Comparison of Spectral Classification Methods in Water Quality

Yıl 2018, Cilt: 39 Sayı: 2, 543 - 549, 29.06.2018

Önder Gürsoy Rutkay Altun

https://doi.org/10.17776/csj.422897

Cited By: 2

Öz

Today, water quality and water pollution can be detected using remote
sensing and its terrestrial components. Remote sensing does not only provide a
quick solution to detect water quality and pollution, but it could also be low
cost. Within the scope of the study, the water quality of the İmranlı area of
the Kızılırmak River, one of the most important water resources of the Sivas
region and the İmranlı dam on the river, was investigated by spectral
classification methods. Water samples were taken from various points on the
river and dam and their chemical oxygen demands were determined in the
laboratory. In addition, the reflectance values of the water samples taken by
the local spectral measurements were examined in order to use as end members
for spectral classification. CHRIS Proba is used as satellite image.  Match filtering (MF), spectral angle mapping
(SAM) and spectral information divergence (SID) methods have been used as the spectral
classification methods and it has been examined which method gives better
results in determining water quality. According to the results, it is
understood that SAM method provides better classification accuracy than other
methods.

Anahtar Kelimeler

Water quality, Spectral Angle Mapper, Match Filtering, Hyperspectral remote sensing, Spectral Information Divergence, CHRIS Proba

Su Kalitesinin Sınıflandırılmasında Spektral Sınıflandırma Yöntemlerinin Karşılaştırılması

Öz

Günümüzde, uzaktan algılama ve yersel bileşenleri kullanılarak su
kalitesi ve su kirliliğinin tespiti yapılabilmektedir. Uzaktan algılama, su
kalitesini ve kirliğini tespit etmede hızlı bir çözüm sunmakla kalmaz aynı
zamanda düşük maliyetli de olabilmektedir. Çalışma kapsamında Sivas bölgesinin
en önemli su kaynaklarından biri olan Kızılırmak nehrinin İmranlı bölgesinin ve
nehir üzerinde bulunan İmranlı barajının su kalitesi spektral sınıflandırma
yöntemleri ile incelenmiştir. Nehir ve barajda çeşitli noktalardan su
numuneleri alınıp bunların laboratuvarda kimyasal oksijen içerikleri tespit
edilmiştir. Buna ek olarak alınan su numunelerinin yersel spektral ölçümlerle
yansıtım değerlerine bakılmıştır. Ölçülen yansıtımlar uç üye olarak alınıp
spektral sınıflandırmada referans olarak kullanılmıştır. Uydu görüntüsü olarak
ise CHRIS Proba kullanılmıştır. Spektral sınıflandırma yöntemleri olarak ise
eşleşen filtreleme (MF), spektral açı haritalama (SAM) ve spektral bilgi ayrımı
(SID) yöntemleri kullanılmış olup hangi yöntemin su kalitesi tespitinde daha
iyi sonuç verdiği irdelenmiştir. Sonuçlara göre SAM yönteminin diğer yöntemlere
göre daha iyi sınıflandırma doğruluğu sağladığı anlaşılmıştır.

Anahtar Kelimeler

Su kalitesi, Hiperspektral uzaktan algılama, CHRIS Proba, Spektral açı haritalama, Spektral bilgi ayrımı, Eşleşen filtreleme

