CSD和CDD结合下的最优遥感特征指数集构建及其在湿地信息提取中的应用

doi:10.12082/dqxxkx.2021.200517

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (6): 1092-1105.doi: 10.12082/dqxxkx.2021.200517

CSD和CDD结合下的最优遥感特征指数集构建及其在湿地信息提取中的应用

赵栋梁¹(), 郭超凡², 吴东丽³, 高星琪¹, 郭逍宇¹^,^*()

1.首都师范大学资源环境与旅游学院,北京 100048
2.安阳师范学院资源环境与旅游学院,安阳 455000
3.中国气象局气象探测中心,北京 100081

收稿日期:2020-05-17 修回日期:2020-12-09 出版日期:2021-06-25 发布日期:2021-08-25
通讯作者: 郭逍宇
作者简介:赵栋梁（1996— ）,男,山西吕梁人,硕士生,主要事从湿地生态过程研究。E-mail: 920240936@qq.com
基金资助:
北京市自然科学基金和北京市教委联合资助重点项目(KZ20190028042)

Construction of Optimal Remote Sensing Feature Index Set based on CSD and CDD and Its Application in Wetland Information Extraction

ZHAO Dongliang¹(), GUO Chaofan², WU Dongli³, GAO Xingqi¹, GUO Xiaoyu¹^,^*()

1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
2. School of Resource Environment and Tourism, Anyang Normal University, Anyang 455000, China
3. Meteorological Observation Center of China Meteorological Administration, Beijing 100081, China

Received:2020-05-17 Revised:2020-12-09 Online:2021-06-25 Published:2021-08-25
Contact: GUO Xiaoyu
Supported by:
Natural Science Foundation of Beijing and Education Commission of Beijing(KZ20190028042)

摘要/Abstract

摘要：

依托中分辨率成像光谱仪完整的数据序列和丰富的光谱信息,遥感特征指数在湿地生态系统发展变化的状态、趋向和规律研究方面发挥着不可替代的优势。传统类间距离判别的遥感特征指数选取中常存在过分依赖数据统计特征、入选指数与目标地类间生态学意义不明确、分类模型普适性差等局限性。基于此,本研究以河北省白洋淀湿地自然保护区为例,提出类可分离性距离判别（Class Separation Discrimination,CSD）与类间距离判别（Class Distance Discrimination,CDD）相结合的方法构建最优遥感特征指数集,并采用QUEST算法和马氏距离判别法构建分类决策树模型用于白洋淀湿地信息的提取研究,尝试克服传统类间距离指数选取中的不足。结果表明：运用CSD和CDD相结合的方法所选取的遥感特征指数在研究区湿地信息提取过程中的总体分类精度达到了91.32%,Kappa系数0.88,较传统的分类与回归树（Classification and Regression Tree,CART）方法,分类精度提高了1.67%;其次选取的最优指数与待提取的湿地类型均具有明确的生态学意义,如挺水植物在立地干湿交替条件下的潴育化过程决定了氧化铁比率IO可成功的将混分的耕地和挺水植物进一步分离;进一步将基于研究区2017年OLI影像构建的CSD和CDD相结合方法与CART方法的模型分别应用于研究区2019年OLI影像进行分类,基于CSD和CDD相结合方法构建的模型分类总体精度和Kappa系数分别为：86.97%、0.83,基于CART方法构建的模型无法满足分类需求,研究结果较好地证明了基于CSD和CDD相结合方法构建的模型在年际之间具有良好的适用性和稳定性。总之,CSD和CDD相结合的方法在不降低湿地信息提取精度的基础上,有效避免了传统遥感特征指数选择方法的局限性,提高了分类模型的普适性,是遥感特征指数选择算法和决策树相结合在湿地信息提取方面的有益尝试。

关键词: 遥感特征指数, 马氏距离, QUEST算法, 马氏距离判别法, 湿地, CART算法, CSD, CDD

Abstract:

