[ { "key": "MEZI5W53", "version": 6839, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/MEZI5W53", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/MEZI5W53", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Kim", "parsedDate": "2023-08-20", "numChildren": 0 }, "data": { "key": "MEZI5W53", "version": 6839, "itemType": "computerProgram", "title": "Awesome Deep Vision", "creators": [ { "creatorType": "programmer", "firstName": "Jiwon", "lastName": "Kim" } ], "abstractNote": "A curated list of deep learning resources for computer vision", "seriesTitle": "", "versionNumber": "", "date": "2023-08-20T19:10:07Z", "system": "", "company": "", "place": "", "programmingLanguage": "", "rights": "", "citationKey": "", "url": "https://github.com/kjw0612/awesome-deep-vision", "accessDate": "2023-08-21T14:07:01Z", "DOI": "", "ISBN": "", "archive": "", "archiveLocation": "", "libraryCatalog": "GitHub", "callNumber": "", "shortTitle": "", "extra": "original-date: 2015-05-18T11:02:08Z", "tags": [], "collections": [ "SJ6FI35Z" ], "relations": {}, "dateAdded": "2023-08-21T14:07:01Z", "dateModified": "2023-08-21T14:07:01Z" } }, { "key": "U2IPL26T", "version": 6838, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/U2IPL26T", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/U2IPL26T", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Gray", "parsedDate": "2023-06-09", "numChildren": 0 }, "data": { "key": "U2IPL26T", "version": 6838, "itemType": "computerProgram", "title": "Awesome Deep Ecology", "creators": [ { "creatorType": "programmer", "firstName": "Patrick", "lastName": "Gray" } ], "abstractNote": "A curated list of deep learning papers in ecology.", "seriesTitle": "", "versionNumber": "", "date": "2023-06-09T01:54:52Z", "system": "", "company": "", "place": "", "programmingLanguage": "", "rights": "", "citationKey": "", "url": "https://github.com/patrickcgray/awesome-deep-ecology", "accessDate": "2023-08-21T13:57:44Z", "DOI": "", "ISBN": "", "archive": "", "archiveLocation": "", "libraryCatalog": "GitHub", "callNumber": "", "shortTitle": "", "extra": "original-date: 2018-07-30T19:29:58Z", "tags": [], "collections": [ "SJ6FI35Z" ], "relations": {}, "dateAdded": "2023-08-21T13:57:44Z", "dateModified": "2023-08-21T13:57:44Z" } }, { "key": "KQJKKYD9", "version": 6837, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/KQJKKYD9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/KQJKKYD9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Christin et al.", "parsedDate": "2018-05-30", "numChildren": 0 }, "data": { "key": "KQJKKYD9", "version": 6837, "itemType": "preprint", "title": "Applications for deep learning in ecology", "creators": [ { "creatorType": "author", "firstName": "Sylvain", "lastName": "Christin" }, { "creatorType": "author", "firstName": "Éric", "lastName": "Hervet" }, { "creatorType": "author", "firstName": "Nicolas", "lastName": "Lecomte" } ], "abstractNote": "A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized several research fields such as bioinformatics or medicine. Yet such a surge of tools and knowledge is still in its infancy in ecology despite the ever-growing size and the complexity of ecological datasets. Here we performed a literature review of deep learning implementations in ecology to identify its benefits in most ecological disciplines, even in applied ecology, up to decision makers and conservationists alike. We also provide guidelines on useful resources and recommendations for ecologists to start adding deep learning to their toolkit. At a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be processed by humans anymore, deep learning could become a necessity in ecology.", "genre": "", "repository": "bioRxiv", "archiveID": "", "place": "", "date": "2018-05-30", "series": "", "seriesNumber": "", "DOI": "10.1101/334854", "citationKey": "", "url": "https://www.biorxiv.org/content/10.1101/334854v1", "accessDate": "2023-08-21T13:56:53Z", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "bioRxiv", "callNumber": "", "rights": "© 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/", "extra": "Pages: 334854\nSection: New Results", "tags": [], "collections": [ "SJ6FI35Z" ], "relations": {}, "dateAdded": "2023-08-21T13:56:53Z", "dateModified": "2023-08-21T13:56:53Z" } }, { "key": "U2EBAGM5", "version": 6836, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/U2EBAGM5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/U2EBAGM5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Wäldchen and Mäder", "parsedDate": "2018", "numChildren": 0 }, "data": { "key": "U2EBAGM5", "version": 6836, "itemType": "journalArticle", "title": "Machine learning for image based species identification", "creators": [ { "creatorType": "author", "firstName": "Jana", "lastName": "Wäldchen" }, { "creatorType": "author", "firstName": "Patrick", "lastName": "Mäder" } ], "abstractNote": "Accurate species identification is the basis for all aspects of taxonomic research and is an essential component of workflows in biological research. Biologists are asking for more efficient methods to meet the identification demand. Smart mobile devices, digital cameras as well as the mass digitisation of natural history collections led to an explosion of openly available image data depicting living organisms. This rapid increase in biological image data in combination with modern machine learning methods, such as deep learning, offers tremendous opportunities for automated species identification. In this paper, we focus on deep learning neural networks as a technology that enabled breakthroughs in automated species identification in the last 2 years. In order to stimulate more work in this direction, we provide a brief overview of machine learning frameworks applicable to the species identification problem. We