Satellite/sensor specification in terms of resolution, life span, level of atmospheric correction, and source of data.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"},{slug:"intechopen-s-chapter-awarded-the-guenther-von-pannewitz-preis-2020-20200715",title:"IntechOpen's Chapter Awarded the Günther-von-Pannewitz-Preis 2020"}]},book:{item:{type:"book",id:"7405",leadTitle:null,fullTitle:"Pattern Recognition - Selected Methods and Applications",title:"Pattern Recognition",subtitle:"Selected Methods and Applications",reviewType:"peer-reviewed",abstract:"Pattern recognition, despite its relatively short history, has already found practical application in many areas of human activity. Systems of pattern recognition usually support people in performing tasks related to ensuring security, including access to premises and devices, detection of unusual changes (e.g. in medicine, cartography, geology), diagnosing technical conditions of devices, and many others. Nevertheless, pattern recognition is probably the most developing area because of the great demand for such solutions in the different areas of our lives. In this book we have collected the experience of scientists from different parts of the world who have researched diverse areas connected directly or indirectly with pattern recognition. We hope that this book will be a treasure trove of knowledge and inspiration for further research in the field of pattern recognition.",isbn:"978-1-78985-500-5",printIsbn:"978-1-78985-499-2",pdfIsbn:"978-1-83880-395-7",doi:"10.5772/intechopen.75291",price:119,priceEur:129,priceUsd:155,slug:"pattern-recognition-selected-methods-and-applications",numberOfPages:118,isOpenForSubmission:!1,isInWos:null,hash:"a9c2940ad153eac48dbefe43bb4ea44c",bookSignature:"Andrzej Zak",publishedDate:"July 31st 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7405.jpg",numberOfDownloads:3931,numberOfWosCitations:0,numberOfCrossrefCitations:7,numberOfDimensionsCitations:8,hasAltmetrics:0,numberOfTotalCitations:15,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 18th 2018",dateEndSecondStepPublish:"July 6th 2018",dateEndThirdStepPublish:"September 4th 2018",dateEndFourthStepPublish:"November 23rd 2018",dateEndFifthStepPublish:"January 22nd 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,editors:[{id:"16539",title:"Dr.",name:"Andrzej",middleName:null,surname:"Zak",slug:"andrzej-zak",fullName:"Andrzej Zak",profilePictureURL:"https://mts.intechopen.com/storage/users/16539/images/system/16539.jpeg",biography:"Prof. Andrzej Zak is a scientific worker and Vice-Dean for Scientific Research at Faculty of Navigation and Naval Weapons of Polish Naval Academy in Gdynia. His area of research includes among others pattern recognition and signal processing in hydroacoustics, computer vision as well as dynamics identification and control of multidimensional objects, control a team of objects. Prof. Zak has published many articles and conference papers covering topics related directly or indirectly to pattern recognition.",institutionString:"Polish Naval Academy",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"3",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"521",title:"Machine Learning and Data Mining",slug:"computer-and-information-science-artificial-intelligence-machine-learning-and-data-mining"}],chapters:[{id:"66534",title:"Introductory Chapter: Pattern Recognition as Cognitive Process",doi:"10.5772/intechopen.85826",slug:"introductory-chapter-pattern-recognition-as-cognitive-process",totalDownloads:425,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Andrzej Zak",downloadPdfUrl:"/chapter/pdf-download/66534",previewPdfUrl:"/chapter/pdf-preview/66534",authors:[{id:"16539",title:"Dr.",name:"Andrzej",surname:"Zak",slug:"andrzej-zak",fullName:"Andrzej Zak"}],corrections:null},{id:"65108",title:"Formation of Inter-Frame Deformation Field of Images Using Reverse Stochastic Gradient Estimation",doi:"10.5772/intechopen.83489",slug:"formation-of-inter-frame-deformation-field-of-images-using-reverse-stochastic-gradient-estimation",totalDownloads:298,totalCrossrefCites:0,totalDimensionsCites:1,signatures:"Alexander Tashlinskii and Pavel Smirnov",downloadPdfUrl:"/chapter/pdf-download/65108",previewPdfUrl:"/chapter/pdf-preview/65108",authors:[null],corrections:null},{id:"65646",title:"Depth Extraction from a Single Image and Its Application",doi:"10.5772/intechopen.84247",slug:"depth-extraction-from-a-single-image-and-its-application",totalDownloads:1026,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Shih-Shuo Tung and Wen-Liang Hwang",downloadPdfUrl:"/chapter/pdf-download/65646",previewPdfUrl:"/chapter/pdf-preview/65646",authors:[null],corrections:null},{id:"67943",title:"Recurrent Level Set Networks for Instance Segmentation",doi:"10.5772/intechopen.84675",slug:"recurrent-level-set-networks-for-instance-segmentation",totalDownloads:413,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Thi Hoang Ngan Le, Khoa Luu, Marios Savvides, Kha Gia Quach and Chi Nhan Duong",downloadPdfUrl:"/chapter/pdf-download/67943",previewPdfUrl:"/chapter/pdf-preview/67943",authors:[null],corrections:null},{id:"65274",title:"Pattern Recognition and Its Application in Solar Radiation Forecasting",doi:"10.5772/intechopen.83503",slug:"pattern-recognition-and-its-application-in-solar-radiation-forecasting",totalDownloads:537,totalCrossrefCites:0,totalDimensionsCites:1,signatures:"Mahmoud Ghofrani, Rasool Azimi and Mastaneh Youshi",downloadPdfUrl:"/chapter/pdf-download/65274",previewPdfUrl:"/chapter/pdf-preview/65274",authors:[null],corrections:null},{id:"68127",title:"Diagnosis of Skin Lesions Based on Dermoscopic Images Using Image Processing Techniques",doi:"10.5772/intechopen.88065",slug:"diagnosis-of-skin-lesions-based-on-dermoscopic-images-using-image-processing-techniques",totalDownloads:836,totalCrossrefCites:6,totalDimensionsCites:6,signatures:"Ihab Zaqout",downloadPdfUrl:"/chapter/pdf-download/68127",previewPdfUrl:"/chapter/pdf-preview/68127",authors:[null],corrections:null},{id:"65444",title:"Novel Formulation of Parzen Data Analysis",doi:"10.5772/intechopen.83781",slug:"novel-formulation-of-parzen-data-analysis",totalDownloads:397,totalCrossrefCites:1,totalDimensionsCites:0,signatures:"David Horn",downloadPdfUrl:"/chapter/pdf-download/65444",previewPdfUrl:"/chapter/pdf-preview/65444",authors:[null],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},relatedBooks:[{type:"book",id:"5774",title:"Underwater Acoustics",subtitle:null,isOpenForSubmission:!1,hash:"f9be56d90357c40ec87f7a9fcaa3c5cf",slug:"advances-in-underwater-acoustics",bookSignature:"Andrzej Zak",coverURL:"https://cdn.intechopen.com/books/images_new/5774.jpg",editedByType:"Edited by",editors:[{id:"16539",title:"Dr.",name:"Andrzej",surname:"Zak",slug:"andrzej-zak",fullName:"Andrzej Zak"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5285",title:"Autonomous Vehicle",subtitle:null,isOpenForSubmission:!1,hash:"74b9f410b2b9b29a6f189c7e39095842",slug:"autonomous-vehicle",bookSignature:"Andrzej Zak",coverURL:"https://cdn.intechopen.com/books/images_new/5285.jpg",editedByType:"Edited by",editors:[{id:"16539",title:"Dr.",name:"Andrzej",surname:"Zak",slug:"andrzej-zak",fullName:"Andrzej Zak"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5327",title:"Data Mining and Knowledge Discovery in Real Life Applications",subtitle:null,isOpenForSubmission:!1,hash:"3e75b0e12e025762803bb937a6c6d459",slug:"data_mining_and_knowledge_discovery_in_real_life_applications",bookSignature:"Julio Ponce and Adem Karahoca",coverURL:"https://cdn.intechopen.com/books/images_new/5327.jpg",editedByType:"Edited by",editors:[{id:"18534",title:"Dr.",name:"Julio",surname:"Ponce",slug:"julio-ponce",fullName:"Julio Ponce"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5687",title:"Pattern Recognition",subtitle:"Techniques, Technology and Applications",isOpenForSubmission:!1,hash:"776a1270a14ebea65bf567dd6dfea1de",slug:"pattern_recognition_techniques_technology_and_applications",bookSignature:"Peng-Yeng Yin",coverURL:"https://cdn.intechopen.com/books/images_new/5687.jpg",editedByType:"Edited by",editors:[{id:"5693",title:"Prof.",name:"Peng-Yeng",surname:"Yin",slug:"peng-yeng-yin",fullName:"Peng-Yeng Yin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3762",title:"Pattern Recognition",subtitle:"Recent Advances",isOpenForSubmission:!1,hash:"3a36addbe6d14d5511152a8572e093b8",slug:"pattern-recognition-recent-advances",bookSignature:"Adam Herout",coverURL:"https://cdn.intechopen.com/books/images_new/3762.jpg",editedByType:"Edited by",editors:[{id:"3806",title:"Dr.",name:"Adam",surname:"Herout",slug:"adam-herout",fullName:"Adam Herout"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3733",title:"Pattern Recognition",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"pattern-recognition",bookSignature:"Peng-Yeng Yin",coverURL:"https://cdn.intechopen.com/books/images_new/3733.jpg",editedByType:"Edited by",editors:[{id:"5693",title:"Prof.",name:"Peng-Yeng",surname:"Yin",slug:"peng-yeng-yin",fullName:"Peng-Yeng Yin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5479",title:"Machine Learning",subtitle:null,isOpenForSubmission:!1,hash:"825720118b3343505f7184cdc8eacdd4",slug:"machine_learning",bookSignature:"Abdelhamid Mellouk and Abdennacer Chebira",coverURL:"https://cdn.intechopen.com/books/images_new/5479.jpg",editedByType:"Edited by",editors:[{id:"13633",title:"Prof.",name:"Abdelhamid",surname:"Mellouk",slug:"abdelhamid-mellouk",fullName:"Abdelhamid