{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:31:39Z","timestamp":1784565099533,"version":"3.55.0"},"reference-count":121,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T00:00:00Z","timestamp":1525910400000},"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>Many remote sensing metrics have been applied in large-scale animal species monitoring and conservation. However, the capabilities of these metrics have not been well compared and assessed. In this study, we investigated the correlation of 21 remote sensing metrics in three categories with the global species richness of three different animal classes using several statistical methods. As a result, we developed a new index by integrating several highly correlated metrics. Of the 21 remote sensing metrics analyzed, evapotranspiration (ET) had the greatest impact on species richness on a global scale (explained variance: 52%). The metrics with a high explained variance on the global scale were mainly in the energy\/productivity category. The metrics in the texture category exhibited higher correlation with species richness at regional scales. We found that radiance and temperature had a larger impact on the distribution of bird richness, compared to their impacts on the distributions of both amphibians and mammals. Three machine learning models (i.e., support vector machine, random forests, and neural networks) were evaluated for metric integration, and the random forest model showed the best performance. Our newly developed index exhibited a 0.7 explained variance for the three animal classes\u2019 species richness on a global scale, with an explained variance that was 20% higher than any of the univariate metrics.<\/jats:p>","DOI":"10.3390\/rs10050739","type":"journal-article","created":{"date-parts":[[2018,5,11]],"date-time":"2018-05-11T03:42:48Z","timestamp":1526010168000},"page":"739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2180-3825","authenticated-orcid":false,"given":"Jinhui","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2708-9183","authenticated-orcid":false,"given":"Shunlin","family":"Liang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China"},{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1038\/35012228","article-title":"Global patterns in biodiversity","volume":"405","author":"Gaston","year":"2000","journal-title":"Nature"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1246752","DOI":"10.1126\/science.1246752","article-title":"The biodiversity of species and their rates of extinction, distribution, and protection","volume":"344","author":"Pimm","year":"2014","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1126\/science.1256014","article-title":"Sensing biodiversity","volume":"346","author":"Turner","year":"2014","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/nature14324","article-title":"Global effects of land use on local terrestrial biodiversity","volume":"520","author":"Newbold","year":"2015","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1038\/35002501","article-title":"Biodiversity hotspots for conservation priorities","volume":"403","author":"Myers","year":"2000","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1126\/science.1187512","article-title":"Global biodiversity: Indicators of recent declines","volume":"328","author":"Butchart","year":"2010","journal-title":"Science"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1111\/j.1523-1739.1990.tb00309.x","article-title":"Indicators for monitoring biodiversity: A hierarchical approach","volume":"4","author":"Noss","year":"1990","journal-title":"Conserv. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1038\/35012221","article-title":"Getting the measure of biodiversity","volume":"405","author":"Purvis","year":"2000","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1126\/science.1072779","article-title":"Geographic range size and determinants of avian species richness","volume":"297","author":"Jetz","year":"2002","journal-title":"Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5925","DOI":"10.1073\/pnas.0608361104","article-title":"Global patterns and determinants of vascular plant diversity","volume":"104","author":"Kreft","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7368","DOI":"10.1073\/pnas.1113867109","article-title":"Global models of ant diversity suggest regions where new discoveries are most likely are under disproportionate deforestation threat","volume":"109","author":"Guenard","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1111\/ddi.12090","article-title":"Conservation implications of divergent global patterns of ant and vertebrate diversity","volume":"19","author":"Jenkins","year":"2013","journal-title":"Divers. Distrib."