Effects of Different Topographic Correction Methods on the Distribution Extraction of Pinus thunbergii Using Remote Sensing Imagery

doi:10.13466/j.cnki.lyzygl.2018.06.022

Abstract

Abstract:

Five topographic correction models (Teillet-regression,VECA,Cosine-C,C,and SCS+C) were employed in the present study in Huangdao,Qingdao to calibrate the Quick-Bird multispectral images combining with fine Digital Elevation Model (DEM) generated by stereo images of domestic ZY-3 satellite.Then,we examined and compared the results of 5 models to evaluate the effects of different topographic correction models on extracting the distribution of pine.The results show that Quick-Bird images corrected by a combination of 2m DEM and 3 models (VECA,C and SCS+C) can better maintain imagery’s spectral characteristic and weaken the effect of mountain shadows than the other 2.Quick-Bird images calibrated by these 3 models have significantly improved the extraction accuracy of Pinus thunbergii.Among these 3 models,VECA is the best one for its elevation of the overall accuracy by 14.05% (from 70.25% to 84.30%) and the Kappa coefficient by 0.19 (from 0.53 to 0.72).This research can provide a reference for the extraction of Pinus thunbergii distribution by using remote sensing imagery.

Key words: topographic correction, Digital Elevation Model (DEM), pine tree, ZY-3, Quick Bird

CLC Number:

S771.8

DENG Shiqing, TAO Huan, LI Cunjun, LIU Rong, HU Haitang. Effects of Different Topographic Correction Methods on the Distribution Extraction of Pinus thunbergii Using Remote Sensing Imagery[J]. FOREST RESOURCES WANAGEMENT, 2018, (6): 138-145.

Figures/Tables 7

Tab.1

Terrain correction models

模型类别	地形校正模型	公式
统计-经验校正模型	Teillet-回归^[20]	$L = m cos i + b$ $L m = L - m × cos i - b + L a$
统计-经验校正模型	VECA^[11]	$λ = L a / (m × cos i + b)$ $L m = L × λ$
朗伯体反射率模型	Cosine-C^[21]	$L m = L + L × co s a i - cos i co s a i$
	C^[22]	$C = b m$ $L m = L × c os θ + C cos i + C$
	SCS+C^[22]	$L m = L cos α cos θ + C cos i + C$

Tab.1

Fig.1

Fig.2

Tab.2

Fig.3

Tab.3

Fig.4

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校正模型	蓝波段		绿波段		红波段		近红外波段
校正模型	均值	标准差	均值	标准差	均值	标准差	均值	标准差
未校正	2.81	3.38	3.27	4.54	3.96	5.82	7.90	9.57
Teillet-回归	2.83	3.38	3.29	4.53	4.00	5.81	7.97	9.48
VECA	2.83	3.42	3.30	4.65	4.02	6.02	8.01	9.93
Cosine-C	2.66	4.08	3.02	5.35	3.63	6.81	7.17	11.28
C	2.88	3.47	3.36	4.73	4.10	6.15	8.19	10.14
SCS+C	2.87	3.34	3.23	4.51	3.92	5.81	7.81	9.60

校正方法	地物类别	总体精度/%	Kappa系数	生产者精度/%	漏分误差/%	用户精度/%	错分误差/%
未校正	松树	70.25	0.53	60.00	40.00	97.50	2.50
未校正	非松树	70.25	0.53	82.14	17.86	93.88	6.12
VECA	松树	84.30	0.72	78.46	21.54	91.07	8.93
VECA	非松树	84.30	0.72	91.07	8.93	98.08	1.92
C	松树	74.38	0.54	72.31	27.69	81.03	18.97
C	非松树	74.38	0.54	76.79	23.21	89.58	10.42
SCS+C	松树	82.64	0.68	78.46	21.54	91.07	8.93
SCS+C	非松树	82.64	0.68	87.50	12.50	92.45	7.55