{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T02:07:47Z","timestamp":1774922867134,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T00:00:00Z","timestamp":1665792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52064039"],"award-info":[{"award-number":["52064039"]}]},{"name":"National Natural Science Foundation of China","award":["61971246"],"award-info":[{"award-number":["61971246"]}]},{"name":"National Natural Science Foundation of China","award":["41861056"],"award-info":[{"award-number":["41861056"]}]},{"name":"National Natural Science Foundation of China","award":["2019GG138"],"award-info":[{"award-number":["2019GG138"]}]},{"name":"National Natural Science Foundation of China","award":["2019GG139"],"award-info":[{"award-number":["2019GG139"]}]},{"name":"National Natural Science Foundation of China","award":["2020GG0073"],"award-info":[{"award-number":["2020GG0073"]}]},{"name":"National Natural Science Foundation of China","award":["2019ZD022"],"award-info":[{"award-number":["2019ZD022"]}]},{"name":"National Natural Science Foundation of China","award":["2019MS04004"],"award-info":[{"award-number":["2019MS04004"]}]},{"name":"National Natural Science Foundation of China","award":["XDA19070102"],"award-info":[{"award-number":["XDA19070102"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["52064039"],"award-info":[{"award-number":["52064039"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["61971246"],"award-info":[{"award-number":["61971246"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["41861056"],"award-info":[{"award-number":["41861056"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["2019GG138"],"award-info":[{"award-number":["2019GG138"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["2019GG139"],"award-info":[{"award-number":["2019GG139"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["2020GG0073"],"award-info":[{"award-number":["2020GG0073"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["2019ZD022"],"award-info":[{"award-number":["2019ZD022"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["2019MS04004"],"award-info":[{"award-number":["2019MS04004"]}]},{"name":"Science and Technology Innovation Guidance Project of Inner Mongolia Autonomous Region","award":["XDA19070102"],"award-info":[{"award-number":["XDA19070102"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["52064039"],"award-info":[{"award-number":["52064039"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["61971246"],"award-info":[{"award-number":["61971246"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["41861056"],"award-info":[{"award-number":["41861056"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["2019GG138"],"award-info":[{"award-number":["2019GG138"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["2019GG139"],"award-info":[{"award-number":["2019GG139"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["2020GG0073"],"award-info":[{"award-number":["2020GG0073"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["2019ZD022"],"award-info":[{"award-number":["2019ZD022"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["2019MS04004"],"award-info":[{"award-number":["2019MS04004"]}]},{"name":"Science and Technology Major Special Project of Inner Mongolia Autonomous Region","award":["XDA19070102"],"award-info":[{"award-number":["XDA19070102"]}]},{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region","award":["52064039"],"award-info":[{"award-number":["52064039"]}]},{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region","award":["61971246"],"award-info":[{"award-number":["61971246"]}]},{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region","award":["41861056"],"award-info":[{"award-number":["41861056"]}]},{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region","award":["2019GG138"],"award-info":[{"award-number":["2019GG138"]}]},{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region","award":["2019GG139"],"award-info":[{"award-number":["2019GG139"]}]},{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region","award":["2020GG0073"],"award-info":[{"award-number":["2020GG0073"]}]},{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region","award":["2019ZD022"],"award-info":[{"award-number":["2019ZD022"]}]},{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region","award":["2019MS04004"],"award-info":[{"award-number":["2019MS04004"]}]},{"name":"Natural Science Foundation of Inner Mongolia Autonomous Region","award":["XDA19070102"],"award-info":[{"award-number":["XDA19070102"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["52064039"],"award-info":[{"award-number":["52064039"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["61971246"],"award-info":[{"award-number":["61971246"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["41861056"],"award-info":[{"award-number":["41861056"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2019GG138"],"award-info":[{"award-number":["2019GG138"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2019GG139"],"award-info":[{"award-number":["2019GG139"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2020GG0073"],"award-info":[{"award-number":["2020GG0073"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2019ZD022"],"award-info":[{"award-number":["2019ZD022"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2019MS04004"],"award-info":[{"award-number":["2019MS04004"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19070102"],"award-info":[{"award-number":["XDA19070102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Pine wilt disease (PWD) is one of the most destructive forest diseases that has led to rapid wilting and mortality in susceptible host pine trees. Spatially explicit detection of pine wood nematode (PWN)-induced infestation is important for forest management, policy making, and practices. Previous studies have mapped forest disturbances in response to various forest diseases and\/or insects over large areas using remote-sensing techniques, but these efforts were often constrained by the limited availability of ground truth information needed for the calibration and validation of moderate-resolution satellite algorithms in the process of linking plot-scale measurements to satellite data. In this study, we proposed a two-level up-sampling strategy by integrating unmanned aerial vehicle (UAV) surveys and high-resolution Radarsat-2 satellite imagery for expanding the number of training samples at the 30-m resampled Sentinel-1 resolution. Random forest algorithms were separately used in the prediction of the Radarsat-2 and Sentinel-1 infestation map