{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T18:06:48Z","timestamp":1775585208622,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,29]],"date-time":"2019-10-29T00:00:00Z","timestamp":1572307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41571332"],"award-info":[{"award-number":["41571332"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, the outbreak of the pine shoot beetle (PSB), Tomicus spp., has caused serious shoots damage and the death of millions of trees in Yunnan pine forests in southwestern China. It is urgent to develop a convincing approach to accurately assess the shoot damage ratio (SDR) for monitoring the PSB insects at an early stage. Unmanned airborne vehicles (UAV)-based sensors, including hyperspectral imaging (HI) and lidar, have very high spatial and spectral resolutions, which are very useful to detect forest health. However, very few studies have utilized HI and lidar data to estimate SDRs and compare the predictive power for mapping PSB damage at the individual tree level. Additionally, the data fusion of HI and lidar may improve the detection accuracy, but it has not been well studied. In this study, UAV-based HI and lidar data were fused to detect PSB. We systematically evaluated the potential of a hyperspectral approach (only-HI data), a lidar approach (only-lidar data), and a combined approach (HI plus lidar data) to characterize PSB damage of individual trees using the Random Forest (RF) algorithm, separately. The most innovative point is the proposed new method to extract the three dimensional (3D) shadow distribution of each tree crown based on a lidar point cloud and the 3D radiative transfer model RAPID. The results show that: (1) for the accuracy of estimating the SDR of individual trees, the lidar approach (R2 = 0.69, RMSE = 12.28%) performed better than hyperspectral approach (R2 = 0.67, RMSE = 15.87%), and in addition, it was useful to detect dead trees with an accuracy of 70%; (2) the combined approach has the highest accuracy (R2 = 0.83, RMSE = 9.93%) for mapping PSB damage degrees; and (3) when combining HI and lidar data to predict SDRs, two variables have the most contributions, which are the leaf chlorophyll content (Cab) derived from hyperspectral data and the return intensity of the top of shaded crown (Int_Shd_top) from lidar metrics. This study confirms the high possibility to accurately predict SDRs at individual tree level if combining HI and lidar data. The 3D radiative transfer model can determine the 3D crown shadows from lidar, which is a key information to combine HI and lidar. Therefore, our study provided a guidance to combine the advantages of hyperspectral and lidar data to accurately measure the health of individual trees, enabling us to prioritize areas for forest health promotion. This method may also be used for other 3D land surfaces, like urban areas.<\/jats:p>","DOI":"10.3390\/rs11212540","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T05:18:26Z","timestamp":1572499106000},"page":"2540","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar"],"prefix":"10.3390","volume":"11","author":[{"given":"Qinan","family":"Lin","sequence":"first","affiliation":[{"name":"Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9355-2338","authenticated-orcid":false,"given":"Huaguo","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Jingxu","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Kan","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Yangyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"889","DOI":"10.2307\/1940551","article-title":"Modifying Lodgepole Pine Stands to Change Susceptibility to Mountain Pine Beetle Attack","volume":"66","author":"Waring","year":"1985","journal-title":"Ecology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/S0048-9697(00)00528-3","article-title":"Assessing the consequences of global change for forest disturbance from herbivores and pathogens","volume":"262","author":"Ayres","year":"2000","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1126\/science.aac6674","article-title":"Planted forest health: The need for a global strategy","volume":"349","author":"Wingfield","year":"2015","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1111\/gcb.13974","article-title":"Simulating the recent impacts of multiple biotic disturbances on forest carbon cycling across the United States","volume":"24","author":"Kautz","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"39","DOI":"10.3390\/f9010039","article-title":"Detecting Shoot Beetle Damage on Yunnan Pine Using Landsat Time-Series Data","volume":"9","author":"Yu","year":"2018","journal-title":"Forests"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0378-1127(98)00376-4","article-title":"Heterogeneity of forest landscapes and the distribution and abundance of the southern pine beetle","volume":"114","author":"Coulson","year":"1999","journal-title":"For. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2011.12.023","article-title":"A general Landsat model to predict canopy defoliation in broadleaf deciduous forests","volume":"119","author":"Townsend","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1007\/s10980-013-9879-8","article-title":"Spatial dynamics of a gypsy moth defoliation outbreak and dependence on habitat characteristics","volume":"28","author":"Foster","year":"2013","journal-title":"Landsc. Ecol."},{"key":"ref_9","first-page":"49","article-title":"Remote sensing of forest insect disturbances: Current state and future directions","volume":"60","author":"Senf","year":"2017","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_10","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_11","first-page":"270","article-title":"Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data","volume":"12","author":"Somers","year":"2010","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2665","DOI":"10.3390\/rs2122665","article-title":"Classification of defoliated trees using tree-level airborne laser scanning data combined with aerial images","volume":"2","author":"Kantola","year":"2010","journal-title":"Remote Sens. Basel"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.isprsjprs.2013.10.010","article-title":"Onisimo Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests","volume":"87","author":"Oumar","year":"2014","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2015.09.019","article-title":"Characterizing spectral\u2013temporal patterns of defoliator and bark beetle disturbances using Landsat time series","volume":"170","author":"Senf","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"Amman, G.D. (1982). Mountain pine beetle\u2014Identification, biology, causes of outbreaks, and entomological research needs. BC-X-Canadian Forestry Service, Pacific Forest Research Centre."