{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T22:02:41Z","timestamp":1768773761233,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T00:00:00Z","timestamp":1632528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sevilleta Long Term Ecological Research","award":["Summer Graduate Research Fellowship"],"award-info":[{"award-number":["Summer Graduate Research Fellowship"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Drought intensity and duration are expected to increase over the coming century in the semiarid western United States due to anthropogenic climate change. Historic data indicate that megadroughts in this region have resulted in widespread ecosystem transitions. Landscape-scale monitoring with remote sensing can help land managers to track these changes. However, special considerations are required: traditional vegetation indices such as NDVI often underestimate vegetation cover in semiarid systems due to short and multimodal green pulses, extremely variable rainfall, and high soil fractions. Multi-endmember spectral mixture analysis (MESMA) may be more suitable, as it accounts for both green and non-photosynthetic soil fractions. To determine the suitability of MESMA for assessing drought vegetation dynamics in the western US, we test multiple endmember selection and model parameters for optimizing the classification of fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S) in semiarid grass- and shrubland in central New Mexico. Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge over six field sessions from May\u2013September 2019. Landsat Thematic Mapper imagery from 2009 (two years pre-drought), and Landsat Operational Land Imager imagery from 2014 (final year of drought), and 2019 (five years post-drought) was unmixed. The best fit model had high levels of agreement with reference plots for all three classes, with R2 values of 0.85 (NPV), 0.67 (GV), and 0.74 (S) respectively. Reductions in NPV and increases in GV and S were observed on the landscape after the drought event, that persisted five years after a return to normal rainfall. Results indicate that MESMA can be successfully applied for monitoring changes in relative vegetation fractions in semiarid grass and shrubland systems in New Mexico.<\/jats:p>","DOI":"10.3390\/rs13193840","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"3840","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Assessing Drought Vegetation Dynamics in Semiarid Grass- and Shrubland Using MESMA"],"prefix":"10.3390","volume":"13","author":[{"given":"Rowan L.","family":"Converse","sequence":"first","affiliation":[{"name":"Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA"},{"name":"Center for the Advancement of Spatial Informatics Research and Education, University of New Mexico, Albuquerque, NM 87131, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher D.","family":"Lippitt","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA"},{"name":"Center for the Advancement of Spatial Informatics Research and Education, University of New Mexico, Albuquerque, NM 87131, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caitlin L.","family":"Lippitt","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"key":"ref_1","unstructured":"Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., P\u00e9an, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., and Gomis, M.I. (2021). IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1126\/science.aaz9600","article-title":"Large Contribution from Anthropogenic Warming to an Emerging North American Megadrought","volume":"368","author":"Williams","year":"2020","journal-title":"Science"},{"key":"ref_3","unstructured":"Hurd, B.H., and Coonrod, J. (2008). Climate Change and Its Implications for New Mexico\u2019s Water Resources and Economic Opportunities, New Mexico State University Agricultural Experiment Station. Technical Report 45."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s00442-015-3233-6","article-title":"Differential Sensitivity to Regional-Scale Drought in Six Central US Grasslands","volume":"177","author":"Knapp","year":"2015","journal-title":"Oecologia"},{"key":"ref_5","unstructured":"Ustin, S.L. (2004). Remote Sensing in Arid Regions: Challenges and Opportunities. Remote Sensing For Natural Resource Management and Environmental Monitoring: Manual of Remote Sensing, John Wiley & Sons. [1st ed.]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1111\/ele.12864","article-title":"Species Reordering, Not Changes in Richness, Drives Long-Term Dynamics in Grassland Communities","volume":"20","author":"Jones","year":"2017","journal-title":"Ecol. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.biocon.2014.12.006","article-title":"Remote Sensing Change Detection for Ecological Monitoring in United States Protected Areas","volume":"182","author":"Willis","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.tplants.2014.10.008","article-title":"Global Satellite Monitoring of Climate-Induced Vegetation Disturbances","volume":"20","author":"McDowell","year":"2015","journal-title":"Trends Plant Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1080\/01431161.2011.620034","article-title":"Derivation of Biomass Information for Semi-Arid Areas Using Remote-Sensing Data","volume":"33","author":"Eisfelder","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.jaridenv.2003.07.001","article-title":"Long-Term Vegetation Monitoring with NDVI in a Diverse Semi-Arid Setting, Central New Mexico, USA","volume":"58","author":"Weiss","year":"2004","journal-title":"J. Arid. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1016\/j.rse.2007.09.007","article-title":"The Impact of Soil Reflectance on the Quantification of the Green Vegetation Fraction from NDVI","volume":"112","author":"Montandon","year":"2008","journal-title":"Remote. Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1890\/09-0302.1","article-title":"The Contribution of Brown Vegetation to Vegetation Dynamics","volume":"91","author":"Okin","year":"2010","journal-title":"Ecology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2015.01.021","article-title":"Assessing the Effects of Site Heterogeneity and Soil Properties When Unmixing Photosynthetic Vegetation, Non-Photosynthetic Vegetation and Bare Soil Fractions from Landsat and MODIS Data","volume":"161","author":"Guerschman","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/S0034-4257(03)00135-4","article-title":"Endmember Selection for Multiple Endmember Spectral Mixture Analysis Using Endmember Average RMSE","volume":"87","author":"Dennison","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/S0034-4257(01)00207-3","article-title":"Practical Limits on Hyperspectral Vegetation Discrimination in Arid and Semiarid Environments","volume":"77","author":"Okin","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2011.12.004","article-title":"Landsat Remote Sensing Approaches for Monitoring Long-Term Tree Cover Dynamics in Semi-Arid Woodlands: Comparison of Vegetation Indices and Spectral Mixture Analysis","volume":"119","author":"Yang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4156","DOI":"10.1080\/01431161.2017.1317940","article-title":"Assessing Drought-Induced Change in a Pi\u00f1on-Juniper Woodland with Landsat: A Multiple Endmember Spectral Mixture Analysis Approach","volume":"38","author":"Brewer","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2005.07.011","article-title":"A Comparison of Methods for Estimating Fractional Green Vegetation Cover within a Desert-to-Upland Transition Zone in Central New Mexico, USA","volume":"98","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"Rudgers, J., Litvak, M., Luo, Y., Miller, T., and Newsome, S. (2016). LTER: Sevilleta (SEV) Site: Climate Variability at Dryland Ecotones, National Science Foundation Project Description."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"167","DOI":"10.2307\/3236602","article-title":"Vegetation Change Following Removal of Keystone Herbivores from Desert Grasslands in New Mexico","volume":"12","author":"Ryerson","year":"2001","journal-title":"J. Veg. Sci."},{"key":"ref_21","unstructured":"LTER (2021, September 04). Sevilleta LTER. Available online: https:\/\/lternet.edu\/site\/sevilleta-lter\/."},{"key":"ref_22","unstructured":"(2020, January 20). PRISM Climate Data. Available online: http:\/\/prism.oregonstate.edu."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s00442-007-0880-2","article-title":"Aboveground Net Primary Production Dynamics in a Northern Chihuahuan Desert Ecosystem","volume":"155","author":"Muldavin","year":"2008","journal-title":"Oecologia"},{"key":"ref_24","unstructured":"(2020, January 20). United States Drought Monitor. Available online: https:\/\/droughtmonitor.unl.edu\/."},{"key":"ref_25","unstructured":"Muldavin, E.H., Shore, G.A., Taugher, K., and Milne, B.T. (1998). A Vegetation Classification and Map for the Sevilleta National Wildlife Refuge, New Mexico: Final Report, New Mexico Natural Heritage and Sevilleta LTER."},{"key":"ref_26","unstructured":"Roberts, D.A., Halligan, K., Dennsion, P., Dudley, K., Somers, B., and Crabb\u00e9, A. (2019). VIPER Tools Version 2.1, UCSB Viper Lab."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2006). Feature Reduction. Remote Sensing Digital Image Analysis: An Introduction, Springer. [4th ed.].","DOI":"10.1007\/3-540-29711-1"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1659\/0276-4741(2005)025[0349:IFCCIA]2.0.CO;2","article-title":"Improved Forest Cover Classification in an Industrialized Mountain Area in Japan","volume":"25","author":"Kachmar","year":"2005","journal-title":"Mt. Res. Dev."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tane, Z., Roberts, D., Veraverbeke, S., Casas, \u00c1., Ramirez, C., and Ustin, S. (2018). Evaluating Endmember and Band Selection Techniques for Multiple Endmember Spectral Mixture Analysis Using Post-Fire Imaging Spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10030389"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1109\/TGRS.2003.812904","article-title":"Evaluation of the Potential of Hyperion for Fire Danger Assessment by Comparison to the Airborne Visible\/Infrared Imaging Spectrometer","volume":"41","author":"Roberts","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2012.08.030","article-title":"Comparing Endmember Selection Techniques for Accurate Mapping of Plant Species and Land Cover Using Imaging Spectrometer Data","volume":"127","author":"Roth","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.07.021","article-title":"Synergies between VSWIR and TIR Data for the Urban Environment: An Evaluation of the Potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey Mission","volume":"117","author":"Roberts","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember Variability in Spectral Mixture Analysis: A Review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1080\/01431161.2017.1388936","article-title":"Multidate MESMA for Monitoring Vegetation Growth Forms in Southern California Shrublands","volume":"39","author":"Lippitt","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1007\/s00442-014-3081-9","article-title":"Effects of Experimentally-Enhanced Precipitation and Nitrogen on Resistance, Recovery and Resilience of a Semi-Arid Grassland after Drought","volume":"176","author":"Xu","year":"2014","journal-title":"Oecologia"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s11258-018-0813-7","article-title":"Legacy Effects of a Regional Drought on Aboveground Net Primary Production in Six Central US Grasslands","volume":"219","author":"Carroll","year":"2018","journal-title":"Plant Ecol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1006\/jare.1996.0096","article-title":"Species Interactions on the Biome Transition Zone in New Mexico: Response of Blue Grama (Bouteloua Gracilis) and Black Grama (Bouteloua Eripoda) to Fire and Herbivory","volume":"34","author":"Gosz","year":"1996","journal-title":"J. Arid. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.rse.2013.01.008","article-title":"Effect of Image Spatial and Spectral Characteristics on Mapping Semi-Arid Rangeland Vegetation Using Multiple Endmember Spectral Mixture Analysis (MESMA)","volume":"132","author":"Thorp","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.rse.2006.09.018","article-title":"Relative Spectral Mixture Analysis\u2014A Multitemporal Index of Total Vegetation Cover","volume":"106","author":"Okin","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.rse.2015.05.004","article-title":"A Multi-Temporal Spectral Library Approach for Mapping Vegetation Species across Spatial and Temporal Phenological Gradients","volume":"167","author":"Dudley","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3840\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:04:58Z","timestamp":1760166298000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3840"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,25]]},"references-count":40,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193840"],"URL":"https:\/\/doi.org\/10.3390\/rs13193840","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,25]]}}}