Based on the complete data sequence and abundant spectral information of the moderate resolution imaging spectrometer, remote sensing feature indices play an irreplaceable role in studying the stage, trend, and law of wetland ecosystem development. There are some limitations in selection of remote sensing feature indices for the traditional inter-class distance discrimination, such as over-dependence on statistical characteristics of data, indefiniteness of the inclusion index and the ecological significance of the target area, and poor applicability of the classification model. Based on this, Baiyangdian Wetland Nature Reserve in Hebei Province is selected as the research area. The method of Class Separation Discrimination (CSD) and Class Distance Discrimination (CDD) is proposed to construct the optimal remote sensing feature index. In addition, QUEST algorithm and Markov distance discrimination method are used to construct the classification decision tree model for the Baiyangdian Wetland extraction, which overcomes the shortcomings in the selection of traditional inter-class distance index. The results show that, firstly, the overall classification accuracy of the remote sensing feature index selected by the method combining CSD and CDD reach 91.32% and the kappa coefficient is 0.88 for the wetland information extraction in the study area. Compared with the traditional Classification and Regression Tree (CART) method, the classification accuracy is improved by 1.67%. Secondly, the selected optimal remote sensing feature index has a clear ecological meaning for the type of wetland extracted. For example, the redoxing process of emergent water plants under alternate dry and wet conditions determines that iron oxide index (IO) can be successfully selected to further separate the mixed cultivated land and emergent water plants. Furthermore, according to the OLI image of the study area in 2017, the decision tree model based on the combination of CSD and CDD and the decision tree model based on CART algorithm are applied to the classification of the OLI image in the study area in 2019. The overall classification accuracy and kappa coefficient of the model based on the combination of CSD and CDD are 86.97% and 0.83, respectively. The model based on cart method cannot meet the classification requirements. The research results show that the model based on the combination of CSD and CDD has good applicability and stability over years. In a word, the combination of CSD and CDD can effectively avoid the limitations of traditional remote sensing feature indices, and improve the applicability of classification model. It is a beneficial attempt to combine remote sensing feature index selection algorithm with decision tree in wetland information extraction.

Key words: remote sensing feature index, Markov distance, QUEST algorithm, Markov distance discrimination, wetland, CART algorithm, CSD, CDD

赵栋梁, 郭超凡, 吴东丽, 高星琪, 郭逍宇. CSD和CDD结合下的最优遥感特征指数集构建及其在湿地信息提取中的应用[J]. 地球信息科学学报, 2021, 23(6): 1092-1105.DOI:10.12082/dqxxkx.2021.200517

ZHAO Dongliang, GUO Chaofan, WU Dongli, GAO Xingqi, GUO Xiaoyu. Construction of Optimal Remote Sensing Feature Index Set based on CSD and CDD and Its Application in Wetland Information Extraction[J]. Journal of Geo-information Science, 2021, 23(6): 1092-1105.DOI:10.12082/dqxxkx.2021.200517

图/表 11

图1

表1

图2

表2

表3

遥感特征指数集"

