盐城海滨湿地植被地上生物量遥感估算研究

doi:10.11849/zrzyxb.2013.12.003

摘要/Abstract

摘要： 以盐城湿地自然保护区核心区的ETM+图像数据和同期野外实测的31 个样方地上生物量干重、湿重数据为数据源，分析了15 个遥感信息变量与湿地植被地上生物量干重、湿重的相关关系，并选择在0.01 水平上显著相关的8 个遥感变量建立一元线性回归模型、一元曲线回归模型以及多元逐步回归模型，并对比得出最优模型，进而计算出整个研究区的地上生物量。研究得出：①与研究区湿地植被地上生物量干重和湿重相关性最大的都是ETM+4 波段，干重的相关系数为0.833，湿重的相关系数为0.796；②研究区植被地上生物量干重和湿重的遥感估算模型都是一元三次函数模型，且干重模型的拟合精度要优于湿重模型；③得到研究区地上生物量干重总重量为2.28×10⁸ kg，湿重总重量为6.10×10⁸ kg。

关键词: 遥感, 回归模型, 盐城保护区, 湿地生物量, Landsat/ETM+数据

Abstract: With the development of remote sensing technology, it has become an important technical means used to investigate vegetation biomass. The biomass of wetland vegetation is an essential index to describe the wetland ecosystem of primary productivity. Therefore, the investigation of wetland vegetation biomass has important practical significance. In this paper, the core area of the Yancheng Natural Reserve was selected as the study area. The ETM+ image on September 24, 2011 and 31 samples of biomass data in the same period were used as the data source to establish the estimation models. The correlation between the 15 remote sensing information variables and measured biomass were analyzed in this paper. The remote sensing information that showed significant correlation at level 0.01 was selected and the estimation models were established based on eight remote sensing information variables. The models included the simple regression models, the curve regression models and the stepwise regression models, the best estimation models were obtained. The total aboveground vegetation biomass of the study area could then be calculated by the best model in this paper. The conclusions of the study were as follows: 1) Both biomass dry weight and fresh weight of the study area has the best positive correlation to the ETM + 4. The coefficient of biomass dry weight was 0.833 and the coefficient of biomass fresh weight was 0.796. 2) A one variable cubic function model was used by both the biomass dry weight models and biomass fresh weight models. The biomass dry weight models were better than the biomass fresh weight models. 3) The total biomass dry weight of the study area was 2.28×10⁸ kg and the biomass fresh weight weights 6.10×10⁸ kg based on the best estimation models. In this study area, dry biomass was mainly between 1000 g/m² and 3000 g/m² and the humid biomass was mainly between 3000 g/m² and 6000 g/m². There was little extreme high biomass for both dry weight and fresh weight, which was mainly distributed in the places of Spartina alterniflora. The weight of S. alterniflora biomass was more than the reed biomass, and that of the reed biomass was more than the Suaeda salsa biomass. The low weight was distributed in places of Suaeda salsa and around the culture pond and neighboring bare flat.

Key words: wetland vegetation biomass, Landsat ETM+, Yancheng Natural Reserve, remote sensing, regression model

中图分类号:

TP79

谭清梅, 刘红玉, 张华兵, 王聪, 侯明行. 盐城海滨湿地植被地上生物量遥感估算研究[J]. 自然资源学报, 2013, 28(12): 2044-2055.

TAN Qing-mei, LIU Hong-yu, ZHANG Hua-bing, WANG Cong, HOU Ming-hang. An Estimation of Aboveground Vegetation Biomass in Coastal Wetland of Yancheng Natural Reserve[J]. JOURNAL OF NATURAL RESOURCES, 2013, 28(12): 2044-2055.

