Monitoring of Calcite Precipitation in Hardwater Lakes with Multi-Spectral Remote Sensing Archives

被引:10
|
作者
Heine, Iris [1 ]
Brauer, Achim [2 ]
Heim, Birgit [3 ]
Itzerott, Sibylle [1 ]
Kasprzak, Peter [4 ]
Kienel, Ulrike [2 ,5 ]
Kleinschmit, Birgit [6 ]
机构
[1] GFZ German Res Ctr Geosci, Helmholtz Ctr Potsdam, Sect 1-4 Remote Sensing, D-14473 Potsdam, Germany
[2] GFZ German Res Ctr Geosci, Helmholtz Ctr Potsdam, Sect 5-2 Climate Dynam & Landscape Evolut, D-14473 Potsdam, Germany
[3] Alfred Wegener Helmholtz Ctr Polar & Marine Res, D-14473 Potsdam, Germany
[4] Leibniz Inst Freshwater Ecol & Inland Fisheries, Dept Expt Limnol, OT Neuglobsow, Alte Fischerhutte 2, D-16775 Stechlin, Germany
[5] Univ Greifswald, Inst Geog & Geol, Friedrich Ludwig Jahn St 16, D-17487 Greifswald, Germany
[6] Tech Univ Berlin, Geoinformat Environm Planning Lab, Str 17 Juni 145, D-10623 Berlin, Germany
关键词
calcium-rich hardwater lakes; Landsat Time series analysis; Sentinel; 2; Northeast German Plain; evaluation of ecological restoration measures; WATER INDEX NDWI; LA-CRUZ SPAIN; MEROMICTIC LAKE; SEDIMENTATION; CONSTANCE; SATELLITE; CARBONATE; STATE;
D O I
10.3390/w9010015
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Calcite precipitation is a common phenomenon in calcium-rich hardwater lakes during spring and summer, but the number and spatial distribution of lakes with calcite precipitation is unknown. This paper presents a remote sensing based method to observe calcite precipitation over large areas, which are an important prerequisite for a systematic monitoring and evaluation of restoration measurements. We use globally archived satellite remote sensing data for a retrospective systematic assessment of past multi-temporal calcite precipitation events. The database of this study consists of 205 data sets that comprise freely available Landsat and Sentinel 2 data acquired between 1998 and 2015 covering the Northeast German Plain. Calcite precipitation is automatically identified using the green spectra and the metric BGR area, the triangular area between the blue, green and red reflectance value. The validation is based on field measurements of CaCO3 concentrations at three selected lakes, Feldberger Haussee, Breiter Luzin and Schmaler Luzin. The classification accuracy (0.88) is highest for calcite concentrations >= 0.7 mg/L. False negative results are caused by the choice of a conservative classification threshold. False positive results can be explained by already increased calcite concentrations. We successfully transferred the developed method to 21 other hardwater lakes in Northeast Germany. The average duration of lakes with regular calcite precipitation is 37 days. The frequency of calcite precipitation reaches from single time detections up to detections nearly every year. False negative classification results and gaps in Landsat time series reduce the accuracy of frequency and duration monitoring, but in future the image density will increase by acquisitions of Sentinel-2a (and 2b). Our study tested successfully the transfer of the classification approach to Sentinel-2 images. Our study shows that 15 of the 24 lakes have at least one phase of calcite precipitation and all events occur between May and September. At the lakes Schmaler Luzin and Feldberger Haussee, we illustrated the influence of ecological restoration measures aiming at nutrient reduction in the lake water on calcite precipitation. Our study emphasizes the high variance of calcite precipitation in hardwater lakes: each lake has to be monitored individually, which is feasible using Landsat and Sentinel-2 time series.
引用
收藏
页数:31
相关论文
共 50 条
  • [31] Comparison and Analysis of the Fusion Algorithms of Multi-spectral and Panchromatic Remote Sensing Image
    Deng, Chao
    Li, Hui-na
    Han, Jie
    ADVANCES IN COMPUTER SCIENCE, INTELLIGENT SYSTEM AND ENVIRONMENT, VOL 1, 2011, 104 : 169 - +
  • [32] Combining multi-spectral and thermal remote sensing to predict forest fire characteristics
    Maffei, Carmine
    Lindenbergh, Roderik
    Menenti, Massimo
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 181 : 400 - 412
  • [33] A NEW METHOD FOR AUTOMATIC FINE REGISTRATION OF MULTI-SPECTRAL REMOTE SENSING IMAGES
    Li, Yang
    Chen, Yunping
    Xue, Zhihang
    Cao, Yongxing
    He, Wenzhu
    Tong, Ling
    2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2015, : 4829 - 4831
  • [34] Mapping multi-spectral remote sensing images using rule extraction approach
    Su, Mu-Chun
    Huang, De-Yuan
    Chen, Jieh-Haur
    Lu, Wei-Zhe
    Tsai, L. -C.
    Lin, Jia-Zheng
    EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (10) : 12917 - 12922
  • [35] Fractal texture signatures for segmentation of multi-spectral remote-sensing images
    Deng, D
    INTERNATIONAL SYMPOSIUM ON MULTISPECTRAL IMAGE PROCESSING, 1998, 3545 : 461 - 464
  • [36] An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing
    Nhamo, Luxon
    Ebrahim, Girma Yimer
    Mabhaudhi, Tafadzwanashe
    Mpandeli, Sylvester
    Magombeyi, Manuel
    Chitakira, Munyaradzi
    Magidi, James
    Sibanda, Mbulisi
    PHYSICS AND CHEMISTRY OF THE EARTH, 2020, 115
  • [37] Study on multi-spectral remote sensing image restoration based on sparse representation
    Qin, Zhentao
    Yang, Ru
    Zhang, Jin
    2017 10TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI), 2017,
  • [38] Character selection of multi-spectral and SAR remote sensing image fusion classification
    Yu, XL
    Qian, GH
    Jia, XG
    JOURNAL OF INFRARED AND MILLIMETER WAVES, 2000, 19 (06) : 449 - 453
  • [39] Demonstration of multi-spectral remote chemical sensing and identification using uncooled detectors
    Holland, SK
    Krauss, RH
    Laufer, G
    SENSORS, AND COMMAND, CONTROL, COMMUNICATIONS, AND INTELLIGENCE(C31) TECHNOLOGIES FOR HOMELAND SECURITY AND HOMELAND DEFENSE III, PTS 1 AND 2, 2004, 5403 : 371 - 377
  • [40] A Classified Adversarial Network for Multi-Spectral Remote Sensing Image Change Detection
    Wu, Yue
    Bai, Zhuangfei
    Miao, Qiguang
    Ma, Wenping
    Yang, Yuelei
    Gong, Maoguo
    REMOTE SENSING, 2020, 12 (13)