{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T18:50:11Z","timestamp":1773687011415,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The emergence of the spectral variation hypothesis (SVH) has gained widespread attention in the remote sensing community as a method for deriving biodiversity information from remotely sensed data. SVH states that spectral heterogeneity on remotely sensed imagery reflects environmental heterogeneity, which in turn is associated with high species diversity and, therefore, could be useful for characterizing landscape biodiversity. However, the effect of phenology has received relatively less attention despite being an important variable influencing plant species spectral responses. The study investigated (i) the effect of phenology on the relationship between spectral heterogeneity and plant species diversity and (ii) explored spectral angle mapper (SAM), the coefficient of variation (CV) and their interaction effect in estimating species diversity. Stratified random sampling was adopted to survey all tree species with a diameter at breast height of &gt; 10 cm in 90 \u00d7 90 m plots distributed throughout the study site. Tree species diversity was quantified by the Shannon diversity index (H\u2032), Simpson index of diversity (D2) and species richness (S). SAM and CV were employed on Landsat-8 data to compute spectral heterogeneity. The study applied linear regression models to investigate the relationship between spectral heterogeneity metrics and species diversity indices across four phenological stages. The results showed that the end of the growing season was the most ideal phenological stage for estimating species diversity, following the SVH concept. During this period, SAM and species diversity indices (S, H\u2032, D2) had an r2 of 0.14, 0.24, and 0.20, respectively, while CV had an r2 of 0.22, 0.22, and 0.25, respectively. The interaction of SAM and CV improved the relationship between the spectral data and H\u2032 and D2 (from r2 of 0.24 and 0.25 to r2 of 0.32 and 0.28, respectively) at the end of the growing season. The two spectral heterogeneity metrics showed differential sensitivity to components of plant diversity. SAM had a high relationship with H\u2032 followed by D2 and then a lower relationship with S throughout the different phenological stages. Meanwhile, CV had a higher relationship with D2 than other plant diversity indices and its relationship with S and H\u2032 remained similar. Although the coefficient of determination was comparatively low, the relationship between spectral heterogeneity metrics and species diversity indices was statistically significant (p &lt; 0.05) and this supports the assertion that SVH could be implemented to characterize plant species diversity. Importantly, the application of SVH should consider (i) the choice of spectral heterogeneity metric in line with the purpose of the SVH application since these metrics relate to components of species diversity differently and (ii) vegetation phenology, which affects the relationship that spectral heterogeneity has with plant species diversity.<\/jats:p>","DOI":"10.3390\/rs13132467","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T11:01:38Z","timestamp":1624532498000},"page":"2467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Investigating the Relationship between Tree Species Diversity and Landsat-8 Spectral Heterogeneity across Multiple Phenological Stages"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1986-5798","authenticated-orcid":false,"given":"Sabelo","family":"Madonsela","sequence":"first","affiliation":[{"name":"Precision Agriculture Research Group, Advanced Agriculture and Food, Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa"},{"name":"School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville 3209, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4435-5375","authenticated-orcid":false,"given":"Moses","family":"Cho","sequence":"additional","affiliation":[{"name":"Precision Agriculture Research Group, Advanced Agriculture and Food, Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa"},{"name":"School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville 3209, South Africa"},{"name":"Department of Plant Science, University of Pretoria, Pretoria 0001, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9917-9754","authenticated-orcid":false,"given":"Abel","family":"Ramoelo","sequence":"additional","affiliation":[{"name":"School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville 3209, South Africa"},{"name":"Risk and Vulnerability Assessment Centre, University of Limpopo, Sovenga 0727, South Africa"},{"name":"Centre for Environmental Studies, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0001, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7358-8111","authenticated-orcid":false,"given":"Onisimo","family":"Mutanga","sequence":"additional","affiliation":[{"name":"School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville 3209, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/S0169-5347(03)00070-3","article-title":"Remote sensing for biodiversity science and conservation","volume":"18","author":"Turner","year":"2003","journal-title":"Trends Ecol. Evol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/S0169-5347(03)00071-5","article-title":"From space to species: Ecological applications for remote sensing","volume":"18","author":"Kerr","year":"2003","journal-title":"Trends Ecol. Evol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.ecoinf.2014.08.006","article-title":"The relationship between the spectral diversity of satellite imagery, habitat heterogeneity, and plant species richness","volume":"24","author":"Warren","year":"2014","journal-title":"Ecol. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1002\/rse2.9","article-title":"Satellite remote sensing to monitor species diversity: Potential and pitfalls","volume":"2","author":"Rocchini","year":"2016","journal-title":"Remote. Sens. Ecol. Conserv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1002\/env.516","article-title":"Quantitative tools for perfecting species lists","volume":"13","author":"Palmer","year":"2002","journal-title":"Environmetrics"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1111\/j.1466-822X.2004.00092.x","article-title":"Patterns of floristic richness in vegetation communities of California: Regional scale analysis with multi-temporal NDVI","volume":"13","author":"Fairbanks","year":"2004","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1890\/1051-0761(2000)010[1861:RSOVPS]2.0.CO;2","article-title":"Remote sensing of vegetation, plant species richness, and regional biodiversity hotspots","volume":"10","author":"Gould","year":"2000","journal-title":"Ecol. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.actao.2008.07.006","article-title":"Linking variability in species composition and MODIS NDVI based on betadiversity measurements","volume":"35","author":"He","year":"2009","journal-title":"Acta Oecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1080\/01431160010014819","article-title":"Interannual variability of NDVI and species richness in Kenya","volume":"23","author":"Oindo","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","first-page":"301","article-title":"NDVI-based productivity and heterogeneity as indicators of plant-species richness in boreal landscapes","volume":"15","author":"Parviainen","year":"2010","journal-title":"Boreal Environ. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"S66","DOI":"10.1890\/07-1201.1","article-title":"The relation between productivity and species diversity in temperate\u2013Arctic marine ecosystems","volume":"89","author":"Witman","year":"2008","journal-title":"Ecology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1111\/j.1365-2699.2012.02731.x","article-title":"Dissecting NDVI-species richness relationships in Hawaiian dry forests","volume":"39","author":"Pau","year":"2012","journal-title":"J. Biogeogr."},{"key":"ref_13","first-page":"106","article-title":"Estimating tree species diversity in the savannah using NDVI and woody canopy cover","volume":"66","author":"Madonsela","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","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_15","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_16","first-page":"1289","article-title":"Mapping Tropical Forest Canopy Diversity Using High-fidelity Imaging Spectroscopy","volume":"24","author":"Asner","year":"2014","journal-title":"Ecol. Appl. Publ. Ecol. Soc. Am."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.rse.2017.01.036","article-title":"The spectral heterogeneity hypothesis does not hold across landscapes","volume":"192","author":"Schmidtlein","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1038\/s41559-018-0551-1","article-title":"Plant Spectral Diversity Integrates Functional and Phylogenetic Components of Biodiversity and Predicts Ecosystem Function","volume":"2","author":"Schweiger","year":"2018","journal-title":"Nat. Ecol. Evol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.ecolind.2009.07.012","article-title":"Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity?","volume":"10","author":"Oldeland","year":"2010","journal-title":"Ecol. Indic."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1146\/annurev.ecolsys.28.1.517","article-title":"Tree-Grass Interactions in Savannas","volume":"28","author":"Scholes","year":"1997","journal-title":"Annu. Rev. Ecol. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.actao.2004.03.008","article-title":"Testing the spectral variation hypothesis by using satellite multispectral images","volume":"26","author":"Rocchini","year":"2004","journal-title":"Acta Oecologica"},{"key":"ref_22","first-page":"31","article-title":"Diversity-habitat heterogeneity relationship at different spatial and temporal scales","volume":"30","year":"2007","journal-title":"Ecography"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"e02145","DOI":"10.1002\/eap.2145","article-title":"Multi-temporal assessment of grassland \u03b1-and \u03b2-diversity using hyperspectral imaging","volume":"30","author":"Gholizadeh","year":"2020","journal-title":"Ecol. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.rse.2015.07.013","article-title":"Application of the photosynthetic light-use efficiency model in a northern Great Plains grassland","volume":"168","author":"Flanagan","year":"2015","journal-title":"Remote. Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/j.1654-109X.2009.01053.x","article-title":"Mapping tree species in temperate deciduous woodland using time-series multi-spectral data","volume":"13","author":"Hill","year":"2010","journal-title":"Appl. Veg. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lopes, M., Fauvel, M., Ouin, A., and Girard, S. (2017). Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation. Remote. Sens., 9.","DOI":"10.3390\/rs9100993"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4048","DOI":"10.1016\/j.rse.2008.05.020","article-title":"Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh","volume":"112","author":"Gilmore","year":"2008","journal-title":"Remote. Sens. Environ."},{"key":"ref_29","first-page":"65","article-title":"Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species","volume":"58","author":"Madonsela","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.ecoinf.2019.04.001","article-title":"Estimating tree species diversity from space in an alpine conifer forest: The Rao\u2019s Q diversity index meets the spectral variation hypothesis","volume":"52","author":"Torresani","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1002\/eap.1669","article-title":"The spatial sensitivity of the spectral het-erogeneity\u2013biodiversity relationship: An experimental test in a prairie grassland","volume":"28","author":"Wang","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.rse.2017.12.014","article-title":"Remote sensing of bio-diversity: Soil correction and data dimension reduction methods improve assessment of \u03b1-diversity (species richness) in prairie ecosystems","volume":"206","author":"Gholizadeh","year":"2018","journal-title":"Remote. Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3514","DOI":"10.1002\/ece3.1155","article-title":"Choosing and using diversity indices: Insights for ecological applications from the German Biodiversity Exploratories","volume":"4","author":"Morris","year":"2014","journal-title":"Ecol. Evol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/S0143-6228(02)00002-4","article-title":"Opposite trends in response for the Shannon and Simpson indices of landscape diversity","volume":"22","author":"Nagendra","year":"2002","journal-title":"Appl. Geogr."