},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rse.2014.09.002","article-title":"Modeling a Historical Mountain Pine Beetle Outbreak Using Landsat MSS and Multiple Lines of Evidence","volume":"155","author":"Assal","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.foreco.2013.03.038","article-title":"Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles","volume":"302","author":"Walter","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.foreco.2014.09.012","article-title":"Mountain pine beetle-caused mortality over eight years in two pine hosts in mixed-conifer stands of the southern Rocky Mountains","volume":"334","author":"West","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"53566","DOI":"10.1117\/1.3662866","article-title":"Combining land surface temperature and shortwave infrared reflectance for early detection of mountain pine beetle infestations in western Canada","volume":"5","author":"Sprintsin","year":"2011","journal-title":"J. Appl. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1016\/j.foreco.2010.03.008","article-title":"Assessing changes in forest fragmentation following infestation using time series Landsat imagery","volume":"259","author":"Coops","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_24","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."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1016\/j.foreco.2009.06.008","article-title":"Monitoring the impacts of mountain pine beetle mitigation","volume":"258","author":"Wulder","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, Q., Huang, H., Yu, L., and Wang, J. (2018). Detection of shoot beetle stress on yunnan pine forest using a coupled LIBERTY2-INFORM simulation. Remote Sens. Basel, 10.","DOI":"10.3390\/rs10071133"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1016\/j.rse.2011.02.018","article-title":"Evaluating the potential of multispectral imagery to map multiple stages of tree mortality","volume":"115","author":"Meddens","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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. Basel"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.rse.2016.10.014","article-title":"Mapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest","volume":"187","author":"Shendryk","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.3390\/rs5084045","article-title":"NASA goddard\u2019s lidar, hyperspectral and thermal (G-LiHT) airborne imager","volume":"5","author":"Cook","year":"2013","journal-title":"Remote Sens. Basel"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1126\/science.aaj1987","article-title":"Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation","volume":"355","author":"Asner","year":"2017","journal-title":"Science"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2018.06.008","article-title":"Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and lidar measurements","volume":"215","author":"Meng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1093\/jee\/tou015","article-title":"A 10-Year Assessment of Hemlock Decline in the Catskill Mountain Region of New York State Using Hyperspectral Remote Sensing Techniques","volume":"108","author":"Hanavan","year":"2015","journal-title":"J. Econ. Entomol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2007.02.032","article-title":"Remote sensing of species mixtures in conifer plantations using lidar height and intensity data","volume":"110","author":"Donoghue","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.rse.2015.08.019","article-title":"Lidar waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters","volume":"173","author":"Hovi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2017.08.010","article-title":"Mapping urban tree species using integrated airborne hyperspectral and lidar remote sensing data","volume":"200","author":"Liu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.isprsjprs.2007.05.008","article-title":"Correction of laser scanning intensity data: Data and model-driven approaches","volume":"62","author":"Pfeifer","year":"2007","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.rse.2006.03.001","article-title":"Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning","volume":"102","author":"Solberg","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.foreco.2007.03.005","article-title":"Assessment of defoliation during a pine sawfly outbreak: Calibration of airborne laser scanning data with hemispherical photography","volume":"250","author":"Hanssen","year":"2007","journal-title":"For. Ecol. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s10980-016-0460-0","article-title":"A multi-scale analysis of western spruce budworm outbreak dynamics","volume":"32","author":"Senf","year":"2017","journal-title":"Landsc. Ecol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"69660C","DOI":"10.1117\/12.777153","article-title":"A novel method for illumination suppression in hyperspectral images","volume":"6966","author":"Ashton","year":"2008","journal-title":"Proc. SPIE"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.rse.2013.01.013","article-title":"RAPID: A Radiosity Applicable to Porous IndiviDual Objects for directional reflectance over complex vegetated scenes","volume":"132","author":"Huang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TGRS.2012.2234755","article-title":"Remote Sensing Image Segmentation by Combining Spectral and Texture Features","volume":"52","author":"Yuan","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S0031-3203(99)00055-2","article-title":"Adaptive document image binarization","volume":"33","author":"Sauvola","year":"2000","journal-title":"Pattern Recogn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.rse.2019.01.031","article-title":"Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline","volume":"223","author":"Hornerob","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2016.03.016","article-title":"Improved progressive TIN densification filtering algorithm for airborne lidar data in forested areas","volume":"117","author":"Zhao","year":"2016","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"75","DOI":"10.14358\/PERS.78.1.75","article-title":"A new method for segmenting individual trees from the lidar point cloud","volume":"78","author":"Li","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Janoutov\u00e1, R., Homolov\u00e1, L., Malenovsk\u00fd, Z., Hanu\u0161, J., Lauret, N., and Gastellu-Etchegorry, J. (2019). Influence of 3D Spruce Tree Representation on Accuracy of Airborne and Satellite Forest Reflectance Simulated in DART. Forests, 10.","DOI":"10.3390\/f10030292"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"802","DOI":"10.2307\/1933693","article-title":"Foliage profile by vertical