This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
S. Díaz et al., “Assessing nature's contributions to people,” Science, vol. 359, no. 6373, pp. 270–272, 2018.DíazS.“Assessing nature's contributions to people,”Science35963732702722018Search in Google Scholar
C. Wang, Z. Guo, S. Wang, L. Wang, and C. Ma, “Improving hyperspectral image classification method for fine land use assessment application using semisupervised machine learning,” Journal of Spectroscopy, vol. 2015, 2015.WangC.GuoZ.WangS.WangL.MaC.“Improving hyperspectral image classification method for fine land use assessment application using semisupervised machine learning,”Journal of Spectroscopy20152015Search in Google Scholar
A. C. Staver, S. Archibald, and S. A. Levin, “The global extent and determinants of savanna and forest as alternative biome states,” science, vol. 334, no. 6053, pp. 230–232, 2011.StaverA. C.ArchibaldS.LevinS. A.“The global extent and determinants of savanna and forest as alternative biome states,”science33460532302322011Search in Google Scholar
G. Forzieri, R. Alkama, D. G. Miralles, and A. Cescatti, “Satellites reveal contrasting responses of regional climate to the widespread greening of Earth,” Science, vol. 356, no. 6343, pp. 1180–1184, 2017.ForzieriG.AlkamaR.MirallesD. G.CescattiA.“Satellites reveal contrasting responses of regional climate to the widespread greening of Earth,”Science3566343118011842017Search in Google Scholar
H. Zhang et al., “High-resolution vegetation mapping using eXtreme gradient boosting based on extensive features,” Remote Sensing, vol. 11, no. 12, p. 1505, 2019.ZhangH.“High-resolution vegetation mapping using eXtreme gradient boosting based on extensive features,”Remote Sensing111215052019Search in Google Scholar
L. Malatesta et al., “Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen),” Journal of Applied Remote Sensing, vol. 7, no. 1, p. 073527, 2013.MalatestaL.“Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen),”Journal of Applied Remote Sensing710735272013Search in Google Scholar
A. Abdollahi and B. Pradhan, “Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model,” Science of The Total Environment, vol. 879, p. 163004, 2023.AbdollahiA.PradhanB.“Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model,”Science of The Total Environment8791630042023Search in Google Scholar
N. Pettorelli, W. F. Laurance, T. G. O’Brien, M. Wegmann, H. Nagendra, and W. Turner, “Satellite remote sensing for applied ecologists: opportunities and challenges,” Journal of Applied Ecology, vol. 51, no. 4, pp. 839–848, 2014.PettorelliN.LauranceW. F.O’BrienT. G.WegmannM.NagendraH.TurnerW.“Satellite remote sensing for applied ecologists: opportunities and challenges,”Journal of Applied Ecology5148398482014Search in Google Scholar
C. Giri, Z. Zhu, and B. Reed, “A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets,” Remote sensing of environment, vol. 94, no. 1, pp. 123–132, 2005.GiriC.ZhuZ.ReedB.“A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets,”Remote sensing of environment9411231322005Search in Google Scholar
X. Zhang, X. Feng, and H. Jiang, “Object-oriented method for urban vegetation mapping using IKONOS imagery,” International Journal of Remote Sensing, vol. 31, no. 1, pp. 177–196, 2010.ZhangX.FengX.JiangH.“Object-oriented method for urban vegetation mapping using IKONOS imagery,”International Journal of Remote Sensing3111771962010Search in Google Scholar
R. C. Sharma, K. Hara, and R. Tateishi, “High-resolution vegetation mapping in japan by combining sentinel-2 and landsat 8 based multi-temporal datasets through machine learning and cross-validation approach,” Land, vol. 6, no. 3, p. 50, 2017.SharmaR. C.HaraK.TateishiR.“High-resolution vegetation mapping in japan by combining sentinel-2 and landsat 8 based multi-temporal datasets through machine learning and cross-validation approach,”Land63502017Search in Google Scholar
V. Lawley, M. Lewis, K. Clarke, and B. Ostendorf, “Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review,” Ecological Indicators, vol. 60, pp. 1273–1283, 2016.LawleyV.LewisM.ClarkeK.OstendorfB.“Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review,”Ecological Indicators60127312832016Search in Google Scholar
A. Abdollahi, Y. Liu, B. Pradhan, A. Huete, A. Dikshit, and N. N. Tran, “Short-time-series grassland mapping using Sentinel-2 imagery and deep learning-based architecture,” The Egyptian Journal of Remote Sensing and Space Science, vol. 25, no. 3, pp. 673–685, 2022.AbdollahiA.LiuY.PradhanB.HueteA.DikshitA.TranN. N.“Short-time-series grassland mapping using Sentinel-2 imagery and deep learning-based architecture,”The Egyptian Journal of Remote Sensing and Space Science2536736852022Search in Google Scholar
