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(2006). A support vector method for anomaly detection in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(8), 2282–2291.\nChen, Y., Nasrabadi, N. M., & Tran, T. D. (2011a). Hyperspectral image classification using dictionary-based sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 49(10), 3973–3985.\nChen, Y., Nasrabadi, N. M., & Tran, T. D. (2011b). Sparse representation for target detection in hyperspectral imagery. IEEE Journal of Selected Topics in Signal Processing, 5(3), 629–640.\nDu, Q., Ren, H., & Chang, C. I. (2003). A comparative study for orthogonal subspace projection and constrained energy minimization. Maryland Univ Baltimore Dept of Computer Science and Electrical Engineering.\nFauvel, M., Benediktsson, J. A., Chanussot, J., & Sveinsson, J. R. (2008). Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 46(11), 3804–3814.\nGehler, P., & Nowozin, S. (2009). On feature combination for multiclass object classification. In 2009 IEEE 12th International Conference on Computer Vision, pp. 221–228.\nHuang, X., & Zhang, L. (2013). An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 51(1), 257–272.\nKraut, S., & Scharf, L. L. (1999). The CFAR adaptive subspace detector is a scale-invariant GLRT. IEEE Transactions on Signal Processing, 47(9), 2538–2541.\nLi, J., Zhang, H., Huang, Y., & Zhang, L. (2014). Hyperspectral image classification by nonlocal joint collaborative representation with a locally adaptive dictionary. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3707–3719.\nLi, W., & Seshia, S. A. (2013). Sparse coding for specification mining and error localization. In Runtime Verification, pp. 64–81.\nLicciardi, G. A., & Chanussot, J. (2015). Nonlinear PCA for visible and thermal hyperspectral images quality enhancement. IEEE Geoscience and Remote Sensing Letters, 12(6), 1228–1231.\nLin, H.-T., Lin, C.-J., & Weng, R. (2007). A note on Platt’s probabilistic outputs for support vector machines. Machine Learning, 68(3), 267–276.\nQian, D., Hsuan, R., & Chein, I. C. (2003). A comparative study for orthogonal subspace projection and constrained energy minimization. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1525–1529.\nScharf, L. L., & Friedlander, B. (1994). Matched subspace detectors. IEEE Transactions on Signal Processing, 42(8), 2146–2157.\nTropp, J., & Wright, S. J. (2010). Computational methods for sparse solution of linear inverse problems. Proceedings of the IEEE, 98(6), 948–958.\nWeldon, T. P., Higgins, W. E., & Dunn, D. F. (1996). Efficient Gabor filter design for texture segmentation. Pattern Recognition, 29(12), 2005–2015.\nWright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Yi, M. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227.\nYang, M., Zhang, L., Zhang, D., & Wang, S. (2012). Relaxed collaborative representation for pattern classification. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2224–2231.\nZhang, L., Yang, M., & Feng, X. (2011). Sparse representation or collaborative representation: Which helps face recognition?. In 2011 IEEE International Conference on Computer Vision (ICCV), pp. 471–478.\nZhang, L., Yang, M., Feng, X., Ma, Y., & Zhang, D. (2012a). Collaborative representation based classification for face recognition. arXiv preprint arXiv:1204.2358.\nZhang, L., Zhang, L., Tao, D., & Huang, X. (2012b). On combining multiple features for hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 50(3), 879–893.\nZhao, C., Li, X., Ren, J., & Marshall, S. (2013). Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery. International Journal of Remote Sensing, 34(24), 8669–8684.\nZhao, C., Li, W., Sanchez-Azofeifa, G. A., Qi, B., & Cui, B. (2016). Improved collaborative representation model with multitask learning using spatial support for target detection in hyperspectral imagery. Journal of Applied Remote Sensing, 10(1), 016009.\nZheng, X., Sun, X., Fu, K., & Wang, H. (2013). Automatic annotation of satellite images via multifeature joint sparse coding with spatial relation constraint. IEEE Geoscience and Remote Sensing Letters, 10(4), 652–656.",{"EN":128},"In this paper, we propose a collaborative representation-based binary hypothesis model with multi-features learning (CRTDBH-MTL) for target detection in hyperspectral imagery. The proposed method contained the following aspects. First, two complementary features extracted by different algorithms are implemented for describing hyperspectral imageries. Next, we apply these features into the unified collaborative representation-based binary hypothesis model (CRTDBH) to acquire a collaborative vector (CV) for each feature. Once the CV is obtained, the sample can be sparsely represented by the training samples from the background-only dictionary under the null hypothesis and the training samples from the target and background dictionaries under the alternative hypothesis. Finally, spatial correlation and spectral similarity of adjacent neighboring pixels are exploited to improve the detection performance. The experimental results suggest that the proposed algorithm shows an outstanding detection performance.",{"EN":130},"Collaborative Representation-Based Binary Hypothesis Model with Multi-features Learning for Target Detection in Hyperspectral Imagery",{"VOID":132},"10.1007\u002Fs12524-018-0752-8","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12524-018-0752-8",[138,156],{"id":139,"sortIndex":21,"researcher":20,"roles":140,"affiliations":142,"properties":153},"991fc933-aa35-4445-8f94-aa30aa1eb47d",[141],"AUTHOR",[143],{"id":20,"sortIndex":21,"affiliation":144,"properties":20},{"id":145,"createTime":146,"updateTime":147,"relativeEntities":148,"slug":149,"properties":150,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"98cf18ad-a429-4af2-907c-cccec694dfef","2023-12-08T05:36:50.293+00:00","2024-12-04T12:35:23.936+00:00",[],"College-of-Information-and-Communication-Engineering-Harbin-Engineering-University-Harbin-China",{"title":151},{"VI":152},"College of Information and Communication Engineering, Harbin Engineering University, Harbin, China",{"title":154},{"VI":155},"Chunhui Zhao",{"id":157,"sortIndex":108,"researcher":20,"roles":158,"affiliations":159,"properties":175},"49921cf7-82ac-4f5f-b56d-a06c71c76e50",[141],[160,165],{"id":20,"sortIndex":21,"affiliation":161,"properties":20},{"id":145,"createTime":146,"updateTime":147,"relativeEntities":162,"slug":149,"properties":163,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":164},{"VI":152},{"id":166,"sortIndex":108,"affiliation":167,"properties":174},"35360b02-af3a-4ff3-81bb-eb8720d2cb11",{"id":168,"createTime":169,"updateTime":169,"relativeEntities":170,"slug":20,"properties":171,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"55a5f3eb-eebd-4eea-9c8c-ac4be3bbb296","2024-02-10T20:26:11.739+00:00",[],{"title":172},{"VI":173},"Department of Earth and Atmospheric Sciences, Alberta Centre for Earth Observation Sciences, University of Alberta, Edmonton, Canada",{},{"title":176},{"VI":177},"Wei Li","ARTICLE",{"url":136,"publisher":180,"properties":208},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":181,"slug":10,"properties":182,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":186,"manageAffiliations":187,"indexDatabases":188,"url":103,"thumbnailPath":20,"statistic":203,"gsStatistic":20,"type":113,"analyzePriority":20},[],{"issn":183,"eissn":184,"title":185},{"VOID":13},{"VOID":15},{"EN":17},[],[],[189,196],{"id":84,"indexDatabase":190,"url":97,"indexYears":98,"academicFieldIds":195,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":191,"label":192,"description":193,"key":94,"publicationTags":194,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"id":64,"indexDatabase":197,"url":79,"indexYears":20,"academicFieldIds":202,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":198,"label":199,"description":200,"key":75,"publicationTags":201,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"impactFactor":21,"impactFactorByYear":204,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":106,"totalPublicationByYear":205,"totalCitation":21,"totalCitationByYear":206,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":207,"hindexLast5Year":21,"hindex":21},{},{"1975":108,"1978":108,"1980":108,"1983":108,"1985":108,"1989":108,"1996":109,"2002":108,"2009":109,"2010":108,"2011":109,"2013":109,"2014":109,"2015":108,"2016":59,"2017":108,"2018":110,"2019":108,"2020":109,"2021":109,"2022":59,"2023":110,"2024":108},{},{},{"volume":209,"pages":211},{"VOID":210},"46",{"VOID":212},"847-862","2018-05-22",2018,false,{"id":217,"createTime":218,"updateTime":218,"relativeEntities":219,"slug":20,"properties":220,"entityType":133,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":229,"fullTextUrl":20,"authors":230,"publicationType":178,"publisherRelationship":270,"citationCount":20,"citationInfo":20,"publishDate":304,"publishYear":305,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":215},"ac2d0530-b3a9-4024-b8e9-eb4798d70597","2024-02-15T23:59:05.287+00:00",[],{"references":221,"abstract":223,"title":225,"doi":227},{"VOID":222},"Chauhan, P., Mohan, M., Sarangi, R. K., Kumari, B. and Nayak, S. (2002). Surface chlorophyll-a estimation in the Bay of Bengal using IRS-P4 ocean colour monitor (OCM) satellite data.International journal of Remote Sensing,23: 1663–1676.