特征类	遥感特征指数	简称	计算方法	参考文献
绿度指数	差值植被指数	DVI	NIR-Red	[25]
	归一化差值植被指数	NDVI	(NIR-Red)/(NIR+Red)	[26]
	重归一化植被指数	RDVI	(NIR-Red)/(SQRT(NIR+Red))	[27]
	绿色归一化植被指数	GNDVI	(NIR-Green)/(NIR+Green)	[28]
	比值植被指数	RVI	NIR/Red	[29]
	缨帽变换绿度分量	GVI	(-0.2848)×Blue+(-0.2435)×Green+(-0.5436)×Red+(0.7243)×NIR+(0.0840)×SWIR_1+(-0.1800)×SWIR_2	[30]
黄度指数	归一化差值耕作指数	NDTI	(SWIR_1-SWIR_2)/(SWIR_1+SWIR_2)	[31]
黄度指数	归一化衰败植被指数	NDSVI	(SWIR_1-Red)/(SWIR_1+Red)	[32]
消除大气影响的植被指数	转换差值植被指数	TDVI	1.5×(NIR-Red)/SQRT(NIR ²+Red ²+0.5)	[33]
	大气阻抗植被指数	ARVI	(NIR-(2×Red-Blue))/(NIR+(2×Red-Blue))	[34]
	增强型植被指数	EVI	2.5×((NIR-Red)/(NIR+6×Red-7.5×Blue+1))	[35]
	可见光大气阻抗指数	VARI	(Green-Red)/(Green+Red-Blue)	[36]
消除土壤背景影像的植被指数	修改型非线性植被指数	MNLI	((NIR²-Red) (1+0.5))/(NIR²+Red+0.5)	[37]
	垂直植被指数	PVI	(NIR-0.96916×Red-0.084726)/SQRT (1+0.96916²)	[38]
	土壤调节植被指数	SAVI	1.5×(NIR-Red)/(NIR+Red+0.5)	[39]
	修改型土壤调节植被指数	MSAVI	0.5×(2×NIR+1-SQRT((2×NIR+1)²)-8×(NIR-Red)))	[40]
	优化土壤调节植被指数	OSAVI	(NIR-Red)/(NIR+Red+0.16)	[41]
湿度指数	缨帽变换湿度分量	WVI	(0.1509)×Blue+0.1973×Green+0.3279×Red+0.3406×NIR-0.7112×SWIR_1-0.4572×SWIR_2	[30]
	归一化红外指数	NDII	(NIR-SWIR_1)/(NIR+SWIR_1)	[42]
	归一化差异水体指数	NDWI	(Green-NIR)/(Green+NIR)	[43]
	改进的归一化差异水体指数	MNDWI	(Green-SWIR_1)/(Green-SWIR_1)	[3]
建筑指数	归一化建筑指数	NDBI	(SWIR_1-NIR)/(SWIR_1-NIR)	[5]
	改进的归一化裸露指数	MNDBI	NDBI+(1-NDVI)	[44]
	归一化三波段指数	NDTBI	(SWIR_2+SWIR_1-Red)/(SWIR_2+SWIR_1+Red)	[45]
	比值居民地指数	RRI	Blue/NIR	[46]
	比值不透水面指数	RISI	(SWIR_1-SWIR_2)/Blue	[47]
土壤指数	裸土指数	BSI	((SWIR_1+Red) -(NIR+Blue))/((SWIR_1+Red) +(NIR+Blue))	[48]
	归一化土壤指数	NDSI	(SWIR_2-Green)/(SWIR_2+Green)	[49]
	氧化铁比率指数	IO	Red/Blue	[50]
	燃烧面积指数	BAI	1/((0.1-Red)²+(0.06-NIR)²))	[51]

