Construction of multidimensional features to identify tea plantations using multisource remote sensing data: A case study of Hangzhou city, China
Tài liệu tham khảo
Akar, 2015, Integrating multiple texture methods and NDVI to the random Forest classification algorithm to detect tea and hazelnut plantation areas in Northeast Turkey, Int. J. Remote Sens., 36, 442, 10.1080/01431161.2014.995276
Aviña-Hernández, 2023, Predictive performance of random forest on the identification of mangrove species in arid environments, Ecol. Inform., 75, 10.1016/j.ecoinf.2023.102040
Azadnia, 2022, Evaluation of hawthorns maturity level by developing an automated machine learning-based algorithm, Ecol. Inform., 71, 10.1016/j.ecoinf.2022.101804
Baldo, 2023, Remote sensing analysis on primary productivity and forest cover dynamics: a Western Ghats India case study, Ecol. Inform., 73, 10.1016/j.ecoinf.2022.101922
Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Brown, 2022, Dynamic World, Near real-time global 10 m land use land cover mapping, Sci. Data., 9, 1, 10.1038/s41597-022-01307-4
Bunting, 2018, The global mangrove watch—a new 2010 global baseline of mangrove extent, Remote Sens., 10, 1669, 10.3390/rs10101669
Chen, 2011
Cheng, 2016, Oil palm mapping using Landsat and PALSAR: a case study in Malaysia, Int. J. Remote Sens., 37, 5431, 10.1080/01431161.2016.1241448
Chu, 2016, Integration of full-waveform LiDAR and hyperspectral data to enhance tea and areca classification, GISci. Remote Sens., 53, 542, 10.1080/15481603.2016.1177249
Dihkan, 2013, Remote sensing of tea plantations using an SVM classifier and pattern-based accuracy assessment technique, Int. J. Remote Sens., 34, 8549, 10.1080/01431161.2013.845317
Dong, 2012, A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA, Remote Sens. Environ., 127, 60, 10.1016/j.rse.2012.08.022
Fei, 2023, Development of a protocol to identify land function based on multifunctionality and suitability analysis: a case study of the Nanyuntai Forest Farm, China, Ecol. Inform., 75, 10.1016/j.ecoinf.2023.102081
Ferchichi, 2022, Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: a systematic literature review, Ecol. Inform., 101552
Genuer, 2010, Variable selection using random forests, Pattern Recogn. Lett., 31, 2225, 10.1016/j.patrec.2010.03.014
Gong, 2022, Prediction of potential distribution of soybean in the frigid region in China with MaxEnt modeling, Ecol. Inform., 72, 10.1016/j.ecoinf.2022.101834
Han-Qiu, 2005, A study on information extraction of water body with the modified normalized difference water index (MNDWI), J. Remote. Sens., 9, 589
Haralick, 1973, Textural features for image classification, IEEE Trans. Syst. Man Cybern., 6, 610, 10.1109/TSMC.1973.4309314
Kumar, 2013, Field hyperspectral data analysis for discriminating spectral behavior of tea plantations under various management practices, Int. J. Appl. Earth Obs., 23, 352
Li, 2012, Soil changes induced by rubber and tea plantation establishment: comparison with tropical rain forest soil in Xishuangbanna, SW China, Environ. Manag., 50, 837, 10.1007/s00267-012-9942-2
Liu, 2021, Identification of plant species in an alpine steppe of Northern Tibet using close-range hyperspectral imagery, Ecol. Inform., 61, 10.1016/j.ecoinf.2021.101213
Liu, 2022, An algorithm for early rice area mapping from satellite remote sensing data in southwestern Guangdong in China based on feature optimization and random Forest, Ecol. Inform., 72, 10.1016/j.ecoinf.2022.101853
Mortimer, 2015, Alder trees enhance crop productivity and soil microbial biomass in tea plantations, Appl. Soil Ecol., 96, 25, 10.1016/j.apsoil.2015.05.012
2021
Perez, 2022, Use of sentinel 2 imagery to estimate vegetation height in fragments of Atlantic Forest, Ecol. Inform., 69, 10.1016/j.ecoinf.2022.101680
Pham, 2018, Optimized rule-based logistic model tree algorithm for mapping mangrove species using ALOS PALSAR imagery and GIS in the tropical region, Environ. Earth Sci., 77, 1, 10.1007/s12665-018-7373-y
