{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T07:35:42Z","timestamp":1772609742360,"version":"3.50.1"},"reference-count":85,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of South Africa","award":["142438"],"award-info":[{"award-number":["142438"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rangelands represent about 25% of the Earth\u2019s land surface but are under severe pressure. Rangeland degradation is a gradually increasing global environmental problem, resulting in temporary or permanent loss of ecosystem functions. Ecological rangeland studies aim to determine the productivity of rangelands as well as the severity of their degradation. Rigorous in situ assessments comprising visual identification of plant species are required as such assessments are perceived to be the most accurate way of monitoring rangeland degradation. However, in situ assessments are expensive and time-consuming exercises, especially when carried out over large areas. In situ assessments are also limited to areas that are accessible. This study aimed to evaluate the effectiveness of multispectral (MS) and hyperspectral (HS) remotely sensed, unmanned aerial vehicle (UAV)-based data and machine learning (random forest) methods to differentiate between 15 dominant Nama Karoo plant species to aid ecological impact surveys. The results showed that MS imagery is unsuitable, as classification accuracies were generally low (37.5%). In contrast, much higher classification accuracies (&gt;70%) were achieved when the HS imagery was used. The narrow bands between 398 and 430 nanometres (nm) were found to be vital for discriminating between shrub and grass species. Using in situ Analytical Spectral Device (ASD) spectroscopic data, additional important wavebands between 350 and 400 nm were identified, which are not covered by either the MS or HS remotely sensed data. Using feature selection methods, 12 key wavelengths were identified for discriminating among the plant species with accuracies exceeding 90%. Reducing the dimensionality of the ASD data set to the 12 key bands increased classification accuracies from 84.8% (all bands) to 91.7% (12 bands). The methodology developed in this study can potentially be used to carry out UAV-based ecological assessments over large and inaccessible areas typical of Karoo rangelands.<\/jats:p>","DOI":"10.3390\/rs16203869","type":"journal-article","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T06:46:52Z","timestamp":1729234012000},"page":"3869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Spectral Discrimination of Common Karoo Shrub and Grass Species Using Spectroscopic Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Christiaan Johannes","family":"Harmse","sequence":"first","affiliation":[{"name":"Northern Cape Department of Agriculture, Environmental Affairs, Land Reform and Rural Development, Eiland Research Station, Upington 8801, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5631-0206","authenticated-orcid":false,"given":"Adriaan","family":"van Niekerk","sequence":"additional","affiliation":[{"name":"Department of Geography & Environmental Studies, Stellenbosch University, Stellenbosch 7602, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1111\/j.1365-2494.2010.00780.x","article-title":"An International Terminology for Grazing Lands and Grazing Animals","volume":"66","author":"Allen","year":"2011","journal-title":"Grass Forage Sci."},{"key":"ref_2","first-page":"37","article-title":"Rangeland Degradation and Restoration: A Global Perspective","volume":"1","author":"Zerga","year":"2015","journal-title":"Point J. Agric. Biotechnol. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.agee.2005.12.015","article-title":"Soil Response to Long-Term Grazing in the Northern Great Plains of North America","volume":"115","author":"Liebig","year":"2006","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_4","first-page":"246","article-title":"Handbook for the Field Assessment of Land Degradation. London: Earthscan In (O\u2019Higgin, RC, Eds), Savannah Woodland Degradation Assessments in Ghana: Integrating Ecological Indicators with Local Perceptions","volume":"3","author":"Stocking","year":"2001","journal-title":"Earth Environ."},{"key":"ref_5","unstructured":"Schwilch, G., Hessel, R., and Verzandvoort, S. (2012). Desire for Greener Land. Options for Sustainable Land Management in Drylands, CTA\u2014Technical Centre for Agricultural and Rural Cooperation."