23.2.8.2 Invasive Plants, Weeds, Exotic Plants

Chapter Contents (Back)
Weeds. Invasive Plants. Exotic Plants. Trees can be weeds in this sense. Close Range Analysis for weeds:
See also Weed Detection, Close Range.

Pearlstine, L.[Leonard], Portier, K.M.[Kenneth M.], Smith, S.E.[Scot E.],
Textural Discrimination of an Invasive Plant, Schinus terebinthifolius, from Low Altitude Aerial Digital Imagery,
PhEngRS(71), No. 3, March 2005, pp. 289-298.
WWW Link. 0509
Texture features derived from first and second order statistics and edge components in high-resolution digital color infrared images were tested for their ability to discriminate Schinus terebinthifolius in multiple linear logistic regressions. BibRef

Olsson, A., van Leeuwen, W., Marsh, S.,
Feasibility of Invasive Grass Detection in a Desertscrub Community Using Hyperspectral Field Measurements and Landsat TM Imagery,
RS(3), No. 10, October 2011, pp. 2283-2304.
DOI Link 1203
BibRef

Jones, D., Pike, S., Thomas, M., Murphy, D.,
Object-Based Image Analysis for Detection of Japanese Knotweed s.l. taxa (Polygonaceae) in Wales (UK),
RS(3), No. 2, February 2011, pp. 319-342.
DOI Link 1203
BibRef

Mirik, M., Ansley, R.,
Utility of Satellite and Aerial Images for Quantification of Canopy Cover and Infilling Rates of the Invasive Woody Species Honey Mesquite (Prosopis Glandulosa) on Rangeland,
RS(4), No. 7, July 2012, pp. 1947-1962.
DOI Link 1208
BibRef

Taylor, S.L.[Sarah L.], Hill, R.A.[Ross A.], Edwards, C.[Colin],
Characterising invasive non-native Rhododendron ponticum spectra signatures with spectroradiometry in the laboratory and field: Potential for remote mapping,
PandRS(81), No. 1, July 2013, pp. 70-81.
Elsevier DOI 1306
Hyperspectral remote sensing; Invasive species; Logistic regression; Species discrimination; Leaf plasticity BibRef

Mirik, M., Ansley, R., Steddom, K., Jones, D., Rush, C., Michels, G., Elliott, N.,
Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier,
RS(5), No. 2, February 2013, pp. 612-630.
DOI Link 1303
BibRef

Hung, C.[Calvin], Xu, Z.[Zhe], Sukkarieh, S.[Salah],
Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV,
RS(6), No. 12, 2014, pp. 12037-12054.
DOI Link 1412
BibRef

Levick, S.R.[Shaun R.], Setterfield, S.A.[Samantha A.], Rossiter-Rachor, N.A.[Natalie A.], Hutley, L.B.[Lindsay B.], MacMaster, D.[Damien], Hacker, J.M.[Jorg M.],
Monitoring the Distribution and Dynamics of an Invasive Grass in Tropical Savanna Using Airborne LiDAR,
RS(7), No. 5, 2015, pp. 5117-5132.
DOI Link 1506
BibRef

Lehmann, J.R.K.[Jan Rudolf Karl], Große-Stoltenberg, A.[André], Römer, M.[Meike], Oldeland, J.[Jens],
Field Spectroscopy in the VNIR-SWIR Region to Discriminate between Mediterranean Native Plants and Exotic-Invasive Shrubs Based on Leaf Tannin Content,
RS(7), No. 2, 2015, pp. 1225-1241.
DOI Link 1503
BibRef

Wallace, C.S.A.[Cynthia S. A.], Walker, J.J.[Jessica J.], Skirvin, S.M.[Susan M.], Patrick-Birdwell, C.[Caroline], Weltzin, J.F.[Jake F.], Raichle, H.[Helen],
Mapping Presence and Predicting Phenological Status of Invasive Buffelgrass in Southern Arizona Using MODIS, Climate and Citizen Science Observation Data,
RS(8), No. 7, 2016, pp. 524.
DOI Link 1608
BibRef

Peerbhay, K.[Kabir], Mutanga, O.[Onisimo], Lottering, R.[Romano], Bangamwabo, V.[Victor], Ismail, R.[Riyad],
Detecting bugweed (Solanum mauritianum) abundance in plantation forestry using multisource remote sensing,
PandRS(121), No. 1, 2016, pp. 167-176.
Elsevier DOI 1609
Remote sensing BibRef

Liu, X.[Xiang], Liu, H.Y.[Hui-Yu], Gong, H.B.[Hai-Bo], Lin, Z.S.[Zhen-Shan], Lv, S.C.[Shi-Cheng],
Appling the One-Class Classification Method of Maxent to Detect an Invasive Plant Spartina alterniflora with Time-Series Analysis,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Dutra Silva, L.[Lara], Brito de Azevedo, E.[Eduardo], Bento Elias, R.[Rui], Silva, L.[Luís],
Species Distribution Modeling: Comparison of Fixed and Mixed Effects Models Using INLA,
IJGI(6), No. 12, 2017, pp. xx-yy.
DOI Link 1801
Invasive species. BibRef

