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Applicability of the Surface Water Extraction Methods Based on China’S GF-2 HD Satellite in Ussuri River, Tonghe County of Northeast China

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Applicability of the Surface Water Extraction Methods Based on China’S GF-2 HD Satellite in Ussuri River, Tonghe County of Northeast China p-ISSN: 0972-6268 Nature Environment and Pollution Technology (Print copies up to 2016) Vol. 19 No. 4 pp. 1537-1545 2020 An International Quarterly Scientific Journal e-ISSN: 2395-3454 Original Research Paper Originalhttps://doi.org/10.46488/NEPT.2020.v19i04.020 Research Paper Open Access Journal Applicability of the Surface Water Extraction Methods Based on China’s GF-2 HD Satellite in Ussuri River, Tonghe County of Northeast China Wenfeng Gong*, Tiedong Liu*†, Yan Jiang** and Philip Stott*** *College of Forestry, Hainan University, Haikou 570228, China **College of Hydraulic and Electrical Engineering, Heilongjiang University, Harbin 150086, China ***School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy 5371, Australia †Corresponding author: Tiedong Liu; [email protected] ABSTRACT Nat. Env. & Poll. Tech. Website: www.neptjournal.com Surface water is the most important and common water resource on earth. Accurate and effective Received: 03-12-2019 mapping and detecting of surface water have been made possible by remote sensing technology, high- Revised: 20-12-2019 resolution satellite data, playing an important role in surface water monitoring and mapping, which has Accepted: 01-03-2020 become the current hot research for water information extraction in recent decades. Therefore, in this paper, we tested and analysed four models to extract water bodies using China’s GF-2 HD satellite (GF- Key Words: 2) image, including Normalized Difference Water Index (NDWI), Modified Shadow Water Index (MSWI), Surface water extraction Support Vector Machine (SVM) and Object-Oriented Method (OOM). The results showed applying GF-2 satellite water extraction models can map surface water with an overall accuracy of 0.8935, 0.9256, 0.9467 and Water index 0.9357, respectively. SVM owns the highest overall accuracy value of 0.9467, followed by OOM. SVM Support vector machine performed significantly better at surface water extraction with kappa coefficients improved by 9.00%, 5.00%, and 2.00%, respectively, which yielded the best results and used to map surfaces water bodies in the study region, while index methods (NDWI and MSWI) are mostly classified into the water and non-water information based on a threshold value, with higher total omission and commission errors at 12.45%, 25.64%, 6.38% and12.87%, respectively. Therefore, we proposed SVM as the best algorithm to identify water body and effectively detect surface water from the GF-2 image. INTRODUCTION Lee et al. 2018). Waterbody information, as an important constituent of remote sensing image, has become the vi- Surface water is one of the vital components of the earth’s tal national geo-information and can be automatically or environment, which is not only the essential for the sur- semi-automatically extracted by integrating remote sensing vival of living beings (Vorosmarty et al. 2000), but also is data with geographic information systems (GIS). Meanwhile, the important basic information for land use/cover change in recent decades, accurate and effective extracting water (LUCC), climate changes, seasonal changes, and environ- from remote sensing data has become indispensable ways mental changes throughout of the world (Alamgir et al. 2016, for the development and utilization of water resources (Du Araral & Wu 2016). Therefore, knowledge of the spatial & Zhou 1998), which also becomes an important branch of distribution of surface water is imperative for assessment remote sensing applications. of water resources, watershed changes, land surface water Due to the ease of processing and obtaining satellite management and environmental monitoring (NRC 2008, Sun image data (Masocha et al. 2018), numerous surface water et al. 2012). Besides, timely monitoring and delivering data extraction algorithms have been developed and applied for re- on the dynamics of surface water are essential for policy and motely sensed imageries (Borton 1989), which focused on the decision-making processes (Frey et al. 2010), especially for following satellite sensors with the different spatial, temporal monitoring floods risk at an emergency. and spectral resolution, including the Moderate-Resolution Remote sensing has advantages of the macroscopic, Imaging Spectro-radio-meter (MODIS) (Khandelwal et al. real-time, periodic repeatability, dynamic access to the land 2017, Ovakoglou et al. 2016), Satellite Pour l’ Observation surface information (Lu et al. 2011), which can provide low- dela Terre (SPOT) (Ji et al. 2009), Advanced Spaceborne cost and reliable information for environmental