D. E. G. Furuya et al., “A Machine Learning Approach for Mapping Forest Vegetation in Riparian Zones in an Atlantic Biome Environment Using Sentinel-2 Imagery,” Remote Sensing, vol. 12, no. 24, p. 4086, 2020.FuruyaD. E. G.“A Machine Learning Approach for Mapping Forest Vegetation in Riparian Zones in an Atlantic Biome Environment Using Sentinel-2 Imagery,”Remote Sensing122440862020Search in Google Scholar
M. Govender, K. Chetty, V. Naiken, and H. Bulcock, “A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation,” Water SA, vol. 34, no. 2, pp. 147–154, 2008.GovenderM.ChettyK.NaikenV.BulcockH.“A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation,”Water SA3421471542008Search in Google Scholar
A. W. Abbas, N. Minallh, N. Ahmad, S. A. R. Abid, and M. A. A. Khan, “K-Means and ISODATA clustering algorithms for landcover classification using remote sensing,” Sindh University Research Journal-SURJ, vol. 48, no. 2, 2016.AbbasA. W.MinallhN.AhmadN.AbidS. A. R.KhanM. A. A.“K-Means and ISODATA clustering algorithms for landcover classification using remote sensing,”Sindh University Research Journal-SURJ4822016Search in Google Scholar
J. A. Richards and J. Richards, Remote sensing digital image analysis. Springer, 1999.RichardsJ. A.RichardsJ.Remote sensing digital image analysisSpringer1999Search in Google Scholar
X. Cheng, Y. Zheng, J. Zhang, and Z. Yang, “Multitask Multisource Deep Correlation Filter for Remote Sensing Data Fusion,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3723–3734, 2020.ChengX.ZhengY.ZhangJ.YangZ.“Multitask Multisource Deep Correlation Filter for Remote Sensing Data Fusion,”IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing13372337342020Search in Google Scholar
A. Abdollahi and M. Yebra, “Forest fuel type classification: Review of remote sensing techniques, constraints and future trends,” Journal of Environmental Management, vol. 342, p. 118315, 2023.AbdollahiA.YebraM.“Forest fuel type classification: Review of remote sensing techniques, constraints and future trends,”Journal of Environmental Management3421183152023Search in Google Scholar
P. Feng, B. Wang, D. Li Liu, and Q. Yu, “Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia,” Agricultural Systems, vol. 173, pp. 303–316, 2019.FengP.WangB.Li LiuD.YuQ.“Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia,”Agricultural Systems1733033162019Search in Google Scholar
P. Macintyre, A. Van Niekerk, and L. Mucina, “Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification,” International Journal of Applied Earth Observation and Geoinformation, vol. 85, p. 101980, 2020.MacintyreP.Van NiekerkA.MucinaL.“Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification,”International Journal of Applied Earth Observation and Geoinformation851019802020Search in Google Scholar
T. Hengl, M. G. Walsh, J. Sanderman, I. Wheeler, S. P. Harrison, and I. C. Prentice, “Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential,” PeerJ, vol. 6, p. e5457, 2018.HenglT.WalshM. G.SandermanJ.WheelerI.HarrisonS. P.PrenticeI. C.“Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential,”PeerJ6e54572018Search in Google Scholar
A. Michez, H. Piégay, L. Jonathan, H. Claessens, and P. Lejeune, “Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery,” International Journal of Applied Earth Observation and Geoinformation, vol. 44, pp. 88–94, 2016.MichezA.PiégayH.JonathanL.ClaessensH.LejeuneP.“Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery,”International Journal of Applied Earth Observation and Geoinformation4488942016Search in Google Scholar
G. De Luca et al., “Object-based land cover classification of cork oak woodlands using UAV imagery and Orfeo ToolBox,” Remote Sensing, vol. 11, no. 10, p. 1238, 2019.De LucaG.“Object-based land cover classification of cork oak woodlands using UAV imagery and Orfeo ToolBox,”Remote Sensing111012382019Search in Google Scholar
L. Parente and L. Ferreira, “Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016,” Remote Sensing, vol. 10, no. 4, p. 606, 2018.ParenteL.FerreiraL.“Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016,”Remote Sensing1046062018Search in Google Scholar
A. Shelestov, M. Lavreniuk, N. Kussul, A. Novikov, and S. Skakun, “Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping,” frontiers in Earth Science, vol. 5, p. 17, 2017.ShelestovA.LavreniukM.KussulN.NovikovA.SkakunS.“Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping,”frontiers in Earth Science5172017Search in Google Scholar
K. Johansen, S. Phinn, and M. Taylor, “Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine,” Remote Sensing Applications: Society and Environment, vol. 1, pp. 36–49, 2015.JohansenK.PhinnS.TaylorM.“Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine,”Remote Sensing Applications: Society and Environment136492015Search in Google Scholar
