2,447
Views
8
CrossRef citations to date
0
Altmetric
Research Article

Comparison of high-resolution NAIP and unmanned aerial vehicle (UAV) imagery for natural vegetation communities classification using machine learning approaches

ORCID Icon &
Article: 2177448 | Received 26 Sep 2022, Accepted 02 Feb 2023, Published online: 21 Feb 2023

References

  • Aaseng, N. E., Almendinger J. C, Dana R. P., Hanson D. S., Lee M. D., Rowe E. R., Rusterholz K. A., Wovcha D. S. 2011. “Minnesota’s Native Plant Community Classification: A Statewide Classification of Terrestrial and Wetland Vegetation Based on Numerical Analysis of Plot Data.” Biological Report 108: 1–24.
  • Adam, E., O. Mutanga, and D. Rugege. 2009. “Multispectral and Hyperspectral Remote Sensing for Identification and Mapping of Wetland Vegetation: A Review.” Wetlands Ecology and Management 18 (3): 281–296. doi:10.1007/s11273-009-9169-z.
  • Ahn, S. H., Jeong D.H., Kim M., Lee T. K., Kim H. K. 2022. “Prediction of Groundwater Quality Index to Assess Suitability for Drinking Purpose Using Averaged Neural Network and Geospatial Analysis.” Hydrology and Earth System Sciences Discussions 1–30.
  • Almeida, T. I. R. D., and D. Souza Filho. 2004. “Principal Component Analysis Applied to Feature-Oriented Band Ratios of Hyperspectral Data: A Tool for Vegetation Studies.” International Journal of Remote Sensing 25 (22): 5005–5023. doi:10.1080/01431160412331270812.
  • Altman, D. G. 1990. Practical Statistics for Medical Research. USA: CRC press.
  • Anderson, J. R. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Vol. 964. Washington, USA: US Government Printing Office.
  • Bailey, R. G. 2004. “Identifying Ecoregion Boundaries.” Environmental Management 34 (1): S14–26. doi:10.1007/s00267-003-0163-6.
  • Bailey, R. G. 2009. Ecosystem Geography: From Ecoregions to Sites. New York, USA: Springer Science & Business Media.
  • Bandos, T. V., L. Bruzzone, and G. Camps-Valls. 2009. “Classification of Hyperspectral Images with Regularized Linear Discriminant Analysis.” IEEE Transactions on Geoscience and Remote Sensing 47 (3): 862–873. doi:10.1109/TGRS.2008.2005729.
  • Bannari, A., Morin D., Bonn F., Huete A. 1995. “A Review of Vegetation Indices.” Remote Sensing Reviews 13 (1–2): 95–120. doi:10.1080/02757259509532298.
  • Bennasar, M., Hicks Y., Setchi R. 2015. “Feature Selection Using Joint Mutual Information Maximisation.” Expert Systems with Applications 42 (22): 8520–8532. doi:10.1016/j.eswa.2015.07.007.
  • Berhane, T. M., C. Lane, Q. Wu, B. Autrey, O. Anenkhonov, V. Chepinoga, and H. Liu. 2018. “Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory.” Remote Sens (Basel) 10 (4): 580. doi:10.3390/rs10040580.
  • Bettinger, P., Boston K., Siry J., Grebner D. L. 2016. Forest Management and Planning. Cambridge, MA: Academic press.
  • Bhatt, P. 2018. Mapping Coastal Wetland and Phragmites on the Hiawatha National Forest Using Unmanned Aerial System (UAS) Imagery: Proof of Concepts. Houghton, MI, USA: Michigan Technological University. doi:10.37099/mtu.dc.etdr/711.
  • Bhatt, P. 2022. 'FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS', Open Access Dissertation, Michigan Technological University. 10.37099/mtu.dc.etdr/1503
  • Bhatt, P., Edson C., Maclean A. 2022b. “Image Processing in Dense Forest Areas Using Unmanned Aerial System (UAS)“. Michigan Tech Publications. doi:10.37099/mtu.dc.michigantech-p/16366.