review selected deep learning approaches for image based species identification and introduce publicly available applications. Eventually, this article aims to provide insights into the current state-of-the-art in automated identification and to serve as a starting point for researchers willing to apply novel machine learning techniques in their biological studies. While modern machine learning approaches only slowly pave their way into the field of species identification, we argue that we are going to see a proliferation of these techniques being applied to the problem in the future. Artificial intelligence systems will provide alternative tools for taxonomic identification in the near future.", "publicationTitle": "Methods in Ecology and Evolution", "publisher": "", "place": "", "date": "2018", "volume": "9", "issue": "11", "section": "", "partNumber": "", "partTitle": "", "pages": "2216-2225", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/2041-210X.13075", "citationKey": "", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13075", "accessDate": "2023-08-21T13:56:37Z", "PMID": "", "PMCID": "", "ISSN": "2041-210X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "", "extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13075", "tags": [ { "tag": "automated species identification", "type": 1 }, { "tag": "computer vision", "type": 1 }, { "tag": "convolutional neural network", "type": 1 }, { "tag": "deep learning", "type": 1 }, { "tag": "images", "type": 1 } ], "collections": [ "SJ6FI35Z" ], "relations": {}, "dateAdded": "2023-08-21T13:56:37Z", "dateModified": "2023-08-21T13:56:37Z" } }, { "key": "5ETEGSKC", "version": 6835, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/5ETEGSKC", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/5ETEGSKC", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Weinstein", "parsedDate": "2018", "numChildren": 0 }, "data": { "key": "5ETEGSKC", "version": 6835, "itemType": "journalArticle", "title": "A computer vision for animal ecology", "creators": [ { "creatorType": "author", "firstName": "Ben G.", "lastName": "Weinstein" } ], "abstractNote": "A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis.", "publicationTitle": "Journal of Animal Ecology", "publisher": "", "place": "", "date": "2018", "volume": "87", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "533-545", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/1365-2656.12780", "citationKey": "", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2656.12780", "accessDate": "2023-08-21T13:56:06Z", "PMID": "", "PMCID": "", "ISSN": "1365-2656", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2017 The Author. Journal of Animal Ecology © 2017 British Ecological Society", "extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2656.12780", "tags": [ { "tag": "automation", "type": 1 }, { "tag": "camera traps", "type": 1 }, { "tag": "ecological monitoring", "type": 1 }, { "tag": "images", "type": 1 }, { "tag": "unmanned aerial vehicles", "type": 1 } ], "collections": [ "SJ6FI35Z" ], "relations": {}, "dateAdded": "2023-08-21T13:56:07Z", "dateModified": "2023-08-21T13:56:07Z" } }, { "key": "FPQEAV7H", "version": 6834, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/FPQEAV7H", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/FPQEAV7H", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Bondre and Patil", "numChildren": 0 }, "data": { "key": "FPQEAV7H", "version": 6834, "itemType": "journalArticle", "title": "Crop disease identification segmentation algorithm based on Mask-RCNN", "creators": [ { "creatorType": "author", "firstName": "Shweta", "lastName": "Bondre" }, { "creatorType": "author", "firstName": "Dipti", "lastName": "Patil" } ], "abstractNote": "Leaf diseases include various diseases like abscission, gummosis, Alternaria solani, vesicatoria, and Pucciniales. Farmers from all around the world are constantly concerned about the health of various plants. Our proposed technology identifies various leaf diseases at an early stage, alerting farmers and allowing them to take necessary control measures. The collection contains 25,362 photos of normal leaves, bacterial spot, rust, early blight, and other disease-infected leaves in Solanum tuberosum, Solanum lycopersicum, Malus, Zea mays, and Piper nigrum. In this study, a transfer learning ResNet 50 algorithm and a mask regional convolutional neural network are used to segment leaf diseases. An improved faster mask regional-convolutional neural network (IFMR-CNN) method is proposed to find the affected areas in the plant. The experiment first gathers photos of leaf disease for preprocessing, then utilizes a VGG annotator to create labels for the data sets, which are split into a test set and training set. The proposed IFMR-CNN approach is capable of localizing and classifying the disease with 96% accuracy. Accuracy is calculated based on the proportion of properly identified leaf samples in a dataset out of all the leaf samples. Accuracy of disease localization in plant leaves can be measured by the percentage of correctly identified regions of interest in the leaf image that contain the disease symptoms.", "publicationTitle": "Agronomy Journal", "publisher": "", "place": "", "date": "", "volume": "n/a", "issue": "n/a", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1002/agj2.21387", "citationKey": "", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/agj2.21387", "accessDate": "2023-08-21T13:53:59Z", "PMID": "", "PMCID": "", "ISSN": "1435-0645", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.", "extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/agj2.21387", "tags": [], "collections": [ "SJ6FI35Z" ], "relations": {}, "dateAdded": "2023-08-21T13:53:59Z", "dateModified": "2023-08-21T13:53:59Z" } }, { "key": "ZYWK42GX", "version": 