Kaynakça

• [1]. Karaman M.,Budakoğlu M., Uca Avcı Z.D., Özelkan E., Bülbül A., Civas M., Tasdelen S., Determination of Seasonal Changes İn Wetlands Using Chris/PROBA Hyperspectral Satellite Images: A Case Study From Acıgöl (Denizli), Turkey, Journal of Environmental Biology., 36 (2015) 73 – 83.
• [2]. Akbulut A. and Akbulut N., The Study of Heavy Metal Pollution and Accumulation in Water, Sediment, and Fish Tissue in Kizilirmak River Basin in Turkey, Environmental Monitoring and Assessment., 167 (2010) 521-526.
• [3]. Taş B., Investigation of Water Quality of Derbent Dam Lake (Samsun) Ekoloji., 60 (2006) 6-15.
• [4]. Giardino C., Candiani G., Bresciani M., Lee Z., Gagliano S., Pepe M., BOMBER: A Tool for Estimating Water Quality and Bottom Properties from Remote Sensing Images, Computers and Geosciences., 45 (2012) 313-318.
• [5]. Olmanson L.G., Brezonik P.L., Bauer M.E., Airborne Hyperspectral Remote Sensing to Assess Spatial Distribution of Water Quality Characteristics in Large Rivers: The Mississippi River and its Tributaries in Minnesota, Remote Sensing of Environment. 130 (2013) 254-265.
• [6]. Kaya Ş., Başar U.G., Karaca M., Şeker D.Z., Assessment of Urban Heat Islands Using Remotely Sensed Data, Ekoloji., 21 (2012) 107-113.
• [7]. Torgersen C.E., Faux R.N., Mcintosh B.A., Poage N.J., Norton D.J., Airborne Thermal Remote Sensing for Water Temperature Assessment in Rivers and Streams, Remote Sensing of Environment., 76 (2001) 386-398.
• [8]. Mattikalli N.M., Richards K.S., Estimation of Surface Water Quality Changes in Response to Land Use Change: Application of the Export Coefficient Model Using Remote Sensing and Geographical Information System, Journal of Environmental Management., 48 (1996) 263-282.
• [9]. Giardino C., Candiani G., Bresciani M., Lee Z., Gagliano S., Pepe M. BOMBER: A tool for estimating water quality and bottom properties from remote sensing images. Computers and Geosciences 2012; 45: 313-318.
• [10]. Urbanski A., Wochna J., Bubak A., Grzybowski I., Matuszewska W.L., Lącka K., Śliwińska M., Wojtasiewicz B., ZajączkowskI B., Application of Landsat 8 Imagery to Regional-Scale Assessment of Lake Water Quality, International Journal of Applied Earth Observation and Geoinformation., 51 (2016) 28-36.
• [11]. Dlamini S., Nhapi I., Gumindoga W., Nhiwatiwa T., Dube T., Assessing the Feasibility of Integrating Remote Sensing and In-Situ Measurements in Monitoring Water Quality Status of Lake Chivero, Zimbabwe, Physics and Chemistry of the Earth., 93 (2016) 2-11.
• [12]. Chawira M., Dube T., Gumindoga W., Remote Sensing Based Water Quality Monitoring in Chivero and Manyame Lakes of Zimbabwe, Physics and Chemistry of the Earth., 66 (2013) 38-44.
• [13]. Umar M., Rhoads B.L., Greenberg J.A., Use of Multispectral Satellite Remote Sensing to Assess Mixing of Suspended Sediment Downstream of Large River Confluences, Journal of Hydrology., 556 (2018) 325-338.
• [14]. Kaya Ş., Şeker D.Z., Tanik A., Temporal Impact of Urbanization on the Protection Zones of Two Drinking Water Reservoirs in Istanbul, Fresenius Environmental Bulletin., 23 (2014) 2984-2989.
• [15]. Marquez L.C.G., Bejarano F.M.T., Espinoza A.C.T., Rodríguez I.R.H., Use of LANDSAT 8 Images for Depth and Water Quality Assessment of El Guájaro Reservoir, Colombia, Journal of South American Earth Sciences., 82 (2018) 231-238.
• [16]. Masocha M., Murwira A., Magadza C.H.D., Hirji R., Dube T., Remote Sensing of Surface Water Quality in Relation to Catchment Condition in Zimbabwe, Physics and Chemistry of the Earth., 100 (2017) 13-18.
