{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:37:24Z","timestamp":1775147844615,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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>The forests and grasslands in the U.S. are vulnerable to global warming and extreme weather events. Current satellites do not provide historical vegetation density images over the long term (more than 50 years), which has restricted the documentation of key ecological processes and their resultant responses over decades due to the absence of large-scale and long-term monitoring studies. We performed point-by-point regression and collected data from 391 tree-ring plots to reconstruct the annual normalized difference vegetation index (NDVI) time-series maps for the contiguous U.S. from 1850 to 2010. Among three machine learning approaches for regressions\u2014Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)\u2014we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. From the Little Ice Age to the present, the NDVI increased by 6.73% across the contiguous U.S., except during some extreme events such as the Dust Bowl drought, during which the averaged NDVI decreased, particularly in New Mexico. The NDVI trend was positive in the Northern Forest, Tropical Humid Forest, Northern West Forest Mountains, Marin West Coast Forests, and Mediterranean California, while other ecoregions showed a negative trend. At the state level, Washington and Louisiana had significantly positive correlations with temperature (p &lt; 0.05). Washington had a significantly negative correlation with precipitation (p &lt; 0.05), whereas Oklahoma had a significantly positive correlation (p &lt; 0.05) with precipitation. This study provides insights into the spatial distribution of paleo-vegetation and its climate drivers. This study is the first to attempt a national-scale reconstruction of the NDVI over such a long period (151 years) using tree rings and machine learning.<\/jats:p>","DOI":"10.3390\/rs16213973","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T08:42:13Z","timestamp":1729845733000},"page":"3973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Mapping the Normalized Difference Vegetation Index for the Contiguous U.S. Since 1850 Using 391 Tree-Ring Plots"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0348-9812","authenticated-orcid":false,"given":"Hang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Ecosystem Science and Management, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada"}]},{"given":"Ichchha","family":"Thapa","sequence":"additional","affiliation":[{"name":"Department of Forestry, Michigan State University, East Lansing, MI 48824, USA"}]},{"given":"Shuang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Southwest University, Chongqing 400715, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6567-6306","authenticated-orcid":false,"given":"Peisi","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"ref_1","unstructured":"Pathak, M., Slade, R., Shukla, P.R., Skea, J., Pichs-Madruga, R., and \u00dcrge-Vorsatz, D. (2024, May 02). Technical summary. Climate Change 2022. Available online: https:\/\/www.ipcc.ch\/report\/ar6\/wg3\/downloads\/report\/IPCC_AR6_WGIII_TechnicalSummary.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1017\/S1355770X10000471","article-title":"The economic costs of extreme weather events: A hydrometeorological CGE analysis for Malawi","volume":"16","author":"Pauw","year":"2011","journal-title":"Environ. Dev. Econ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ummenhofer, C.C., and Meehl, G.A. (2017). Extreme weather and climate events with ecological relevance: A review. Philos. Trans. R. Soc. B Biol. Sci., 372.","DOI":"10.1098\/rstb.2016.0135"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1097\/JOM.0b013e31817d32da","article-title":"Climate change, extreme weather events, and us health impacts: What can we say?","volume":"51","author":"Mills","year":"2009","journal-title":"J. Occup. Environ. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2207536119","DOI":"10.1073\/pnas.2207536119","article-title":"Trends of extreme US weather events in the changing climate","volume":"119","author":"Shenoy","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.foreco.2018.09.006","article-title":"Tree mortality following drought in the central and southern Sierra Nevada, California, US","volume":"432","author":"Fettig","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s10841-023-00525-4","article-title":"Extreme weather impacts on butterfly populations in Southern Texas, USA","volume":"28","author":"Zerlin","year":"2024","journal-title":"J. Insect Conserv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.14358\/PERS.72.10.1155","article-title":"Historical record of Landsat global coverage","volume":"72","author":"Goward","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"53","DOI":"10.2307\/1934662","article-title":"Environment in relation to age of bristlecone pines","volume":"50","author":"LaMarche","year":"1969","journal-title":"Ecology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.earscirev.2006.12.002","article-title":"North American drought: Reconstructions, causes, and consequences","volume":"81","author":"Cook","year":"2007","journal-title":"Earth-Sci. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2011.565080","article-title":"Land cover mapping applications with MODIS: A literature review","volume":"5","author":"Mas","year":"2012","journal-title":"Int. J. Digit. Earth"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1029\/00EO00076","article-title":"Tree-ring data document 16th century megadrought over North America","volume":"81","author":"Stahle","year":"2000","journal-title":"EOS Trans. Am. Geophys. Union"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, H., Speer, J.H., Malubeni, C.C., and Wilson, E. (2024). Reconstructing a Fine Resolution Landscape of Annual Gross Primary Product (1895\u20132013) with Tree-Ring Indices. Remote Sens., 16.","DOI":"10.3390\/rs16193744"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"25835","DOI":"10.1029\/94JD02007","article-title":"Summer temperatures across northern North America: Regional reconstructions from 1760 using tree-ring densities","volume":"99","author":"Briffa","year":"1994","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1080\/01431160050029611","article-title":"Correlation between maximum latewood density of annual tree rings and NDVI based estimates of forest productivity","volume":"21","author":"Malmstrom","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","first-page":"1","article-title":"Identifying climatic controls on ring width: The timing of correlations between tree rings and NDVI","volume":"12","author":"Kaufmann","year":"2008","journal-title":"Earth Interact."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1515\/geochr-2015-0091","article-title":"Picea schrenkiana tree-ring chronologies development and vegetation index reconstruction for the Alatau Mountains, Central Asia","volume":"45","author":"Zhang","year":"2018","journal-title":"Geochronometria"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1080\/01431160500056931","article-title":"Relationships between tree growth and NDVI of grassland in the semi-arid grassland of north China","volume":"26","author":"Liang","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, H., Thapa, I., and Speer, J.H. (2021). Fine-scale NDVI reconstruction back to 1906 from tree-rings in the greater Yellowstone ecosystem. Forests, 12.","DOI":"10.3390\/f12101324"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1175\/1520-0442(1999)012<1145:DRFTCU>2.0.CO;2","article-title":"Drought reconstructions for the continental United States","volume":"12","author":"Cook","year":"1999","journal-title":"J. Clim."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, H., Speer, J.H., and Thapa, I. (2024). Reconstructing and mapping annual net primary productivity (NPP) since 1940 using tree rings in Southern Indiana, US. J. Geophys. Res. Biogeosciences, 129.","DOI":"10.1029\/2023JG007929"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Seiler, R., Kirchner, J.W., Krusic, P.J., Tognetti, R., Houlie, N., Andronico, D., Cullotta, S., Egli, M., D\u2019Arrigo, R., and Cherubini, P. (2017). Insensitivity of tree-ring growth to temperature and precipitation sharpens the puzzle of enhanced pre-eruption NDVI on Mt. Etna (Italy). PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169297"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"317","DOI":"10.2307\/2389523","article-title":"Effects of defoliation on radial stem growth and photosynthesis in the mountain birch (Betula pubescens ssp. tortuosa)","volume":"6","author":"Hoogesteger","year":"1992","journal-title":"Funct. Ecol."},{"key":"ref_24","unstructured":"Speer, J.H. (2010). Fundamentals of Tree-Ring Research, University of Arizona Press."},{"key":"ref_25","first-page":"45","article-title":"The smoothing spline: A new approach to standardizing forest interior tree-ring width series for dendroclimatic studies","volume":"41","author":"Cook","year":"1981","journal-title":"Tree-Ring Bull."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A general regression neural network","volume":"2","author":"Specht","year":"1991","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/ICDAR.1995.598994","article-title":"Random decision forests","volume":"Volume 1","author":"Ho","year":"1995","journal-title":"In Proceedings of 3rd International Conference on Document Analysis and Recognition"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Spiess, A.N., and Neumeyer, N. (2010). An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: A Monte Carlo approach. BMC Pharmacol., 10.","DOI":"10.1186\/1471-2210-10-6"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1057\/jt.2009.5","article-title":"The correlation coefficient: Its values range between +1\/\u22121, or do they?","volume":"17","author":"Ratner","year":"2009","journal-title":"J. Target. Meas. Anal. Mark."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s10584-006-9171-x","article-title":"Tree-ring reconstructed megadroughts over North America since AD 1300","volume":"83","author":"Stahle","year":"2007","journal-title":"Clim. Chang."