\nChauhan, P., Nagamani, P. V. and Nayak, S. (2005). Artificial neural network (ANN) based algorithms for chlorophyll estimation in Arabian Sea.Indian Journal of Marine Sciences,34: 368–373.\nCipollini, P., Corsini, G., Diani, M. and Grasso, R. (2001). Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks.IEEE Transactions on Geoscience and Remote Sensing,39: 1508–1524.\nGordon, H.R. (1997). Atmospheric correction of ocean colour imagery in the earth observing system era,Journal of Geophysical Research,102: 17081–17106.\nGordon, H.R. and Morel, A. (1983). Remote Assessment of ocean color for interpretation of satellite visible imagery: a review. In Lecture Notes on Coastal and Estuarine Studies, Vol.4, M. Bowmen (ed.), Spinger-Verlag, 1-114.\nGross, L., Thiria, S., Frouin, R. and Mitchell, B.G. (2000). Artificial neural networks for modeling the transfer function between marine reflectance and phytoplankton chlorophyll-a concentration.Journal of Geophysical Research,105: 3483–3495.\nHu, Y. H. and Hwang J. N. (2002). Handbook of Neural Network Signal Processing. Edited by Y.H. Hu and J. N. Hwang, CRC press, Florida.\nIOCCG (2000). Remote Sensing of Ocean Colour in Coastal, and other optically complex waters, Satyendranath S. (ed.). Report s of the International Ocean-Colour Coordinating Group, No. 3, IOCCG, Dartmouth, Canada.\nKeiner, L.E. and Brown, C.W. (1999). Estimating oceanic chlorophyll concentrations with neural networks.International Journal of Remote Sensing,120: 189–194.\nLavender, S.J. and Nagur, C.R.C. (2002). Mapping coastal waters with high-resolution imagery: atmospheric correction of multi-height airborne imagery.Applied Optics,4: 50–55.\nMorel, A. (1991). Light and marine photosynthesis: A spectral model with geochemical and climatological implications.Progress in Oceanography,26: 263–306.\nO’Reilly, J.E., Maritorena, S., Mitchell, B.G., Scigal, D.A., Carder, K.L., Graver, S.A., Kahru, M. and McClain, C.R. (1998). Ocean colour chlorophyll algorithms for SeaWiFS.Journal of Geophysical Research,98: 22827–22841.\nRuddick, K.G., Ovidio, F. and Rijkeboer, M. (2000). Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters.Applied Optics,39: 897–912.\nSiegel, D.A., Wang M.H., Maritorena, S. and Robinson, W. (2000). Atmospheric correction of satellite ocean colour imagery: the black pixel assumption.Applied Optics,39:3582–3591.\nTanaka, A., Kishino, M., Doerffer, R., Schiller, H.,Oishi, T. and Kubota, T. (2004). Development of a neural network algorithm for retrieving concentrations of chlorophyll, suspended sediment matter and yellow substance from radiance data of the ocean colour and temperature scanner.Journal of Ocenaography,50: 519–530.",{"EN":224},"An artificial neural network (ANN) based chlorophyll-a algorithm was developed to estimate chlorophyll-a concentration using OCEANSAT-I Ocean Colour Monitor (OCM) satellite-data. A multi-layer perceptron (MLP) type neural network was trained using simulated reflectances (~60,000 spectra) with known chlorophyll-a concentration, corresponding to the first five spectral bands of OCM. The correlation coefficient(r\n                2) andRMSE for the log transformed training data was found to be 0.99 and 0.07, respectively. The performance of the developed ANN-based algorithm was tested with the global SeaWiFS Bio-optical Algorithm Mini Workshop (SeaBAM) data (~919 spectra), 0.86 and 0.13 were observed asr\n                2 andRMSE for the test data set. The algorithm was further validated with thein-situ bio-optical data collected in the northeastern Arabian Sea (~215 spectra), ther\n                2 andRMSE were observed as 0.87 and 0.12 for this regional data set. Chlorophyll-a images were generated by applying the weight and bias matrices obtained during the training, on the normalized water leaving radiances (nL\n                W) obtained from the OCM data after atmospheric correction. The chlorophyll-a image generated using ANN based algorithm and global Ocean Chlorophyll-4 (OC4) algorithm was compared. Chlorophyll-a estimated using both the algorithms showed a good correlation for the open ocean regions. However, in the coastal waters the ANN algorithm estimated relatively smaller concentrations, when compared to OC4 estimated chlorophyll-a.\n              ",{"EN":226},"Estimation of chlorophyll-A concentration using an artificial neural network (ANN)-based algorithm with oceansat-I OCM data",{"VOID":228},"10.1007\u002FBF03013488","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF03013488",[231,246,258],{"id":232,"sortIndex":108,"researcher":20,"roles":233,"affiliations":234,"properties":243},"b80a8f13-c1d4-4280-8b3d-3d2358ed82ac",[141],[235],{"id":20,"sortIndex":21,"affiliation":236,"properties":20},{"id":237,"createTime":238,"updateTime":238,"relativeEntities":239,"slug":20,"properties":240,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"de91c2ac-6e4c-42aa-808f-0bdd2a060802","2024-02-15T23:59:05.347+00:00",[],{"title":241},{"VI":242},"Marine and Coastal Environment Division, Marine and Earth Sciences Group, Space Applications Centre (ISRO), Ahmedabad, India",{"title":244},{"VI":245},"Prakash Chauhan",{"id":247,"sortIndex":109,"researcher":20,"roles":248,"affiliations":249,"properties":255},"8f8acaf7-9aea-497e-a0b3-34dd24d38621",[141],[250],{"id":20,"sortIndex":21,"affiliation":251,"properties":20},{"id":237,"createTime":238,"updateTime":238,"relativeEntities":252,"slug":20,"properties":253,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":254},{"VI":242},{"title":256},{"VI":257},"R. M. Dwivedi",{"id":259,"sortIndex":21,"researcher":20,"roles":260,"affiliations":261,"properties":267},"83da4836-0955-4817-9f4b-01267a0d3850",[141],[262],{"id":20,"sortIndex":21,"affiliation":263,"properties":20},{"id":237,"createTime":238,"updateTime":238,"relativeEntities":264,"slug":20,"properties":265,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":266},{"VI":242},{"title":268},{"VI":269},"P. V. Nagamani",{"url":229,"publisher":271,"properties":299},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":272,"slug":10,"properties":273,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":277,"manageAffiliations":278,"indexDatabases":279,"url":103,"thumbnailPath":20,"statistic":294,"gsStatistic":20,"type":113,"analyzePriority":20},[],{"issn":274,"eissn":275,"title":276},{"VOID":13},{"VOID":15},{"EN":17},[],[],[280,287],{"id":84,"indexDatabase":281,"url":97,"indexYears":98,"academicFieldIds":286,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":282,"label":283,"description":284,"key":94,"publicationTags":285,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"id":64,"indexDatabase":288,"url":79,"indexYears":20,"academicFieldIds":293,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":289,"label":290,"description":291,"key":75,"publicationTags":292,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"impactFactor":21,"impactFactorByYear":295,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":106,"totalPublicationByYear":296,"totalCitation":21,"totalCitationByYear":297,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":298,"hindexLast5Year":21,"hindex":21},{},{"1975":108,"1978":108,"1980":108,"1983":108,"1985":108,"1989":108,"1996":109,"2002":108,"2009":109,"2010":108,"2011":109,"2013":109,"2014":109,"2015":108,"2016":59,"2017":108,"2018":110,"2019":108,"2020":109,"2021":109,"2022":59,"2023":110,"2024":108},{},{},{"volume":300,"pages":302},{"VOID":301},"35",{"VOID":303},"201-207","2007-09-01",2007,{"id":307,"createTime":308,"updateTime":309,"relativeEntities":310,"slug":311,"properties":312,"entityType":133,"verifyStatus":134,"verifyTime":309,"verifyNote":135,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":321,"fullTextUrl":20,"authors":322,"publicationType":178,"publisherRelationship":417,"citationCount":20,"citationInfo":20,"publishDate":451,"publishYear":452,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":215},"6deeac59-747d-4d5e-adfd-51542b9b9105","2024-01-27T16:07:57.568+00:00","2025-02-14T23:59:02.711+00:00",[],"Description-of-Salient-Features-Combined-with-Local-Self-Similarity-for-SAR-Image-Registration",{"references":313,"abstract":315,"title":317,"doi":319},{"VOID":314},"Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4), 509–522.