measurements","volume":"50","author":"MacArthur","year":"1969","journal-title":"Ecology"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Almeida, D.R.A.D., Stark, S.C., Shao, G., Schietti, J., Nelson, B.W., Silva, C.A., Gorgens, E.B., Valbuena, R., Papa, D.D.A., and Brancalion, P.H.S. (2019). Optimizing the Remote Detection of Tropical Rainforest Structure with Airborne lidar: Leaf Area Profile Sensitivity to Pulse Density and Spatial Sampling. Remote Sens. Basel, 11.","DOI":"10.3390\/rs11010092"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Martin, R., Chadwick, K., Brodrick, P., Carranza-Jimenez, L., Vaughn, N., and Asner, G. (2018). An Approach for Foliar Trait Retrieval from Airborne Imaging Spectroscopy of Tropical Forests. Remote Sens. Basel, 10.","DOI":"10.3390\/rs10020199"},{"key":"ref_52","first-page":"207","article-title":"Tree species classification using plant functional traits from lidar and hyperspectral data","volume":"73","author":"Shi","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.isprsjprs.2018.02.002","article-title":"Important lidar metrics for discriminating forest tree species in Central Europe","volume":"137","author":"Shi","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","first-page":"45","article-title":"A comprehensive but efficient framework of proposing and validating feature parameters from airborne lidar data for tree species classification","volume":"46","author":"Lin","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3280","DOI":"10.3390\/rs5073280","article-title":"Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model","volume":"5","author":"Rivera","year":"2013","journal-title":"Remote Sens. Basel"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TGRS.2013.2238242","article-title":"Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions","volume":"52","author":"Verrelst","year":"2014","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2018.04.023","article-title":"Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy","volume":"211","author":"Ferreira","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"PROSPECT+SAIL models: A review of use for vegetation characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(90)90100-Z","article-title":"PROSPECT: A model of leaf optical properties spectra","volume":"34","author":"Jacquemoud","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1109\/TGRS.2007.895844","article-title":"Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies","volume":"45","author":"Verhoef","year":"2007","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.rse.2017.03.004","article-title":"PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle","volume":"193","author":"Gitelson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2016.09.015","article-title":"Retrieval of forest leaf functional traits from HySpex imagery using radiative transfer models and continuous wavelet analysis","volume":"122","author":"Ali","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_63","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_64","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","article-title":"Empirical characterization of random forest variable importance measures","volume":"52","author":"Archer","year":"2008","journal-title":"Comput. Stat. Data Ann."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.patcog.2010.08.011","article-title":"Mining data with random forests: A survey and results of new tests","volume":"44","author":"Verikas","year":"2011","journal-title":"Pattern Recogn."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1080\/01431161.2012.713142","article-title":"Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data","volume":"34","author":"Ahmed","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","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. Basel"},{"key":"ref_69","first-page":"18","article-title":"Classification and Regression by RandomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R. News"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_71","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_72","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"1","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1046\/j.1469-8137.1999.00424.x","article-title":"Assessing Leaf Pigment Content and Activity with a Reflectometer","volume":"143","author":"Gamon","year":"2010","journal-title":"N. Phytol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(94)90079-5","article-title":"Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands","volume":"50","author":"Carter","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(98)00059-5","article-title":"Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches","volume":"66","author":"Blackburn","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"578","DOI":"10.2135\/cropsci2005.0059","article-title":"Spectral Reflectance Indices as a Potential Indirect Selection Criteria for Wheat Yield under Irrigation","volume":"46","author":"Babar","year":"2006","journal-title":"Crop. Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2665","DOI":"10.1016\/j.rse.2007.12.011","article-title":"Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies","volume":"112","author":"Pontius","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.compag.2014.05.014","article-title":"Automatic threshold method and optimal wavelength selection for insect-damaged vegetable soybean detection using hyperspectral images","volume":"106","author":"Ma","year":"2014","journal-title":"Compute. Electr. Agricult."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2014.12.020","article-title":"On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images","volume":"159","author":"Tochon","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.foreco.2019.03.064","article-title":"Intra-annual Ips typographus outbreak monitoring using a multi-temporal GIS analysis based on hyperspectral and ALS data in the Bia\u0142owie\u017ca Forests","volume":"442","author":"Mielcarek","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.foreco.2004.07.018","article-title":"Ecology and management of the spruce bark beetle Ips typographus\u2014A review of recent research","volume":"202","author":"Wermelinger","year":"2004","journal-title":"For. Ecol. Manag."},{"key":"ref_82","first-page":"486","article-title":"Spatio-temporal impact of climate change on the activity and voltinism of the spruce bark beetle","volume":"15","author":"Jonsson","year":"2009","journal-title":"IPS. Typogr."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.foreco.2011.04.023","article-title":"Quantifying spatio-temporal dispersion of bark beetle infestations in epidemic and non-epidemic conditions","volume":"262","author":"Kautz","year":"2011","journal-title":"For. Ecol. 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