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1657\/1523-0430(2003)035[0489:EOLIAC]2.0.CO;2","article-title":"Estimates of Little Ice Age climate inferred through historical rephotography, northern Uinta Mountains, USA","volume":"35","author":"Munroe","year":"2003","journal-title":"Arct. Antarct. Alp. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1177\/09596836211011656","article-title":"Climate-induced treeline mortality during the termination of the Little Ice Age in the Greater Yellowstone Ecoregion, USA","volume":"31","author":"Rochner","year":"2021","journal-title":"Holocene"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"16200","DOI":"10.1073\/pnas.0707896104","article-title":"Tracing the effects of the Little Ice Age in the tropical lowlands of eastern Mesoamerica","volume":"104","author":"Caballero","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bolles, K.C., and Forman, S.L. (2018). Evaluating landscape degradation along climatic gradients during the 1930s dust bowl drought from panchromatic historical aerial photographs, United States Great Plains. Front. Earth Sci., 6.","DOI":"10.3389\/feart.2018.00153"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2936","DOI":"10.3390\/su7032936","article-title":"North American soil degradation: Processes, practices, and mitigating strategies","volume":"7","author":"Baumhardt","year":"2015","journal-title":"Sustainability"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1007\/s00484-021-02170-5","article-title":"Inter and intra-annual links between climate, tree growth and NDVI: Improving the resolution of drought proxies in conifer forests","volume":"65","author":"Camarero","year":"2021","journal-title":"Int. J. Biometeorol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1007\/s10980-019-00896-7","article-title":"Simulating forest cover change in the northeastern US: Decreasing forest area and increasing fragmentation","volume":"34","author":"Adams","year":"2019","journal-title":"Landsc. Ecol."},{"key":"ref_39","first-page":"e02198","article-title":"The loss of vegetation cover has distinct but short-term impact on multiple vertebrate taxa in a grassland ecosystem","volume":"38","author":"Mdluli","year":"2022","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.epsl.2011.05.021","article-title":"Cold conditions in Antarctica during the Little Ice Age\u2014Implications for abrupt climate change mechanisms","volume":"308","author":"Bertler","year":"2011","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.5194\/essd-15-1005-2023","article-title":"Four-century history of land transformation by humans in the United States (1630\u20132020): Annual and 1 km grid data for the HIStory of LAND changes (HISLAND-US)","volume":"15","author":"Li","year":"2023","journal-title":"Earth Syst. Sci. Data."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Park, S.Y., Yoo, J.W., Song, S.K., Kim, C.H., and Lee, S.H. (2022). Numerical study on advective fog formation and its characteristic associated with cold water upwelling. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0267895"},{"key":"ref_43","first-page":"72","article-title":"Sunshine and Cloudiness in the United States","volume":"58","author":"Visher","year":"1944","journal-title":"Sci. Mon."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"292","DOI":"10.2307\/1932397","article-title":"Wet seasons in the United States: How wet and how frequent","volume":"31","author":"Visher","year":"1950","journal-title":"Ecology"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Engstr\u00f6m, J., Jafarzadegan, K., and Moradkhani, H. (2020). Drought vulnerability in the United States: An integrated assessment. Water, 12.","DOI":"10.3390\/w12072033"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1002\/joc.6026","article-title":"Spatial and temporal patterns of drought in Oklahoma (1901\u20132014)","volume":"39","author":"Tian","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.1007\/s10584-020-02934-9","article-title":"Agricultural impacts of climate change in Indiana and potential adaptations","volume":"163","author":"Bowling","year":"2020","journal-title":"Clim. Chang."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, Z., Luo, T., Wang, X., Wang, A., and Zhang, D. (2024). Reconstruction of NDVI based on Larix gmelinii tree-rings during June\u2013September 1759\u20132021. Front. For. Glob. Chang., 7.","DOI":"10.3389\/ffgc.2024.1283956"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1007\/s10668-016-9870-z","article-title":"Seasonal dynamics of vegetation of the central Loess Plateau (China) based on tree rings and their relationship to climatic warming","volume":"19","author":"Wang","year":"2017","journal-title":"Environ. Dev. Sustain."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/3973\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:20:36Z","timestamp":1760113236000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/3973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"references-count":49,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16213973"],"URL":"https:\/\/doi.org\/10.3390\/rs16213973","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]}}}