Alvarez-Taboada, F.[Flor], Paredes, C.[Claudio], Julián-Pelaz, J.[Julia],
Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Martin, F.M.[François-Marie], Müllerová, J.[Jana], Borgniet, L.[Laurent], Dommanget, F.[Fanny], Breton, V.[Vincent], Evette, A.[André],
Using Single- and Multi-Date UAV and Satellite Imagery to Accurately Monitor Invasive Knotweed Species,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Louargant, M.[Marine], Jones, G.[Gawain], Faroux, R.[Romain], Paoli, J.N.[Jean-Noël], Maillot, T.[Thibault], Gée, C.[Christelle], Villette, S.[Sylvain],
Unsupervised Classification Algorithm for Early Weed Detection in Row-Crops by Combining Spatial and Spectral Information,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Pflanz, M.[Michael], Nordmeyer, H.[Henning], Schirrmann, M.[Michael],
Weed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Bah, M.D.[M Dian], Hafiane, A.[Adel], Canals, R.[Raphael],
Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Tarantino, C.[Cristina], Casella, F.[Francesca], Adamo, M.[Maria], Lucas, R.[Richard], Beierkuhnlein, C.[Carl], Blonda, P.[Palma],
Ailanthus altissima mapping from multi-temporal very high resolution satellite images,
PandRS(147), 2019, pp. 90-103.
Elsevier DOI 1901
Invasive species, Alien species, mapping, multi-temporal WorldView-2 data, Remote sensing, Novel ecosystems BibRef

Rasti, P.[Pejman], Ahmad, A.[Ali], Samiei, S.[Salma], Belin, E.[Etienne], Rousseau, D.[David],
Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Mbaabu, P.R.[Purity Rima], Ng, W.T.[Wai-Tim], Schaffner, U.[Urs], Gichaba, M.[Maina], Olago, D.[Daniel], Choge, S.[Simon], Oriaso, S.[Silas], Eckert, S.[Sandra],
Spatial Evolution of Prosopis Invasion and its Effects on LULC and Livelihoods in Baringo, Kenya,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Zhu, X.D.[Xu-Dong], Meng, L.X.[Ling-Xuan], Zhang, Y.H.[Yi-Hui], Weng, Q.H.[Qi-Hao], Morris, J.[James],
Tidal and Meteorological Influences on the Growth of Invasive Spartina alterniflora: Evidence from UAV Remote Sensing,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Abeysinghe, T.[Tharindu], Milas, A.S.[Anita Simic], Arend, K.[Kristin], Hohman, B.[Breann], Reil, P.[Patrick], Gregory, A.[Andrew], Vázquez-Ortega, A.[Angélica],
Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Luo, Q.[Qian], Song, J.L.[Jin-Ling], Yang, L.[Lei], Wang, J.[Jindi],
Improved Spring Vegetation Phenology Calculation Method Using a Coupled Model and Anomalous Point Detection,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Farooq, A.[Adnan], Jia, X.P.[Xiu-Ping], Hu, J.K.[Jian-Kun], Zhou, J.[Jun],
Multi-Resolution Weed Classification via Convolutional Neural Network and Superpixel Based Local Binary Pattern Using Remote Sensing Images,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

See also Superpixel-Based Graphical Model for Remote Sensing Image Mapping. BibRef

Masemola, C., Cho, M.A., Ramoelo, A.,
Assessing the Effect of Seasonality on Leaf and Canopy Spectra for the Discrimination of an Alien Tree Species, Acacia Mearnsii, From Co-Occurring Native Species Using Parametric and Nonparametric Classifiers,
GeoRS(57), No. 8, August 2019, pp. 5853-5867.
IEEE DOI 1908
geophysics computing, pattern classification, time series, vegetation, vegetation mapping, native plant species, random forest (RF) BibRef

Bayat, M.[Mahmoud], Noi, P.T.[Phan Thanh], Zare, R.[Rozita], Bui, D.T.[Dieu Tien],
A Semi-empirical Approach Based on Genetic Programming for the Study of Biophysical Controls on Diameter-Growth of Fagus orientalis in Northern Iran,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Dash, J.P.[Jonathan P.], Watt, M.S.[Michael S.], Paul, T.S.H.[Thomas S. H.], Morgenroth, J.[Justin], Pearse, G.D.[Grant D.],
Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Kiala, Z.[Zolo], Mutanga, O.[Onisimo], Odindi, J.[John], Peerbhay, K.[Kabir],
Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Ghoussein, Y.[Youssra], Nicolas, H.[Hervé], Haury, J.[Jacques], Fadel, A.[Ali], Pichelin, P.[Pascal], Hamdan, H.A.[Hussein Abou], Faour, G.[Ghaleb],
Multitemporal Remote Sensing Based on an FVC Reference Period Using Sentinel-2 for Monitoring Eichhornia crassipes on a Mediterranean River,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
Water hyacinth. BibRef

