基于PROSAIL模型和多角度遥感数据的森林叶面积指数反演

doi:10.11707/j.1001-7488.20210410

Abstract

Abstract:

Objective: The aim of this study was to improve the inversion accuracy of vegetation LAI (leaf area index) on the regional scale based on multi-angle PROBA/CHRIS remote sensing data and field measured data, and provide a new method and model for regional soil erosion remote sensing quantitative monitoring. Method: In this study, Mount Zijin and Mount Mufu in Nanjing were selected as the study areas. Through the methods of field experiments, remote sensing image, radiation transfer model and mathematical models, this study established the LAI inversion model of random forest model based on PROSPECT+SAIL (scattering by arbitrarily inclined leaves) model—PROSAIL model and multi-angle PROBA/CHRIS (project for on-board autonomy/compact high resolution imaging spectrometer) remote sensing data. The sensitivity analysis and applicability evaluation of PROSAIL model were carried out, and the optimal LAI inversion model was also determined. The accuracies were verified and evaluated by the ground measured LAI values. Result: The sensitivity of input parameters of the PROSAIL model was LAI>C_ab (chlorophyll a and b content)>C_m (leaf dry matter content)>S_L (hotspot parameters)>N (blade internal structure parameters)>C_w (equivalent water thickness). The accuracy of the canopy reflectance simulated by the PROSAIL model was 0°> 36°>-36°>55°>-55°. In the single angle LAI inversion models, the accuracy of forward observation angle 55° was the highest, and the R² (coefficient of determination), RMSE(root mean square error) and MAPE(mean absolute percentage error) were 0.915 7, 0.235 7 and 0.042 6, respectively. Compared with the traditional vertical observation, the R² of the 55° model increased by 0.75%, and the RMSE and MAPE decreased by 3.76% and 5.12%, respectively. Compared with the nonlinear regression model, the R² of the 55° model increased by 0.7%, and the RMSE and MAPE decreased by 15.40% and 11.98%, respectively. The accuracy of single angle inversion models was 55°>36°>0°>-55°>-36°. In the LAI inversion models based on the multi-angle data, the three angles combination of 0°, 36° and 55° had the highest accuracy with the R², RMSE and MAPE of 0.918 4, 0.231 9 and 0.041 5, respectively. Compared with single angle 55°, the R² of the three angles combination model increased by 0.29%, and the RMSE and MAPE decreased by 1.61% and 2.58%, respectively. Compared with the traditional vertical observation, the R² of the three angles combination model increased by 1.05%, and the RMSE and MAPE decreased by 5.31% and 7.57%, respectively. Compared with the nonlinear regression model, the R² of the three angle combination model increased by 0.79%, and the RMSE and MAPE increased by 6.72% and 9.19%, respectively. The LAI in the western region of Mount Zijin was between 0.44 and 6.70, and the average LAI of forest was 3.04. The spatial distribution pattern of LAI in the western woodland of Mount Zijin showed "high in the north and south and low in the middle" as a whole. Conclusion: The optimal LAI inversion model was the random forest LAI inversion model based on three angles combination (0°, 36°, 55°). On the one hand, increasing the observation angle can provide more information about vegetation crown structure, and the inversion accuracy of LAI will increase with the increase of observation angle. However, on the other hand, too many observation angles will lead to more uncertainty in pixel space resampling, leaf shadow, soil shadow and so on, and the accuracy of LAI inversion will decrease. No matter single angle and multi-angle data, the accuracy of random forest LAI inversion model was higher than that of nonlinear regression model, which indicated that the random forest model can obviously improve the inversion accuracy of LAI and was suitable for the inversion of LAI on the regional scale. Multi-angle remote sensing data can reflect the three-dimensional structure information of vegetation and the characteristics of multi-dimensional spatial structure of ground objects, and significantly improve the low accuracy of LAI inversion by traditional vertical observation, thus effectively improve the inversion accuracy of vegetation LAI.

Key words: leaf area index(LAI), multi-angle PROBA/CHRIS remote sensing data, PROSAIL model, random forest(RF) model

CLC Number:

S771.8

Ying Pan,Mingming Ding,Jie Lin,Qiao Dai,Geng Guo,Linlin Cui. Inversion of Forest Leaf Area Index Based on PROSAIL Model and Multi-Angle Remote Sensing Data[J]. Scientia Silvae Sinicae, 2021, 57(4): 90-106.