表3

图3

图4

图5

图6

表4

图7

参考文献 56

[1]	嘎力巴. 基于遥感指数地物信息提取方法对比研究[D]. 哈尔滨:哈尔滨师范大学, 2017.
	[A comparative study on the extraction method of surface features based on remote sensing index[D]. Harbin: Normal University, 2017. ]
[2]	李喆, 胡蝶, 赵登忠, 等. 宽波段遥感植被指数研究进展综述[J]. 长江科学院院报, 2015,32(1):125-130.
	[ Li Z, Hu D, Zhao D Z, et al. Research advance of broadband vegetation index using remotely sensed images[J]. Journal of Yangtze River Scientific Research Institute, 2015,32(1):125-130. ]
[3]	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(5):589-595.
	[ Xu H Q. A study on information extraction of water body with the modified normalized difference water index (MNDWI)[J]. Journal of Remote Sensing, 2005(5):589-595. ]
[4]	徐涵秋. 城市遥感生态指数的创建及其应用[J]. 生态学报, 2013,33(24):7853-7862.
	[ Xu H Q. A remote sensing urban ecological index and its application[J]. Acta Ecologica Sinica, 2013,33(24):7853-7862. ]
[5]	查勇, 倪绍祥, 杨山. 一种利用TM图像自动提取城镇用地信息的有效方法[J]. 遥感学报, 2003,7(1):37-40,82.
	[ Zha Y, Ni S X, Yang S. An effective approach to automatically extract urban land-use from TM imagery[J]. Journal of Remote Sensing, 2003,7(1):37-40,82. ]
[6]	贾坤, 姚云军, 魏香琴, 等. 植被覆盖度遥感估算研究进展[J]. 地球科学进展, 2013,28(7):774-782.
	[ Jia K, Yao Y J, Wei X Q, et al. A review on fractional vegetation cover estimation using remote sensing[J]. Advances in Earth Science, 2013,28(7):774-782. ]
[7]	吴志杰, 邹丹, 黄绍霖. 山地城市扩展及生态变化遥感分析[J]. 亚热带资源与环境学报, 2016,11(3):81-87,94.
	[ Wu Z J, Zou D, Huang S L. Analysis of urban expansion and ecological changes based on remote sensing imagery in mountainous city[J]. Journal of Subtropical Resources and Environment, 2016,11(3):81-87,94. ]
[8]	佟光臣, 林杰, 陈杭, 等. 1994—2014年连云港市赣榆区生态变化评估[J]. 水土保持研究, 2016,23(6):352-357.
	[ Tong G C, Lin J, Chen H, et al. Ecological change assessment in Ganyu of Lianyungang city from 1994 to 2014[J]. Research of Soil and Water Conservation, 2016,23(6):352-357. ]
[9]	那晓东, 张树清, 孔博, 等. 基于决策树方法的淡水沼泽湿地信息提取——以三江平原东北部为例[J]. 遥感技术与应用, 2008(4):365-372,355.
	[ Na X D, Zhang S Q, Kong B, et al. The extraction of freshwater marsh wetland information based on decision tree algorithm: A case study in the northeast of the Sanjiang Plain[J]. Remote Sensing Technology and Application, 2008(4):365-372,355. ]
[10]	雷璇, 杨波, 蒋卫国, 等. 东洞庭湿地植被格局变化及其影响因素[J]. 地理研究, 2012,31(3):461-470.
	[ Le X, Yang B, Jiang W G, et al. Vegetation pattern changes and their influencing factors in the east Dongting lake wetland[J]. Geographical Research, 2012,31(3):461-470. ]
[11]	孟超, 王计平, 支晓蓉, 等. 基于GIS的县域森林景观空间格局等级特征研究[J]. 农业机械学报, 2018,49(10):187-194,204.
	[ Meng C, Wang J P, Zhi X R, et al. Spatial characteristics of forest landscape in county level based on GIS[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018,49(10):187-194,204. ]
[12]	许明明, 张良培, 杜博, 等. 基于类别可分性的高光谱图像波段选择[J]. 计算机科学, 2015,42(4):274-275,296.
	[ Xu M M, Zhang L P, Du B, et al. Supervised band selection based on class separability for hyperspectral Image[J]. Computer Science, 2015,42(4):274-275,296. ]
[13]	孙滨峰, 赵红, 陈立才, 等. 基于植被指数选择算法和决策树的生态系统识别[J]. 农业机械学报, 2019,50(6):194-200.
	[ Sun B F, Zhao H, Cheng L C, et al. Identification of ecosystems based on vegetation indices selection algorithm and decision tree[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019,50(6):194-200. ]
[14]	林川, 宫兆宁, 赵文吉. 基于中分辨率TM数据的湿地水生植被提取[J]. 生态学报, 2010,30(23):6460-6469.
	[ Lin C, Gong Z N, Zhao W J. The extraction of wetland hydrophytes types based on medium resolution TM data[J]. Acta Ecologica Sinica, 2010,30(23):6460-6469. ]
[15]	李行, 毛定山, 张连蓬. 高光谱遥感影像波段选择算法评价方法研究[J]. 地理与地理信息科学, 2006,22(6):34-37.
	[ Li X, Mao D S, Zhang L P. Evaluation method for band selection algorithm of hyperspectral image[J]. Geography and Geo-Information Science, 2006,22(6):34-37. ]
[16]	王京, 卢善龙, 吴炳方, 等. 近40年来白洋淀湿地土地覆被变化分析[J]. 地球信息科学学报, 2010,12(2):2292-2300.
	[ Wang J, Lu S L, Wu B F, et al. Land cover change in Baiyangdian wetland[J]. Journal of Geo-information Science, 2010,12(2):2292-2300. ]
[17]	江波, 陈媛媛, 肖洋, 等. 白洋淀湿地生态系统最终服务价值评估[J]. 生态学报, 2017,37(8):2497-2505.