R. Sluiter and E. Pebesma, “Comparing techniques for vegetation classification using multi-and hyperspectral images and ancillary environmental data,” International Journal of Remote Sensing, vol. 31, no. 23, pp. 6143–6161, 2010.SluiterR.PebesmaE.“Comparing techniques for vegetation classification using multi-and hyperspectral images and ancillary environmental data,”International Journal of Remote Sensing3123614361612010Search in Google Scholar
M. Pal, “Random forest classifier for remote sensing classification,” International journal of remote sensing, vol. 26, no. 1, pp. 217–222, 2005.PalM.“Random forest classifier for remote sensing classification,”International journal of remote sensing2612172222005Search in Google Scholar
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone,” Remote sensing of Environment, vol. 202, pp. 18–27, 2017.GorelickN.HancherM.DixonM.IlyushchenkoS.ThauD.MooreR.“Google Earth Engine: Planetary-scale geospatial analysis for everyone,”Remote sensing of Environment20218272017Search in Google Scholar
H. Tamiminia, B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli, and B. Brisco, “Google Earth Engine for geo-big data applications: A meta-analysis and systematic review,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, pp. 152–170, 2020.TamiminiaH.SalehiB.MahdianpariM.QuackenbushL.AdeliS.BriscoB.“Google Earth Engine for geo-big data applications: A meta-analysis and systematic review,”ISPRS Journal of Photogrammetry and Remote Sensing1641521702020Search in Google Scholar
M. Wu et al., “Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion,” Scientific Reports, vol. 8, no. 1, pp. 1–12, 2018.WuM.“Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion,”Scientific Reports811122018Search in Google Scholar
R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Transactions on systems, man, and cybernetics, no. 6, pp. 610–621, 1973.HaralickR. M.ShanmugamK.DinsteinI. H.“Textural features for image classification,”IEEE Transactions on systems, man, and cybernetics66106211973Search in Google Scholar
A. Abdollahi and B. Pradhan, “Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI),” Sensors, vol. 21, no. 14, p. 4738, 2021.AbdollahiA.PradhanB.“Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI),”Sensors211447382021Search in Google Scholar
V. N. Mishra, R. Prasad, P. K. Rai, A. K. Vishwakarma, and A. Arora, “Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data,” Earth Science Informatics, vol. 12, no. 1, pp. 71–86, 2019.MishraV. N.PrasadR.RaiP. K.VishwakarmaA. K.AroraA.“Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data,”Earth Science Informatics12171862019Search in Google Scholar
H. Zhang, J. Kang, X. Xu, and L. Zhang, “Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi’an County, Heilongjiang province, China,” Computers and Electronics in Agriculture, vol. 176, p. 105618, 2020.ZhangH.KangJ.XuX.ZhangL.“Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi’an County, Heilongjiang province, China,”Computers and Electronics in Agriculture1761056182020Search in Google Scholar
Y. Du, Y. Zhang, F. Ling, Q. Wang, W. Li, and X. Li, “Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band,” Remote Sensing, vol. 8, no. 4, p. 354, 2016.DuY.ZhangY.LingF.WangQ.LiW.LiX.“Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band,”Remote Sensing843542016Search in Google Scholar
R. Xu, J. Liu, and J. Xu, “Extraction of high-precision urban impervious surfaces from sentinel-2 multispectral imagery via modified linear spectral mixture analysis,” Sensors, vol. 18, no. 9, p. 2873, 2018.XuR.LiuJ.XuJ.“Extraction of high-precision urban impervious surfaces from sentinel-2 multispectral imagery via modified linear spectral mixture analysis,”Sensors18928732018Search in Google Scholar
P. D’Odorico, A. Gonsamo, A. Damm, and M. E. Schaepman, “Experimental evaluation of Sentinel-2 spectral response functions for NDVI time-series continuity,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 3, pp. 1336–1348, 2013.D’OdoricoP.GonsamoA.DammA.SchaepmanM. E.“Experimental evaluation of Sentinel-2 spectral response functions for NDVI time-series continuity,”IEEE Transactions on Geoscience and Remote Sensing513133613482013Search in Google Scholar
L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.BreimanL.“Random forests,”Machine Learning4515322001Search in Google Scholar
A. Puissant, S. Rougier, and A. Stumpf, “Object-oriented mapping of urban trees using Random Forest classifiers,” International Journal of Applied Earth Observation and Geoinformation, vol. 26, pp. 235–245, 2014.PuissantA.RougierS.StumpfA.“Object-oriented mapping of urban trees using Random Forest classifiers,”International Journal of Applied Earth Observation and Geoinformation262352452014Search in Google Scholar
