{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:06:01Z","timestamp":1775837161238,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,13]],"date-time":"2021-02-13T00:00:00Z","timestamp":1613174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003359","name":"Generalitat Valenciana","doi-asserted-by":"publisher","award":["AICO\/2020\/246"],"award-info":[{"award-number":["AICO\/2020\/246"]}],"id":[{"id":"10.13039\/501100003359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 \u00d7 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now.<\/jats:p>","DOI":"10.3390\/rs13040681","type":"journal-article","created":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T05:54:49Z","timestamp":1613282089000},"page":"681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information"],"prefix":"10.3390","volume":"13","author":[{"given":"Sergio","family":"Morell-Monz\u00f3","sequence":"first","affiliation":[{"name":"Instituto de Investigaci\u00f3n para la Gesti\u00f3n Integrada de Zonas Costeras, Universitat Polit\u00e8cnica de Val\u00e8ncia, C\/Paraninfo, 1, 46730 Grau de Gandia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8042-5628","authenticated-orcid":false,"given":"Mar\u00eda-Teresa","family":"Sebasti\u00e1-Frasquet","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n para la Gesti\u00f3n Integrada de Zonas Costeras, Universitat Polit\u00e8cnica de Val\u00e8ncia, C\/Paraninfo, 1, 46730 Grau de Gandia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0854-5358","authenticated-orcid":false,"given":"Javier","family":"Estornell","sequence":"additional","affiliation":[{"name":"Geo-Environmental Cartography and Remote Sensing Group, Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2010.09.008","article-title":"Land cover classification of VHR airborne images for citrus grove identification","volume":"66","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","unstructured":"Instituto Valenciano de Investigaciones Agrarias (IVIA) (2020, December 25). Citricultura Valenciana: Gesti\u00f3n Integrada de Plagas y Enfermedades en C\u00edtricos. Available online: http:\/\/gipcitricos.ivia.es\/citricultura-valenciana."},{"key":"ref_3","unstructured":"Ministerio de Agricultura y Pesca, Alimentaci\u00f3n y Medio Ambiente (2019). ESYRCE: Encuesta Sobre Superficies y Rendimientos del a\u00f1o 2019, Ministerio de Agricultura y Pesca, Alimentaci\u00f3n y Medio Ambiente."},{"key":"ref_4","first-page":"81","article-title":"Viabilidad y competitividad del sistema citr\u00edcola valenciano","volume":"52","author":"Noguera","year":"2010","journal-title":"Bolet\u00edn Asoc. Ge\u00f3grafos Espa\u00f1oles"},{"key":"ref_5","unstructured":"Ministerio de Agricultura y Pesca, Alimentaci\u00f3n y Medio Ambiente (2008). ESYRCE: Encuesta Sobre Superficies y Rendimientos del a\u00f1o 2008, Ministerio de Agricultura y Pesca, Alimentaci\u00f3n y Medio Ambiente."},{"key":"ref_6","unstructured":"Ministerio de Agricultura y Pesca, Alimentaci\u00f3n y Medio Ambiente (2018). ESYRCE: Encuesta Sobre Superficies y Rendimientos del a\u00f1o 2018, Ministerio de Agricultura y Pesca, Alimentaci\u00f3n y Medio Ambiente."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.agee.2005.11.027","article-title":"A coherent set of future land use change scenarios for Europe","volume":"114","author":"Rounsevell","year":"2006","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"L\u00f6w, F., Prishchepov, F., Waldner, F., Dubovyk, O., Akramkhanov, A., Biradar, C., and Lamers, J. (2018). Mapping Cropland Abandonment in the Aral Sea Basin with MODIS Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10020159"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.rse.2012.05.019","article-title":"Mapping abandoned agriculture with multi-temporal MODIS satellite data","volume":"124","author":"Kuemmerle","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.rse.2015.03.028","article-title":"Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series","volume":"163","author":"Estel","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2018.02.050","article-title":"Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series","volume":"210","author":"Yin","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ecolind.2017.06.022","article-title":"Using multi-seasonal Landsat imagery for rapid identification of abandoned land in areas affected by urban sprawl","volume":"96","author":"Kienast","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.isprsjprs.2006.10.003","article-title":"Land cover classification and economic assessment of citrus groves using remote sensing","volume":"61","author":"Schrivastava","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","first-page":"35","article-title":"Obtaining agricultural land cover in Sentinel-2 satellite images with drone image injection using Random Forest in Google Earth Engine","volume":"56","author":"Montilla","year":"2020","journal-title":"Rev. Teledetecci\u00f3n"},{"key":"ref_15","first-page":"35","article-title":"Deep learning for agricultural land use classification from Sentinel-2","volume":"56","author":"Gilabert","year":"2020","journal-title":"Rev. Teledetecci\u00f3n"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vajsov\u00e1, B., Fasbender, D., Wirnhardt, C., Lemajic, S., and Devos, W. (2020). Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12142195"},{"key":"ref_17","unstructured":"Vajsov\u00e1, B., Fasbender, D., Wirnhardt, C., Lemajic, S., Sima, A., and Astrand, P. (2019). Applicability Limits of Sentine-2 Data Compared to Higher Resolution Imagery for CAP Checks by Monitoring, Publications Office of the European Union. JRC Technical Report: JRC115564."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Morell-Monz\u00f3, S., Estornell, J., and Sebasti\u00e1-Frasquet, M.T. (2020). Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas. Remote Sens., 12.","DOI":"10.3390\/rs12122062"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1016\/0031-3203(90)90135-8","article-title":"Texture classification using texture spectrum","volume":"23","author":"Wang","year":"1990","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Laws, K.I. (1980). Texture Image Segmentation. [Ph.D. Dissertation, University Southern California].","DOI":"10.21236\/ADA083283"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/0031-3203(91)90143-S","article-title":"Unsupervised texture segmentation using Gabor filters","volume":"24","author":"Jain","year":"1991","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: The wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.cageo.2009.05.003","article-title":"Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification","volume":"36","author":"Balaguer","year":"2010","journal-title":"Comput. Geosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1080\/01431161.2016.1278314","article-title":"Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales","volume":"38","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lan, Z., and Liu, Y. (2018). Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7050175"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9020","DOI":"10.3390\/rs70709020","article-title":"Mapping Robinia Pseudoacacia Forest Health Conditions by Using Combined Spectral, Spatial, and Textural Information Extracted from IKONOS Imagery and Random Forest Classifier","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jia, L., Zhou, Z., and Li, B. (2012, January 1\u20133). Study of SAR Image Texture Feature Extraction Based on GLCM in Guizhou Karst Mountainous Region. In Proceedings of the 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, Nanjing, China.","DOI":"10.1109\/RSETE.2012.6260741"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.isprsjprs.2016.08.010","article-title":"Description and validation of a new set of object-based temporal geostatistical features for land-use\/land-cover change detection","volume":"121","author":"Ruiz","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.compag.2011.02.007","article-title":"A feature extraction software tool for agricultural object-based image analysis","volume":"76","author":"Ruiz","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compag.2012.10.005","article-title":"Automated extraction of tree and plot-based parameters in citrus orchards from aerial images","volume":"90","author":"Recio","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"710908","DOI":"10.1117\/12.801737","article-title":"Multi-stage robust scheme for citrus identification from high resolution airborne images","volume":"Volume 7109","author":"Bruzzone","year":"2008","journal-title":"Image and Spatial Processing for Remote Sensing XIV"},{"key":"ref_35","unstructured":"Vi\u00f1als, M.J. (1995). Secuencias Estratigr\u00e1ficas y Evoluci\u00f3n Morfol\u00f3gica del Extremo Meridional del Golfo de Valencia (Cullera-D\u00e9nia). El Cuaternario del Pa\u00eds Valenciano, Universitat de Val\u00e8ncia-AEQUA. [1st ed.]."},{"key":"ref_36","unstructured":"Vi\u00f1als, M.J. (1996). El Marjal de Oliva-Pego: Geomorfolog\u00eda y Evoluci\u00f3n de un Humedal Costero Mediterr\u00e1neo, Conselleria de Agricultura y Medio Ambiente, Generalitat Valenciana. [1st ed.]."},{"key":"ref_37","unstructured":"Geleralitat Valenciana (2020, December 25). Portal Estad\u00edstico de la Generalitat Valenciana. Fichas Municipales 2020, Available online: http:\/\/www.pegv.gva.es\/es\/fichas."},{"key":"ref_38","unstructured":"Ilich, A. (2020, December 25). GLCMTextures. Available online: http:\/\/doi.org\/10.5281\/zenodo.4310187."},{"key":"ref_39","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8424","DOI":"10.3390\/rs6098424","article-title":"A Multichannel Gray Level Co-Ocurrence Matrix for Multi\/Hyperspectral Image Texture Representation","volume":"6","author":"Huang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1080\/01431161.2014.995276","article-title":"Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey","volume":"36","author":"Akar","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8703","DOI":"10.1080\/01431161.2018.1490976","article-title":"Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong","volume":"39","author":"Jin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","unstructured":"Hall-Beyer, M. (2017). GLCM Texture: A Tutorial v. 3.0 March 2017. PRISM Univ. Calg. Digit. Repos."},{"key":"ref_44","unstructured":"Breiman, L. (1999). Random Forests\u2014Random Features, Statistics Department, University of California. Technical Report 567."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_48","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_49","unstructured":"Kunh, M. (2020, December 25). Caret: Classification and Regression Training, Available online: https:\/\/cran.r-project.org\/web\/packages\/caret\/caret.pdf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.isprsjprs.2020.07.013","article-title":"Improved land cover map of Iran using Sentinel imagery withing Google Earth Engine and novel automatic workflow for land cover classification using migrated training samples","volume":"167","author":"Ghorbanian","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kursa, M.B., and Rudncki, A. (2010). Feature Selection with Boruta Package. J. Stat. Softw., Available online: https:\/\/www.jstatsoft.org\/article\/view\/v036i11.","DOI":"10.18637\/jss.v036.i11"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1093\/bib\/bbx124","article-title":"Evaluation of variable selection methods for random forests and omics data sets","volume":"20","author":"Degenhardt","year":"2017","journal-title":"Brief. Bioinform."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kumar, S.S., and Shaikh, T. (2017, January 6\u20137). Empirical Evaluation of the Performance of Feature Selection Approaches on Random Forests. Proceedings of the 2017 International Conference on Computer and Applications (ICCA), Doha, United Arab Emirates.","DOI":"10.1109\/COMAPP.2017.8079769"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"271","DOI":"10.3233\/FI-2010-288","article-title":"Boruta\u2014A system for Feature Selection","volume":"101","author":"Kursa","year":"2010","journal-title":"Fundam. Inform."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/0098-3004(96)00009-X","article-title":"Automated derivation of geographic window sizes for use in remote sensing digital image texture","volume":"22","author":"Franklin","year":"1996","journal-title":"Comput. Geosci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/681\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:23:44Z","timestamp":1760160224000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/681"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,13]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13040681"],"URL":"https:\/\/doi.org\/10.3390\/rs13040681","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,13]]}}}