{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T16:10:25Z","timestamp":1762445425847,"version":"build-2065373602"},"reference-count":97,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T00:00:00Z","timestamp":1688774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Research Council"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Comparisons of recent global forest products at higher resolutions that are only available annually have shown major disagreements among forested areas in highly fragmented landscapes. A holistic reductionist framework and colourimetry were applied to create a chorologic typology of environmental indicators to map forest extent with an emphasis on large-scale performance, interpretability\/communication, and spatial\u2013temporal scalability. Interpretation keys were created to identify forest and non-forest features, and a set of candidate tree cover indices were developed and compared with a decision matrix of prescribed criteria. The candidate indices were intentionally limited to those applying only the visible and NIR bands to obtain the highest possible resolution and be compatible with commonly available multispectral satellites and higher resolution sensors, including aerial and potentially UAV\/drone sensors. A new High-Resolution Tree Cover Index (HRTCI) in combination with the Green band was selected as the best index based on scores from the decision matrix. To further improve the performance of the indices, the chorologic typology included two insolation indices, a water index and a NIR surface saturation index, to exclude any remaining spectrally similar but unrelated land cover features such as agriculture, water, and built-up features using a process of elimination. The approach was applied to the four seasons across a wide range of ecosystems in south-eastern Australia, with and without regionalisation, to identify which season produces the most accurate results for each ecoregion and to assess the potential for mitigating the spatial\u2013temporal scaling effects of the Modifiable Spatio-Temporal Unit Problem. Autumn was found to be the most effective season, yielding overall accuracies of 94.19% for the full extent, 95.79% for the temperate zone, and 95.71% for the arid zone. It produced the greatest spatial agreement between two recognised global products, the GEDI forest heights extent and the ESA WorldCover Tree cover class. The performance, transparency, and scalability of the approach should provide the basis for a framework for globally relatable forest monitoring.<\/jats:p>","DOI":"10.3390\/rs15143457","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:47:35Z","timestamp":1688950055000},"page":"3457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["High Resolution Forest Masking for Seasonal Monitoring with a Regionalized and Colourimetrically Assisted Chorologic Typology"],"prefix":"10.3390","volume":"15","author":[{"given":"Ricardo A.","family":"Aravena","sequence":"first","affiliation":[{"name":"Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of NSW, Sydney, NSW 2052, Australia"}]},{"given":"Mitchell B.","family":"Lyons","sequence":"additional","affiliation":[{"name":"Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of NSW, Sydney, NSW 2052, Australia"}]},{"given":"David A.","family":"Keith","sequence":"additional","affiliation":[{"name":"Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of NSW, Sydney, NSW 2052, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"key":"ref_1","unstructured":"Lindenmayer, D., and Franklin, J. (2002). Conserving Forest Biodiversity: A Comprehensive Multiscaled Approach, Bibliovault OAI Repository, The University of Chicago Press."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Corlett, R., and Primack, R. (2011). Tropical Rain Forests: An Ecological and Biogeographical Comparison, John Wiley & Sons. [2nd ed.].","DOI":"10.1002\/9781444392296"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.envsci.2012.04.010","article-title":"Choice of forest map has implication for policy analysis: A case study on the EU biofuel target","volume":"22","author":"Seebach","year":"2012","journal-title":"Environ. Sci. Policy"},{"key":"ref_4","unstructured":"Schepaschenko, D., Lesiv, M., See, L.M., Fritz, S., Shvidenko, A., Perger, C., D\u00fcrauer, M., Kraxner, F., Schepaschenko, M., and McCallum, I. (2015, January 7\u201311). A citizen science application for improving the spatial distribution of global forests. Proceedings of the XIV World Forestry Congress, Durban, South Africa."