A. Ghosh, R. Sharma, and P. Joshi, “Random forest classification of urban landscape using Landsat archive and ancillary data: Combining seasonal maps with decision level fusion,” Applied Geography, vol. 48, pp. 31–41, 2014.GhoshA.SharmaR.JoshiP.“Random forest classification of urban landscape using Landsat archive and ancillary data: Combining seasonal maps with decision level fusion,”Applied Geography4831412014Search in Google Scholar
Q. Feng, J. Liu, and J. Gong, “UAV remote sensing for urban vegetation mapping using random forest and texture analysis,” Remote Sensing, vol. 7, no. 1, pp. 1074–1094, 2015.FengQ.LiuJ.GongJ.“UAV remote sensing for urban vegetation mapping using random forest and texture analysis,”Remote Sensing71107410942015Search in Google Scholar
V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, “An assessment of the effectiveness of a random forest classifier for land-cover classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, pp. 93–104, 2012.Rodriguez-GalianoV. F.GhimireB.RoganJ.Chica-OlmoM.Rigol-SanchezJ. P.“An assessment of the effectiveness of a random forest classifier for land-cover classification,”ISPRS Journal of Photogrammetry and Remote Sensing67931042012Search in Google Scholar
N. Ghasemkhani, S. S. Vayghan, A. Abdollahi, B. Pradhan, and A. Alamri, “Urban Development Modeling Using Integrated Fuzzy Systems, Ordered Weighted Averaging (OWA), and Geospatial Techniques,” Sustainability, vol. 12, no. 3, p. 809, 2020.GhasemkhaniN.VayghanS. S.AbdollahiA.PradhanB.AlamriA.“Urban Development Modeling Using Integrated Fuzzy Systems, Ordered Weighted Averaging (OWA), and Geospatial Techniques,”Sustainability1238092020Search in Google Scholar
F. Schiefer et al., “Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 170, pp. 205–215, 2020.SchieferF.“Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks,”ISPRS Journal of Photogrammetry and Remote Sensing1702052152020Search in Google Scholar
R. G. Pontius Jr and M. Millones, “Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment,” International Journal of Remote Sensing, vol. 32, no. 15, pp. 4407–4429, 2011.PontiusR. G.JrMillonesM.“Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment,”International Journal of Remote Sensing3215440744292011Search in Google Scholar
S. Lobser and W. Cohen, “MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data,” International Journal of Remote Sensing, vol. 28, no. 22, pp. 5079–5101, 2007.LobserS.CohenW.“MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data,”International Journal of Remote Sensing2822507951012007Search in Google Scholar
J. Delegido, J. Verrelst, L. Alonso, and J. Moreno, “Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content,” Sensors, vol. 11, no. 7, pp. 7063–7081, 2011.DelegidoJ.VerrelstJ.AlonsoL.MorenoJ.“Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content,”Sensors117706370812011Search in Google Scholar
J. M. Lachin, “Introduction to sample size determination and power analysis for clinical trials,” Controlled clinical trials, vol. 2, no. 2, pp. 93–113, 1981.LachinJ. M.“Introduction to sample size determination and power analysis for clinical trials,”Controlled clinical trials22931131981Search in Google Scholar
D. R. Cutler et al., “Random forests for classification in ecology,” Ecology, vol. 88, no. 11, pp. 2783–2792, 2007.CutlerD. R.“Random forests for classification in ecology,”Ecology8811278327922007Search in Google Scholar
E. C. B. de Colstoun, M. H. Story, C. Thompson, K. Commisso, T. G. Smith, and J. R. Irons, “National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier,” Remote sensing of Environment, vol. 85, no. 3, pp. 316–327, 2003.de ColstounE. C. B.StoryM. H.ThompsonC.CommissoK.SmithT. G.IronsJ. R.“National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier,”Remote sensing of Environment8533163272003Search in Google Scholar
A. M. Cingolani, D. Renison, M. R. Zak, and M. R. Cabido, “Mapping vegetation in a heterogeneous mountain rangeland using Landsat data: an alternative method to define and classify land-cover units,” Remote sensing of environment, vol. 92, no. 1, pp. 84–97, 2004.CingolaniA. M.RenisonD.ZakM. R.CabidoM. R.“Mapping vegetation in a heterogeneous mountain rangeland using Landsat data: an alternative method to define and classify land-cover units,”Remote sensing of environment92184972004Search in Google Scholar
R. C. Sharma, K. Hara, and R. Tateishi, “High-resolution vegetation mapping in japan by combining sentinel-2 and landsat 8 based multi-temporal datasets through machine learning and cross-validation approach,” Land Degradation, vol. 6, no. 3, p. 50, 2017.SharmaR. C.HaraK.TateishiR.“High-resolution vegetation mapping in japan by combining sentinel-2 and landsat 8 based multi-temporal datasets through machine learning and cross-validation approach,”Land Degradation63502017Search in Google Scholar