  • Bhatt, P., Maclean A., Dickinson Y., Kumar C. 2022a. “Fine-Scale Mapping of Natural Ecological Communities Using Machine Learning Approaches.” Remote Sensing 14 (3): 563. doi:10.3390/rs14030563.
  • Boser, B. E., Guyon I. M., Vapnik V. N. 1992. “A Training Algorithm for Optimal Margin Classifiers.” Paper presented at the Proceedings of the fifth annual workshop on Computational learning theory, July 27 - 29, 1992, Pennsylvania, Pittsburgh, USA.
  • Bradter, U., Thom T. J., Altringham J. D., Kunin W. E., Benton T. G. 2011. “Prediction of National Vegetation Classification Communities in the British Uplands Using Environmental Data at Multiple Spatial Scales, Aerial Images and the Classifier Random Forest.” The Journal of Applied Ecology 48 (4): 1057–1065. doi:10.1111/j.1365-2664.2011.02010.x.
  • Breiman, L. 1999. “Random Forests.” UC Berkeley TR567.
  • Breiman, L., and A. Cutler. 2007. “Random Forests-Classification Description.” Department of Statistics, Berkeley 2.
  • Brieman, L., 1984. Classification and Regression Tree Analysis. Boca, Raton, FL: CRC Press. doi:10.1201/9781315139470.
  • Brown, T., Meysembourg P., Host G. E. 2013. “Geospatial Modeling of Native Plant Communities of Minnesota’s Laurentian Mixed Forest.“ University of Minnesota Duluth. https://hdl.handle.net/11299/187333
  • Buchanan, G., Pearce‐Higgins J., Grant M., Robertson D., Waterhouse T. 2005. “Characterization of Moorland Vegetation and the Prediction of Bird Abundance Using Remote Sensing.” Journal of Biogeography 32 (4): 697–707. doi:10.1111/j.1365-2699.2004.01187.x.
  • Burton, T. A. 1993. “Averaged Neural Networks.” Neural Networks 6 (5): 677–680. doi:10.1016/S0893-6080(05)80111-X.
  • Clark, M. L., D. A. Roberts, and D. B. Clark. 2005. “Hyperspectral Discrimination of Tropical Rain Forest Tree Species at Leaf to Crown Scales.” Remote Sensing of Environment 96 (3–4): 375–398. doi:10.1016/j.rse.2005.03.009.
  • Cohen, J. G., Albert D. A., Slaughter B. S., Kost M. A. 2014. A Field Guide to the Natural Communities of Michigan. Michigan, USA: Michigan State University Press.
  • Cohen, J.G., Kost, M.A., Slaughter, B.S., Albert, D.A., Lincoln, J.M., Kortenhoven, A.P., Wilton, C.M., Enander, H.D., Korroch, K.M. 2020. Michigan Natural Community Classification [web application]. Michigan Natural Features Inventory, Michigan State University Extension, Lansing, Michigan. https://mnfi.anr.msu.edu/communities/classification
  • Congalton, R. G., and K. Green. 2019. Assessing the Accuracy of Remotely Sensed Data : Principles and Practices. Third Edition ed. Milton: Chapman and Hall/CRC.
  • Corcoran, J., Knight J. F., Gallant A. L. 2013. “Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota.” Remote Sensing 5 (7): 3212–3238. doi:10.3390/rs5073212.
  • Curtis, J. T. 1959. The Vegetation of Wisconsin: An Ordination of Plant Communities. Madison, WI: University of Wisconsin Pres.
  • Dronova, I., Gong P., Wang L., Zhong L. 2015. “Mapping Dynamic Cover Types in a Large Seasonally Flooded Wetland Using Extended Principal Component Analysis and Object-Based Classification.” Remote Sensing of Environment 158: 193–206. doi:10.1016/j.rse.2014.10.027.
  • Dunteman, G. H. 1989. In Lewis-Beck, Michael(ed) Basic Concepts of Principal Components Analysis. London: SAGE Publications Ltd. 15–22.
  • Fangfang, L., and B. Xiao. 2011. “Aquatic Vegetation Mapping Based on Remote Sensing Imagery: An Application to Honghu Lake.” Paper presented at the 2011 International Conference on Remote Sensing Nanjing, China, Environment and Transportation Engineering.