Mellouk"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5380",title:"Pattern Recognition",subtitle:"Analysis and Applications",isOpenForSubmission:!1,hash:"8295f3734b5a2292ab59813ccfe4579c",slug:"pattern-recognition-analysis-and-applications",bookSignature:"S. Ramakrishnan",coverURL:"https://cdn.intechopen.com/books/images_new/5380.jpg",editedByType:"Edited by",editors:[{id:"116136",title:"Dr.",name:"Srinivasan",surname:"Ramakrishnan",slug:"srinivasan-ramakrishnan",fullName:"Srinivasan Ramakrishnan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],ofsBooks:[]},correction:{item:{id:"74251",slug:"corrigendum-to-enhancing-soil-properties-and-maize-yield-through-organic-and-inorganic-nitrogen-and",title:"Corrigendum to: Enhancing Soil Properties and Maize Yield through Organic and Inorganic Nitrogen and Diazotrophic Bacteria",doi:null,correctionPDFUrl:"https://cdn.intechopen.com/pdfs/74251.pdf",downloadPdfUrl:"/chapter/pdf-download/74251",previewPdfUrl:"/chapter/pdf-preview/74251",totalDownloads:null,totalCrossrefCites:null,bibtexUrl:"/chapter/bibtex/74251",risUrl:"/chapter/ris/74251",chapter:{id:"71840",slug:"enhancing-soil-properties-and-maize-yield-through-organic-and-inorganic-nitrogen-and-diazotrophic-ba",signatures:"Arshad Jalal, Kamran Azeem, Marcelo Carvalho Minhoto Teixeira Filho and Aeysha Khan",dateSubmitted:"May 29th 2019",dateReviewed:"March 6th 2020",datePrePublished:"April 20th 2020",datePublished:"June 17th 2020",book:{id:"9345",title:"Sustainable Crop Production",subtitle:null,fullTitle:"Sustainable Crop Production",slug:"sustainable-crop-production",publishedDate:"June 17th 2020",bookSignature:"Mirza Hasanuzzaman, Marcelo Carvalho Minhoto Teixeira Filho, Masayuki Fujita and Thiago Assis Rodrigues Nogueira",coverURL:"https://cdn.intechopen.com/books/images_new/9345.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"76477",title:"Dr.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"190597",title:"Dr.",name:"Marcelo Carvalho Minhoto",middleName:null,surname:"Teixeira Filho",fullName:"Marcelo Carvalho Minhoto Teixeira Filho",slug:"marcelo-carvalho-minhoto-teixeira-filho",email:"mcm.teixeira-filho@unesp.br",position:null,institution:{name:"Sao Paulo State University",institutionURL:null,country:{name:"Brazil"}}},{id:"322298",title:"Dr.",name:"Aeysha",middleName:null,surname:"Khan",fullName:"Aeysha Khan",slug:"aeysha-khan",email:"fhw9uhfig@gmail.com",position:null,institution:null},{id:"322299",title:"Dr.",name:"Kamran",middleName:null,surname:"Azeem",fullName:"Kamran Azeem",slug:"kamran-azeem",email:"gisfgiog34sg@gmail.com",position:null,institution:null},{id:"322301",title:"Dr.",name:"Arshad",middleName:null,surname:"Jalal",fullName:"Arshad Jalal",slug:"arshad-jalal",email:"gisfgiog3465sg@gmail.com",position:null,institution:null}]}},chapter:{id:"71840",slug:"enhancing-soil-properties-and-maize-yield-through-organic-and-inorganic-nitrogen-and-diazotrophic-ba",signatures:"Arshad Jalal, Kamran Azeem, Marcelo Carvalho Minhoto Teixeira Filho and Aeysha Khan",dateSubmitted:"May 29th 2019",dateReviewed:"March 6th 2020",datePrePublished:"April 20th 2020",datePublished:"June 17th 2020",book:{id:"9345",title:"Sustainable Crop Production",subtitle:null,fullTitle:"Sustainable Crop Production",slug:"sustainable-crop-production",publishedDate:"June 17th 2020",bookSignature:"Mirza Hasanuzzaman, Marcelo Carvalho Minhoto Teixeira Filho, Masayuki Fujita and Thiago Assis Rodrigues Nogueira",coverURL:"https://cdn.intechopen.com/books/images_new/9345.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"76477",title:"Dr.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"190597",title:"Dr.",name:"Marcelo Carvalho Minhoto",middleName:null,surname:"Teixeira Filho",fullName:"Marcelo Carvalho Minhoto Teixeira Filho",slug:"marcelo-carvalho-minhoto-teixeira-filho",email:"mcm.teixeira-filho@unesp.br",position:null,institution:{name:"Sao Paulo State University",institutionURL:null,country:{name:"Brazil"}}},{id:"322298",title:"Dr.",name:"Aeysha",middleName:null,surname:"Khan",fullName:"Aeysha Khan",slug:"aeysha-khan",email:"fhw9uhfig@gmail.com",position:null,institution:null},{id:"322299",title:"Dr.",name:"Kamran",middleName:null,surname:"Azeem",fullName:"Kamran Azeem",slug:"kamran-azeem",email:"gisfgiog34sg@gmail.com",position:null,institution:null},{id:"322301",title:"Dr.",name:"Arshad",middleName:null,surname:"Jalal",fullName:"Arshad Jalal",slug:"arshad-jalal",email:"gisfgiog3465sg@gmail.com",position:null,institution:null}]},book:{id:"9345",title:"Sustainable Crop Production",subtitle:null,fullTitle:"Sustainable Crop Production",slug:"sustainable-crop-production",publishedDate:"June 17th 2020",bookSignature:"Mirza Hasanuzzaman, Marcelo Carvalho Minhoto Teixeira Filho, Masayuki Fujita and Thiago Assis Rodrigues Nogueira",coverURL:"https://cdn.intechopen.com/books/images_new/9345.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"76477",title:"Dr.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"10826",leadTitle:null,title:"Artificial Muscles",subtitle:null,reviewType:"peer-reviewed",abstract:"This book will be a self-contained collection of scholarly papers targeting an audience of practicing researchers, academics, PhD students and other scientists. The contents of the book will be written by multiple authors and edited by experts in the field.",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"2f86f1caeed80b392ec14ecd61def8e7",bookSignature:"",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10826.jpg",keywords:null,numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 25th 2020",dateEndSecondStepPublish:"December 16th 2020",dateEndThirdStepPublish:"February 14th 2021",dateEndFourthStepPublish:"May 5th 2021",dateEndFifthStepPublish:"July 4th 2021",remainingDaysToSecondStep:"a month",secondStepPassed:!0,currentStepOfPublishingProcess:1,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:null},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"62698",title:"Monitoring Water Siltation Caused by Small-Scale Gold Mining in Amazonian Rivers Using Multi-Satellite Images",doi:"10.5772/intechopen.79725",slug:"monitoring-water-siltation-caused-by-small-scale-gold-mining-in-amazonian-rivers-using-multi-satelli",body:'\nThe Amazon river basin is the largest in the world, draining approximately 7,500,000 km2 and discharging 20% of the global riverine waters into the ocean [1]. Within this territory, which includes eight countries in South America (Brazil, Bolivia, Colombia, Ecuador, Guyana, Peru, Suriname, and Venezuela), several economic activities are threatening water quality. The three activities with the most impact are hydropower dams, agribusiness expansion (deforestation) and, mostly, mining activities [2, 3, 4, 5]. The political and economic context in Brazil is even more concerning, given the construction of several hydropower dams and the recent changes in environmental laws to benefit agribusiness and mining activities in the Amazon region. With regard to mining, although it usually takes place in small areas compared to cattle and soybean production, for example, the environmental impacts such as area degradation, water siltation, and metal contamination are more severe and intense than those of other land use changes [6].
\nIn the Amazon, substantial small-scale gold mining (SSGM) activities started in the 1950s at a few sites, called “garimpos.” In the 1980s, encouraged by the high gold price, hundreds of thousands of people migrated to mining sites, causing an intense “gold rush” in order to escape complete social downgrading [7]. The SSGM decreased in the 1990s due to overexploitation of superficial gold, gold price stagnation, and national economic crises. However, within recent years, the gold price has risen from US$ 400 per ounce (28.3 grams) in 2005 to US$ 1300 in 2016 encouraging a new gold rush in the Amazon region [8]. In Brazil alone, approximately 130,000 small-scale gold miners are responsible for 30 tons per year [9, 10]. This production generates at least R$ 26 million per month for the regional economy [11].
\nDespite its financial contribution, the semi-mechanized nature of small-scale gold mining activities often generates a legacy of extensive environmental degradation, both during operations and well after mining activities have ceased [7]. SSGM takes place mostly over alluvial deposits (river network) using either dredges or water-jet systems that cause dislodging of bottom or topsoil, respectively [11, 12]. The discharge of sediment into the water has severe impacts on the water quality, such as decreasing light availability for primary production [13] and changing benthic [14] and fish communities [15]. Currently, these socio-environmental impacts can be intensified since legislation is currently being debated in the Brazilian congress, which would weaken existing environmental/indigenous laws and to slash funding for environmental protection agencies.
\nTechnically, monitoring of environmental impacts caused by SSGM, such as quantification of water siltation using satellite images, is rarely performed either due to lack of water quality data [e.g., total suspended solids (TSS)] or because of limitation of satellite image specifications such spatial resolution [16, 17]. Today, with the availability of 10 m images such as MSI/Sentinel-2, even narrow rivers (up to 30 m width) can be monitored.
