{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:19:38Z","timestamp":1773512378502,"version":"3.50.1"},"reference-count":104,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T00:00:00Z","timestamp":1533254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004629","name":"Ministry for Business Innovation and Employment","doi-asserted-by":"publisher","award":["CO4X1306"],"award-info":[{"award-number":["CO4X1306"]}],"id":[{"id":"10.13039\/501100004629","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth\u2019s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.<\/jats:p>","DOI":"10.3390\/rs10081216","type":"journal-article","created":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T11:03:26Z","timestamp":1533294206000},"page":"1216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":123,"title":["UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health"],"prefix":"10.3390","volume":"10","author":[{"given":"Jonathan P.","family":"Dash","sequence":"first","affiliation":[{"name":"Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046, New Zealand"}]},{"given":"Grant D.","family":"Pearse","sequence":"additional","affiliation":[{"name":"Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046, New Zealand"}]},{"given":"Michael S.","family":"Watt","sequence":"additional","affiliation":[{"name":"Scion, 10 Kyle St, P. O. Box 29237, Christchurch 8440, New Zealand"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1093\/forestry\/cpx016","article-title":"Quantifying the influence of seedlot and stand density on growth, wood properties and the economics of growing radiata pine","volume":"91","author":"Moore","year":"2017","journal-title":"Forestry"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1139\/cjfr-2016-0220","article-title":"Spatial prediction of optimal final stand density for even-aged plantation forests using productivity indices","volume":"47","author":"Watt","year":"2017","journal-title":"Can. J. For. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Klapste, J., Suontama, M., Telfer, E., Graham, N., Low, C., Stovold, T., McKinley, R., and Dungey, H. (2017). Exploration of genetic architecture through sib-ship reconstruction in advanced breeding population of Eucalyptus nitens. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0185137"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s11295-014-0830-1","article-title":"Genetic parameters and clone by environment interactions for growth and foliar nutrient concentrations in radiata pine on 14 widely diverse New Zealand sites","volume":"11","author":"Li","year":"2015","journal-title":"Tree Genet. Genomes"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1139\/cjfr-2016-0422","article-title":"Fertilization can compensate for decreased water availability by increasing the efficiency of stem volume production per unit of leaf area for loblolly pine (Pinus taeda) stands","volume":"47","author":"Maggard","year":"2017","journal-title":"Can. J. For. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.foreco.2015.08.001","article-title":"Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning","volume":"357","author":"Watt","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40490-016-0065-z","article-title":"Multi-sensor modelling of a forest productivity index for radiata pine plantations","volume":"46","author":"Watt","year":"2016","journal-title":"N. Z. J. For. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rse.2017.08.002","article-title":"Optimising prediction of forest leaf area index from discrete airborne lidar","volume":"200","author":"Pearse","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.envpol.2005.12.051","article-title":"Impact of ozone on Mediterranean forests: A review","volume":"144","author":"Paoletti","year":"2006","journal-title":"Environ. Pollut."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.scitotenv.2017.03.135","article-title":"Ozone exposure affects tree defoliation in a continental climate","volume":"596\u2013597","author":"Marco","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.1111\/gcb.12900","article-title":"Modelling the influence of predicted future climate change on the risk of wind damage within New Zealand\u2019s planted forests","volume":"21","author":"Moore","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.07.007","article-title":"Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak","volume":"131","author":"Dash","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1111\/efp.12305","article-title":"A worldwide perspective on the management and control of Dothistroma needle blight","volume":"46","author":"Bulman","year":"2016","journal-title":"For. Pathol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1094\/PHYTO.2003.93.12.1524","article-title":"Assessment of Dothistroma Needle Blight of Pinus radiata Using Airborne Hyperspectral Imagery","volume":"93","author":"Coops","year":"2003","journal-title":"Phytopathology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1016\/j.rse.2011.09.009","article-title":"A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests","volume":"115","author":"Meigs","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1016\/j.rse.2009.03.008","article-title":"Mapping insect defoliation in Scots pine with MODIS time-series data","volume":"113","author":"Eklundh","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1080\/01431160410001716923","article-title":"Mapping insect-induced tree defoliation and mortality using coarse spatial resolution satellite imagery","volume":"26","author":"Fraser","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pasquarella, V.J., Bradley, B.A., and Woodcock, C.E. (2017). Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series. Forests, 8.","DOI":"10.3390\/f8080275"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.foreco.2011.10.008","article-title":"Managing drought-induced mortality in Pinus radiata plantations under climate change conditions: A local approach using digital camera data","volume":"265","author":"Stone","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2006.03.012","article-title":"Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation","volume":"103","author":"Coops","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4427","DOI":"10.1080\/01431160802566439","article-title":"Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery","volume":"30","author":"Hicke","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"29","DOI":"10.2989\/20702620.2012.748255","article-title":"Discriminating the occurrence of pitch canker fungus in Pinus radiata trees using QuickBird imagery and artificial neural networks","volume":"75","author":"Poona","year":"2013","journal-title":"South. For."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2013.11.013","article-title":"Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers","volume":"88","author":"Mutanga","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","first-page":"12","article-title":"Remote sensing for precision forestry","volume":"64","author":"Dash","year":"2016","journal-title":"N. Z. J. For."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2017.03.019","article-title":"Use of partial-coverage UAV data in sampling for large scale forest inventories","volume":"194","author":"Puliti","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Miller, E., Dandois, J.P., Detto, M., and Hall, J.S. (2017). Drones as a Tool for Monoculture Plantation Assessment in the Steepland Tropics. Forests, 8.","DOI":"10.3390\/f8050168"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5558\/tfc2017-012","article-title":"Unmanned aerial systems for precision forest inventory purposes: A review and case study","volume":"93","author":"Goodbody","year":"2017","journal-title":"For. Chron."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kachamba, D.J., \u00d8rka, H.O., Gobakken, T., Eid, T., and Mwase, W. (2016). Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland. Remote Sens., 8.","DOI":"10.3390\/rs8110968"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wallace, L., Lucieer, A., Malenovsk\u00fd, Z., Turner, D., and Vop\u011bnka, P. (2016). Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests, 7.","DOI":"10.3390\/f7030062"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Messinger, M., Asner, G.P., and Silman, M. (2016). Rapid Assessments of Amazon Forest Structure and Biomass Using Small Unmanned Aerial Systems. Remote Sens., 8.","DOI":"10.3390\/rs8080615"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2938","DOI":"10.1080\/01431161.2016.1219425","article-title":"Updating residual stem volume estimates using ALS- and UAV-acquired stereo-photogrammetric point clouds","volume":"38","author":"Goodbody","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.1080\/01431161.2016.1230290","article-title":"Use of remotely sensed data to characterize weed competition in forest plantations","volume":"38","author":"Watt","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cardil, A., Vepakomma, U., and Brotons, L. (2017). Assessing Pine Processionary Moth Defoliation Using Unmanned Aerial Systems. Forests, 8.","DOI":"10.3390\/f8100402"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"15467","DOI":"10.3390\/rs71115467","article-title":"Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level","volume":"7","author":"Honkavaara","year":"2015","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s10661-015-4996-2","article-title":"Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system","volume":"188","author":"Michez","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cruz, H., Eckert, M., Meneses, J., and Mart\u00ednez, J.F. (2016). Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs). Sensors, 16.","DOI":"10.3390\/s16060893"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s10846-016-0464-7","article-title":"Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles","volume":"88","author":"Yuan","year":"2017","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mokro\u0161, M., V\u00fdbo\u0161\u0165ok, J., Mergani\u010d, J., Hollaus, M., Barton, I., Kore\u0148, M., Toma\u0161t\u00edk, J., and \u010cer\u0148ava, J. (2017). Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery. Forests, 8.","DOI":"10.3390\/f8090306"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Samiappan, S., Turnage, G., McCraine, C., Skidmore, J., Hathcock, L., and Moorhead, R. (2017). Post-Logging Estimation of Loblolly Pine (Pinus taeda) Stump Size, Area and Population Using Imagery from a Small Unmanned Aerial System. Drones, 1.","DOI":"10.3390\/drones1010004"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fraser, R.H., van der Sluijs, J., and Hall, R.J. (2017). Calibrating Satellite-Based Indices of Burn Severity from UAV-Derived Metrics of a Burned Boreal Forest in NWT, Canada. Remote Sens., 9.","DOI":"10.3390\/rs9030279"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1002\/rse2.51","article-title":"Combining drones and satellite tracking as an effective tool for informing policy change in riparian habitats: A proboscis monkey case study","volume":"4","author":"Stark","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1080\/01431161.2017.1280638","article-title":"Mapping orangutan habitat and agricultural areas using Landsat OLI imagery augmented with unmanned aircraft system aerial photography","volume":"38","author":"Szantoi","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1080\/01431161.2017.1280637","article-title":"UAV data for multi-temporal Landsat analysis of historic reforestation: A case study in Costa Rica","volume":"38","author":"Marx","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.rse.2017.10.007","article-title":"Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference","volume":"204","author":"Puliti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Abdollahnejad, A., Panagiotidis, D., and Surov\u00fd, P. (2018). Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pl\u00e9iades Data. Forests, 9.","DOI":"10.3390\/f9020085"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"377","DOI":"10.5424\/fs\/2013223-04417","article-title":"Remote monitoring of forest insect defoliation-A Review","volume":"22","author":"Olthoff","year":"2013","journal-title":"For. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2012.10.029","article-title":"Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery","volume":"129","author":"Garrity","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.foreco.2014.02.037","article-title":"Spatial and temporal patterns of Landsat-based detection of tree mortality caused by a mountain pine beetle outbreak in Colorado, USA","volume":"322","author":"Meddens","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_49","first-page":"295","article-title":"Applicability of a vegetation indices-based method to map bark beetle outbreaks in the High Tatra Mountains","volume":"58","author":"Bucha","year":"2015","journal-title":"Ann. For. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3680","DOI":"10.1016\/j.rse.2008.05.005","article-title":"Estimation of insect infestation dynamics using a temporal sequence of Landsat data","volume":"112","author":"Goodwin","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1016\/j.rse.2011.09.002","article-title":"Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland","volume":"115","author":"Eitel","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_52","unstructured":"Hewitt, A.E. (2010). New Zealand Soil Classification, Manaaki Whenua Press. [3rd ed.]."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.1080\/014311699211994","article-title":"The use of the empirical line method to calibrate remotely sensed data to reflectance","volume":"20","author":"Smith","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/S0034-4257(01)00205-X","article-title":"Calibrating images from different dates to \u2018like-value\u2019 digital counts","volume":"77","author":"Furby","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1080\/0143116032000160499","article-title":"Relations between NDVI and tree productivity in the central Great Plains","volume":"25","author":"Wang","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.rse.2003.07.010","article-title":"IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region","volume":"88","author":"Goetz","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1016\/j.foreco.2009.06.011","article-title":"Forecasting tree mortality using change metrics derived from MODIS satellite data","volume":"258","author":"Verbesselt","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.compag.2012.12.002","article-title":"Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees","volume":"91","author":"Sankaran","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1071\/BT07031","article-title":"Quantitative assessment of stand condition and its relationship to physiological stress in stands of Eucalyptus camaldulensis (Myrtaceae)","volume":"55","author":"Cunningham","year":"2007","journal-title":"Aust. J. Bot."},{"key":"ref_60","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote sensing of chlorophyll concentration in higher plant leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.rse.2005.05.009","article-title":"Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection","volume":"97","author":"Healey","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_65","unstructured":"GDAL Development Team (2018). GDAL\u2014Geospatial Data Abstraction Library, Version 2.2.1, Open Source Geospatial Foundation."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/0169-2070(88)90132-X","article-title":"Forecasting manpower demand and supply: A model for the accounting profession in Canada","volume":"4","author":"Harvey","year":"1988","journal-title":"Int. J. Forecast."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s12665-018-7228-6","article-title":"A review on missing hydrological data processing","volume":"77","author":"Gao","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_68","first-page":"1","article-title":"Mice: Multivariate Imputation by Chained Equations in R","volume":"45","year":"2011","journal-title":"J. Stat. Softw."},{"key":"ref_69","unstructured":"Harrell, F.E., and Dupont, C. (2018). Hmisc: Harrell Miscellaneous, Available online: ftp:\/\/sourceforge.mirror.ac.za\/cran\/web\/packages\/Hmisc\/Hmisc.pdf."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","article-title":"MissForest\u2014Non-parametric missing value imputation for mixed-type data","volume":"28","author":"Stekhoven","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_72","unstructured":"R Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1093\/forestry\/cpu054","article-title":"Methods for estimating multivariate stand yields and errors using k-NN and aerial laser scanning","volume":"88","author":"Dash","year":"2015","journal-title":"Forestry"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1093\/forestry\/cpv048","article-title":"Characterising forest structure using combinations of airborne laser scanning data, RapidEye satellite imagery and environmental variables","volume":"89","author":"Dash","year":"2016","journal-title":"Forestry"},{"key":"ref_75","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_76","doi-asserted-by":"crossref","unstructured":"Mellor, A., Haywood, A., Stone, C., and Jones, S. (2013). The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification. Remote Sens., 5.","DOI":"10.3390\/rs5062838"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Criminisi, A., Shotton, J., and Konukoglu, E. (2012). Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning, NOW Publishers.","DOI":"10.1561\/9781601985415"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Dash, J.P., Pearse, G.D., Watt, M.S., and Paul, T. (2017). Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection. Remote Sens., 9.","DOI":"10.3390\/rs9020156"},{"key":"ref_79","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1093\/biostatistics\/kxj011","article-title":"Survival Ensembles","volume":"7","author":"Hothorn","year":"2006","journal-title":"Biostatistics"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.L., Zeileis, A., and Hothorn, T. (2007). Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution. BMC Bioinform., 8.","DOI":"10.1186\/1471-2105-8-25"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional Variable Importance for Random Forests. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.envpol.2005.01.032","article-title":"Diagnosis of abiotic and biotic stress factors using the visible symptoms in foliage","volume":"137","author":"Vollenweider","year":"2005","journal-title":"Environ. Pollut."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1093\/jxb\/erl123","article-title":"Hyperspectral remote sensing of plant pigments","volume":"58","author":"Blackburn","year":"2007","journal-title":"J. Exp. Bot."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"S67","DOI":"10.1016\/j.rse.2008.10.019","article-title":"Retrieval of foliar information about plant pigment systems from high resolution spectroscopy","volume":"113","author":"Ustin","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"S78","DOI":"10.1016\/j.rse.2008.10.018","article-title":"Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies","volume":"113","author":"Kokaly","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2015.01.008","article-title":"Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework","volume":"102","author":"Croft","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1080\/01431169408954109","article-title":"Ratios of leaf reflectances in narrow wavebands as indicators of plant stress","volume":"15","author":"Carter","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5353","DOI":"10.1080\/01431161.2015.1095369","article-title":"Detection of mountain pine beetle-killed ponderosa pine in a heterogeneous landscape using high-resolution aerial imagery","volume":"36","author":"Gartner","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.rse.2005.03.007","article-title":"Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery","volume":"96","author":"White","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.foreco.2005.09.021","article-title":"Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities","volume":"221","author":"Wulder","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1016\/j.rse.2010.05.018","article-title":"Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data","volume":"114","author":"Dennison","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11119-004-5321-1","article-title":"A review on remote sensing of weeds in agriculture","volume":"5","author":"Thorp","year":"2004","journal-title":"Precis. Agric."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.rse.2009.11.005","article-title":"Landsat TM\/ETM+ and tree-ring based assessment of spatiotemporal patterns of the autumnal moth (Epirrita autumnata) in northernmost Fennoscandia","volume":"114","author":"Babst","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1016\/j.rse.2009.05.006","article-title":"Monitoring the spatio-temporal dynamics of geometrid moth outbreaks in birch forest using MODIS-NDVI data","volume":"113","author":"Jepsen","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1080\/01431160802549419","article-title":"Siberian silkmoth outbreak pattern analysis based on SPOT VEGETATION data","volume":"30","author":"Kharuk","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.rse.2010.09.013","article-title":"Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks","volume":"115","author":"Spruce","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2015.11.010","article-title":"Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa","volume":"112","author":"Lottering","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"677","DOI":"10.2307\/2657068","article-title":"Leaf Optical Properties in Higher Plants: Linking Spectral Characteristics to Stress and Chlorophyll Concentration","volume":"88","author":"Carter","year":"2001","journal-title":"Am. J. Bot."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(99)00016-4","article-title":"Development of an Index of Balsam Fir Vigor by Foliar Spectral Reflectance","volume":"69","author":"Luther","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"4249","DOI":"10.1080\/01431161.2010.486413","article-title":"Discriminating the early stages of Sirex noctilio infestation using classification tree ensembles and shortwave infrared bands","volume":"32","author":"Ismail","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"283","DOI":"10.14358\/PERS.69.3.283","article-title":"Mountain pine beetle red-attack forest damage classification using stratified Landsat TM data in British Columbia, Canada","volume":"69","author":"Franklin","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/S0034-4257(03)00112-3","article-title":"Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage","volume":"86","author":"Skakun","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"2729","DOI":"10.1016\/j.rse.2008.01.010","article-title":"Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring","volume":"112","author":"Wulder","year":"2008","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1216\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:16:21Z","timestamp":1760195781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,3]]},"references-count":104,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["rs10081216"],"URL":"https:\/\/doi.org\/10.3390\/rs10081216","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,3]]}}}