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1007\/s10712-019-09538-8","article-title":"The importance of consistent validation of global forest aboveground biomass products","volume":"40","author":"Duncanson","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, H., Zeng, Z., Wu, J., Peng, L., Lakshmi, V., Yang, H., and Liu, J. (2020). Large Uncertainty on Forest Area Change in the Early 21st Century among Widely Used Global Land Cover Datasets. Remote Sens., 12.","DOI":"10.3390\/rs12213502"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vangi, E., D\u2019amico, G., Francini, S., Giannetti, F., Lasserre, B., Marchetti, M., McRoberts, R.E., and Chirici, G. (2021). The Effect of Forest Mask Quality in the Wall-to-Wall Estimation of Growing Stock Volume. Remote Sens., 13.","DOI":"10.3390\/rs13051038"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3991","DOI":"10.5194\/bg-13-3991-2016","article-title":"Analysing the uncertainty of estimating forest carbon stocks in China","volume":"13","author":"Yue","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_9","unstructured":"Lund, H.G. (2018). rev* Definitions of Forest, Deforestation, Afforestation, and Reforestation, Forest Information Services. Misc. pagination: Note, this paper has been continuously updated since 1998."},{"key":"ref_10","first-page":"18","article-title":"Remote sensing in forest mapping, monitoring and measurement","volume":"18","author":"Rajashekar","year":"2019","journal-title":"J. Gov."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.envsci.2013.06.002","article-title":"Exploring different forest definitions and their impact on developing REDD+ reference emission levels: A case study for Indonesia","volume":"33","author":"Romijn","year":"2013","journal-title":"Environ. Sci. Policy"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fisher, A., Day, M., Gill, T., Roff, A., Danaher, T., and Flood, N. (2016). Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia. Remote Sens., 8.","DOI":"10.3390\/rs8060515"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1080\/01431161.2016.1266112","article-title":"A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series","volume":"38","author":"Gill","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping global forest canopy height through integration of GEDI and Landsat data","volume":"253","author":"Potapov","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.rse.2015.02.011","article-title":"Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics","volume":"162","author":"Schepaschenko","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1080\/01431161.2015.1058539","article-title":"Tropical forest cover dynamics for Latin America using Earth observation data: A review covering the continental, regional, and local scale","volume":"36","author":"Fleckenstein","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","unstructured":"Avitabile, V., Camia, A., and Pilli, R. (2020). The Biomass of European Forests. An Integrated Assessment of Forest Biomass Maps, Field Plots and National Statistics, Publications Office of the European Union."},{"key":"ref_18","first-page":"101979","article-title":"Primitives as building blocks for constructing land cover maps","volume":"85","author":"Saah","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Myroniuk, V., Kutia, M., Sarkissian, A.J., Bilous, A., and Liu, S. (2020). Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification. Remote Sens., 12.","DOI":"10.3390\/rs12010187"},{"key":"ref_20","first-page":"101909","article-title":"Mapping woody vegetation cover across Australia\u2019s arid rangelands: Utilising a machine-learning classification and low-cost Remotely Piloted Aircraft System","volume":"83","author":"Barnetson","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1126\/science.1248817","article-title":"Response to Comment on \u201cHigh-resolution global maps of 21st-century forest cover change\u201d","volume":"344","author":"Hansen","year":"2014","journal-title":"Science"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1007\/s13280-016-0772-y","article-title":"When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration","volume":"45","author":"Chazdon","year":"2016","journal-title":"AMBIO"},{"key":"ref_24","unstructured":"Tsendbazar, N., Li, L., Koopman, M., Carter, S., Herold, M., Georgieva, I., and Lesiv, M. (2022, December 01). WorldCover Product Validation Report V1.1. Available online: https:\/\/esa-worldcover.s3.amazonaws.com\/v100\/2020\/docs\/WorldCover_PVR_V1.1.pdf."},{"key":"ref_25","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2022, December 01). ESA WorldCover 10 m 2020 v100. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/ESA_WorldCover_v100?hl=en."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kinnebrew, E., Ochoa-Brito, J.I., French, M., Mills-Novoa, M., Shoffner, E., and Siegel, K. (2022). Biases and limitations of Global Forest Change and author-generated land cover maps in detecting deforestation in the Amazon. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0268970"},{"key":"ref_27","first-page":"389","article-title":"Use and misuse of landscape indices","volume":"19","author":"Harbin","year":"2003","journal-title":"Landsc. Ecol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, J., and Li, H. (2006). Concepts of Scale and Scaling, Springer.","DOI":"10.1007\/1-4020-4663-4_1"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.ecocom.2008.10.011","article-title":"Factors limiting our understanding of ecological scale","volume":"6","author":"Wheatley","year":"2009","journal-title":"Ecol. Complex."