  • Feng, Q., Liu J., Gong J. 2015. “UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis.” Remote Sensing 7 (1): 1074–1094. doi:10.3390/rs70101074.
  • Fernández-Manso, A., Fernández-Manso O., Quintano C. 2016. “SENTINEL-2A Red-Edge Spectral Indices Suitability for Discriminating Burn Severity.” International Journal of Applied Earth Observation and Geoinformation 50: 170–175. doi:10.1016/j.jag.2016.03.005.
  • Franklin, S. E., and O. S. Ahmed. 2018. “Deciduous Tree Species Classification Using Object-Based Analysis and Machine Learning with Unmanned Aerial Vehicle Multispectral Data.” International Journal of Remote Sensing 39 (15–16): 5236–5245. doi:10.1080/01431161.2017.1363442.
  • Gómez, D., . 2019. “Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data.” Remote Sensing 11 (15): 1745. doi:10.3390/rs11151745.
  • Gong, P., Pu R., Yu B. 1997. “Conifer Species Recognition: An Exploratory Analysis of in situ Hyperspectral Data.” Remote Sensing of Environment 62 (2): 189–200. doi:10.1016/S0034-4257(97)00094-1.
  • Gunn, S. R. 1998. “Support Vector Machines for Classification and Regression.” ISIS Technical Report 14 (1): 5–16.
  • Guo, X., Wang M., Jia M., Wang W. 2021. “Estimating Mangrove Leaf Area Index Based on Red-Edge Vegetation Indices: A Comparison Among UAV, WorldView-2 and Sentinel-2 Imagery.” International Journal of Applied Earth Observation and Geoinformation 103: 102493. doi:10.1016/j.jag.2021.102493.
  • Guyon, I., and A. Elisseeff. 2003. “An Introduction to Variable and Feature Selection.” Journal of Machine Learning Research 3 (Mar): 1157–1182.
  • Hall-Beyer, M. 2017. “Practical Guidelines for Choosing GLCM Textures to Use in Landscape Classification Tasks Over a Range of Moderate Spatial Scales.” International Journal of Remote Sensing 38 (5): 1312–1338. doi:10.1080/01431161.2016.1278314.
  • Hansen, M. C., R. S. Defries, J. R. G. Townshend, and R. Sohlberg. 2010. “Global Land Cover Classification at 1 Km Spatial Resolution Using a Classification Tree Approach.” International Journal of Remote Sensing 21 (6–7): 1331–1364. doi:10.1080/014311600210209.
  • Hanson, D. S., and B. Hargrave. 1996. “Development of a Multilevel Ecological Classification System for the State of Minnesota.” Environmental Monitoring and Assessment 39 (1): 75–84. doi:10.1007/BF00396137.
  • Haralick, R. M., K. Shanmugam, and I. Dinstein. 1973. “Textural Features for Image Classification.” IEEE Transactions on Systems, Man, and Cybernetics 6: 610–621. doi:10.1109/TSMC.1973.4309314.
  • Hayes, M. M., S. N. Miller, and M. A. Murphy. 2014. “High-Resolution Landcover Classification Using Random Forest.” Remote Sensing Letters 5 (2): 112–121. doi:10.1080/2150704X.2014.882526.
  • Hill, M. J. 2013. “Vegetation Index Suites as Indicators of Vegetation State in Grassland and Savanna: An Analysis with Simulated SENTINEL 2 Data for a North American Transect.” Remote Sensing of Environment 137: 94–111. doi:10.1016/j.rse.2013.06.004.
  • Hogland, J., N. Anderson, J. St. Peter, J. Drake, and P. Medley. 2018. “Mapping Forest Characteristics at Fine Resolution Across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data.” ISPRS International Journal of Geo-Information 7 (4). doi:10.3390/ijgi7040140.
  • Homer, C., Huang C., Yang L., Wylie B. K., Coan M. 2004. “Development of a 2001 National Land-Cover Database for the United States.” Photogrammetric Engineering & Remote Sensing 70 (7): 829–840. doi:10.14358/PERS.70.7.829.
  • Homoya, M. A., Abrell D. B., Aldrich J. A., Post T. W. 1984. “The Natural Regions of Indiana.” Paper presented at the Proceedings of the Indiana Academy of Science.