},{"key":"ref_35","first-page":"846","article-title":"Determinants of woody cover in African savannas","volume":"438","author":"Sankaran","year":"2005","journal-title":"Nat. Cell Biol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1046\/j.1365-2486.2003.00577.x","article-title":"The importance of low atmospheric CO2 and fire in promoting the spread of grasslands and savannas","volume":"9","author":"Bond","year":"2003","journal-title":"Glob. Chang. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"67","DOI":"10.2989\/10220110409485835","article-title":"The Kruger Experience: Ecology and Management of Savanna Heterogeneity\u2013Johan du Toit, Kevin Rogers and Harry Biggs (eds) 2003","volume":"21","author":"Scogings","year":"2004","journal-title":"Afr. J. Range Forage Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1046\/j.1365-2028.2000.00217.x","article-title":"Trends in woody vegetation cover in the Kruger National Park, South Africa, between 1940 and 1998","volume":"38","author":"Eckhardt","year":"2000","journal-title":"Afr. J. Ecol."},{"key":"ref_39","first-page":"1","article-title":"Factors influencing the adaptation and distribution of Colophospermum mopane in southern Africa\u2019s mopane savannas-A review","volume":"44","author":"Makhado","year":"2014","journal-title":"Bothalia-Afr. Biodivers. Conserv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"026004","DOI":"10.1117\/1.JRS.10.026004","article-title":"Satellite-based land use mapping: Comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery","volume":"10","author":"Pervaiz","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.biocon.2006.01.004","article-title":"The importance of nutrient hot-spots in the conservation and management of large wild mammalian herbivores in semi-arid savannas","volume":"130","author":"Grant","year":"2006","journal-title":"Biol. Conserv."},{"key":"ref_42","unstructured":"Scholes, R.J., Bond, W.J., and Eckhardt, H.C. (2013). Vegetation dynamics in the Kruger ecosystem. The Kruger Experience: Ecology and Management of Savanna Heterogeneity, Island Press."},{"key":"ref_43","first-page":"4133","article-title":"Improving Discrimination of Savanna Tree Species Through a Multiple-Endmember Spectral Angle Mapper Approach: Canopy-Level Analysis","volume":"48","author":"Cho","year":"2010","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kaszta, \u017b., Van De Kerchove, R., Ramoelo, A., Cho, M., Madonsela, S., Mathieu, R., and Wolff, E. (2016). Seasonal separation of African savanna components using worldview-2 imagery: A comparison of pixel-and object-based approaches and selected classifica-tion algorithms. Remote Sens., 8.","DOI":"10.3390\/rs8090763"},{"key":"ref_45","unstructured":"Richter, R., and Schl\u00e4pfer, D. (2015). Atmospheric\/Topographic Correction for Satellite Imagery, DLR\u2014German Aerospace Center. DLR Report."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1023\/A:1011093014141","article-title":"Teacher\u2019s Aide Variogram Interpretation and Modeling","volume":"33","author":"Gringarten","year":"2001","journal-title":"Math. Geol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.5194\/bg-11-2401-2014","article-title":"Using Moran\u2019s I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China","volume":"11","author":"Fu","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jongman, E., and Jongman, S.R.R. (1995). Data Analysis in Community and Landscape Ecology, Cambridge University Press.","DOI":"10.1017\/CBO9780511525575"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.rse.2012.07.010","article-title":"Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system","volume":"125","author":"Cho","year":"2012","journal-title":"Remote. Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.isprsjprs.2015.04.007","article-title":"Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data","volume":"105","author":"Naidoo","year":"2015","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/TGRS.2004.830549","article-title":"Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries","volume":"42","author":"Keshava","year":"2004","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Colwell, R.K. III (2009). 1 Biodiversity: Concepts, Patterns, and Measurement. The Princeton Guide to Ecology, Walter de Gruyter GmbH.","DOI":"10.1515\/9781400833023.257"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5","DOI":"10.2307\/3545743","article-title":"Statistics and Partitioning of Species Diversity, and Similarity among Multiple Communities","volume":"76","author":"Lande","year":"1996","journal-title":"Oikos"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1038\/163688a0","article-title":"Measurement of diversity","volume":"163","author":"Simpson","year":"1949","journal-title":"Nature"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Daly, A.J., Baetens, J.M., and De Baets, B. (2018). Ecological Diversity: Measuring the Unmeasurable. Mathematics, 6.","DOI":"10.3390\/math6070119"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.isprsjprs.2017.10.008","article-title":"Remote sensing of species diversity using Landsat 8 spectral variables","volume":"133","author":"Madonsela","year":"2017","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/S0034-4257(02)00054-8","article-title":"Determining land surface fractional cover from NDVI and rainfall time series for a savanna ecosystem","volume":"82","author":"Scanlon","year":"2002","journal-title":"Remote. Sens. Environ."},{"key":"ref_59","first-page":"583","article-title":"Leaf green-up in a semi-arid African savanna -separating tree and grass responses to environmental cues","volume":"18","author":"Archibald","year":"2007","journal-title":"J. Veg. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:23:03Z","timestamp":1760163783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,24]]},"references-count":59,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132467"],"URL":"https:\/\/doi.org\/10.3390\/rs13132467","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,24]]}}}