基于Sentinel数据的滇池湖滨湿地地上生物量反演

张国飞; 岳彩荣; 章皖秋

doi:doi:10.11805/tkyda2019095

太赫兹科学与电子信息学报, 2020, 18 (1): 142, 网络出版: 2020-04-13

基于Sentinel数据的滇池湖滨湿地地上生物量反演

Above-ground biomass estimation in Kunming Dianchi lake wetland using Sentinel imagery

论文大纲

张国飞 ^*岳彩荣章皖秋

作者单位

西南林业大学林学院, 云南昆明 650224

地上生物量湿地 Sentinel遥感图像滇池湖滨 Above-Ground Biomass wetland Sentinel imagery Kunming Dianchi lake

摘要

为了评估基于Sentine 1/2影像数据反演滇池湖滨带湿地森林地上生物量(AGB)的效果和能力, 以Sentinel-1 A/B(SAR)和Sentinel-2 A/B(多光谱)卫星图像为数据源, 获取SAR双极化后向散射系数、多光谱波段、植被指数和林冠生物物理变量等因子, 利用线性回归和机器学习算法, 建立了多个滇池湖滨湿地生物量反演模型。所有模型与滇池湖滨湿地样地地上生物量的相关性为0.619~0.84, 均方根误差(RMSE)范围为40.14~59.7 t/ha, 其中基于SAR的模型反演精确度最低; 在多光谱波段中, 红色和红边(波段4,5和7)与生物量有很好的相关性; 叶面积指数(LAI)模型是生物量反演的最佳变量组合(r=0.84,RMSE=40.14); 基于Sentine 1/2影像数据反演滇池湖滨带湿地地上生物量具有可行性。

Abstract

For evaluating the potential of Sentinel imagery for the inversion of Above-Ground Biomass(AGB) of Kunming Dianchi lake wetland, Sentinel SAR and multispectral imagery are used as data sources respectively, and various biomass prediction models are developed through the conventional linear regression and other machine learning algorithms. SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables are extracted. These models have 0.619-0.84 correlation agreement of observed and predicted values, and root mean square error of 40.14-59.7 t/ha. The SAR-based model has the lowest accuracy. Among the Sentinel-2 multispectral bands, the red and red edge bands(band 4,5 and 7), are the best variable set combination for biomass prediction. The model based on the biophysical variable―Leaf Area Index(LAI) derived from Sentinel-2 is more accurate in predicating the overall AGB. The study demonstrates encouraging results in biomass mapping of Dianchi lake wetland by using the freely accessible and relatively high-resolution Sentinel imagery.

参考文献

[1] TODD M J,MUNEEPEERAKUL Ｒ,PUMO D,et al. Hydrological drivers of wetland vegetation community distribution within Everglades National Park,Florida[J]. Advances in Water Resources, 2010,33(10):1279-1289.

[2] 王树功,黎夏,周永章. 湿地植被生物量测算方法研究进展[J]. 地理与地理信息科学, 2004,20(5):104-109. (WANG Shugong,LI Xiang,ZHOU Yongzhang. Progress of method for wetland vegetation biomass[J]. Geography and Geo- Information Science, 2004,20(5):104-109.)

[3] GTINERALP I,FILIPPI A M,RANDALL J. Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling[J]. International Journal of Applied Earth Observation and Geoinformation, 2014(33):l19-126．

[4] 刘莉,韩美,刘玉斌,等. 黄河三角洲自然保护区湿地植被生物量空间分布及其影响因素[J]. 生态学报, 2017,37(13): 4346-4355. (LIU Li,HAN Mei,LIU Yubin,et al. Spatial distribution of wetland vegetation biomass and its influencing factors in the Yellow River Delta Nature Reserve[J]. Acta Ecologica Sinica, 2017,37(13):4346-4355.)

[5] 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 YEH Gar-On,WANG Shugong,et al. Estimating Mangrove wetland biomass using radar remote sensing[J]. Journal of Remote Sensing, 2006,10(3):387-396.)

[6] 谭清梅,刘红玉,张华兵,等. 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 Qingmei,LIU Hongyu,ZHANG Huabing,et al. An estimation of aboveground vegetation biomass in coastal wetland of Yancheng natural reserve[J]. Journal of Natural Resources, 2013,28(12):2044-2055.)

[7] LU D,MAUSEl P,BRONDI Zio,et al. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin[J]. Forest Ecology and Management, 2004,198(1/2/3):149-167.

[8] SIMARD Marc,ZHANG Keqi,RIVERA-MONROY Victor H,et al. Mapping height and biomass of mangrove forests in everglades national park with SRTM elevation data[J]. Photogrammetric Engineering & Remote Sensing, 2006,72(3):299-311.