Villarreal, M.L.[Miguel L.], Soulard, C.E.[Christopher E.], Waller, E.K.[Eric K.],
Landsat Time Series Assessment of Invasive Annual Grasses Following Energy Development,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

de Castro, A.I.[Ana I.], Peña, J.M.[José M.], Torres-Sánchez, J.[Jorge], Jiménez-Brenes, F.M.[Francisco M.], Valencia-Gredilla, F.[Francisco], Recasens, J.[Jordi], López-Granados, F.[Francisca],
Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
BibRef

Sabat-Tomala, A.[Anita], Raczko, E.[Edwin], Zagajewski, B.[Bogdan],
Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Labonté, J.[Joanie], Drolet, G.[Guillaume], Sylvain, J.D.[Jean-Daniel], Thiffault, N.[Nelson], Hébert, F.[Francois], Girard, F.[Francois],
Phenology-Based Mapping of an Alien Invasive Species Using Time Series of Multispectral Satellite Data: A Case-Study with Glossy Buckthorn in Québec, Canada,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Tian, Y.L.[Yan-Lin], Jia, M.M.[Ming-Ming], Wang, Z.M.[Zong-Ming], Mao, D.H.[De-Hua], Du, B.J.[Bao-Jia], Wang, C.[Chao],
Monitoring Invasion Process of Spartina alterniflora by Seasonal Sentinel-2 Imagery and an Object-Based Random Forest Classification,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Liu, Y.F.[Yi-Fei], Ma, J.[Jun], Wang, X.X.[Xin-Xin], Zhong, Q.Y.[Qiao-Yan], Zong, J.M.[Jia-Min], Wu, W.B.[Wan-Ben], Wang, Q.[Qing], Zhao, B.[Bin],
Joint Effect of Spartina alterniflora Invasion and Reclamation on the Spatial and Temporal Dynamics of Tidal Flats in Yangtze River Estuary,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Sheffield, K.[Kathryn], Dugdale, T.[Tony],
Supporting Urban Weed Biosecurity Programs with Remote Sensing,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Sivakumar, A.N.V.[Arun Narenthiran Veeranampalayam], Li, J.[Jiating], Scott, S.[Stephen], Psota, E.[Eric], Jhala, A.J.[Amit J.], Luck, J.D.[Joe D.], Shi, Y.[Yeyin],
Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Masemola, C.[Cecilia], Cho, M.A.[Moses Azong], Ramoelo, A.[Abel],
Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa,
PandRS(166), 2020, pp. 153-168.
Elsevier DOI 2007
Invasive alien plant, Radiative Transfer Model, PROSAIL, Sentinel-2, Leaf Area Index, Canopy Chlorophyll Content BibRef

Worqlul, A.W.[Abeyou W.], Ayana, E.K.[Essayas K.], Dile, Y.T.[Yihun T.], Moges, M.A.[Mamaru A.], Dersseh, M.G.[Minychl G.], Tegegne, G.[Getachew], Kibret, S.[Solomon],
Spatiotemporal Dynamics and Environmental Controlling Factors of the Lake Tana Water Hyacinth in Ethiopia,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Gée, C.[Christelle], Denimal, E.[Emmanuel],
RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Cabezas, M.[Mariano], Kentsch, S.[Sarah], Tomhave, L.[Luca], Gross, J.[Jens], Caceres, M.L.L.[Maximo Larry Lopez], Diez, Y.[Yago],
Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Dutta, D.[Dipanwita], Chen, G.[Gang], Chen, C.[Chen], Gagné, S.A.[Sara A.], Li, C.L.[Chang-Lin], Rogers, C.[Christa], Matthews, C.[Christopher],
Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Pepe, M.[Monica], Pompilio, L.[Loredana], Gioli, B.[Beniamino], Busetto, L.[Lorenzo], Boschetti, M.[Mirco],
Detection and Classification of Non-Photosynthetic Vegetation from PRISMA Hyperspectral Data in Croplands,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Peteinatos, G.G.[Gerassimos G.], Reichel, P.[Philipp], Karouta, J.[Jeremy], Andújar, D.[Dionisio], Gerhards, R.[Roland],
Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Liu, X.[Xiang], Liu, H.Y.[Hui-Yu], Datta, P.[Pawanjeet], Frey, J.[Julian], Koch, B.[Barbara],
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,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Singh, G.[Geethen], Reynolds, C.[Chevonne], Byrne, M.[Marcus], Rosman, B.[Benjamin],
A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Haagsma, M.[Marja], Page, G.F.M.[Gerald F. M.], Johnson, J.S.[Jeremy S.], Still, C.[Christopher], Waring, K.M.[Kristen M.], Sniezko, R.A.[Richard A.], Selker, J.S.[John S.],
Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Symptom Severity in Pinus strobiformis Seedlings of Different Genotypes,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Zou, K.L.[Kun-Lin], Chen, X.[Xin], Zhang, F.[Fan], Zhou, H.[Hang], Zhang, C.L.[Chun-Long],
A Field Weed Density Evaluation Method Based on UAV Imaging and Modified U-Net,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Ronay, I.[Inbal], Ephrath, J.E.[Jhonathan E.], Eizenberg, H.[Hanan], Blumberg, D.G.[Dan G.], Maman, S.[Shimrit],
Hyperspectral Reflectance and Indices for Characterizing the Dynamics of Crop-Weed Competition for Water,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Pfitzner, K.[Kirrilly], Bartolo, R.[Renee], Whiteside, T.[Tim], Loewensteiner, D.[David], Esparon, A.[Andrew],
Hyperspectral Monitoring of Non-Native Tropical Grasses over Phenological Seasons,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Bolch, E.A.[Erik A.], Hestir, E.L.[Erin L.], Khanna, S.[Shruti],
Performance and Feasibility of Drone-Mounted Imaging Spectroscopy for Invasive Aquatic Vegetation Detection,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Benjamin, A.R.[Adam R.], Abd-Elrahman, A.[Amr], Gettys, L.A.[Lyn A.], Hochmair, H.H.[Hartwig H.], Thayer, K.[Kyle],
Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Bransky, N.[Nathaniel], Sankey, T.[Temuulen], Sankey, J. .B.[Joel B.], Johnson, M.[Matthew], Jamison, L.[Levi],
Monitoring Tamarix Changes Using WorldView-2 Satellite Imagery in Grand Canyon National Park, Arizona,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
Tamarisk -- invasive shrub. BibRef

