浏览全部资源
扫码关注微信
Published： 2019-7 ，
Accepted： 4 April 2018
扫 描 看 全 文
方馨蕊, 温兆飞, 陈吉龙, 吴胜军, 黄远洋, 马茂华. 2019. 随机森林回归模型的悬浮泥沙浓度遥感估算. 遥感学报, 23(4): 756–772
Fang X R, Wen Z F, Chen J L, Wu S J, Huang Y Y and Ma M H. 2019. Remote sensing estimation of suspended sediment concentration based on Random Forest Regression Model. Journal of Remote Sensing, 23(4): 756–772
三峡工程蓄水以来，清水下泄，坝下游河段发生了长时间、长距离的沿程冲刷，河流悬浮泥沙浓度发生改变，给沿岸生态系统带来了不利影响。随机森林算法灵活、稳健，已被广泛应用于各类生态环境变量的回归预测分析，但其在水体悬浮泥沙浓度估算方面的能力尚未得到充分认识。基于泥沙站点监测数据和MODIS卫星遥感反射率数据，通过构建随机森林非参数回归预测模型，对三峡工程坝下游宜昌至城陵矶河段在建坝前后14年间(2002年—2015年)各月的悬浮泥沙浓度进行遥感估算。研究表明：(1)基于随机森林的悬浮泥沙浓度估算模型表现较好，模型预测值与实测值间相关性好、预测精度高，优于其他模型(线性回归、支持向量机、人工神经网络模型)。(2)在参与模型构建的MODIS波段变量中，红波段被认为是最重要的预测变量，但不能单独使用它进行预测，悬浮泥沙遥感预测需要多变量共同参与。(3)将悬浮泥沙数据按季节分类所构建的随机森林模型，其平均误差为0.46 mg/L，平均相对均方根误差为12.33%，估算效果最优，能够满足较高精度下悬浮泥沙浓度估算的需求。综上，可以考虑以季节为划分依据，用随机森林回归模型估算悬浮泥沙浓度，并用于后期坝下游河道悬浮泥沙浓度时空反演。
Since 2003 when the Three-Gorge Dam (TGD) was in impoundment
the dam abundantly blocks suspended sediment and cause clear water flowing through the dam
which induces scouring effect on the beds and banks of the Yangtze river below the dam.Furthermore
the altered Suspended Sediment Concentration (SSC) has adversely affected the downstream coastal environment. In this study
the random forest model was applied for SSC estimation. The model is flexible and robust
and can be used for regression analysis of ecological environment variables. Yet
its ability in estimating SSC in aquatic environment has not been fully understood. On the basis of the monitoring data of SSC and satellite remote sensing reflectance data from 2002 to 2015
this study estimated the SSC in Yichang–Chenglingji downstream reach of the TGD by constructing a non-parametric regression model using random forest. The results showed that:(1) the random forest model could effectively monitor SSC
and the correlation coefficient and prediction accuracy were significantly improved from those of other models (linear regression
support vector machine
and artificial neural network model).(2) the red band is a suitable predictor for SSC in the random forest model
but cannot be independently used for forecasting. SSC remote sensing prediction requires multivariate co-participation. (3)By using the random forest model
the average root mean square error of the seasonal division was 0.46 mg/L
and the average relative root mean square error was 12.33%. These values met the needs of high-precision SSC estimation. In conclusion
this study reveals that the season shall be considered as temporal factors to estimate SSC and then prepare for the subsequent SSC spatiotemporal inversion. Which is of great help to reveal the TGD’s downstream river sediment evolution
and understand the regional distribution of sediment and sediment variation process in the future.
