{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T22:25:45Z","timestamp":1776464745162,"version":"3.51.2"},"reference-count":91,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,10]],"date-time":"2021-07-10T00:00:00Z","timestamp":1625875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Anhui Provincial Natural Science Foundation of China","award":["2008085QD193"],"award-info":[{"award-number":["2008085QD193"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["JZ2020HGQA0189"],"award-info":[{"award-number":["JZ2020HGQA0189"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["JZ2021HGTA0055"],"award-info":[{"award-number":["JZ2021HGTA0055"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The distribution of forest tree species provides crucial data for regional forest management and ecological research. Although medium-high spatial resolution remote sensing images are widely used for dynamic monitoring of forest vegetation phenology and species identification, the use of multiresolution images for similar applications remains highly uncertain. Moreover, it is necessary to explore to what extent spectral variation is responsible for the discrepancies in the estimation of forest phenology and classification of various tree species when using up-scaled images. To clarify this situation, we studied the forest area in Harqin Banner in northeast China by using year-round multiple-resolution time-series images (at four spatial resolutions: 4, 10, 16, and 30 m) and eight phenological metrics of four deciduous forest tree species in 2018, to explore potential impacts of relevant results caused by various resolutions. We also investigated the effect of using up-scaled time-series images by comparing the corresponding results that use pixel-aggregation algorithms with the four spatial resolutions. The results indicate that both phenology and classification accuracy of the dominant forest tree species are markedly affected by the spatial resolution of time-series remote sensing data (p &lt; 0.05): the spring phenology of four deciduous forest tree species first rises and then falls as the image resolution varies from 4 to 30 m; similarly, the accuracy of tree species classification increases as the image resolution varies from 4 to 10 m, and then decreases as the image resolution gradually falls to 30 m (p &lt; 0.05). Therefore, there remains a profound discrepancy between the results obtained by up-scaled and actual remote sensing data at the given spatial resolutions (p &lt; 0.05). The results also suggest that combining phenological metrics and time-series NDVI data can be applied to identify the regional dominant tree species across different spatial resolutions, which would help advance the use of multiscale time-series satellite data for forest resource management.<\/jats:p>","DOI":"10.3390\/rs13142716","type":"journal-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:23:36Z","timestamp":1626049416000},"page":"2716","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["How Spatial Resolution Affects Forest Phenology and Tree-Species Classification Based on Satellite and Up-Scaled Time-Series Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4825-3942","authenticated-orcid":false,"given":"Kaijian","family":"Xu","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China"},{"name":"Institute for Spatial Information Intelligence Analysis and Application, Hefei University of Technology, Hefei 230009, China"},{"name":"Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei 230009, China"}]},{"given":"Zhaoying","family":"Zhang","sequence":"additional","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"}]},{"given":"Wanwan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China"}]},{"given":"Ping","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China"},{"name":"Institute for Spatial Information Intelligence Analysis and Application, Hefei University of Technology, Hefei 230009, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9766-5313","authenticated-orcid":false,"given":"Jibo","family":"Yue","sequence":"additional","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"},{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Yaping","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China"}]},{"given":"Jun","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.rse.2015.10.032","article-title":"Suitability of global forest change data to report forest cover estimates at national level in Gabon","volume":"173","author":"Sannier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1080\/17538947.2017.1301581","article-title":"Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon","volume":"10","author":"Feng","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1038\/s41559-019-0842-1","article-title":"Global buffering of temperatures under forest canopies","volume":"3","author":"Frenne","year":"2019","journal-title":"Nat. Ecol. Evol."},{"key":"ref_4","first-page":"101","article-title":"Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation","volume":"18","author":"Heinzel","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_6","first-page":"1632","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."},{"key":"ref_7","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s11676-015-0088-y","article-title":"Drone remote sensing for forestry research and practices","volume":"26","author":"Tang","year":"2015","journal-title":"J. For. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.rse.2017.09.031","article-title":"Fractional cover mapping of spruce and pine at 1 ha resolution combining very high and medium spatial resolution satellite imagery","volume":"204","author":"Immitzer","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.3390\/s90301768","article-title":"Scale issues in remote sensing: A review on analysis, processing and modeling","volume":"9","author":"Wu","year":"2009","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.06.012","article-title":"Accurate mapping of forest types using dense seasonal Landsat time-series","volume":"96","author":"Zhu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2015.10.004","article-title":"The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data","volume":"171","author":"Roth","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","first-page":"144","article-title":"Assessing the potential of multi-seasonal high resolution Pl\u00e9iades satellite imagery for mapping urban tree species","volume":"71","author":"Pu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.rse.2015.05.004","article-title":"A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients","volume":"167","author":"Dudley","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K. (2019). Forest stand species mapping using the Sentinel-2 time series. Remote Sens., 11.","DOI":"10.3390\/rs11101197"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1109\/TGRS.2011.2158548","article-title":"Data fusion of different spatial resolution remote sensing images applied to forest-type mapping","volume":"49","author":"Kempeneers","year":"2011","journal-title":"IEEE T. Geocsi. Remote."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2012.04.001","article-title":"Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology","volume":"123","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"102257","article-title":"Impact of the number of dates and their sampling on a NDVI time series reconstruction methodology to monitor urban trees with Ven\u03bcs satellite","volume":"95","author":"Adeline","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, K.J., Tian, Q.J., Zhang, Z.Y., Yue, J.B., and Chang, C.T. (2020). Tree species (genera) identification with GF-1 time-series in a forested landscape, Northeast China. Remote Sens., 12.","DOI":"10.3390\/rs12101554"},{"key":"ref_20","first-page":"102208","article-title":"Exploring the potential of land surface phenology and seasonal cloud free composites of one year of Sentinel-2 imagery for tree species mapping in a mountainous region","volume":"94","author":"Kollert","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, W.Y., Sanesi, G., and Lafortezza, R. (2019). Remote sensing in urban forestry: Recent applications and future directions. Remote Sens., 11.","DOI":"10.3390\/rs11101144"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/j.1654-109X.2009.01053.x","article-title":"Mapping tree species in temperate deciduous woodland using time-series multi-spectral data","volume":"13","author":"Hill","year":"2010","journal-title":"Appl. Veg. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11518","DOI":"10.3390\/rs61111518","article-title":"Land cover classification of Landsat data with phenological features extracted from time series MODIS NDVI data","volume":"6","author":"Jia","year":"2014","journal-title":"Remote Sens."},{"key":"ref_24","first-page":"102207","article-title":"Sentinel-2 time series based optimal features and time window for mapping invasive Australian native Acacia species in KwaZulu Natal, South Africa","volume":"93","author":"Masemola","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2012.10.026","article-title":"Estimating the fractional cover of growth forms and bare surface in savannas. A multi-resolution approach based on regression tree ensembles","volume":"129","author":"Gessner","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/0034-4257(95)00153-0","article-title":"Forest classification of Southeast Asia using NOAA AVHRR data","volume":"54","author":"Achard","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0034-4257(02)00051-2","article-title":"Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data","volume":"82","author":"Xiao","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"Yu, X.F., Zhuang, D.F., Chen, H., and Hou, X.Y. (2004, January 20\u201324). Forest classification based on MODIS time series and vegetation phenology. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pimple, U., Sitthi, A., Simonetti, D., Pungkul, S., Leadprathom, K., and Chidthaisong, A. (2017). Topographic correction of Landsat TM-5 and Landsat OLI-8 imagery to improve the performance of forest classification in the mountainous terrain of Northeast Thailand. Sustainability, 9.","DOI":"10.3390\/su9020258"},{"key":"ref_30","first-page":"1","article-title":"Forest cover mapping in post-Soviet Central Asia using multi-resolution remote sensing imagery","volume":"7","author":"Yin","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/17538947.2012.713190","article-title":"Global characterization and monitoring of forest cover using Landsat data: Opportunities and challenges","volume":"5","author":"Townshend","year":"2012","journal-title":"Int. J. Digit. Earth"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1007\/s10310-010-0233-6","article-title":"Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution","volume":"16","author":"Ota","year":"2011","journal-title":"J. For. Res."},{"key":"ref_33","first-page":"145","article-title":"Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery","volume":"54","author":"Pu","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","first-page":"2538","article-title":"Forest species discrimination in an Alpine mountain area using a fuzzy classification of multi-temporal SPOT (HRV) data","volume":"4","author":"Puzzolo","year":"2003","journal-title":"IEEE Int. Geosci. Remote Sens. Symp."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Persson, M., Lindberg, E., and Reese, H. (2018). Tree species classification with multi-temporal Sentinel-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10111794"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wessel, M., Brandmeier, M., and Tiede, D. (2018). Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on Sentinel-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10091419"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"Gomez","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"083691","DOI":"10.1117\/1.JRS.8.083691","article-title":"Using phenological metrics and the multiple classifier fusion method to map land cover types","volume":"8","author":"Liu","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_39","first-page":"88","article-title":"Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery","volume":"44","author":"Michez","year":"2015","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_40","first-page":"3794","article-title":"Classifying forest dominant trees species based on high dimensional time-series NDVI data and differential transform methods","volume":"39","author":"Xu","year":"2019","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"796","DOI":"10.17520\/biods.2019197","article-title":"Classification and identification of plant species based on multi-source remote sensing data: Research progress and prospect","volume":"27","author":"Kong","year":"2019","journal-title":"Biodivers. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.agrformet.2018.03.010","article-title":"Scaling up spring phenology derived from remote sensing images","volume":"256\u2013257","author":"Peng","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111511","DOI":"10.1016\/j.rse.2019.111511","article-title":"A review of vegetation phenological metrics extraction using time-series, multispectral satellite data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.isprsjprs.2017.09.002","article-title":"Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States","volume":"132","author":"Peng","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ge, Q., Dai, J., Cui, H., and Wang, H. (2016). Spatiotemporal variability in start and end of growing season in China related to climate variability. Remote Sens., 8.","DOI":"10.3390\/rs8050433"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, L., Cao, R., Shen, M., Chen, J., and Zhang, X. (2019). How does scale effect influence spring vegetation phenology estimated from satellite-derived vegetation indexes?. Remote Sens., 11.","DOI":"10.3390\/rs11182137"},{"key":"ref_47","first-page":"381","article-title":"Mapping rice paddy distribution by using time series HJ blend data and phenological parameters","volume":"22","author":"Liu","year":"2018","journal-title":"J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13021-018-0097-1","article-title":"Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna","volume":"13","author":"Schwieder","year":"2018","journal-title":"Carbon Balanc. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"324","DOI":"10.2747\/1548-1603.48.3.324","article-title":"Mapping plant functional types at multiple spatial resolutions using imaging spectrometer data","volume":"48","author":"Schaaf","year":"2011","journal-title":"GISci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"7113","DOI":"10.1080\/01431161.2013.817712","article-title":"The effect of spectral and spatial degradation of hyperspectral imagery for the sclerophyll tree species classification","volume":"34","author":"Cruz","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1533656","article-title":"How up-scaling of remote-sensing images affects land-cover classification by comparison with multiscale satellite images","volume":"40","author":"Xu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2017.01.001","article-title":"Exploration of scaling effects on coarse resolution land surface phenology","volume":"190","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tian, J.Q., Zhu, X.L., Wu, J., Shen, M.G., and Chen, J. (2020). Coarse-resolution satellite images overestimate urbanization effects on vegetation spring phenology. Remote Sens., 12.","DOI":"10.3390\/rs12010117"},{"key":"ref_54","first-page":"585","article-title":"Scale-dependent errors in the estimation of land-cover proportions: Implications for global land-cover datasets","volume":"60","author":"Moody","year":"1994","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_55","first-page":"657","article-title":"Review of up-scaling of quantitative remote sensing","volume":"28","author":"Luan","year":"2013","journal-title":"Adv. Earth Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s11707-018-0713-0","article-title":"Spatio-temporal analysis of phenology in Yangtze river delta based on MODIS NDVI time series from 2001 to 2015","volume":"13","author":"Wang","year":"2019","journal-title":"Front. Earth Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2015.12.023","article-title":"An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data","volume":"174","author":"Shao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_59","first-page":"3493","article-title":"Evaluation of five commonly used atmospheric correction algorithms for multi-temporal aboveground forest carbon storage estimation","volume":"37","author":"Xu","year":"2017","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"TIMESAT-a program for analyzing time-series of satellite sensor data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"05030","DOI":"10.1088\/1748-9326\/8\/4\/045030","article-title":"Impact of vegetation onset time on the net primary productivity in a mountainous island in Pacific Asia","volume":"8","author":"Chang","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_62","first-page":"1","article-title":"Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010","volume":"21","author":"Yang","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.rse.2006.11.025","article-title":"AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005","volume":"108","author":"Heumann","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Jiao, F.S., Liu, H.Y., Xu, X.J., Gong, H.B., and Lin, Z.S. (2020). Trend evolution of vegetation phenology in China during the period of 1981\u20132016. Remote Sens., 12.","DOI":"10.3390\/rs12030572"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s41748-019-00106-z","article-title":"Identifying agricultural systems using SVM classification approach based on phenological metrics in a semi-arid region of Morocco","volume":"3","author":"Lebrini","year":"2019","journal-title":"Earth Syst. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Sothe, C., de Almeida, C.M., Liesenberg, V., and Schimalski, M.B. (2017). Evaluating Sentinel-2 and Landsat-8 data to map sucessional forest stages in a subtropical forest in southern Brazil. Remote Sens., 9.","DOI":"10.3390\/rs9080838"},{"key":"ref_67","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_68","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","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."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"106955","DOI":"10.1016\/j.ecolind.2020.106955","article-title":"Mapping forest age and characterizing vegetation structure and species composition in tropical dry forests","volume":"120","author":"Dupuy","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/tgis.12164","article-title":"Hyperspectral remote sensing classifications: A perspective survey","volume":"20","author":"Chutia","year":"2016","journal-title":"Trans. GIS"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/JSTARS.2013.2282166","article-title":"Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms","volume":"7","author":"Shang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"9184","DOI":"10.3390\/rs70709184","article-title":"Detection of convective initiation using meteorological imager onboard communication, ocean, and meteorological satellite based on machine learning approaches","volume":"7","author":"Han","year":"2015","journal-title":"Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Neuwirth, M., Bck, S., Brenner, H., and Atzberger, C. (2019). Optimal input features for tree epecies classification in central Europe based on multi-temporal Sentinel-2 data. Remote Sens., 11.","DOI":"10.3390\/rs11222599"},{"key":"ref_76","first-page":"419","article-title":"Accuracy assessment of satellite derived land-gover data: A review","volume":"60","author":"Janssen","year":"1994","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.isprsjprs.2017.01.016","article-title":"Accuracy assessment of seven global land cover datasets over China","volume":"125","author":"Yang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_78","first-page":"464","article-title":"The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area","volume":"52","author":"Richter","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.3390\/rs70202089","article-title":"Comparative analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD sensor data for grassland monitoring applications","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_80","first-page":"107","article-title":"Remote sensing classification of marsh wetland with different resolution images","volume":"7","author":"Li","year":"2016","journal-title":"J. Resour. Ecol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"6097","DOI":"10.1080\/2150704X.2016.1252471","article-title":"Creating a basic customizable framework for crop detection using Landsat imagery","volume":"37","author":"Lessel","year":"2016","journal-title":"Int J. Remote Sens."},{"key":"ref_82","first-page":"1961","article-title":"Accuracy analysis of up-scaling data: A case study with land use data in Xilin Gol of Inner Mongolia, China","volume":"31","author":"Hu","year":"2012","journal-title":"Geogr. Res."},{"key":"ref_83","first-page":"426","article-title":"Image Quality Evaluation of Multi-Scale Resampling in Geometric Correction","volume":"47","author":"Zhang","year":"2013","journal-title":"J. Huazhong Norm. Univ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2013.01.011","article-title":"Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM\/ETM+ data","volume":"132","author":"Melaas","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_85","first-page":"1511","article-title":"Age information retrieval of Larix gmelinii forest using Sentinel-2 data","volume":"24","author":"Tang","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(97)81622-7","article-title":"Spatial thresholds, image-objects, and upscaling: A multiscale evaluation","volume":"62","author":"Hay","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S0034-4257(02)00102-5","article-title":"Radiative transfer based scaling of LAI retrieval from reflectance data of different resolutions","volume":"84","author":"Tian","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"3503","DOI":"10.1080\/01431161.2012.716537","article-title":"Impact of nonlinearity and discontinuity on the spatial scaling effects of the leaf area index retrieved from remotely sensed data","volume":"34","author":"Wu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Jiang, J., Xiao, Z., Wang, J., and Song, J. (2016). Multiscale estimation of leaf area index from satellite observations based on an ensemble multiscale filter. Remote Sens., 8.","DOI":"10.3390\/rs8030229"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.inffus.2017.06.005","article-title":"Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach","volume":"40","author":"Wu","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.rse.2018.03.014","article-title":"Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island","volume":"215","author":"Vrieling","year":"2018","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2716\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:28:40Z","timestamp":1760164120000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2716"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,10]]},"references-count":91,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142716"],"URL":"https:\/\/doi.org\/10.3390\/rs13142716","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,10]]}}}