{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T13:15:38Z","timestamp":1775222138385,"version":"3.50.1"},"reference-count":87,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"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>Accurate spatial distribution information of native, mixed, and tame grasslands is essential for maintaining ecosystem health in the Prairie. This research aimed to use the latest monitoring technology to assess the remaining grasslands in Saskatchewan\u2019s mixed grassland ecoregion (MGE). The classification approach was based on 78 raster-based variables derived from big remote sensing data of multispectral optical space-borne sensors such as MODIS and Sentinel-2, and synthetic aperture radar (SAR) space-borne sensors such as Sentinel-1. Principal component analysis (PCA) was used as a data dimensionality reduction technique to mitigate big data load and improve processing time. Random Forest (RF) was used in the classification process and incorporated the selected variables from 78 satellite-based layers and 2385 reference training points. Within the MGE, the overall accuracy of the classification was 90.2%. Native grassland had 98.20% of user\u2019s accuracy and 88.40% producer\u2019s accuracy, tame grassland had 81.4% user\u2019s accuracy and 93.8% producer\u2019s accuracy, whereas mixed grassland class had very low user\u2019s accuracy (45.8%) and producer\u2019s accuracy 82.83%. Approximately 3.46 million hectares (40.2%) of the MGE area are grasslands (33.9% native, 4% mixed, and 2.3% tame). This study establishes a novel analytical framework for reliable grassland mapping using big data, identifies future challenges, and provides valuable information for Saskatchewan and North America decision-makers.<\/jats:p>","DOI":"10.3390\/rs13244972","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T11:00:23Z","timestamp":1638874823000},"page":"4972","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2539-6972","authenticated-orcid":false,"given":"Nasem","family":"Badreldin","sequence":"first","affiliation":[{"name":"Department of Soil Science, University of Manitoba, 13 Freedman Crescent, Winnipeg, MB R3T 2N2, Canada"}]},{"given":"Beatriz","family":"Prieto","sequence":"additional","affiliation":[{"name":"Habitat Unit, Saskatchewan Ministry of Environment, Fish, Wildlife and Lands Branch, 3211 Albert St., Regina, SK S4S 5W6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6398-2334","authenticated-orcid":false,"given":"Ryan","family":"Fisher","sequence":"additional","affiliation":[{"name":"Habitat Unit, Saskatchewan Ministry of Environment, Fish, Wildlife and Lands Branch, 3211 Albert St., Regina, SK S4S 5W6, Canada"},{"name":"Royal Saskatchewan Museum, 2340 Albert St., Regina, SK S4P 2V7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"595","DOI":"10.4141\/CJSS07094","article-title":"Potential Impacts of Climate Change on Grazing Capacity of Native Grasslands in the Canadian Prairies","volume":"88","author":"Thorpe","year":"2008","journal-title":"Can. J. Soil Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1023\/A:1025585527169","article-title":"Monitoring the Conservation of Grassland Habitats, Prairie Ecozone, Canada","volume":"88","author":"Gauthier","year":"2003","journal-title":"Environ. Monit. Assess."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1111\/j.1461-0248.2004.00686.x","article-title":"Confronting a Biome Crisis: Global Disparities of Habitat Loss and Protection","volume":"8","author":"Hoekstra","year":"2004","journal-title":"Ecol. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1111\/j.1523-1739.2008.01022.x","article-title":"Predicting Risk of Habitat Conversion in Native Temperate Grasslands","volume":"22","author":"Stephens","year":"2008","journal-title":"Conserv. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.rse.2018.10.003","article-title":"A Novel Technique Using LiDAR to Identify Native-Dominated and Tame-Dominated Grasslands in Canada","volume":"218","author":"Fisher","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1046\/j.1523-1739.2002.00530.x","article-title":"Habitat Loss and Extinction in the Hotspots of Biodiversity","volume":"16","author":"Brooks","year":"2002","journal-title":"Conserv. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15","DOI":"10.2307\/1933177","article-title":"Preliminary Classification of Grasslands in Saskatchewan","volume":"44","author":"Looman","year":"1963","journal-title":"Ecology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"386","DOI":"10.2307\/1930904","article-title":"The Fescue Grassland in Saskatchewan","volume":"34","author":"Coupland","year":"1953","journal-title":"Ecology"},{"key":"ref_9","first-page":"368","article-title":"Carbon Sequestration and Growth of Six Common Tree and Shrub Shelterbelts in Saskatchewan, Canada","volume":"97","author":"Amichev","year":"2016","journal-title":"Can. J. Soil Sci."},{"key":"ref_10","unstructured":"Hammermeister, A., Gauthier, D., and McGovern, K. (2001). Saskatchewan\u2019s Native Prairie: Statistics of a Vanishing Ecosystem and Dwindling Resource, Native Plant Society of Saskatchewan Inc."