{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:42:21Z","timestamp":1774986141867,"version":"3.50.1"},"reference-count":125,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T00:00:00Z","timestamp":1692144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to destruction and land use change. Therefore, the identification and monitoring of gardens in urban areas in dry regions and their impact on the ecosystem are the aims of this study. The data utilized are aerial and Sentinel-2 images (2018\u20132022) for Yazd Township in Iran. Several satellite and aerial image fusion methods were employed and compared. The root mean square error (RMSE) of horizontal shortcut connections (HSC) and color normalization (CN) were the highest compared to other methods with values of 18.37 and 17.5, respectively, while the Ehlers method showed the highest accuracy with a RMSE value of 12.3. The normalized difference vegetation index (NDVI) was then calculated using the images with 15 cm spatial resolution retrieved from the fusion. Aerial images were classified by NDVI and digital surface model (DSM) using object-oriented methods. Different object-oriented classification methods were investigated, including support vector machine (SVM), Bayes, random forest (RF), and k-nearest neighbor (KNN). SVM showed the greatest accuracy with overall accuracy (OA) and kappa of 86.2 and 0.89, respectively, followed by RF with OA and kappa of 83.1 and 0.87, respectively. Separating the gardens using NDVI, DSM, and aerial images from 2018, the images were fused in 2022, and the current status of the gardens and associated changes were classified into completely dried, drying, acceptable, and desirable conditions. It was found that gardens with a small area were more prone to destruction, and 120 buildings were built in the existing gardens in the region during 2018\u20132022. Moreover, the monitoring of land surface temperature (LST) showed an increase of 14 \u00b0C in the areas that were changed from gardens to buildings.<\/jats:p>","DOI":"10.3390\/rs15164053","type":"journal-article","created":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T10:08:09Z","timestamp":1692180489000},"page":"4053","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0309-3774","authenticated-orcid":false,"given":"Fahime","family":"Arabi Aliabad","sequence":"first","affiliation":[{"name":"Department of Arid Lands Management, Faculty of Natural Resources and Desert Studies, Yazd University, Yazd 8915818411, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6083-1517","authenticated-orcid":false,"given":"Hamidreza","family":"Ghafarian Malamiri","sequence":"additional","affiliation":[{"name":"Department of Geography, Yazd University, Yazd 8915818411, Iran"},{"name":"Department of Geoscience and Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands"}]},{"given":"Alireza","family":"Sarsangi","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 1417935840, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4715-5160","authenticated-orcid":false,"given":"Aliihsan","family":"Sekertekin","sequence":"additional","affiliation":[{"name":"Department of Architecture and Town Planning, Vocational School of Higher Education for Technical Sciences, Igdir University, Igdir 76002, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5165-1773","authenticated-orcid":false,"given":"Ebrahim","family":"Ghaderpour","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences & CERI Research Centre, Sapienza University of Rome, Piazzale Aldo-Moro, 5, 00185 Rome, Italy"},{"name":"Earth and Space Inc., Calgary, AB T3A 5B1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mirzaee, S., and Mirzakhani Nafchi, A. (2023). Monitoring Spatiotemporal Vegetation Response to Drought Using Remote Sensing Data. Sensors, 23.","DOI":"10.3390\/s23042134"},{"key":"ref_2","first-page":"103241","article-title":"Coherency and phase delay analyses between land cover and climate across Italy via the least-squares wavelet software","volume":"118","author":"Ghaderpour","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, D., Liu, S., Feng, W., Lai, F., Li, H., Zou, C., Zhang, N., and Zan, M. (2022). Research on Vegetation Cover Changes in Arid and Semi-Arid Region Based on a Spatio-Temporal Fusion Model. Forests, 13.","DOI":"10.3390\/f13122066"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ghorbanian, A., Mohammadzadeh, A., and Jamali, S. (2022). Linear and Non-Linear Vegetation Trend Analysis throughout Iran Using Two Decades of MODIS NDVI Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14153683"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Almalki, R., Khaki, M., Saco, P.M., and Rodriguez, J.F. (2022). Monitoring and Mapping Vegetation Cover Changes in Arid and Semi-Arid Areas Using Remote Sensing Technology: A Review. Remote Sens., 14.","DOI":"10.3390\/rs14205143"},{"key":"ref_6","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":"Kellert","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","first-page":"102334","article-title":"Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method","volume":"100","author":"Lindberg","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","first-page":"101960","article-title":"Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data","volume":"84","author":"Modzelewska","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"100032","DOI":"10.1016\/j.fecs.2022.100032","article-title":"Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province, China","volume":"9","author":"Wang","year":"2022","journal-title":"For. Ecosyst."