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

摘要/Abstract

摘要：

目的: 基于多角度PROBA/CHRIS遥感数据和野外实测数据，结合PROSAIL模型和随机森林模型反演森林叶面积指数（LAI），以提高植被LAI遥感反演精度，为区域土壤侵蚀遥感定量监测提供新的方法和模型。方法: 以南京市紫金山和幕府山为研究区，采用野外调查、遥感影像、辐射传输模型与数学模型相结合的方法，构建基于PROSAIL模型和多角度PROBA/CHRIS遥感数据的随机森林LAI反演模型，对PROSAIL模型进行敏感性分析和适用性评价，确定最佳LAI反演模型，并利用地面实测LAI进行精度验证和评价。结果: PROSAIL模型中各输入参数敏感性大小为LAI>叶绿素a、b含量C_ab>叶片干物质含量C_m>热点参数S_L>叶片内部结构参数N>等效水厚度C_w；模拟的冠层反射率精度大小为0°>36°>-36°>55°>-55°。单角度LAI反演模型中，前向观测角55°精度最高，其决定系数（R²）、均方根误差（RMSE）和平均绝对百分误差（MAPE）分别为0.915 7、0.235 7和0.042 6；相比于传统垂直观测，55°模型的R²提高0.75%，RMSE和MAPE分别降低3.76%和5.12%；相比于非线性回归模型，单角度随机森林LAI反演模型的R²提高0.7%，RMSE和MAPE分别降低15.40%和11.98%；单角度LAI反演模型精度由高到低依次为55°、36°、0°、-55°、-36°。多角度LAI反演模型中，3角度组合（0°、36°、55°）LAI反演精度最高，其R²、RMSE和MAPE分别为0.918 4、0.231 9和0.041 5，相比于单角度55°，R²提高0.29%，RMSE和MAPE分别降低1.61%和2.58%；相比于传统垂直观测，3角度组合模型的R²提高1.05%，RMSE和MAPE分别降低5.31%和7.57%；相比于非线性回归模型，多角度随机森林LAI反演模型的R²提高0.79%，RMSE和MAPE分别降低6.72%和9.19%。紫金山西部区域LAI介于0.44~6.70之间，林地LAI均值为3.04；紫金山西部林地LAI整体上呈北部和南部高、中间低的空间分布格局。结论: 最佳LAI反演模型为基于3角度组合（0°、36°、55°）的随机森林LAI反演模型；一方面，增加观测角度可提供更多植被冠层结构信息，LAI反演精度随观测角度增加而增加，但另一方面，观测角度过多会使像元空间重采样、叶片阴影和土壤阴影等问题带来更多不确定性，LAI反演精度反而下降；无论是单角度还是多角度数据，随机森林LAI反演模型精度均高于非线性回归模型，随机森林模型能够明显提高LAI反演精度，适用于区域植被LAI反演；多角度遥感数据能够反映森林立体结构信息和地物多维空间结构特征，显著改善传统垂直观测数据反演LAI精度较低的问题，从而有效提高植被LAI反演精度。

关键词: 叶面积指数, 多角度PROBA/CHRIS遥感数据, PROSAIL模型, 随机森林模型

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

中图分类号:

S771.8

潘颖,丁鸣鸣,林杰,代侨,郭赓,崔琳琳. 基于PROSAIL模型和多角度遥感数据的森林叶面积指数反演[J]. 林业科学, 2021, 57(4): 90-106.

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