{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T11:14:02Z","timestamp":1780658042724,"version":"3.54.1"},"reference-count":60,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T00:00:00Z","timestamp":1625356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007446","name":"King Khalid University","doi-asserted-by":"publisher","award":["R.G.P2\/75\/41"],"award-info":[{"award-number":["R.G.P2\/75\/41"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The present paper proposes a novel fuzzy-VORS (vigor, organization, resilience, ecosystem services) model by integrating fuzzy logic and a VORS model to predict ecosystem health conditions in Abha city of Saudi Arabia from the past to the future. In this study, a support vector machine (SVM) classifier was utilized to classify the land use land cover (LULC) maps for 1990, 2000, and 2018. The LULCs dynamics in 1990\u20132000, 2000\u20132018, and 1990\u20132018 were computed using delta (\u0394) change and Markovian transitional probability matrix. The future LULC map for 2028 was predicted using the artificial neural network-cellular automata model (ANN-CA). The machine learning algorithms, such as random forest (RF), classification and regression tree (CART), and probability distribution function (PDF) were utilized to perform sensitivity analysis. Pearson\u2019s correlation technique was used to explore the correlation between the predicted models and their driving variables. The ecosystem health conditions for 1990\u20132028 were predicted by integrating the fuzzy inference system with the VORS model. The results of LULC maps showed that urban areas increased by 334.4% between 1990 and 2018. Except for dense vegetation, all the natural resources and generated ecosystem services have been decreased significantly due to the rapid and continuous urbanization process. A future LULC map (2028) showed that the built-up area would be 343.72 km2. The new urban area in 2028 would be 169 km2. All techniques for sensitivity analysis showed that proximity to urban areas, vegetation, and scrubland are highly sensitive to land suitability models to simulate and predict LULC maps of 2018 and 2028. Global sensitivity analysis showed that fragmentation or organization was the most sensitive parameter for ecosystem health conditions.<\/jats:p>","DOI":"10.3390\/rs13132632","type":"journal-article","created":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T22:35:22Z","timestamp":1625438122000},"page":"2632","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Novel Technique for Modeling Ecosystem Health Condition: A Case Study in Saudi Arabia"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6155-3720","authenticated-orcid":false,"given":"Javed","family":"Mallick","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed","family":"AlQadhi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6680-9791","authenticated-orcid":false,"given":"Swapan","family":"Talukdar","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Gour Banga, Malda 732101, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed Ali","family":"Bindajam","sequence":"additional","affiliation":[{"name":"Department of Architecture and Planning, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5779-1382","authenticated-orcid":false,"given":"Abu Reza Md. Towfiqul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amal Saad","family":"Dajam","sequence":"additional","affiliation":[{"name":"Department of Biology, College of Science, King Khalid University, Abha 62529, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"138452","DOI":"10.1016\/j.scitotenv.2020.138452","article-title":"Spatial differences of ecosystem services and their driving factors: A comparation analysis among three urban agglomerations in China\u2019s Yangtze River Economic Belt","volume":"725","author":"Luo","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Guo, L., and Sang, W. 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