Error Propagation Environmental Modeling Gis
Bhutan Bolivia Bosnia And Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory Brunei Darussalam Bulgaria Burundi Burkina Faso Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo, The Democratic Republic Of The Congo Cook Islands Costa Rica Croatia Cyprus Czech Republic Cïte D'ivoire Denmark Djibouti Dominican Republic Dominica Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands (Malvinas) Faroe Islands Fiji Finland France French Southern Territories French Polynesia French Guiana Gabon Gambia Georgia Germany Ghana Gibraltar Grenada Greenland Greece Guam Guatemala Guadeloupe Guernsey Guinea-Bissau Guinea Guyana Haiti Heard Island And Mcdonald Islands Holy See (Vatican City State) Hong Kong Honduras Hungary Iceland India Indonesia Iraq Ireland Isle Of Man Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Korea, Republic Of Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libyan Arab Jamahiriya Liechtenstein Lithuania Luxembourg Macedonia, The Former Yugoslav Republic Of Macao Madagascar Mali Malta Maldives Malaysia Malawi Martinique Marshall Islands Mauritania Mauritius Mayotte Mexico Micronesia, Federated States Of Moldova, Republic Of Montserrat Montenegro Monaco Mongolia Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Netherlands Antilles New Zealand New Caledonia Nicaragua Nigeria Niger Niue Norway Northern Mariana Islands Norfolk Island Oman Pakistan Palau Palestinian Territory, Occupied Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Qatar Reunion Romania Russian Federation Rwanda Saint Martin Saint Pierre And Miquelon Saint Vincent And The Grenadines Saint Lucia Saint Barthƒlemy Saint Kitts And Nevis Saint Helena Samoa San Marino Sao Tome And Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Georgia And The South Sandwich Islands South Africa Spain Sri Lanka Suriname Svalbard And Jan Mayen Swaziland Sweden Switzerland Taiwan Tajikistan Tanzania, United Republic Of Thailand Timor-Leste Togo Tokelau Ton
Request full-text Error Propagation in Environmental Modeling with GISChapter · January 1998 with 43 Reads Publisher: Taylor and Francis1st Gerard Heuvelink38.55 · Wageningen University & ResearchDo you want to read the rest of this chapter?Request full-text CitationsCitations276ReferencesReferences1Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index"Classification-derived area class maps, such as land use/cover and crop type maps, are routinely used as input data for various environmental modeling tasks, such as natural disaster prediction modeling, crop yield assessment, and spatial estimation of air https://www.crcpress.com/Error-Propagation-in-Environmental-Modelling-with-GIS/Heuvelink/p/book/9780748407439 pollution [1][2][3]. Since class maps are used as inputs into environmental models, any errors arising during classification may propagate to the applied model outputs, hence leading to error propagation problems [4]. Therefore, it is of critical importance to generate reliable classification results for further analysis. "[Show abstract] [Hide abstract] ABSTRACT: Traditional classification accuracy assessments based on summary statistics https://www.researchgate.net/publication/224839892_Error_Propagation_in_Environmental_Modeling_with_GIS from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-derived ambiguity index with classification accuracy values at reference data locations. As for the ambiguity measure, a novel discrimination capability index (DCI) is defined from per class posteriori probabilities and then calibrated via logistic regression to derive soft probabilities. Integration of indicator-coded reference data with soft probabilities is finally carried out for mapping classification accuracy. It is demonstrated via a case study involving classification of multi-temporal and multi-sensor SAR datasets, that the proposed approach can provide a map of locally-varying accuracy values, while respecting the overall accuracy derived from the confusion matrix. It can also highlight areas where the benefit of data fusion was significant. It is expected that the indicator approach presented in this paper could be a useful methodology for assessing the spatial quality of classificat
Industry Education Engineering & Technology Environment & Agriculture Environment and Sustainability Food Science & Technology Geography Health and Social Care Humanities Information Science error propagation Language & Literature Law Mathematics & Statistics Medicine, Dentistry, Nursing & Allied Health Museum and Heritage Studies Physical Sciences Politics & International Relations Social Sciences Sports and error propagation environmental Leisure Tourism, Hospitality and Events Urban Studies Information for Authors Editors Librarians Societies Open access Overview Open journals Open Select Cogent OA Help and info Help FAQs Press releases Contact us Commercial services Connect with Taylor & Francis © Informa Group plc Privacy policy & cookies Terms & conditions Accessibility Registered in England & Wales No. 3099067 5 Howick Place | London | SW1P 1WG Accept This website uses cookies to ensure you get the best experience on our website