6833, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/ZYWK42GX", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/ZYWK42GX", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/46733/items/XQM7U5KH", "type": "application/json" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } } }, "data": { "key": "ZYWK42GX", "version": 6833, "parentItem": "XQM7U5KH", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2021-07-26T06:52:27Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/34244409", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2021-07-26T06:52:27Z", "dateModified": "2021-07-26T06:52:27Z" } }, { "key": "XQM7U5KH", "version": 6833, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/XQM7U5KH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/XQM7U5KH", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Azpeitia et al.", "parsedDate": "2021-07-09", "numChildren": 1 }, "data": { "key": "XQM7U5KH", "version": 6833, "itemType": "journalArticle", "title": "Cauliflower fractal forms arise from perturbations of floral gene networks", "creators": [ { "creatorType": "author", "firstName": "Eugenio", "lastName": "Azpeitia" }, { "creatorType": "author", "firstName": "Gabrielle", "lastName": "Tichtinsky" }, { "creatorType": "author", "firstName": "Marie Le", "lastName": "Masson" }, { "creatorType": "author", "firstName": "Antonio", "lastName": "Serrano-Mislata" }, { "creatorType": "author", "firstName": "Jérémy", "lastName": "Lucas" }, { "creatorType": "author", "firstName": "Veronica", "lastName": "Gregis" }, { "creatorType": "author", "firstName": "Carlos", "lastName": "Gimenez" }, { "creatorType": "author", "firstName": "Nathanaël", "lastName": "Prunet" }, { "creatorType": "author", "firstName": "Etienne", "lastName": "Farcot" }, { "creatorType": "author", "firstName": "Martin M.", "lastName": "Kater" }, { "creatorType": "author", "firstName": "Desmond", "lastName": "Bradley" }, { "creatorType": "author", "firstName": "Francisco", "lastName": "Madueño" }, { "creatorType": "author", "firstName": "Christophe", "lastName": "Godin" }, { "creatorType": "author", "firstName": "Francois", "lastName": "Parcy" } ], "abstractNote": "Vegetal fractals\nCauliflower, along with dahlias and daisies, develop as phyllotactic spirals. Azpeitia et al. combined modeling with experimental investigation to clarify the gene-regulatory network that sets up a multitude of undeveloped flowers to form a cauliflower curd. Irrepressible inflorescence identity genes in the context of dysfunctional meristems and slow internode elongation result in piles of incomplete flowers. If meristem size drifts during organogenesis, then the conical structures of the Romanesco form emerge in fractal formation.\nScience, abg5999, this issue p. 192\nThroughout development, plant meristems regularly produce organs in defined spiral, opposite, or whorl patterns. Cauliflowers present an unusual organ arrangement with a multitude of spirals nested over a wide range of scales. How such a fractal, self-similar organization emerges from developmental mechanisms has remained elusive. Combining experimental analyses in an Arabidopsis thaliana cauliflower-like mutant with modeling, we found that curd self-similarity arises because the meristems fail to form flowers but keep the “memory” of their transient passage in a floral state. Additional mutations affecting meristem growth can induce the production of conical structures reminiscent of the conspicuous fractal Romanesco shape. This study reveals how fractal-like forms may emerge from the combination of key, defined perturbations of floral developmental programs and growth dynamics.\nShifts in floral regulatory networks determine the emergence of fractal patterns in growing cauliflowers.\nShifts in floral regulatory networks determine the emergence of fractal patterns in growing cauliflowers.", "publicationTitle": "Science", "publisher": "", "place": "", "date": "2021/07/09", "volume": "373", "issue": "6551", "section": "", "partNumber": "", "partTitle": "", "pages": "192-197", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1126/science.abg5999", "citationKey": "", "url": "https://science.sciencemag.org/content/373/6551/192", "accessDate": "2021-07-26T06:52:26Z", "PMID": "", "PMCID": "", "ISSN": "0036-8075, 1095-9203", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "science.sciencemag.org", "callNumber": "", "rights": "Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. https://www.sciencemag.org/about/science-licenses-journal-article-reuseThis is an article distributed under the terms of the Science Journals Default License.", "extra": "PMID: 34244409", "tags": [], "collections": [ "2G2TBGDU" ], "relations": {}, "dateAdded": "2021-07-26T06:52:26Z", "dateModified": "2021-07-26T06:52:26Z" } }, { "key": "TQIPTGM4", "version": 6832, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/TQIPTGM4", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/TQIPTGM4", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "TQIPTGM4", "version": 6832, "itemType": "webpage", "title": "Strange Attractors", "creators": [], "abstractNote": "", "websiteTitle": "", "websiteType": "", "date": "", "publisher": "", "place": "", "DOI": "", "citationKey": "", "url": "http://sprott.physics.wisc.edu/sa.htm", "accessDate": "2019-10-16T06:53:48Z", "shortTitle": "", "language": "", "rights": "", "extra": "", "tags": [], "collections": [ "ZB79C2R3" ], "relations": {}, "dateAdded": "2019-10-16T06:53:48Z", "dateModified": "2019-10-16T06:53:48Z" } }, { "key": "WI4WZGJN", "version": 6831, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/WI4WZGJN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/WI4WZGJN", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Gomes and Maillard", "parsedDate": "2016-06-08", "numChildren": 