Kaivosoja, J.[Jere], Hautsalo, J.H.[Ju-Ho], Heikkinen, J.[Jaakko], Hiltunen, L.[Lea], Ruuttunen, P.[Pentti], Näsi, R.[Roope], Niemeläinen, O.[Oiva], Lemsalu, M.[Madis], Honkavaara, E.[Eija], Salonen, J.[Jukka],
Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Larson, K.B.[Kyle B.], Tuor, A.R.[Aaron R.],
Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Mattivi, P.[Pietro], Pappalardo, S.E.[Salvatore Eugenio], Nikolic, N.[Nebojša], Mandolesi, L.[Luca], Persichetti, A.[Antonio], de Marchi, M.[Massimo], Masin, R.[Roberta],
Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Brooks, C.[Colin], Weinstein, C.[Charlotte], Poley, A.[Andrew], Grimm, A.[Amanda], Marion, N.[Nicholas], Bourgeau-Chavez, L.[Laura], Hansen, D.[Dana], Kowalski, K.[Kurt],
Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive Phragmites australis in Treatment Areas Enrolled in an Adaptive Management Program,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Hu, C.S.[Cheng-Song], Sapkota, B.B.[Bishwa B.], Thomasson, J.A.[J. Alex], Bagavathiannan, M.V.[Muthukumar V.],
Influence of Image Quality and Light Consistency on the Performance of Convolutional Neural Networks for Weed Mapping,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Huang, T.C.[Tie-Cheng], Ding, X.J.[Xiao-Juan], Zhu, X.[Xuan], Chen, S.J.[Shu-Jiang], Chen, M.Y.[Meng-Yu], Jia, X.[Xiang], Lai, F.B.[Feng-Bing], Zhang, X.L.[Xiao-Li],
Assessment of Poplar Looper (Apocheima cinerarius Erschoff) Infestation on Euphrates (Populus euphratica) Using Time-Series MODIS NDVI Data Based on the Wavelet Transform and Discriminant Analysis,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Quan, L.Z.[Long-Zhe], Li, H.D.[Heng-Da], Li, H.L.[Hai-Long], Jiang, W.[Wei], Lou, Z.X.[Zhao-Xia], Chen, L.Q.[Li-Qing],
Two-Stream Dense Feature Fusion Network Based on RGB-D Data for the Real-Time Prediction of Weed Aboveground Fresh Weight in a Field Environment,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Anderson, C.J.[Connor J.], Heins, D.[Daniel], Pelletier, K.C.[Keith C.], Bohnen, J.L.[Julia L.], Knight, J.F.[Joseph F.],
Mapping Invasive Phragmites australis Using Unoccupied Aircraft System Imagery, Canopy Height Models, and Synthetic Aperture Radar,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Anderson, C.J.[Connor J.], Heins, D.[Daniel], Pelletier, K.C.[Keith C.], Knight, J.F.[Joseph F.],
Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis,
RS(15), No. 14, 2023, pp. 3511.
DOI Link 2307
BibRef

