{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T01:43:32Z","timestamp":1781833412073,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2013,6,4]],"date-time":"2013-06-04T00:00:00Z","timestamp":1370304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping and monitoring forest extent is a common requirement of regional forest inventories and public land natural resource management, including in Australia. The state of Victoria, Australia, has approximately 7.2 million hectares of mostly forested public land, comprising ecosystems that present a diverse range of forest structures, composition and condition. In this paper, we evaluate the performance of the Random Forest (RF) classifier, an ensemble learning algorithm that has recently shown promise using multi-spectral satellite sensor imagery for large area feature classification. The RF algorithm was applied using selected Landsat Thematic Mapper (TM) imagery metrics and auxiliary terrain and climatic variables, while the reference data was manually extracted from systematically distributed plots of sample aerial photography and used for training (75%) and accuracy (25%) assessment. The RF algorithm yielded an overall accuracy of 96% and a Kappa statistic of 0.91 (confidence interval (CI) 0.909\u20130.919) for the forest\/non-forest classification model, given a Kappa maximised binary threshold value of 0.5. The area under the receiver operating characteristic plot produced a score of 0.91, also indicating high model performance. The framework described in this study contributes to the operational deployment of a robust, but affordable, program, able to collate and process large volumes of multi-sourced data using open-source software for the production of consistent and accurate forest cover maps across the full spectrum of Victorian sclerophyll forest types.<\/jats:p>","DOI":"10.3390\/rs5062838","type":"journal-article","created":{"date-parts":[[2013,6,4]],"date-time":"2013-06-04T12:24:56Z","timestamp":1370348696000},"page":"2838-2856","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":163,"title":["The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification"],"prefix":"10.3390","volume":"5","author":[{"given":"Andrew","family":"Mellor","sequence":"first","affiliation":[{"name":"School of Mathematical and Geospatial Sciences, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia"},{"name":"Victorian Department of Environment and Primary Industries, 8 Nicholson Street, East Melbourne, VIC 3002, Australia"},{"name":"Joint Remote Sensing Research Program, School of Geography, Planning and Environmental Management, University of Queensland, St Lucia, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Haywood","sequence":"additional","affiliation":[{"name":"Victorian Department of Environment and Primary Industries, 8 Nicholson Street, East Melbourne, VIC 3002, Australia"},{"name":"Joint Remote Sensing Research Program, School of Geography, Planning and Environmental Management, University of Queensland, St Lucia, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine","family":"Stone","sequence":"additional","affiliation":[{"name":"New South Wales Department of Primary Industries, P.O. Box 100, Beecroft, NSW 2119, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Jones","sequence":"additional","affiliation":[{"name":"School of Mathematical and Geospatial Sciences, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2013,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.rse.2009.12.013","article-title":"Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data","volume":"114","author":"McRoberts","year":"2010","journal-title":"Remote Sens. Environ"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ecolind.2006.11.004","article-title":"Sustainable forest management reporting in Australia","volume":"8","author":"Howell","year":"2008","journal-title":"Ecol. Indic"},{"key":"ref_3","first-page":"285","article-title":"Forest area estimation using sample surveys and Landsat MSS and TM data","volume":"64","author":"Deppe","year":"1998","journal-title":"Photogramm. Eng. Remote Sensing"},{"key":"ref_4","unstructured":"Department of Agriculture Fisheries and Forestry (2012). Australia\u2019s Forest at a Glance, Department of Agriculture Fisheries and Forestry."},{"key":"ref_5","unstructured":"Australian Surveying and Land Information Group (1990). Atlas of Australian Resources (Vol. 6, Vegetation), Australian Surveying and Land Information Group."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/00049182.2011.546316","article-title":"Landscape controls on structural variation in Eucalypt vegetation communities: Woronora Plateau, Australia","volume":"42","author":"Jenkins","year":"2011","journal-title":"Aust. Geogr"},{"key":"ref_7","unstructured":"Jacobs, M (1955). Growth Habits of the Eucalypts, Forestry and Timber Bureau."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1080\/00049158.2001.10676169","article-title":"Mapping forest cover, Kimberley Region of Western Australia","volume":"64","author":"Behn","year":"2001","journal-title":"Australian Forestry"},{"key":"ref_9","unstructured":"Bhandari, S (2011). Monitoring Forest Dynamics using Time Series of Satellite Image Data in Queensland, Australia. PhD Dissertation, The University of Queensland, Brisbane, QLD, Australia,."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shimoda, H., Gholz, H.L., and Nakane, K. (1997). The Use of Remote Sensing in the Modeling of Forest Productivity, Springer.","DOI":"10.1007\/978-94-011-5446-8"},{"key":"ref_11","unstructured":"Montreal Process Implementation Group for Australia (2008). Australia\u2019s State of the Forests Report 2008, Montreal Process Implementation Group for Australia."