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.ecolecon.2006.03.021","article-title":"Biodiversity and ecosystem services: A multi-scale empirical study of the relationship between species richness and net primary production","volume":"61","author":"Costanza","year":"2007","journal-title":"Ecol. Econ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13384","DOI":"10.1073\/pnas.0704469104","article-title":"Species richness, hotspots, and the scale dependence of range maps in ecology and conservation","volume":"104","author":"Hurlbert","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","unstructured":"Pardieck, K.L., Ziolkowski, D.J., Lutmerding, M., Campbell, K., and Hudson, M.-A.R. (2017). North American Breeding Bird Survey Dataset 1966-2013, Version 2016.0, Patuxent Wildlife Research Center. Available online: www.pwrc.usgs.gov\/BBS\/RawData\/."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Constable, H., Guralnick, R., Wieczorek, J., Spencer, C.L., and Peterson, A.T. (2010). Vertnet: A new model for biodiversity data sharing. PLoS Biol., 8.","DOI":"10.1371\/journal.pbio.1000309"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1111\/2041-210X.12209","article-title":"Using modestr to download, import and clean species distribution records","volume":"5","author":"Garciarosello","year":"2014","journal-title":"Methods Ecol. Evol."},{"key":"ref_19","unstructured":"IUCN (2018, May 08). The Iucn Red List of Threatened Species. Available online: http:\/\/www.iucnredlist.org\/."},{"key":"ref_20","unstructured":"GBIF (2018, May 08). Gbif Data Portal. Available online: http:\/\/www.gbif.org\/."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1080\/01431160117096","article-title":"Using remote sensing to assess biodiversity","volume":"22","author":"Nagendra","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07038992.1995.10874590","article-title":"Comparison of possible multispectral classification schemes for tree crowns individually delineatedon high spatial resolution meis images","volume":"21","author":"Gougeon","year":"1995","journal-title":"Can. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9322","DOI":"10.1073\/pnas.0810306106","article-title":"A global assessment of endemism and species richness across island and mainland regions","volume":"106","author":"Kier","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4534","DOI":"10.1073\/pnas.071034898","article-title":"Multiscale assessment of patterns of avian species richness","volume":"98","author":"Rahbek","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1111\/geb.12365","article-title":"A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling","volume":"24","author":"Tuanmu","year":"2015","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1177\/0309133307079054","article-title":"Development of a large area biodiversity monitoring system driven by remote sensing","volume":"31","author":"Duro","year":"2007","journal-title":"Prog. Phys. Geogr."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1086\/285144","article-title":"Energy and large-scale patterns of animal- and plant-species richness","volume":"137","author":"Currie","year":"1991","journal-title":"Am. Nat."},{"key":"ref_28","unstructured":"Rosenzweig, M.L., and Abramsky, Z. (1993). Species Diversity in Ecological Communities, University of Chicago Press."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"aaf8957","DOI":"10.1126\/science.aaf8957","article-title":"Positive biodiversity-productivity relationship predominant in global forests","volume":"354","author":"Liang","year":"2016","journal-title":"Science"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"132","DOI":"10.3389\/fgene.2015.00132","article-title":"Continental scale patterns and predictors of fern richness and phylogenetic diversity","volume":"6","author":"Nagalingum","year":"2015","journal-title":"Front. Genet."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wilson, A.M., and Jetz, W. (2016). Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol., 14.","DOI":"10.1371\/journal.pbio.1002415"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1111\/j.1461-0248.2004.00671.x","article-title":"Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness","volume":"7","author":"Currie","year":"2004","journal-title":"Ecol. Lett."},{"key":"ref_33","unstructured":"Hansson, L., Fahrig, L., and Merriam, G. (2012). Mosaic Landscapes and Ecological Processes, Springer Science & Business Media."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1038\/34899","article-title":"Energy availability and habitat heterogeneity predict global riverine fish diversity","volume":"391","author":"Guegan","year":"1998","journal-title":"Nature"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"496","DOI":"10.2307\/3544109","article-title":"Species-energy theory: An extension of species-area theory","volume":"41","author":"Wright","year":"1983","journal-title":"Oikos"},{"key":"ref_36","first-page":"877","article-title":"Two decades of homage to santa rosalia: Toward a general theory of diversity","volume":"21","author":"Brown","year":"1981","journal-title":"Integr. Comp. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1086\/282070","article-title":"Homage to santa rosalia or why are there so many kinds of animals","volume":"93","author":"Hutchinson","year":"1959","journal-title":"Am. Nat."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2017.04.018","article-title":"A comparison of dynamic habitat indices derived from different modis products as predictors of avian species richness","volume":"195","author":"Hobi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1098\/rspb.2006.3484","article-title":"Species richness changes lag behind climate change","volume":"273","author":"Menendez","year":"2006","journal-title":"Proc. R. Soc. B Biol. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1046\/j.1523-1739.2003.01389.x","article-title":"Potential impact of global climate change on species richness of long-distance migrants","volume":"17","author":"Lemoine","year":"2003","journal-title":"Conserv. Biol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1146\/annurev.en.28.010183.000323","article-title":"Plant architecture and the diversity of phytophagous insects","volume":"28","author":"Lawton","year":"1983","journal-title":"Annu. Rev. Entomol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"McCoy, E.D., and Bell, S.S. (1991). Habitat structure: The evolution and diversification of a complex topic. Habitat Structure, Springer.","DOI":"10.1007\/978-94-011-3076-9_1"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1046\/j.0305-0270.2003.00994.x","article-title":"Animal species diversity driven by habitat heterogeneity\/diversity: The importance of keystone structures","volume":"31","author":"Tews","year":"2004","journal-title":"J. Biogeogr."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1038\/523403a","article-title":"Agree on biodiversity metrics to track from space","volume":"523","author":"Skidmore","year":"2015","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/nature03289","article-title":"A biodiversity intactness index","volume":"434","author":"Scholes","year":"2005","journal-title":"Nature"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1007\/s10531-015-0947-z","article-title":"Remote sensing for biodiversity monitoring: A review of methods for biodiversity indicator extraction and assessment of progress towards international targets","volume":"24","author":"Petrou","year":"2015","journal-title":"Biodivers. Conserv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1111\/j.1523-1739.2008.01083.x","article-title":"Delivering a global, terrestrial, biodiversity observation system through remote sensing","volume":"23","author":"Buchanan","year":"2009","journal-title":"Conserv. Biol."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"E2602","DOI":"10.1073\/pnas.1302251110","article-title":"Global patterns of terrestrial vertebrate diversity and conservation","volume":"110","author":"Jenkins","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5081","DOI":"10.1073\/pnas.1418034112","article-title":"Us protected lands mismatch biodiversity priorities","volume":"112","author":"Jenkins","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cai, D., Guo, S., Guan, Y., Fraedrich, K., Nie, Y., Liu, X., and Bian, X. (2016). Spatial-temporal dynamics of china\u2019s terrestrial biodiversity: A dynamic habitat index diagnostic. Remote Sens., 8.","DOI":"10.3390\/rs8030227"},{"key":"ref_52","unstructured":"Golestani, A., and Gras, R. (2012, January 24\u201326). Using Machine Learning Techniques for Identifying Important Characteristics to Predict Changes in Species Richness in Ecosim, an Individual-Based Ecosystem Simulation. Proceedings of the World Congress on Engineering and Computer Science, San Francisco, CA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Olaya-Mar\u00edn, E.J., Mart\u00ednez-Capel, F., and Vezza, P. (2013). A comparison of artificial neural networks and random forests to predict native fish species richness in mediterranean rivers. Knowl. Manag. Aquat. Ecosyst.","DOI":"10.1051\/kmae\/2013052"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"043120","DOI":"10.1063\/1.3514011","article-title":"Regularity analysis of an individual-based ecosystem simulation","volume":"20","author":"Golestani","year":"2010","journal-title":"Chaos"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.simpat.2009.09.012","article-title":"Species abundance patterns in an ecosystem simulation studied through fisher\u2019s logseries","volume":"18","author":"Devaurs","year":"2010","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.envsoft.2010.12.001","article-title":"Data-driven fuzzy habitat suitability models for brown trout in spanish mediterranean rivers","volume":"26","author":"Mouton","year":"2011","journal-title":"Environ. Model. Softw."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.ecoinf.2011.05.001","article-title":"A comparison of modeling techniques to predict juvenile 0+ fish species occurrences in a large river system","volume":"6","author":"Leclere","year":"2011","journal-title":"Ecol. Inform."