\nThe scientific hypothesis of this research is that naturally low-turbid rivers (clear and black waters, based on the Sioli [18] and Junk [19] classification of the Amazonian water types) and some tributaries that are heavily impacted by SSGM tailing discharge are becoming “white” waters (naturally turbid waters), directly affecting the aquatic and benthic ecosystems.
\nTo test the hypothesis of whether or not SSGM activities are responsible for the “whitening” process in some Amazonian rivers, we need to investigate the spatial-temporal TSS distribution along these rivers throughout the years.
\nRecently, Lobo et al. [20] have estimated TSS in the Tapajós river using an empirical model based on measured TSS and radiometric data. The effects of TSS derived from mining activities on both the inherent and apparent optical properties were quantified. The authors concluded that the inorganic nature of mine tailings is the main component affecting the underwater scalar irradiance in the Tapajós river basin. For tributaries with low or no influence of mine tailings, waters are relatively more absorbent. On the other hand, with TSS loadings from mining operations, the scattering process prevails over the absorption coefficient at the green and red wavelengths. This change in load and seasonality might affect, in the long run, biota composition of a previous clear water environment to a distinct light availability regime as the river becomes subjected to mining operations.
\nThis approach worked well for the study area, and it seems a very promising approach for other areas in the Amazon region that are facing the same SSGM impacts in order to provide easy-access information for land use management in very remote areas with little financial support. However, further extension to other study areas in the Brazilian Amazon scale using different satellite data requires improvement on image processing to handle a large database and requires validation of the methods used for SSGM area mapping and TSS estimation prior to extend it to additional areas.
\nTherefore, the purpose of this chapter is to inform the main activities carried out toward a monitoring system for quantifying water siltation caused by SSGM in the Amazon rivers using multi-satellite data. In the assessment phase, the activities aimed to evaluate the viability of retrieving total suspended solids (TSS) from rivers other than Tapajós using images of several satellite missions.
\nAs a part of the assessment phase, a monitoring system was designed for areas selected according to the following criteria: (i) river basins that naturally present low sediment content (clear and blackwaters according to Junk [19], as opposed to white waters), in which discharge of mining tailings can have a negative impact on biodiversity and on the local economy; (ii) areas where SSGM is actively occurring over topsoils dominated by inorganic fine particles [21]; and (iii) rivers detectable by medium spatial resolution images or high resolution (at least 30 m wide). Considering these criteria, four areas were defined: (A) the Tapajós river, (B) the Amanã river, (C) Peixoto de Azevedo river, and (D) the Xingu river (Figure 1).
\nStudy areas in the Brazilian Amazon. States of Acre (AC), Amapá (AP), Amazonas (AM), Maranhão (MA), Pará (PA), Rondônia (RO), Roraima (RR), and Tocantins (TO). (A) The Tapajós river basin (PA). (B) The Amanã river (AM). (C) The Teles Pires river, Peixoto Azevedo region (MT). (D) The Fresco river at Xingu river basin (PA).
The first step was to select cloud-free images from the web platforms for each sensor. The images were downloaded from USGS, ESA, or DGI website servers (Table 1). In this chapter, few images are used here to illustrate the methodology. The second step was to convert top of the atmosphere (TOA) reflectance into surface reflectance (ρw). Considering the different sensors, the image correction may have different correction approaches (Table 1).
\nSatellite/sensor | \nLife span | \nSwath (km) | \nResolutions | \nAtm. correction | \nAvailable at | \n|||
---|---|---|---|---|---|---|---|---|
Spatial | \nTemperature (days) | \nRadiometric | \nSpectral | \n|||||
Landsat-1&2/MSS | \n1973–1978 | \n170 | \n60 m | \n16 | \n8 bit | \n550, 650, 750, and 900 nm | \nCorrected | \n\n | \n
Landsat-5/thematic mapper (TM)/ | \n1983–1997 | \n170 | \n30 m | \n16 | \n8 bit | \n470, 550, 660, 830, 1600, and 2200 nm | \nCorrected | \n\n | \n
IRS/LISS-III | \n2011– | \n141 | \n23 m | \n5 | \n10 bit | \n550, 660, 810, and 1600 nm | \nFLAASH | \n\n | \n
RapidEye | \n2012–2015 | \n77 | \n5 m | \n6 | \n12 bit | \n470, 550, 660, 705, and 820 nm | \nFLAASH | \n\n | \n
Landsat-8/OLI | \n2014– | \n170 | \n30 m | \n16 | \n12 bit | \n440, 470, 550, 660, 830, 1600, and 2200 nm | \nACOLITE or 6S | \n\n | \n
CBERS-4/MUX | \n2015– | \n120 | \n20 m | \n16 | \n8 bit | \n470, 550, 660, 705, and 820 nm | \n6S | \n\n | \n
Sentinel-2/ESA | \n2015– | \n290 | \n10 m | \n5 | \n12 bit | \n470, 550, 660, 705, and 820 nm | \nSen2Cor | \n\n | \n
Satellite/sensor specification in terms of resolution, life span, level of atmospheric correction, and source of data.
The second step was to correct the atmospheric effects of original images to surface reflectance. The atmospheric correction (AC) process is necessary for intercalibration of the images in order to compare images from different sensors. The atmospheric correction method for each sensor is chosen according to the output quality and time/processing cost. Only physical-based methods are applied, such as 6S [22], FLAASH [23], ACOLITE [24], and Sen2Cor [25]. For all physical methods, the aerosol optical thickness (AOT) data and water vapor (among other environmental conditions) are required as input to run the model. Some methods retrieve this information from the image (image-based method, such as Sen2Cor and ACOLITE), and others (such as 6S) require the user to indicate this information [26, 27]. The procedure assures that any variation on water-leaving reflectance is due to changes in the water constituents and not to atmospheric effects, neither to intercomparison deviations related to images acquired with different sensors at different times/dates.
\nThe third step was to estimate TSS from corrected satellite images. In this assessment phase, we tested the empirical model developed by Lobo et al. [20] based on in situ radiometric and TSS measurements taken in April 2011 [23 sample points were taken during high water level (water depth ~8 m)] and September 2012 [16 sample points were taken during low water level (water depth ~3 m)]. The measured in situ reflectance (ρw) was resampled for Landsat-5/TM spectral band. Then, to establish an empirical relationship between measured TSS and measured reflectance data, the TSS concentrations measured at 39 sample points were used. To evaluate the use of satellite images on TSS retrieving, this empirical algorithm was applied on two satellite image sets acquired at the same period of field campaigns: Landsat-5/TM (April, 2011) and IRS/LISS-III (September, 2012). To do so, the algorithm was inverted so the user can extract TSS from ρw. In the case of Landsat-5/TM and LISS-III, the following algorithm is applied:
\nwhere ρw is the surface reflectance at red band, a = 2.272, b = 2.468, and c = 2.154.
\nConsidering the different spectral resolution of the orbital sensors, the radiometric measurements were resampled to the sensor’s specification during the assessment phase. As a result, specific empirical curves were generated for each sensor (Table 2).
\nSatellite/sensor | \nRed band (ρw) | \nParameters for Eq. 1 | \n|||
---|---|---|---|---|---|
Range (nm) | \nCentered (nm) | \na | \nb | \nc | \n|
Landsat-1&2/MSS | \n600–700 | \n650 | \n2.272 | \n2.558 | \n2.230 | \n
Landsat-5/Thematic Mapper (TM) | \n630–690 | \n660 | \n2.272 | \n2.468 | \n2.154 | \n
Landsat-5/OLI | \n640–670 | \n655 | \n2.272 | \n2.516 | \n2.182 | \n
CBERS-4/MUX | \n630–690 | \n660 | \n2.272 | \n2.471 | \n2.156 | \n
Sentinel-2 | \n640–680 | \n665 | \n2.272 | \n2.469 | \n2.188 | \n
IRS/LISS-III | \n630–690 | \n660 | \n2.272 | \n2.468 | \n2.154 | \n
RapidEye | \n630–685 | \n658 | \n2.272 | \n2.484 | \n2.163 | \n
Parameters to retrieve TSS from several satellites/sensors using the atmospherically corrected red band.
After estimating TSS from satellite images, the values were clustered into six TSS concentration classes ranging from 0 to 5, 10, 20, 50, 120, and >120 mgL−1. Finally, the water surface area for each TSS class was tabulated (in km2) in order to evaluate the amount of water surface with increased TSS values (>20 mgL−1), as opposed to pristine conditions (<20 mgL−1) of clear waters.
\nResults of TSS estimation for study areas A–D (Figure 1), from the assessment phase, are presented along with relevant information to characterize SSGM activities and their discharge of sediment into the water.
\nThe lower section of the Tapajós river basin (study area A) located in the State of Pará (Brazil) covers about 130,370 km2. In terms of SSGM, more than 300 small-scale gold mines with more than 50,000 miners cover approximately 230 km2 [28]. As a result of SSGM activities, the TSS distribution over the Tapajós river and the main tributaries (Crepori and Jamanxim rivers) was extensively presented by Lobo et al. [20]. The authors indicated that the upstream section of the Tapajós river is naturally classified as “clear water” [16, 18, 29]. This class presented relatively low TSS (~5.0 mgL−1), low dissolved organic matter (absorption coefficient for colored dissolved organic matter, acdom < 2.5 m−1), and low chlorophyll-a concentration (chl-a < 1.0 μgL−1), thus resulting in a relatively deep euphotic zone at 1% of total income irradiance (Z1% ~6.0 m). In these waters, the suspended sediment has a considerable amount of organic matter (~30% of TSS), composed mostly of allochthonous plant debris [30]. The characteristics and concentration of the suspended sediments change abruptly as the Tapajós river receives clay-rich tributaries, such as the heavily mined Crepori river (TSS ~111.3 mgL−1, particulate organic matter <3%, and euphotic depth ~2.0 m). The sediment plume from the Crepori river only fully mixes with the Tapajós river waters at about 200 km downstream after passing through rapids [16, 20]. After these rapids, as the water velocity decreases, the fine suspended solids sink, and concentrations decrease to values similar to those of the upstream Tapajós river (see Telmer et al. [16]). Similar to the Tapajós river, TSS at the Jamanxim river increases as it receives a sediment-rich discharge from the Novo and the Tocantins sub-basins subject to mining operations. In the low water level season (IRS/LISS-III acquired on September 16, 2012), for example, TSS values of about 115.0 mgL−1 were estimated for the Crepori river (Figure 2).