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.3390\/rs4061781","article-title":"Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories","volume":"4","author":"Clerici","year":"2012","journal-title":"Remote Sens"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.3390\/rs3071427","article-title":"Evaluating the remote sensing and inventory-based estimation of biomass in the western carpathians","volume":"3","author":"Moisen","year":"2011","journal-title":"Remote Sens"},{"key":"ref_15","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"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/S0378-1127(96)03753-X","article-title":"Current approaches to modelling the environmental niche of eucalypts: implication for management of forest biodiversity","volume":"85","author":"Austin","year":"1996","journal-title":"Forest Ecol. Manag"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6956","DOI":"10.1080\/01431161.2012.695095","article-title":"Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests","volume":"33","author":"Khalyani","year":"2012","journal-title":"Int. J. Remote Sens"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1080\/01431160210154948","article-title":"A non-parametric supervised classification of vegetation types on the Kaibab National Forest using decision trees","volume":"24","author":"Joy","year":"2003","journal-title":"Int. J. Remote Sens"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1016\/j.rse.2007.08.025","article-title":"Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments","volume":"112","author":"Sesnie","year":"2008","journal-title":"Remote Sens.Environ"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0378-1127(99)00272-8","article-title":"Incorporation of digital elevation models with Landsat-TM data to improve land cover classification accuracy","volume":"128","author":"Fahsi","year":"2000","journal-title":"Forest Ecol. Manag"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1126\/science.248.4952.212","article-title":"Deforestation history of the eastern rainforests of Madagascar from satellite images","volume":"248","author":"Green","year":"1990","journal-title":"Science"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1191\/0309133305pp432ra","article-title":"Satellite remote sensing of forest resources: Three decades of research development","volume":"29","author":"Boyd","year":"2005","journal-title":"Progr. Phys. Geogr"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1080\/01431160500142145","article-title":"Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon","volume":"26","author":"Lu","year":"2005","journal-title":"Int. J. Remote Sens"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1080\/014311600210263","article-title":"Strategies for tropical forest deforestation assessment using satellite data","volume":"21","author":"Tucker","year":"2000","journal-title":"Int. J. Remote Sens"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S0034-4257(01)00296-6","article-title":"A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery","volume":"80","author":"Rogan","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6379","DOI":"10.1080\/01431161.2010.510490","article-title":"Use of MODIS NDVI data to improve forest-area estimation","volume":"32","author":"Maselli","year":"2011","journal-title":"Int. J. Remote Sens"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s10661-009-1243-8","article-title":"Multiscale satellite and spatial information and analysis framework in support of a large-area forest monitoring and inventory update","volume":"170","author":"Wulder","year":"2010","journal-title":"Environ. Monit. Assess"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/JSTARS.2009.2021959","article-title":"The impact of phenological variation on texture measures of remotely sensed imagery","volume":"2","author":"Culbert","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1080\/0143116042000192367","article-title":"A multiscale texture analysis procedure for improved forest stand classification","volume":"25","author":"Coburn","year":"2004","journal-title":"Int. J. Remote Sens"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"810","DOI":"10.3390\/rs4040810","article-title":"Improved forest biomass and carbon estimations using texture measures from worldview-2 satellite data","volume":"4","author":"Eckert","year":"2012","journal-title":"Remote Sens"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2006.02.022","article-title":"Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery","volume":"102","author":"Kayitakire","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.proenv.2011.02.009","article-title":"Incorporating Spatial Variability Measures in Land-cover Classification using Random Forest","volume":"3","author":"Ghimire","year":"2011","journal-title":"Procedia Environ. Sci"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/S0304-3800(00)00354-9","article-title":"Predictive habitat distribution models in ecology","volume":"135","author":"Guisan","year":"2000","journal-title":"Ecol. Model"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ecolmodel.2005.01.030","article-title":"Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species\u2019 current and future distributions","volume":"186","author":"Beaumont","year":"2005","journal-title":"Ecol. Model"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1177\/030913339501900403","article-title":"Predictive vegetation mapping: Geographic modelling of biospatial patterns in relation to environmental gradients","volume":"19","author":"Franklin","year":"1995","journal-title":"Progr. Phys. Geogr"},{"key":"ref_38","unstructured":"Random Forest. Available online: http:\/\/www.stat.berkeley.edu\/~breiman\/RandomForests\/cc_home.htm."