• [17]. Olmanson L.G., Brezonik P.L., Finlay J.C., Bauer M.E., Comparison of Landsat 8 and Landsat 7 for Regional Measurements of CDOM and Water Clarity in Lakes, Remote Sensing of Environment., 185 (2016) 119-128.
• [18]. Kiefer I., Odermatt D., Anneville O., Wüest A., Bouffard D., Application of Remote Sensing for the Optimization of In-Situ Sampling for Monitoring of Phytoplankton Abundance in a Large Lake, Science of the Total Environment., 527 (2015) 493-506.
• [19]. Dörnhöfer K. and Oppelt N., Remote Sensing for Lake Research and Monitoring - Recent advances, Ecological Indicators., 64 (2016) 105-122.
• [20]. Lotfinasabasl S., Gunale V.R., Khosroshahi M., Applying Geographic Information Systems and Remote Sensing for Water Quality Assessment of Mangrove Forest, Acta Ecologica Sinica., 38 (2018) 135-143.
• [21]. Rostom N.G., Shalaby A.A., Issa Y.M., Afifi A.A., Evaluation of Mariut Lake Water Quality Using Hyperspectral Remote Sensing and Laboratory Works, The Egyptian Journal of Remote Sensing and Space Science., 20 (2017) 39-48.
• [22]. Abdelmalik, K. W., Role of Statistical Remote Sensing for Inland Water Quality Parameters Prediction, Egyptian Journal of Remote Sensing and Space Science., (2016); doi 10.1016/j.ejrs.2016.12.002: 1-8.
• [23]. Rautiainen M., Lang M., Mõttus M., Kuusk A., Nilson T., Kuusk J., Lükk T. Multi-angular reflectance properties of a hemiboreal forest: An analysis using CHRIS PROBA data. Remote Sensing of Environment 2008; 112: 2627–2642.
• [24]. Chan Wai J.C., Beckers P., Spanhove T., Vanden Borre J., An Evaluation of Ensemble Classifiers for Mapping Natura 2000 Heathland in Belgium Using Spaceborne Angular Hyperspectral (CHRIS/Proba) Imagery, International Journal of Applied Earth Observation and Geoinformation., 18 (2012) 13-22.
• [25]. Demarchi L., Chan J.C.W., Ma J., Canters F., Mapping Impervious Surfaces from Superresolution Enhanced CHRIS/Proba Imagery Using Multiple Endmember Unmixing, ISPRS Journal of Photogrammetry and Remote Sensing., 72 (2012) 99-112.
• [26]. Republic of Turkey, Ministry of Environment and Urbanization, Laws on Management of Water Pollution www.csb.gov.tr/db/cygm/editordosya/YON-25687SKKY.docx URL (accessed on 15.02.2014).
• [27]. Gürsoy Ö., Birdal A.C., Özyonar F., Kasaka E., Determining And Monitoring The Water Quality Of Kizilirmak River Of Turkey: First Results, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015 36th International Symposium on Remote Sensing of Environment, 11-15 May 2015, Berlin, Germany. [27] Millán V.E.G., Sanchez-Azofeifa G.A., Malvárez G.C., Mapping Tropical Dry Forest Succession with CHRIS/PROBA Hyperspectral Images Using Nonparametric Decision Trees, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing., 8 (2015) 3081-3094.
• [28]. Gürsoy Ö., Canbaz O., Gökçe A., Atun R., Spectral Classification in Lithological Mapping; A Case Study of Matched Filtering, Cumhuriyet Science Journal., 38 (2017) 731-737.
• [29]. Park B., Windham W.R., Lawrence K.C., Smith D.P., Contaminant Classification of Poultry Hyperspectral Imagery Using a Spectral Angle Mapper Algorithm, Biosystems Engineering., 96 (2017) 323-333.
• [30]. Girouard G. and Bannari A., Validated Spectral Angle Mapper Algorithm for Geological Mapping: Comparative Study Between QuickBird and Landsat-TM. XXth ISPRS Congress Istanbul, 2004: 599-604.
• [31]. Chang C., Spectral Information Divergence for Hyperspectral Image Analysis, IEEE International Geoscience and Remote Sensing Symposium Hamburg, 1999: 509-511.