{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:51Z","timestamp":1760147031000,"version":"build-2065373602"},"reference-count":89,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Environment","award":["CUP B76C18000890001"],"award-info":[{"award-number":["CUP B76C18000890001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Technological advances in Earth observation made images characterized by high spatial and temporal resolutions available, nevertheless bringing with them the radiometric heterogeneity of small geographical entities, often also changing in time. Among small geographical entities, hill lakes exhibit a widespread distribution, and their census is sometimes partial or shows unreliable data. High resolution and heterogeneity have boosted the development of geographic object-based image analysis algorithms. This research analyzes which is the most suitable period for acquiring satellite images to identify and delimitate hill lakes. This is achieved by analyzing the spectral separability of the surface reflectance of hill lakes from surrounding bare or vegetated soils and by implementing a semiautomatic procedure to enhance the segmentation phase of a GEOBIA algorithm. The proposed procedure was applied to high spatial resolution satellite images acquired in two different climate periods (arid and temperate), corresponding to dry and vegetative seasons. The segmentation parameters were tuned by minimizing an under- and oversegmentation metric on surfaces and perimeters of hill lakes selected as the reference. The separability of hill lakes from their surrounding was evaluated using Euclidean and divergence metrics both in the arid and temperate periods. The classification accuracy was evaluated by calculating the error matrix and normalized error matrix. Classes\u2019 reflectances in the image acquired in the arid period show the highest average separability (3\u20134 higher than in the temperate one). The segmentation based on the reference areas performs more than that based on the reference perimeters (metric \u2248 20% lower). Both separability metrics and classification accuracies indicate that images acquired in the arid period are more suitable than temperate ones to map hill lakes.<\/jats:p>","DOI":"10.3390\/rs15010262","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:00:59Z","timestamp":1672628459000},"page":"262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["On the Choice of the Most Suitable Period to Map Hill Lakes via Spectral Separability and Object-Based Image Analyses"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2778-4680","authenticated-orcid":false,"given":"Antonino","family":"Maltese","sequence":"first","affiliation":[{"name":"Department of Engineering, Universit\u00e0 degli Studi di Palermo, 90128 Palermo, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"413","DOI":"10.5721\/EuJRS20144724","article-title":"Automatic Three-Dimensional Features Extraction: The Case Study of L\u2019Aquila for Collapse Identification after April 06, 2009 Earthquake","volume":"47","author":"Baiocchi","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object Based Image Analysis for Remote Sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for Object-Based Image Analysis (OBIA): A Review of Algorithms and Challenges from Remote Sensing Perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","first-page":"555","article-title":"Object-Oriented Image Processing in an Integrated GIS\/Remote Sensing Environment and Perspectives for Environmental Applications","volume":"2","author":"Blaschke","year":"2000","journal-title":"Environ. Inf. Plan. Politics Public"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/S0304-3800(03)00139-X","article-title":"A Multi-Scale Segmentation\/Object Relationship Modelling Methodology for Landscape Analysis","volume":"168","author":"Burnett","year":"2003","journal-title":"Ecol. Model."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/S0924-2716(02)00161-2","article-title":"Automated Analysis of Ultra High Resolution Remote Sensing Data for Biotope Type Mapping: New Possibilities and Challenges","volume":"57","author":"Ehlers","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lef\u00e8vre, S., Sheeren, D., and Tasar, O. (2019). A Generic Framework for Combining Multiple Segmentations in Geographic Object-Based Image Analysis. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020070"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.isprsjprs.2011.02.006","article-title":"Unsupervised Image Segmentation Evaluation and Refinement Using a Multi-Scale Approach","volume":"66","author":"Johnson","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated Parameterisation for Multi-Scale Image Segmentation on Multiple Layers","volume":"88","author":"Csillik","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2788","DOI":"10.1109\/JSTARS.2018.2846551","article-title":"A Systematic Extraction Approach for Mapping Glacial Lakes in High Mountain Regions of Asia","volume":"11","author":"Zhao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"148535","DOI":"10.1109\/ACCESS.2021.3124453","article-title":"A New Vegetation Index in Short-Wave Infrared Region of Electromagnetic Spectrum","volume":"9","author":"Cimtay","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1109\/TGRS.2012.2208466","article-title":"A New Short-Wave Infrared (SWIR) Method for Quantitative Water Fraction Derivation and Evaluation With EOS\/MODIS and Landsat\/TM Data","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2707","DOI":"10.1007\/s11629-020-6255-4","article-title":"Evaluation of Effective Spectral Features for Glacial Lake Mapping by Using Landsat-8 OLI Imagery","volume":"17","author":"Zhang","year":"2020","journal-title":"J. Mt. Sci."},{"key":"ref_15","first-page":"251","article-title":"Lacs collinaires en Tunisie semi-aride","volume":"5","author":"Talineau","year":"1994","journal-title":"Sci. Et Changements Plan\u00e9taires\/S\u00e9cheresse"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"366","DOI":"10.30955\/gnj.001036","article-title":"Hill Lakes: Innovative Approach for Sustainable Rural Management in the Semi-Arid Areas in Tunisia","volume":"15","author":"Boufaroua","year":"2013","journal-title":"Glob. NEST J."},{"key":"ref_17","unstructured":"(2021, December 14). Deep Learning for Extracting Water Body from Landsat Imagery. Semantic Scholar. Available online: https:\/\/www.semanticscholar.org\/paper\/DEEP-LEARNING-FOR-EXTRACTING-WATER-BODY-FROM-Yang-Tian\/220b6d870bc3616ac3cf9d9801000c4f16bdcd7c."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3482","DOI":"10.1109\/JSTARS.2017.2692959","article-title":"Small Reservoirs Extraction in Semiarid Regions Using Multitemporal Synthetic Aperture Radar Images","volume":"10","author":"Amitrano","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/22797254.2017.1297540","article-title":"Object-Based Water Body Extraction Model Using Sentinel-2 Satellite Imagery","volume":"50","author":"Kaplan","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/TGRS.2008.2004805","article-title":"Suitability and Limitations of ENVISAT ASAR for Monitoring Small Reservoirs in a Semiarid Area","volume":"47","author":"Liebe","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","unstructured":"Liang, S. (2018). 6.07\u2014Remote Sensing of Urban Environments. Comprehensive Remote Sensing, Elsevier."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liang, S., and Wang, J. (2020). Chapter 1\u2014A Systematic View of Remote Sensing. Advanced Remote Sensing, Academic Press. [2nd ed.].","DOI":"10.1016\/B978-0-12-815826-5.00001-5"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liuzzo, L., Puleo, V., Nizza, S., and Freni, G. (2020). Parameterization of a Bayesian Normalized Difference Water Index for Surface Water Detection. Geosciences, 10.","DOI":"10.3390\/geosciences10070260"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/978-3-030-58811-3_57","article-title":"Post-Processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images","volume":"Volume 12252","author":"Sarzana","year":"2020","journal-title":"Lecture Notes in Computer Science"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"165","DOI":"10.5194\/adgeo-49-165-2019","article-title":"Small Reservoirs for a Sustainable Water Resources Management","volume":"49","author":"Casadei","year":"2019","journal-title":"Adv. Geosci."},{"key":"ref_26","first-page":"1","article-title":"Une tentative de d\u00e9limitation et de sch\u00e9matisation des climats intertropicaux","volume":"36","year":"1961","journal-title":"G\u00e9ocarrefour"},{"key":"ref_27","unstructured":"(2022, December 24). Regione Siciliana Assessorato Agricoltura e Foreste, Gruppo IV\u2014Servizi Allo Sviluppo, Unit\u00e0 di Agrometeorologia Climatologia della Sicilia. Available online: http:\/\/www.sias.regione.sicilia.it\/."