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1093\/bib\/bbq011","article-title":"Letter to the editor: Stability of Random Forest importance measures","volume":"12","author":"Calle","year":"2011","journal-title":"Briefings Bioinf"},{"key":"ref_40","unstructured":"The GNUManifesto. Available online: http:\/\/www.gnu.org\/gnu\/manifesto.html."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rocchini, D., Delucchi, L., Bacaro, G., Cavallini, P., Feilhauer, H., Foody, G.M., He, K.S., Nagendra, H., Porta, C., and Ricotta, C. (2012). Calculating landscape diversity with information-theory based indices: A GRASS GIS solution. Ecol. Inform., in press.","DOI":"10.1016\/j.ecoinf.2012.04.002"},{"key":"ref_42","unstructured":"GRASS Development Team Geographic Resources Analysis Support System (GRASS) Software; Version 6.4; Open Source Geospatial Foundation Project. Available online: http:\/\/grass.osgeo.org."},{"key":"ref_43","unstructured":"R Development Core Team Available online: http:\/\/www.R-project.org."},{"key":"ref_44","first-page":"36","article-title":"Using the R-GRASS Interface: Current Status","volume":"1","author":"Bivand","year":"2007","journal-title":"OSGeo Journal"},{"key":"ref_45","unstructured":"The Python Language Reference. Available online: http:\/\/docs.python.org\/release\/3.2\/reference\/index.html."},{"key":"ref_46","unstructured":"Viridans Ecosystems and Vegetation. Available online: http:\/\/www.viridans.com\/ECOVEG\/."},{"key":"ref_47","unstructured":"Department of Sustainability and Environment Victorian Forest Monitoring Program. Available onine: http:\/\/www.dse.vic.gov.au\/forests\/managing-our-forests\/forest-sustainability\/victorian-forest-monitoring-program."},{"key":"ref_48","unstructured":"Mellor, A., and Haywood, A (2010, January 13). Remote Sensing Victoria\u2019s Public Land Forests\u2014A Two Tiered Synoptic Approach. Alice Springs, Australia."},{"key":"ref_49","unstructured":"National Forest Inventory (2003). Australia\u2019s State of the Forests Report 2003, Bureau of Rural Sciences."},{"key":"ref_50","unstructured":"Food and Agriculture Organization of the United Nations (2001). Global Forest Resources Assessment 2000, FAO."},{"key":"ref_51","unstructured":"Arrowsmith, C., Bellman, C., Cartwright, W., Jones, S., and Shortis, M. (2013). Progress in Geospatial Science Research, Publishing Solutions."},{"key":"ref_52","unstructured":"Earth Explorer. Availiable online: http:\/\/earthexplorer.usgs.gov."},{"key":"ref_53","unstructured":"CSIRO One-second SRTM digital elevation model. Available online: http:\/\/www.csiro.au\/Outcomes\/Water\/Water-information-systems\/One-second-SRTM-Digital-Elevation-Model.aspx."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3390\/rs5010083","article-title":"An operational scheme for deriving standardised surface reflectance from Landsat TM\/ETM+ and SPOT HRG imagery for Eastern Australia","volume":"5","author":"Flood","year":"2013","journal-title":"Remote Sens"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approach to texture","volume":"67","author":"Haralich","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_56","unstructured":"Paget, M.J., and King, E.A. (2008). MODIS Land Data Sets for the Australian Region, CSIRO Marine and Atmospheric Research."},{"key":"ref_57","unstructured":"Houlder, D., Hutchinson, M., Nix, H., and McMahon, J (2001). ANUCLIM; Version 5.1, Centre for Resource and Environmental Studies."},{"key":"ref_58","first-page":"18","article-title":"Classification and regression by RandomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v023.i11","article-title":"PresenceAbsence: An R package for Presence-Absence Model analysis","volume":"23","author":"Freeman","year":"2008","journal-title":"J. Stat. Softw"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S0304-3800(00)00322-7","article-title":"Evaluating the predictive performance of habitat models developed using logistic regression","volume":"133","author":"Pearce","year":"2000","journal-title":"Ecol. Model"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s10980-008-9215-x","article-title":"On the accuracy of landscape pattern analysis using remote sensing data","volume":"23","author":"Shao","year":"2008","journal-title":"Landscape Ecol"},{"key":"ref_62","unstructured":"RPy Python interface to the R Programming Language. Available online: http:\/\/rpy.sourceforge.net."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ"},{"key":"ref_64","unstructured":"Woodgate, P., and Black, P (1988). Forest Cover Changes in Victoria 1869\u20131987, Remote Sensing Group, Lands and Forests Division, Dept. of Conservation, Forests and Lands."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"033540","DOI":"10.1117\/1.3216031","article-title":"Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery","volume":"3","author":"Armston","year":"2009","journal-title":"J. Appl. Remote Sens"},{"key":"ref_66","first-page":"1155","article-title":"The effect of training strategies on supervised classification at different spatial resolutions","volume":"68","author":"Chen","year":"2002","journal-title":"Photogramm. Eng. Remote Sensing"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/6\/2838\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:47:10Z","timestamp":1760219230000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/6\/2838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,6,4]]},"references-count":66,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2013,6]]}},"alternative-id":["rs5062838"],"URL":"https:\/\/doi.org\/10.3390\/rs5062838","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,6,4]]}}}