},{"key":"ref_30","first-page":"5","article-title":"Main aspects of system hierarchy in ecological landscape research","volume":"17","author":"Richling","year":"2013","journal-title":"Misc. Geogr. Reg. Stud. Dev."},{"key":"ref_31","first-page":"169","article-title":"Certain Effects of Grouping upon the Size of the Correlation Coefficient in Census Track Material","volume":"29","author":"Gehlke","year":"1934","journal-title":"J. Am. Stat. Assoc. Suppliment"},{"key":"ref_32","unstructured":"Openshaw, S. (1984). The Modifiable Areal Unit Problem; Concepts and Techniques in Modern Geography, GeoBooks."},{"key":"ref_33","unstructured":"\u00c7\u00f6ltekin, A., De Sabbata, S., Willi, C., Vontobel, I., Pfister, S., Kuhn, M., and Lacayo, M. (2011). Modifiable Temporal Unit Problem, International Cartographic Association. Available online: http:\/\/www.geo.unizh.ch\/~sdesabba\/docs\/ModifiableTemporalUnitProblem.pdf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5194\/bg-9-71-2012","article-title":"Linear trends in seasonal vegetation time series and the modifiable temporal unit problem","volume":"9","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_35","unstructured":"Jacquez, G. (2021, March 22). It\u2019s about Space and Time: From the Modifiable Areal Unit Problem (MAUP) to the Modifiable Temporal Unit Problem (MTUP) to the Modifiable Spatio-Temporal Unit Problem (MSTUP). Available online: https:\/\/www.biomedware.com\/its-about-space-and-time-from-the-modifiable-areal-unit-problem-maup-to-the-modifiable-temporal-unit-problem-mtup-to-the-modifiable-spatio-temporal-unit-problem-mstup\/."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cheng, T., and Adepeju, M. (2014). Modifiable Temporal Unit Problem (MTUP) and Its Effect on Space-Time Cluster Detection. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0100465"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Avelino, A.F.T., Baylis, K., and Honey-Ros\u00e9s, J. (2016). Goldilocks and the Raster Grid: Selecting Scale when Evaluating Conservation Programs. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0167945"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.biocon.2009.09.020","article-title":"Extent and conservation of tropical dry forests in the Americas","volume":"143","year":"2010","journal-title":"Biol. Conserv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1002\/rse2.24","article-title":"From imagery to ecology: Leveraging time series of all available Landsat observations to map and monitor ecosystem state and dynamics","volume":"2","author":"Pasquarella","year":"2016","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Goodchild, M., and Gopal, S. (1989). Accuracy of Spatial Databases, Taylor and Francis.","DOI":"10.1201\/b12612"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1080\/07038992.1999.10874735","article-title":"Remote Sensing Contributions to the Scale Issue","volume":"25","author":"Marceau","year":"1999","journal-title":"Can. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1007\/s00267-003-5181-x","article-title":"Ecoregions and Ecoregionalization: Geographical and Ecological Perspectives","volume":"34","author":"Loveland","year":"2004","journal-title":"Environ. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Land Resources: Monitoring, Modeling and Mapping, Taylor & Francis Group.","DOI":"10.1201\/b19322"},{"key":"ref_44","first-page":"233","article-title":"Ecological boundaries in the context of hierarchy theory","volume":"92","author":"Yarrow","year":"2008","journal-title":"Bio Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1016\/j.ecolind.2015.01.042","article-title":"A remote sensing spatio-temporal framework for interpreting sparse indicators in highly variable arid landscapes","volume":"60","author":"Lawley","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Venkatappa, M., Sasaki, N., Shrestha, R.P., Tripathi, N.K., and Ma, H.-O. (2019). Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform. Remote Sens., 11.","DOI":"10.3390\/rs11131514"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0034-4257(02)00079-2","article-title":"Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data","volume":"83","author":"Hansen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1111\/j.1469-8137.2010.03284.x","article-title":"Remote sensing of plant functional types","volume":"186","author":"Ustin","year":"2010","journal-title":"New Phytol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cushman, S.A., Littell, J., and McGarigal, K. (2010). The Problem of Ecological Scaling in Spatially Complex, Nonequilibrium Ecological Systems, Springer.","DOI":"10.1007\/978-4-431-87771-4_3"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/0034-4257(74)90003-0","article-title":"Vegetation canopy reflectance","volume":"3","author":"Colwell","year":"1974","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/0034-4257(94)90155-4","article-title":"Visible-near infrared spectral reflectance of landscape components in western Oregon","volume":"47","author":"Goward","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/S0034-4257(97)00042-4","article-title":"Vegetation canopy PAR absorptance and NDVI: An assessment for ten tree species with the SAIL model","volume":"61","author":"Huemmrich","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1016\/j.rse.2007.07.023","article-title":"Use of a dark object concept and support vector machines to automate forest cover change analysis","volume":"112","author":"Huang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"993","DOI":"10.2307\/2269350","article-title":"Remote Sensing of Forest Biophysical Structure Using Mixture Decomposition and Geometric Reflectance Models","volume":"5","author":"Hall","year":"1995","journal-title":"Ecol. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.isprsjprs.2019.11.018","article-title":"Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review","volume":"159","author":"Gao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","unstructured":"Van Der Meer, F.D., and De Jong, S.M. (2001). Imaging Spectrometry: Basic Principles and Prospective Applications, Kluwer."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1111\/j.0033-0124.1962.14510.x","article-title":"On the concept of areal differentiation","volume":"14","author":"Hartshorne","year":"1962","journal-title":"Prof. Geogr."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Richardson, D., Castree, N., Goodchild, M.F., Kobayashi, A., Liu, W., and Marston, R.A. (2017). International Encyclopedia of Geography: People, the Earth, Environment and Technology, John Wiley & Sons.","DOI":"10.1002\/9781118786352"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3097\/LO.201856","article-title":"A Renewed Approach to the ABC Landscape Assessment Method: An Applicaton to Muntanyes d\u2019Ordal, Barcelona Metropolitan Area","volume":"56","author":"Serrano","year":"2018","journal-title":"Landsc. Online"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Aravena, R.A., Lyons, M.B., Roff, A., and Keith, D.A. (2021). A Colourimetric Approach to Ecological Remote Sensing: Case Study for the Rainforests of South-Eastern Australia. Remote Sens., 13.","DOI":"10.3390\/rs13132544"},{"key":"ref_61","unstructured":"Kauth, R.J., and Thomas, G.S. (July, January 29). The Tasselled-Cap\u2014A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, IN, USA."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/965139.807362","article-title":"Color spaces for computer graphics","volume":"12","author":"Joblove","year":"1978","journal-title":"Comput. Graph."},{"key":"ref_64","unstructured":"Whittaker, R.H. (1970). Communities and Ecosystems, Macmillan."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1093\/biosci\/bix014","article-title":"An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm","volume":"67","author":"Dinerstein","year":"2017","journal-title":"Bioscience"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/BF00036045","article-title":"The El Ni\u00f1o\/Southern Oscillation and Australian Vegetation","volume":"91","author":"Nicholls","year":"1991","journal-title":"Vegetation"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5181","DOI":"10.5194\/bg-11-5181-2014","article-title":"Land surface phenological response to decadal climate variability across Australia using satellite remote sensing","volume":"11","author":"Broich","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"23113","DOI":"10.1038\/srep23113","article-title":"The importance of interacting climate modes on Australia\u2019s contribution to global carbon cycle extremes","volume":"6","author":"Cleverly","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5085","DOI":"10.5194\/bg-13-5085-2016","article-title":"Reviews and syntheses: Australian vegetation phenology: New insights from satellite remote sensing and digital repeat photography","volume":"13","author":"Moore","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_70","first-page":"297","article-title":"Long-Term Detection of Global Vegetation Phenology from Satellite Instruments","volume":"16","author":"Zhang","year":"2012","journal-title":"Phenol. Clim. Chang."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"271","DOI":"10.14358\/PERS.78.3.271","article-title":"Seasonal Variation in Land-Cover Classification Accuracy in a Diverse Region","volume":"78","author":"Sinha","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"10017","DOI":"10.3390\/rs70810017","article-title":"Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest","volume":"7","author":"Karlson","year":"2015","journal-title":"Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13021-018-0097-1","article-title":"Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna","volume":"13","author":"Schwieder","year":"2018","journal-title":"Carbon Balance Manag."},{"key":"ref_75","first-page":"1120","article-title":"Ocean Shores to Desert Dunes: The Native Vegetation of New South Wales and the ACT","volume":"54","author":"Schmid","year":"2005","journal-title":"Taxon"},{"key":"ref_76","unstructured":"Keith, D., and Simpson, C. (2018). Vegetation Formations and Classes of NSW (Version 3.03), VIS_ID 3848."