三峡工程坝下游河段水环境遥感随机森林遥感监测MODIS泥沙反演
TGD(Three Gorges Dam)downstream channel of damremote sensing of aquatic environmentrandom forestremote sensing monitoringMODISsediment inversion
Bian M, Skidmore A K, Schlerf M, Wang T J, Liu Y F, Zeng R and Fei T. 2013. Predicting foliar biochemistry of tea (Camellia sinensis) using reflectance spectra measured at powder, leaf and canopy levels. ISPRS Journal of Photogrammetry and Remote Sensing, 78: 148–156
Birth G S and McVey G R. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60(6): 640–643
Bowers D G, Hill P S and Braithwaite K M. 2014. The effect of particulate organic content on the remote sensing of marine suspended sediments. Remote Sensing of Environment, 144: 172–178
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5–32
程天文, 赵楚年. 1985. 我国主要河流入海径流量、输沙量及对沿岸的影响. 海洋学报, 7(4): 460–471
Cheng T W and Zhao C N. 1985. The runoff, sediment discharge and its impacts on the coastal waters of China’s major rivers. Acta Oceanologica Sinica, 7(4): 460–471
丁莉东, 吴昊, 王长健, 覃志豪, 章其祥. 2006. MODIS图像湖泊水体信息的快速识别与制图. 海洋测绘, 26(6): 31–34
Ding L D, Wu H, Wang C J, Qin Z H and Zhang Q X. 2006. Quick recognition and mapping of Lake Water information based on MODIS image. Hydrographic Surveying and Charting, 26(6): 31–34
范德芹, 朱文泉, 潘耀忠, 姜楠. 2013. 基于狄克松检验的NDVI时序数据噪声检测及其在数据重建中的应用. 遥感学报, 17(5): 1158–1174
Fan D Q, Zhu W Q, Pan Y Z and Jiang N. 2013. Noise detection for NDVI time series based on Dixon’s test and application in data reconstruction. Journal of Remote Sensing, 17(5): 1158–1174
樊辉, 黄海军, 唐军武. 2007. 黄河口水体光谱特性及悬沙浓度遥感估测. 武汉大学学报(信息科学版), 32(7): 601–604
Fan H, Huang H J and Tang J W. 2007. Spectral signature of waters in Huanghe estuary and estimation of suspended sediment concentration from remote sensing data. Geomatics and Information Science of Wuhan University, 32(7): 601–604
Francke T, López-Tarazón J A and Schröder B. 2008. Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests. Hydrological Processes, 22(25): 4892–4904
高志斌, 段光磊. 2006. 边界条件对三峡坝下游河床演变影响. 人民长江, 37(12): 92–94
Gao Z B and Duan G L. 2006. Influence of boundary condition on downstream river bed evolution of TGP. Yangtze River, 37(12): 92–94
龚玲, 钟成华, 邓春光. 2006. 水体中悬浮泥沙对藻类生长的影响. 农业环境科学学报, 25(S2): 687–689
Gong L, Zhong C H and Deng C G. 2006. Effect of suspended sand particles on growth of algae in aquatic system. Journal of Agro-Environment Science, 25(S2): 687–689
韩震. 2004. 海岸带淤泥质潮滩和Ⅱ类水体悬浮泥沙遥感信息提取与定量反演研究. 上海: 华东师范大学
Han Z. 2004. Remote Sensing Information Extraction and Quantitative Inversion Research of Silt Tidal Flat and Suspended Sediment of Case II Waters in Coast Zone. Shanghai: East China Normal University
Ismail R, Mutanga O and Kumar L. 2010. Modeling the potential distribution of pine forests susceptible to Sirex noctilio infestations in Mpumalanga, South Africa. Transactions in GIS, 14(5): 709–726
姜杰. 2004. 悬浮泥沙浓度遥感反演模式研究. 南京: 南京师范大学
Jiang J. 2004. A Study on Retrieval and Change Analysis of Suspended Sediment Concentration. Nanjing: Nanjing Normal University
江佳乐, 刘湘南, 刘美玲, 毕晓庆. 2014. 基于随机森林的香港海域海表盐度遥感反演模型. 海洋通报, 33(3): 333–341