},{"key":"ref_11","first-page":"65","article-title":"Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species","volume":"58","author":"Madonsela","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rahman, M.F.F., Fan, S., Zhang, Y., and Chen, L. (2022). A comparative study on application of unmanned aerial vehicle systems in agriculture. Agriculture, 11.","DOI":"10.3390\/agriculture11010022"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ahmadi, P., Mansor, S., Farjad, B., and Ghaderpour, E. (2022). Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma. Remote Sens., 14.","DOI":"10.3390\/rs14051239"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Grybas, H., and Congalton, R.G. (2021). A comparison of multi-temporal RGB and multispectral UAS imagery for tree species classification in heterogeneous New Hampshire Forests. Remote Sens., 13.","DOI":"10.3390\/rs13132631"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Belcore, E., Pittarello, M., Lingua, A.M., and Lonati, M. (2021). Mapping riparian habitats of natura 2000 network (91E0*, 3240) at individual tree level using UAV multi-temporal and multi-spectral data. Remote Sens., 13.","DOI":"10.3390\/rs13091756"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2020.04.017","article-title":"Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery","volume":"165","author":"Johansen","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, Q., Zhang, Y., Xie, M., Zhao, Z., Yang, L., Liu, J., and Hou, D. (2023). Estimation of Fv\/Fm in spring wheat using UAV-Based multispectral and RGB imagery with multiple machine learning methods. Agronomy, 13.","DOI":"10.3390\/agronomy13041003"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hegarty-Craver, M., Polly, J., O\u2019Neil, M., Ujeneza, N., Rineer, J., Beach, R.H., and Temple, D.S. (2020). Remote crop mapping at scale: Using satellite imagery and UAV-acquired data as ground truth. Remote Sens., 12.","DOI":"10.3390\/rs12121984"},{"key":"ref_19","unstructured":"Shamshiri, R.R., Hameed, I.A., Balasundram, S.K., Ahmad, D., Weltzien, C., and Yamin, M. (2018). Agricultural Robots-Fundamentals and Applications, IntechOpen."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.5194\/isprs-archives-XLII-3-2185-2018","article-title":"Research on Remote Sensing Image Classification Based on Feature Level Fusion","volume":"XLII-3","author":"Yuan","year":"2018","journal-title":"ISPRS\u2014Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Aliabad, F.A., Malamiri, H.R.G., Shojaei, S., Sarsangi, A., Ferreira, C.S.S., and Kalantari, Z. (2022). Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2. Remote Sens., 14.","DOI":"10.3390\/rs14133227"},{"key":"ref_22","unstructured":"Neale, C.M.U., and Maltese, A. (2016). Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, SPIE."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Yan, W., An, S., Gao, W., Jia, J., Tao, S., and Wang, W. (2023). A Spatio-Temporal Fusion Framework of UAV and Satellite Imagery for Winter Wheat Growth Monitoring. Drones, 7.","DOI":"10.3390\/drones7010023"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lu, T., Wan, L., Qi, S., and Gao, M. (2023). Land Cover Classification of UAV Remote Sensing Based on Transformer\u2014CNN Hybrid Architecture. Sensors, 23.","DOI":"10.3390\/s23115288"},{"key":"ref_25","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":"Corpetti","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Navarro, J.A., Algeet, N., Fern\u00e1ndez-Landa, A., Esteban, J., Rodr\u00edguez-Noriega, P., and Guill\u00e9n-Climent, M.L. (2019). Integration of UAV, Sentinel-1, and Sentinel-2 data for mangrove plantation aboveground biomass monitoring in Senegal. Remote Sens., 11.","DOI":"10.3390\/rs11010077"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s12517-015-2109-8","article-title":"Fusion of very high-resolution UAV images with criteria-based image fusion algorithm","volume":"9","author":"Yilmaz","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, L., Shi, Y., Liu, B., Hovis, C., Duan, Y., and Shi, Z. (2019). Finer classification of crops by fusing UAV images and Sentinel-2A data. Remote Sens., 11.","DOI":"10.3390\/rs11243012"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"116290","DOI":"10.1016\/j.geoderma.2022.116290","article-title":"Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire","volume":"430","author":"Marcos","year":"2023","journal-title":"Geoderma"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105686","DOI":"10.1016\/j.compag.2020.105686","article-title":"Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data","volume":"177","author":"Daryaei","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Molt\u00f3, E. (2022). Fusion of different image sources for improved monitoring of agricultural plots. Sensors, 