\nStudy area A. TSS concentration with classes ranging from 0.1 to 200 mg L−1 using Eq. 1 along the Tapajós river derived from IRS/LISS-III (September 16, 2012).
In terms of surface water impacted by water siltation, Table 3 shows that Jamanxim river and tributaries present 54% of total surface water (174.3 km2) with increased TSS levels (>20 mgL−1), mostly because its tributaries Novo and Tocantinzinho rivers present high TSS levels (Figure 2). The Crepori river also presents elevated TSS values for 91% of total water surface (31.1 km2). The discharge of these tributaries into the Tapajós river affects 30% of total surface water (1208.8 km2) of the Tapajós main channel. These results confirm that Tapajós river and tributaries have been subject to intense water siltation derived from small-scale gold mining that are changing water quality conditions from clear water (<20 mgL−1) in pristine conditions to white waters (TSS levels higher than 20 mgL−1).
\nStudy area | \nRiver | \nExtension (km) | \nPercentage (%) of surface area per class of TSS (mgL−1) from total | \nTotal km2 | \n||||||
---|---|---|---|---|---|---|---|---|---|---|
0–5 | \n10 | \n20 | \n50 | \n120 | \n>120 | \nTSS > 20 | \n||||
A | \nTapajos main channel | \n483.3 | \n27% | \n43% | \n27% | \n3% | \n0% | \n0% | \n30% | \n1280.8 | \n
\n | Jamanxim and tributaries | \n707.2 | \n21% | \n25% | \n14% | \n21% | \n17% | \n1% | \n54% | \n174.3 | \n
\n | Crepori river | \n228.7 | \n7% | \n2% | \n2% | \n9% | \n70% | \n11% | \n91% | \n31.1 | \n
B | \nAmana river | \n95.1 | \n40% | \n29% | \n5% | \n6% | \n14% | \n5% | \n31% | \n15.1 | \n
C | \nXingu main channel | \n185.5 | \n87% | \n11% | \n2% | \n0% | \n0% | \n0% | \n2% | \n229.6 | \n
\n | Fresco and tributaries | \n266.7 | \n43% | \n5% | \n2% | \n47% | \n0% | \n4% | \n53% | \n39.2 | \n
D | \nTeles Pires main channel | \n120.5 | \n87% | \n12% | \n0% | \n0% | \n0% | \n0% | \n1% | \n50.2 | \n
\n | Peixoto de Azevedo river | \n140.4 | \n20% | \n64% | \n16% | \n1% | \n0% | \n0% | \n16% | \n14.2 | \n
Total water surface mapped for four study areas in the Brazilian Amazon.
Indication of percentage of surface area per class of TSS (mgL−1) from total area mapped.
Moreover, the recent research applied a sediment modeling approach on Crepori basin to simulate the impacts of past land use-cover change (LUCC) on TSS in the river, comparing these impacts to the effects of gold mining activity in TSS [31]. When comparing the TSS simulated with the 1998–2012 scenario and the estimates conducted by Lobo et al. [20] for the same period, on average, about 14% of TSS estimated by Lobo et al. [20] for high water season is derived by diffuse soil erosion, whereas this proportion is about 6%, on average, for the low water period. Therefore, this suggests that the remaining proportion of TSS measured and estimated by Lobo et al. [20], that is, over 86% of TSS estimated/measured, can be attributed to the gold mining activity.
\nThe Amanã river (study area B) is located between the state of Pará and Amazonas, and most of the SSGM is related to Tapajós gold domain as well (Santos et al. [30]). The inclusion of this area holds on the need for information about SSGM impacts on aquatic systems by the Environmental Protection Agency (ICMBio) for a better management of protected areas in the Amazon region. Recently, a federal police operation shuts down a group of about hundred miners that used to exploit gold illegally for the past 10 years. Local police agents estimate that approximately US$ 10,000 were generated with small-scale gold mining; they also claim that illegal activities caused intense area degradation for more than 70 hectares as well as mercury and cyanide contamination (both used in the gold extraction process) [32].
\nIn terms of water siltation, preliminary results from Landsat-8/OLI (July 29, 2016) indicate TSS values higher than 120 mgL−1 where mining sites are present (Figure 3). TSS concentration decreases as the river enters the protected area (Flona de Pau-Rosa). In terms of surface water with increased TSS levels, Table 3 indicates that 31% of 15.1 km2 presents elevated TSS, mostly located in the upstream section of the river, as shown in Figure 3. The TSS values only decrease when the river reaches a more stable and wider section downstream. Overall, these results confirm that the Amanã river has also been subject to intense water siltation derived from small-scale gold mining that are changing water quality conditions from clear water (<20 mgL−1) in pristine conditions to white waters (TSS levels higher than 20 mgL−1).
\nStudy Area B (Amanã river). TSS concentration with classes ranging from 0.1 to 200 mgL−1 using Eq. 1 along the Amanã river located in the border between Pará and Amazonas states derived from Landsat-8/OLI (July 29, 2016).
The Peixoto de Azevedo river (study area C) is located upstream of the Tapajós (so-called Teles Pires river), characterized by natural clear water (TSS < 20 mgL−1). This region is marked also by high deforestation rate, mostly due to the conversion of forest into pasture and agriculture fields [33]. SSGM was intense from 1970 to 1998, but recently the activity is not as intense as it had been before [30]. However, the sediment plume caused either by current mining activity or by degraded areas is still detectable by satellite images (Figure 4). The TSS estimation using Landsat-8/OLI (August 06, 2016) for the Peixoto de Azevedo river was up to 20 mgL−1 as opposed to the water from the Tapajós upstream (Teles Pires), with TSS lower than 20 mgL−1. The water surface with increased TSS (>20 mgL−1), however, was only 16% for Peixoto de Azevedo (out of 14.2 km2 mapped) and 1% of Teles Pires river (out of 50.2 km2). The water siltation in the Peixoto de Azevedo river is not too high, indicating that the sediment discharged by current small-scale gold mining is not enough to change the water quality as it occurs in the Tapajós and Amanã rivers.
\nStudy area C (Peixoto de Azevedo river/Teles Pires). TSS concentration using Eq. 1 along the Peixoto Azevedo river derived from Landsat-8/OLI (August 06, 2016).
The Xingu river, likewise the Tapajós river, has its headwaters in the Brazilian central shield, and as a consequence, it also has clear water characterized by low TSS concentration [19]. The Xingu river basin presents several indigenous lands and protected areas that have been threatened by SSGM for decades [34]. Today, intense SSGM is taking place in the Fresco river (Figure 5) at the borders of the Kayapó territory. As mining activities within indigenous land are prohibited in Brazil, a recent federal police operation had closed an illegal mining activity in the Kayapó land where drafts, dredges, guns, and mercury were apprehended [35].
\nStudy area D (Fresco and Xingu rivers). TSS concentration using Eq. 1 along the Fresco river derived from Sentinel-2 (July 18, 2016).
As a result of intense SSGM in this region, the TSS estimation using Sentinel-2 image (July 18, 2016) shows TSS higher than 200 mgL−1 in the Branco river, which in turn discharges into the Fresco river. At the São Félix do Xingu region, the Fresco river presented TSS concentration of up to 50 mgL−1. For the Xingu Main channel, only 2% of total water surface (229.6 km2) shows TSS above 10 mgL−1, which corresponds to the sediment-rich Fresco river discharge area. In fact, 39.2 km2 of the Fresco river and tributaries is analyzed in this study (Figure 5); 53% presents TSS above 10 mgL−1 (Table 3). Once more, the results confirm that Fresco river and tributaries have been subject to intense water siltation derived from small-scale gold mining that are changing water quality conditions from clear water (<20 mgL−1), in pristine conditions, to white waters (TSS levels higher than 20 mgL−1).
\nGiven the favorable results derived from the first phase of assessment of the methodology, the next steps of this research (second phase) include the establishment of an image-processing framework to correct a large imagery base of multiple sensors in order to build a time series from the 1970s to present, and validation of the empirical model designed by Lobo et al. [20] with field campaigns in the selected areas to estimate TSS from the corrected imagery.
\nThe approach that will be used in the viability assessment for building a time series follows the method applied by Lobo et al. [20], which takes dark dense vegetation (DDV) as reference for atmospheric correction. The main input parameters such as AOT, ozone, and water vapor will be optimized until the forest spectra match those from the imagery calibrated with radiometric measurements (Landsat-5/TM and IRS/LISS-III calibrated with in situ radiometric measurements in April 2011 and September 2012).
\nOnce all images are atmospherically corrected and organized into a database, they are ready for TSS estimation using the parameters in Table 2. The challenge here is to validate the application of empirical model designed by Lobo et al. [20] into the additional areas to estimate TSS from the corrected imagery. Although the selected areas present similar characteristics of river basin conditions and mining techniques, slight variation of sediment composition or even the presence of dissolved organic matter can be observed among these areas. Therefore, an effort to sample water in the extended areas (B–D) in order to validate the empirical model is key for a broader application and water quality monitoring purposes. The application of the empirical model to other areas needs a validation process that includes TSS and radiometry acquisition.
\nProducts derived from historical Landsat-1&2/MSS and Landsat-5/TM data (1973–2011) as well as current data from Sentinel-2, Landsat-8/OLI, and RapidEye will be available online at INPE (
In addition to the direct use for SSGM controlling and monitoring by these national agencies, the applicability of these products for aquatic research is enormous. In fact, evaluation of light attenuation caused by mining tailing has shown that, in general, the euphotic zone in impacted rivers has decreased to at least half of nonimpacted rivers [36]. Several studies can use TSS distribution as an input for hydrological and sediment transportation models; studies on light attenuation and consequences to the biota, as well as socioeconomic studies, will benefit from this research.