\nCanny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.\nChatfield, K., Philbin, J., & Zisserman, A. (2009). Efficient retrieval of deformable shape classes using local self-similarities. IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops 2009), 264–271.\nDa Cunha, A. L., Zhou, J., & Do, M. N. (2006). The nonsubsampled contourlet transform: theory, design, and applications. IEEE Transactions on Image Processing, 15(10), 3089–3101.\nFreeman, W. T., & Adelson, E. H. (1991). The design and use of steerable filters. IEEE Transactions on Pattern Analysis & Machine Intelligence, 9, 891–906.\nGoshtasby, A. A. (2005). 2-D and 3-D image registration: for medical, remote sensing, and industrial applications. John Wiley & Sons.\nHarris, C., & Stephens, M. (1988). A combined corner and edge detector. In Alvey Vision Conference, 15, 147–151.\nKrig, S. (2014). Computer vision metrics: Survey, taxonomy, and analysis. Apress.\nLe Moigne, J., Netanyahu, N. S., & Eastman, R. D. (2011). Image registration for remote sensing. Cambridge University Press.\nLiu, J., & Zeng, G. (2012). Description of interest regions with oriented local self-similarity. Optics Communications, 285(10), 2549–2557.\nLiu, J., Zeng, G., & Fan, J. (2012). Fast local self-similarity for describing interest regions. Pattern Recognition Letters, 33(9), 1224–1235.\nLowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.\nMaes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997). Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16(2), 187–198.\nMindru, F., Tuytelaars, T., Van Gool, L., & Moons, T. (2004). Moment invariants for recognition under changing viewpoint and illumination. Computer Vision and Image Understanding, 94(1), 3–27.\nPalenichka, R. M., & Zaremba, M. B. (2010). Automatic extraction of control points for the registration of optical satellite and lidar images. IEEE Transactions on Geoscience and Remote Sensing, 48(7), 2864–2879.\nPei, S. C., & Lin, C. N. (1995). Image normalization for pattern recognition. Image and Vision Computing, 13(10), 711–723.\nShechtman, E., & Irani, M. (2007). Matching local self-similarities across images and videos. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’07), 1–8.\nSmith, S. M., & Brady, J. M. (1997). SUSAN—a new approach to low level image processing. International Journal of Computer Vision, 23(1), 45–78.\nTeague, M. R. (1980). Image analysis via the general theory of moments. JOSA, 70(8), 920–930.\nTorabi, A., & Bilodeau, G. A. (2011). Local self-similarity as a dense stereo correspondence measure for themal-visible video registration. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011, 61–67.\nTorabi, A., & Bilodeau, G. A. (2013a). Local self-similarity-based registration of human rois in pairs of stereo thermal-visible videos. Pattern Recognition, 46(2), 578–589.\nTorabi, A., & Bilodeau, G. A. (2013b). A LSS-based registration of stereo thermal–visible videos of multiple people using belief propagation. Computer Vision and Image Understanding, 117(12), 1736–1747.\nTuytelaars, T., & Mikolajczyk, K. (2008). Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, 3(3), 177–280.\nWunsch, P., & Laine, A. F. (1995). wavelet descriptors for multiresolution recognition of handprinted characters. Pattern Recognition, 28(8), 1237–1249.\nYang, L. J., Tian, Z., & Zhao, W. (2014). A new affine invariant feature extraction method for sar image registration. International Journal of Remote Sensing, 35(20), 7219–7229.\nYao, S., Pan, S., Wang, T., Zheng, C., Shen, W., & Chong, Y. (2015). A new pedestrian detection method based on combined hog and lss features. Neurocomputing, 151, 1006–1014.\nYe, Y., & Shan, J. (2014). A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences. ISPRS Journal of Photogrammetry and Remote Sensing, 90, 83–95.\nZitova, B., & Flusser, J. (2003). Image registration methods: a survey. Image and Vision Computing, 21(11), 977–1000.",{"EN":316},"Local feature descriptor plays an important role in image representation and is helpful to further image processing. This paper proposes a local feature descriptor based registration method for synthetic aperture radar (SAR) images. The proposed method starts with identifying evenly distributed features by applying the divided salient image disk (SID) extraction method. To describe the shape content of local neighborhood, local self-similarity (LSS) descriptor is built in the local normalized region with a suitable size for every detected feature. Finally, the correspondence is found by measuring the similarity between LSS descriptors. The registration experiments on SAR images demonstrate that the proposed method can be applied to SAR image registration.",{"EN":318},"Description of Salient Features Combined with Local Self-Similarity for SAR Image Registration",{"VOID":320},"10.1007\u002Fs12524-016-0584-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12524-016-0584-3",[323,338,350,383,395],{"id":324,"sortIndex":109,"researcher":20,"roles":325,"affiliations":326,"properties":335},"2c971d1a-dd17-48c3-9a1d-3a09ef868bf0",[141],[327],{"id":20,"sortIndex":21,"affiliation":328,"properties":20},{"id":329,"createTime":330,"updateTime":330,"relativeEntities":331,"slug":20,"properties":332,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"40b07fda-d206-45dd-8391-b82ad9b83644","2024-01-17T03:30:58.155+00:00",[],{"title":333},{"VI":334},"Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an, People’s Republic of China",{"title":336},{"VI":337},"Wei Zhao",{"id":339,"sortIndex":59,"researcher":20,"roles":340,"affiliations":341,"properties":347},"8a942431-4bec-442e-865c-eadd5d2e232e",[141],[342],{"id":20,"sortIndex":21,"affiliation":343,"properties":20},{"id":329,"createTime":330,"updateTime":330,"relativeEntities":344,"slug":20,"properties":345,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":346},{"VI":334},{"title":348},{"VI":349},"Jinhuan 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China",{},{"id":365,"sortIndex":109,"affiliation":366,"properties":374},"efe487a6-ac42-44aa-8452-fcf7a64bf8e4",{"id":367,"createTime":368,"updateTime":368,"relativeEntities":369,"slug":370,"properties":371,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"611c8267-56b7-4518-bf86-2650525aa42d","2024-04-21T04:37:42.124+00:00",[],"State-Key-Laboratory-of-Remote-Sensing-Science-Beijing-People-s-Republic-of-China",{"title":372},{"EN":373},"State Key Laboratory of Remote Sensing Science, Beijing, People’s Republic of China",{},{"id":20,"sortIndex":21,"affiliation":376,"properties":20},{"id":329,"createTime":330,"updateTime":330,"relativeEntities":377,"slug":20,"properties":378,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":379},{"VI":334},{"title":381},{"VI":382},"Zheng 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China",{},{"title":415},{"VI":416},"Lijuan Yang",{"url":321,"publisher":418,"properties":446},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":419,"slug":10,"properties":420,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":424,"manageAffiliations":425,"indexDatabases":426,"url":103,"thumbnailPath":20,"statistic":441,"gsStatistic":20,"type":113,"analyzePriority":20},[],{"issn":421,"eissn":422,"title":423},{"VOID":13},{"VOID":15},{"EN":17},[],[],[427,434],{"id":84,"indexDatabase":428,"url":97,"indexYears":98,"academicFieldIds":433,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":429,"label":430,"description":431,"key":94,"publicationTags":432,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"id":64,"indexDatabase":435,"url":79,"indexYears":20,"academicFieldIds":440,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":436,"label":437,"description":438,"key":75,"publicationTags":439,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"impactFactor":21,"impactFactorByYear":442,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":106,"totalPublicationByYear":443,"totalCitation":21,"totalCitationByYear":444,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":445,"hindexLast5Year":21,"hindex":21},{},{"1975":108,"1978":108,"1980":108,"1983":108,"1985":108,"1989":108,"1996":109,"2002":108,"2009":109,"2010":108,"2011":109,"2013":109,"2014":109,"2015":108,"2016":59,"2017":108,"2018":110,"2019":108,"2020":109,"2021":109,"2022":59,"2023":110,"2024":108},{},{},{"volume":447,"pages":449},{"VOID":448},"45",{"VOID":450},"131-138","2016-05-05",2016,{"id":454,"createTime":455,"updateTime":455,"relativeEntities":456,"slug":20,"properties":457,"entityType":133,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":466,"fullTextUrl":20,"authors":467,"publicationType":178,"publisherRelationship":498,"citationCount":20,"citationInfo":20,"publishDate":532,"publishYear":452,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":215},"321bced9-cd20-459a-b7a3-78ff07b5b2cb","2023-12-28T23:57:21.155+00:00",[],{"references":458,"abstract":460,"title":462,"doi":464},{"VOID":459},"Asafou, A. (2002). Environmental economy for non economists. Translated by siavash dehghanian and Zakaria farajzadeh, Mashhad (p. 45). Iran: Ferdousi University Press.