{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T11:44:48Z","timestamp":1775735088088,"version":"3.50.1"},"reference-count":19,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Social Science Foundation Project","award":["20CGL011"],"award-info":[{"award-number":["20CGL011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2022,5,11]]},"abstract":"<jats:p>In order to improve the effect of land resource management, this paper combines the Internet of Things technology and GIS technology to build an intelligent management system for gradient resources to improve the efficiency of land resource management. Aiming at the hybrid intelligent model of wetland resource remote sensing monitoring technology, this paper analyzes and studies the remote sensing image processing theory. Moreover, this paper studies in detail remote sensing image restoration, TM image reflectivity simulation imaging, image enhancement technology, optimal band selection based on the characteristics of wetland resources, expert decision analysis, deep mining of image data, knowledge reasoning, and decision tree analysis to form a theoretical support system for a hybrid intelligent classification model for wetland resources. The research shows that the intelligent management system of land resources based on the Internet of Things and GIS technology has a good effect in the collection and processing of land resource information and can effectively improve the management efficiency of land resources.<\/jats:p>","DOI":"10.1155\/2022\/2216581","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T16:36:34Z","timestamp":1652286994000},"page":"1-13","source":"Crossref","is-referenced-by-count":5,"title":["Intelligent Management of Land Resources Based on Internet of Things and GIS Technology"],"prefix":"10.1155","volume":"2022","author":[{"given":"Hao","family":"Gong","sequence":"first","affiliation":[{"name":"School of Land Science & Technology, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0319-4344","authenticated-orcid":true,"given":"Chen","family":"He","sequence":"additional","affiliation":[{"name":"Faculty of Accounting, Hubei University of Economics, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1177\/0734242X18815950"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s41324-019-00295-9"},{"issue":"12a","key":"3","first-page":"9336","article-title":"Monitoring and prediction of urban expansion using multilayer perceptron neural network by remote sensing and GIS technologies: a case study from Istanbul Metropolitan City","volume":"27","author":"M. 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