Study on Wetland Extraction Based on the Synthetic Identification Method in the Baiyangdian Wetland, Hebei Province

Abstract

Abstract:

As a valuable natural resource, wetland plays an important role on maintaining ecological balance, improving ecological environment, preventing and remedying pollution etc. Based on the remote sensing data of Landsat 8 OLI, the reasonable methods for extracting area information of wetlands and water bodies in the Bai-yangdian Wetland were determined. The improved index method and spectral relationship method were used to quantitatively extract the area of wetlands and open water bodies respectively. Most of the Baiyangdian Wetland are covered by open water bodies and reeds. Therefore, the reed area can be determined by subtracting the area of open water bodies from that of wetlands. A synthetic method for extracting wetlands and related components in the Baiyangdian Wetland was summarized. It can provide a reference for extracting area information of subjects in the Bai-yangdian Wetland by using remote sensing technique.

Key words: Baiyangdian Wetland, open water body, reed, improved index method, synthetic method

CLC Number:

TP79
P641

WANG Kailin, ZHAO Kai, LI Haitao, ZHANG Baoyun, LI Wenpeng. Study on Wetland Extraction Based on the Synthetic Identification Method in the Baiyangdian Wetland, Hebei Province[J]. Geoscience, 2017, 31(06): 1294-1300.

Figures/Tables 10

References 17

[1]	Office of International Standards and Legal Affairs, UNESCO. Convention on Wetlands of International Importance Especially as Waterfowl Habitat[EB/OL]. (1971-02-02)[2017-02-14].http://portal.unesco.org/en/ev.php-URL_ID=15398&URL_DO=DO_TOPIC&URL_SECTION=201.html.
[2]	金晓媚, 高萌萌, 柯珂, 等. 巴丹吉林沙漠湖泊遥感信息提取及动态变化趋势[J]. 科技导报, 2014, 32(8):15-21.
[3]	ASHRAF M, NAWAZ R. A comparison of change detection analyses using different band algebras for Baraila Wetland with Nasa’s multi-temporal Landsat Dataset[J]. Journal of Geographic Information System, 2015, 7(1):1-19. DOI URL
[4]	李博伦, 遆超普, 颜晓元. Landsat 8陆地成像仪影像的缨帽变换推导[J]. 测绘科学, 2016, 41(4):102-107.
[5]	ZENG Z Y. A new method of data transformation for satellite images: I. Methodology and transformation equations for TM images[J]. International Journal of Remote Sensing, 2007, 28(18):4095-4124. DOI URL
[6]	贾永红, 李芳芳. 一种新的湿地信息遥感提取方法研究[J]. 华中师范大学学报(自然科学版), 2007, 41(4):641-644.
[7]	MCFEETERS S K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7):1425-1432. DOI URL
[8]	徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005, 9(5):589-595.
[9]	闫霈, 张友静, 张元. 利用增强型水体指数(EWI)和GIS去噪音技术提取半干旱地区水系信息的研究[J]. 遥感信息, 2007, 7(6):62-67.
[10]	曹荣龙, 李存军, 刘良云, 等. 基于水体指数的密云水库面积提取及变化监测[J]. 测绘科学, 2008, 33(2):158-160.
[11]	周成虎, 骆剑承, 杨晓梅, 等. 遥感影像地学理解与分析[M]. 北京: 科学出版社, 1999: 68-86.
[12]	ZHU C M, LUO J C, SHEN Z F, et al. Wetland mapping in the Balqash Lake Basin using multi-source remote sensing data and topographic features synergic retrieval[J]. Procedia Environmental Sciences, 2011, 10(part C):2718-2724. DOI URL
[13]	CAMPOS J C, SILLERO N, BRITO J C. Normalized difference water indexes have dissimilar performances in detecting seasonal and permanent water in the Sahara-Sahel transition zone[J]. Journal of Hydrology, 2012, 464/465(13):438-446. DOI URL
[14]	沈占锋, 李均力, 于新菊. 基于协同计算的白洋淀湿地时序水体信息提取[J]. 地球信息科学学报, 2016, 18(5):690-698. DOI
[15]	林皓波, 吴新玲, 刘菲, 等. 白洋淀湿地资源的遥感监测[J]. 中南林业科技大学学报, 2012, 32(4):127-130.
[16]	江波, 肖洋, 马文勇, 等. 1974-2011年白洋淀土地覆盖时空变化特征[J]. 湿地科学与管理, 2016, 12(1):38-42.
[17]	王军. Kappa系数在一致性评价中的应用研究[D]. 成都: 四川大学, 2006: 17-53.

波段	波段名称	波长范围/μm	分辨率/m
1	海岸波段	0.433~0.453	30
2	蓝光	0.450~0.515	30
3	绿光	0.525~0.600	30
4	红光	0.630~0.680	30
5	近红外	0.845~0.885	30
6	短波红外1	1.560~1.660	30
7	短波红外2	2.100~2.300	30
8	全色	0.500~0.680	15
9	卷云波段	1.360~1.390	30

波段	波段名称	波长范围/μm	分辨率/m
1	海岸波段	0.433~0.453	30
2	蓝光	0.450~0.515	30
3	绿光	0.525~0.600	30
4	红光	0.630~0.680	30
5	近红外	0.845~0.885	30
6	短波红外1	1.560~1.660	30
7	短波红外2	2.100~2.300	30
8	全色	0.500~0.680	15
9	卷云波段	1.360~1.390	30

指数方法	公式
KT₃	缨帽变换^[4]
LBV-B	1.126 971×TM_Green+0.673348×TM_Red+ 0.077966×TM_NIR-1.878287×TM_SWIR1+159^[5]
WI	(KT₃+TM_Green)>(TM_NIR+TM_Red)^[6]
M-KT₃	N(KT₃)-N(NIR)

指数方法	公式
KT₃	缨帽变换^[4]
LBV-B	1.126 971×TM_Green+0.673348×TM_Red+ 0.077966×TM_NIR-1.878287×TM_SWIR1+159^[5]
WI	(KT₃+TM_Green)>(TM_NIR+TM_Red)^[6]
M-KT₃	N(KT₃)-N(NIR)

提取方法	提取结果/个	目视解译/个		总精度/%	Kappa 系数
提取方法	提取结果/个	湿地	非湿地	总精度/%	Kappa 系数
KT₃	湿地	21 683	86	94.7	0.89
KT₃	非湿地	2 896	31 141	94.7	0.89
LBV-B	湿地	21 175	37	93.8	0.87
LBV-B	非湿地	3 404	31 190	93.8	0.87
WI	湿地	20 423	0	92.0	0.83
WI	非湿地	4 456	31 227	92.0	0.83
M-KT₃	湿地	22 683	46	96.5	0.93
M-KT₃	非湿地	1 896	31 181	96.5	0.93