\nAydin Yonca, N., Kentel, E., & Duzgun, S. (2010). GIS-based environmental asseeement of wind energy system for spatial planinng case study from wentern Turkey. Renewable & Sustainable Energy Reviews, 14(1), 364–373.\nBaban, S., & Parry, T. (2001). Developing and applying a GIS-assisted approach to locating wind farms in the UK. Renewable Energy, 24(1), 59–74.\nBirgit, S. (2011). Evaluation of Ecological Capability in Iran. Environmental Studies, 39(2), 75-- 86.\nDjamai, M., & KASBADJI Merzouk, N. (2011). Wind farm feasibility study and site selection in Adrar, Algeria. Energy Procedia, 6, 136–142.\nEastman, R. J. (2006). Guide To GIS And Image Processing. (p. 328). Massachusetts: USA Clark University Press.\nGipe, P. (1995). Wind energy comes of Age (p. 560). New York: Wiley.\nGorsevski, P. V., Cathcart, S. C., Mirzaei, G., Jamali, M. M., Ye, X., & Gomezdelcampo, E. (2013). A group-based spatial decision support system for wind farm site selection in northwest Ohio. Energy Policy, 55, 374–385.\nHansen, H. (2005). GIS-based multicriteria analysis of wind farm development, Scan GIS’2005: Proceedings of the 10th Scandinavian Research Conference on Geographical Information Science. Department of Planning and Environment, pp. 75–87.\nHydrocarbon Balance Sheet of Iran. (2005). International Institute of Energy, Ministry of Petroleum Republic of Iran, p. 400. (In Persian)\nJoselin Herbert, G. M., Iniyan, S., & Amutha, D. (2014). A review of technical issues on the development of wind farms. Renewable & Sustainable Energy Reviews, 32, 619–641.\nJozi, S. A., Aghapour, P., Poshtegal, M. K., & Zaredar, N. (2010). Presentation of strategic management plan in ecotourism development through SWOT (case study: qeshm island). Journal of Food, Agriculture and Environment, 8(2), 1123–1132.\nKheirkhah Zarkesh, M. M., Ghoddusi, J., Zaredar, N., Soltani, M. J., Jafari, S., & Ghadirpour, A. (2010). Application of spatial analytical hierarchy process model in land use planning. Journal of Food, Agriculture and Environment, 8(2), 970–975.\nKim, J. Y., Oh, K. Y., Kang, K. S., & Lee, J. S. (2013). Site selection of offshore wind farms around the Korean peninsula through economic evaluation. Renewable Energy, 54, 189–195.\nMalczewski, J. (1999). GIS And Multi Criteria Decission Analysis. Translated By: Parhizkar, A., Gilandeh, A.G. (p. 597). Tehran, Iran: Samt Publication.\nQazvin Meteorological organization, (2007). Available at: http:\u002F\u002Fwww.qazvinmet.ir\u002F?type=static&lang=1&id=40\nRezaei-Moghaddam, K., & Karami, E. (2004). A multiple criteria evaluation of sustainable agricultural development models using AHP. Environment, Development and Sustainability, 10(4), 407–426.\nTahmoriyan, F. (2007). Principles Of Environmental Management. (p. 216). Tehran, Iran: Fadak Press.\nUyan, M. (2013). GIS-based solar farms site selection using analytic hierarchy process (AHP) in karapinar region, Konya\u002FTurkey. Renewable & Sustainable Energy Reviews, 28, 11–17.\nVan Haaren, R., & Fthenakis, V. (2011). GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): evaluating the case for New York State. Renewable & Sustainable Energy Reviews, 15(7), 3332–3340.\nYunna, W., & Geng, S. (2014). Multi-criteria decision making on selection of solar–wind hybrid power station location: a case of China. Energy Conversion and Management, 81, 527–533.\nYun-na, W., Yi-sheng, Y., Tian-tian, F., Li-na, K., Wei, L., & Luo-jie, F. (2013). Macro-site selection of wind\u002Fsolar hybrid power station based on ideal matter-element model. International Journal of Electrical Power & Energy Systems, 50, 76–84.\nZadmehdi, J. (2006). Analysis Of Wind Data Stations Located In The Sepidroud Valleys And The Plains Of Qazvin. Renewable Energy Of Iran, p. 135. (In Persian)",{"EN":461},"The aim of this study was to determine suitable areas for construction of wind farm in Takestan Plain as one of the main wind-prone areas in Qazvin Province. To this end, one of the widely-used multi criteria decision making techniques (Analytical Hierarchy Process (AHP)) was used. The site selection criteria were divided into three main groups of environmental, technical and geographical. Further, buffer zones around wind farms were considered as constraint and excluded from site selection analysis. After weighting the criteria, wind speed was recognized as the top-priority criterion while slope and distance from population centers were ranked second and third, respectively. The obtained results indicated that the western areas of Takistan County, over an area of 433 ha, have a great potential for establishment of a wind farm.",{"EN":463},"Application of Multi Criteria Decision-Making Technique in Site Selection of Wind Farm- a Case Study of Northwestern Iran",{"VOID":465},"10.1007\u002Fs12524-015-0517-6","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12524-015-0517-6",[468,483],{"id":469,"sortIndex":21,"researcher":20,"roles":470,"affiliations":471,"properties":480},"c3ea98b9-0d66-4080-8515-d1cb963f902f",[141],[472],{"id":20,"sortIndex":21,"affiliation":473,"properties":20},{"id":474,"createTime":475,"updateTime":475,"relativeEntities":476,"slug":20,"properties":477,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"8bbbf464-cc16-4eec-8522-4b1a1d188b3c","2023-12-28T23:57:21.172+00:00",[],{"title":478},{"VI":479},"Department of Environment, Islamic Azad University, Shahrood, Iran",{"title":481},{"VI":482},"Sahar Rezaian",{"id":484,"sortIndex":108,"researcher":20,"roles":485,"affiliations":486,"properties":495},"6c6f2c17-640d-4869-acc6-bbcfc218f95f",[141],[487],{"id":20,"sortIndex":21,"affiliation":488,"properties":20},{"id":489,"createTime":490,"updateTime":490,"relativeEntities":491,"slug":20,"properties":492,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"59d1f85a-1e29-463e-8c33-5ced5c1fd11c","2023-12-28T23:57:21.186+00:00",[],{"title":493},{"VI":494},"Department of Environment, Islamic Azad University, Tehran, Iran",{"title":496},{"VI":497},"Seyed Ali Jozi",{"url":466,"publisher":499,"properties":527},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":500,"slug":10,"properties":501,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":505,"manageAffiliations":506,"indexDatabases":507,"url":103,"thumbnailPath":20,"statistic":522,"gsStatistic":20,"type":113,"analyzePriority":20},[],{"issn":502,"eissn":503,"title":504},{"VOID":13},{"VOID":15},{"EN":17},[],[],[508,515],{"id":84,"indexDatabase":509,"url":97,"indexYears":98,"academicFieldIds":514,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":510,"label":511,"description":512,"key":94,"publicationTags":513,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"id":64,"indexDatabase":516,"url":79,"indexYears":20,"academicFieldIds":521,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":517,"label":518,"description":519,"key":75,"publicationTags":520,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"impactFactor":21,"impactFactorByYear":523,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":106,"totalPublicationByYear":524,"totalCitation":21,"totalCitationByYear":525,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":526,"hindexLast5Year":21,"hindex":21},{},{"1975":108,"1978":108,"1980":108,"1983":108,"1985":108,"1989":108,"1996":109,"2002":108,"2009":109,"2010":108,"2011":109,"2013":109,"2014":109,"2015":108,"2016":59,"2017":108,"2018":110,"2019":108,"2020":109,"2021":109,"2022":59,"2023":110,"2024":108},{},{},{"volume":528,"pages":530},{"VOID":529},"44",{"VOID":531},"803-809","2016-02-16",{"id":534,"createTime":535,"updateTime":536,"relativeEntities":537,"slug":538,"properties":539,"entityType":133,"verifyStatus":134,"verifyTime":536,"verifyNote":135,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":548,"fullTextUrl":20,"authors":549,"publicationType":178,"publisherRelationship":633,"citationCount":20,"citationInfo":20,"publishDate":667,"publishYear":668,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":215},"30685523-4fed-4ce9-85da-8f9dd15e0b69","2023-12-07T09:21:20.954+00:00","2024-12-09T23:56:36.953+00:00",[],"A-Novel-Approach-to-Estimate-Diffuse-Attenuation-Coefficients-for-QuickBird-Satellite-Images-A-Case-Study-at-Kish-Island-the-Persian-Gulf",{"references":540,"abstract":542,"title":544,"doi":546},{"VOID":541},"Bierwirth, P., Lee, T., & Burne, R. (1993). Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery. Photogrammetric Engineering and Remote Sensing; (United States), 59(3).\nBoss, E., & Zaneveld, J. R. V. (2003). The effect of bottom substrate on inherent optical properties: evidence of biogeochemical processes. Limnology and Oceanography, 346–354.\nBrock, J. C., Wright, C. W., Kuffner, I. B., Hernandez, R., & Thompson, P. (2006). Airborne lidar sensing of massive stony coral colonies on patch reefs in the northern Florida reef tract. Remote Sensing of Environment, 104(1), 31–42.\nCuevas-Jiménez, A., Ardisson, P. L., & Condal, A. R. (2002). Mapping of shallow coral reefs by colour aerial photography. International Journal of Remote Sensing, 23(18), 3697–3712. doi:10.1080\u002F01431160110075640.\nElvidge, C., Dietz, J., Berkelmans, R., Andréfouët, S., Skirving, W., Strong, Tuttle, B. (2004). Satellite observation of Keppel Islands (Great Barrier Reef) 2002 coral bleaching using IKONOS data. Coral Reefs, 23(1). doi:10.1007\u002Fs00338-003-0364-8.\nHolden, H., & Ledrew, E. (1999). Hyperspectral identification of coral reef features. International Journal of Remote Sensing, 20(13), 2545–2563. doi:10.1080\u002F014311699211921.\nHolden, H., & LeDrew, E. (2002). Measuring and modeling water column effects on hyperspectral reflectance in a coral reef environment. Remote Sensing of Environment, 81(2), 300–308.\nHolden, H., & LeDrew, E. (2008). An examination of variability in vertical radiometric profiles in a coral reef environment. Journal of Coastal Research, 241, 224–231. doi:10.2112\u002F05-0446.1.\nJupp, D. L. B. (1988). Background and extensions to depth of penetration (DOP) mapping in shallow coastal waters. Proceedings of the Symposium on Remote Sensing of the Coastal Zone, Gold Coast, Queensland, pp. IV.2.1–IV.2.19.\nKabiri, K., Pradhan, B., Rezai, H., Ghobadi, Y., & Moradi, M. (2012). Fluctuation of sea surface temperature in the Persian Gulf and its impact on coral reef communities around Kish Island. Colloquium on Humanities, Science & Engineering Research (CHUSER 2012), December 2012, Kota Kinabalu, Sabah, Malaysia, 164–167.\nKabiri, K., Pradhan, B., Samimi-Namin, K., & Moradi, M. (2012b). Detecting coral bleaching, using QuickBird multi-temporal data: a feasibility study at Kish Island, the Persian Gulf. Estuarine, Coastal and Shelf Science, 117, 273–281. doi:10.1016\u002Fj.ecss.2012.12.006.\nKarpouzli, E., Malthus, T. J., & Place, C. J. (2004). Hyperspectral discrimination of coral reef benthic communities in the western Caribbean. Coral Reefs, 23(1), 141–151.\nKaufman, Y. J., Wald, A. E., Remer, L. A., Gao, B. C., Li, R. R., & Flynn, L. (1997). The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5), 1286–1298.\nLyzenga, D. R. (1978). Passive remote-sensing techniques for mapping water depth and bottom features. Applied Optics, 17, 379–383.\nLyzenga, D. R. (1981). Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International Journal of Remote Sensing, 2, 71–82.\nMatthew, M. W., Adler-Golden, S. M., Berk, A., Richtsmeier, S. C., Levine, R. Y., Bernstein, L. S., et al. (2000). Status of atmospheric correction using a MODTRAN4-based algorithm: DTIC document.\nMishra, D. R., Narumalani, S., Rundquist, D., & Lawson, M. (2005). Characterizing the vertical diffuse attenuation coefficient for downwelling irradiance in coastal waters: Implications for water penetration by high resolution satellite data. ISPRS Journal of Photogrammetry and Remote Sensing, 60(1), 48–64. doi:10.1016\u002Fj.isprsjprs.2005.09.003.\nMishra, D., Narumalani, S., Rundquist, D., & Lawson, M. (2006). Benthic habitat mapping in tropical marine environments using QuickBird multispectral data. Photogrammetric Engineering and Remote Sensing, 72(9), 1037.\nMueller, J. L. (2000). SeaWiFS algorithm for the diffuse attenuation coefficient, K (490), using water-leaving radiances at 490 and 555 nm. SeaWiFS Postlaunch Calibration and Validation Analyses, part 3(11), 24–27.\nMumby, P. J., & Edwards, A. J. (2000). Water column correction approaches. In E. P. Green, P. J. Mumby, A. J. Edwards, & C. D. Clark (Eds.), Remote sensing handbook for tropical coastal management. Paris: Unesco. 316 pp.\nNagamani, P. V., Chauhan, P., Sanwlani, N., & Ali, M. M. (2012). Artificial Neural Network (ANN) based inversion of benthic substrate bottom type and bathymetry in optically shallow waters—initial model results. Journal of the Indian Society of Remote Sensing, 40(1), 137–143. doi:10.1007\u002Fs12524-011-0142-y.\nPope, R. M., & Fry, E. S. (1997). Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements. Applied Optics, 36(33), 8710. doi:10.1364\u002FAO.36.008710.\nPurkis, S. J., & Pasterkamp, R. (2004). Integrating in situ reef-top reflectance spectra with Landsat TM imagery to aid shallow-tropical benthic habitat mapping. Coral Reefs, 23(1), 5–20. doi:10.1007\u002Fs00338-003-035.\nStumpf, R. P., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48(1), 547–556.\nThanikachalam, M., & Ramachandran, S. (2003). Shoreline and coral reef ecosystem changes in gulf of Mannar, Southeast coast of India. Journal of the Indian Society of Remote Sensing, 31(3), 157–173. doi:10.1007\u002Fbf03030823.\nWashington, M., Kirui, P., Cho, H. J., & Wafo-Soh, C. (2012). Data-driven correction for light attenuation in shallow waters. Remote Sensing Letters, 3(4), 335–342. doi:10.1080\u002F01431161.2011.597791.\nWerdell, P. J., & Bailey, S. W. (2005). An improved bio-optical data set for ocean color algorithm development and satellite data product validation. Remote Sensing of Environment, 98(1), 122–140.\nWerdell, P. J., & Roesler, C. S. (2003). Remote assessment of benthic substrate composition in shallow waters using multispectral reflectance. Limnology and Oceanography, 557–567.",{"EN":543},"Diffuse attenuation coefficient (k\n                \n                  d\n                ) is a critical parameter for benthic habitat mapping using remotely sensed data. This research attempted to develop a new approach to estimate k\n                \n                  d\n                 in blue and green bands of QuickBird satellite image based on the integration of Lyzenga’s method and updated NASA-k\n                \n                  d\n                \n                \n                  490\n                 algorithm. To do this, the Lyzenga’s method was utilized to determine the ratio of k\n                \n                  d\n                 in different bands of QuickBird satellite image. Additionally, NASA-k\n                \n                  d\n                \n                \n                  490\n                 algorithm was applied to determine k\n                \n                  d\n                \n                \n                  490\n                 by using remotely sensed reflectance values of blue (R\n                \n                  rs\n                \n                \n                  Blue\n                ) and green (R\n                \n                  rs\n                \n                \n                  Green\n                ) bands in each pixel of QuickBird satellite image. Since the aforementioned algorithm has been developed for other types of sensors, an approach using weighted mean value of parameters for SeaWiFS, MERIS, VIIRS, and OCTS sensors were employed to estimate parameter values for QuickBird image. After determining the k\n                \n                  d\n                \n                \n                  490\n                 values as k\n                \n                  d\n                 for blue band, the k\n                \n                  d\n                 values for green and red bands were subsequently obtained by using Lyzenga’s method. Then, Mumby and Edwards’ method was employed as evidence to evaluate the accuracy of the results achieved from newly developed approach. Eventually, the maximum likelihood classifier was implemented during pre and post correction steps to examine the capability of the proposed approach. The final results proved to be consistent in the areas deeper than 2 m between estimated k\n                \n                  d\n                 values using the proposed approach and the results obtained from Mumby and Edwards’ method. On the other hand, the values estimated for extremely shallow areas seem to be overestimated. Furthermore, results demonstrated an increment of ~16 % in the overall accuracy of the classification.",{"EN":545},"A Novel Approach to Estimate Diffuse Attenuation Coefficients for QuickBird Satellite Images: A Case Study at Kish Island, the Persian