing Inversion of Effective Leaf Area Index of Four Coniferous Forest Types and Their Spatial Distribution Rule in Changbai Mountain[J]. Scientia Silvae Sinicae, 2024, 60(5): 127-138.

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类型 Type	样地数量 Plot number	最小值 Minimum	最大值 Maximum	平均值 Mean	标准差 Standard deviation	偏度 Skewness	峰度 Peakness
长白落叶松Larix olgensis forest	14	1.65	3.45	2.51	0.27	1.27	2.24
樟子松Pinus sylvestris var. mongolica forest	15	1.86	3.79	2.72	0.23	2.32	7.79
红松Pinus koraiensis forest	15	1.06	3.24	1.95	0.24	1.32	3.19
红皮云杉Picea koraiensis forest	14	1.72	4.37	2.87	0.31	0.26	?0.05
全样本Full samples	58	1.06	4.37	2.43	0.38	1.62	4.75

名称及来源 Name and source	公式 Formula
增强植被指数Enhanced vegetation index (EVI)	2.5× (b8?b4) / (1+ b8 + 6×b4?7.5× b2+1)
反红边叶绿素指数Inverted red-edge chlorophyll index (IRECI)	(b7 ? b4) × (b6/b5)
改进简单植被指数Modified simple ratio (MSR)	[(b7/b4) – 1]/[(b7/b4) +1]
归一化水体指数Normalized difference water index (NDWI)	(b2 - b4) / (b2 + b4)
归一化植被指数Normalized difference vegetation index(NDVI)	(b8a ? b4) / (b8a + b4)
土壤调节植被指数Soil adjusted vegetation index (SAVI)	1.5× (b8a? b4) / (b8a+ b4+0.5)
简单植被指数Simple ratio (SR)	b8a / b4

类型 Type	斑块数 Patches number	面积 Area/hm²	平均斑块面积 Average area of patches/hm²	制图精度 Mapping accuracy（%）	用户精度 User accuracy（%）
长白落叶松Larix olgensis forest	2 552	5 344.85	2.09	89.5	90.2
樟子松Pinus sylvestris var. mongolica forest	148	245.58	1.66	87.6	89.4
红松Pinus koraiensis forest	780	1 319.52	1.69	91.2	92.7
红皮云杉Picea koraiensis forest	763	1 085.61	1.42	90.4	92.3

植被指数 Vegetation index	长白落叶松 Larix olgensis forest	樟子松 Pinus sylvestris var. mongolica forest	红松 Pinus koraiensis forest	红皮云杉 Picea koraiensis forest	全样本 Full sample
EVI	0.629**	0.682**	0.367*	0.782**	0.602**
IRECI	0.891**	0.859**	0.905**	0.602**	0.714**
MSR	0.868**	0.805**	0.891**	0.685**	0.778**
NDWI	?0.819**	?0.763**	?0.795**	?0.650**	?0.685**
NDVI	0.844**	0.805**	0.816**	0.708**	0.723**
SAVI	0.767**	0.784**	0.641**	0.628**	0.631**
SR	0.710**	0.621**	0.776**	0.396*	0.620**

类型 Type	变量 Variable	斜率 Slope	截距 Incept	P	R²	RMSE	MAE	RPD
长白落叶松Larix olgensis forest	IRECI	0.691	0.006	0.000	0.813	0.435	0.427	1.657
樟子松Pinus sylvestris var. mongolica forest	IRECI	0.513	0.558	0.000	0.771	0.619	0.434	1.982
红松Pinus koraiensis forest	IRECI	0.667	0.203	0.000	0.842	0.374	0.446	1.736
红皮云杉Picea koraiensis forest	EVI	1.132	?0.439	0.000	0.693	0.891	0.605	1.608
全样本Full samples	MSR	0.551	0.479	0.000	0.615	1.364	0.911	1.015

Remote Sensing Inversion of Effective Leaf Area Index of Four Coniferous Forest Types and Their Spatial Distribution Rule in Changbai Mountain

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类型 Type	地理因子 Geographical factors	斜率 Slope	截距 Incept	P	R²
长白落叶松Larix olgensis forest	海拔 Altitude	?0.000 4	2.191	0.019*	0.001
	坡度 Slope	0.010 5	1.910	0.000**	0.028
	坡向 Aspect	0.000 1	2.030	0.302	0.001
樟子松Pinus sylvestris var. mongolica forest	海拔 Altitude	?0.000 3	2.246	0.052	0.017
	坡度 Slope	0.009 1	1.745	0.004**	0.036
	坡向 Aspect	0.000 4	1.765	0.071	0.015
红松Pinus koraiensis forest	海拔 Altitude	?0.003 5	5.845	0.000**	0.241
	坡度 Slope	0.042 9	1.701	0.000**	0.218
	坡向 Aspect	?0.000 7	2.401	0.083	0.007
红皮云杉Picea koraiensis forest	海拔 Altitude	?0.002 2	4.306	0.000**	0.043
	坡度 Slope	?0.006 4	1.816	0.354	0.003
	坡向 Aspect	0.000 3	1.731	0.233	0.004

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