Jiang J L, Liu X N, Liu M L and Bi X Q. 2014. Remote sensing retrieval model of sea surface salinity in Hong Kong waters based on the random forest. Marine Science Bulletin, 33(3): 333–341
姜刘志. 2014. 三峡蓄水后长江中下游水文情势变化特征及其对鱼类的影响研究. 北京: 中国科学院大学
Jiang L Z. 2014. Assessment of Hydrological Alteration and its Impacts on Fish Population in the Middle-Lower Yangtze River after the Impoundment of the Three Gorges Dam. Beijing: University of Chinese Academy of Sciences
金龙, 况雪源, 黄海洪, 覃志年, 王业宏. 2004. 人工神经网络预报模型的过拟合研究. 气象学报, 62(1): 62–70
Jin L, Kuang X Y, Huang H H, Qin Z N and Wang Y H. 2004. Study on the overfitting of the artificial neural network forecasting model. Acta Meteorologica Sinica, 62(1): 62–70
李洪灵, 张鹰, 姜杰. 2006. 基于遥感方法反演悬浮泥沙分布. 水科学进展, 17(2): 242–245
Li H L, Zhang Y and Jiang J. 2006. Study on the inversion model for the suspended sediment concentration in remote sensing technology. Advances in Water Science, 17(2): 242–245
李四海, 恽才兴. 2001. 河口表层悬浮泥沙气象卫星遥感定量模式研究. 遥感学报, 5(2): 154–160
Li S H and Yun C X. 2001. A study on the quantitative model of the suspended sediment concentration from the meteorological satellite imagery. Journal of Remote Sensing, 5(2): 154–160
李义天, 孙昭华, 邓金运, 张为. 2004. 泥沙输移变化与长江中游水患. 泥沙研究(2): 33–39
Li Y T, Sun Z H, Deng J Y and Zhang W. 2004. Variation of sediment transport and flood disaster in the middle reach of Yangtze River. Journal of Sediment Research(2): 33–39
李致博. 2012. 基于支持向量机的海洋悬浮物浓度遥感反演模型研究. 北京: 中国地质大学(北京)
Li Z B. 2012. Based Support Vector Machine Retrieval Model for Ocean Suspended Solids Remote Sensing Concentration. Beijing: China University of Geosciences (Beijing)
梁栋, 管青松, 黄文江, 黄林生, 杨贵军. 2013. 基于支持向量机回归的冬小麦叶面积指数遥感反演. 农业工程学报, 29(7): 117–123
Liang D, Guan Q S, Huang W J, Huang L S and Yang G J. 2013. Remote sensing inversion of leaf area index based on support vector machine regression in winter wheat. Transactions of the Chinese Society of Agricultural Engineering, 29(7): 117–123
Liaw A and Wiener M. 2002. Classification and regression by randomForest. R News, 2-3: 18–22
林承达, 周斌, 马琪, 姜萌薪. 2014. 基于遥感反演长江中游地区悬浮泥沙研究. 长江流域资源与环境, 23(8): 1119–1124
Lin C D, Zhou B, Ma Q and Jiang M X. 2014. Study on the inversion model of the suspended sediment in the middle Yangtze River based on remote sensing technology. Resources and Environment in the Yangtze Basin, 23(8): 1119–1124
Liu H Q and Huete A. 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2): 457–465
刘小斌, 卢金友, 林木松. 2006. 三峡工程对长江中下游河道影响分析//水电2006国际研讨会论文集. 昆明: 中国水利学会中国水力发电工程学会中国大坝委员会
Liu X B, Lu J Y and Lin M S. 2006. The influence of the Three Gorges Project on the middle and lower reach of the Yangtze River//Proceedings of 2006 International Symposium on Hydropower. Kunming: China Dam Committee of China Hydropower Engineering Society, China Water Conservancy Society
楼琇林, 黄韦艮. 2003. 基于人工神经网络的赤潮卫星遥感方法研究. 遥感学报, 7(2): 125–130
Lou X L and Huang W G. 2003. An artificial Neural Network Method for detecting red tides with NOAA AVHRR imagery. Journal of Remote Sensing, 7(2): 125–130
梅安新, 彭望琭, 秦其明, 刘慧平. 2001. 遥感导论. 北京: 高等教育出版社: 236-238
Mei A X, Peng W L, Qin Q M and Liu H P. 2001. An Introduction to Remote Sensing. Beijing: Higher Education Press: 236-238