\nWe hypothesize that because of intensification of mining activities in the Brazilian Amazon, clear water rivers such as Tapajós and Xingu rivers and its tributaries are becoming or may become permanently turbid waters (so-called white waters in the Amazonian context). This chapter informs the main activities carried out to develop a monitoring system for quantifying water siltation caused by SSGM in the Amazon rivers using multi-satellite data in order to investigate this hypothesis.
\nAs a result of the first assessment phase, a multi-satellite approach was developed based on TSS algorithm proposed by Lobo et al. [20]. To do so, radiometric in situ data were resampled to several sensors’ specifications, such as Landsat-8/OLI and Sentinel-2, and applied to clear water rivers subject to intense sediment discharged by small-scale gold mining in order to recover TSS concentration. Except for Peixoto de Azevedo (study area C), the results confirm that Tapajós (A), Amanã (B), and Fresco river and tributaries (D) have been subject to intense water siltation derived from small-scale gold mining that are changing water quality conditions from clear water (<20 mgL−1), in pristine conditions, to white waters (TSS levels higher than 20 mgL−1).
\nIn order to establish a monitoring system of water siltation, the next steps of this research (second phase) include an image-processing framework to correct a large imagery base of multiple sensors and validation of the empirical model designed by Lobo et al. [20] with field campaigns in the selected areas to estimate TSS from the corrected imagery.
\nThe authors acknowledge financial support from FAPESP (Process Nos. 2011/23594-8 and 2008/07537-1) and CNPq (Process Nos. 237930/2012-9 and 150835/2015-9). We would like to thank all the LabISA (Instrumentation Laboratory for Aquatic Systems at INPE) staff, particularly to Lino de Carvalho and Felipe Menino Carlos, for their support on data processing. Thanks to Lauren Pansegrouw for the English revision.
\nThe authors declare no conflict of interest.
In the UK, Third-Sector Organisations (TSOs) are a collective term for voluntary and community agencies, charities, and social enterprises, of which a sub-section provides health and social care via independent and value-driven services [1]. Recent audits of the whole sector reveal a notable presence, with over 160,000 organisations and nearly 1-million employees and volunteers operating in the UK [1]. Across many high-income countries, it is an area which is growing rapidly as governments seek to harness their innovation and local capabilities [1, 2]. Given their nature, TSOs tend to be highly regarded for their proximity to the community, welcoming facilities, and the ability to engage those with complex and chronic needs [1, 2, 3, 4].
Despite the potential benefits of TSOs, little research has been undertaken to evidence their impact and effectiveness [2, 3]. Research applicable to many mental health care TSOs in the UK, including systematic reviews [2], national audits [1] and interviews with mental health charities [3], highlight the clinical and economic barriers affecting the production and utilisation of practice-based evidence (PBE). Many are constrained by tight budgets and scarce resources and often exist as ‘micro-entities’ making bidding processes and research prohibitively expensive [1, 4]. The evidence that has been produced has been characterised as low in quality, lacking methodological rigour, theoretical modelling, and reliance on non-representative stakeholder feedback [2, 3]. Access to learning is equally challenging with constraints on resources to review the latest research literature [3, 4].
For TSOs to overcome these challenges, there must be greater alignment of needs and priorities between providers, commissioners, policymakers and academic institutions. One approach to optimising the production and sharing of knowledge has been to form collaborative learning networks (CLNs) of services using a similar treatment model or methodology for generating evidence [5]. By partnering with similar providers, these networks enable organisations to explore, share and integrate learning across a network, maximising the potential for practice-based learning. CLNs have demonstrable potential within the UK mental health care sector, having reported success in the Improving Access to Psychological Therapy (IAPT) programme [6] and Children and Young People’s [5] services. The IAPT programme, which is a national government-funded initiative for English primary mental health services, has been an influential driver in generating public domain service performance data. Having mandated sessional measurement across all services over a decade ago, it has recently achieved pre-and-post outcomes completion rates of 98% for clients completing therapy [7]. These high levels of data completeness are essential for supporting CLNs [6].
The quality implementation framework (QIF) [8] has been previously used as a schematic structure to introduce practice changes, including routine outcome monitoring (ROM), within mental health care services [9]. This model synthesises 25 implementation methods from almost 2000 evaluation reports, comprising 4 action phases and 14 critical steps [8]. Combined with research on the value of CLNs, an initiative was undertaken to bring together multiple TSOs delivering mental health care to enhance service quality. This chapter describes the rationale, process, and outcome of this initiative across its initial start-up and first year of operation using a traditional storytelling structure, with reference to the QIF [8] and other implementation frameworks [10, 11, 12, 13].
Implementation science is the scientific study of techniques to enhance the quality and effectiveness of health services by advancing the systematic uptake of evidence-based practice (EBP) in routine clinical settings [14]. The learning from the field demonstrates the gap between what is shown to be effective to what is implemented in practice [14]. According to the QIF, in preparation for implementing practice change, agents must assess the host setting and build capacity, meeting with the service, analysing its infrastructure, surveying and training practitioners, and securing buy-in [8, 9]. Regardless of how well-founded and robust the evidence may be, it is no guarantee it will be accepted and readily adopted by stakeholders [9, 15]. Persuasive communication is therefore critical for framing research findings for specific contexts to enhance their uptake and impact [16]. The power of storytelling is increasingly recognised as an effective technique for transforming attitudes, perceptions and behaviours as they summarise concepts simply, quickly and effectively, appealing directly to a stakeholder’s values and interest [16]. For instance, within UK mental health care services, storytelling as a technique has been associated with rapid improvements in data quality [9]. It is for this reason, our chapter aims to share the experiential learning and evaluation of this CLN for mental health care TSOs using a traditional storytelling outline, describing its setting, characters, plot, and themes.
To overcome the challenges of effective service development, a CLN was devised to support TSOs in the collection and use of data to inform the future development of operational practice. Inspired by the Institute for Healthcare Improvement’s (IHI) [12] ‘Breakthrough Series’ Collaborative Model and implementation science research [11, 12, 13, 14], this initiative intended to break new ground by working in close partnership with TSOs to generate evidence and inform quality improvement. The framework integrated implementation techniques using plan, do, study, act (PDSA) cycles [10] focusing on specific areas of service delivery and, as modelled by the QIF, create a structure for implementation [8, 9]. This would become known as the service improvement learning collaborative (SILC).
Working in partnership, TSOs were invited to upgrade their measurement system to a more sophisticated software platform providing additional reporting features relevant for service operation and development [17]. Services were required to verify their commitment and autonomy at a managerial, board and trustee level to commence on a year-long journey to profile and engage with subject-relevant resources and attend monthly mentorship sessions and quarterly overnight residentials. A memorandum of understanding was devised to emphasise that membership was contingent on full-service participation and this was incorporated into the development of an implementation plan [8, 9].
This project took place over the course of a year, focusing on a different challenge each quarter, including a focus on data collection, session attendance, endings, and clinical outcomes. The project commenced with a planning meeting involving introductions, training and attitudinal surveys. With reference to the QIF, these steps were undertaken to assess the fit between the organisation’s aspirations and readiness for change, allowing for open discussion and early feedback [8, 9]. Across the project, there were monthly supportive calls with an assigned mentor from the research team, and quarterly in-person residential meetings with fellow TSOs, each supported by in-depth data profiling throughout. The purpose of the mentorship and residential sessions were to support participants in monitoring aspects of service quality and provide supportive feedback mechanisms which, according to the QIF, are critical post-implementation support strategies [8]. To improve future applications, the end of the year culminated in a summative conference with fellow mental health services to share the findings from the project’s first year in operation [8, 9, 10]. A diagram of the SILC CLN model, including the induction, mentorship, residentials and summative conference, is outlined in Figure 1.
The SILC CLN model, adapted from the IHI [10] ‘breakthrough series’ collaborative model.
The QIF emphasises the criticality in creating an implementation team to oversee its rollout and set targets and agree off-track remedial action [8, 9]. The SILC project team was assembled in 2016, consisting of academics and clinicians with extensive experience in the field of talking therapies and service design [9]. This team was responsible for developing learning resources, providing mentorship support and tracking data through the relevant quarterly themes of service development. The team also worked directly with individual service leads to cascade learning and implement practice change, compiling routine reflective case notes and disseminating learning throughout the network.
A series of prospective pilot services were approached and recruited in early 2017, subject to expressions of interest and eligibility criteria. The SILC initiative was specifically aimed at mental health care TSOs using CORE IMS computerised quality evaluation systems [17] to obtain evidence on their delivery and strengthen their position for funding and benchmarking. Those eligible had been using CORE outcome measurement systems for over 5 years, primarily as an administrative tool to log clinical activity. Within all but one TSO expressing interest, there was little analysis of the data being undertaken, and no indication of it being used clinically or to enhance service quality. Prospective services were using traditional pre and post-therapy measurement approaches, acquiring outcomes data for around 40–50% of clients; a rate which is representative of the field and this methodology generally [18]. Many were also experiencing high rates of non-attendance and attrition, plus modest clinical outcomes for those with outcomes data.
The exploration phase of Aarons, Hurlburt and Horwitz [11] conceptual model for implementation identifies the importance of inner and outer contexts. In this project, it seems early withdrawal during the recruitment stages was due to a combination of socio-political factors and lack of absorptive capacity which impeded progress [11]. What had started as 12 prospective members soon halved to only six. Various reasons were given but discontinuation was mostly cited as being due to managerial turnover, lack of capacity for change, and workforce restructuring, or resistance. By contrast, the remaining TSOs demonstrated their levels of commitment via an initial attitudinal survey which, when disseminated to all practitioners (n = 49), achieved a high response rate of around 80%.