	[ Jiang B, Chen Y Y, Xiao Y, et al. Evaluation of the economic value of final ecosystem services from the Baiyangdian wetland[J]. Acta Ecologica Sinica, 2017,37(8):2497-2505. ]
[18]	刘茂峰, 高彦春, 甘国靖. 白洋淀流域年径流变化趋势及气象影响因子分析[J]. 资源科学, 2011,33(8):1438-1445.
	[ Liu M F, Gao Y C, Gan G J. Long-term trends in annual runoff and the impact of meteorological factors in the Baiyangdian watershed[J]. Resources Science, 2011,33(8):1438-1445. ]
[19]	张敏, 宫兆宁, 赵文吉, 等. 近30年来白洋淀湿地景观格局变化及其驱动机制[J]. 生态学报, 2016,36(15):4780-4791.
	[ Zhang M, Gong Z N, Zhao W J, et al. Landscape pattern change and the driving forces in Baiyangdian wetland from 1984 to 2014[J]. Acta Ecologica Sinica, 2016,36(15):4780-4791. ]
[20]	刘俊国, 赵丹丹, 叶斌. 雄安新区白洋淀生态属性辨析及生态修复保护研究[J]. 生态学报, 2019,39(9):3019-3025.
	[ Liu J G, Zhao D D, Ye B. Ecological attributes restoration and protection of the Baiyangdian in Xiong'an New Area[J]. Acta Ecologica Sinica, 2019,39(9):3019-3025. ]
[21]	张磊, 宫兆宁, 王启为, 等. Sentinel-2影像多特征优选的黄河三角洲湿地信息提取[J]. 遥感学报, 2019,23(2):313-326.
	[ Zhang L, Gong Z N, Wang Q W, et al. Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images[J]. Journal of Remote Sensing, 2019,23(2):313-326. ]
[22]	全国湿地资源调查与监测技术规程[M]. 中华人民共和国国家林业局, 2001.
	[ Technical specification for national wetland resources investigation and monitoring[M]. State Forestry Administration of the people's Republic of China, 2001. ]
[23]	王雪宏, 栗云召, 孟焕, 等. 黄河三角洲新生湿地植物群落分布格局[J]. 地理科学, 2015,35(8):1021-1026. doi: 10.13249/j.cnki.sgs.2015.08.1021
	[ Wang X H, Li Y Z, Meng H, et al. Distribution pattern of plant community in new-born coastal wetland in the Yellow River Delta[J]. Scientia Geographica Sinica, 2015,35(8):1021-1026. ]
[24]	梅安新. 遥感导论[M]. 北京: 高等教育出版社, 2001.
	[ Mei A X. Introduction to remote sensing[M]. Beijing: High Education Press, 2001. ]
[25]	Jordan C F. Derivation of leaf-area index from quality of light on the forest floor[J]. Ecology, 1969,50(4):663-666. doi: 10.2307/1936256
[26]	Aparicio N, Villfgas D, Royo C, et al. Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions[J]. International Journal of Remote Sensing, 2004,25(6):1131-1152. doi: 10.1080/0143116031000116967
[27]	Roujean J L, Breon F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements[J]. Remote Sensing of Environment, 1995,51(3):375-384. doi: 10.1016/0034-4257(94)00114-3
[28]	Gitelson A A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996,58(3):289-298. doi: 10.1016/S0034-4257(96)00072-7
[29]	Birth G S, McVey G R. Measuring the color of growing turf with a reflectance spectrophotometer[J]. Agronomy Journal, 1968,60(6):640-643. doi: 10.2134/agronj1968.00021962006000060016x
[30]	Lee W S, Alchanatis V, Yang C, et al. Sensing technologies for precision specialty crop production[J]. Computers & Electronics in Agriculture, 2010,74(1):2-33.
[31]	Deventer V, Ward A D, Gowda P H, et al. Using thematic mapper data to identify contrasting soil plains and tillage practices[J]. Photogrammetric Engineering & Remote Sensing, 1997,63(1):87-93.
[32]	Qi J, Marsett R, Heilman P, et al. RANGES improves satellite-based information and land cover assessments in southwest United States[J]. Eos Transactions American Geophysical Union, 2013,83(51):601-606.
[33]	Bannari A, Asalhi H, Teillet P M. Transformed Difference Vegetation Index (TDVI) for vegetation cover mapping[C]. IEEE International Geoscience & Remote Sensing Symposium, 2002.
[34]	Kaufman Y J, Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS[J]. IEEE Trans Geo Remote Sens, 1992,30(2):261-270. doi: 10.1109/36.134076
[35]	Nagler P L, Scott R L, Westenburg C, et al. Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers[J]. Remote Sensing of Environment, 2005,97(3):337-351. doi: 10.1016/j.rse.2005.05.011
[36]	Gitelson A A, Kaufman Y J, Stark R, et al. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 2002,80(1):76-87. doi: 10.1016/S0034-4257(01)00289-9