},{"key":"ref_6","unstructured":"Nachtergaele, F., Petri, M., Biancalani, R., van Lynden, G., van Velthuizen, H., and Bloise, M. (2010). Global Land Degradation Information System (GLADIS). Beta Version. An Information Database for Land Degradation Assessment at Global Level, Food and Agriculture Organization of the United Nations (FAO). Land Degradation Assessment in Drylands Technical Report."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Von Braun, J., and Gatzweiler, F.W. (2014). Marginality: Addressing the Nexus of Poverty, Exclusion and Ecology, Springer Nature.","DOI":"10.1007\/978-94-007-7061-4"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Barbier, E.B., and Hochard, J.P. (2016). Does Land Degradation Increase Poverty in Developing Countries?. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0152973"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41893-018-0155-4","article-title":"Land Degradation and Poverty","volume":"1","author":"Barbier","year":"2018","journal-title":"Nat. Sustain."},{"key":"ref_10","unstructured":"Hoffmann, T., Todd, S., Ntshona, Z., and Turner, S. (2014). Land Degradation in South Africa, University of Cape Town."},{"key":"ref_11","unstructured":"Middleton, N., and Thomas, D. (1997). World Atlas of Desertification, Arnold, Hodder Headline, PLC. [2nd ed.]."},{"key":"ref_12","unstructured":"Hoffman, T., and Ashwell, A. (2001). Nature Divided: Land Degradation in South Africa, University of Cape Town Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1002\/pan3.10260","article-title":"Land Degradation in South Africa: Justice and Climate Change in Tension","volume":"3","author":"Mani","year":"2021","journal-title":"People Nat."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1016\/j.jaridenv.2009.04.022","article-title":"Climate and Environmental Change in Arid Central Asia: Impacts, Vulnerability, and Adaptations","volume":"73","author":"Lioubimtseva","year":"2009","journal-title":"J. Arid Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/sajs.2014\/20130353","article-title":"Observed and Modelled Trends in Rainfall and Temperature for South Africa: 1960\u20132010","volume":"110","author":"MacKellar","year":"2014","journal-title":"S. Afr. J. Sci."},{"key":"ref_16","unstructured":"Barry, P.S., Mendenhall, J., Jarecke, P., Folkman, M., Pearlman, J., and Markham, B. (2002, January 24\u201328). EO-1 Hyperion Hyperspectral Aggregation and Comparison with EO-1 Advanced Land Imager and Landsat 7 ETM+. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.foreco.2006.12.015","article-title":"Comparison of Image and Rapid Field Assessments of Riparian Zone Condition in Australian Tropical Savannas","volume":"240","author":"Johansen","year":"2007","journal-title":"For. Ecol. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110652","DOI":"10.1016\/j.jenvman.2020.110652","article-title":"Using Remote Sensing to Characterize Riparian Vegetation: A Review of Available Tools and Perspectives for Managers","volume":"267","author":"Huylenbroeck","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mureriwa, N., Adam, E., Sahu, A., and Tesfamichael, S. (2016). Examining the Spectral Separability of Prosopis Glandulosa from Co-Existent Species Using Field Spectral Measurement and Guided Regularized Random Forest. Remote Sens., 8.","DOI":"10.3390\/rs8020144"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cris\u00f3stomo de Castro Filho, H., Ab\u00edlio de Carvalho J\u00fanior, O., Ferreira de Carvalho, O.L., Pozzobon de Bem, P., dos Santos de Moura, R., Olino de Albuquerque, A., Silva, C.R., Ferreira, P.H.G., Guimar\u00e3es, R.F., and Gomes, R.A.T. (2020). Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12162655"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyypp\u00e4, J., Saari, H., P\u00f6l\u00f6nen, I., and Imai, N.N. (2017). Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sens., 9.","DOI":"10.3390\/rs9030185"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1111\/jbi.12199","article-title":"Modelling Species Distributions with Remote Sensing Data: Bridging Disciplinary Perspectives","volume":"40","author":"Cord","year":"2013","journal-title":"J. Biogeogr."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, D., Meng, R., Morrison, B.D., McMahon, A., Hantson, W., Hayes, D.J., Breen, A.L., Salmon, V.G., and Serbin, S.P. (2020). A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra. Remote Sens., 12.","DOI":"10.3390\/rs12162638"},{"key":"ref_24","first-page":"68","article-title":"Detection, Quantification and Monitoring of Prosopis in the Northern Cape Province of South Africa Using Remote Sensing and GIS","volume":"2","author":"Kotze","year":"2013","journal-title":"S. Afr. J. Geomat."