  • Hoque, N., Bhattacharyya D. K., Kalita J. K. 2014. “MIFS-ND: A Mutual Information-Based Feature Selection Method.” Expert Systems with Applications 41 (14): 6371–6385. doi:10.1016/j.eswa.2014.04.019.
  • Huang, C., Davis L. S., Townshend J. R. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23 (4): 725–749. doi:10.1080/01431160110040323.
  • Hughes, G. 1968. “On the Mean Accuracy of Statistical Pattern Recognizers.” IEEE Transactions on Information Theory 14 (1): 55–63. doi:10.1109/TIT.1968.1054102.
  • Hyvärinen, A., and E. Oja. 2000. “Independent Component Analysis: Algorithms and Applications.” Neural Networks 13 (4–5): 411–430. doi:10.1016/S0893-6080(00)00026-5.
  • Inventory, Florida Natural Areas. 1990. Guide to the Natural Communities of Florida. Florida, USA: Florida Natural Areas Inventory and Florida Department of Natural Resources.
  • Jackson, M. T. 1979. “A Classification of Indiana Plant Communities.” Paper presented at the Proceedings of the Indiana Academy of Science.
  • Jensen, J. R. 2015. Introductory Digital Image Processing: A Remote Sensing Perspective, 4th edition. Glenview, IL, USA: Pearson.
  • Jerome, D. S. 2006. Landforms of the Upper Peninsula, Michigan. Marquette, MI, USA: Natural Resources Conservation Service. In, 56.
  • Juel, A., Groom G. B., Svenning J. C., Ejrnaes R. 2015. “Spatial Application of Random Forest Models for Fine-Scale Coastal Vegetation Classification Using Object Based Analysis of Aerial Orthophoto and DEM Data.” International Journal of Applied Earth Observation and Geoinformation 42: 106–114. doi:10.1016/j.jag.2015.05.008.
  • Kavhu, B., Mashimbye Z. E., Luvuno L. 2021. “Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/cover Classification Using Deep Learning and Machine Learning.” Remote Sensing 13 (24): 5054. doi:10.3390/rs13245054.
  • Kearsley, J. B. 1999. “Inventory and Vegetation Classification of Floodplain Forest Communities in Massachusetts.” Rhodora, Vol. 101, No. 906, 105–135. https://www.jstor.org/stable/23313353
  • Kohavi, R. 1995. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” Paper presented at the Ijcai.
  • Kuhn, M. 2015. Caret: Classification and Regression Training. Astrophysics Source Code Library.
  • Kuhn, M., P. Balbo, M. E. Banker, R. M. Czerwinski, M. Kuhn, T. S. Maurer, J. -B. Telliez, F. Vincent, and A. J. Wittwer. 2020. The Advantages of Describing Covalent Inhibitor in vitro Potencies by IC50 at a Fixed Time Point. IC50 Determination of Covalent Inhibitors Provides Meaningful Data to Medicinal Chemistry for SAR Optimization. The R Journal29. 10.1016/j.bmc.2020.115865
  • Kulkarni, A. D., and B. Lowe. 2016. “Random Forest Algorithm for Land Cover Classification. Computer Science Faculty Publications and Presentations. http://hdl.handle.net/10950/341
  • Kumar, C., S. Chatterjee, T. Oommen, and A. Guha. 2020. “Automated Lithological Mapping by Integrating Spectral Enhancement Techniques and Machine Learning Algorithms Using AVIRIS-NG Hyperspectral Data in Gold-Bearing Granite-Greenstone Rocks in Hutti, India.” International Journal of Applied Earth Observation and Geoinformation 86: 102006. doi:10.1016/j.jag.2019.102006.
  • Landis, J. R., and G. G. Koch. 1977. “The Measurement of Observer Agreement for Categorical Data.” Biometrics 33: 159–174. doi:10.2307/2529310.
  • Lane, C., Liu H., Autrey B. C., Anenkhonov O. A., Chepinoga V. V., Wu Q. 2014. “Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach.” Remote Sensing 6 (12): 12187–12216. doi:10.3390/rs61212187.