[37]	Gong P, Pu R, Biging G S, et al. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data[J]. IEEE Transactions on Geoence & Remote Sensing, 2003,41(6):1355-1362.
[38]	Richardson A J, Wiegand C L. Distinguishing vegetation from soil background information[J]. Pe&Rs, 1977,43(12):1541-1552.
[39]	Huete A R. A Soil-Adjusted Vegetation Vndex (SAVI)[J]. Remote Sensing of Environment, 1988,25(3):295-309. doi: 10.1016/0034-4257(88)90106-X
[40]	Huete A R. A modified soil adjusted vegetation index[J]. Remote Sens Envrionment, 2015,48(2):119-126.
[41]	Geneviève R, Michael S, Frédéric Bt. Optimization of soil-adjusted vegetation indices[J]. Remote Sensing of Environment, 1996,55(2):95-107. doi: 10.1016/0034-4257(95)00186-7
[42]	Wagtendonk J W V, Root R R, Key C H. Comparison of AVIRIS and Landsat ETM+detection capabilities for burn severity[J]. Remote Sensing of Environment, 2004,92(3):397-408. doi: 10.1016/j.rse.2003.12.015
[43]	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: 10.1080/01431169608948714
[44]	吴宏安, 蒋建军, 周杰, 等. 西安城市扩张及其驱动力分析[J]. 地理学报, 2005,60(1):143-150.
	[ Wu H A, Jiang J G, Zhou J, et al. Dynamics of urban expansion in Xi'an city using Landsat TM/ETM+ data[J]. Acta Geographica Sinica, 2005,60(1):143-150. ]
[45]	饶萍, 王建力, 王勇. 基于多特征决策树的建设用地信息提取[J]. 农业工程学报, 2014,30(12):233-240.
	[ Rao P, Wang J L, Wang Y. Extraction of information on construction land based on multi-feature decision tree classification[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014,30(12):233-240. ]
[46]	吴宏安, 蒋建军, 张海龙, 等. 比值居民地指数在城镇信息提取中的应用[J]. 南京师范大学报(自然科学版), 2006,29(3):118-121.
	[ Wu H G, Jiang J J, Zhang H L, et al. Application of ratio resident-area index to retrieve urban residential areas based on Landsat TM data[J]. Journal of Nan Jing Normal University (Natural Science), 2006,29(3):118-121. ]
[47]	尹毅, 黄哲. 天水市不透水表面提取及时空变化分析[J]. 安徽农业科学, 2013,41(29):11893-11895.
	[ Yin i, Huang Z. Impervious surface extraction and temporal-spatial change analysis in Tianshui city[J]. Journal of Anhui Agricultural Sciences, 2013,41(29):11893-11895. ]
[48]	晓琴, 孙丹峰, 张凤荣. 基于遥感的北京山区植被覆盖景观格局动态分析[J]. 山地学报, 2003,21(3):272-280.
	[ Li X Q, Sun D F, Zhang D F. Landscape pattern analysis on change in the fraction of green vegetation based on remotely sensed data in Beijing Mountainous Area[J]. Journal of mountain science, 2003,21(3):272-280. ]
[49]	RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments[J]. International Journal of Applied Earth Observations & Geoinformation, 2015,39:40-48.
[50]	Dogan H M. Mineral composite assessment of Kelkit River Basin in Turkey by means of remote sensing[J]. Journal of Earth System ence, 2009,118(6):701-710.
[51]	Chuvieco E, Mart M P, Palacios A. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination[J]. International Journal of Remote Sensing, 2002,23(23):5103-5110. doi: 10.1080/01431160210153129
[52]	那晓东, 张树清, 李晓峰, 等. 基于QUEST决策树兼容多源数据的淡水沼泽湿地信息提取[J]. 生态学杂志, 2009,28(2):357-365.
	[ Na X D, Zhang S Q, Li X F, et al. Fresh water marsh wetland information extraction based on QUEST decision tree integrating with multi-sourcedata[J]. Chinese Journal of Ecology, 2009,28(2):357-365. ]
[53]	吴健生, 潘况一, 彭建, 等. 基于QUEST决策树的遥感影像土地利用分类——以云南省丽江市为例[J]. 地理研究, 2012,31(11):1973-1980.
	[ Wu J S, Pan K Y, Peng J, et al. Research on the accuracy of TM images land-use classification based on QUEST decision tree:A case study Lijing in Yunnan[J]. Geographical Research, 2012,31(11):1973-1980. ]
[54]	Dogan H M. Mineral composite assessment of Kelkit River Basin in Turkey by means of remote sensing[J]. Journal of Earth System ence, 2009,118(6):701-710.
[55]	张甘霖, 龚子同. 氧化还原形态特征模式与形成机理解析[J]. 土壤学进展, 1994,22(3):54-56.
	[ Zhang G L, Gong Z T. Analysis on the morphology characteristic pattern and formation mechanism of REDOX[J]. Progress In Soil Science, 1994,22(3):54-56. ]
[56]	朱金峰, 周艺, 王世新, 等. 1975—2018年白洋淀湿地变化分析[J]. 遥感学报, 2019,23(5):971-986.
	[ Zhu J F, Zhou Y, Wang S X, et al. Analysis of changes of Baiyangdian wetland from 1975 to 2018 based on remote sensing[J]. Journal of Remote Sensing, 2019,23(5):971-986. ]