},{"key":"ref_77","unstructured":"NSW Department of Planning and Environment (2020). NSW Landuse 2017 v1.2."},{"key":"ref_78","unstructured":"Butler, C., Lucieer, V., Walsh, P., Flukes, E., and Johnson, C. (2017). Seamap Australia [Version 1.0] the Development of a National Benthic Marine Classification Scheme for the Australian Continental Shelf, Institute for Marine and Antarctic Studies, University of Tasmania. Final Report to the Australian National Data Service (ANDS) High Values Collection #19."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/BF00131171","article-title":"The land unit\u2014A fundamental concept in landscape ecology, and its applications","volume":"3","author":"Zonneveld","year":"1989","journal-title":"Landsc. Ecol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"143","DOI":"10.2307\/3858585","article-title":"The Epidemiology of Forest Decline in Eastern Deciduous Forests","volume":"5","author":"Loucks","year":"1998","journal-title":"Northeast. Nat."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Aravena, R.A., Lyons, M.B., and Keith, D.A. (2023). Holistic Reduction to Compare and Create New Indices for Global Inter-Seasonal Monitoring: Case Study for High Resolution Surface Water Mapping. Remote Sens., 15.","DOI":"10.3390\/rs15082063"},{"key":"ref_82","first-page":"112148","article-title":"High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015","volume":"252","author":"Hadjikakou","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1641\/0006-3568(2004)054[0535:LRIEAO]2.0.CO;2","article-title":"Landsat\u2019s Role in Ecological Applications of Remote Sensing","volume":"54","author":"Cohen","year":"2004","journal-title":"Bioscience"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Wang, R., Cherkauer, K., and Bowling, L. (2016). Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8040269"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2006.01.003","article-title":"Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction","volume":"101","author":"Jiang","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1109\/TGRS.2006.874140","article-title":"Automatic Spectral Rule-Based Preliminary Mapping of Calibrated Landsat TM and ETM+ Images","volume":"44","author":"Baraldi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1038\/s41597-020-00580-5","article-title":"Outlining where humans live, the World Settlement Footprint 2015","volume":"7","author":"Marconcini","year":"2020","journal-title":"Sci. Data"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"107488","DOI":"10.1016\/j.dib.2021.107488","article-title":"Pan-tropical Sentinel-2 cloud-free annual composite datasets","volume":"39","author":"Simonetti","year":"2021","journal-title":"Data Brief"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2008). The Elements of Statistical Learning, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"100019","DOI":"10.1016\/j.srs.2021.100019","article-title":"UAV & satellite synergies for optical remote sensing applications: A literature review","volume":"3","author":"Corpetti","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1080\/15481603.2022.2083791","article-title":"Multi-sensor and multi-platform consistency and interoperability between UAV, Planet CubeSat, Sentinel-2, and Landsat reflectance data","volume":"59","author":"Jiang","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Hashimoto, N., Saito, Y., Maki, M., and Homma, K. (2019). Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields. Remote Sens., 11.","DOI":"10.3390\/rs11182119"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Guo, Y., Senthilnath, J., Wu, W., Zhang, X., Zeng, Z., and Huang, H. (2019). Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform. Sustainability, 11.","DOI":"10.3390\/su11040978"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Revill, A., Florence, A., MacArthur, A., Hoad, S., Rees, R., and Williams, M. (2020). Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations. Remote Sens., 12.","DOI":"10.3390\/rs12111843"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/22797254.2020.1806734","article-title":"Near-real time forest change detection using PlanetScope imagery","volume":"53","author":"Francini","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Herndon, K., Muench, R., Cherrington, E., and Griffin, R. (2020). An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel. Sensors, 20.","DOI":"10.3390\/s20020431"},{"key":"ref_97","first-page":"601","article-title":"A Threshold Method for Robust and Fast Estimation of Land-Surface Phenology Using Google Earth Engine","volume":"14","author":"Descalsferrando","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3457\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:09:08Z","timestamp":1760126948000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,8]]},"references-count":97,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143457"],"URL":"https:\/\/doi.org\/10.3390\/rs15143457","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,7,8]]}}}