Mouta, N.[Nuno], Silva, R.[Renato], Pais, S.[Silvana], Alonso, J.M.[Joaquim M.], Gonçalves, J.F.[João F.], Honrado, J.[João], Vicente, J.R.[Joana R.],
'The Best of Two Worlds': Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Iqbal, I.M.[Iram M.], Balzter, H.[Heiko], Firdaus-e-Bareen, Shabbir, A.[Asad],
Identifying the Spectral Signatures of Invasive and Native Plant Species in Two Protected Areas of Pakistan through Field Spectroscopy,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Parker, K.[Kelsey], Elmes, A.[Arthur], Boucher, P.[Peter], Hallett, R.A.[Richard A.], Thompson, J.E.[John E.], Simek, Z.[Zachary], Bowers, J.[Justin], Reinmann, A.B.[Andrew B.],
Crossing the Great Divide: Bridging the Researcher-Practitioner Gap to Maximize the Utility of Remote Sensing for Invasive Species Monitoring and Management,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Lan, Y.B.[Yu-Bin], Huang, K.H.[Kang-Hua], Yang, C.[Chang], Lei, L.C.[Luo-Cheng], Ye, J.H.[Jia-Hang], Zhang, J.L.[Jian-Ling], Zeng, W.[Wen], Zhang, Y.[Yali], Deng, J.Z.[Ji-Zhong],
Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Schulze-Brüninghoff, D.[Damian], Wachendorf, M.[Michael], Astor, T.[Thomas],
Potentials and Limitations of WorldView-3 Data for the Detection of Invasive Lupinus polyphyllus Lindl. in Semi-Natural Grasslands,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Shen, M.[Ming], Tang, M.F.[Mao-Feng], Li, Y.K.[Ying-Kui],
Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Rakhmatuiln, I.[Ildar], Kamilaris, A.[Andreas], Andreasen, C.[Christian],
Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Eide, A.[Austin], Koparan, C.[Cengiz], Zhang, Y.[Yu], Ostlie, M.[Michael], Howatt, K.[Kirk], Sun, X.[Xin],
UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Koco, Š.[Štefan], Dubravská, A.[Anna], Vilcek, J.[Jozef], Grulová, D.[Daniela],
Geospatial Approaches to Monitoring the Spread of Invasive Species of Solidago spp.,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Etienne, A.[Aaron], Ahmad, A.[Aanis], Aggarwal, V.[Varun], Saraswat, D.[Dharmendra],
Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Sabat-Tomala, A.[Anita], Raczko, E.[Edwin], Zagajewski, B.[Bogdan],
Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Reedha, R.[Reenul], Dericquebourg, E.[Eric], Canals, R.[Raphael], Hafiane, A.[Adel],
Transformer Neural Network for Weed and Crop Classification of High Resolution UAV Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Liang, S.[Shuang], Gong, Z.N.[Zhao-Ning], Wang, Y.C.[Ying-Cong], Zhao, J.[Jiafu], Zhao, W.J.[Wen-Ji],
Accurate Monitoring of Submerged Aquatic Vegetation in a Macrophytic Lake Using Time-Series Sentinel-2 Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Mafanya, M.[Madodomzi], Tsele, P.[Philemon], Zengeya, T.[Tsungai], Ramoelo, A.[Abel],
An assessment of image classifiers for generating machine-learning training samples for mapping the invasive Campuloclinium macrocephalum (Less.) DC (pompom weed) using DESIS hyperspectral imagery,
PandRS(185), 2022, pp. 188-200.
Elsevier DOI 2202
Image classifiers, Training samples, Pompom weed, Spectral angle mapper, Maximum likelihood, DESIS BibRef