  • Lasaponara, R. 2006. “On the Use of Principal Component Analysis (PCA) for Evaluating Interannual Vegetation Anomalies from SPOT/VEGETATION NDVI Temporal Series.” Ecological Modelling 194 (4): 429–434. doi:10.1016/j.ecolmodel.2005.10.035.
  • Lillesand, T., Kiefer R. W., Chipman J. 2015. Remote Sensing and Image Interpretation, Seventh Edition. Hoboken, NJ, USA: John Wiley & Sons.
  • Lindenmayer, D. B., and J. F. Franklin. 2002. Conserving Forest Biodiversity: A Comprehensive Multiscaled Approach. Washington DC, USA: Island press.
  • Mahdianpari, M., Salehi B., Mohammadimanesh F., Brisco B., Mahdavi S., Amani M., Granger J. E. 2018. “Fisher Linear Discriminant Analysis of Coherency Matrix for Wetland Classification Using PolSar Imagery.” Remote Sensing of Environment 206: 300–317. doi:10.1016/j.rse.2017.11.005.
  • Mahesh, P., and P. M. Mather. 2003. “An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification.” Remote Sensing of Environment 86 (4): 554–565. doi:10.1016/s0034-4257(03)00132-9.
  • Maillard, P. 2003. “Comparing Texture Analysis Methods Through Classification.” Photogrammetric Engineering & Remote Sensing 69 (4): 357–367. doi:10.14358/PERS.69.4.357.
  • Maxwell, A. E., M. P. Strager, T. A. Warner, C. A. Ramezan, A. N. Morgan, and C. E. Pauley. 2019. “Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations.” Remote Sensing 11 (12). doi:10.3390/rs11121409.
  • Maxwell, A. E., T. A. Warner, and F. Fang. 2018. “Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review.” International Journal of Remote Sensing 39 (9): 2784–2817. doi:10.1080/01431161.2018.1433343.
  • Maxwell, A. E., T. A. Warner, B. C. Vanderbilt, and C. A. Ramezan. 2017. “Land Cover Classification and Feature Extraction from National Agriculture Imagery Program (NAIP) Orthoimagery: A Review.” Photogrammetric Engineering & Remote Sensing 83 (11): 737–747. doi:10.14358/PERS.83.10.737.
  • McHugh, M. L. 2012. “Interrater Reliability: The Kappa Statistic.” Biochemia Medica 22 (3): 276–282. doi:10.11613/BM.2012.031.
  • Momm, H. G., R. ElKadiri, and W. Porter. 2020. “Crop-Type Classification for Long-Term Modeling: An Integrated Remote Sensing and Machine Learning Approach.” Remote Sensing 12 (3): 449. doi:10.3390/rs12030449.
  • Monahan, W. B., C. E. Arnspiger, P. Bhatt, Z. An, F. J. Krist, T. Liu, R. P. Richard, 2022. “A Spectral Three-Dimensional Color Space Model of Tree Crown Health.” PLoS One 17 (10): e0272360. doi:10.1371/journal.pone.0272360.
  • Monahan, W., C. Arnspiger, P. Bhatt, A. Zhongming, F. Krist Jr., T. Liu, R. Richard, 2022. Data and Code From: A Spectral Three-Dimensional Color Space Model of Tree Crown Health. DRYAD. doi:10.5061/dryad.wm37pvmpp.
  • Mountrakis, G., Im J., Ogole C. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
  • Munyati, C. 2004. “Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on the Kafue Flats, Zambia.” Geocarto International 19 (3): 11–22. doi:10.1080/10106040408542313.
  • Noss, R. F. 1987. “From Plant Communities to Landscapes in Conservation Inventories: A Look at the Nature Conservancy (USA).” Biological Conservation 41 (1): 11–37. doi:10.1016/0006-3207(87)90045-0.
  • Prasad, A. M., L. R. Iverson, and A. Liaw. 2006. “Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction.” Ecosystems (New York, NY) 9 (2): 181–199. doi:10.1007/s10021-005-0054-1.
  • Ripley, B. D. 2007. Pattern Recognition and Neural Networks. Cambridge, England: Cambridge university press.