[9] DUBE T,GARA T W,MUTANGA O,et al. Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor[J]. Geocarto International, 2018,33(2):1-11.

[10] DUSSEUX P,HUBERT-MOY L,CORPETTI T,et al. Evaluation of SPOT imagery for the estimation of grassland biomass[J]. International Journal of Applied Earth Observation and Geoinformation, 2015,38(1):72-77.

[11] Sentinel-1_Team. Sentinel-1 user handbook[EB/OL]. (2013)[2018-06-01]. https://sentinels.copernicus.eu/web/sentinel/ user-guides/sentinel-1-sar.

[12] Sentinel-2_Team. 2015. Sentinel-2 user handbook[EB/OL]. (2015)[2018-06-01]. https://sentinels.copernicus.eu/web/ sentinel/user-guides/ sentinel-2-msi.

[13] 程晋昕,余凌翔,鲁韦坤. 基于高分辨率遥感影像的滇池湖滨湿地植被类型监测[J]. 云南地理环境研究, 2013,25(6): 1-7. (CHENG Jinxin,YU Lingxiang,LU Weikun. Vegetation types monitoring of Dianchi lakeside wetland based on high-spatial resolution remote sensing imagery[J]. Yunnan Geographic Environment Research, 2013,25(6):1-7.)

[14] SNAP 2016. Sentinels application platform software ver. 5.0.0[EB/OL]. (2016)[2018-06-01]. http://step.esa.int/main/ toolboxes/snap/.

[15] 黄兴召,王泽夫,徐小牛. 生物量转换因子连续函数的拟合方法比较[J]. 浙江农林大学学报, 2017,34(5):775-781. (HUANG Xingzhao,WANG Zefu,XU Xiaoniu. Comparison of fitting approaches with biomass expansion factor equations[J]. Journal of Zhejiang A & F University, 2017,34(5):775-781.)

[16] 刘婷婷,陈军,庄建磊,等. 杨树人工林生物量及其碳储量的研究[J]. 中国科技财富, 2009(4):192. (LIU Tingting, CHEN Jun,ZHUANG Jianlei,et al. Biomass and carbon storage of poplar plantations[J]. China Science and Technology Fortune Magazine, 2009(4):192).

[17] 梁建平,马大喜,毛德华,等. 双台河口国际重要湿地芦苇地上生物量遥感估算[J]. 国土资源遥感, 2016,28(3):60-66. (LIANG Jianping,MA Daxi,MAO Dehua,et al. Remote sensing based estimation of Phragmites australis aboveground biomass in Shuangtai Estuary nature reserve[J]. Remote Sensing for Land & Resources, 2016,28(3):60-66.)

[18] KOMIYAMA A,POUNGPARN S,KATO S. Common allometric equations for estimating the tree weight of mangroves[J]. Journal of Tropical Ecology, 2005,21(4):471-477.

[19] CHRISTENSEN L K,BENNEDSEN B S,JORGENSEN R N,et al. Modeling nitrogen and phosphorus content at early growth stages in spring barley using hyperspectral line scanning[J]. Biosystems Engineering, 2004,88(1):19-24.

[20] PROISY C,MITCHELL A,LUCAS R,et al. Estimation of mangrove biomass using multifrequency radar data[EB/OL]. (2013-05-20)[2018-06-01]. https://www.researchgate.net/publication/228770186_Estimation_of_Mangrove_Biomass_using_ Multifrequency_Radar_Data_Application_to_Mangroves_of_French_Guiana_and_Northern_Australia.

[21] KUMAR L,SINHA P,TAYLOR S,et al. Review of the use of remote sensing for biomass estimation to support renewable energy generation[J]. Journal of Applied Remote Sensing, 2015,9(1):097696.

[22] SINHA S,JEGANATHAN C,SHARMA L K,et al. A review of radar remote sensing for biomass estimation[J]. International Journal of Environmental Science and Technology, 2015,12(5):1779-1792.

[23]SAATCHI S S,HOUGHTON R A,ALVAL D S R C,et al. Distribution of aboveground live biomass in the Amazon basin[J]. Global Change Biology, 2007,13(4):816–837.

张国飞, 岳彩荣, 章皖秋. 基于Sentinel数据的滇池湖滨湿地地上生物量反演[J]. 太赫兹科学与电子信息学报, 2020, 18(1): 142. ZHANG Guofei, YUE Cairong, ZHANG Wanqiu. Above-ground biomass estimation in Kunming Dianchi lake wetland using Sentinel imagery[J]. Journal of terahertz science and electronic information technology, 2020, 18(1): 142.

基于Sentinel数据的滇池湖滨湿地地上生物量反演

关于本站 Cookie 的使用提示

全站搜索

基于Sentinel数据的滇池湖滨湿地地上生物量反演

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索