induced by PWN. After data acquisition in Muping District during August and September 2021, we first verified the ability of a deep-learning-based object detection algorithm (i.e., YOLOv5 model) in the detection of infested trees from coregistered UAV-based RGB images (Average Precision (AP) of larger than 70% and R2 of 0.94). A random forest algorithm trained using the up-sampling UAV infestation map reference and corresponding Radarsat-2 pixel values was then used to produce the Radarsat-2 infestation map, resulting in an overall accuracy of 72.57%. Another random forest algorithm trained using the Radarsat-2 infestation pixels with moderate and high severity (i.e., an infestation severity of larger than 0.25, where the value was empirically set based on a trade-off between classification accuracy and infection detectability) and corresponding Sentinel-1 pixel values was subsequently used to predict the Sentinel-1 infestation map, resulting in an overall accuracy of 87.63%, where the validation data are Radarsat-2 references rather than UAV references. The Sentinel-1 map was also validated by independent UAV surveys, with an overall accuracy of 76.30% and a Kappa coefficient of 0.45. We found that the expanded training samples by the integration of UAV and Radarsat-2 strengthened the medium-resolution Sentinel-1-based prediction model of PWD. This study demonstrates that the proposed method enables effective PWN infestation mapping over multiple scales.<\/jats:p>","DOI":"10.3390\/rs14205164","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"5164","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Integrating Multi-Scale Remote-Sensing Data to Monitor Severe Forest Infestation in Response to Pine Wilt Disease"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiujuan","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China"},{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China"}]},{"given":"Yongxin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China"}]},{"given":"Pingping","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"given":"Tong","family":"Tong","sequence":"additional","affiliation":[{"name":"College of Forestry, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Linyuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Forestry, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Yuejuan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7704-6023","authenticated-orcid":false,"given":"Ting","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5831-9344","authenticated-orcid":false,"given":"Yun","family":"Su","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"given":"Xiaoqi","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"given":"Wenxue","family":"Fu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Beijing 100094, China"}]},{"given":"Xiaojun","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119890","DOI":"10.1016\/j.foreco.2021.119890","article-title":"A multi-point aggregation trend of the outbreak of pine wilt disease in China over the past 20 years","volume":"505","author":"Hao","year":"2022","journal-title":"For. Ecol. Manag."},{"key":"ref_2","first-page":"102363","article-title":"A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level","volume":"101","author":"Yu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112847","DOI":"10.1016\/j.rse.2021.112847","article-title":"Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest","volume":"269","author":"Meng","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2006.06.007","article-title":"Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation","volume":"105","author":"Coops","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.isprsjprs.2021.05.007","article-title":"Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images","volume":"177","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113198","DOI":"10.1016\/j.rse.2022.113198","article-title":"Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review","volume":"280","author":"Berger","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.rse.2014.09.034","article-title":"SAR and optical remote sensing: Assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system","volume":"156","author":"Lehmann","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112159","DOI":"10.1016\/j.rse.2020.112159","article-title":"SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery","volume":"252","author":"Bouvet","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112240","DOI":"10.1016\/j.rse.2020.112240","article-title":"Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)","volume":"255","author":"Huo","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wei\u00df, T., Ramsauer, T., Jagdhuber, T., L\u00f6w, A., and Marzahn, P. (2021). Sentinel-1 Backscatter Analysis and Radiative Transfer Modeling of Dense Winter Wheat Time Series. Remote Sens., 13.","DOI":"10.3390\/rs13122320"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ahmad, U., Alvino, A., and Marino, S. (2021). A Review of Crop Water Stress Assessment Using Remote Sensing. Remote Sens., 13.","DOI":"10.3390\/rs13204155"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2015.08.025","article-title":"Radar Burn Ratio for fire severity estimation at canopy level: An example for temperate forests","volume":"170","author":"Tanase","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ma, L., Yu, D., Feng, K., Wang, X., and Song, J. (2021). Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China. Remote Sens., 14.","DOI":"10.3390\/rs14010148"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Melancon, A.M., Molthan, A.L., Griffin, R.E., Mecikalski, J.R., Schultz, L.A., and Bell, J.R. (2021). Random Forest Classification of Inundation Following Hurricane Florence (2018) via L-Band Synthetic Aperture Radar and Ancillary Datasets. Remote Sens., 13.","DOI":"10.3390\/rs13245098"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113040","DOI":"10.1016\/j.rse.2022.113040","article-title":"Site-specific scaling of remote sensing-based estimates of woody cover and aboveground biomass for mapping long-term tropical dry forest degradation status","volume":"276","author":"Fremout","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2017.02.014","article-title":"Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery","volume":"193","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.isprsjprs.2020.04.012","article-title":"Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data","volume":"164","author":"Chauhan","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112731","DOI":"10.1016\/j.rse.2021.112731","article-title":"Integration of multi-scale remote sensing data for reindeer lichen fractional cover mapping in Eastern Canada","volume":"267","author":"He","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, X., Tong, T., Luo, T., Wang, J., Rao, Y., Li, L., Jin, D., Wu, D., and Huang, H. (2022). Retrieving the Infected Area of Pine Wilt Disease-Disturbed Pine Forests from Medium-Resolution Satellite Images Using the Stochastic Radiative Transfer Theory. Remote Sens., 14.","DOI":"10.3390\/rs14061526"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, L., Chen, J., Mu, X., Li, W., Yan, G., Xie, D., and Zhang, W. (2020). Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation. Remote Sens., 12.","DOI":"10.3390\/rs12020298"},{"key":"ref_23","first-page":"102686","article-title":"Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach","volume":"107","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"118397","DOI":"10.1016\/j.foreco.2020.118397","article-title":"Individual tree detection and species classification of Amazonian palms using UAV images and deep learning","volume":"475","author":"Ferreira","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isprsjprs.2020.10.015","article-title":"Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks","volume":"170","author":"Schiefer","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","first-page":"102456","article-title":"A review on deep learning in UAV remote sensing","volume":"102","author":"Osco","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nezami, S., Khoramshahi, E., Nevalainen, O., P\u00f6l\u00f6nen, I., and Honkavaara, E. (2020). Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.20944\/preprints202002.0334.v1"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.biosystemseng.2020.03.021","article-title":"Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier","volume":"194","author":"Hu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2019.01.030","article-title":"Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data","volume":"224","author":"Luoto","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","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":"Emilien","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111561","DOI":"10.1016\/j.rse.2019.111561","article-title":"Assessment of rice growth conditions in a semi-arid region of India using the Generalized Radar Vegetation Index derived from RADARSAT-2 polarimetric SAR data","volume":"237","author":"Mandal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 26). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_34","first-page":"2778","article-title":"TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios","volume":"2021","author":"Zhu","year":"2021","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2533","DOI":"10.1016\/j.rse.2009.07.002","article-title":"Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability","volume":"113","author":"Martinuzzi","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.rse.2019.03.025","article-title":"UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data","volume":"227","author":"Kattenborn","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"115678","DOI":"10.1016\/j.eswa.2021.115678","article-title":"Fully component selection: An efficient combination of feature selection and principal component analysis to increase model performance","volume":"186","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1056\/NEJMoa042980","article-title":"Mutations in TERT, the gene for telomerase reverse transcriptase, in aplastic anemia","volume":"352","author":"Yamaguchi","year":"2005","journal-title":"N. Engl. J. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TGRS.2006.886176","article-title":"Target scattering decomposition in terms of roll-invariant target parameters","volume":"45","author":"Touzi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3452","DOI":"10.1109\/TGRS.2010.2076285","article-title":"Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues","volume":"49","author":"Arii","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1117\/12.300620","article-title":"Feature-motivated Sinclair matrix (sphere\/diplane\/helix) decomposition and its application to target sorting for land feature classification","volume":"Volume 3120","author":"Krogager","year":"1997","journal-title":"Wideband Interferometric Sensing and Imaging Polarimetry"},{"key":"ref_45","first-page":"102946","article-title":"Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery","volume":"112","author":"Puliti","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rse.2003.10.018","article-title":"Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems","volume":"89","author":"Stow","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"112560","DOI":"10.1016\/j.rse.2021.112560","article-title":"Detecting subtle change from dense Landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","volume":"263","author":"Ye","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13436","DOI":"10.3390\/rs71013436","article-title":"Scale Issues Related to the Accuracy Assessment of Land Use\/Land Cover Maps Produced Using Multi-Resolution Data: Comments on \u201cThe Improvement of Land Cover Classification by Thermal Remote Sensing\u201d","volume":"7","author":"Johnson","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","first-page":"102493","article-title":"Estimating mangrove leaf area index based on red-edge vegetation indices: A comparison among UAV, WorldView-2 and Sentinel-2 imagery","volume":"103","author":"Guo","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2017.12.011","article-title":"Quantifying the relative contributions of vegetation and soil moisture conditions to polarimetric C-Band SAR response in a temperate peatland","volume":"206","author":"Millard","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105990","DOI":"10.1016\/j.compag.2021.105990","article-title":"Soil exchangeable cations estimation using Vis-NIR spectroscopy in different depths: Effects of multiple calibration models and spiking","volume":"182","author":"Zhao","year":"2021","journal-title":"Comput. Electron. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5164\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:54:53Z","timestamp":1760144093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,15]]},"references-count":53,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205164"],"URL":"https:\/\/doi.org\/10.3390\/rs14205164","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,15]]}}}