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植被类型 Vegetation types	样点数 Sample number	最小值 Min.	最大值 Max.	平均值 Mean	偏度 Skewness	峰度 Kurtosis	变异系数 Coefficient of variation (%)
阔叶林 Broadleaf forest	59	1.70	6.30	3.90	-0.08	0.77	24.36
针叶林 Coniferous forest	18	2.28	4.61	3.62	-0.38	-0.74	20.58
针阔混交林 Coniferous-broadleaf forest	37	2.82	6.70	4.06	0.85	0.92	22.48
全样本 Full sample	114	1.70	6.70	3.91	0.23	0.84	23.36

数据类型 Data type	成像时间 Imaging time	波谱范围 Band range	分辨率 Resolution ratio
PROBA/CHRIS	2015-09-07	0.4~1.1	17

参数设置 Parameter setting	分析参数 Analysis parameters
参数设置 Parameter setting	LAI	N	C_ab	C_w	C_m	S_L
LAI	1-0.5-7	3.8	3.8	3.8	3.8	3.8
N	1.50	1-0.1-2.5	1.50	1.50	1.50	1.50
C_ab	44.72	44.72	20-5-70	44.72	44.72	44.72
C_w	0.015 3	0.015 3	0.015 3	0.006-0.02-0.06	0.015 3	0.015 3
C_m	0.008 3	0.008 3	0.008 3	0.008 3	0.004-0.002-0.04	0.008 3
S_L	0.15	0.15	0.15	0.15	0.15	0.01-0.05-0.31
θ_s	61.88	61.88	61.88	61.88	61.88	61.88
θ_v	20.18	20.18	20.18	20.18	20.18	20.18
φ	214.36	214.36	214.36	214.36	214.36	214.36

植被指数 Vegetation indices	表达式 Expressions	参考文献 References
比值植被指数 Ratio vegetation index(RVI)	ρ_NIR/ρ_RED	Pearson et al., 1972
归一化植被指数Normalized difference vegetation index(NDVI)	(ρ_NIR-ρ_RED)/(ρ_NIR+ρ_RED)	Rouse, 1974
垂直植被指数 Perpendicular vegetation index(PVI)	$\sqrt {{{\left({0.355{\rho _{{\rm{NIR}}}} - 0.149{\rho _{{\rm{RED}}}}} \right)}^2} + {{\left({0.355{\rho _{{\rm{RED}}}} - 0.852{\rho _{{\rm{NI}}R}}} \right)}^2}} $	Jackson et al., 1980
差值植被指数 Difference vegetation index(DVI)	ρ_NIR-ρ_RED	Clevers, 1986
土壤调节植被指数 Soil adjusted vegetation index(SAVI)	1.5(ρ_NIR-ρ_RED)/(ρ_NIR+ρ_RED+0.5)	Huete, 1988
增强型植被指数 Enhanced vegetation index(EVI)	2.5(ρ_NIR-ρ_RED)/(ρ_NIR+6.0ρ_RED-7.5ρ_BLUE+1)	Huete et al., 1994
修正土壤调节植被指数 Modified soil adjusted vegetation index(MSAVI)	$\left[ {2{\rho _{{\rm{NIR}}}} + 1 - \sqrt {{{\left({2{\rho _{{\rm{NIR}}}} + 1} \right)}^2} - 8\left({{\rho _{{\rm{NIR}}}} - {\rho _{{\rm{RED}}}}} \right)} } \right]/2$	Qi et al., 1994
结构不敏感色素(植被)指数 Structure insensitive pigment (vegetation) index(SIPI)	(ρ_NIR-ρ_BLUE)/(ρ_NIR+ρ_BLUE)	Pe?uelas et al., 1995

等级 Level	LAI	N	C_ab	C_m	C_w	S_L
1	0.332 86	0.021 60	0.261 82	0.068 09	0.011 88	0.023 62
2	0.227 62	0.026 81	0.085 11	0.058 56	0.011 42	0.021 18
3	0.161 23	0.039 96	0.034 61	0.051 89	0.011 00	0.017 98
4	0.121 63	0.047 22	0.023 76	0.046 93	0.010 61	0.015 47
5	0.096 81	0.050 54	0.016 87	0.043 08	0.010 26	0.013 49
平均值 Mean	0.188 03	0.037 23	0.084 43	0.053 71	0.011 03	0.018 35

Inversion of Forest Leaf Area Index Based on PROSAIL Model and Multi-Angle Remote Sensing Data

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变化参数 Variable parameters	参数描述 Parametric description	参数值 Parameter values
变化参数 Variable parameters	参数描述 Parametric description	最小值 Min.	最大值 Max.	步长 Step
LAI	叶面积指数 Leaf area index	0.5	6.5	0.02
C_ab/(μg·cm^-2)	叶片叶绿素a、b含量 Chlorophyll a and b content	20	70	5
θ_v/(°)	观测天顶角 Observation zenith angle	20.18，39.47，37.27，57.65，56.04
φ	太阳-卫星相对方位角 Sun-satellite relative azimuth angle	214.36，111.77，243.56，103.82，246.91

常量参数 Constant parameters	参数描述 Parametric description	参数值 Parameter values
C_w/cm	叶片等效水厚度 Leaf equivalent water thickness	0.015 3
C_m/(g·cm^-2)	叶片干物质含量 Leaf dry matter content	0.008 3
N	叶片结构参数 Leaf structure parameter	1.5
θ_s	太阳天顶角 Sun zenith angle	61.88
S_L	热点效应参数 Hot spot effect parameter	0.15
ALA	平均叶倾角 Average leaf inclination angle	Spherical