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/S0034-4257(98)00078-9","article-title":"Textural Analysis of Historical Aerial Photography to Characterize Woody Plant Encroachment in South African Savanna","volume":"66","author":"Hudak","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"29","DOI":"10.5194\/isprsarchives-XL-2-29-2014","article-title":"Bush encroachment monitoring using multi-temporal landsat data and random forests","volume":"10","author":"Symeonakis","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","first-page":"89","article-title":"Automatic Classification of Google Earth Images for a Larger Scale Monitoring of Bush Encroachment in South Africa","volume":"50","author":"Ludwig","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","first-page":"209","article-title":"Wavelet-Based Detection of Bush Encroachment in a Savanna Using Multi-Temporal Aerial Photographs and Satellite Imagery","volume":"35","author":"Shekede","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111624","DOI":"10.1016\/j.rse.2019.111624","article-title":"Mapping Cropping Intensity in China Using Time Series Landsat and Sentinel-2 Images and Google Earth Engine","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat Land Cover Classification Methods: A Review. Remote Sens., 9.","DOI":"10.3390\/rs9090967"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A Survey of Image Classification Methods and Techniques for Improving Classification Performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"AbdelRahman, M.A.E., Afifi, A.A., and Scopa, A. (2021). A Time Series Investigation to Assess Climate Change and Anthropogenic Impacts on Quantitative Land Degradation in the North Delta, Egypt. ISPRS Int. J. Geoinf., 11.","DOI":"10.3390\/ijgi11010030"},{"key":"ref_33","first-page":"352","article-title":"Field Hyperspectral Data Analysis for Discriminating Spectral Behavior of Tea Plantations under Various Management Practices","volume":"23","author":"Kumar","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","first-page":"102008","article-title":"Is It Possible to Discern Striga Weed (Striga Hermonthica) Infestation Levels in Maize Agro-Ecological Systems Using in-Situ Spectroscopy?","volume":"85","author":"Mudereri","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the Mean Accuracy of Statistical Pattern Recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"16091","DOI":"10.3390\/rs71215820","article-title":"Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Walsh, O.S., Marshall, J.M., Nambi, E., Jackson, C.A., Ansah, E.O., Lamichhane, R., McClintick-Chess, J., and Bautista, F. (2023). Wheat Yield and Protein Estimation with Handheld and Unmanned Aerial Vehicle-Mounted Sensors. Agronomy, 13.","DOI":"10.3390\/agronomy13010207"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Neupane, K., and Baysal-Gurel, F. (2021). Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review. Remote Sens., 13.","DOI":"10.3390\/rs13193841"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"963","DOI":"10.5194\/isprs-archives-XLI-B1-963-2016","article-title":"Light-Weight Multispectral UAV Sensors and Their Capabilities for Predicting Grain Yield and Detecting Plant Diseases","volume":"41","author":"Nebiker","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Song, B., and Park, K. (2020). Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index. Remote Sens., 12.","DOI":"10.3390\/rs12030387"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2015.03.019","article-title":"An Automatic Object-Based Method for Optimal Thresholding in UAV Images: Application for Vegetation Detection in Herbaceous Crops","volume":"114","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Carvajal-Ram\u00edrez, F., da Silva, J.R.M., Ag\u00fcera-Vega, F., Mart\u00ednez-Carricondo, P., Serrano, J., and Moral, F.J. (2019). Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV. Remote Sens., 11.","DOI":"10.3390\/rs11090993"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Guan, S., Fukami, K., Matsunaka, H., Okami, M., Tanaka, R., Nakano, H., Sakai, T., Nakano, K., Ohdan, H., and Takahashi, K. (2019). Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs. Remote Sens., 11.","DOI":"10.3390\/rs11020112"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Jaramillo, V., Fries, A., and Bendix, J. (2019). AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV). Remote Sens., 11.","DOI":"10.3390\/rs11121413"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.1080\/01431161.2016.1264030","article-title":"Utility of Unmanned Aerial Vehicles for Mapping Invasive Plant Species: A Case Study on Yellow Flag Iris (Iris pseudacorus L.)","volume":"38","author":"Hill","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"de Castro, A.I., Pe\u00f1a, J.M., Torres-S\u00e1nchez, J., Jim\u00e9nez-Brenes, F.M., Valencia-Gredilla, F., Recasens, J., and L\u00f3pez-Granados, F. (2020). Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture. Remote Sens., 12.","DOI":"10.3390\/rs12010056"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dash, J.P., Watt, M.S., Paul, T.S.H., Morgenroth, J., and Pearse, G.D. (2019). Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11151812"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Marques, P., P\u00e1dua, L., Ad\u00e3o, T., Hru\u0161ka, J., Peres, E., Sousa, A., and Sousa, J.J. (2019). UAV-Based Automatic Detection and Monitoring of Chestnut Trees. Remote Sens., 11.","DOI":"10.3390\/rs11070855"},{"key":"ref_49","first-page":"55","article-title":"Remote Sensing as a Tool for Monitoring Plant Invasions: Testing the Effects of Data Resolution and Image Classification Approach on the Detection of a Model Plant Species Heracleum Mantegazzianum (Giant Hogweed)","volume":"25","author":"Pergl","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_50","first-page":"324","article-title":"Nama-Karoo Biome. The vegetation of South Africa, Lesotho and Swaziland","volume":"19","author":"Mucina","year":"2006","journal-title":"Strelitzia"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pfitzner, K., Bartolo, R., Whiteside, T., Loewensteiner, D., and Esparon, A. (2021). Hyperspectral Monitoring of Non-Native Tropical Grasses over Phenological Seasons. Remote Sens., 13.","DOI":"10.3390\/rs13040738"},{"key":"ref_52","unstructured":"Meyer, T.C. (1992). Weikapasiteitstudies Op Veld in Die Ariede Karoo. [Master\u2019s Thesis, University of the Orange Free State]. unpublished."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"612","DOI":"10.2307\/2404657","article-title":"Vegetation Changes (1949\u201371) in a Semi-Arid, Grassy Dwarf Shrubland in the Karoo, South Africa: Influence of Rainfall Variability and Grazing by Sheep","volume":"32","author":"Roux","year":"1995","journal-title":"J. Appl. Ecol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"100307","DOI":"10.1016\/j.ancene.2021.100307","article-title":"Anthropogenic Impacts and Implications for Ecological Restoration in the Karoo, South Africa","volume":"36","author":"Milton","year":"2021","journal-title":"Anthropocene"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.jaridenv.2018.02.005","article-title":"Impact of Sheep Grazing Intensity on Vegetation at the Arid Karoo Stocking Rate Trial after 27 Years, Carnarvon, South Africa","volume":"155","year":"2018","journal-title":"J. Arid Environ."},{"key":"ref_56","unstructured":"(2012). Trimble Trimble R8 GNSS System. Trimble Datasheet, Trimble Navigation Limited."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"096033","DOI":"10.1117\/1.JRS.9.096033","article-title":"Exploring the Potential of in Situ Hyperspectral Data and Multivariate Techniques in Discriminating Different Fertilizer Treatments in Grasslands","volume":"9","author":"Sibanda","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Olsson, P.-O., Vivekar, A., Adler, K., Garcia Millan, V.E., Koc, A., Alamrani, M., and Eklundh, L. (2021). Radiometric Correction of Multispectral Uas Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor. Remote Sens., 13.","DOI":"10.3390\/rs13040577"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"055006","DOI":"10.1088\/1748-9326\/abf464","article-title":"Multiscale Mapping of Plant Functional Groups and Plant Traits in the High Arctic Using Field Spectroscopy, UAV Imagery and Sentinel-2A Data","volume":"16","author":"Thomson","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the Sampling Error of the Difference between Correlated Proportions or Percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.compag.2017.11.027","article-title":"A Novel Approach for Vegetation Classification Using UAV-Based Hyperspectral Imaging","volume":"144","author":"Ishida","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yan, Y., Deng, L., Liu, X., and Zhu, L. (2019). Application of UAV-Based Multi-Angle Hyperspectral Remote Sensing in Fine Vegetation Classification. Remote Sens., 11.","DOI":"10.3390\/rs11232753"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5236","DOI":"10.1080\/01431161.2017.1363442","article-title":"Deciduous Tree Species Classification Using Object-Based Analysis and Machine Learning with Unmanned Aerial Vehicle Multispectral Data","volume":"39","author":"Franklin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Gini, R., Sona, G., Ronchetti, G., Passoni, D., and Pinto, L. (2018). Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures. ISPRS Int. J. Geoinf., 7.","DOI":"10.3390\/ijgi7080315"},{"key":"ref_66","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_67","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1007\/s11119-017-9528-3","article-title":"Weed Detection by UAV: Simulation of the Impact of Spectral Mixing in Multispectral Images","volume":"18","author":"Louargant","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Lisein, J., Michez, A., Claessens, H., and Lejeune, P. (2015). Discrimination of Deciduous Tree Species from Time Series of Unmanned Aerial System Imagery. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0141006"},{"key":"ref_69","first-page":"88","article-title":"Mapping of Riparian Invasive Species with Supervised Classification of Unmanned Aerial System (UAS) Imagery","volume":"44","author":"Michez","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Weil, G., Lensky, I.M., Resheff, Y.S., and Levin, N. (2017). Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection. Remote Sens., 9.","DOI":"10.3390\/rs9111130"},{"key":"ref_71","first-page":"255","article-title":"Section of Toxicology on Geigeria ornativa","volume":"54","author":"Joubert","year":"1983","journal-title":"J. S. Afr. Vet. Assoc."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.2307\/3869507","article-title":"Ultraviolet Radiation and Plants: Burning Questions","volume":"4","author":"Stapleton","year":"1992","journal-title":"Plant Cell"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1034\/j.1399-3054.2003.1170101.x","article-title":"Molecular Events Following Perception of Ultraviolet-B Radiation by Plants","volume":"117","author":"Strid","year":"2003","journal-title":"Physiol. Plant."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s10535-007-0145-2","article-title":"UV-B Response of Green and Etiolated Barley Seedlings","volume":"51","author":"Fedina","year":"2007","journal-title":"Biol. Plant."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Valenta, K., Dimac-Stohl, K., Baines, F., Smith, T., Piotrowski, G., Hill, N., Kuppler, J., and Nevo, O. (2020). Ultraviolet Radiation Changes Plant Color. BMC Plant Biol., 20.","DOI":"10.1186\/s12870-020-02471-8"},{"key":"ref_76","unstructured":"Court, D. (2010). Succulent Flora of Southern Africa (Revised Edition), Struik Nature. [3rd ed.]."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Gibson, A.C. (1996). Succulent Photosynthetic Organs. Structure-Function Relations of Warm Desert Plants, Springer.","DOI":"10.1007\/978-3-642-60979-4"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.porgcoat.2006.08.023","article-title":"UV Protective Coatings: A Botanical Approach","volume":"58","author":"Jacobs","year":"2007","journal-title":"Prog. Org. Coat."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.3390\/rs71013895","article-title":"Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure","volume":"7","author":"Dandois","year":"2015","journal-title":"Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Feroz, S., and Abu Dabous, S. (2021). Uav-Based Remote Sensing Applications for Bridge Condition Assessment. Remote Sens., 13.","DOI":"10.3390\/rs13091809"},{"key":"ref_82","first-page":"857","article-title":"Maximizing Autonomous Performance of Fixed-Wing Unmanned Aerial Vehicle to Reduce Motion Blur in Taken Images","volume":"232","author":"Oktay","year":"2018","journal-title":"Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"33","DOI":"10.5194\/isprsarchives-XL-1-W4-33-2015","article-title":"UAV Image Blur\u2013Its Influence and Ways to Correct It","volume":"40","author":"Sieberth","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"34515","DOI":"10.1117\/1.JRS.13.034515","article-title":"Developing Optimized Spectral Indices Using Machine Learning to Model Fusarium Circinatum Stress in Pinus Radiata Seedlings","volume":"13","author":"Poona","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"3764","DOI":"10.1109\/JSTARS.2014.2329763","article-title":"Using Boruta-Selected Spectroscopic Wavebands for the Asymptomatic Detection of Fusarium Circinatum Stress","volume":"7","author":"Poona","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. 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