0 }, "data": { "key": "WI4WZGJN", "version": 6831, "itemType": "journalArticle", "title": "Detection of Tree Crowns in Very High Spatial Resolution Images", "creators": [ { "creatorType": "author", "firstName": "Marilia Ferreira", "lastName": "Gomes" }, { "creatorType": "author", "firstName": "Philippe", "lastName": "Maillard" } ], "abstractNote": "The requirements for advanced knowledge on forest resources have led researchers to develop efficient methods to provide detailed information about trees. Since 1999, orbital remote sensing has been providing very high resolution (VHR) image data. The new generation of satellite allows individual tree crowns to be visually identifiable. The increase in spatial resolution has also had a profound effect in image processing techniques and has motivated the development of new object-based procedures to extract information. Tree crown detection has become a major area of research in image analysis considering the complex nature of trees in an uncontrolled environment. This chapter is subdivided into two parts. Part I offers an overview of the state of the art in computer detection of individual tree crowns in VHR images. Part II presents a new hybrid approach developed by the authors that integrates geometrical-optical modeling (GOM), marked point processes (MPP), and template matching (TM) to individually detect tree crowns in VHR images. The method is presented for two different applications: isolated tree detection in an urban environment and automatic tree counting in orchards with an average performance rate of 82% for tree detection and above 90% for tree counting in orchards.", "publicationTitle": "Environmental Applications of Remote Sensing", "publisher": "", "place": "", "date": "2016-06-08", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.5772/62122", "citationKey": "", "url": "https://www.intechopen.com/books/environmental-applications-of-remote-sensing/detection-of-tree-crowns-in-very-high-spatial-resolution-images", "accessDate": "2019-08-30T07:02:28Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.intechopen.com", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "TTEZGI5H" ], "relations": {}, "dateAdded": "2019-08-30T07:02:28Z", "dateModified": "2019-08-30T07:02:28Z" } }, { "key": "K3BMKEJZ", "version": 6830, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/K3BMKEJZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/K3BMKEJZ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Fletcher and Fortin", "parsedDate": "2019-02-27", "numChildren": 1 }, "data": { "key": "K3BMKEJZ", "version": 6830, "itemType": "book", "title": "Spatial Ecology and Conservation Modeling: Applications with R", "creators": [ { "creatorType": "author", "firstName": "Robert", "lastName": "Fletcher" }, { "creatorType": "author", "firstName": "Marie-Josée", "lastName": "Fortin" } ], "abstractNote": "This book provides a foundation for modern applied ecology. Much of current ecology research and conservation addresses problems across landscapes and regions, focusing on spatial patterns and processes. This book is aimed at teaching fundamental concepts and focuses on learning-by-doing through the use of examples with the software R. It is intended to provide an entry-level, easily accessible foundation for students and practitioners interested in spatial ecology and conservation.", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "1st ed. 2018", "date": "27. Februar 2019", "publisher": "Springer", "place": "New York, NY", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "523", "ISBN": "978-3-030-01988-4", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Spatial Ecology and Conservation Modeling", "language": "Englisch", "libraryCatalog": "Amazon", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "483UU5HE" ], "relations": {}, "dateAdded": "2019-08-12T11:17:18Z", "dateModified": "2019-08-12T11:17:24Z" } }, { "key": "KRSD6F88", "version": 6830, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/KRSD6F88", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/KRSD6F88", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/46733/items/K3BMKEJZ", "type": "application/json" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } } }, "data": { "key": "KRSD6F88", "version": 6830, "parentItem": "K3BMKEJZ", "itemType": "attachment", "linkMode": "linked_url", "title": "Amazon.com Link", "accessDate": "2019-08-12T11:17:18Z", "url": "https://www.amazon.de/Spatial-Ecology-Conservation-Modeling-Applications/dp/3030019888/ref=sr_1_13?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&keywords=r+ecology&qid=1565608294&s=books-intl-de&sr=1-13", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2019-08-12T11:17:18Z", "dateModified": "2019-08-12T11:17:18Z" } }, { "key": "LPYHDEAI", "version": 6829, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/LPYHDEAI", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/LPYHDEAI", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Bayr and Puschmann", "parsedDate": "2019-03-01", "numChildren": 0 }, "data": { "key": "LPYHDEAI", "version": 6829, "itemType": "journalArticle", "title": "Automatic detection of woody vegetation in repeat landscape photographs using a convolutional neural network", "creators": [ { "creatorType": "author", "firstName": "Ulrike", "lastName": "Bayr" }, { "creatorType": "author", "firstName": "Oskar", "lastName": "Puschmann" } ], "abstractNote": "Repeat photography is an efficient method for documenting long-term landscape changes. So far, the usage of repeat photographs for quantitative analyses is limited to approaches based on manual classification. In this paper, we demonstrate the application of a convolutional neural network (CNN) for the automatic detection and classification of woody