Prasad, 2022, Evaluation and comparison of the earth observing sensors in land cover/land use studies using machine learning algorithms, Ecol. Inform., 68, 10.1016/j.ecoinf.2021.101522
Qin, 2015, Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI, ISPRS-J. Photogramm. Remote Sens., 109, 1, 10.1016/j.isprsjprs.2015.08.010
Qin, 2017, Annual dynamics of forest areas in South America during 2007–2010 at 50-m spatial resolution, Remote Sens. Environ., 201, 73, 10.1016/j.rse.2017.09.005
Sanlier, 2018, A minireview of effects of white tea consumption on diseases, Trends Food Sci. Technol., 82, 82, 10.1016/j.tifs.2018.10.004
Sanlier, 2018, Tea consumption and disease correlations, Trends Food Sci. Technol., 78, 95, 10.1016/j.tifs.2018.05.026
Santos, 2023, Predicting eucalyptus plantation growth and yield using Landsat imagery in Minas Gerais, Brazil, Ecol. Inform., 102120, 10.1016/j.ecoinf.2023.102120
Shimada, 2014, New global forest/non-forest maps from ALOS PALSAR data (2007–2010), Remote Sens. Environ., 155, 13, 10.1016/j.rse.2014.04.014
Song, 2023, Species classification from hyperspectral leaf information using machine learning approaches, Ecol. Inform., 102141
Sotille, 2022, UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest, Ecol. Inform., 71, 10.1016/j.ecoinf.2022.101768
Steinbach, 2023, Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales, Ecol. Inform., 75, 10.1016/j.ecoinf.2023.102032
Su, 2016, Land use changes to cash crop plantations: crop types, multilevel determinants and policy implications, Land Use Policy, 50, 379, 10.1016/j.landusepol.2015.10.003
Su, 2017, Economic benefit and ecological cost of enlarging tea cultivation in subtropical China: characterizing the trade-off for policy implications, Land Use Policy, 66, 183, 10.1016/j.landusepol.2017.04.044
Thomas, 2017, Distribution and drivers of global mangrove forest change, 1996–2010, PLoS One, 12, 10.1371/journal.pone.0179302
Toosi, 2022, Citrus orchard mapping in juybar, Iran: analysis of ndvi time series and feature fusion of multi-source satellite imageries, Ecol. Inform., 70, 10.1016/j.ecoinf.2022.101733
Torbick, 2010, Monitoring rice agriculture in the Sacramento Valley, USA with multitemporal PALSAR and MODIS imagery, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 4, 451, 10.1109/JSTARS.2010.2091493
Torbick, 2017, Monitoring rice agriculture across Myanmar using time series Sentinel-1 assisted by Landsat-8 and PALSAR-2, Remote Sens., 9, 119, 10.3390/rs9020119
Van Deventer, 1997, Using thematic mapper data to identify contrasting soil plains and tillage practices, Photogramm. Eng. Remote. Sens., 63, 87
Van Niel, 2005, On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification, Remote Sens. Environ., 98, 468, 10.1016/j.rse.2005.08.011
Wang, 2019, Mapping tea plantations from multi-seasonal Landsat-8 OLI imageries using a random forest classifier, J. Indian Soc. Remote Sens., 47, 1315, 10.1007/s12524-019-01014-5
Xiao, 2005, Mapping paddy rice agriculture in southern China using multi-temporal MODIS images, Remote Sens. Environ., 95, 480, 10.1016/j.rse.2004.12.009
Xiong, 2021, Mapping the spatial distribution of tea plantations with 10 m resolution in Fujian Province using Google earth engine, J. Geogr. Sci., 23, 1325
Xu, 2018, Quantifying spatial-temporal changes of tea plantations in complex landscapes through integrative analyses of optical and microwave imagery, Int. J. Appl. Earth Obs. Geoinf., 73, 697
Zha, 2003, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, Int. J. Remote Sens., 24, 583, 10.1080/01431160304987
Zhang, 2019, Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images, J. Remote Sens., 23, 313
Zhang, 2019, Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016, Remote Sens. Environ., 224, 74, 10.1016/j.rse.2019.01.038
Zhou, 2015, Feature-location analyses for identification of urban tree species from very high resolution remote sensing data, Ecol. Inform., 29, 16, 10.1016/j.ecoinf.2015.06.002
Zhu, 2022, Identification of soybean based on Sentinel-1/2 SAR and MSI imagery under a complex planting structure, Ecol. Inform., 72, 10.1016/j.ecoinf.2022.101825