Wetland Plant Extraction Based on the Time Series Landsat NDVI in Dongting Lake Area

doi:10.13466/j.cnki.lyzygl.2017.04.016

Abstract

Abstract:

As an important ecological system,wetland of lake groups and river system in Dongting Lake area is essential for the ecological environment.Due to the continuous disturbance of human activities and globe climate change,wetland in Dongting Lake area has degraded and it’s urgent to monitor the wetland change timely.In this paper,we used Landsat8 OLI data and MODIS data to get the time series Landsat NDVI data based on spatial and temporal adaptive reflectance fusion model (STARFM).Then,the Savitzky-Golay (S-G) filter was employed to smooth the time series Landsat NDVI data.With the phonological calendar of plant wetland and the computation of Jeffries-Matsushita distance (J-M),and through selecting validation data randomly throughout the study area for many times,we got the best J-M distance and the optimal Landsat NDVI data combination.Support vector machine was used to map wetland distribution of study area.Results showed that this method could map wetland fields effectively,and get a high overall precision of 91.52% with the Kappa coefficient of 0.85,and overall accuracy and Kappa coefficient were improved about 4.16% and 0.03,respectively,compared with using single date Landsat8 OLI spectral data.Especially,the precision of plant wetland,such as sedge,reed,polar and paddy,were improved about 2.35%,0.67%,10.47% and 4.75% for user accuracy and 3.57%,2.31%,10.11% and 6.21% for producer accuracy.The research can provide an important way to solve the problem of missing data on monitoring wetland.

Key words: time series, NDVI, STARFM, Dongting Lake area, wetland vegetation

CLC Number:

S757.2
TP79

LIU Xiaonong, XING Yuanjun, LUO Peng. Wetland Plant Extraction Based on the Time Series Landsat NDVI in Dongting Lake Area[J]. FOREST RESOURCES WANAGEMENT, 2017, 0(4): 103-109.

Figures/Tables 8

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References 21

[1]	王红娟, 姜加虎, 黄群. 东洞庭湖湿地景观变化研究[J]. 长江流域资源与环境, 2007,16(6):732-737.
[2]	唐玥, 谢永宏, 李峰, 等. 基于Landsat的近20余年东洞庭湖湿地草洲变化研究[J]. 长江流域资源与环境, 2013,22(11):1484-1492.
[3]	杨波, 廖丹霞, 李京, 等. 东洞庭湖湿地生态系统健康状态与水位关系研究[J]. 长江流域资源与环境, 2014,23(8):1145-1152.
[4]	陈燕芬, 牛振国, 胡胜杰, 等. 基于MODIS时间序列数据的洞庭湖湿地动态监测[J]. 水利学报, 2016,47(9):1093-1104.
[5]	邓帆, 王学雷, 厉恩华, 等. 1993—2010年洞庭湖湿地动态变化[J]. 湖泊科学, 2012,24(4):571-576.
[6]	蒋卫国, 潘英姿, 侯鹏, 等. 洞庭湖区湿地生态系统健康综合评价[J]. 地理研究, 2009,28(6):1665-1672.
[7]	郑建蕊, 蒋卫国, 周廷刚, 等. 洞庭湖区湿地景观指数选取与格局分析[J]. 长江流域资源与环境, 2010,19(3):305-310.
[8]	杨利, 谢炳庚, 秦建新, 等. 三峡建坝前后洞庭湖区湿地景观格局变化[J]. 自然资源学报, 2013,28(12):2068-2080.
[9]	Gao F, Masek J, Schwaller M, et al. On the blending of the Landsat and MODIS surface reflectance:predicting daily Landsat surface reflectance[J]. IEEE Transactions on Geoscience & Remote Sensing, 2006,44(8):2207-2218.
[10]	张猛, 曾永年. 基于多时相Landsat数据融合的洞庭湖区水稻面积提取[J]. 农业工程学报, 2015,31(13):178-185.
[11]	Hilker T, Wulder M A, Coops N C, et al. A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS[J]. Remote Sensing of Environment, 2009,113(8):1613-1627.
[12]	Zhu X L, Jin C, Feng G, et al. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions.[J]. Remote Sensing of Environment, 2010,114(11):2610-2623.
[13]	Wu M Q. Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model[J]. Journal of Applied Remote Sensing, 2012,6(1):063507. doi: 10.1117/1.JRS.6.063507
[14]	Hilker T, Wulder M A, Coops N C, et al. Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model[J]. Remote Sensing of Environment, 2009,113(9):1988-1999. doi: 10.1016/j.rse.2009.05.011
[15]	邬明权, 王长耀, 牛铮. 利用多源时序遥感数据提取大范围水稻种植面积[J]. 农业工程学报, 2010,26(7):240-244.
[16]	Jia K, Liang S, Zhang N, et al. Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data[J]. Journal of Photogrammetry & Remote Sensing, 2014,93(7):49-55.
[17]	李儒, 张霞, 刘波, 等. 遥感时间序列数据滤波重建算法发展综述[J]. 遥感学报, 2009,13(2):335-341. doi: 10.11834/jrs.20090257
[18]	Jönsson P, Eklundh L. Seasonality extraction and noise removal by function fitting to time-series of satellite sensor data[J]. IEEE Transactions of Geoscience and Remote Sensing, 2002,40(8), 1824-1832. doi: 10.1109/TGRS.2002.802519
[19]	Hao Pengyu, Wang Li, Niu Zheng, et al. The potential of time series merged from Landsat-5 TM and HJ-1 CCD for crop classification:A case study for Bole and Manas counties in XinJiang,China[J]. Remote Sensing, 2014,6(8), 7610-7631. doi: 10.3390/rs6087610
[20]	臧淑英, 张策, 张丽娟, 等. 遗传算法优化的支持向量机湿地遥感分类——以洪河国家级自然保护区为例[J]. 地理科学, 2012,32(4):434-441.
[21]	殷书柏, 李冰, 沈方. 湿地定义研究进展[J]. 湿地科学, 2014,2(4):504-514.

