{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T21:01:28Z","timestamp":1771016488575,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,6,21]],"date-time":"2018-06-21T00:00:00Z","timestamp":1529539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX12AO04H S01"],"award-info":[{"award-number":["NNX12AO04H S01"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deciduousness in dry tropical forests results in substantial seasonal changes to canopy gap fractions. The characterization of such structural properties over large areas is necessary for understanding energy and nutrient distribution within forest ecosystems. However, a spatial extrapolation of measurements from relatively few, spatially-concentrated field observations can yield estimated values that have questionable accuracy and precision at regional scales. This paper uses linear regression models to compare measurements of canopy gap fraction from in situ digital cover photography in the dry tropical forest of the Southern Yucat\u00e1n, Mexico, to measurements of seasonal vegetation change based on three vegetation indices\u2014the Normalized Difference Vegetation Index (NDVI), two-band Enhanced Vegetation Index (EVI2), and the Normalized Difference Water Index (NDWI)\u2014derived from Landsat-7 ETM+ and Landsat-8 Operational Land Imager (OLI) data to gauge the ability of standardized combinations of multispectral reflectance data to accurately describe the intensity of deciduousness that occurs during the dry season. Discrete observations are compared, as well as spatially summarized values at coarser spatial scales. Model R2 values are greater at coarse spatial scales for all vegetation indices. Models of in situ measurements of gap fraction and Landsat NDWI normalized seasonal change exhibit stronger correlation than do models that feature NDVI or EVI2 (R\u00b2 = 0.751 and Mean Absolute Error = 0.04 after aggregation, R\u00b2 = 0.552 and MAE = 0.07 for observation-level data). Based on its comparatively strong correlation with field observations, NDWI is adapted to a Moderate Resolution Imaging Spectroradiometer (MODIS) time series and used for spatial extrapolation and the monitoring of canopy conditions. NDWI values derived from MODIS data are regressed against Tropical Rainforest Measuring Misson (TRMM) rainfall data over the period 2000\u20132011, and the regression results are compared to those of a prior study that used regression to explain the variation of a MODIS EVI using TRMM rainfall data. A MODIS NDWI time series reveals stronger correlation (R\u00b2 = 0.48 in deciduous forests) with TRMM accumulated (three-month) rainfall data than a MODIS EVI time series. The results indicate that an NDWI time series can accurately describe a variability of canopy leaf abundance during the dry season and could be an alternative basis of long-term monitoring of season phenology in a dry tropical forest.<\/jats:p>","DOI":"10.3390\/rs10070979","type":"journal-article","created":{"date-parts":[[2018,6,22]],"date-time":"2018-06-22T02:46:21Z","timestamp":1529635581000},"page":"979","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Cross-Scale Correlation between In Situ Measurements of Canopy Gap Fraction and Landsat-Derived Vegetation Indices with Implications for Monitoring the Seasonal Phenology in Tropical Forests Using MODIS Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6334-4175","authenticated-orcid":false,"given":"Nicholas","family":"Cuba","sequence":"first","affiliation":[{"name":"Graduate School of Geography, Clark University, 950 Main St., Worcester, MA 01610, USA"},{"name":"Institute at Brown for Environment and Society, Brown University, 85 Waterman St., Providence, RI 02912, USA"}]},{"given":"John","family":"Rogan","sequence":"additional","affiliation":[{"name":"Graduate School of Geography, Clark University, 950 Main St., Worcester, MA 01610, USA"}]},{"given":"Deborah","family":"Lawrence","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, University of Virginia, 291 McCormick Rd., Charlottesville, VA 22904, USA"}]},{"given":"Christopher","family":"Williams","sequence":"additional","affiliation":[{"name":"Graduate School of Geography, Clark University, 950 Main St., Worcester, MA 01610, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1093\/treephys\/22.15-16.1065","article-title":"How the environment, canopy structure, and canopy physiological functioning influence carbon, water and energy fluxes of a temperate broadleaf deciduous forest\u2014An assessment with the biophysical model CANOAK","volume":"22","author":"Baldocchi","year":"2002","journal-title":"Tree Physiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1046\/j.1365-2486.2001.00383.x","article-title":"Global response of terrestrial ecosystem structure and function to CO2 and climate change: Results from six dynamic global vegetation models","volume":"7","author":"Cramer","year":"2001","journal-title":"Glob. Chang. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wulder, M., and Franklin, S. (2003). Rationale and Conceptual Framework for Classification Approaches to Assess Forest Resources and Properties. Remote Sensing of Forest Environments, Springer.","DOI":"10.1007\/978-1-4615-0306-4_10"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"045023","DOI":"10.1088\/1748-9326\/2\/4\/045023","article-title":"Monitoring and estimating tropical forest carbon stocks: Making REDD a reality","volume":"2","author":"Gibbs","year":"2007","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"024005","DOI":"10.1088\/1748-9326\/6\/2\/021002","article-title":"Painting the world REDD: Addressing scientific barriers to monitoring emissions from tropical forests","volume":"6","author":"Asner","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3713","DOI":"10.1111\/gcb.12627","article-title":"A large-scale field assessment of carbon stocks in human-modified tropical forests","volume":"20","author":"Berenguer","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"E5224","DOI":"10.1073\/pnas.1412999111","article-title":"Amazonian landscapes and the bias in field studies of forest structure and biomass","volume":"111","author":"Marvin","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.tree.2007.05.001","article-title":"Regional ecosystem structure and function: Ecological insights from remote sensing of tropical forests","volume":"22","author":"Chambers","year":"2007","journal-title":"Trends Ecol. Evol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.rse.2005.10.022","article-title":"Green leaf phenology at Landsat resolution: Scaling from field to the satellite","volume":"100","author":"Fisher","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Land cover change detection. Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, CRC Press.","DOI":"10.1201\/b19322"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Browning, D.M., Karl, J.W., Morin, D., Richardson, A.D., and Tweedie, C.E. (2017). Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem. Remote Sens., 9.","DOI":"10.3390\/rs9101071"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1080\/15481603.2013.778559","article-title":"Modeling dry season deciduousness in Mexican Yucat\u00e1n forest using MODIS EVI data (2000\u20132011)","volume":"50","author":"Cuba","year":"2013","journal-title":"GISci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Azofeifa, A., Powers, J.S., Fernandes, G.W., and Quesada, M. (2014). Azofeifa, A. A Review of Remote Sensing of Tropical Dry Forests. Tropical Dry Forests in the Americas: Ecology, Conservation, and Management, CRC Press.","DOI":"10.1201\/b15417"},{"key":"ref_14","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. (1974). Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation, NASA\/GSFC Type III Final Report."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.3390\/s8042136","article-title":"Relationship between remotely-sensed Vegetation Indices, canopy attributes and plant physiological processes: What Vegetation Indices can and cannot tell us about the landscape","volume":"8","author":"Glenn","year":"2008","journal-title":"Sensors"},{"key":"ref_17","first-page":"77","article-title":"The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies","volume":"49","author":"Hardisky","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2014.10.001","article-title":"Fusing Landsat and SAR time series to detect deforestation in the tropics","volume":"156","author":"Reiche","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the archive\u2014How free data has enabled the science and monitoring promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/S0378-1127(01)00508-4","article-title":"Deforestation in the southern Yucat\u00e1n peninsular region: An integrative approach","volume":"154","author":"Turner","year":"2001","journal-title":"For. Ecol. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2015.12.039","article-title":"On the interest of penetration depth, canopy area, and volume metrics to improve Lidar-based models of forest parameters","volume":"175","author":"Renaud","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.rse.2006.04.005","article-title":"Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates","volume":"103","author":"Eriksson","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/S0034-4257(01)00281-4","article-title":"Estimation of tropical forest structural characteristics using large-footprint lidar","volume":"79","author":"Drake","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"22","DOI":"10.2307\/2388716","article-title":"Phenology of Canopy Trees of a Tropical Deciduous Forest in Mexico","volume":"22","author":"Bullock","year":"1990","journal-title":"Biotropica"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2016.02.018","article-title":"Imaging phenology; scaling from camera plots to landscapes","volume":"177","author":"Nijland","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s10745-012-9557-5","article-title":"Persistence of Swidden Cultivation in the Face of Globalization: A Case Study from Communities in Calakmul, Mexico","volume":"41","author":"Schmook","year":"2013","journal-title":"Hum. Ecol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Turner, B.L., Geoghegan, J., and Foster, D. (2004). Forest Types and Their Implications. Integrate Land-Change Science and Tropical Deforestation in the Southern Yucatan: Final Frontiers, Oxford University Press.","DOI":"10.1093\/oso\/9780199245307.001.0001"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1080\/01431160903527413","article-title":"A step-wise land-cover classification of the tropical forests of the Southern Yucatan, Mexico","volume":"32","author":"Schmook","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1080\/15481603.2017.1403136","article-title":"Local variability in the timing and intensity of tropical dry forest deciduousness is explained by differences in forest stand age","volume":"3","author":"Cuba","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1890\/05-1106","article-title":"Land change in the southern Yucat\u00e1n and Calakmul Biosphere Reserve: Effects on habitat and biodiversity","volume":"17","author":"Vester","year":"2007","journal-title":"Ecol. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1175\/1520-0442(1999)012<1577:TMDOMA>2.0.CO;2","article-title":"The midsummer drought over Mexico and Central America","volume":"12","author":"Magana","year":"1999","journal-title":"J. Clim."