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.rse.2010.01.007","article-title":"Predictive mapping of reef fish species richness, diversity and biomass in zanzibar using ikonos imagery and machine-learning techniques","volume":"114","author":"Knudby","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_59","unstructured":"IUCN (2017, November 12). The Iucn Red List of Threatened Species. Version 2010-4. Available online: http:\/\/www.iucnredlist.org\/info\/categories_criteria2001.html."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.rse.2004.12.011","article-title":"Improvements of the modis terrestrial gross and net primary production global data set","volume":"95","author":"Zhao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1016\/j.ecolind.2008.11.003","article-title":"Demonstration of a satellite-based index to monitor habitat at continental-scales","volume":"9","author":"Coops","year":"2009","journal-title":"Ecol. Indic."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.ecolind.2008.01.007","article-title":"The development of a canadian dynamic habitat index using multi-temporal satellite estimates of canopy light absorbance","volume":"8","author":"Coops","year":"2008","journal-title":"Ecol. Indic."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1080\/17538947.2015.1116623","article-title":"Characterization of spatial relationships between three remotely sensed indirect indicators of biodiversity and climate: A 21years\u2019 data series review across the canadian boreal forest","volume":"9","author":"Perez","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1016\/j.rse.2011.02.019","article-title":"Improvements to a modis global terrestrial evapotranspiration algorithm","volume":"115","author":"Mu","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2007.04.015","article-title":"Development of a global evapotranspiration algorithm based on modis and global meteorology data","volume":"111","author":"Mu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_66","first-page":"205","article-title":"Evaporation and environment","volume":"Volume 19","author":"Monteith","year":"1965","journal-title":"The State and Movement of Water in Living Organisms. Symposium of the Society of Experimental Biology"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"9260","DOI":"10.3390\/rs6109260","article-title":"Application of modis land products to assessment of land degradation of alpine rangeland in northern india with limited ground-based information","volume":"6","author":"Tasumi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_68","unstructured":"Mu, Q., Zhao, M., and Running, S.W. (2013). Modis Global Terrestrial Evapotranspiration (et) Product (Nasa Mod16a2\/a3), NASA. Algorithm Theoretical Basis Document, Collection."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s11442-012-0935-0","article-title":"Land surface emissivity change in china from 2001 to 2010","volume":"22","author":"Wang","year":"2012","journal-title":"J. Geogr. Sci."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1111\/ele.12277","article-title":"Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales","volume":"17","author":"Stein","year":"2014","journal-title":"Ecol. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/385252a0","article-title":"Habitat heterogeneity as a determinant of mammal species richness in high-energy regions","volume":"385","author":"Kerr","year":"1997","journal-title":"Nature"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1641\/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2","article-title":"Terrestrial ecoregions of the world: A new map of life on earth","volume":"51","author":"Olson","year":"2009","journal-title":"Bioscience"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"199","DOI":"10.2307\/3298564","article-title":"The Global 200: Priority ecoregions for global conservation","volume":"89","author":"Olson","year":"2002","journal-title":"Ann. Mo. Bot. Gard."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"478","DOI":"10.3390\/rs2020478","article-title":"Assessing plant diversity in a dry tropical forest: Comparing the utility of landsat and ikonos satellite images","volume":"2","author":"Nagendra","year":"2010","journal-title":"Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1080\/01621459.1988.10478639","article-title":"Locally weighted regression: An approach to regression analysis by local fitting","volume":"83","author":"Cleveland","year":"1988","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_78","unstructured":"Loehlin, J.C. (1998). Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis, Lawrence Erlbaum Associates Publishers."