regrowth vegetation in repeat landscape photographs. We also tested if the classification results based on the automatic approach can be used for quantifying changes in woody vegetation cover between image pairs. The CNN was trained with 50 × 50 pixel tiles of woody vegetation and non-woody vegetation. We then tested the classifier on 17 pairs of repeat photographs to assess the model performance on unseen data. Results show that the CNN performed well in differentiating woody vegetation from non-woody vegetation (accuracy = 87.7%), but accuracy varied strongly between individual images. The very similar appearance of woody vegetation and herbaceous species in photographs made this a much more challenging task compared to the classification of vegetation as a single class (accuracy = 95.2%). In this regard, image quality was identified as one important factor influencing classification accuracy. Although the automatic classification provided good individual results on most of the 34 test photographs, change statistics based on the automatic approach deviated from actual changes. Nevertheless, the automatic approach was capable of identifying clear trends in increasing or decreasing woody vegetation in repeat photographs. Generally, the use of repeat photography in landscape monitoring represents a significant added value to other quantitative data retrieved from remote sensing and field measurements. Moreover, these photographs are able to raise awareness on landscape change among policy makers and public as well as they provide clear feedback on the effects of land management.", "publicationTitle": "Ecological Informatics", "publisher": "", "place": "", "date": "March 1, 2019", "volume": "50", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "220-233", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Ecological Informatics", "DOI": "10.1016/j.ecoinf.2019.01.012", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1574954118303121", "accessDate": "2019-08-12T11:02:06Z", "PMID": "", "PMCID": "", "ISSN": "1574-9541", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Landscape change", "type": 1 }, { "tag": "Landscape monitoring", "type": 1 }, { "tag": "Machine learning", "type": 1 }, { "tag": "Photo monitoring", "type": 1 }, { "tag": "Repeat photography", "type": 1 }, { "tag": "Vegetation succession", "type": 1 } ], "collections": [ "F4CZA7XX" ], "relations": {}, "dateAdded": "2019-08-12T11:02:06Z", "dateModified": "2019-08-12T11:02:09Z" } }, { "key": "BJJTMXPB", "version": 6828, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/BJJTMXPB", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/BJJTMXPB", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Yu et al.", "parsedDate": "2019-05-01", "numChildren": 0 }, "data": { "key": "BJJTMXPB", "version": 6828, "itemType": "journalArticle", "title": "Landscape ecology development supported by geospatial technologies: A review", "creators": [ { "creatorType": "author", "firstName": "Huan", "lastName": "Yu" }, { "creatorType": "author", "firstName": "Xiangmeng", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Bo", "lastName": "Kong" }, { "creatorType": "author", "firstName": "Ruopu", "lastName": "Li" }, { "creatorType": "author", "firstName": "Guangxing", "lastName": "Wang" } ], "abstractNote": "Numerous technologies have contributed to the recent development of landscape ecology, especially the geospatial technological advances constitute a revolution in landscape ecology. Extensive applications of geospatial technologies, such as fractal theory, geographic information systems (GIS) and remote sensing (RS) in landscape ecology research, suggest the necessity of such a review of research progress in this field. In this study, a brief introduction to fractal theory, GIS and RS and how they were applied in landscape ecology were provided first. Then, the current state-of-the-art was summarized and analyzed as reference for further promoting the development of landscape ecology science and its applications. Finally, opportunities and challenges of landscape ecology applications using these new technologies were discussed and concluded for future research. It was contended that a combination and integration of these technologies can substantially advance the study of landscape ecology for data acquisition, process modeling, scale transformation, result analysis and visualization. However, as no theoretical framework and application cases of applying an integration of fractal theory, GIS and RS techniques to landscape ecology research have been established, further studies are still much needed.", "publicationTitle": "Ecological Informatics", "publisher": "", "place": "", "date": "May 1, 2019", "volume": "51", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "185-192", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Ecological Informatics", "DOI": "10.1016/j.ecoinf.2019.03.006", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1574954119300329", "accessDate": "2019-08-12T10:59:11Z", "PMID": "", "PMCID": "", "ISSN": "1574-9541", "archive": "", "archiveLocation": "", "shortTitle": "Landscape ecology development supported by geospatial technologies", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Fractal theory", "type": 1 }, { "tag": "Geographic information systems", "type": 1 }, { "tag": "Geospatial technologies", "type": 1 }, { "tag": "Landscape ecology", "type": 1 }, { "tag": "Remote sensing", "type": 1 } ], "collections": [ "5CPIDRP5" ], "relations": {}, "dateAdded": "2019-08-12T10:59:11Z", "dateModified": "2019-08-12T10:59:11Z" } }, { "key": "SCT85HFE", "version": 6827, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/SCT85HFE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/SCT85HFE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Moen