Gulf",{"VOID":547},"10.1007\u002Fs12524-013-0293-0","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs12524-013-0293-0",[550,565,577,602,621],{"id":551,"sortIndex":109,"researcher":20,"roles":552,"affiliations":553,"properties":562},"35b42e26-b828-4965-8efc-21f50df947fe",[141],[554],{"id":20,"sortIndex":21,"affiliation":555,"properties":20},{"id":556,"createTime":557,"updateTime":557,"relativeEntities":558,"slug":20,"properties":559,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"76d72737-092a-487e-8a92-21791230da14","2023-12-07T09:21:21.035+00:00",[],{"title":560},{"VI":561},"Department of Civil Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia",{"title":563},{"VI":564},"Helmi Zulhaidi Mohd Shafri",{"id":566,"sortIndex":110,"researcher":20,"roles":567,"affiliations":568,"properties":574},"8c9dcb2e-64a4-4e3a-8aca-e411e424928a",[141],[569],{"id":20,"sortIndex":21,"affiliation":570,"properties":20},{"id":556,"createTime":557,"updateTime":557,"relativeEntities":571,"slug":20,"properties":572,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":573},{"VI":561},{"title":575},{"VI":576},"Shattri Bin Mansor",{"id":578,"sortIndex":59,"researcher":20,"roles":579,"affiliations":580,"properties":599},"b3ce89fe-8764-4f05-9e39-a83c9d621063",[141],[581,591],{"id":582,"sortIndex":108,"affiliation":583,"properties":590},"891e9031-8272-4b89-8afa-9ebd26b77897",{"id":584,"createTime":585,"updateTime":585,"relativeEntities":586,"slug":20,"properties":587,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"2546e6b8-2aa9-4a53-85c4-7a927d26f5c6","2024-01-20T02:27:22.350+00:00",[],{"title":588},{"VI":589},"Netherlands Centre for Biodiversity Naturalis, Leiden, The Netherlands",{},{"id":20,"sortIndex":21,"affiliation":592,"properties":20},{"id":593,"createTime":594,"updateTime":594,"relativeEntities":595,"slug":20,"properties":596,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"b104200f-aa1c-446e-9864-d0fcecec788a","2023-12-07T09:21:21.049+00:00",[],{"title":597},{"VI":598},"Department of Satellite Oceanography, Iranian National Institute for Oceanography, Tehran, Iran",{"title":600},{"VI":601},"Kaveh Samimi-Namin",{"id":603,"sortIndex":21,"researcher":20,"roles":604,"affiliations":605,"properties":618},"fdcd6a09-da5c-48b4-a14d-9cf56c014c04",[141],[606,613],{"id":607,"sortIndex":108,"affiliation":608,"properties":612},"11c98efc-3007-4027-b5d8-481bb2d82ebd",{"id":593,"createTime":594,"updateTime":594,"relativeEntities":609,"slug":20,"properties":610,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":611},{"VI":598},{},{"id":20,"sortIndex":21,"affiliation":614,"properties":20},{"id":556,"createTime":557,"updateTime":557,"relativeEntities":615,"slug":20,"properties":616,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":617},{"VI":561},{"title":619},{"VI":620},"Keivan 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J. R, Hardy, E. E, Roach, J. T. and Wirmer, R. E. (1976). A land use land cover classification system for use with remote sensing data.Professional Paper No. 964 USGS, Reston Verginia, 28p.\nKrishna, N. D. R., Westinga. W. and Huizing, H. (1999). Monitoring land cover changes using geoinformatics in some communal lands of Zimbabwe,Proceedings of Intern. Conf. On Geoinformatics: Beyond 2000, Dehradun, March 1999.\nLillesand, T. M. and Kieffer. R. W. (1994). Remote Sensing and Image Interpretation, 3rd] edition, John Wiley & Sons, Inc.\nMinakshi, R. C. and Sharma. P. K. (1999). Land use\u002F land cover mapping and change detection using satellite data — A case study of Dehlon Block, District Ludhiana, Punjab.,Jour. Ind. Soc. Remote Sensing,27(2): 115–121.\nRoy, P. S, Ranganath, B. K. Diwakar, P. G, Vohra, T. P. S, Bhan, S. K, Singh. I.J. and Pandian, V. C. (1991). Tropical forest type mapping and monitoring using remote sensing.Int. J. of Remote Sensing,12(11):2205–2225.\nSkidmore, A. K., Witske Bijker, Karin Schmidt and Lalit Kumar. 1997. Use of remote sensing and GIS for sustainable land management, ITC Journal,1997-3\u002F4, pp. 302–315.",{"EN":729},"Availability of remote sensing data from earth observation satellites has made it convenient to map and monitor land use\u002Fland cover at regional to local scales. A land cover map is very critical for a various planning activities including watershed planning. The spectral and spatial resolutions are major constraints for mapping the crop resources at microlevel. The cropping pattern zones have been mapped using the false color composite, physiography, irrigation and toposheets. The IRS LISS-III data is classified into various categories depending on spectral reflectance from crop canopy and are overlaid on cropping zones map. The re-classified resultant map provides land use\u002Fland cover information including dominant cropping systems. 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E. (1972). Coastal geomorphology of India, Orient Longman, New Delhi, 222p.\nAhmed, M. (1986). Analysis of LANDS AT imagery for mineral and water resources of Tamilnadu, Pro. Inat. Semi. Photos and Remote Sensing for Developed Countries. New Delhi, pp.74–91.\nArjan Rajasuriya, Maizan Hassan Maniku, Subramanian, B.R. and Jason Rubene (1999). Coral reef ecosystems in South Asia. In: Coral reef degradation in the Indian Ocean, (Eds.: Olof Linden and Niki Sporrong). CORDIO Publishers, Stockholm, Sweden, pp. 11–24.\nBahuguna, A. and Nayak, S.R. (1994). Mapping of the coral reefs of Tamilnadu using satellite data. Scientific Note: SAC\u002FRSA\u002FRSAG\u002FDOD-COS\u002FSN\u002F 07\u002F94, Space Application Center, Ahmedabad, India.\nBruckner, H. (1989). Late quaternary shorelines in India, In: Late Quaternary Sea-Level Correlation and Applications, (Eds.: D.B. Scott), pp. 169–194.\nChandrasekeran, N. (1996). Sediment transport along barrier islands and its impact on conservation of coral reef in the Gulf of Mannar. Proc. Reg. Seminar on Conservation of Coral Reefs in Gulf of Mannar.\nDhandapani, P. (1992). A study on the changes of coastal morphology of some island of Gulf of Mannar using satellite data, Proc. of the Silver Jubilee Seminar, URS, pp.250–253.\nDhandapani, P. (1997). The effect of human activities in the Gulf of Mannar Biosphere and the needed remedial measurements: A casestudy. In: Island Ecosystem and Sustainable Development (Eds: B. Gangwar and K. Chandra) National Science Association, pp.169#x2013;175.\nGrassle, J.F., Laserre, P., Mcintyre, A.D. and Ray, G.C. (1990). Marine biodiversity and ecosystem function. Biology International.,23:19p.\nGrigg, R.W. and Dollar, S.J. (1990). Natural and anthropogenic disturbance on coral reefs, In: Ecosystem of the World Coral Reefs (Eds.: Z. Dubinsky). Elsevier Science Publishers, New York,25:439–452.\nHolden and Ledrew, (1999). Hyperspectral identification of coral reef features. Inter. J. Remo. Sens,17:703–719.\nHubbard, D.K. and Scaturo, D. (1985). Growth rate of seven species of scleractinian corals from Cane Bay and Salt River. USVI Bulletin Marine Science,36:325–338.\nHubbard, D.K., Burke, R.B. and Gill, I.P. (1986). Style of reef accretion along a steep, shelf-edge reef. Jour. Sedimentary Petrology,56:848–861.\nJoshi, A.B. (1995). Coastal erosion-An overview. In: Course Material on Coastal Erosion, Protection and Coastal Zone Management, Beach Erosion Board, Ministry of Water Resources, Government of India, New Delhi,2:1–26.\nLoveson, V.J. and Rajamanickam, V.G. (1987). Coastal geomorphology of the south Tamilnadu, India. Proc. Nat. Symp. Remote Sensing in Land Transformation and Management, Hyderabad, (Eds.: S.K. Bhan and V.K. Jha). pp.115–129.\nLoveson, V.J. and Rajamanickam, G.V. (1988a). Progradation as evidenced around as evidenced around a submerged ancient port, Periapatnam, Tamilnadu, India. Int. J. Land. Sys. Eclo. Studies,12: 94–98.\nLoveson, V.J. and Rajamanickam, G.V. (1988b). Evidences for phenomena of emergence along southern Tamilnadu coast through remote sensing techniques. Tamil Civilization,5:80–90.\nMacintyre, I. (1988). Modern coral reefs of western Atlantic: New geologic prospective. Bull. American Asso. Petroleum Geology,72:1360–1369.\nMahadevan, S. and Nayar, K.N. (1972). Distribution of coral reefs in Gulf of Mannar and Palk Bay and their exploitation and utilization. Proc. Symp. Coral Reef, Mandapam, pp. 181 -190.\nMorelock, J., Grove, K. and Hernandez, M. (1983). Fish School, An Asset Coral Science,220:1047–1049.\nNayak S.R.et al. (1991). Manul for mapping of coastal wetlands\u002Flandforms and shoreline changes using satellite data. Technical Note: IRS-UP\u002FSAC\u002FMCE\u002F TN\u002F32\u002F91, Space Application Center, Ahmedabad, India.\nPillai, C.S.G. (1975). An assessment of the effect of environmental and human interference on coral reefs of Palk Bay and Gulf of Mannar along the Indian coast. Seafood Export Jour.,7:1–13.