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fisette, T., Rollin, P., Aly, Z., Campbell, L., Daneshfar, B., Filyer, P., Smith, A., Davidson, A., Shang, J., and Jarvis, I. (2013, January 12\u201316). AAFC Annual Crop Inventory: Status and Challenges. Proceedings of the Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Fairfax, VA, USA.","DOI":"10.1109\/Argo-Geoinformatics.2013.6621920"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1093\/jpe\/rtw005","article-title":"Satellite Remote Sensing of Grasslands: From Observation to Management","volume":"9","author":"Ali","year":"2016","journal-title":"J. Plant Ecol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s40808-017-0300-5","article-title":"The Application of Satellite-Based Model and Bi-Stable Ecosystem Balance Concept to Monitor Desertification in Arid Lands, a Case Study of Sinai Peninsula","volume":"3","author":"Badreldin","year":"2017","journal-title":"Modeling Earth Syst. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"S18","DOI":"10.1111\/j.1442-8903.2006.00287.x","article-title":"Integrating Vegetation Field Surveys with Remotely Sensed Data","volume":"7","author":"Reinke","year":"2006","journal-title":"Ecol. Manag. Restor."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1093\/jpe\/rtm005","article-title":"Remote Sensing Imagery in Vegetation Mapping: A Review","volume":"1","author":"Xie","year":"2008","journal-title":"J. Plant Ecol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kolecka, N., Ginzler, C., Pazur, R., Price, B., and Verburg, P. (2018). Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10081221"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2015.10.012","article-title":"Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges","volume":"115","author":"Li","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/s10661-019-7934-x","article-title":"Spatiotemporal Dynamics of Urbanization and Cropland in the Nile Delta of Egypt Using Machine Learning and Satellite Big Data: Implications for Sustainable Development","volume":"191","author":"Badreldin","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_19","first-page":"6","article-title":"Data Management: Controlling Data Volume, Velocity, and Variety","volume":"6","author":"Laney","year":"2001","journal-title":"Appl. Deliv. Strateg."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1145\/2627534.2627557","article-title":"Big Data Classification: Problems and Challenges in Network Intrusion Prediction with Machine Learning","volume":"41","author":"Suthaharan","year":"2014","journal-title":"ACM SIGMETRICS Perform. Eval. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hogland, J., and Anderson, N. (2017). Function Modeling Improves the Efficiency of Spatial Modeling Using Big Data from Remote Sensing. Big Data Cogn. Comput., 1.","DOI":"10.3390\/bdcc1010003"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/07038992.2019.1711366","article-title":"Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform","volume":"46","author":"Mahdianpari","year":"2020","journal-title":"Can. J. Remote Sens."},{"key":"ref_23","unstructured":"Acton, D.F., Padbury, G.A., and Stushnoff, C.T. (1998). The Ecoregions of Saskatchewan, Saskatchewan Environment and Resource Management, Canadian Plains Research Center."},{"key":"ref_24","unstructured":"Gauthier, D.A., Patino, L., and McGovern, K. (2002). Status of Native Prairie Habitat, Prairie Ecozone, Saskatchewan, Canadian Plains Research Centre. Project Report to Wildlife Habitat Canada, Number 8.65A.1R-01\/02."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0167-1987(98)00105-6","article-title":"Management Effects on Soil C Storage on the Canadian Prairies","volume":"47","author":"Janzen","year":"1998","journal-title":"Soil Tillage Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"14008","DOI":"10.1088\/1748-9326\/7\/1\/014008","article-title":"Successes of Soil Conservation in the Canadian Prairies Highlighted by a Historical Decline in Blowing Dust","volume":"7","author":"Thomas","year":"2012","journal-title":"Environ. Res. Lett."},{"key":"ref_27","first-page":"177","article-title":"Relationship between Plant Species Diversity and Grassland Condition","volume":"54","author":"Bai","year":"2001","journal-title":"Rangel. Ecol. Manag. \/J. Range Manag. Arch."},{"key":"ref_28","unstructured":"Didan, K. (2015). MOD13Q1 MODIS\/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V006, NASA EOSDIS LP DAAC."},{"key":"ref_29","first-page":"136","article-title":"Temporal Optimisation of Image Acquisition for Land Cover Classification with Random Forest and MODIS Time-Series","volume":"34","author":"Nitze","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","unstructured":"Didan, K., Barreto Munoz, A., Solano, R., and Huete, A. (2015). MODIS Vegetation Index User\u2019s Guide (MOD13 Series), Vegetation Index and Phenology Lab, University of Arizona."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.cageo.2016.08.020","article-title":"MODIStsp: An R Package for Automatic Preprocessing of MODIS Land Products Time Series","volume":"97","author":"Busetto","year":"2016","journal-title":"Comput. Geosci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Petrovska, B., Zdravevski, E., Lameski, P., Corizzo, R., \u0160tajduhar, I., and Lerga, J. (2020). Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification. Sensors, 20.","DOI":"10.3390\/s20143906"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1007\/s10618-019-00616-4","article-title":"Unsupervised Dimensionality Reduction versus Supervised Regularization for Classification from Sparse Data","volume":"33","author":"Clark","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105288","DOI":"10.1016\/j.compag.2020.105288","article-title":"A Comparative Study on Dimensionality Reduction of Dielectric Spectral Data for the Classification of Basal Stem Rot (BSR) Disease in Oil Palm","volume":"170","author":"Khaled","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1080\/17445647.2017.1372316","article-title":"Fusion of Sentinel-1a and Sentinel-2A Data for Land Cover Mapping: A Case Study in the Lower Magdalena Region, Colombia","volume":"13","author":"Clerici","year":"2017","journal-title":"J. Maps"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1080\/22797254.2018.1457937","article-title":"Atmospheric Correction of Landsat-8\/OLI and Sentinel-2\/MSI Data Using ICOR Algorithm: Validation for Coastal and Inland Waters","volume":"51","author":"Sterckx","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Filipponi, F. (2019). Sentinel-1 GRD Preprocessing Workflow. Proceedings, 18.","DOI":"10.3390\/ECRS-3-06201"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1038\/d41586-018-03924-9","article-title":"Ecology\u2019s Remote-Sensing Revolution","volume":"556","author":"Kwok","year":"2018","journal-title":"Nature"},{"key":"ref_40","unstructured":"Hajduch, G. (2018). Masking \u201cNo-Value\u201d Pixels on GRD Products Generated by the Sentinel-1 ESA IPF, European Space Agency (ESA)."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/02757259409532206","article-title":"Speckle Filtering of Synthetic Aperture Radar Images: A Review","volume":"8","author":"Lee","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1080\/2150704X.2016.1212419","article-title":"Best Practices for the Reprojection and Resampling of Sentinel-2 Multi Spectral Instrument Level 1C Data","volume":"7","author":"Roy","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1890\/0012-9658(2000)081[3178:CARTAP]2.0.CO;2","article-title":"Classification and Regression Trees: A Powerful yet Simple Technique for Ecological Data Analysis","volume":"81","author":"Fabricius","year":"2000","journal-title":"Ecology"},{"key":"ref_44","unstructured":"Liaw, A., and Wiener, M. (2021, December 01). Breiman and Cutler\u2019s Random Forests for Classification and Regression. The Comprehensive R Archive Network (CRAN). Available online: http:\/\/math.furman.edu\/~dcs\/courses\/math47\/R\/library\/randomForest\/html\/00Index.html."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random Forest Classifier for Remote Sensing Classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"111630","DOI":"10.1016\/j.rse.2019.111630","article-title":"Explaining the Unsuitability of the Kappa Coefficient in the Assessment and Comparison of the Accuracy of Thematic Maps Obtained by Image Classification","volume":"239","author":"Foody","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s10708-017-9805-8","article-title":"Prairie or Planted? Using Time-Series NDVI to Determine Grassland Characteristics in Montana","volume":"83","author":"Olimb","year":"2017","journal-title":"GeoJournal"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1109\/JSTARS.2015.2416713","article-title":"Discriminating Native and Nonnative Grasses in the Dry Mixedgrass Prairie with MODIS NDVI Time Series","volume":"8","author":"McInnes","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/00690805.2000.9714334","article-title":"Slope Angle and Slope Length Solutions for GIS","volume":"29","author":"Hickey","year":"2000","journal-title":"Cartography"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40965-019-0066-y","article-title":"TWI Computation: A Comparison of Different Open Source GISs","volume":"4","author":"Mattivi","year":"2019","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1080\/01431168508948283","article-title":"Canopy Reflectance, Photosynthesis and Transpiration","volume":"6","author":"Sellers","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_57","first-page":"1541","article-title":"Distinguishing Vegetation from Soil Background Information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_58","unstructured":"Clevers, J.G.P.W., De Jong, S.M., Epema, G.F., and Addink, E.A. (2000). MERIS and The Red-Edge Index. Proceedings of the Second EARSeL Workshop on Imaging Spectroscopy, Springer."},{"key":"ref_59","unstructured":"Guyot, G., and Baret, F. (1988, January 12\u201318). Utilisation de la haute resolution spectrale pour suivre l\u2019etat des couverts vegetaux. Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing, Aussois, France."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.asr.2006.02.034","article-title":"Evaluation of the MERIS Terrestrial Chlorophyll Index (MTCI)","volume":"39","author":"Dash","year":"2007","journal-title":"Adv. Space Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","unstructured":"Huete, A.R., Justice, C., and van Leeuwen, W. (1999). MODIS Vegetation Index (MOD13), Department of Environmental Sciences, University of Virginia. Algorithm Theoretical Basis Document (ATBD)."