数据类型	行列号	获取日期
Landsat8 OLI	123/39	2015年云量<5%的所有影像
	123/40
	124/39
	124/40
MODIS 13Q1	h27v05	2015年1月—2015年12月共23期
MODIS 13Q1	h27v06	2015年1月—2015年12月共23期

NDVI编号	NDVI对应日期	NDVI对应天数/d
1	5月09日	129
2	5月25日	145
3	6月10日	161
4	6月26日	177
5	7月12日	193
6	7月28日	209
7	8月13日	225
8	8月29日	241
9	9月14日	257
10	9月30日	273
11	10月16日	289
12	11月01日	305
13	11月17日	311

Landsat NDVI 数据组合序号	J-M值
Landsat NDVI 数据组合序号	芦苇-双季稻	苔草-一季稻	苔草-芦苇	双季稻-一季稻	双季稻-芦苇	一季稻-芦苇
1 4 6 8	1.873	1.813	1.785	1.826	1.831	1.782
1 4 6 8 10	1.894	1.839	1.822	1.852	1.848	1.825
1 3 4 6 8 10	1.924	1.857	1.842	1.863	1.891	1.849
1 3 4 6 8 10 13	1.918	1.934	1.919	1.938	1.946	1.927
1 3 4 6 8 9 10 13	1.988	1.974	1.972	1.989	1.978	1.969
1 3 4 6 7 8 9 10 13	1.999	2.000	1.998	1.992	2.000	2.000
1 3 4 5 6 7 8 9 10 13	2.000	2.000	2.000	1.999	2.000	2.000
1 3 4 5 6 7 8 9 10 11 13	2.000	2.000	2.000	1.999	2.000	2.000

数据集	分类方法	湿地类型	生产者精度/%	用户精度/%	总体分类精度/%	Kappa系数
单时相Landsat	SVM	水体	97.43	96.27	87.36	0.82
		泥沙滩地	91.03	92.19
		苔草沼泽	78.45	81.74
		芦苇沼泽	88.26	90.17
		杨树林沼泽	78.45	80.26
		水田	82.06	85.37
时序Landsat	SVM	水体	96.24	98.31	91.52	0.85
		泥沙滩地	94.26	93.98
		苔草沼泽	82.13	84.09
		芦苇沼泽	90.57	90.84
		杨树林沼泽	88.56	90.73
		水田	88.27	90.12