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, 0(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

References 24

[1]	杨子祥, 王健敏, 陈晓鸣, 等. 松墨天牛在云南松树干的垂直分布研究[J]. 林业科学研究, 2010,23(4):607-611.
[2]	蒲智, 刘萍, 杨辽, 等. 城市绿地信息提取中的遥感影像阴影校正[J]. 北京林业大学学报, 2009,31(2):80-85.
[3]	徐伟燕, 孙睿, 金志凤. 基于资源三号卫星影像的茶树种植区提取[J].农业工程学报, 2016(S1):161-168.
[4]	王文泉, 陈永富, 李肇晨, 等. 基于面向对象的热带林分类方法研究[J]. 南京林业大学学报:自然科学版, 2017,41(3):117-123.
[5]	董德进, 周国模, 杜华强, 等. 6种地形校正方法对雷竹林地上生物量遥感估算的影响[J]. 林业科学, 2011,47(12):1-8. doi: 10.11707/j.1001-7488.20111201
[6]	段四波, 阎广建. 山区遥感图像地形校正模型研究综述[J]. 北京师范大学学报:自然科学版, 2007,43(3):362-366.
[7]	高永年. 遥感影像地形校正理论基础与方法应用[M]. 北京: 科学出版社, 2013.
[8]	Wen J G, Qiang L. Parametrized BRDF for atmospheric and topographic correction and albedo estimation in jiangxi rugged terrain,China[J]. International Journal of Remote Sensing, 2009,30(11):2875-2896. doi: 10.1080/01431160802558618
[9]	Wen J G, Liu Q, Liu Q H, et al. Scale effect and scale correction of land-surface albedo in rugged terrain[J]. International Journal of Remote Sensing, 2009,30(20):5397-5420. doi: 10.1080/01431160903130903
[10]	Liu S H, Liu Q, Liu Q, et al. The angular and spectral kernel model for BRDF and albedo retrieval[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2010,3(3):241-256.
[11]	高永年, 张万昌. 遥感影像地形校正研究进展及其比较实验[J]. 地理研究, 2008,27(2):467-477.
[12]	Richter R, Kellenberger T, Kaufmann H. Comparison of topographic correction methods[J]. Remote Sensing, 2009,1(3):184-196. doi: 10.3390/rs1030184
[13]	林起楠, 黄华国, 陈玲, 等. 陡峭山区影像的半经验地形校正[J]. 遥感学报, 2017,21(5):776-784.
[14]	刘时城, 温仲明, 陶宇, 等. 不同地形校正方法对刺槐林遥感提取的影响[J]. 北京林业大学学报, 2017,39(5):25-33.
[15]	穆悦, 安裕伦, 王喆, 等. 不同地形校正模型计算地形复杂山区地表反射率的对比[J]. 山地学报, 2014,32(3):257-266.
[16]	张超, 王人潮, 赵春江, 等. 山区高分辨率遥感影像地形辐射校正方法研究[J]. 浙江大学学报:农业与生命科学版, 2006,32(6):693-698.
[17]	闻建光, 柳钦火, 肖青, 等. 复杂山区光学遥感反射率计算模型[J]. 中国科学, 2008,38(11):1419-1427.
[18]	薛东剑, 李增元, 郑洁, 等. 利用DEM进行SAR图像模拟及地形辐射校正[J].测绘通报, 2017(7):14-17.
[19]	宿渊源, 张景发, 何仲太, 等. 资源卫星三号DEM数据在活动构造定量研究中的应用评价[J]. 国土资源遥感, 2015,27(4):122-130. doi: 10.6046/gtzyyg.2015.04.19
[20]	Teillet P M, Guindon B, Goodenough D G. On the slope-aspect correction of multispectral scanner data[J]. Canadian Journal of Remote Sensing, 1982,8(2):1537-1540.
[21]	Civco D L. Topographic normalization of Landsat thematic mapper digital imagery[J]. Photogrammetric Engineering & Remote Sensing, 1989,55(1):135-43.
[22]	Soenen S A, Peddle D R, Coburn C A. SCS+C:a modified Sun-canopy-sensor topographic correction in forested terrain[J]. IEEE Transactions on Geoscience & Remote Sensing, 2005,43(9):2148-2159.
[23]	吕利利, 颉耀文, 董龙龙. 基于不同地形校正模型的影像反射率对比分析[J]. 遥感技术与应用, 2017,32(4):751-759.
[24]	丁一帆, 尤红建, 张浩, 等. 面向高分辨率遥感影像的地形辐射校正方法[J]. 北京航空航天大学学报, 2018,44(1):27-35.

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