},{"key":"ref_28","unstructured":"(2022, December 24). Regione Siciliana Assessorato Risorse Agricole e Alimentari Dipartimento Interventi Strutturali SIAS\u2014Servizio Informativo Agrometeorologico Siciliano. Available online: http:\/\/www.sias.regione.sicilia.it\/."},{"key":"ref_29","unstructured":"Didan, K. (2022, December 24). MOD13Q1 MODIS\/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V006, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod13q1v006\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/S0034-4257(99)00022-X","article-title":"MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data","volume":"69","author":"Huete","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_31","unstructured":"ORNL DAAC (2018). MODIS and VIIRS Land Products Global Subsetting and Visualization Tool."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Capodici, F., Cammalleri, C., Francipane, A., Ciraolo, G., La Loggia, G., and Maltese, A. (2020). Soil Water Content Diachronic Mapping: An FFT Frequency Analysis of a Temperature\u2013Vegetation Index. Geosciences, 10.","DOI":"10.3390\/geosciences10010023"},{"key":"ref_33","first-page":"31","article-title":"A Review on Image Segmentation Techniques with Remote Sensing Perspective","volume":"38","author":"Dey","year":"2010","journal-title":"Environ. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jackson, J.A. (2010, January 14\u201316). Automated Image Segmentation for Synthetic Aperture Radar Feature Extraction. Proceedings of the IEEE 2010 National Aerospace & Electronics Conference, Dayton, OH, USA.","DOI":"10.1109\/NAECON.2010.5712922"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s11554-017-0717-0","article-title":"Polarimetric Synthetic Aperture Radar Image Segmentation by Convolutional Neural Network Using Graphical Processing Units","volume":"15","author":"Wang","year":"2018","journal-title":"J. Real-Time Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s12665-022-10376-y","article-title":"Cosine-Similarity Watershed Algorithm for Water-Body Segmentation Applying Deep Neural Network Classifier","volume":"81","author":"Gautam","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1111\/coin.12339","article-title":"Water-Body Segmentation from Satellite Images Using Kapur\u2019s Entropy-Based Thresholding Method","volume":"36","author":"Babu","year":"2020","journal-title":"Comput. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/LGRS.2019.2926412","article-title":"Multiscale Refinement Network for Water-Body Segmentation in High-Resolution Satellite Imagery","volume":"17","author":"Duan","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"155787","DOI":"10.1109\/ACCESS.2019.2949635","article-title":"Multiscale Features Supported DeepLabV3+ Optimization Scheme for Accurate Water Semantic Segmentation","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/LGRS.2018.2794545","article-title":"Automatic Water-Body Segmentation From High-Resolution Satellite Images via Deep Networks","volume":"15","author":"Miao","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Weng, L., Xu, Y., Xia, M., Zhang, Y., Liu, J., and Xu, Y. (2020). Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040256"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7422","DOI":"10.1109\/JSTARS.2021.3098678","article-title":"Deep-Learning-Based Multispectral Satellite Image Segmentation for Water Body Detection","volume":"14","author":"Yuan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lu, M., Ji, S., Yu, H., and Nie, C. (2021). Rich CNN Features for Water-Body Segmentation from Very High Resolution Aerial and Satellite Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13101912"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","article-title":"Efficient Graph-Based Image Segmentation","volume":"59","author":"Felzenszwalb","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shepherd, J.D., Bunting, P., and Dymond, J.R. (2019). Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination. Remote Sens., 11.","DOI":"10.3390\/rs11060658"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.cageo.2013.08.007","article-title":"Remote Sensing and GIS Software Library (RSGISLib)","volume":"62","author":"Bunting","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/14498596.2019.1615011","article-title":"Multi-Resolution Segmentation Parameters Optimization and Evaluation for VHR Remote Sensing Image Based on MeanNSQI and Discrepancy Measure","volume":"66","author":"Chen","year":"2021","journal-title":"J. Spat. Sci."