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.agrformet.2017.04.011","article-title":"Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms","volume":"242","author":"Yao","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Eitrich, T., and Lang, B. (2005, January 25\u201327). Parallel Tuning of Support Vector Machine Learning Parameters for Large and Unbalanced Data Sets. Proceedings of the International Symposium on Computational Life Science, Konstanz, Germany.","DOI":"10.1007\/11560500_23"},{"key":"ref_81","first-page":"409","article-title":"The nature of statistical learning theory","volume":"38","author":"Vapnik","year":"1995","journal-title":"Technometrics"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2013.11.013","article-title":"Detecting sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers","volume":"88","author":"Mutanga","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_83","unstructured":"Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C.-C., Lin, C.-C., and Meyer, M.D. (2017, November 12). Package \u2018e1071\u2019; R Package Version; 2017. Available online: https:\/\/cran.r-project.org\/web\/packages\/e1071\/index.html."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ecolmodel.2017.03.006","article-title":"Effects of species prevalence on the performance of predictive models","volume":"354","author":"Sor","year":"2017","journal-title":"Ecol. Model."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1177\/026119291604400603","article-title":"Comparing the coral and random forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials","volume":"44","author":"Cassano","year":"2016","journal-title":"Altern. Lab. Anim."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_87","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_88","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s10452-007-9093-3","article-title":"Applications of artificial neural networks predicting macroinvertebrates in freshwaters","volume":"41","author":"Goethals","year":"2007","journal-title":"Aquat. Ecol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0304-3800(99)00092-7","article-title":"Artificial neural networks as a tool in ecological modelling, an introduction","volume":"120","author":"Lek","year":"1999","journal-title":"Ecol. Model."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/0304-3800(95)00142-5","article-title":"Application of neural networks to modelling nonlinear relationships in ecology","volume":"90","author":"Lek","year":"1996","journal-title":"Ecol. Model."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.enbuild.2015.11.013","article-title":"The neural network predictive model for heat island intensity in Seoul","volume":"110","author":"Lee","year":"2016","journal-title":"Energy Build."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/B978-0-444-63536-5.00008-9","article-title":"Species distribution models for sustainable ecosystem management","volume":"Volume 27","author":"Boets","year":"2015","journal-title":"Developments in Environmental Modelling"},{"key":"ref_93","unstructured":"Ripley, B., Venables, W., and Ripley, M.B. (2017, November 12). Package \u2018nnet\u2019; R Package Version 7.3-12; 2016. Available online: https:\/\/cran.r-project.org\/web\/packages\/nnet\/index.html."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/BF02239720","article-title":"The biodiversity challenge: Expanded hot-spots analysis","volume":"10","author":"Myers","year":"1990","journal-title":"Environmentalist"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1038\/nature02422","article-title":"Effectiveness of the global protected area network in representing species diversity","volume":"428","author":"Rodrigues","year":"2004","journal-title":"Nature"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1890\/03-8006","article-title":"Energy, water, and broad-scale geographic patterns of species richness","volume":"84","author":"Hawkins","year":"2003","journal-title":"Ecology"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1890\/0012-9658(2006)87[1234:TIOPOT]2.0.CO;2","article-title":"The influence of productivity on the species richness of plants: A critical assessment","volume":"87","author":"Gillman","year":"2006","journal-title":"Ecology"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1038\/nature15374","article-title":"Biodiversity increases the resistance of ecosystem productivity to climate extremes","volume":"526","author":"Isbell","year":"2015","journal-title":"Nature"},{"key":"ref_99","unstructured":"Wright, D.H., Currie, D.J., and Maurer, B.A. (1993). Energy supply and patterns of species richness on local and regional scales. Species Diversity in Ecological Communities: Historical and Geographical Perspectives, University of Chicago Press."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1146\/annurev.ecolsys.30.1.257","article-title":"The relationship between productivity and species richness","volume":"30","author":"Waide","year":"2003","journal-title":"Annu. Rev. Ecol. Evol. System."