22.","DOI":"10.3390\/s22176642"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"113205","DOI":"10.1016\/j.rse.2022.113205","article-title":"Mapping tree species proportions from satellite imagery using spectral-spatial deep learning","volume":"280","author":"Bolyn","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"De Giglio, M., Greggio, N., Goffo, F., Merloni, N., Dubbini, M., and Barbarella, M. (2019). Comparison of pixel-and object-based classification methods of unmanned aerial vehicle data applied to coastal dune vegetation communities: Casal borsetti case study. Remote Sens., 11.","DOI":"10.3390\/rs11121416"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S.R., Nyirenda, V., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover. Use Mapping: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhen, Z., Chen, S., Yin, T., and Gastellu-Etchegorry, J.P. (2023). Improving Crop Mapping by Using Bidirectional Reflectance Distribution Function (BRDF) Signatures with Google Earth Engine. Remote Sens., 15.","DOI":"10.3390\/rs15112761"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tarantino, C., Forte, L., Blonda, P., Vicario, S., Tomaselli, V., Beierkuhnlein, C., and Adamo, M. (2021). Intra-annual sentinel-2 time-series supporting grassland habitat discrimination. Remote Sens., 13.","DOI":"10.3390\/rs13020277"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kluczek, M., Zagajewski, B., and Kycko, M. (2022). Airborne HySpex hyperspectral versus multitemporal Sentinel-2 images for mountain plant communities mapping. Remote Sens., 14.","DOI":"10.3390\/rs14051209"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kluczek, M., Zagajewski, B., and Zwijacz-Kozica, T. (2023). Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15030844"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine learning classification of mediterranean forest habitats in google earth engine based on seasonal sentinel-2 time-series and input image composition optimisation. Remote Sens., 13.","DOI":"10.3390\/rs13040586"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, H., Datta, P., Frey, J., and Koch, B. (2020). Mapping an invasive plant Spartina alterniflora by combining an ensemble one-class classification algorithm with a phenological NDVI time-series analysis approach in middle coast of Jiangsu, China. Remote Sens., 12.","DOI":"10.3390\/rs12244010"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bollas, N., Kokinou, E., and Polychronos, V. (2021). Comparison of sentinel-2 and UAV multispectral data for use in precision agriculture: An application from northern Greece. Drones, 5.","DOI":"10.3390\/drones5020035"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Maimaitijiang, M., Sagan, V., Sidike, P., Daloye, A.M., Erkbol, H., and Fritschi, F.B. (2020). Crop monitoring using satellite\/UAV data fusion and machine learning. Remote Sens., 12.","DOI":"10.3390\/rs12091357"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, P.C., Chiang, Y.C., and Weng, P.Y. (2020). Imaging using unmanned aerial vehicles for agriculture land use classification. Agriculture, 10.","DOI":"10.3390\/agriculture10090416"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.11.032","article-title":"The global Landsat archive: Status, consolidation, and direction","volume":"185","author":"Wulder","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/LGRS.2008.2001636","article-title":"Split-window coefficients for land surface temperature retrieval from low-resolution thermal infrared sensors","volume":"5","author":"Sobrino","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3979","DOI":"10.1016\/j.asr.2021.02.019","article-title":"Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province, Iran","volume":"67","author":"Zarei","year":"2021","journal-title":"Adv. Space Res."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land","volume":"120","author":"Rott","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Crommelinck, S., Bennett, R., Gerke, M., Nex, F., Yang, M.Y., and Vosselman, G. (2016). Review of automatic feature extraction from high-resolution optical sensor data for UAV-based cadastral mapping. Remote Sens., 8.","DOI":"10.3390\/rs8080689"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Maulit, A., Nugumanova, A., Apayev, K., Baiburin, Y., and Sutula, M. (2023). A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan. Data, 8.","DOI":"10.3390\/data8050088"},{"key":"ref_51","first-page":"48","article-title":"An evaluation on fixed wing and multi-rotor UAV images using photogrammetric image processing","volume":"7","author":"Tahar","year":"2013","journal-title":"Int. J. Comput. Electr. Autom. Control Inf. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2015.10.004","article-title":"Remote sensing platforms and sensors: A survey","volume":"115","author":"Toth","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures, and data correction workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Palsson, F., Sveinsson, J.R., Benediktsson, J.A., and Aan\u00e6s, H. (2010, January 25\u201330). Image fusion for classification of high-resolution images based on mathematical morphology. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5654167"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1109\/36.763269","article-title":"Some terms of reference in data fusion","volume":"37","author":"Lucien","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite-derived land surface temperature: Current status and perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Laben, C.A., and Brower, B.V. (2000). Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. (6,011,875), U.S. Patent."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"239","DOI":"10.5194\/isprsarchives-XL-1-W1-239-2013","article-title":"How to pan-sharpen images using the gram-schmidt pan-sharpen metho\u2014A recipe","volume":"XL-1\/W1","author":"Maurer","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3230","DOI":"10.1109\/TGRS.2007.901007","article-title":"Improving component substitution pansharpening through multivariate regression of MS + Pan data","volume":"45","author":"Aiazzi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/014311698215748","article-title":"Multisensor image fusion in remote sensing: Concepts, methods and applications","volume":"19","author":"Pohl","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","first-page":"1325","article-title":"Reconstruction of Multispatial, Multispectral Image Data Using Spatial Frequency Content","volume":"46","author":"Schowengerdt","year":"1980","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/LGRS.2004.834804","article-title":"A fast intensity hue-saturation fusion technique with spectral adjustment for IKONOS imagery","volume":"1","author":"Tu","year":"2004","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_63","first-page":"459","article-title":"The use of intensity-hue-saturation transformations for merging spot panchromatic and multispectral image data","volume":"56","author":"Carper","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhang, X., Dai, X., Zhang, X., Hu, Y., Kang, Y., and Jin, G. (2023). Improved Generalized IHS Based on Total Variation for Pansharpening. Remote Sens., 15.","DOI":"10.3390\/rs15112945"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5491","DOI":"10.1080\/01431160412331270830","article-title":"Spatially Adaptive Multi-resolution Multispectral Image Fusion","volume":"25","author":"Park","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","unstructured":"Shamshad, A., Wan Hussin, W.M.A., and Mohd Sanusi, S.A. (2004, January 16\u201318). Comparison of Different Data Fusion Approaches for Surface Features Extraction Using Quickbird Images. Proceedings of the GISIDEAS, Hanoi, Vietnam."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1080\/17538947.2013.869266","article-title":"Remote sensing image fusion: An update in the context of digital earth","volume":"7","author":"Pohl","year":"2014","journal-title":"Int. J. Digit. Earth."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Shuangao, W., Padmanaban, R., Mbanze, A.A., Silva, J.M., Shamsudeen, M., Cabral, P., and Campos, F.S. (2021). Using satellite image fusion to evaluate the impact of land use changes on ecosystem services and their economic values. Remote Sens., 13.","DOI":"10.3390\/rs13050851"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.isprsjprs.2006.11.002","article-title":"FFT-enhanced IHS transform for fusing high-resolution satellite images FFT-enhanced IHS transform method for fusing high-resolution satellite images","volume":"61","author":"Ehlers","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"013107","DOI":"10.1117\/1.OE.53.1.013107","article-title":"Nearest-neighbor diffusion-based pansharpening algorithm for spectral images","volume":"53","author":"Sun","year":"2014","journal-title":"Opt. Eng."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/34.56205","article-title":"Scale-space and edge detection using anisotropic diffusion","volume":"12","author":"Perona","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_72","unstructured":"Padwick, C., Deskevich, M., and Pacifici, F. (2010, January 26\u201330). WorldView-2 pan-sharpening. Proceedings of the ASPRS 2010 Annual Conference, San Diego, CA, USA."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1080\/19479832.2013.778335","article-title":"A comparative study of various pixel-based image fusion techniques as applied to an urban environment","volume":"4","author":"Dahiya","year":"2013","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_74","unstructured":"Geospatial Hexagon (2023, August 14). ERDAS Imagine Help Guide. Available online: https:\/\/hexagonusfederal.com\/-\/media\/Files\/IGS\/Resources\/Geospatial%20Product\/ERDAS%20IMAGINE\/img%20pd1.ashx?la=en."},{"key":"ref_75","first-page":"128","article-title":"Projective pan sharpening algorithm. In Multispectral Imaging for Terrestrial Applications","volume":"2818","author":"Lindgren","year":"1996","journal-title":"Int. J. Opt. Photonics"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Jel\u00e9nek, J., Kopa\u010dkov\u00e1, V., Kouck\u00e1, L., and Mi\u0161urec, J. (2016). Testing a modified PCA-based sharpening approach for image fusion. Remote Sens., 8.","DOI":"10.3390\/rs8100794"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"11171","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zhang, D., Li, D., Zhou, L., and Wu, J. (2023). Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach. Sensors, 23.","DOI":"10.3390\/s23042180"},{"key":"ref_80","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_81","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land cover classification using Google Earth Engine and random forest classifier\u2014The role of image composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Zhang, L., Liu, Z., Ren, T., Liu, D., Ma, Z., Tong, L., Zhang, C., Zhou, T., Zhang, X., and Li, S. (2020). Identification of seed maize fields with high spatial resolution and multiple spectral remote sensing using random forest classifier. Remote Sens., 12.","DOI":"10.3390\/rs12030362"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.commatsci.2019.01.006","article-title":"Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels","volume":"160","author":"Gola","year":"2019","journal-title":"Comput. Mater. Sci."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Yousefi, S., Mirzaee, S., Almohamad, H., Al Dughairi, A.A., Gomez, C., Siamian, N., Alrasheedi, M., and Abdo, H.G. (2022). Image classification and land cover mapping using sentinel-2 imagery: Optimization of SVM parameters. Land, 11.","DOI":"10.3390\/land11070993"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Taheri Dehkordi, A., Valadan Zoej, M.J., Ghasemi, H., Ghaderpour, E., and Hassan, Q.K. (2022). A new clustering method to generate training samples for supervised monitoring of long-term water surface dynamics using Landsat data through Google Earth Engine. Sustainability, 14.","DOI":"10.3390\/su14138046"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Sahour, H., Kemink, K.M., and O\u2019Connell, J. (2022). Integrating SAR and optical remote sensing for conservation-targeted wetlands mapping. Remote Sens., 14.","DOI":"10.3390\/rs14010159"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1006593614256","article-title":"A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms","volume":"11","author":"Wettschereck","year":"1997","journal-title":"Artif. Intell. Rev."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Noi Tnh, P., and Kappas, M. (2018). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_90","first-page":"93","article-title":"Estimation and mapping forest attributes using \u201ck-nearest neighbor\u201d method on IRS-p6 lISS III satellite image data","volume":"7","author":"Abedi","year":"2015","journal-title":"Ecol. Balk."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Pacheco, A.D.P., Junior, J.A.D.S., Ruiz-Armenteros, A.M., and Henriques, R.F.F. (2021). Assessment of k-nearest neighbor and random forest classifiers for mapping forest fire areas in central portugal using landsat-8, sentinel-2, and terra imagery. Remote Sens., 13.","DOI":"10.3390\/rs13071345"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Matvienko, I., Gasanov, M., Petrovskaia, A., Kuznetsov, M., Jana, R., Pukalchik, M., and Oseledets, I. (2022). Bayesian Aggregation Improves Traditional Single-Image Crop Classification Approaches. Sensors, 22.","DOI":"10.3390\/s22228600"},{"key":"ref_93","first-page":"102318","article-title":"Tree species classification using Sentinel-2 imagery and Bayesian inference","volume":"100","author":"Axelsson","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant remote sensing vegetation indices: A review of developments and application","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_96","unstructured":"McKinnon, T., and Hoff, P. (2017). Comparing RGB-Based Vegetation Indices with NDVI for Drone Based Agricultural Sensing, AGBX. AGBX021-17."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Sishodia, R.P., Ray, R.L., and Singh, S.K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sens., 12.","DOI":"10.3390\/rs12193136"},{"key":"ref_98","unstructured":"Govaerts, B., and Verhulst, N. (2010). The Normalized Difference Vegetation Index (NDVI) GreenSeekerTM Handheld Sensor: Toward the Integrated Evaluation of Crop Management, CIMMYT."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1038\/s41598-020-57750-z","article-title":"Quantitative monitoring of leaf area index in wheat of different plant types by integrating nDVi and Beer-Lambert law","volume":"10","author":"Tan","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.rse.2016.10.025","article-title":"On the performance of remote sensing time series reconstruction methods\u2014A spatial comparison","volume":"187","author":"Zhou","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ecolind.2014.07.031","article-title":"Spatio-temporal analysis of vegetation variation in the Yellow River Basin","volume":"51","author":"Jiang","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"769","DOI":"10.3390\/earth3030044","article-title":"Comparative Assessment of UAV and Sentinel-2 NDVI and GNDVI for Preliminary Diagnosis of Habitat Conditions in Burunge Wildlife Management Area, Tanzania","volume":"3","author":"Mangewa","year":"2022","journal-title":"Earth"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"118950","DOI":"10.1016\/j.foreco.2021.118950","article-title":"The impact