影像编号	获取日期	获取卫星	分辨率/m	行列号	影像质量
A	2017-09-28	Landsat8_OLI	30	123/33	良好
B	2019-09-18	Landsat8_OLI	30	123/33	良好

一级分类	二级分类	三级分类	说明
湿地	水体		主要有河流、湖泊、水库、沟渠、鱼塘等
	水生植被	挺水植被	主要指芦苇、香蒲和莲等植物
	水生植被	沉水植被	主要指狐尾藻、金鱼藻、马来眼子菜、篦齿眼子菜等植物
非湿地	耕地		主要有水稻、小麦、玉米等农作物
非湿地	建筑用地		主要指包括城镇、乡村、工矿、交通等各类建设用地

白洋淀湿地类型	2017年CSD+CDD		2017年CART		2019年CSD+CDD		2019年CART
白洋淀湿地类型	用户精度/%	制图精度/%	用户精度/%	制图精度/%	用户精度/%	制图精度/%	用户精度/%	制图精度/%
水体	93.37	75.52	86.05	99.02	98.96	78.77	83.02	87.04
建设用地	94.90	96.47	99.09	80.91	99.59	94.21	99.30	62.81
耕地	96.48	92.43	72.77	77.74	97.07	78.53	99.61	6.63
挺水植被	88.42	95.21	99.39	93.22	75.74	96.97	45.66	100.00
沉水植被	71.80	86.22	83.18	86.76	72.43	88.32	0	0
总体精度/%	91.32		89.65		86.97		56.21
Kappa系数	0.88		0.86		0.83		0.37