et al.", "parsedDate": "2019-05-27", "numChildren": 0 }, "data": { "key": "SCT85HFE", "version": 6827, "itemType": "journalArticle", "title": "Deep learning for cellular image analysis", "creators": [ { "creatorType": "author", "firstName": "Erick", "lastName": "Moen" }, { "creatorType": "author", "firstName": "Dylan", "lastName": "Bannon" }, { "creatorType": "author", "firstName": "Takamasa", "lastName": "Kudo" }, { "creatorType": "author", "firstName": "William", "lastName": "Graf" }, { "creatorType": "author", "firstName": "Markus", "lastName": "Covert" }, { "creatorType": "author", "firstName": "David Van", "lastName": "Valen" } ], "abstractNote": "A Review on applications of deep machine learning in image analysis that offers practical guidance for biologists.", "publicationTitle": "Nature Methods", "publisher": "", "place": "", "date": "2019-05-27", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1038/s41592-019-0403-1", "citationKey": "", "url": "https://www.nature.com/articles/s41592-019-0403-1", "accessDate": "2019-05-29T06:30:13Z", "PMID": "", "PMCID": "", "ISSN": "1548-7105", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "En", "libraryCatalog": "www.nature.com", "callNumber": "", "rights": "2019 Springer Nature America, Inc.", "extra": "", "tags": [], "collections": [ "F4CZA7XX" ], "relations": {}, "dateAdded": "2019-05-29T06:30:13Z", "dateModified": "2019-05-29T06:30:13Z" } }, { "key": "LS92H76F", "version": 6826, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/LS92H76F", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/LS92H76F", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "LS92H76F", "version": 6826, "itemType": "webpage", "title": "Handbook of Spatial Analysis - Theory and Application with R Insee Méthodes N°131 - October 2018", "creators": [], "abstractNote": "", "websiteTitle": "", "websiteType": "", "date": "", "publisher": "", "place": "", "DOI": "", "citationKey": "", "url": "https://www.insee.fr/en/information/3635545", "accessDate": "2019-05-13T06:22:22Z", "shortTitle": "", "language": "", "rights": "", "extra": "", "tags": [], "collections": [ "626V5AQ8" ], "relations": {}, "dateAdded": "2019-05-13T06:22:22Z", "dateModified": "2019-05-13T06:23:53Z" } }, { "key": "DQNGI8P3", "version": 6824, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/DQNGI8P3", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/DQNGI8P3", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Garcia et al.", "parsedDate": "2017-12-08", "numChildren": 0 }, "data": { "key": "DQNGI8P3", "version": 6824, "itemType": "journalArticle", "title": "Geostatistics and Geographic Information System to Analyze the Spatial Distribution of the Diversity of Anastrepha Species (Diptera: Tephritidae): the Effect of Forest Fragments in an Urban Area", "creators": [ { "creatorType": "author", "firstName": "A. G.", "lastName": "Garcia" }, { "creatorType": "author", "firstName": "M. R.", "lastName": "Araujo" }, { "creatorType": "author", "firstName": "K.", "lastName": "Uramoto" }, { "creatorType": "author", "firstName": "J. M. M.", "lastName": "Walder" }, { "creatorType": "author", "firstName": "R. A.", "lastName": "Zucchi" } ], "abstractNote": "Abstract. Fruit flies are among the most damaging insect pests of commercial fruit in Brazil. It is important to understand the landscape elements that may fav", "publicationTitle": "Environmental Entomology", "publisher": "", "place": "", "date": "2017/12/08", "volume": "46", "issue": "6", "section": "", "partNumber": "", "partTitle": "", "pages": "1189-1194", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Environ Entomol", "DOI": "10.1093/ee/nvx145", "citationKey": "", "url": "https://academic.oup.com/ee/article/46/6/1189/4210700", "accessDate": "2019-04-12T13:17:04Z", "PMID": "", "PMCID": "", "ISSN": "0046-225X", "archive": "", "archiveLocation": "", "shortTitle": "Geostatistics and Geographic Information System to Analyze the Spatial Distribution of the Diversity of Anastrepha Species (Diptera", "language": "en", "libraryCatalog": "academic.oup.com", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "626V5AQ8" ], "relations": {}, "dateAdded": "2019-04-12T13:17:04Z", "dateModified": "2019-04-12T13:17:04Z" } }, { "key": "BSXUBRK2", "version": 6823, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/BSXUBRK2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/BSXUBRK2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "BSXUBRK2", "version": 6823, "itemType": "book", "title": "", "creators": [], "abstractNote": "", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "", "publisher": "", "place": "", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "", "ISBN": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2019-02-07T10:09:45Z", "dateModified": "2019-02-07T10:09:45Z" } }, { "key": "XQGCR7IY", "version": 6822, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/XQGCR7IY", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/XQGCR7IY", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "XQGCR7IY", "version": 6822, "itemType": "webpage", "title": "Spatio-Temporal Statistics with R", "creators": [], "abstractNote": "The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these", "websiteTitle": "CRC Press", "websiteType": "", "date": "", "publisher": "", "place": "", "DOI": "", "citationKey": "", "url": "https://www.crcpress.com/Spatio-Temporal-Statistics-with-R/Wikle-Zammit-Mangion-Cressie/p/book/9781138711136", "accessDate": "2019-02-07T10:08:54Z", "shortTitle": "", "language": "en", "rights": "", "extra": "", "tags": [], "collections": [ "626V5AQ8" ], "relations": {}, "dateAdded": "2019-02-07T10:08:54Z", "dateModified": "2019-02-07T10:08:54Z" } }, { "key": "VYQXI7WM", "version": 