{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:42:00Z","timestamp":1773898920126,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T00:00:00Z","timestamp":1544140800000},"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>Understanding forest cover changes is especially important in highly threatened and understudied tropical dry forest landscapes. This research uses Landsat images and a Random Forest classifier (RF) to map old-growth, secondary, and plantation forests and to evaluate changes in their coverage in Ecuador. We used 46 Landsat-derived predictors from the dry and wet seasons to map these forest types and to evaluate the importance of having seasonal variables in classifications. Initial RF models grouped old-growth and secondary forest as a single class because of a lack of secondary forest training data. The model accuracy was improved slightly from 92.8% for the wet season and 94.6% for the dry season to 95% overall by including variables from both seasons. Derived land cover maps indicate that the remaining forest in the landscape occurs mostly along the coastline in a matrix of pastureland, with less than 10% of the landscape covered by plantation forests. To obtain secondary forest training data and evaluate changes in forest cover, we conducted a change analysis between the 1990 and 2015 images. The results indicated that half of the forests present in 1990 were cleared during the 25-year study period and highlighted areas of forest regrowth. We used these areas to extract secondary forest training data and then re-classified the landscape with secondary forest as a class. Classification accuracies decreased with more forest classes, but having data from both seasons resulted in higher accuracy (87.9%) compared to having data from only the wet (85.8%) or dry (82.9%) seasons. The produced cover maps classified the majority of previously identified forest areas as secondary, but these areas likely correspond to forest regrowth and to degraded forests that structurally resemble secondary forests. Among the few areas classified as old-growth forests are known reserves. This research provides evidence of the importance of using bi-seasonal Landsat data to classify forest types and contributes to understanding changes in forest cover of tropical dry forests.<\/jats:p>","DOI":"10.3390\/rs10121980","type":"journal-article","created":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T03:36:41Z","timestamp":1544413001000},"page":"1980","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Understanding Land Cover Change in a Fragmented Forest Landscape in a Biodiversity Hotspot of Coastal Ecuador"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8048-5380","authenticated-orcid":false,"given":"Xavier","family":"Haro-Carri\u00f3n","sequence":"first","affiliation":[{"name":"Department of Geography, University of Florida, Gainesville, FL 32611, USA"},{"name":"School of Natural Resources and Environment, University of Florida, Gainesville, FL 32611, USA"}]},{"given":"Jane","family":"Southworth","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Florida, Gainesville, FL 32611, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_2","unstructured":"Food and Agriculture Organization of the United Nations (2015). Global Forest Resources Assessment 2015: How Are the World\u2019s Forests Changing?, Food and Agriculture Organization of the United Nations."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global Consequences of Land Use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10980-015-0270-9","article-title":"A global evaluation of forest interior area dynamics using tree cover data from 2000 to 2012","volume":"31","author":"Riitters","year":"2016","journal-title":"Landsc. Ecol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1111\/j.1523-1739.2009.01338.x","article-title":"The Potential for Species Conservation in Tropical Secondary Forests","volume":"23","author":"Chazdon","year":"2009","journal-title":"Conserv. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1126\/science.287.5459.1770","article-title":"Global Biodiversity Scenarios for the Year 2100","volume":"287","author":"Sala","year":"2000","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1146\/annurev.energy.28.050302.105459","article-title":"Dynamics of Land-Use and Land-Cover Change in Tropical Regions","volume":"28","author":"Lambin","year":"2003","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"20666","DOI":"10.1073\/pnas.0704119104","article-title":"The emergence of land change science for global environmental change and sustainability","volume":"104","author":"Turner","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"271","DOI":"10.4155\/cmt.10.30","article-title":"Estimating tropical deforestation from Earth observation data","volume":"1","author":"Achard","year":"2010","journal-title":"Carbon Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"16732","DOI":"10.1073\/pnas.0910275107","article-title":"Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s","volume":"107","author":"Gibbs","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.envsci.2007.01.010","article-title":"Earth observations for estimating greenhouse gas emissions from deforestation in developing countries","volume":"10","author":"DeFries","year":"2007","journal-title":"Environ. Sci. Policy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1126\/science.1070656","article-title":"Determination of Deforestation Rates of the World\u2019s Humid Tropical Forests","volume":"297","author":"Achard","year":"2002","journal-title":"Science"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1111\/j.1523-1739.2009.01333.x","article-title":"A Contemporary Assessment of Change in Humid Tropical Forests","volume":"23","author":"Asner","year":"2009","journal-title":"Conserv. Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1126\/science.1118051","article-title":"Selective logging in the Brazilian Amazon","volume":"310","author":"Asner","year":"2005","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1016\/j.rse.2007.11.013","article-title":"Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets","volume":"112","author":"Herold","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.2307\/2399468","article-title":"Management of Habitat Fragments in a Tropical Dry Forest: Growth","volume":"75","author":"Janzen","year":"1988","journal-title":"Ann. Mo. Bot. Gard."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1046\/j.0950-091x.2001.00153.x-i1","article-title":"Research Priorities for Neotropical Dry Forests1","volume":"37","author":"Quesada","year":"2005","journal-title":"Biotropica"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1111\/j.1365-2699.2005.01424.x","article-title":"A global overview of the conservation status of tropical dry forests","volume":"33","author":"Miles","year":"2006","journal-title":"J. Biogeogr."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Zachos, F.E., and Habel, J.C. (2011). Global Biodiversity Conservation: The Critical Role of Hotspots. Biodiversity Hotspots, Springer.","DOI":"10.1007\/978-3-642-20992-5"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1046\/j.1523-1739.2002.00530.x","article-title":"Habitat Loss and Extinction in the Hotspots of Biodiversity","volume":"16","author":"Brooks","year":"2002","journal-title":"Conserv. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1017\/S0266467417000062","article-title":"Effect of distance from edge on exotic grass abundance in tropical dry forests bordering pastures in Ecuador","volume":"33","year":"2017","journal-title":"J. Trop. Ecol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1126\/science.1155365","article-title":"Beyond deforestation: Restoring forests and ecosystem services on degraded lands","volume":"320","author":"Chazdon","year":"2008","journal-title":"Science"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1098\/rstb.2006.1990","article-title":"Rates of change in tree communities of secondary Neotropical forests following major disturbances","volume":"362","author":"Chazdon","year":"2007","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1126\/science.1248753","article-title":"Comment on \u201cHigh-resolution global maps of 21st-century forest cover change\u201d","volume":"344","author":"Tropek","year":"2014","journal-title":"Science"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3893","DOI":"10.1007\/s10531-010-9936-4","article-title":"Does plantation forestry restore biodiversity or create green deserts? A synthesis of the effects of land-use transitions on plant species richness","volume":"19","author":"Bremer","year":"2010","journal-title":"Biodivers. Conserv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1007\/s10531-008-9380-x","article-title":"Plantation forests and biodiversity: Oxymoron or opportunity?","volume":"17","author":"Brockerhoff","year":"2008","journal-title":"Biodivers. Conserv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.3390\/rs5062795","article-title":"Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series","volume":"5","author":"Senf","year":"2013","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.rse.2013.07.008","article-title":"Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier","volume":"139","author":"Grinand","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Connette, G., Oswald, P., Songer, M., and Leimgruber, P. (2016). Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar\u2019s Tanintharyi Region. Remote Sens., 8.","DOI":"10.3390\/rs8110882"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5660","DOI":"10.3390\/rs70505660","article-title":"Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery","volume":"7","author":"Fagan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2885","DOI":"10.1080\/01431160903140803","article-title":"The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees","volume":"31","author":"Sesnie","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"273","DOI":"10.2307\/2399563","article-title":"Biological Extinction in Western Ecuador","volume":"78","author":"Dodson","year":"1991","journal-title":"Ann. Mo. Bot. Gard."},{"key":"ref_36","unstructured":"Cuesta-Camacho, F., Peralvo, M.F., Ganzenm\u00fcller, A., S\u00e1enz, M., Novoa, J., Riofr\u00edo, G., and Beltr\u00e1n, K. (2006). Identificaci\u00f3n de Vac\u00edos de Conservaci\u00f3n para la Biodiversidad Terrestre en el Ecuador Continental, EcoCiencia, The Nature Conservancy, Conservation International, Ministerio del Ambiente del Ecuador."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.envsci.2011.04.007","article-title":"Bridging the gap between forest conservation and poverty alleviation: The Ecuadorian Socio Bosque program","volume":"14","author":"Bravo","year":"2011","journal-title":"Environ. Sci. Policy"},{"key":"ref_38","first-page":"13","article-title":"Vegetaci\u00f3n","volume":"75","author":"Neill","year":"1999","journal-title":"Monogr. Syst. Bot. Mo. Bot. Gard."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1111\/j.1756-1051.1994.tb00628.x","article-title":"The composition and structure of a dry, semideciduous forest in western Ecuador","volume":"14","author":"Josse","year":"1994","journal-title":"Nord. J. Bot."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1017\/S0376892999000181","article-title":"Traditional resource-use systems and tropical deforestation in a multi-ethnic region in North-west Ecuador","volume":"26","author":"Sierra","year":"1999","journal-title":"Environ. Conserv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1111\/j.1865-1682.2007.01013.x","article-title":"Epidemiological Patterns of Foot-and-Mouth Disease Worldwide: Global FMD epidemiology","volume":"55","author":"Rweyemamu","year":"2008","journal-title":"Transbound. Emerg. Dis."},{"key":"ref_42","unstructured":"(2018, November 16). Gap-Filling Landsat 7 SLC-off Single Scenes Using ERDAS Imagine 2014TM Landsat Missions, Available online: https:\/\/landsat.usgs.gov\/gap-filling-landsat-7-slc-single-scenes-using-erdas-imagine-TM."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1139\/cjfr-2014-0562","article-title":"Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance","volume":"46","author":"Freeman","year":"2016","journal-title":"Can. J. For. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1080\/0143116031000139917","article-title":"An assessment of Landsat TM band 6 thermal data for analysing land cover in tropical dry forest regions","volume":"25","author":"Southworth","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.foreco.2004.03.048","article-title":"Relationships between forest stand parameters and LandsatTM spectral responses in the Brazilian Amazon Basin","volume":"198","author":"Lu","year":"2004","journal-title":"For. Ecol. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1890\/1051-0761(2003)013[0352:LFPWSH]2.0.CO;2","article-title":"Linking floristic patterns with soild heteregeneity and satellite imagery in Ecuadorian Amazonia","volume":"13","author":"Tuomisto","year":"2003","journal-title":"Ecol. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.apgeog.2012.06.014","article-title":"Land cover change analysis of a Mediterranean area in Spain using different sources of data: Multi-seasonal Landsat images, land surface temperature, digital terrain models and texture","volume":"35","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1111\/j.1466-822X.2006.00232.x","article-title":"Using remote sensing image texture to study habitat use patterns: A case study using the polymorphic white-throated sparrow (Zonotrichia albicollis)","volume":"15","author":"Tuttle","year":"2006","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random forests for classification in ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_55","unstructured":"Freeman, E.A., Frescino, T.S., and Moisen, G.G. (2016). ModelMap: An R Package for Model Creation and Map Production. R Package Version, 4\u20136."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1980\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:31:58Z","timestamp":1760196718000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1980"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,7]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["rs10121980"],"URL":"https:\/\/doi.org\/10.3390\/rs10121980","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,7]]}}}