CSD和CDD结合下的最优遥感特征指数集构建及其在湿地信息提取中的应用

Construction of Optimal Remote Sensing Feature Index Set based on CSD and CDD and Its Application in Wetland Information Extraction

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 56

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	赵泉华, 冯林达, 李玉. 基于最优极化特征组合的SAR影像湿地分类[J]. 地球信息科学学报, 2021, 23(4): 723-736.
[2]	周伟, 李浩然, 石佩琪, 谢利娟, 杨晗. 三江源区毒杂草型退化草地植被光谱特征分析[J]. 地球信息科学学报, 2020, 22(8): 1735-1742.
[3]	刘卫华, 王思远, 马元旭, 申明, 游永发, 海凯, 吴林霖. 一种湿地河流叶绿素a遥感反演方法[J]. 地球信息科学学报, 2020, 22(10): 2062-2077.
[4]	吴瑞娟, 何秀凤, 王静. 结合像元级与对象级的滨海湿地变化检测方法[J]. 地球信息科学学报, 2020, 22(10): 2078-2087.
[5]	耿仁方,付波霖,蔡江涛,陈晓雨,蓝斐芜,余杭洺,李青逊. 基于无人机影像和面向对象随机森林算法的岩溶湿地植被识别方法研究[J]. 地球信息科学学报, 2019, 21(8): 1295-1306.
[6]	陈柯欣, 丛丕福, 卢伟志, 曲丽梅. CA-Markov与LCM模型的黄河三角洲湿地变化模拟比较[J]. 地球信息科学学报, 2019, 21(12): 1903-1910.
[7]	詹国旗, 杨国东, 王凤艳, 辛秀文, 国策, 赵强. 基于特征空间优化的随机森林算法在GF-2影像湿地分类中的研究[J]. 地球信息科学学报, 2018, 20(10): 1520-1528.
[8]	夏热帕提·阿不来提, 刘高焕, 刘庆生, 黄翀, 管续栋. 近30年刘家峡以下黄河上游河道湿地演变规律与驱动力分析[J]. 地球信息科学学报, 2017, 19(8): 1116-1131.
[9]	张旸, 胡德勇, 陈姗姗. 北京城区不透水地表盖度变化及对地表温度的影响[J]. 地球信息科学学报, 2017, 19(11): 1504-1513.
[10]	易凤佳, 黄端, 刘建红, 邱娟, 施媛媛, 李仁东. 汉江流域湿地变化及其生态健康评价[J]. 地球信息科学学报, 2017, 19(1): 70-79.
[11]	廖柳文, 秦建新. 环长株潭城市群湿地生态安全研究[J]. 地球信息科学学报, 2016, 18(9): 1217-1226.
[12]	方朝阳, 邬浩, 陶长华, 高丹, 周华. 鄱阳湖南矶湿地景观信息高分辨率遥感提取[J]. 地球信息科学学报, 2016, 18(6): 847-856.
[13]	陈建龙, 王语檬, 侯淑涛, 杨厚翔. 面向对象和DEM的ZY-1 02C影像湿地提取研究[J]. 地球信息科学学报, 2015, 17(9): 1102-1109.
[14]	段浩, 潘世兵, 李琳, 袁海峰. 敦煌西湖自然保护区湿地演化及驱动力分析[J]. 地球信息科学学报, 2015, 17(2): 222-228.
[15]	史学丽, 张芳, 周文艳, 张艳武. CG-LTDR地表覆盖数据对BCC_AVIM 1.0陆面温度模拟的影响研究[J]. 地球信息科学学报, 2015, 17(11): 1294-1303.