Measurement of vegetation parameters and error analysis based on Monte Carlo method

doi:10.1007/s11442-018-1507-8

Abstract

Abstract:

In this paper we bring up a Monte Carlo theory based method to measure the ground vegetation parameters, and make quantitative description of the error. The leaf area index is used as the example in the study. Its mean and variance stability at different scales or in different time is verified using both the computer simulation and the statistics of remotely sensed images. And the error of Monte Carlo sampling method is analyzed based on the normal distribution theory and the central-limit theorem. The results show that the variance of leaf area index in the same area is stable at certain scales or in the same time of different years. The difference between experimental results and theoretical ones is small. The significance of this study is to establish a measurement procedure of ground vegetation parameters with an error control system.

Key words: remote sensing, vegetation parameter, error analysis, GLASS LAI

Boyi LIANG, Suhong LIU. Measurement of vegetation parameters and error analysis based on Monte Carlo method[J].Journal of Geographical Sciences, 2018, 28(6): 819-832.

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References 38

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			Sampling quantity	30	50	70	100	150	200	300	500
Low level	Computer simulation	Variation	Theoretical value	0.049	0.029	0.021	0.015	0.010	0.007	0.005	0.003
		Variation	Real value	0.056	0.032	0.022	0.016	0.011	0.008	0.006	0.003
		Mean	Theoretical value	2.535	2.535	2.535	2.535	2.535	2.535	2.535	2.535
		Mean	Real value	2.531	2.546	2.541	2.536	2.537	2.534	2.540	2.537
	GLASS LAI	Variation	Theoretical value	0.028	0.017	0.012	0.008	0.006	0.004	0.003	0.002
		Variation	Real value	0.031	0.018	0.013	0.009	0.006	0.005	0.003	0.002
		Mean	Theoretical value	1.234	1.234	1.234	1.234	1.234	1.234	1.234	1.234
		Mean	Real value	1.243	1.240	1.228	1.231	1.230	1.233	1.231	1.231
Median level	Computer simulation	Variation	Theoretical value	0.212	0.127	0.091	0.064	0.042	0.032	0.021	0.013
		Variation	Real value	0.245	0.153	0.105	0.076	0.047	0.035	0.024	0.015
		Mean	Theoretical value	5.086	5.086	5.086	5.086	5.086	5.086	5.086	5.086
		Mean	Real value	5.073	5.091	5.097	5.079	5.083	5.084	5.087	5.091
	GLASS LAI	Variation	Theoretical value	0.039	0.023	0.017	0.012	0.008	0.006	0.004	0.002
		Variation	Real value	0.038	0.024	0.016	0.011	0.008	0.006	0.004	0.002
		Mean	Theoretical value	2.115	2.115	2.115	2.115	2.115	2.115	2.115	2.115
		Mean	Real value	2.099	2.122	2.118	2.117	2.113	2.115	2.115	2.115
High level	Computer simulation	Variation	Theoretical value	0.495	0.297	0.212	0.148	0.099	0.074	0.049	0.030
		Variation	Real value	0.532	0.352	0.270	0.176	0.122	0.085	0.054	0.035
		Mean	Theoretical value	8.086	8.086	8.086	8.086	8.086	8.086	8.086	8.086
		Mean	Real value	8.089	8.096	8.087	8.081	8.103	8.090	8.081	8.096
	GLASS LAI	Variation	Theoretical value	0.056	0.033	0.024	0.017	0.011	0.008	0.006	0.003
		Variation	Real value	0.047	0.030	0.020	0.015	0.009	0.007	0.005	0.003
		Mean	Theoretical value	4.498	4.498	4.498	4.498	4.498	4.498	4.498	4.498
		Mean	Real value	4.493	4.498	4.501	4.503	4.504	4.505	4.502	4.500

Sampling quantity	0-3%	3%-5%	5%-10%	10%-20%	>20%	Average
30	3.07%	0.18%	0.75%	3.24%	0.76%	1.60%
50	1.95%	1.60%	1.45%	1.86%	0.24%	1.42%
70	3.33%	2.34%	1.94%	3.07%	0.66%	2.27%
100	2.82%	2.84%	1.35%	1.36%	0.03%	1.68%
150	3.20%	0.95%	1.59%	0.52%		1.57%
200	3.97%	1.55%	2.12%	0.30%		1.99%
300	3.31%	1.96%	1.32%	0.03%		1.66%
500	5.99%	3.75%	2.24%			3.99%