Miller R L and McKee B A. 2004. Using MODIS Terra 250 m imagery to map concentrations of total suspended matter in coastal waters. Remote Sensing of Environment, 93(1/2): 259–266
Novo E M L M, Steffen C A and Braga C Z F. 1991. Results of a laboratory experiment relating spectral reflectance to total suspended solids. Remote Sensing of Environment, 36(1): 61–72
裴雪原, 臧淑英, 那晓东. 2014. MODIS MOD13Q1植被产品介绍及快速预处理. 哈尔滨师范大学自然科学学报, 30(2): 65–67, 77
Pei X Y, Zang S Y and Na X D. 2014. The introductions and rapid pretreatment of MODIS MOD13Q1 Vegetation product. Natural Science Journal of Harbin Normal University, 30(2): 65–67, 77
Prasad A M, Iverson L R and Liaw A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9(2): 181–199
乔晓景, 何报寅, 张文, 李元征, 苏振华. 2013. 基于MODIS的长江中游河段悬浮泥沙浓度反演. 长江流域资源与环境, 22(8): 1090–1095
Qiao X J, He B Y, Zhang W, Li Y Z and Su Z H. 2013. MODIS-based retrieval and change analysis of suspended sediment concentration in middle Yangtze River. Resources and Environment in the Yangtze Basin, 22(8): 1090–1095
Richardson A J and Wiegand C L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12): 1541–1552
Rouse J W Jr, Haas R H, Schell J A and Deering D W. 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. PAPER-A20. NASA Special Publication: 309
Smith C and Croke B. 2005. Sources of uncertainty in estimating suspended sediment load//Proceedings of Symposium S1 Held During the Seventh IAHS Scientific Assembly. Foz do Igaussu, Brazil: IAHS-AISH Publication: 136-143
Stephens D and Diesing M. 2015. Towards quantitative spatial models of seabed sediment composition. PLoS One, 10(11): e0142502
田婷, 孙成明, 刘涛, 郭斗斗, 王力坚, 陈瑛瑛. 2013. 高光谱遥感技术及其在草地及植被中的应用. 安徽农业科学, 41(7): 3192–3195
Tian T, Sun C M, Liu T, Guo D D, Wang L J and Chen Y Y. 2013. Hyperspectral remote sensing and its applications in grassland and vegetation. Journal of Anhui Agricultural Sciences, 41(7): 3192–3195
王繁, 凌在盈, 周斌, 宋立松, 王新. 2009. MODIS监测河口水体悬浮泥沙质量浓度的短期变异. 浙江大学学报(工学版), 43(4): 755–759
Wang F, Ling Z Y, Zhou B, Song L S and Wang X. 2009. MODIS images monitoring short-period variation of estuary surface water suspended sediment concentration. Journal of Zhejiang University (Engineering Science), 43(4): 755–759
王强, 沈永平, 陈英武. 2006. 支持向量机规则提取. 国防科技大学学报, 28(2): 106–110
Wang Q, Shen Y P and Chen Y W. 2006. Rule extraction from support vector machines. Journal of National University of Defense Technology, 28(2): 106–110
Wang W, Yang X, Liu G, Zhou H, Ma W, Yu Y and Li Z. 2016. Random forest classification of sediments on exposed intertidal flats using ALOS-2 Quad-Polarimetric SAR data. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8: 1191-1194 [DOI: 10.5194/isprsarchives-XLI-B8-1191-2016]
王晓青, 李哲, 吕平毓, 郭劲松. 2007. 三峡库区悬移质泥沙对磷污染物的吸附解吸特性. 长江流域资源与环境, 16(1): 31–36
Wang X Q, Li Z, Lv P Y and Guo J S. 2007. Adsorption and desorption of phosphorus on suspended particles in the Three Gorges Area. Resources and Environment in the Yangtze Basin, 16(1): 31–36
王晓青, 吕平毓, 胡长霜. 2006. 三峡库区悬移质泥沙对TP、TN等的吸附影响. 人民长江, 37(7): 15–17
Wang X Q, Lv P Y and Hu C S. 2006. Influence of suspended sediment in TGP reservoir on absorption of TP, TN etc. Yangtze River, 37(7): 15–17