Kiala, Z.[Zolo], Odindi, J.[John], Mutanga, O.[Onisimo],
Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Curtarelli, M.P.[Marcelo Pedroso], Kurtz, D.J.[Diego Jacob], Salgueiro, T.P.[Taisa Pereira],
Identifying Priority Areas for Vegetation Management in the Context of Energy Distribution Networks Using PlanetScope Images,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Dmitriev, P.A.[Pavel A.], Kozlovsky, B.L.[Boris L.], Kupriushkin, D.P.[Denis P.], Dmitrieva, A.A.[Anastasia A.], Rajput, V.D.[Vishnu D.], Chokheli, V.A.[Vasily A.], Tarik, E.P.[Ekaterina P.], Kapralova, O.A.[Olga A.], Tokhtar, V.K.[Valeriy K.], Minkina, T.M.[Tatiana M.], Varduni, T.V.[Tatiana V.],
Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zhu, W.Q.[Wen-Qing], Ren, G.[Guangbo], Wang, J.P.[Jian-Ping], Wang, J.[Jianbu], Hu, Y.[Yabin], Lin, Z.Y.[Zhao-Yang], Li, W.[Wei], Zhao, Y.J.[Ya-Jie], Li, S.[Shibao], Wang, N.[Ning],
Monitoring the Invasive Plant Spartina alterniflora in Jiangsu Coastal Wetland Using MRCNN and Long-Time Series Landsat Data,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Sheffield, K.J.[Kathryn J.], Clements, D.[Daniel], Clune, D.J.[Darryl J.], Constantine, A.[Angela], Dugdale, T.M.[Tony M.],
Detection of Aquatic Alligator Weed (Alternanthera philoxeroides) from Aerial Imagery Using Random Forest Classification,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Simpson, M.D.[Morgan David], Akbari, V.[Vahid], Marino, A.[Armando], Prabhu, G.N.[G. Nagendra], Bhowmik, D.[Deepayan], Rupavatharam, S.[Srikanth], Datta, A.[Aviraj], Kleczkowski, A.[Adam], Sujeetha, J.A.R.P.[J. Alice R. P.], Anantrao, G.G.[Girish Gunjotikar], Poduvattil, V.K.[Vidhu Kampurath], Kumar, S.[Saurav], Maharaj, S.[Savitri], Hunter, P.D.[Peter D.],
Detecting Water Hyacinth Infestation in Kuttanad, India, Using Dual-Pol Sentinel-1 SAR Imagery,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Ade, C.[Christiana], Khanna, S.[Shruti], Lay, M.[Mui], Ustin, S.L.[Susan L.], Hestir, E.L.[Erin L.],
Genus-Level Mapping of Invasive Floating Aquatic Vegetation Using Sentinel-2 Satellite Remote Sensing,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Andujar, D.[Dionisio], Martinez-Guanter, J.[Jorge],
An Overview of Precision Weed Mapping and Management Based on Remote Sensing,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Salim, S.[Samla], Sarath, R.,
An improved invasive weed optimization enabled Shepard convolutional neural network for classification of breast cancer,
IJIST(32), No. 5, 2022, pp. 1521-1534.
DOI Link 2209
breast cancer classification, histopathological image, invasive weed optimization, morphological operation, water wave optimization BibRef

Fraccaro, P.[Paolo], Butt, J.[Junaid], Edwards, B.[Blair], Freckleton, R.P.[Robert P.], Childs, D.Z.[Dylan Z.], Reusch, K.[Katharina], Comont, D.[David],
A Deep Learning Application to Map Weed Spatial Extent from Unmanned Aerial Vehicles Imagery,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Gutiérrez-Lazcano, L.[Lucia], Camacho-Bello, C.J.[César J.], Cornejo-Velazquez, E.[Eduardo], Arroyo-Núñez, J.H.[José Humberto], Clavel-Maqueda, M.[Mireya],
Cuscuta spp. Segmentation Based on Unmanned Aerial Vehicles (UAVs) and Orthomasaics Using a U-Net Xception-Style Model,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Ribeiro, J.W.[José W.], Harmon, K.[Kristopher], Leite, G.A.[Gabriel Augusto], de Melo, T.N.[Tomaz Nascimento], LeBien, J.[Jack], Campos-Cerqueira, M.[Marconi],
Passive Acoustic Monitoring as a Tool to Investigate the Spatial Distribution of Invasive Alien Species,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Chávez, R.O.[Roberto O.], Estay, S.A.[Sergio A.], Lastra, J.A.[José A.], Riquelme, C.G.[Carlos G.], Olea, M.[Matías], Aguayo, J.[Javiera], Decuyper, M.[Mathieu],
npphen: An R-Package for Detecting and Mapping Extreme Vegetation Anomalies Based on Remotely Sensed Phenological Variability,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Gao, B.T.[Bing-Tao], Yu, L.F.[Lin-Feng], Ren, L.[Lili], Zhan, Z.Y.[Zhong-Yi], Luo, Y.Q.[You-Qing],
Early Detection of Dendroctonus valens Infestation at Tree Level with a Hyperspectral UAV Image,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Innangi, M.[Michele], Marzialetti, F.[Flavio], di Febbraro, M.[Mirko], Acosta, A.T.R.[Alicia Teresa Rosario], de Simone, W.[Walter], Frate, L.[Ludovico], Finizio, M.[Michele], Perna, P.V.[Priscila Villalobos], Carranza, M.L.[Maria Laura],
Coastal Dune Invaders: Integrative Mapping of Carpobrotus sp. pl. (Aizoaceae) Using UAVs,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Gallo, I.[Ignazio], Ur Rehman, A.[Anwar], Dehkordi, R.H.[Ramin Heidarian], Landro, N.[Nicola], La Grassa, R.[Riccardo], Boschetti, M.[Mirco],
Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset from UAV Images,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Chaudhuri, G.[Gargi], Mishra, N.B.[Niti B.],
Detection of Aquatic Invasive Plants in Wetlands of the Upper Mississippi River from UAV Imagery Using Transfer Learning,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Mallmann, C.L.[Caroline Lorenci], Filho, W.P.[Waterloo Pereira], Dreyer, J.B.B.[Jaqueline B. B.], Tabaldi, L.A.[Luciane A.], Durgante, F.M.[Flavia Machado],
Leaf-Level Field Spectroscopy to Discriminate Invasive Species (Psidium guajava L. and Hovenia dulcis Thunb.) from Native Tree Species in the Southern Brazilian Atlantic Forest,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Domingo, D.[Dario], Pérez-Rodríguez, F.[Fernando], Gómez-García, E.[Esteban], Rodríguez-Puerta, F.[Francisco],
Assessing the Efficacy of Phenological Spectral Differences to Detect Invasive Alien Acacia dealbata Using Sentinel-2 Data in Southern Europe,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Anderson, C.J.[Connor J.], Heins, D.[Daniel], Pelletier, K.C.[Keith C.], Knight, J.F.[Joseph F.],
Improving Machine Learning Classifications of Phragmites australis Using Object-Based Image Analysis,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Iqbal, I.M.[Iram M.], Balzter, H.[Heiko], Firdaus-e-Bareen, Shabbir, A.[Asad],
Mapping Lantana camara and Leucaena leucocephala in Protected Areas of Pakistan: A Geo-Spatial Approach,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Amarasingam, N.[Narmilan], Hamilton, M.[Mark], Kelly, J.E.[Jane E.], Zheng, L.H.[Li-Hong], Sandino, J.[Juan], Gonzalez, F.[Felipe], Dehaan, R.L.[Remy L.], Cherry, H.[Hillary],
Autonomous Detection of Mouse-Ear Hawkweed Using Drones, Multispectral Imagery and Supervised Machine Learning,
RS(15), No. 6, 2023, pp. 1633.
DOI Link 2304
BibRef