\nPillai, C.S.G. (1969). The distribution of coral on a reefs at Mandapam (Palk Bay) South India. Jour. Marine Biological Association, India, pp.62–72.\nRamanujam, N. and Mukesh, M.V. (1998). Geomorphology of Tuticorin Group of Island. In: Biodiversity of Gulf of Mannar Marine Biosphere Reserve. (Eds.: M. Rajeswari, K. Anand, Dorairaj and A. Parida). M.S. Swaminathan Research Foundation, Chennai. pp.32–37.\nRamanujam, N., Mukesh, M.V., Sabeen, H.M. and Preeja, N.B. (1995). Morphological variation in some islands in the Gulf of Mannar, India. Jour. Geol. Sur. India,45:703–708.\nRamasamy, S.M. (1996). Remote sensing and geomorphic processes modeling along Tamil Nadu coast, India. Int. Jour, of Remote Sensing (in press).\nRamasamy, S.M. (1990). Marine environment modeling using.thematic mapper data. NNRMS Bulletin,13: 43–44.\nRamasamy, S.M. (1989). Morpho- tectonic evolution of east and west coast of Indian peninsula. Geol. Sur. of India, Special Publish. Arabian Sea Seminar,24:333–339.\nRamasamy, S.M. and Balaji, S. (1995). Remote sensing and Pleistocene tectonics of Southern Indian Peninsula. Int. Jour, of Remote sensing, Taylors and Francis London,16(13): 2375–2391.\nRamasamy, S.M., Joyce, E.B and Ian Bishop (2001). Tectonically induced environmental problems on and off Pondicherry coast, Tamil Nadu, India-A vision throught remote sensing. 22nd Asian Conference on Remote Sensing, held at Singapore, Nov. 5-9,2001.\nSewell, R.B.S. (1935).Geographic and Occanographic research in Indian waters-VIII, Studies on corals and coral formations of Indian water. Mem. Asiat. Soc. Beng,9: 461–539.\nSilas, E.G. Mahadevan, S. and Nayar, N. (1985). Existing and proposed marine park and reserves in India-A review. Proc. Symp. Endangered Marine Animais and Marine Parks, Cochain, Marine Biological Society of India,3: 36p.\nSmith, S.V. and Buddemeier, (1993). Global change and coral reef ecosystems. Annual review of Ecological System,23: 89–118.\nStoddart, D.R. and Pillai, C.S.G. (1972). Raised reefs of Ramanathapuram district, south India. Trans. Institute, British Geographical Society,56:111–125.\nThanikachalam, M. and Ramachandran, S. (2002a). Remote Sensing and GIS Techniques for Monitoring and Conservation of Coral Reef in Gulf of Manar, Southeast Coast of India. Nat. Symp. Conservation of Eastern Gnats, held at Tirupathi from March 24-26, 2002, 112p.http:\u002F\u002Fwww.envis-eptri-org\u002Fimages\u002Fabstracts.pdf\nThanikachalam, M. and Ramachandran, S. (2002b). Management of coral reefs in Gulf of Mannar using Remote Sensing and GIS techniques-with reference to coastal geomorphology and landuse. Map Asia 2002, Asian conference on GIS, GPS, Aerial Photography and Remote Sensing, held at Bankok from August 7-9, 2002,http:\u002F\u002F www.gisdevelopment.net.\nThanikachalam, M. and Ramachandran, S. (2002c). Conservation of coral reefs in Gulf of Mannar: A remote sensing and GIS approach. Indian Society of Geomatics (ISG) Newsletter, Special Issue on Coastal & Marine Environment, 8(2&3): 65–71.\nThanikachalam, M. and Ramachandran, S. (2002d). Remote sensing and GIS techniques for mapping coastal geomorphology in Gulf of Mannar, southwest coast of Bay of Bengal. ISPRS Technical Commission VII Symposium on Resources and Environment Monitoring and ISRS Annual Convention, held at Hyderabad, India, pp.58–63.\nUNEP (1985). Environmental problems of the marine and coastal areas of India. National Report, UNEP Regional Seas Report and Studies, 53: 33p.\nUNEP\u002FIUCN (1993). Marine protected areas needs in the South Asian Seas region. (Eds: John C. Pernetta). IUCN Publishers, Gland, Switzerland,2: 23–37\nVenkataramanujam, K. and Santhanam, R. (1985). Coral reef fishery resources of Tuticorin (S. India). Proc. 5th Int. Coral Reef Congress, Tahiti,2: 391p.\nVenkataramanujam, K. and Santhanam, R. (1989). Gorgonian resources of the east coast of India and methods of their conservation. Proc. 4th Int. Coral Reef Symp. Manila,1:259–262.\nVenkataramanujam, R., Santhanam, R. and Sukamaran, N. (1981). Coral resources of Tuticorin (S. India) and methods of their conservation. Proc. 4th Int. Coral Reef Symp. Manila, pp.259–262.",{"EN":853},"Changes in shoreline, coral reef and seafloor have been mapped using remote sensing satellite data of IRS LISS-III (1998), IRS LISS-II (1988), Survey of India Topographic sheet (1969), Naval Hydrographic Chart (NHO) 1975 and bathymetry data (1999) with ARC-INFO and ARC-VIEW GIS. The analysis of multi-date shoreline maps showed that 4.34 and 23.49 km2 of the mainland coast and 4.14 and 3.31 km2 areas of island coast have been eroded and accreted, respectively, in the Gulf of Mannar. The analysis of multi-date coral reef maps showed that 25.52 km2 of reef area and 2.16 km2 of reef vegetation in Gulf of Mannar have been lost over a period of ten years. The analysis of multi-date bathymetry data indicates that the depth of seafloor has decreased along the coast and around the islands in the study area. The average reduction of depth in seafloor has been estimated as 0.51m over a period of twenty four years. The increased suspended sediment concentration due to coastal and island erosion, and raised reef due to emerging of coast by tectonic movement are responsible for coral reef degradation in the Gulf of Mannar. 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Parthasarathy Rao",{"url":20,"publisher":1008,"properties":1036},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1009,"slug":10,"properties":1010,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1014,"manageAffiliations":1015,"indexDatabases":1016,"url":103,"thumbnailPath":20,"statistic":1031,"gsStatistic":20,"type":113,"analyzePriority":20},[],{"issn":1011,"eissn":1012,"title":1013},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1017,1024],{"id":84,"indexDatabase":1018,"url":97,"indexYears":98,"academicFieldIds":1023,"indexDatabaseRanking":102},{"id":86,"createTime":87,"updateTime":88,"relativeEntities":1019,"label":1020,"description":1021,"key":94,"publicationTags":1022,"standard":20},[],{"EN":91,"VI":91},{"EN":91,"VI":93},[96],[100,101],{"id":64,"indexDatabase":1025,"url":79,"indexYears":20,"academicFieldIds":1030,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":1026,"label":1027,"description":1028,"key":75,"publicationTags":1029,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81,82],{"impactFactor":21,"impactFactorByYear":1032,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":106,"totalPublicationByYear":1033,"totalCitation":21,"totalCitationByYear":1034,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":1035,"hindexLast5Year":21,"hindex":21},{},{"1975":108,"1978":108,"1980":108,"1983":108,"1985":108,"1989":108,"1996":109,"2002":108,"2009":109,"2010":108,"2011":109,"2013":109,"2014":109,"2015":108,"2016":59,"2017":108,"2018":110,"2019":108,"2020":109,"2021":109,"2022":59,"2023":110,"2024":108},{},{},{"volume":1037,"pages":1039,"issue":1041},{"VOID":1038},"10",{"VOID":1040},"35-44",{"VOID":1042},"1",{"total":21,"publishYear":20,"statisticByYear":1044},{},"1982-06-01",1982,[1048,1051],{"id":20,"text":1049,"url":20,"identifiers":1050},"Ayyangar R.S. 1978. Experiment to evolve methods of separation and identification of Agricultural crops from multispectral information. Proc. 12th int. symp on Remote Sensing of Environment. Environmental Research Institute of Michigan, Ann Arbor, Michigan, U.S.A.",{},{"id":20,"text":1052,"url":20,"identifiers":1053},"Ayyangar R.S., M.V. Krishna Rao, PP. Nageswara Rao and SS Budwal 1979. Use of spectral band ratio for identifying crop stages in multispectral data analysis (unpublished)",{},{"id":1055,"createTime":1056,"updateTime":1057,"relativeEntities":1058,"slug":1059,"properties":1060,"entityType":133,"verifyStatus":134,"verifyTime":1057,"verifyNote":135,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1069,"fullTextUrl":20,"authors":1070,"publicationType":178,"publisherRelationship":1143,"citationCount":20,"citationInfo":20,"publishDate":1176,"publishYear":214,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":215},"c57aab33-d021-498f-8544-f565830833e9","2023-11-25T16:13:31.035+00:00","2024-12-08T23:54:19.253+00:00",[],"Application-of-Remote-Sensing-in-Monitoring-Unsustainable-Wetlands-Case-Study-Hamun-Wetland",{"references":1061,"abstract":1063,"title":1065,"doi":1067},{"VOID":1062},"Amiri, F., Rahdari, V., Maleki Najafabadi, S., Pradhan, B., & Tabatabaei, T. (2014). Multi-temporal landsat images based on eco-environmental change analysis in and around Chah Nimeh reservoir, Sistan and Balochestan (Iran). Environmental Earth Sciences, 72(3), 801–809.\nBaghdadi, N., Bernier, M., Gauthier, R., & Neeson, I. (2001). Evaluation of C-band SAR data for wetlands mapping. International Journal of Remote Sensing, 22, 71–88.