6821, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/VYQXI7WM", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/VYQXI7WM", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Lister and Leites", "parsedDate": "2018-06-24", "numChildren": 0 }, "data": { "key": "VYQXI7WM", "version": 6821, "itemType": "journalArticle", "title": "Modeling and simulation of tree spatial patterns in an oak-hickory forest with a modular, hierarchical spatial point process framework", "creators": [ { "creatorType": "author", "firstName": "Andrew J.", "lastName": "Lister" }, { "creatorType": "author", "firstName": "Laura P.", "lastName": "Leites" } ], "abstractNote": "Modeling and simulating tree spatial distribution in complex forests is important to ecologists and applied scientists who seek to both understand pattern-creating biological processes and create realistic model forests that can be used for hypothesis testing and sampling experiments. Several patterns of tree spatial distribution can co-occur in a forest. Clustering can occur due to localized patterns of growth and mortality of larger trees and corresponding regeneration of smaller trees, while trees of medium size can exhibit more uniform patterns. Inter-tree interaction may be characterized by asymmetry of competitive strength, with larger individuals having a disproportionate influence on smaller individuals. Many point process modeling approaches exist, but few have incorporated hierarchical principles that describe inter-tree competition. Those that do sometimes assume symmetric interaction among trees, which can be unrealistic. None of the existing models allow for the use of different model types at each level of the hierarchy, something that could provide a more realistic representation of the patterns displayed by trees of different size. In this study, we model and simulate a forest using a novel, modular, hierarchical approach that allows for the use of different model types at each hierarchy level, and incorporates asymmetrical interactions as well as the effects of environmental covariates. The forest is a mid-successional 8-ha stem-mapped oak-hickory watershed in Pennsylvania, USA. Results suggest that asymmetrical interactions based on tree size do exist, and these are mediated by the effects of topography. The hierarchical models reproduce the spatial patterns found in the original data better than non-hierarchical versions of the same models. The flexibility afforded by the modularity of our modeling framework will allow simulation of forests with varying levels of complexity as well as the testing of ecological hypotheses about drivers of spatial pattern creation.", "publicationTitle": "Ecological Modelling", "publisher": "", "place": "", "date": "June 24, 2018", "volume": "378", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "37-45", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Ecological Modelling", "DOI": "10.1016/j.ecolmodel.2018.03.012", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0304380018301108", "accessDate": "2018-06-07T07:31:23Z", "PMID": "", "PMCID": "", "ISSN": "0304-3800", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Competition", "type": 1 }, { "tag": "Facilitation", "type": 1 }, { "tag": "Hierarchical models", "type": 1 }, { "tag": "Point process models", "type": 1 }, { "tag": "Spatial patterns", "type": 1 } ], "collections": [ "626V5AQ8" ], "relations": {}, "dateAdded": "2018-06-07T07:31:23Z", "dateModified": "2018-06-07T07:31:23Z" } }, { "key": "3IZ3IW5B", "version": 6820, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/3IZ3IW5B", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/3IZ3IW5B", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Box", "parsedDate": "1976", "numChildren": 0 }, "data": { "key": "3IZ3IW5B", "version": 6820, "itemType": "journalArticle", "title": "Science and Statistics", "creators": [ { "creatorType": "author", "firstName": "George E. P.", "lastName": "Box" } ], "abstractNote": "Aspects of scientific method are discussed: In particular, its representation as a motivated iteration in which, in succession, practice confronts theory, and theory, practice. Rapid progress requires sufficient flexibility to profit from such confrontations, and the ability to devise parsimonious but effective models, to worry selectively about model inadequacies and to employ mathematics skillfully but appropriately. The development of statistical methods at Rothamsted Experimental Station by Sir Ronald Fisher is used to illustrate these themes.", "publicationTitle": "Journal of the American Statistical Association", "publisher": "", "place": "", "date": "1976", "volume": "71", "issue": "356", "section": "", "partNumber": "", "partTitle": "", "pages": "791-799", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.2307/2286841", "citationKey": "", "url": "http://www.jstor.org/stable/2286841", "accessDate": "2018-03-28T14:10:26Z", "PMID": "", "PMCID": "", "ISSN": "0162-1459", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "JSTOR", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "3VDMEEUC" ], "relations": {}, "dateAdded": "2018-03-28T14:10:26Z", "dateModified": "2018-03-28T14:10:26Z" } }, { "key": "TJNI2PNE", "version": 6819, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/TJNI2PNE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/TJNI2PNE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Zuur et al.", "parsedDate": "2010-03-01", "numChildren": 0 }, "data": { "key": "TJNI2PNE", "version": 6819, "itemType": "journalArticle", "title": "A protocol for data exploration to avoid common statistical problems", "creators": [ { "creatorType": "author", "firstName": "Alain F.", "lastName": "Zuur" }, { "creatorType": "author", "firstName": "Elena N.", "lastName": "Ieno" }, { "creatorType": "author", "firstName": "Chris