王茵茵, 齐雁冰, 陈洋, 解飞. 2016. 基于多分辨率遥感数据与随机森林算法的土壤有机质预测研究. 土壤学报, 53(2): 342–354
Wang Y Y, Qi Y B, Chen Y and Xie F. 2016. Prediction of soil organic matter based on multi-resolution remote sensing data and random forest algorithm. Acta Pedologica Sinica, 53(2): 342–354
王志辉, 易善桢. 2007. 不同指数模型法在水体遥感提取中的比较研究. 科学技术与工程, 7(4): 534–537
Wang Z H and Yi S Z. 2007. Comparison and research on the different index models used in water extraction by remote sensing. Science Technology and Engineering, 7(4): 534–537
王正兴, 刘闯, Huete A. 2003. 植被指数研究进展: 从AVHRR-NDVI到MODIS-EVI. 生态学报, 23(5): 979–987
Wang Z X, Liu C and Huete A. 2003. From AVHRR-NDVI to MODIS-EVI: advances in vegetation index research. Acta Ecologica Sinca, 23(5): 979–987
温兆飞. 2017. 三峡水库消落带地上净初级生产力时空变化及其影响因素分析. 重庆: 中国科学院大学(中国科学院重庆绿色智能技术研究院)
Wen Z F. 2017. Spatial and Seasonal Patterns of Aboveground Net Primary Productivity and Their Responses to Environmental Factors in the Drawdown Zone of the Three Gorges Reservoir, China. Chongqing: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences
邬国锋, 崔丽娟, 纪伟涛. 2009. 基于时间序列MODIS影像的鄱阳湖丰水期悬浮泥沙浓度反演及变化. 湖泊科学, 21(2): 288–297
Wu G F, Cui L J and Ji W T. 2009. Time-series MODIS images-based retrieval and change analysis of suspended sediment concentration during flood period in Lake Poyang. Journal of Lake Sciences, 21(2): 288–297
谢贻发, 胡耀辉, 刘正文, 谢贵水. 2007. 沉积物再悬浮对沉水植物生长的影响研究. 环境科学学报, 27(1): 18–22
Xie Y F, Hu Y H, Liu Z W and Xie G S. 2007. Effects of sediment resuspension on the growth of submerged plants. Acta Scientiae Circumstantiae, 27(1): 18–22
翟伟康. 2006. MODIS大气校正及渤海水色时空分布特征研究. 大连: 大连海事大学
Zhai W K. 2006. Atmospheric Correction of MODIS and Analysis Space-Time Distribution Characteristics for Ocean Color in Bohai Sea. Dalian: Dalian Maritime University
张恒喜, 郭基联, 朱家元, 虞健飞. 2002. 小样本多元数据分析方法及应用. 西安: 西北工业大学出版社: 153-154
Zhang H X, Guo J L, Zhu J Y and Yu J F. 2002. Multivariate Data Analysis Methods and Applications with Few Observations. Xi’an: Northwestern Polytechnical University Press: 153-154
张雷, 王琳琳, 张旭东, 刘世荣, 孙鹏森, 王同立. 2014. 随机森林算法基本思想及其在生态学中的应用——以云南松分布模拟为例. 生态学报, 34(3): 650–659
Zhang L, Wang L L, Zhang X D, Liu S R, Sun P S and Wang T L. 2014. The basic principle of random forest and its applications in ecology: a case study of Pinus yunnanensis. Acta Ecologica Sinica, 34(3): 650–659
张文. 2012. 基于智能计算的水体悬浮物遥感反演研究——以长江中游为例. 北京: 中国科学院大学
Zhang W. 2012. Remote Sensing Retrieval of Suspended Matters Based on Intelligent Calculation——A Case Study of Middle Yangtze River. Beijing: University of Chinese Academy of Sciences
张卫军, 魏立鹏, 渠庚. 2013. 三峡工程运用后荆江不同河型河道演变分析. 水利科技与经济, 19(11): 56–59
Zhang W J, Wei L P and Qu G. 2013. The analysis of channel evolution of Jingjiang River in different types after operation of Three Gorges Project. Water Conservancy Science and Technology and Economy, 19(11): 56–59
Zimmermann A, Francke T and Elsenbeer H. 2012. Forests and erosion: Insights from a study of suspended-sediment dynamics in an overland flow-prone rainforest catchment. Journal of Hydrology, 428-429: 170–181
相关文章
相关作者
相关机构
京公网安备11010802024621