\nBayley, P. B. (1995). Understanding large river: Floodplain ecosystems. BioScience, 45, 153–158.\nBerberoglu, S., Yilmaz, K. T., & Ozkan, C. (2004). Mapping and monitoring of coastal wetlands of Cukurova Delta in the Eastern Mediterranean region. Biodiversity and Conservation, 13, 615–633.\nBortels, L., Chan, J. C.-W., Merken, R., & Koedam, N. (2011). Long-term monitoring of wetlands along the Western-Greek Bird migration route using Landsat and ASTER satellite images: Amvrakikos Gulf (Greece). Journal for Nature Conservation, 19, 215–223.\nCardona, B. M., Ripolles, J., & Martinez, L. (2013). Wetland inundation monitoring by the synergistic use of ENVISAT\u002FASAR imagery and ancillary spatial data. Remote Sensing of Environment, 139, 171–184.\nCarter, V. (2009). Remote sensing for wetland mapping and inventory. Water International, 6(4), 177–185.\nCastaneda, C. J. H., Herrero, J., & Auxiliadora, C. (2005). Landsat monitoring of playa-lakes in the Spanish Monegros desert. Journal of Arid Environments, 63, 497–516.\nDahl, T. E. (2004). Remote sensing as a tool for monitoring wetland habitat change. In Monitoring science and technology symposium: Unifying knowledge or sustainability ill the western hemisphere.\nDong, Z., Wang, Z., Liu, D., Li, L., Ren, C., Tang, X., Jia, M., & Liu, C. (2013). Assessment of habitat suitability for waterbirds in the West Songnen Plain. Ecological Engineering, 55, 94–100.\nDownard, R., Endter-Wada, J., & Kettenring, K. M. (2014). Adaptive wetland management in an uncertain and changing arid environment. Ecology and Society, 19(2), 23.\nFeng, L., Chuanmin, H., Xiaoling, C., Xiaobin, C., Liqiao, T., & Wenxia, G. (2012). Assessment of inundation changes of Poyang Lake using MODIS observations between 2000 and 2010. Remote Sensing of Environment, 121, 80–92.\nFeng, X. Q., Zhang, G. X., & Yin, X. R. (2010). Study on the hydrological responses to climate change in Wuyuer River Basin on the SWAT model. Progress in Geography, 29, 827–832.\nGao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.\nGarcia, K., Lasco, R., Ines, A., Lyon, B., & Pulhin, F. (2013). Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines. Applied Geography, 44, 12–22.\nHalls, A. J. (1997). Wetlands, biodiversity and the Ramsar convention: The role of the convention on 3 wetlands in the conservation and wise use of biodiversity. Gland: Ramsar Convention Bureau.\nHuang, C., Peng, Y., Lang, M., Yeo, I.-Y., & McCarty, G. (2014). Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data. Remote Sensing of Environment, 141, 231–242.\nKassawmar, N. T., Rao, K. R. M., & Abraha, G. L. (2011). An integrated approach for spatio-temporal variability analysis of wetlands: A case study of Abaya and Chamo lakes, Ethiopia. Environmental Monitoring and Assessment, 180, 313–324.\nKerr, P. C., Martyr, R. C., Donahue, A. S., Hope, M. E., Westerink, J. J., & Luettich, R. A. (2013). U.S. IOOS Coastal and ocean modeling tested: Evaluation of tide, wave, and hurricane surge response sensitivities to mesh resolution and friction in the Gulf of Mexico. Journal of Geophysical Research: Oceans, 118, 4633–4661.\nMaleki, S., Soffianian, A. R., Koupaei, S. S., Saatchi, S., Pourmanafi, S., & Sheikholeslam, F. (2016). Habitat mapping as a tool for water birds conservation planning in an arid zone wetland: The case study Hamun wetland. Ecological Engineering, 95, 594–603.\nMwaniki, M. W., Moeller, M. S., Schellmann, G. (2015). A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualising lineaments: A case study of central region Kenya. In: 36th international symposium on remote sensing of environment, Berlin, Germany.\nOzesmi, S. L., & Bauer, M. (2002). Satellite remote sensing of wetlands. Wetlands Ecology and Management, 10, 381–402.\nPlug, L. J., Walls, C., & Scott, B. M. (2008). Tundra lake changes from 1978 to 2001 on the Tuktoyaktuk Peninsula, western Canadian Arctic. Geophysical Research Letters, 35, 03502.\nPowell, S. L., Cohen, W. B., Healey, S. P., Kennedy, R. E., Moisen, G. G., & Pierce, K. B. (2010). Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sensing of Environment, 114, 1053–1068.\nRahdari, V., Maleki Najafabadi, S., Afsari, K. H., Abtin, E., & Piri, H. (2012). Change detection of Hmoun wild life refuge using RS & GIS. Remote sensing and GIS Journal, 3(2), 5970.\nRogers, A. S., & Kearney, M. S. (2004). Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. International Journal of Remote Sensing, 25, 2317–2335.\nRoy, D., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., et al. (2016). Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185, 57–70.\nRuan, R., Feng, X., & She, Y. (2007). Fusion of RADARSAT SAR and ETM + imagery for identification of fresh waterwetland. In Proceedings of the SPIE 6752 (pp. 675221–675231). Nanjing: China.\nShamohammadi, Z., & Maleki, S. (2011). The Life of human. Yazd: Jahad Daneshgahi.\nThomas, R. F., Kingsford, R. T., Lu, Y., & Hunter, S. J. (2011). Landsat mapping of annual inundation (1979–2006) of the Macquarie Marshes in semi-arid Australia. International Journal of Remote Sensing, 32, 4545–4569.\nThorley, N., Clandillon, S., & Fraipont, P. De. (1997). The contribution of space borne SAR and optical data in5 monitoring flood events: Examples in northern and southern France. Hydrological Processes, 11, 1409–1413.\nWidis, D. C., BenDor, T. K., & Deegan, M. (2015). Prioritizing Wetland Restoration Sites: A review and application to a large-scale coastal restoration program. Ecological Restoration., 33(4), 358–377.\nZhang, L., Yang, J., Li, P., & Zhang, L. (2014). Seasonal inundation monitoring and vegetation pattern6 mapping of the Erguna floodplain by means of a RADARSAT-2 fully polarimetric time series. Remote Sensing of Environment, 152, 426–440.",{"EN":1064},"Monitoring wetland as one of the important parts of the global ecosystem is necessary for conservational programs. But, usually, collecting in situ data is restricted in these areas because of their remote locations, vast area and dynamic conditions. Remote sensing provides a cost effective tool to investigate hydrological patterns and the seasonal trend of changes in wetlands. In this paper, Land-use\u002Fland-cover change during water inundation period of Hamun wetland was investigated in order to determine change trend during this period. Hamun wetland is an unsustainable ecosystem, and monitoring this wetland is essential for conservation goals. This trend is critical for decision makers in order to plan the conservational scheme in all unsustainable ecosystems. To reach this objective, the land-use\u002Fland-cover maps during inundation period of Hamun were produced using Landsat 8 time series images. The results of accuracy assessment showed the classification of water and vegetation have the highest accuracy (94% and 93%, respectively). And the accuracy of plants in the water classes was the lowest (water–veg = 89.9%, veg–water 1 = 88.8%, veg–water 2 = 87.6%). This means the higher misclassification is in determining the vegetation in the water. Then, the changes in the land-cover classes in relation to wetland inundation were investigated. Results of land-use\u002Fland-cover change illustrate the regions that were suitable for water birds but lost their suitability when the wetland dried out. These areas are crucial for water bird’s conservation. Satellite data determined these areas with acceptable accuracy.",{"EN":1066},"Application of Remote Sensing in Monitoring Unsustainable Wetlands: Case Study Hamun Wetland",{"VOID":1068},"10.1007\u002Fs12524-018-0842-7","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs12524-018-0842-7",[1071,1087,1099,1114,1126],{"id":1072,"sortIndex":109,"researcher":20,"roles":1073,"affiliations":1074,"properties":1084},"58276925-f846-42d2-abec-ac63664c77bb",[141],[1075],{"id":20,"sortIndex":21,"affiliation":1076,"properties":20},{"id":1077,"createTime":1078,"updateTime":1078,"relativeEntities":1079,"slug":1080,"properties":1081,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"637a68aa-de6d-4434-b9be-a3fe79fb3925","2024-04-07T14:36:35.503+00:00",[],"Department-of-Natural-Resources-Isfahan-University-of-Technology-Isfahan-Iran",{"title":1082},{"VI":1083},"Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran",{"title":1085},{"VI":1086},"Saeid Soltani Koupaei",{"id":1088,"sortIndex":108,"researcher":20,"roles":1089,"affiliations":1090,"properties":1096},"b87c0912-b5f4-433d-b5c9-7e9bcf18f9be",[141],[1091],{"id":20,"sortIndex":21,"affiliation":1092,"properties":20},{"id":1077,"createTime":1078,"updateTime":1078,"relativeEntities":1093,"slug":1080,"properties":1094,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1095},{"VI":1083},{"title":1097},{"VI":1098},"Alireza 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