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2480","DOI":"10.3390\/s8042480","article-title":"Inter-Comparison of ASTER and MODIS Surface Reflectance and Vegetation Index Products for Synergistic Applications to Natural Resource Monitoring","volume":"8","author":"Miura","year":"2008","journal-title":"Sensors"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"L11402","DOI":"10.1029\/2006GL026457","article-title":"Three-Band Model for Noninvasive Estimation of Chlorophyll, Carotenoids, and Anthocyanin Contents in Higher Plant Leaves","volume":"33","author":"Gitelson","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0034-4257(98)00046-7","article-title":"Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll A+b, and Total Carotenoid Content in Eucalyptus Leaves","volume":"66","author":"Datt","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_66","unstructured":"Baret, F., Guyot, G., and Major, D. (1989, January 10\u201314). TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects on LAI and APAR Estimation. Proceedings of the 12th Canadian Symposium on Remote Sensing and IGARSS\u201989, Vancouver, BC, Canada."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1071\/BT98042","article-title":"Remote Sensing of Water Content in Eucalyptus Leaves","volume":"47","author":"Datt","year":"1999","journal-title":"Aust. J. Bot."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3744","DOI":"10.3390\/s8063744","article-title":"Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index","volume":"8","author":"Pu","year":"2008","journal-title":"Sensors"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TGRS.1984.350619","article-title":"A Physically-Based Transformation of Thematic Mapper Data\u2014The TM Tasseled Cap","volume":"GE-22","author":"Crist","year":"1984","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A Review of Vegetation Indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4113","DOI":"10.1080\/01431160410001698870","article-title":"Crop Yield Estimation by Satellite Remote Sensing","volume":"25","author":"Ferencz","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.2135\/cropsci2007.01.0031","article-title":"Relationships between Blue- and Red-Based Vegetation Indices and Leaf Area and Yield of Alfalfa","volume":"47","author":"Hancock","year":"2007","journal-title":"Crop Sci."},{"key":"ref_74","first-page":"183","article-title":"Estimation of Canopy-Average Surface-Specific Leaf Area Using Landsat TM Data","volume":"66","author":"Lymburne","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/014311698215919","article-title":"Spectral Indices for Estimating Photosynthetic Pigment Concentrations: A Test Using Senescent Tree Leaves","volume":"19","author":"Blackburn","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_76","unstructured":"Kauth, R., and Thomas, G. (1976). The Tasselled Cap\u2014A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by LANDSAT. Symposium on Machine Processing of Remotely Sensed Data, The Laboratory for Applications of Remote Sensing, Purdue University."},{"key":"ref_77","first-page":"77","article-title":"The Influence of Soil Salinity, Growth Form, and Leaf Moisture on the Spectral Reflectance of Spartina Alternifolia Canopies","volume":"49","author":"Hardinsky","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1016\/j.rse.2008.06.005","article-title":"Calibration and Validation of Hyperspectral Indices for the Estimation of Broadleaved Forest Leaf Chlorophyll Content, Leaf Mass per Area, Leaf Area Index and Leaf Canopy Biomass","volume":"112","author":"Soudani","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S1672-6308(07)60027-4","article-title":"New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice","volume":"14","author":"Wang","year":"2007","journal-title":"Rice Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_81","unstructured":"Al-Khaier, F. (2003). Soil Salinity Detection Using Satellite Remote Sensing. [Master\u2019s Thesis, Universiteit Twenten]."},{"key":"ref_82","unstructured":"Misra, P.N., Wheeler, S.G., and Oliver, R.E. (1977). Kauth-Thomas Brightness and Greenness Axes, NASA."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Gadal, S., Ouerghemmi, W., Gadal, S., and Ouerghemmi, W. (2019). Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030129"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"3503","DOI":"10.1080\/01431160110063779","article-title":"Sensitivity of Vegetation Indices to Substrate Brightness in Hyper-Arid Environment: The Makhtesh Ramon Crater (Israel) Case Study","volume":"22","author":"Schmidt","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.07.012","article-title":"Classification of Ponds from High-Spatial Resolution Remote Sensing: Application to Rift Valley Fever Epidemics in Senegal","volume":"106","author":"Lacaux","year":"2007","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4972\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:42:36Z","timestamp":1760168556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/4972"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,7]]},"references-count":87,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13244972"],"URL":"https:\/\/doi.org\/10.3390\/rs13244972","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,7]]}}}