Danilevicz, M.F.[Monica F.], Rocha, R.L.[Roberto Lujan], Batley, J.[Jacqueline], Bayer, P.E.[Philipp E.], Bennamoun, M.[Mohammed], Edwards, D.[David], Ashworth, M.B.[Michael B.],
Segmentation of Sandplain Lupin Weeds from Morphologically Similar Narrow-Leafed Lupins in the Field,
RS(15), No. 7, 2023, pp. 1817.
DOI Link 2304
BibRef

Raja, G.[Gunasekaran], Philips, N.D.[Nisha Deborah], Ramasamy, R.K.[Ramesh Krishnan], Dev, K.[Kapal], Kumar, N.[Neeraj],
Intelligent Drones Trajectory Generation for Mapping Weed Infested Regions Over 6G Networks,
ITS(24), No. 7, July 2023, pp. 7506-7515.
IEEE DOI 2307
Trajectory, Splines (mathematics), Optimization, Genetic algorithms, 6G mobile communication, Drones, non-uniform rational B-splines BibRef

Du, B.[Bobo], Ding, X.L.[Xiao-Long], Ji, C.[Chao], Lin, K.[Kejian], Guo, J.[Jing], Lu, L.[Longhui], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Wang, N.[Ning],
Estimating Leymus chinensis Loss Caused by Oedaleus decorus asiaticus Using an Unmanned Aerial Vehicle (UAV),
RS(15), No. 17, 2023, pp. 4352.
DOI Link 2310
BibRef

Zhao, J.S.[Jiang-San], Berge, T.W.[Therese With], Geipel, J.[Jakob],
Transformer in UAV Image-Based Weed Mapping,
RS(15), No. 21, 2023, pp. 5165.
DOI Link 2311
BibRef

Khan, S.D.[Sultan Daud], Basalamah, S.[Saleh], Lbath, A.[Ahmed],
Weed-Crop Segmentation in Drone Images with a Novel Encoder-Decoder Framework Enhanced via Attention Modules,
RS(15), No. 23, 2023, pp. 5615.
DOI Link 2312
BibRef

Huang, T.C.[Tie-Cheng], Yang, T.[Tong], Wang, K.[Kun], Huang, W.J.[Wen-Jiang],
Assessing the Current and Future Potential Distribution of Solanum rostratum Dunal in China Using Multisource Remote Sensing Data and Principal Component Analysis,
RS(16), No. 2, 2024, pp. 271.
DOI Link 2402
BibRef

Thürkow, F.[Florian], Lorenz, C.G.[Christopher Günter], Pause, M.[Marion], Birger, J.[Jens],
Advanced Detection of Invasive Neophytes in Agricultural Landscapes: A Multisensory and Multiscale Remote Sensing Approach,
RS(16), No. 3, 2024, pp. 500.
DOI Link 2402
BibRef

de Figueiredo Meyer, M.[Manuel], Gonçalves, J.A.[José Alberto], Bio, A.M.F.[Ana Maria Ferreira],
Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation,
RS(16), No. 4, 2024, pp. 652.
DOI Link 2402
BibRef