  • Robichaud, C. D., and R. C. Rooney. 2017. “Long-Term Effects of a Phragmites Australis Invasion on Birds in a Lake Erie Coastal Marsh.” Journal of Great Lakes Research 43 (3): 141–149. doi:10.1016/j.jglr.2017.03.018.
  • Rodriguez-Galiano, V. F., Chica-Olmo M., Abarca-Hernandez F., Atkinson P. M., Jeganathan C. 2012b. “Random Forest Classification of Mediterranean Land Cover Using Multi-Seasonal Imagery and Multi-Seasonal Texture.” Remote Sensing of Environment 121: 93–107. doi:10.1016/j.rse.2011.12.003.
  • Rodriguez-Galiano, V. F., Ghimire B., Rogan J., Chica-Olmo M., Rigol-Sanchez J. P. 2012a. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
  • Rouse, J. W., Haas R. H., Schell J. A., Deering D. W. 1974. “Monitoring Vegetation Systems in the Great Plains with ERTS.” NASA Special Publication 351: 309.
  • Ruiliang, P., and S. Landry. 2012. “A Comparative Analysis of High Spatial Resolution IKONOS and WorldView-2 Imagery for Mapping Urban Tree Species.” Remote Sensing of Environment 124: 516–533. doi:10.1016/j.rse.2012.06.011.
  • Schafale, M. P., and A. S. Weakley. 1990. “Classification of the Natural Communities of North Carolina.” In Third Approximation. North Carolina Department of Environment, Health, and Natural Resources, Division of Parks and Recreation. Raleigh, NC: Natural Heritage Program, Raleigh 326.
  • Schulz, K., Hänsch R., Sörgel U. 2018. “Machine Learning Methods for Remote Sensing Applications: An Overview.” Earth Resources and Environmental Remote Sensing/GIS Applications IX 10790: 1079002.
  • Shah, C. A., Anderson I., Gou Z., Hao S., Leason A. 2007b. “Towards the Development of Next Generation Remote Sensing Technology–Erdas Imagine Incorporates a Higher Order Feature Extraction Technoque Based on Ica.” Paper presented at the Proceedings of the ASPRS 2007 Annual Conference May 7-11, 2007 Tampa, Florida.
  • Shah, C. A., Arora M. K., Robila S. A., Varshney P. K. 2002. “ICA Mixture Model Based Unsupervised Classification of Hyperspectral Imagery.” Paper presented at the Applied Imagery Pattern Recognition Workshop, 2002. Proceedings Washington, DC, USA.
  • Shah, C. A., Varshney P. K., Arora M. K. 2007a. “ICA Mixture Model Algorithm for Unsupervised Classification of Remote Sensing Imagery.” International Journal of Remote Sensing 28 (8): 1711–1731. doi:10.1080/01431160500462121.
  • Shang, J., Liu, J., Ma, B., Zhao, T., Jiao, X., Geng, X., Huffman, T., Kovacs, J., Walters, D., et al. 2015. “Mapping Spatial Variability of Crop Growth Conditions Using RapidEye Data in Northern Ontario, Canada.” Remote Sensing of Environment 168: 113–125. doi:10.1016/j.rse.2015.06.024.
  • Shuang, L., L. Xu, Y. Jing, H. Yin, X. Li, and X. Guan. 2021. “High-Quality Vegetation Index Product Generation: A Review of NDVI Time Series Reconstruction Techniques.” International Journal of Applied Earth Observation and Geoinformation 105: 102640. doi:10.1016/j.jag.2021.102640.
  • Sloan, J. L. 2017. National Unmanned Aircraft Systems Project Office: U.S. Geological Survey. Denver, Colorado: Agisoft PhotoScan Workflow.
  • Sperduto, D. D., and W. F. Nichols. 2004. Natural Communities of New Hampshire: UNH Cooperative Extension. Durham, NH: New Hampshire Natural Heritage Bureau.
  • Taghizadeh-Mehrjardi, R., Schmidt, K., Amirian-Chakan, A., Rentschler, T., Zeraatpisheh, M., Sarmadian, F., Valavi, R., Davatgar, N., Behrens, T., Scholten, T., et al. 2020. “Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space.” Remote Sensing 12 (7): 1095. doi:10.3390/rs12071095.