changes at Thermal Emission and Reflection Radiometer (ASTER) local, regional, and global scales, with their long-collected (Huang et al. 2008 ), Advanced Very High Resolution Ra- repeatable and even real-time data (Melesse et al. 2007, diometer (AVHRR) (Zhou et al. 1996), Thematic Mapper 1538 Wenfeng Gong et al. esciy Ussuri Rir betwee i a ussia. series dataHence (MSS,TM,ETM+ i is paper and OLI) (Acharyaalgris et al. have 2018, e rsHence, in this r paper, iiyig the algorithms ar have is been iproposed AlanaziGF &-2 Ghrefa iui 2013, Alesheikhwater body et al. iex2007, Senay s et al. (Noforr identifyingie iferncewater bodies withater GF-2 Index, including NDWI water body 2016, Wang et al. 2018, Yang et al. 2010, Zhang et al. 2016), index methods (Normalized Difference Water Index, NDWI, and othersMoii (Lu et ha al. 2011, WarPeng et Indexal. 2018, WI Wang ),et surisal. Modified ssiiai Shadow Water s Index, MSWI), ur supervised ecr classi - 2012).Maci Besides, although many VM) studies a have rs been s conducted (Object fication-ri methods e (Support OOVector Machine The t method,i sSVM) most of large is surfacesuy waterar areto limitedpply tothe small ifere water bodies, meth sand t others i methods surace (Object-Oriented ter iri Method, OOM). y The such ascrig small ponds, i narrow war rivers, y and shallowi water at athe objectives detec of this studya are: tolan apply surace the different ar methods i edge of rivers, which accurately cannot be extracted due to to obtain the surface water information by comparing with the limitedrtheas spatial Ciresolution. regi uiiy a uitaivelywater body index, aci SVM and OOM, suia to detect and map land Inexrc the recent surace decade, ter with the r improvement he vaui of the spatial curcy. surface water in Northeast China region qualitatively and resolution, visiting time and spectral resolution of remote-sens- quantitatively, to achieve the suitable method to extract ing images,MATERIALS more and more high-resolutionAND METHODS satellite data can surface water from the evaluation accuracy. be widely applied to extract and map surface water, such as ChineseStudy Gaofen-1/2, Region Quick-Bird, WorldView, IKONOS, MATERIALS AND METHODS Rapid Eye and so on (Sawaya et al. 2003, Wasowski et al. Study Region 2012,The Tatar Ussuri et al. 2018). Rir In addition,is the ury the study on rir water betwee body i a ussia (ig. 1) suy area is extractionloca from at GF-1/2 the cunce series images has become Heiongiang a hot re- (AmurThe Ussuri ir River a is Ussuri the boundary Rir river i between elonged China and search topic rsury in China (Chen rgis et al. 2015, Li located et al. 2015, betwee Peng 47°53'Russia (Fig.-48°10' 1). The study iu area is located at the -confluence133° et al. 2018, Song et al. 2015), though several studies have of the Heilongjiang (Amur) River and Ussuri River, which tested9' the performance iu. of I water belongs indices using cine GF-2 sensor in so belonged imate to transboundary i aua regions rciii and located between China (Liu et al. 2019, Zou average et al. 2019). accuua However, this temperure new and 47°53’ is N-48°10’ res N latitude The suy and 132°47’ rgi E-133° is covere 9’ E longi - advancedby rs sensor has, i unfortunately, i blac not si been iexploited is to suia map tude. fr allIt belongs is to the cr continental cuii monsoon a climate re withr the surfacea swater at ur county scales uius in Northeast China, ras especially sattere iannual he precipitationi over ofed 532.7 i mm, rs the average levai accumulated at Ussuri River between China and Russia. temperature is 2435 degrees. The study region is covered by rnge r rgi rrai is i i suhwes a i ras Fi Fig. rapi 1: Geographic ocati location of of the study stu region. rion. Vol. 19,GF No.-2 4, Image 2020 • Nature Preprocessing Environment and Pollution Technology I suy th F-2 iages (L1A rus ii panchric iage i a rsui and ui-scr iage i rsui 4m, wer cuire September The rmeters ii he suy ra ollec r gi i Ta Meai ih-rsui si imagery o - go uity s cuy s or-recii y usi riona polyia coefii C iin rmote ssi sar ENVI . The verge r ea sur u as ss ha ies r ac iage Meai eac iage s rc UTM one i WGS- u gru r is wer i r sa aeri gr September iage a The g rmeters map, ui -sci ii da he suyre cairte ra ollec i r riri gi i airai Ta Meaitool i ENVI ih r-rsui rw iia si ur imagery (DN) o - surace go rfl ectanceuity us s a cuy satmsri or-recii rrci y usi was rionaaie polyia usi Fascoefii Li-of C-Sig spheri iin lysis rmote ssi sar ENVI . The verge r ea sur u as ss ha Specr yperus (FLAASH) ue. ies r ac iage Meai eac iage s rc UTM one i WGS- u gru r is wer i r sa aeri gr iage a gTabl m:ap, ajr ui rameters-sci da t GFre- cairteimage. i riri airai tool i ENVI Prametrr rw iia ur (DN) Rsoluisurace rfl romatic/rsoluiectance us a ulti -sctral ra atmsri rrciSpectral range was aie usi FasParomatic Li-of-Sig spheri lysis0.45-0.90μm Specr yperus (FLAASH) ue.
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