S.", "lastName": "Elphick" } ], "abstractNote": "", "publicationTitle": "Methods in Ecology and Evolution", "publisher": "", "place": "", "date": "2010/03/01", "volume": "1", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "3-14", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/j.2041-210X.2009.00001.x", "citationKey": "", "url": "https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/j.2041-210X.2009.00001.x", "accessDate": "2018-03-28T14:05:29Z", "PMID": "", "PMCID": "", "ISSN": "2041-210X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "besjournals.onlinelibrary.wiley.com", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "HKU863U2" ], "relations": {}, "dateAdded": "2018-03-28T14:05:29Z", "dateModified": "2018-03-28T14:05:29Z" } }, { "key": "E42YVTNW", "version": 6817, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/E42YVTNW", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/E42YVTNW", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Turner and Gardner", "parsedDate": "2015-11-03", "numChildren": 0 }, "data": { "key": "E42YVTNW", "version": 6817, "itemType": "book", "title": "Landscape Ecology in Theory and Practice: Pattern and Process", "creators": [ { "creatorType": "author", "firstName": "Monica G.", "lastName": "Turner" }, { "creatorType": "author", "firstName": "Robert H.", "lastName": "Gardner" } ], "abstractNote": "", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "2nd ed. 2015 edition", "date": "November 3, 2015", "publisher": "Springer", "place": "New York", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "482", "ISBN": "978-1-4939-2793-7", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Landscape Ecology in Theory and Practice", "language": "English", "libraryCatalog": "Amazon", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "5CPIDRP5" ], "relations": {}, "dateAdded": "2018-03-09T12:25:28Z", "dateModified": "2018-03-09T12:25:28Z" } }, { "key": "KPTF9FB9", "version": 6816, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/KPTF9FB9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/KPTF9FB9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Gergel and Turner", "parsedDate": "2017-04-01", "numChildren": 0 }, "data": { "key": "KPTF9FB9", "version": 6816, "itemType": "book", "title": "Learning Landscape Ecology: A Practical Guide to Concepts and Techniques", "creators": [ { "creatorType": "editor", "firstName": "Sarah E.", "lastName": "Gergel" }, { "creatorType": "editor", "firstName": "Monica G.", "lastName": "Turner" } ], "abstractNote": "", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "2nd ed. 2017 edition", "date": "April 1, 2017", "publisher": "Springer", "place": "New York, NY, U.S.A", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "350", "ISBN": "978-1-4939-6372-0", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "Learning Landscape Ecology", "language": "English", "libraryCatalog": "Amazon", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "5CPIDRP5" ], "relations": {}, "dateAdded": "2018-03-09T12:25:07Z", "dateModified": "2018-03-09T12:25:07Z" } }, { "key": "KP5W2RFD", "version": 6815, "library": { "type": "group", "id": 46733, "name": "Ecological Modelling", "links": { "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/46733/items/KP5W2RFD", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ecological_modelling/items/KP5W2RFD", "type": "text/html" } }, "meta": { "createdByUser": { "id": 628510, "username": "marcel.", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/marcel.", "type": "text/html" } } }, "creatorSummary": "Young et al.", "parsedDate": "2017-04-01", "numChildren": 0 }, "data": { "key": "KP5W2RFD", "version": 6815, "itemType": "journalArticle", "title": "A survival guide to Landsat preprocessing", "creators": [ { "creatorType": "author", "firstName": "Nicholas E.", "lastName": "Young" }, { "creatorType": "author", "firstName": "Ryan S.", "lastName": "Anderson" }, { "creatorType": "author", "firstName": "Stephen M.", "lastName": "Chignell" }, { "creatorType": "author", "firstName": "Anthony G.", "lastName": "Vorster" }, { "creatorType": "author", "firstName": "Rick", "lastName": "Lawrence" }, { "creatorType": "author", "firstName": "Paul H.", "lastName": "Evangelista" } ], "abstractNote": "Landsat data are increasingly used for ecological monitoring and research. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These issues can, at best, make determining the correct preprocessing workflow a difficult and time-consuming task and, at worst, lead to erroneous results. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co-registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well tested, easily available, and sufficiently documented. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms.", "publicationTitle": "Ecology", "publisher": "", "place": "", "date": "April 1, 2017", "volume": "98", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "920-932", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Ecology", "DOI": "10.1002/ecy.1730", "citationKey": "", "url": "http://onlinelibrary.wiley.com/doi/10.1002/ecy.1730/abstract", "accessDate": "2018-03-09T12:15:52Z", "PMID": "", "PMCID": "", "ISSN": "1939-9170", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "atmospheric correction", "type": 1 }, { "tag": "change detection", "type": 1 }, { "tag": "decision tree", "type": 1 }, { "tag": "ecology", "type": 1 }, { "tag": "image", "type": 1 }, { "tag": "normalization", "type": 1 }, { "tag": "radiometric correction", "type": 1 }, { "tag": "remote sensing", "type": 1 }, { "tag": "review", "type": 1 }, { "tag": "satellite", "type": 1 }, { "tag": "topographic correction", "type": 1 }, { "tag": "workflow", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2018-03-09T12:15:52Z", "dateModified": "2018-03-09T12:15:52Z" } } ]