  • Tassi, A., and M. Vizzari. 2020. “Object-Oriented Lulc Classification in Google Earth Engine Combining Snic, Glcm, and Machine Learning Algorithms.” Remote Sensing 12 (22): 3776. doi:10.3390/rs12223776.
  • Taylor, J. C., Brewer, T.R., Bird, A.C., et al. 2000. “Monitoring Landscape Change in the National Parks of England and Wales Using Aerial Photo Interpretation and GIS.” International Journal of Remote Sensing 21 (13–14): 2737–2752. doi:10.1080/01431160050110269.
  • Team, R. C. 2013. “R: A Language and Environment for Statistical Computing.“ R Foundation for Statistical Computing. https://www.r-project.org/index.html
  • Tucker, C. J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8 (2): 127–150. doi:10.1016/0034-4257(79)90013-0.
  • USDA. 2022. “2018 Michigan Image Dates.” USDA, Accessed 8 July 2022. https://naip-image-dates-usdaonline.hub.arcgis.com/datasets/8abca94b0db34143b3b5cdd2c99e7fe9_0/about.
  • Van Etten Adam, Lindenbaum Dave, Bacastow Todd M. 2018. “Spacenet: A Remote Sensing Dataset and Challenge Series.” arXiv preprint arXiv: 180701232 doi:https://doi.org/10.48550/arXiv.1807.01232.
  • Vapnik, V. 2013. “The Nature of Statistical Learning theory: Springer Science & Business Media Springer 1–334 .”
  • Vogelmann, J. E., Howard, S.M., Yang, L., Larson, C.R., Wylie, B.K., Van Driel, N. 2001. “Completion of the 1990s National Land Cover Data Set for the Conterminous United States from Landsat Thematic Mapper Data and Ancillary Data Sources.” Photogrammetric Engineering and Remote Sensing 67: 650-655, 657-659, 661–662.
  • White, M. A., and G. E. Host. 2008. “Forest Disturbance Frequency and Patch Structure from Pre-European Settlement to Present in the Mixed Forest Province of Minnesota, USA.” Canadian Journal of Forest Research 38 (8): 2212–2226. doi:10.1139/X08-065.
  • Whitlatch, R. B. 1977. “Seasonal Changes in the Community Structure of the Macrobenthos Inhabiting the Intertidal Sand and Mud Flats of Barnstable Harbor, Massachusetts.” The Biological Bulletin 152 (2): 275–294. doi:10.2307/1540565.
  • Whittaker, R. H. 1962. “Classification of Natural Communities.” The Botanical Review 28 (1): 1–239. doi:10.1007/BF02860872.
  • Wilson, D. C., and A. R. Ek. 2017. “Imputing Plant Community Classifications for Forest Inventory Plots.” Ecological Indicators 80: 327–336. doi:10.1016/j.ecolind.2017.04.043.
  • Wolf, A. F. 2012. “Using WorldView-2 Vis-NIR Multispectral Imagery to Support Land Mapping and Feature Extraction Using Normalized Difference Index Ratios.” Paper presented at the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII.
  • Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S.M., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M. 2018. “A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies.” ISPRS Journal of Photogrammetry and Remote Sensing 146: 108–123. doi:10.1016/j.isprsjprs.2018.09.006.
  • Zhang, W., Liu, H., Wu, W., Zhan, L., Wei, J. 2020. “Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability.” Remote Sensing 12 (10): 1620. doi:10.3390/rs12101620.
  • Zheng, C., Abd-Elrahman A., Whitaker V. 2021. “Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming.” Remote Sensing 13 (3): 531. doi:10.3390/rs13030531.
  • Zhong, L., Hu L., Zhou H. 2019. “Deep Learning Based Multi-Temporal Crop Classification.” Remote Sensing of Environment 221: 430–443. doi:10.1016/j.rse.2018.11.032.
  • Zhu, Y., Liu K., Liu L., Myint S. W., Wang S., Liu H., He Z. 2017. “Exploring the Potential of Worldview-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms.” Remote Sensing 9 (10): 1060. doi:10.3390/rs9101060.