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/j.1744-7429.2005.00073.x","article-title":"Regional-Scale Variation in Litter Production and Seasonality in Tropical Dry Forests of Southern Mexico","volume":"37","author":"Lawrence","year":"2005","journal-title":"Biotropica"},{"key":"ref_33","first-page":"19","article-title":"Sequ\u00edas en el Sur de la Pen\u00ednsula de Yucat\u00e1n: An\u00e1lisis de la variabilidad anual y estacional de la precipitaci\u00f3n","volume":"78","author":"Nickl","year":"2012","journal-title":"Investig. Geogr."},{"key":"ref_34","unstructured":"Leal, W., Alves, F., Caeiro, S., and Azeiteiro, U.M. (2014). Precipitation Variability and Adaptation Strategies in the Southern Yucat\u00e1n Peninsula, Mexico: Integrating Local Knowledge with Quantitative Analysis. International Perspectives on Climate Change, Springer International Publising."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.cosust.2015.08.014","article-title":"Land system science and the social-environmental system: The case of Southern Yucatan Peninsular Region (SPYR) project","volume":"19","author":"Turner","year":"2016","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Van Hoek, M., Jia, L., Zhou, J., Zheng, C., and Menenti, M. (2016). Early Drought Detection by Spectral Analysis of Satellite Time Series of Precipitation and Normalized Difference Vegetation Index. Remote Sens., 8.","DOI":"10.3390\/rs8050422"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4358","DOI":"10.1016\/j.rse.2008.08.005","article-title":"Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index","volume":"112","author":"Boer","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.3390\/rs1041298","article-title":"Measurement of Crown Cover and Leaf Area Index Using Digital Cover Photography and Its Application to Remote Sensing","volume":"1","author":"Pekin","year":"2009","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.agrformet.2012.09.002","article-title":"Estimation of canopy properties in deciduous forests with digital hemispherical and cover photography","volume":"168","author":"Chianucci","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.agrformet.2016.03.008","article-title":"Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography","volume":"222","author":"Hwang","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_41","unstructured":"United States Geological Survey (2016, March 15). Product Guide: Landsat 4\u20137 Climate Data Record (CDR) Surface Reflectance. Version 5.8, Available online: http:\/\/landsat.usgs.gov\/CDR_LSR.php."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","unstructured":"United States Geological Survey (2016, March 15). Product Guide: Provisional Landsat 8 Surface Reflectance Code (LaSRC) Product, Version 1.4, Available online: http:\/\/landsat.usgs.gov\/CDR_LSR.php."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Env."},{"key":"ref_45","unstructured":"Didan, K. (2015). MOD13A3 MODIS\/Terra Vegetation Indices Monthly L3 Global 1 km SIN Grid V006."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1175\/JHM560.1","article-title":"The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales","volume":"8","author":"Huffman","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1080\/01431161.2010.520344","article-title":"Hurricane disturbance mapping using MODIS EVI data in the southeastern Yucatan, Mexico","volume":"2","author":"Rogan","year":"2011","journal-title":"Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/s00442-006-0629-3","article-title":"Water source partitioning among trees growing on shallow karst soils in a seasonally dry tropical climate","volume":"152","author":"Querejeta","year":"2007","journal-title":"Oecologia"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Dirzo, R., Young, H.S., Mooney, H.A., and Ceballos, G. (2011). Physiological Mechanisms Underlying the Seasonality of Leaf Senescence and Renewal in Seasonally Dry Tropical Forest Trees. Seasonally Dry Tropical Forests: Ecology and Conservation, Island Press.","DOI":"10.5822\/978-1-61091-021-7"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.2307\/1941447","article-title":"The Problem of Pattern and Scale in Ecology","volume":"73","author":"Levin","year":"1992","journal-title":"Ecology"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1080\/2150704X.2012.749360","article-title":"Evaluating MODIS active fire products in subtropical Yucat\u00e1n forest","volume":"4","author":"Cheng","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1088\/1748-9326\/aa5968","article-title":"Will seasonally dry tropical forests be sensitive or resistant to future changes in rainfall regimes?","volume":"12","author":"Allen","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2015.12.017","article-title":"Matching the phenology of Net Ecosystem Exchange and vegetation indices estimated with MODIS and FLUXNET in-situ obervations","volume":"174","author":"Balzarolo","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"12314","DOI":"10.3390\/rs70912314","article-title":"A Phenology-Based Method for Monitoring Woody and Herbaceous Vegetation in Mediterranean Forests from NDVI Time Series","volume":"7","author":"Helman","year":"2015","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/979\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:09:31Z","timestamp":1760195371000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/979"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,21]]},"references-count":54,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["rs10070979"],"URL":"https:\/\/doi.org\/10.3390\/rs10070979","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,21]]}}}