{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:42:51Z","timestamp":1771026171344,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,23]],"date-time":"2019-02-23T00:00:00Z","timestamp":1550880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (\u03b2 diversity) and species diversity at aggregate spatial scales (\u03b3 diversity). Shannon indices of \u03b3 and \u03b2 diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or an unmanned aerial vehicle (UAV). Partial least squares regression (PLSR) models explained 59\u201385% of variance in \u03b3 diversity and 68\u201379% of variance in \u03b2 diversity using spatial heterogeneity in canopy optical properties. Variation in both \u03b3 and \u03b2 diversity were associated most strongly with heterogeneity in reflectance in blue (350\u2013370 nm), red (660\u2013770 nm), and near infrared (810\u20131050 nm) wavebands. Modeled diversity was more sensitive by a factor of three to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, \u03b2 diversity was greater, but \u03b3 diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both \u03b3 and \u03b2 diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved.<\/jats:p>","DOI":"10.3390\/rs11040458","type":"journal-article","created":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T03:06:52Z","timestamp":1551064012000},"page":"458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1197-8800","authenticated-orcid":false,"given":"H. Wayne","family":"Polley","sequence":"first","affiliation":[{"name":"Grassland, Soil &amp; Water Research Laboratory, USDA-Agricultural Research Service, Temple, TX 76502, USA"}]},{"given":"Chenghai","family":"Yang","sequence":"additional","affiliation":[{"name":"Southern Plains Agricultural Research Center, USDA-Agricultural Research Service, College Station, TX 77845, USA"}]},{"given":"Brian J.","family":"Wilsey","sequence":"additional","affiliation":[{"name":"Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8291-6316","authenticated-orcid":false,"given":"Philip A.","family":"Fay","sequence":"additional","affiliation":[{"name":"Grassland, Soil &amp; Water Research Laboratory, USDA-Agricultural Research Service, Temple, TX 76502, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,23]]},"reference":[{"key":"ref_1","unstructured":"Mooney, H.A., Cushman, J.H., Medina, E., Sala, O.E., and Schulze, E.D. (1996). Biodiversity and Ecosystem Functioning in Grasslands. Functional Roles of Biodiversity: A Global Perspective, Wiley."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1073\/pnas.1309492111","article-title":"Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes","volume":"111","author":"Werling","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1038\/s41559-018-0647-7","article-title":"Multiple facets of biodiversity drive the diversity-stability relationship","volume":"2","author":"Craven","year":"2018","journal-title":"Nat. Ecol. Evol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1111\/ele.12088","article-title":"Predicting ecosystem stability from community composition and biodiversity","volume":"16","author":"Isbell","year":"2013","journal-title":"Ecol. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1890\/09-1162.1","article-title":"General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding","volume":"91","author":"Hector","year":"2010","journal-title":"Ecology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1086\/673915","article-title":"Species richness and the temporal stability of biomass production: A new analysis of recent biodiversity experiments","volume":"183","author":"Gross","year":"2014","journal-title":"Am. Nat."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1111\/ele.12928","article-title":"Quantifying effects of biodiversity on ecosystem functioning across times and places","volume":"21","author":"Isbell","year":"2018","journal-title":"Ecol. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"10219","DOI":"10.1073\/pnas.1220333110","article-title":"Several scales of biodiversity affect ecosystem multifunctionality","volume":"110","author":"Pasari","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/nature10282","article-title":"High plant diversity is needed to maintain ecosystem services","volume":"477","author":"Isbell","year":"2011","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.ecoinf.2010.06.001","article-title":"Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges","volume":"5","author":"Rocchini","year":"2010","journal-title":"Ecol. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1002\/rse2.15","article-title":"Framing the concept of satellite remote sensing essential biodiversity variables: Challenges and future directions","volume":"2","author":"Pettorelli","year":"2016","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1126\/science.1229931","article-title":"Essential biodiversity variables","volume":"339","author":"Pereira","year":"2013","journal-title":"Science"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, R., Gamon, J., Emmerton, C., Li, H., Nestola, E., Pastorello, G., and Menzer, O. (2016). Integrated analysis of productivity and biodiversity in a southern Alberta prairie. Remote Sens., 8.","DOI":"10.3390\/rs8030214"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.rse.2007.03.018","article-title":"Effects of spatial and spectral resolution in estimating ecosystem \u03b1-diversity by satellite imagery","volume":"111","author":"Rocchini","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.agee.2010.01.016","article-title":"Fine-scale assessment of hay meadow productivity and plant diversity in the European Alps using field spectrometric data","volume":"137","author":"Fava","year":"2010","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"M\u00f6ckel, T., Dalmayne, J., Schmid, B., Prentice, H., and Hall, K. (2016). Airborne hyperspectral data predict fine-scale plant species diversity in grazed dry grasslands. Remote Sens., 8.","DOI":"10.3390\/rs8020133"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.rse.2018.10.037","article-title":"Detecting prairie biodiversity with airborne remote sensing","volume":"221","author":"Gholizadeh","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1111\/2041-210X.12941","article-title":"Measuring \u03b2-diversity by remote sensing: A challenge for biodiversity monitoring","volume":"9","author":"Rocchini","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.1890\/0012-9658(2006)87[2697:AOEBDU]2.0.CO;2","article-title":"Analyzing or explaining beta diversity? Understanding the targets of different methods of analysis","volume":"87","author":"Tuomisto","year":"2006","journal-title":"Ecology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1890\/1051-0761(2003)013[0352:LFPWSH]2.0.CO;2","article-title":"Linking floristic patterns with soil heterogeneity and satellite imagery in Ecuadorian Amazonia","volume":"13","author":"Tuomisto","year":"2003","journal-title":"Ecol. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecoinf.2013.05.004","article-title":"Assessment of fine-scale plant species beta diversity using WorldView-2 satellite spectral dissimilarity","volume":"18","author":"Dalmayne","year":"2013","journal-title":"Ecol. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s10021-009-9247-3","article-title":"Primary productivity and water balance of grassland vegetation on three soils in a continuous CO2 gradient: Initial results from the Lysimeter CO2 Gradient Experiment","volume":"12","author":"Fay","year":"2009","journal-title":"Ecosystems"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"213","DOI":"10.2307\/1218190","article-title":"Evolution and measurement of species diversity","volume":"21","author":"Whittaker","year":"1972","journal-title":"Taxon"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1890\/06-1736.1","article-title":"Partitioning diversity into independent alpha and beta components","volume":"88","author":"Jost","year":"2007","journal-title":"Ecology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1890\/11-1817.1","article-title":"Proposing a resolution to debates on diversity partitioning","volume":"93","author":"Chao","year":"2012","journal-title":"Ecology"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1016\/j.jqsrt.2010.03.007","article-title":"Brightness-normalized Partial Least Squares Regression for hyperspectral data","volume":"111","author":"Feilhauer","year":"2010","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1137\/0905052","article-title":"The collinearity problem in linear regressions. The partial least squares (PLS) approach to generalized inverses","volume":"5","author":"Wold","year":"1984","journal-title":"SIAM J. Sci. Stat. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/01621459.1993.10476299","article-title":"Linear model selection by cross-validation","volume":"88","author":"Shao","year":"1993","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1111\/geb.12413","article-title":"Contrasting beta diversity among regions: How do classical and multivariate approaches compare?","volume":"25","author":"Bennett","year":"2015","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1111\/j.1469-8137.2010.03536.x","article-title":"Sources of variability in canopy reflectance and the convergent properties of plants","volume":"189","author":"Ollinger","year":"2011","journal-title":"New Phytol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/4\/458\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:34:17Z","timestamp":1760186057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/4\/458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,23]]},"references-count":30,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11040458"],"URL":"https:\/\/doi.org\/10.3390\/rs11040458","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,23]]}}}