The six services joining the project ranged in size, geographical location and clinical specialism. Annual throughput ranged from around 80–300 clients per organisation. Clinical support specialisms included psychological support for female victims of domestic abuse; women on low incomes; parenting; unpaid carers; and general counselling support. Informed by QIF support strategies, each service was assigned a mentor from the SILC project team using a consultation and matching process [8, 9]. Members received regular updates via a monthly blog post on the project’s website (
Expanding on the story structure framework, this section will incorporate a generic narrative mountain structure, breaking down the plot by its background, rising action, climax, falling action, and resolution.
During each quarter, the project team worked with each TSO to produce an implementation plan including a set of targets, infographics, quality checklists, report templates and mentorship support, with PDSA cycles to structure the process [8, 9, 10]. Many of these tools required regular, in-depth auditing of data recorded during assessment, treatment, and discharge. Analyses were complemented by attitudinal surveys to front-line practitioners focusing on their perceptions and experiences across each quarter. Services were encouraged to reflect and communicate their learning at the quarterly residential meetings, while critically appraising fellow member’s contributions.
Throughout the project, it became clear that an organisation’s success in addressing the challenges depended on their relationship with the process of using measurement questionnaires and how deeply practitioners and clients were engaged in responding to feedback. The team later conceptualised this as a development cycle with four distinct evolutionary stages that described the operational depth of practitioners’ relationship with measurement: Pre and post-therapy measurement using paper forms; measurement at every session using paper forms; digital measurement at every session using tablets or computers; and digital measurement at every session tracking and sharing outcome progress directly with clients throughout the entire therapeutic encounter. It was recognised that services which were further along in this cycle had an inverse relationship with measurement in terms of its input and value towards stakeholders. Those in the later stages were able to maximise the value for clients that in turn benefitted other groups including practitioners, service management, and boards/funders. Conversely, those operating in the earlier stages were limited in their value to certain groups, typically to the boards/funders. Figure 2 shows a conceptual model of this, including the resulting value for stakeholders.
The evolutionary stages of measurement within SILC TSOs illustrating the development cycle and value to stakeholders.
Conceptual implementation models highlight how the structures and processes that exist within organisations have an influence on the adoption of practice changes during the active implementation phases [8, 10, 11]. Within the SILC project, it was observed that completing paper forms, particularly at every session, generated huge administrative and inefficient burdens for members. This created barriers for practitioners looking to use data as feedback to enhance client outcomes and develop their clinical skills. During the year, most organisations evolved their administrative processes by replacing paper with digital methods, recording via electronic tablets. The services most successful in achieving the optimal rates for each quarterly challenge described understanding measurement as a construct and extension of the client. By focusing on creating the maximum value of measurement for clients, a myriad of other benefits at different stakeholder levels was also reported [19]. Naturally, some services were more equipped than others in accessing the appropriate technologies.
During the project, one of the participating TSOs withdrew due to a turnover in management and evolving financial pressures. Two other services experienced management turnover during the project which, although not impacting on their participation, did require additional input and training from the SILC project team. Practitioner turnover was understood to be common in TSOs [2, 3, 4], however, the rate of turnover concentrated at a managerial level had not been anticipated. For services with a complex management structure, this too complicated the sharing of learning and addressing each quarterly challenge. It was discovered that when managers with an on-hand leadership style were absent, this would impact on key aspects of their service operation, including the collection of high-quality data.
Another key challenge regarded the issue of session attendance and unplanned endings. A list of categorical reasons for why a session was not attended was compiled to record each time this occurred. Although the reasons recorded for cancellations were high, this was not the case for those who did not attend (DNA) (no advanced warning given) despite subsequent sessions being attended in approximately half of all instances. The most common reason for cancellations during the second quarter (n = 482) was ‘Health Problems’ (40%) while for DNAs (n = 160) it was ‘Unknown’ or ‘Not Recorded’ (76%). The absence of reasons recorded despite sessions being subsequently attended suggests practitioners either forgot or did not feel comfortable exploring why a session had been missed. This is concerning as DNAs were found to be indicative of an unplanned ending.
Definitions are important and have shown to vary the reported unplanned ending rate [20]. During the project, the unplanned ending rate reduced from 32% at baseline to 27% at the end of the third quarter, however defining and interpreting these rates revealed notable issues. Among the participating members, there were multiple interpretations about what constituted a planned versus unplanned ending. Given its inherently subjective nature and potentially negative connotations, this limited the analysis somewhat. However, the links between session non-attendance and unplanned endings were consistent across all services and tended to occur early in treatment, as described in the next section.
One of the aims of the SILC project was to provide services with regular analyses to inform delivery and operation. This section reports on some of the headline findings along with extract quotes from two of the SILC TSOs. Systems-level modelling demonstrates the importance of considering the interrelationships between individual practice elements as opposed to solely focusing on each in isolation [11, 21]. Although the challenges during each quarter were distinct, the areas of overlap were noteworthy. Not only was session non-attendance linked with unplanned endings, but those TSOs with the longest standing commitment to high-quality data also reported the highest rates of clinical improvement.
One major shift during the first quarter was to adopt sessional ROM, moving from traditional pre and post-therapy measurement approaches. This process was supported by a dedicated project member auditing and feeding back information to services. By the end of the first quarter, pre-and-post outcome completion rates increased from an average of 65% at baseline to 98%, while by the end of the year, this was 97%, with all TSOs achieving above 90% and half achieving 100% completion rates (Figure 3). These values were almost identical to the IAPT programme’s recent achievement of 98%, a decade after its first site implementation [7].
Improvement of pre-and-post outcome measures completion rates for all SILC TSOs, 1 year before-and-after the project.
At the start of the second quarter, members began to record session non-attendance, including when an appointment was cancelled (by client) or the client DNA (no advanced warning given). One of the primary areas of interest was understood when sessions being missed were most likely to occur. Aggregating each service’s datasets, the total number of appointments per sequential session number was tallied to assess what proportion was recorded as either cancelled or DNA. Including only session numbers with over 10 appointments each, it was possible to chart this data (Figure 4). It was identified that cancellations as a proportion tended to increase the longer therapy progressed; although this might be due to a lower number of appointments at these stages. DNAs as a proportion did not exceed 10% for any session number although they did tend to occur earlier in therapy, with sessions 2–5 reporting the highest rates of 7–8%. The occurrence of DNAs declined somewhat as therapy progressed, possibly due to contracting which discharged clients after missed appointments without prior notice. Focusing on session non-attendance helped determine the scale of the challenge and how the pattern of cancellations and DNAs differed, prompting two participating services to a revise their policy in the interests of equitable access and service efficiency.
The rate of appointment non-attendance per session number showing a higher proportion of DNAs earlier and cancellations later in therapy, across all SILC TSOs.
For the third quarter, the focus shifted to exploring the nature of unplanned endings. An analysis was undertaken to explore the potential associations between unplanned endings and the rate of non-attendance during therapy. This analysis found that, across all services, there was a link between session absence and ultimate attrition, especially regarding DNAs. For all TSOs, the DNA rate for clients with an unplanned (13%) versus planned (2%) ending was around 6½ times difference, ranging from 2 to 18 times across providers (Figure 5). By the end of the third quarter, those with planned endings attended almost 3 times more sessions (11) than those with unplanned endings (4) and were more likely to report reliable improvement for planned (62%, n = 226) versus unplanned (36%, n = 70) endings.
A comparison of session non-attendance reporting a higher rate for unplanned versus planned endings across all SILC TSOs.
To assess how the pattern of non-attendance varied during therapy per ending type, session numbers and total appointments recorded were banded across all services (Figure 6). This analysis found that again, non-attendance was indicative of an unplanned ending, with higher rates of cancellations and DNAs. For those with an unplanned ending, it also revealed that while DNAs as a proportion were reduced in the lower session number bandings (2–4; 5%), they remained consistent at around 17–21%, excluding the 14–16 banding which reported a rate of 30%. Similar to the overall patterns of attendance, cancellations as a proportion of all appointments tended to increase the longer therapy progressed but again, this could be explained by a decrease in appointments recorded during these later subgroup stages.
A comparison of session non-attendance bandings showing a steady DNA rate and increasing cancellations for unplanned versus planned endings, across all SILC TSOs.
In the final quarter, the project focused on clinical outcomes and understanding therapist variation and trajectories of change. To identify a possible dose-effect, an analysis was undertaken to assess the rates of change across individual domains of the CORE-OM (wellbeing, problems, functioning, and risk) within the one service using the full 34-item measure, as opposed to the shorter CORE-10 which does not record all domains [17]. A pattern of average scores were mapped relative to individual session numbers up to the 10th session (for clients having 10+ appointments each) for those who reported reliable improvement (n = 130; 891 sessions) versus those who reported no reliable change (n = 39 clients; 243 sessions) or reliable deterioration (n = 7 clients; 53 sessions) (Figure 7). Based on this analysis, most of the score changes tended to occur early in treatment for those reporting reliable improvement, with an average decrease in scores of −6.1 across the first four sessions, remaining steady between sessions four to seven (−0.5), and then decreasing steadily from sessions seven to 10 (−2.3). For those reporting no reliable change or reliable deterioration, scores generally remained steady, with average changes ranging from 0.2 to 1.7. This suggests the first four sessions were important for identifying clients who were likely to improve or not. This triggered the integration of a flag feature to remind practitioners to review progress early in therapy to identify those at-risk of showing no change to provide additional support.
A pattern-of-change comparison across the CORE-OM per session number illustrating early improvements for clients reporting reliable improvement compared with no reliable change or reliable deterioration.
Informed by the QIF, improvement for future applications requires learning from experience [8]. To gauge the experiences of those participating in the project, a brief semi-structured interview was conducted at the end of the year to explore what service managers thought of the initiative, and how they might improve it for future services embarking on a similar journey of collaborative learning. The boxes below contain extracts from these interviews with two self-selected TSOs.