指标 Index	-55°	-36°	0°	36°	55°
RMSE	0.127 2	0.065 2	0.047 3	0.054 7	0.091 3
MAPE	0.654 7	0.412 5	0.376 1	0.393 7	0.421 7

角度数目 Angle number	组合数 Combination number	角度组合形式 Angle combination forms
单角度 Single angle	5	0°，36°，-36°，55°，-55°
2角度 Two angles	10	(0°，36°)，(0°，-36°)，(0°，55°)，(0°，-55°)，(36°，-36°)，(36°，55°)，(36°，-55°)，(-36°，55°)，(-36°，-55°)，(55°，-55°)
3角度 Three angles	10	(0°，36°，-36°)，(0°，36°，55°)，(0°，36°，-55°)，(0°，-36°，55°)，(0°，-36°，-55°)，(0°，55°，-55°)，(36°，-36°，55°)，(36°，-36°，-55°)，(36°，55°，-55°)，(-36°，55°，-55°)
4角度 Four angles	5	(0°，36°，-36°，55°)，(0°，36°，-36°，-55°)，(0°，36°，55°，-55°)，(0°，-36°，55°，-55°)，(36°，-36°，55°，-55°)
5角度 Five angles	1	(0°，36°，-36°，55°，-55°)

观测角度 Observation angle	R²	RMSE	MAPE
-55°	0.908 3	0.245 8	0.046 1
-36°	0.641 1	0.486 2	0.060 1
0°	0.908 9	0.244 9	0.044 9
36°	0.909 9	0.243 6	0.043 7
55°	0.915 7	0.235 7	0.042 6

角度数目 Angle number	观测角度组合 Observation angle combination	R²	RMSE	MAPE
2角度 Two angles	0°，55°	0.917 6	0.233 0	0.042 3
3角度 Three angles	0°，36°，55°	0.918 4	0.231 9	0.041 5
4角度 Four angles	0°，36°，55°，-55°	0.917 7	0.232 9	0.041 6
5角度 Five angles	0°，36°，-36°，55°，-55°	0.914 3	0.237 6	0.043 4

角度数目 Angle number	观测角度/角度组合 Observation angle/angle combination	模型 Models	R²	RMSE	MAPE
单角度 Single angle	55°	y = 0.053 9exp(7.875 0PVI)	0.909 3	0.278 6	0.048 4
2角度 Two angles	0°，36°	y = 0.028 7exp(5.713 1PVI)	0.910 6	0.267 8	0.047 4
3角度 Three angles	0°，36°，55°	y = 0.045 2exp(8.297 7PVI)	0.911 2	0.248 6	0.045 7
4角度 Four angles	0°，36°，36°，-55°	y = 0.031 5exp(4.246 4PVI)	0.904 8	0.280 4	0.049 0
5角度 Five angles	0°，36°，-36°，55°，-55°	y = 0.027 1exp(3.376 0PVI)	0.901 8	0.284 5	0.051 3

[1]	You Haotian, Xing Yanqiu, Peng Tao, Ding Jianhua. Research on the Effect of Side-Overlap between Airborne LiDAR Adjacent Swaths on the Coniferous Forest Structural Parameters Estimation [J]. Scientia Silvae Sinicae, 2018, 54(6): 109-118.
[2]	Lin Jie;Zhang Jinchi;Gu Zheyan;Wu Yümin. Quantitative Assessment of Vegetation Cover and Management Factor Based on Leaf Area Index and Remote Sensing [J]. , 2013, 49(2): 86-92.
[3]	Li Haiyang;Fan Wenyi;Yu Ying;Yang Xiguang. Leaf Area Index Retrieval Based on Prospect,Liberty and Geosail Models [J]. Scientia Silvae Sinicae, 2011, 47(9): 75-81.

植被指数 Vegetation indices	-55°	-36°	0°	36°	55°
RVI	0.191 7	0.300 3^*	0.362 2^**	0.287 5^**	0.179 6^**
NDVI	0.162 7	0.265 5^*	0.327 4^**	0.253 3^**	0.149 9
PVI	0.903 5^**	0.900 4^**	0.907 6^**	0.900 0^**	0.892 1^**
DVI	0.871 1^**	0.883 8^**	0.894 0^**	0.882 6^**	0.862 5^**
SAVI	0.760 1^**	0.827 1^**	0.848 8^**	0.821 9^**	0.753 3^**
EVI	0.809 6^**	0.852 3^**	0.867 9^**	0.848 9^**	0.803 3^**
MSAVI	0.735 5^**	0.832 5^**	0.859 2^**	0.824 7^**	0.721 9^**
SIPI	0.567 6^**	0.708 6^**	0.748 7^**	0.699 5^**	0.558 0^**