},{"key":"ref_101","first-page":"523","article-title":"Species diversity in ecological communities: Historical and geographical perspectives","volume":"42","author":"Pavlik","year":"1995","journal-title":"Madro\u00f1o"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Rosenzweig, M.L. (1995). Species Diversity in Space and Time, Cambridge University Press.","DOI":"10.1017\/CBO9780511623387"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1080\/11956860.1999.11682541","article-title":"Some general propositions about the study of spatial patterns of species richness","volume":"6","author":"Currie","year":"1999","journal-title":"Ecoscience"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1038\/nclimate3223","article-title":"Species\/\u2019traits influenced their response to recent climate change","volume":"7","author":"Pacifici","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/BF00138846","article-title":"Lags in vegetation response to greenhouse warming","volume":"15","author":"Davis","year":"1989","journal-title":"Clim. Chang."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1073\/pnas.1204651110","article-title":"Persistent effects of a severe drought on amazonian forest canopy","volume":"110","author":"Saatchi","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rse.2013.08.022","article-title":"Using satellite based soil moisture to quantify the water driven variability in ndvi: A case study over mainland australia","volume":"140","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1111\/gcb.12945","article-title":"Time-lag effects of global vegetation responses to climate change","volume":"21","author":"Wu","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"594","DOI":"10.2307\/1932254","article-title":"On bird species diversity","volume":"42","author":"MacArthur","year":"1961","journal-title":"Ecology"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1111\/brv.12135","article-title":"Terminology and quantification of environmental heterogeneity in species-richness research","volume":"90","author":"Stein","year":"2015","journal-title":"Biol. Rev."},{"key":"ref_111","first-page":"796","article-title":"Environmental heterogeneity, species diversity and co-existence at different spatial scales","volume":"21","author":"Tamme","year":"2010","journal-title":"J. Veg. Sci."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s00442-013-2623-x","article-title":"A negative heterogeneity\u2013diversity relationship found in experimental grassland communities","volume":"173","author":"Gazol","year":"2013","journal-title":"Oecologia"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s00442-012-2398-5","article-title":"Microfragmentation concept explains non-positive environmental heterogeneity\u2013diversity relationships","volume":"171","author":"Laanisto","year":"2013","journal-title":"Oecologia"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1111\/j.1466-8238.2010.00615.x","article-title":"Cross-scale variation in species richness\u2013environment associations","volume":"20","author":"Belmaker","year":"2011","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ecoinf.2012.03.003","article-title":"Support vector machines to map rare and endangered native plants in pacific islands forests","volume":"9","author":"Pouteau","year":"2012","journal-title":"Ecol. Inform."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1198\/016214504000000098","article-title":"Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data","volume":"99","author":"Lee","year":"2004","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1109\/TGRS.2015.2409563","article-title":"Global land surface fractional vegetation cover estimation using general regression neural networks from modis surface reflectance","volume":"53","author":"Jia","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Yang, L., Jia, K., Liang, S., Liu, J., and Wang, X. (2016). Comparison of four machine learning methods for generating the glass fractional vegetation cover product from modis data. Remote Sens., 8.","DOI":"10.3390\/rs8080682"},{"key":"ref_119","unstructured":"Sun, C., and Vilalta, R. (2007, January 18\u201320). Data selection using sash trees for support vector machines. Proceedings of the Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Zhai, J., Li, C., and Li, T. (2013, January 13\u201316). Sample selection based on kl divergence for effectively training svm. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Washington, DC, USA.","DOI":"10.1109\/SMC.2013.823"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1111\/j.1461-0248.2011.01736.x","article-title":"Impacts of climate change on the future of biodiversity","volume":"15","author":"Bellard","year":"2012","journal-title":"Ecol. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/739\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:04:11Z","timestamp":1760195051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,10]]},"references-count":121,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["rs10050739"],"URL":"https:\/\/doi.org\/10.3390\/rs10050739","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,10]]}}}