},{"key":"ref_48","unstructured":"Karak\u0131\u015f, S., Marangoz, A., and Buyuksalih, G. (2006, January 14\u201316). Analysis of Segmentation Parameters in Ecognition Software Using High Resolution Quickbird Ms Imagery. Proceedings of the ISPRS Workshop on Topographic Mapping from Space, Ankara, Turkey."},{"key":"ref_49","unstructured":"(2022, July 16). Mean Shift: A Robust Approach toward Feature Space Analysis|IEEE Journals & Magazine|IEEE Xplore. Available online: https:\/\/ieeexplore.ieee.org\/document\/1000236."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Michel, J., Feuvrier, T., and Inglada, J. (2009, January 12\u201317). Reference Algorithm Implementations in OTB: Textbook Cases. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417483"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2012.01.007","article-title":"Discrepancy Measures for Selecting Optimal Combination of Parameter Values in Object-Based Image Analysis","volume":"68","author":"Liu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3089","DOI":"10.1016\/j.aej.2018.10.001","article-title":"Determination of Optimum Segmentation Parameter Values for Extracting Building from Remote Sensing Images","volume":"57","year":"2018","journal-title":"Alex. Eng. J."},{"key":"ref_53","first-page":"479","article-title":"Contribution to the Assessment of Segmentation Quality for Remote Sensing Applications","volume":"37","author":"Weidner","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.geomorph.2014.02.028","article-title":"Assessment of Multiresolution Segmentation for Delimiting Drumlins in Digital Elevation Models","volume":"214","author":"Eisank","year":"2014","journal-title":"Geomorphology"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ramezan, C.A., Warner, T.A., and Maxwell, A.E. (2019). Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification. Remote Sens., 11.","DOI":"10.3390\/rs11020185"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1007\/s10601-016-9267-5","article-title":"Domain Reduction Techniques for Global NLP and MINLP Optimization","volume":"22","author":"Puranik","year":"2017","journal-title":"Constraints"},{"key":"ref_57","unstructured":"Swain, P.H., and Davis, S.M. (1979). Remote Sensing; The Quantitative Approach, McGraw-Hill College."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/TPAMI.1981.4767177","article-title":"Remote Sensing: The Quantitative Approach","volume":"PAMI-3","author":"Swain","year":"1981","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Shivakumar, B.R., and Rajashekararadhya, S.V. (2017, January 22\u201324). Spectral Similarity for Evaluating Classification Performance of Traditional Classifiers. Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, Chennai, India.","DOI":"10.1109\/WiSPNET.2017.8300111"},{"key":"ref_60","unstructured":"(2021, December 13). Optimum Band Selection for Supervised Classification of Multispectral Data. Semantic Scholar. Available online: https:\/\/www.semanticscholar.org\/paper\/Optimum-Band-Selection-for-Supervised-of-Data\/9693651d1240e3034a8049fbc0cd1c5cbaf65428."},{"key":"ref_61","unstructured":"Renard, X., Laugel, T., and Detyniecki, M. (2021). Understanding Prediction Discrepancies in Machine Learning Classifiers. arXiv."},{"key":"ref_62","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., M\u00fcller, A., Nothman, J., and Louppe, G. (2018). Scikit-Learn: Machine Learning in Python. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support Vector Machines in Remote Sensing: A Review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1007\/s12040-013-0339-2","article-title":"Decision Tree Approach for Classification of Remotely Sensed Satellite Data Using Open Source Support","volume":"122","author":"Sharma","year":"2013","journal-title":"J. Earth Syst. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/0098-3004(94)00082-6","article-title":"Neural Network Classification of Remote-Sensing Data","volume":"21","author":"Miller","year":"1995","journal-title":"Comput. Geosci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Liu, L., and \u00d6zsu, M.T. (2009). Nearest Neighbor Classification. Encyclopedia of Database Systems, Springer.","DOI":"10.1007\/978-0-387-39940-9"},{"key":"ref_67","unstructured":"Yamane, T. (1967). Statistics: An Introductory Analysis, Harper and Row. [2nd ed.]."},{"key":"ref_68","first-page":"10","article-title":"Comparative Performance of Multi-Source Reference Data to Assess the Accuracy of Classified Remotely Sensed Imagery: Example of Landsat 8 OLI Across Kigali City-Rwanda 2015","volume":"4","author":"Nkomeje","year":"2017","journal-title":"Int. J. Eng. Work."