Service A: Interview Extracts |
Our first question was how is it going to work for our clients? Building that value for them, and the practitioners, giving them a value to the work. This is not a measurement, it’s not an outcome, it’s an aide to the process, something that helps the work with clients. And then, once we all understood that, we could have an open conversation about why we might want something like this. You really need that opportunity to embed it early on though. It completely allowed us to cement and consolidate how we work. I mean the data the project provided, really cemented what we were doing, how we were doing, we were using data in the right way, but it also gave us ways to look at data differently, what we could do, so it was an enhancing experience. That allowed us to feel quite proud of what we do, and have it validated, which for us a charity tucked away from others, that was a nice thing to have it validated on that level. I did that like kind of cyclical journey, that it’s not linear, we’ve got new practitioners all the time, we’ve just got 8 new practitioners in now, and they’re going back through that loop. They’re doing their first data clean this week where I’m just putting them through all the information, right we need to go through and see, right this is done, this is done, and you keep on embedding it, keeping the data quality up really. Constant, it must be really because when I’ve dipped out of the service, it went a little bit, my practitioners got a little bit complacent. I think one of the biggest things for us, the 4-session thing, spotting that. We actively use that in supervision now, so it’s really looking at, from that first session, you can see it quite clearly. So, there’s more focus in those first 4 sessions, really looking at what the client needs, with a view to contracting through goals, further through that process. So that we’re really meeting those needs, making that environment that’s conducive then to achieving good outcomes. We’re about sharing good practice, we’re about empowerment, we’re about creating choice and all those things. Being part of SILC fitted with part of the ethos so it was nice to go and be there in that capacity with other services. There’s something about talking to someone who’s been through it, we’re just through it. It’s that kind of picking their brains and have you thought this? For me it’s about credentialing the sector, it’s about professionalism, it’s about best practice, it’s about evidence base, not being afraid to strive, to get to those levels, and get good outcomes and be accountable for that. I don’t think therapy is any different from if you go to a shop to buy something you expect it to be good quality. I don’t see why in therapy, clients shouldn’t expect it to be any different. |
Service E: Interview Extracts |
Having the support from the team that was specific to our service, having experts on hand when you needed them. Keeping on top of the data quality is not as easy without the help of the project team, and our monthly calls and the little tool pointing out the problems… Whereas sitting down and finding the problems myself is another matter. I personally enjoy getting involved in things like this. I find it very stimulating. It ticked a lot of boxes for me, in terms of what we wanted for the service, but also for me personally, it was an interest. You couldn’t have designed it better for me… So, I think without that personal interest and enthusiasm it wouldn’t have happened. I think I’m very fortunate in that I’ve got a very good group of people, I think credit needs to go where it\'s due, they are a group of people who are motivated and supportive and I think all we did was talk about, well this is going to be a benefit to the service, and they’re all very committed to the service and they came, I suppose, with open minds. That’s been one of the key things for me, has been the experience of being part of the learning collaborative. And I think that is so valuable, personally and also for the service, because you’re going through a journey with other services, their journey’s different but there are similar issues. It’s just that ability to share learning and connect with people who have a similar job, are having similar issues. When you have something, and they say, yeah that’s happened to me. And for me, it takes away that sense of being in your own little bubble, in your own little service, which I wouldn’t say is isolating but that you’re not part of anything else. The learning collaborative made you feel part of something bigger with some connections, and yeah, doing the same thing you’re doing, I thought it’s fabulous, it’s brilliant. It’s the practical stuff, we’ve become a service that does sessional measurement using tablets, that’s the way we do things now. We’ve changed the way we manage DNAs, we have an appreciation of data quality, and that’s not just me, the team come along and say why haven’t I got 100%? Why is this only saying 90%? Can we have a look where that 10% has gone? So, there is an appreciation now of the importance of good data. In fact, the things that SILC was meant to address, are the things that have changed in our service. It’s a no-brainer. Why wouldn’t you? I can’t see any reason why you wouldn’t, unless you haven’t got the support to see it through. Know your organisation, know that you’ve got that support, to be able to put the time into it, those are the two caveats, otherwise, it’s a no-brainer. |
In keeping with the IHI’s [10] collaborative learning model framework, the first year of the SILC project culminated in a summative conference. Nearly 100 delegates were in attendance, each representing a range of different sectors within the field of talking therapies. Both the project team and self-selected SILC TSOs held a discussion regarding their experiential learning during the first year of the project. There was a consensus at the event about the operational challenges facing modern-day talking therapy services. While systems were becoming increasingly sophisticated, the training and support necessary to build in-house expertise were reportedly difficult to access due to time and resource constraints, a saturated and uncertain field, and isolated working practices. Providers, particularly in the third-sector, desired the opportunity to work in partnership with others to share learning and enhance theirs and the sector’s organisational and therapeutic models further.
With the first stage complete, the SILC project has amassed a wealth of learning which will be converted into a modular learning programme, providing a resource for future applications of the network [8, 9]. This will replicate the CLN model and invite existing SILC members to act as guest speakers and offer unique support and valuable insights to newly recruited collaborative members. There are three existing SILC TSOs who have declared their interest and commitment to continuing with the project. Due to a turnover in management and decrease in contribution, two members have since withdrawn. The next phase of the initiative will focus on expanding the network, building on the existing knowledge and aggregate data to support ongoing analyses and resource development.
Themes are the essence of a story, the central constructs which reflect the actions, perceptions and experiences of the characters in their situational contexts. They represent the underlying ‘big ideas’ which transcend the distinctions between settings and circumstances and help conceptualise elements and links between them. This is important given the lack of guiding conceptual models for the sustainment phase of implementation [11]. Listed below is a discussion on some of the key themes both the participating services and project team uncovered during this stage of the project.
The unintegrated nature of TSOs in the UK means there can be obstructions to developing and integrating EBP [2, 3, 4]. Within the field of talking therapies, determining what constitutes as EBP has been criticised for its reliance on controlled study methodologies which, due to their somewhat artificial nature, are considered detached from the clinical realities of routine practice settings [22, 23]. Certain advocates support a PBE approach to complement and address these limitations [24]. However, PBE relies on the collection of robust, aggregate datasets across multiple organisations sharing a common system or model.
Fragmentation, isolated working practices, and resource constraints can limit TSOs generating the PBE necessary to support their delivery [2, 3, 4]. Indeed, the primary interest from prospective members in this project was overcoming these barriers and demonstrating they were treating clients effectively. By pooling experience, resources and expertise around a central, unifying theme, TSOs were able to systematically explore, assess, understand and reflect upon key aspects of service quality development. Through iterative cycles, strategic improvement models and coordinated and collaborative dialogue [10], services were able to generate timely and actionable insights that were relevant to their unique circumstances. Testing practice changes on small scales, using focused inquiry and PDSA cycles, helped achieve small wins which, according to evaluation theories, can be an effective strategy for boosting perceived capabilities [6, 10].
Replicating previous research findings [2, 3], access to a supportive academic project team was deemed invaluable for producing, mentoring and synthesising analyses and learning across the network. However, liaising with several TSOs proved to be a lengthier and more complicated process than first envisaged; an experience which is echoed elsewhere [6]. This identifies an important obstacle for sustaining CLNs, particularly those undertaking continuous analyses. It might be that by offsetting resources to a project team, this creates a more efficient process within individual services as it shares the expertise around a common need. If this were true, then it could prove more efficient and cost-effective for TSOs overall.
Given the central communicative nature of CLNs, it is important these channels are equitable. Within the third-sector, organisations tend to differ in size and can be equally varied in their operational modelling [2, 3]. This inequity in size and visibility could feasibly leverage greater influence over smaller providers to work towards their agenda. To overcome the challenges of distinct delivery models within CLNs, a central governing platform using cooperative representation could therefore be valuable for identifying topics of interests and establishing a dictionary of terms. Similarly, these communication channels ought to use terminology that is consistent and agreed upon, particularly around subjective concepts such as ending types as doing so would ensure greater validity and reliability in data analytics [20].
Many implementation frameworks emphasise the planning stages as critical to successfully embedding innovation [8, 11, 12, 13]. Because implementation can be a complex process involving integrating existing practices with new, it typically requires a well-planned, structured and iterative process, addressing the various philosophical and practical barriers that can occur regularly [9, 15, 21]. It is within these contexts that supportive leadership can be a facilitating factor [2, 11, 15, 21, 25]. Without effective leadership to track, monitor and effectively champion the merging of practices, any expended effort can unravel [9, 15, 25]. Those in leadership positions need to be present and well-respected, retaining a detailed awareness and understanding of delivery and operation [15]. Service quality development through CLNs therefore appears to be reliant on management structures and local leadership.
In considering the scale of change and level of turnover in TSOs, particularly at a managerial level, the reliance on leadership highlights a notable barrier. Given the project team tended to work exclusively through managers brokering knowledge and training, their absence ultimately affected their organisation’s participation and operational processes. It could be argued this was a side-effect of the chosen methodology which may have benefitted from a broader involvement and contribution among the workforce. Advocates across the field recommend ensuring a local champion is permanently in place, advising that those departing a service provide sufficient training to those replacing them [9, 15, 26]. While this recommendation is practical, how it applies to TSOs is perhaps more complicated.
Continually nurturing the operational climate through sustained involvement and being present can help resolve the functional mechanisms of feedback systems [15, 25, 26, 27, 28]. A perceived lack of presence in the project among some practitioners served to undermine the initial enthusiasm and positive ethos established at the project’s outset. Services which thrived tended to dedicate additional time and resources to sharing information in an open and accessible manner. This actively engaged the workforce in the minutiae of feedback informed treatment (FIT) [28] and encouraged more open dialogue. The literature on FIT teaches the value of routinely soliciting responses from clients about treatment progress, aiding practitioners at a therapeutic level [28, 29, 30]. However, there is an additional service-level which could also help inform practitioners and other stakeholders about enhancing client engagement and outcomes. By combining a FIT model with a feedback informed service, practitioners could have timely access to relevant learning. With reference to the QIF [8], supportive feedback mechanisms will be relevant to all stakeholder levels and through aggregate data, the client voice can be made accessible to all, helping sustain innovation.