of land-use legacies and recent management on natural disturbance susceptibility in mountain forests","volume":"484","author":"Stritih","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1007\/s40808-020-01007-1","article-title":"A comparative assessment of the accuracies of split-window algorithms for retrieving of land surface temperature using Landsat 8 data","volume":"7","author":"Aliabad","year":"2021","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"103692","DOI":"10.1016\/j.infrared.2021.103692","article-title":"Comparison of the accuracy of daytime land surface temperature retrieval methods using Landsat 8 images in arid regions","volume":"115","author":"Aliabad","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1007\/s12665-009-0286-z","article-title":"Landsat data to evaluate urban expansion and determine land use\/land cover changes in Penang Island, Malaysia","volume":"60","author":"Tan","year":"2010","journal-title":"Environ. Earth Sci."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.3390\/rs6054345","article-title":"Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling","volume":"6","author":"Vlassova","year":"2014","journal-title":"Remote Sens."},{"key":"ref_111","first-page":"101984","article-title":"Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method","volume":"85","author":"Neinavaz","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/S0034-4257(00)00171-1","article-title":"A comparative study of land surface emissivity retrieval from NOAA data","volume":"75","author":"Sobrino","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_113","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS, NASA Special Publication; Texas A&M University."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1080\/01431169208904248","article-title":"Percentage vegetation cover of a degrading rangeland from SPOT","volume":"13","author":"Dymond","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_115","first-page":"15","article-title":"Comparison of the Accuracies of Different Methods for Estimating Atmospheric Water Vapor in the Retrieval of Land Surface Temperature Using Landsat 8 Images","volume":"9","author":"Aliabad","year":"2021","journal-title":"Desert Manag."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1080\/2150704X.2015.1089363","article-title":"NDVI-based split-window algorithm for precipitable water vapor retrieval from Landsat-8 TIRS data over land area","volume":"6","author":"Wang","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1007\/s10708-022-10744-y","article-title":"Comparison of neural network methods (fuzzy ARTMAP, Kohonen and Perceptron) and maximum likelihood efficiency in preparation of land use map","volume":"88","author":"Aliabad","year":"2023","journal-title":"GeoJournal"},{"key":"ref_118","unstructured":"Aliabad, F., Zare, M., and Ghafarian Malamiri, H.R. (2023). Investigating the retrieval possibility of land surface temperature images of Landsat 8 in desert areas using harmonic analysis of time series (HANTS). Infrared Phys. Technol, under review."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"026001","DOI":"10.1117\/1.JRS.10.026001","article-title":"Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery","volume":"10","author":"Ai","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Rahimzadeganasl, A., Alganci, U., and Goksel, C. (2019). An approach for the pan sharpening of very high resolution satellite images using a CIELab color based component substitution algorithm. Appl. Sci., 9.","DOI":"10.3390\/app9235234"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Al-Najjar, H.A., Kalantar, B., Pradhan, B., Saeidi, V., Halin, A.A., Ueda, N., and Mansor, S. (2019). Land cover classification from fused DSM and UAV images using convolutional neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11121461"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Marcinkowska-Ochtyra, A., Zagajewski, B., Raczko, E., Ochtyra, A., and Jaroci\u0144ska, A. (2018). Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10040570"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.3390\/rs70202046","article-title":"Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery","volume":"7","author":"Burai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Bento, N.L., Ferraz, G.A.E.S., Amorim, J.D.S., Santana, L.S., Barata, R.A.P., Soares, D.V., and Ferraz, P.F.P. (2023). Weed Detection and Mapping of a Coffee Farm by a Remotely Piloted Aircraft System. Agronomy, 13.","DOI":"10.3390\/agronomy13030830"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, W., Sun, Y., Chang, C., Yu, J., and Zhang, W. (2021). Fusion of multispectral aerial imagery and vegetation indices for machine learning-based ground classification. Remote Sens., 13.","DOI":"10.3390\/rs13081411"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4053\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:35:14Z","timestamp":1760128514000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,16]]},"references-count":125,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15164053"],"URL":"https:\/\/doi.org\/10.3390\/rs15164053","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,16]]}}}