参考文献

[1] 周德明, 宫辉力, 胡金明, 等. 中国湿地卫星遥感的应用研究[J]. 遥感技术与应用, 2006, 21(6): 577-583.[ZHOU De-ming, GONG Hui-li, HU Jin-ming, et al. Application of satellite remote sensing technology to wetland research. Remote Sensing Technology and Application, 2006, 21(6): 577-583.]
[2] Moreau S, Bosseno R, Gu X F, et al. Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR[J]. Remote Sensing of Environment, 2003, 85: 516-529.
[3] 马泽清, 刘琪璟, 徐雯佳, 等. 基于TM 遥感影像的湿地松林生物量研究[J]. 自然资源学报, 2008, 23(3): 467-478.[MA Ze-qing, LIU Qi-jing, XU Wen-jia, et al. Study on biomass of Pinus elliottii forest in subtropical china assisted with remote sensing. Journal of Natural Resources, 2008, 23(3):467-478.]
[4] Kasischke E S, Smith K B, Bourgeau-chavez L L. Monitoring south Florida wetlands using ERS-1 SAR imagery[J]. Photogram Metric Engineering and Remote Sensing, 1997, 63(3): 281-291.
[5] Kasischke E S, Smith K B, Bourgeau-chavez L L, et al. Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery[J]. Remote Sensing of Environment, 2003(88): 423-441.
[6] 李旭文, 季耿善, 杨静. 太湖梅粱湖蓝藻生物量遥感估算[J]. 国土资料遥感, 1995, 2(2): 23-28.[LI Xu-wen, JI Geng-qiu, YANG Jing. Estimating cyanophyta biomass standing crops in Meiliang gulf of Lake Taihu by satellite remote sensing. Remote Sensing for Land Resources, 1995, 2(2): 23-28.]
[7] 李仁东, 刘纪远. 应用Landsat ETM 数据估算鄱阳湖湿生植被生物量[J]. 地理学报, 2001, 56(5): 532-540.[LI Ren-dong, LIU Ji-yuan. An estimation of wetland vegetation biomass in the Poyang Lake using Landsat ETM Data. Acta Geographica Sinica, 2001, 56(5): 532-540.]
[8] 黎夏, 刘凯, 王树功. 珠江口红树林湿地演变的遥感分析[J]. 地理学报, 2006, 61(1):26-34.[LI Xia, LIU Kai, WANG Shu-gong. Mangrove wetland changes in the Pearl River Estuary using remote sensing. Acta Geographica Sinica, 2006, 61(1): 26-34.]
[9] 黎夏, 叶嘉安, 王树功, 等. 红树林湿地植被生物量的雷达遥感估算[J]. 遥感学报, 2006, 10(3): 387-396.[LI Xia, Anthony Gar-on Yeh, WANG Shu-gong, et al. Estimating mangrove wetland biomass using radar remote sensing. Journal of Remote Sensing, 2006, 10(3): 387-396.]
[10] Willem F B. Biomass dynamics of seagrasses and the role of mangrove and seagrass vegetation as different nutrient source for aintertidal ecosystem[J]. Aquatic Botany, 2000(66): 227.
[11] 邵成, 陈中林, 董厚德. 辽河河口湿地芦苇的生长及生物量研究[J]. 辽宁大学学报: 自然科学版, 1995, 22(1): 89-94.[SHAO Cheng, CHENG Zhong-ling, DONG Hou-de. A study on the growth and biomass of Phragmites communis in Liaohe estuary wetland. Journal of Liaoning University: Natural Science Edition, 1995, 22(1): 89-94.]
[12] 李健, 舒晓波, 陈水森. 基于Landsat-TM 数据鄱阳湖湿地植被生物量遥感监测模型的建立[J]. 广州大学学报: 自然科学版, 2005, 4(6): 494-498.[LI Jian, SHU Xiao-bo, CHEN Shui-sen. Establishment of wetland vegetation biomass model by in-situ and remote sensing observation in Poyang Lake area. Journal of Guangzhou University: Natural Science Edition, 2005, 4(6): 494-498.]
[13] 牛志春, 倪绍祥. 青海湖环湖地区草地植被生物量遥感监测模型[J]. 地理学报, 2003, 8(5): 695-702.[NIU Zhi-chun, NI Shao-xiang. Study on models for monitoring of grassland biomass ground Qinghai Lake assisted by remote sensing. Acta Geographica Sinica, 2003, 58(5): 695-702.]
[14] 何池全.毛果苔草湿地枯落物及其地下生物量动态[J]. 应用生态学报, 2003, 14(3): 363-366.[HE Chi-quan. Dynamics of litter and underground biomass in Carex lasiocarpa wetland on Sanjinag Plain. Chinese Journal of Applied Ecology, 2003, 14(3): 363-366.]
[15] Jukka Miettinen, Soo Chin Liew. Estimation of biomass distribution in Peninsular Malaysia and in the islands of Sumatra, Java and Borneo based on multi-resolution remote sensing land cover analysis[J]. Mitigation and Adaptation Strategies for Global Change, 2009, 14: 357-373.
[16] 李爽, 张祖陆, 周德民. 湿地植被地上生物量遥感估算模型研究——以洪河湿地自然保护区为例[J]. 地理研究, 2011, 30(2): 278-290.[LI Shuang, ZHANG Zu-lu, ZHOU De-min. An estimation of aboveground vegetation biomass in a national natural reserve using remote sensing. Geographical Research, 2011, 30(2): 278-290.]
[17] 罗彩莲, 陈杰, 乐通朝. 基于FLAASH 模型的Landsat ETM+卫星影像大气校正[J]. 防护林科技, 2008(5): 46-48.[LUO Cai-lian, CHEN Jie, LE Tong-chao. Atmospheric correction on Landsat ETM+ satellite image based in FLAASH model. Protection Forest Science and Technology, 2008(5): 46-48.]
[18] 胡上序, 焦力成. 人工神经元计算导论[M]. 北京: 科学出版社, 1994: 190-220.[HU Shang-xu, JIAO Li-cheng. The Introduction to Artificial Neural Computation. Beijing: Science Press, 1994: 190-220.]
[19] 徐建华. 现代地理学中的数学方法[M]. 北京: 高等教育出版社, 2004.[XU Jian-hua. Mathematical Methods in Contemporary Geography. Beijing: Higher Education Press, 2004.]
[20] 赵英时, 等. 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2003: 74.[ZHAO Ying-shi, et al. Theory and Method of Analysis of Remote Sensing Application. Beijing: Science Press, 2003: 74.]
[21] Thomas M Lillesand, Ralph W Kiefer. 遥感与图像解译[M]. 北京: 电子工业出版社, 2003: 286.[Thomas M Lillesand, Ralph W Kiefer. Remote Sensing and Image Interpretation. Beijing: Publishing House of Electronics Industry, 2003: 286.]
[22] 田庆久, 闵祥军. 植被指数研究进展[J]. 地球科学进展, 1998, 13(4): 327-333.[TIAN Qing-jiu, MIN Xiang-jun. Advances in study on vegetation indices. Advance in Earth Sciences, 1998, 13(4): 327-333.]
[23] Elhadi A, Onisimo M, Denis R. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review[J]. Wetlands Ecological Management, 2010, 18: 281-296.
[24] Lei Ji, Bruce K Wylie, Dana R Nossov, et al. Estimating aboveground biomass in interior Alaska with Landsat data and field measurements[J]. International Journal of Applied Earth Observation and Geoinformation, 2012, 18: 451-461.
[25] Donald C R, Sunil N, Ram M N. A review of wetlands remote sensing and defining new considerations[J]. Remote Sensing Reviews, 2001, 20(3): 207-226.