Based on the learning from this initiative and relevant national and international research [2, 3, 4], there appears to be a significant resource challenge facing TSOs. Although many report having an interest in quality improvement [3], the constraints on providers including turnover, financial pressures and limited budgets, appear to greatly impact their ability to generate data and engage in practice development [2, 3, 4]. For a sector that relies heavily on volunteers, some of whom are in trainee positions [1], preserving a level of local expertise represents a continual challenge, particularly as systems become more expansive, specialised and costly. Although the CLN was a means to pool and share resources, supporting the implementation phase [11], external pressures had a notable influence on its integration, process and overall output. The level of attrition at the beginning and eventual withdrawal of others highlights the scale of this challenge. Consequently, this further demonstrates the criticality of the QIF phases in thoroughly assessing the fit between the host setting’s aspirations and readiness for change [8, 9].
Given the sheer scale of change and advancing pace of new technologies, feedback systems and innovations are becoming increasingly sophisticated while at the same time, access to training and support might not be keeping pace [3, 31]. For many, including attendees at the summative conference and across the wider literature [3], allocating resources to this endeavour might be considered non-feasible as few can afford or justify it economically. This issue is further compounded by the fluctuating and isolated nature of services as well as barriers in accessing the literature due to subscription paywalls [2, 3]. Accordingly, this highlights the need to consider the additional training and support required when adopting new innovations.
Despite its limitations, a CLN could address some of the resource challenges identified, increasing the opportunities for learning. Disseminating feedback throughout a network might help overcome some of the barriers to accessing research and forming partnerships [5, 6, 10]. Shared learning across all levels of the network, could foster a broader culture of openness and training, supporting collaboration across multiple platforms, while also generating an asset for feeding back insights across the sector. Undoubtedly, this would rely on the aggregation of robust datasets and communication platform to support this process [5].
The experiences from this project revealed the influence of organisational factors and infrastructure on the uptake of practice changes. Although research on the integration of feedback systems and ROM have identified numerous practical barriers, much of the emphasis has focused on practitioners [9, 15, 31, 32, 33, 34, 35, 36, 37]. Indeed, positive attitudes towards feedback have been shown to facilitate the effect on clinical outcomes improvement, while resistance can have the opposite effect [33, 38, 39, 40]. Resistance reportedly stems underlying performance anxiety or negativity about the relevance and utility of the practice [9, 15]. However, the learning from this project highlights how positivity and motivation might not be sufficient in isolation.
Despite the generally positive attitudes from the survey and among the management mentees, itself likely a result of the selection process, many TSOs still encountered challenges, many of which appeared to be due to limitations in the infrastructure and frustrations with the technology. This, in turn, affected their capacity to use the system, something which is shown to be a facilitator in implementing EBP [25, 27, 31]. Restrictive and frustrated working practices can lead to negative perceptions forming [25, 27, 36, 41], suggesting attitudes might be mediated by how user-friendly and engaging a system is. For TSOs facing time and resource constraints, the simplicity of a feedback system is perhaps more pivotal. In these circumstances, systems may benefit from a uniform, standardised approach so that training and support can be refined and accessible via fully integrated and self-led instructional packages [32]. In terms of the QIF [8], the critical steps for assessing the needs and resources, capacity, and pre-implementation training would benefit from accessible resources which are intuitive and easy to understand.
Traditionally, measurement in TSOs have been undertaken to satisfy the needs of boards and funders and to a lesser extent, service managers [3, 4]. The pressures on services have meant that pre and post-measurement approaches have dominated, with its purpose serving mainly administrative rather than clinical needs [3, 9]. ROM established a method for improving data quality and representativeness, although the emphasis regarding its clinical utility or use in service development has only recently been advanced [7]. This illustrates how the focus and value of measurement have been positioned to satisfy a broader sector-level drive. However, by framing measurement in a way to maximise the value for clients, as observed in this project, there appear to be many cumulative gains for all stakeholders, including practitioners, service managers and boards/funders.
Across each of the common challenges, there seemed to be a critical period, usually within the first four to six sessions, which correlated with eventual outcome. For instance, a large proportion of DNAs tended to occur early in treatment which were a useful indicator of an unplanned ending, and by extension, a reduced chance of reliable improvement [20]. For clients reporting reliable improvement in one TSO, most change seemed to occur during the first four sessions, while those reporting no reliable change or reliable deterioration showed little change across a 10-session period. This emulates the wider literature which identifies the initial stages as being a useful indicator for a client’s subsequent engagement and outcome [42, 43, 44, 45]. Accordingly, this trend highlights the criticality of early engagement and warrants a further discussion about the implications of keeping clients involved in therapy who report no change or attend infrequently. Evidence has shown that decisions to prolong or conclude therapy despite a lack of positive therapeutic change can be influenced by subjective beliefs, norms and attitudes, sometimes superseding what feedback monitoring and practice guidelines recommend [45].
According to the literature, the clinical benefit of measurement can be mediated by a practitioner’s engagement and attitude towards outcomes monitoring [33, 38, 39]. Moreover, timely access to feedback has been shown to be a critical factor in the use of data among practitioners [27, 34, 36, 46]. TSOs which encourage open dialogue and pay greater attention to this information could produce cumulative benefits in each of the quarterly themes identified [10, 30, 47]. An organisational culture of openness and commitment to learning was important and replicates findings reported elsewhere [15, 46]. Additionally, giving practitioners access to service-level data might assist them in overcoming residual ambivalence because its application to service quality development is readily observable.
For those interested in implementing a CLN to support TSOs, there are several recommendations based on this project’s findings. Firstly, recording high-quality data is crucial to this model. Securing high-quality data helps support the network and aggregate learning by effectively threading the client voice throughout all stakeholder levels. Promoting client engagement in the process of measurement is an effective strategy for enhancing data quality and building the opportunities for clinical application [9, 31, 47]. Because of this, it is important that implementation teams do not underestimate the infrastructure necessary to support practitioners working to deliver these innovations [15, 32, 35, 46]. While pooling resources can help overcome challenges relating to cost and access to expertise, without a shared framework and understanding of the key concepts, a CLN and its associated analyses are likely to be impacted. In keeping with the wider literature, access to expertise and committed project team can be beneficial for supporting the network [2, 3, 5, 6, 9]. Focusing on distinct areas of service delivery through iterative improvement cycles and acknowledging their interdependency can help achieve cumulative benefits through the combination of smaller gains [6, 21, 25]. For TSOs, the role of leadership and effects of turnover cannot be understated. While it might not be feasible in TSOs to ensure a local champion is always in place, it is valuable to build a system that enables receptiveness towards continual practice innovation. A broader involvement and contribution among the workforce through wider supportive feedback mechanisms represents one effective strategy to overcome this.
TSOs represent a valuable and growing player in the provision of mental health care, yet many are constrained by limited budgets, isolated working practices, and a constantly shifting workforce. Together, these make producing and accessing evidence difficult, further limiting the sector from credentialing their impact and engaging in service development. To overcome these challenges, a CLN was implemented involving six TSOs and a dedicated project team to share learning and resources with the aim of improving delivery and operation in the areas of data quality, session attendance, unplanned endings and clinical outcomes. The CLN was inspired by the IHI collaborative model [10] framework for integrating and testing improvements using PDSA cycles and the implementation process was guided by the QIF [8]. It was found that introducing ROM substantially improved data quality which acted as the bedrock for all subsequent analyses and discussion. There appeared to be strong links between each of the common challenges, including increased non-attendance being associated with the occurrence of an unplanned ending, itself linked with a lower chance of reliable improvement. Overall, this approach to generating timely and relevant practice-based insight through partnership working and mentorship support proved to be effective for stimulating service quality enhancement. Although TSOs face many unique challenges, including high staff turnover and strained budgets, those with on-hand and inspirational leadership and commitment towards maximising the value of measurement for clients reported most success.
This work was supported by the Artemis Trust (No grant number). The funder had no further role in the design, collection, analysis, interpretation and compilation of this paper and no financial interests or benefits have arisen from the direct applications of this research.
Scott Steen and John Mellor-Clark declare they have no conflicts of interest.
The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
",metaTitle:"Open Access Publishing Fees",metaDescription:"Open Access Publishing Fees",metaKeywords:null,canonicalURL:"/page/OA-publishing-fees",contentRaw:'[{"type":"htmlEditorComponent","content":"As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 118,000 international scientists and researchers.
\\n\\nThe Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
\\n\\nOAPF Publishing Options
\\n\\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
\\n\\nServices included are:
\\n\\nSee our full list of services here.
\\n\\nWhat isn't covered by the Open Access Publishing Fee?
\\n\\nIf your manuscript:
\\n\\nYour Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\\n\\nOpen Access Funding
\\n\\nTo explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\\n\\nFor Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\\n\\nAdded Value of Publishing with IntechOpen
\\n\\nChoosing to publish with IntechOpen ensures the following benefits:
\\n\\nBenefits of Publishing with IntechOpen
\\n\\nAs a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 118,000 international scientists and researchers.
\n\nThe Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
\n\nOAPF Publishing Options
\n\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
\n\nServices included are:
\n\nSee our full list of services here.
\n\nWhat isn't covered by the Open Access Publishing Fee?
\n\nIf your manuscript:
\n\nYour Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\nOpen Access Funding
\n\nTo explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\n\nFor Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\n\nAdded Value of Publishing with IntechOpen
\n\nChoosing to publish with IntechOpen ensures the following benefits:
\n\nBenefits of Publishing with IntechOpen
\n\n