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Hoekstra, M., Jiang, M., Clausi, D.A., and Duguay, C. (2020). Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling. Remote Sens., 12.","DOI":"10.3390\/rs12091425"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely Randomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_71","first-page":"397","article-title":"Accuracy Assessment: A User\u2019s Perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"611","DOI":"10.4236\/ijg.2017.84033","article-title":"Accuracy Assessment of Land Use\/Land Cover Classification Using Remote Sensing and GIS","volume":"8","author":"Rwanga","year":"2017","journal-title":"IJG"},{"key":"ref_74","unstructured":"(2021, December 27). Analysis of Classification Results of Remotely Sensed Data and Evaluation of Classification Algorithms. Semantic Scholar. Available online: https:\/\/www.semanticscholar.org\/paper\/Analysis-of-classification-results-of-remotely-data-Zhuang-Engel\/58e57a879542a0a7711eebc6d7311555731da9b8."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1080\/014311699212560","article-title":"Quality Assessment of Image Classification Algorithms for Land-Cover Mapping: A Review and a Proposal for a Cost-Based Approach","volume":"20","author":"Smits","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_76","first-page":"1671","article-title":"Assessing Landsat Classification Accuracy Using Discrete Multivariate Analysis Statistical Techniques","volume":"49","author":"Congalton","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1002\/bimj.4710370705","article-title":"Raking Kappa: Describing Potential Impact of Marginal Distributions on Measures of Agreement","volume":"37","author":"Agresti","year":"1995","journal-title":"Biom. J."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"743","DOI":"10.14358\/PERS.70.6.743","article-title":"A Critical Evaluation of the Normalized Error Matrix in Map Accuracy Assessment","volume":"70","author":"Stehman","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/0895-4356(93)90018-V","article-title":"Bias, Prevalence and Kappa","volume":"46","author":"Byrt","year":"1993","journal-title":"J. Clin. Epidemiol."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Fleiss, J.L., Levin, B., Paik, M.C., and Fleiss, J. (2003). Statistical Methods for Rates & Proportions, Wiley-Interscience. [3rd ed.].","DOI":"10.1002\/0471445428"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0304-3800(92)90003-W","article-title":"Comparing Global Vegetation Maps with the Kappa Statistic","volume":"62","author":"Monserud","year":"1992","journal-title":"Ecol. Model."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"6445","DOI":"10.5194\/hess-21-6445-2017","article-title":"Monitoring Small Reservoirs\u2019 Storage with Satellite Remote Sensing in Inaccessible Areas","volume":"21","author":"Avisse","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Pipitone, C., Maltese, A., Dardanelli, G., Brutto, M.L., and Loggia, G.L. (2018). Monitoring Water Surface and Level of a Reservoir Using Different Remote Sensing Approaches and Comparison with Dam Displacements Evaluated via GNSS. Remote Sens., 10.","DOI":"10.3390\/rs10010071"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Maltese, A., Pipitone, C., Dardanelli, G., Capodici, F., and Muller, J.-P. (2021). Toward a Comprehensive Dam Monitoring: On-Site and Remote-Retrieved Forcing Factors and Resulting Displacements (GNSS and PS\u2013InSAR). Remote Sens., 13.","DOI":"10.3390\/rs13081543"},{"key":"ref_86","first-page":"1","article-title":"Soil Water Content Measurement Using Hyper-Spectral Remote Sensing Techniques\u2014A Case Study from North-Western Part of Tamil Nadu, India","volume":"14","author":"Divya","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_87","first-page":"287","article-title":"The Classification of Submerged Vegetation Using Hyperspectral MIVIS Data","volume":"49","author":"Ciraolo","year":"2006","journal-title":"Ann. Geophys."},{"key":"ref_88","first-page":"103026","article-title":"Trophic State Assessment of Optically Diverse Lakes Using Sentinel-3-Derived Trophic Level Index","volume":"114","author":"Liu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1080\/17538947.2020.1829112","article-title":"ClimateCharts.Net\u2014An Interactive Climate Analysis Web Platform","volume":"14","author":"Zepner","year":"2021","journal-title":"Int. J. Digit. 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