{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:23:50Z","timestamp":1778664230513,"version":"3.51.4"},"reference-count":20,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2009,1,8]],"date-time":"2009-01-08T00:00:00Z","timestamp":1231372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Measuring heterogeneity in satellite imagery is an important task to deal with. Most measures of spectral diversity have been based on Shannon Information theory. However, this approach does not inherently address different scales, ranging from local (hereafter referred to alpha diversity) to global scales (gamma diversity). The aim of this paper is to propose a method for measuring spectral heterogeneity at multiple scales based on rarefaction curves. An algorithmic solution of rarefaction applied to image pixel values (Digital Numbers, DNs) is provided and discussed.<\/jats:p>","DOI":"10.3390\/s90100303","type":"journal-article","created":{"date-parts":[[2009,1,8]],"date-time":"2009-01-08T12:46:04Z","timestamp":1231418764000},"page":"303-310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Algorithmic Foundation of Spectral Rarefaction for Measuring Satellite Imagery Heterogeneity at Multiple Spatial Scales"],"prefix":"10.3390","volume":"9","author":[{"given":"Duccio","family":"Rocchini","sequence":"first","affiliation":[{"name":"Dipartimento di Scienze Ambientali \u201cG. Sarfatti\u201d, Universit\u00e0 di Siena, via P.A. Mattioli 4, 53100 Siena, Italy"},{"name":"TerraData environmetrics, Universit\u00e0 di Siena, via P.A. 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