Zagajewski, B.[Bogdan], Kluczek, M.[Marcin], Zdunek, K.B.[Karolina Barbara], Holland, D.[David],
Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping,
RS(16), No. 4, 2024, pp. 636.
DOI Link 2402
BibRef

Sabat-Tomala, A.[Anita], Raczko, E.[Edwin], Zagajewski, B.[Bogdan],
Airborne Hyperspectral Images and Machine Learning Algorithms for the Identification of Lupine Invasive Species in Natura 2000 Meadows,
RS(16), No. 3, 2024, pp. 580.
DOI Link 2402
BibRef

Valero-Jorge, A.[Alexey], Zayas, R.G.D.[Roberto González-De], Matos-Pupo, F.[Felipe], Becerra-González, A.L.[Angel Luis], Álvarez-Taboada, F.[Flor],
Mapping and Monitoring of the Invasive Species Dichrostachys cinerea (Marabú) in Central Cuba Using Landsat Imagery and Machine Learning (1994-2022),
RS(16), No. 5, 2024, pp. 798.
DOI Link 2403
BibRef


Wang, Y.[Yuemin], Ha, T.[Thuan], Aldridge, K.[Kathryn], Duddu, H.[Hema], Shirtliffe, S.[Steve], Stavness, I.[Ian],
Weed Mapping with Convolutional Neural Networks on High Resolution Whole-Field Images,
CVPPA23(505-514)
IEEE DOI 2401
BibRef

Melki, P.[Paul], Bombrun, L.[Lionel], Diallo, B.[Boubacar], Dias, J.[Jérôme], Costa, J.P.D.[Jean-Pierre Da],
Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification,
CVPPA23(614-623)
IEEE DOI 2401
BibRef

Celikkan, E.[Ekin], Saberioon, M.[Mohammadmehdi], Herold, M.[Martin], Klein, N.[Nadja],
Semantic Segmentation of Crops and Weeds with Probabilistic Modeling and Uncertainty Quantification,
CVPPA23(582-592)
IEEE DOI 2401
BibRef

Schmidt, P.[Patrick], Güldenring, R.[Ronja], Nalpantidis, L.[Lazaros],
Sift-guided Saliency-based Augmentation for Weed Detection in Grassland Images: Fusing Classic Computer Vision with Deep Learning,
CVS23(137-147).
Springer DOI 2312
BibRef

Rozendo, G.B.[Guilherme Botazzo], Roberto, G.F.[Guilherme Freire], do Nascimento, M.Z.[Marcelo Zanchetta], Neves, L.A.[Leandro Alves], Lumini, A.[Alessandra],
Weeds Classification with Deep Learning: An Investigation Using Cnn, Vision Transformers, Pyramid Vision Transformers, and Ensemble Strategy,
CIARP23(I:229-243).
Springer DOI 2312
BibRef

Steininger, D.[Daniel], Trondl, A.[Andreas], Croonen, G.[Gerardus], Simon, J.[Julia], Widhalm, V.[Verena],
The CropAndWeed Dataset: a Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation,
WACV23(3718-3727)
IEEE DOI 2302
Location awareness, Training, Image segmentation, Annotations, Crops, Training data, Benchmark testing, Applications: Agriculture, visual reasoning BibRef

Iancu, O.D.[Ovidiu Dan], Yang, K.[Kara], Man, H.[Han], Menard, T.C.[Theresa Cabrera],
An Automated and Scalable ML Solution for Mapping Invasive Species: the Case of the Australian Tree Fern in Hawaiian Forests,
RealWorld23(140-147)
IEEE DOI 2302
Training, Web services, Pipelines, Training data, Vegetation, Organizations BibRef

Elias, N.[Nathan],
Deep Learning Methodology for Early Detection and Outbreak Prediction of Invasive Species Growth,
WACV23(6324-6332)
IEEE DOI 2302
Training, Deep learning, Solid modeling, Laser radar, Manuals, Predictive models, Animals/Insects BibRef

Amziane, A.[Anis], Losson, O.[Olivier], Mathon, B.[Benjamin], Dumenil, A.[Aurélien], Macaire, L.[Ludovic],
Frame-based reflectance estimation from multispectral images for weed identification in varying illumination conditions,
IPTA20(1-7)
IEEE DOI 2206
Reflectivity, Image segmentation, Lighting, Estimation, Vegetation mapping, Tools, Cameras, Multispectral imaging, Linescan camera BibRef

Gimenez, R., Lassalle, G., Hédacq, R., Elger, A., Dubucq, D., Credoz, A., Jennet, C., Fabre, S.,
Exploitation of Spectral and Temporal Information for Mapping Plant Species in a Former Industrial Site,
ISPRS21(B3-2021: 559-566).
DOI Link 2201
BibRef

Liu, J., Hossain, M.D., Chen, D.,
A Procedure for Identifying Invasive Wild Parsnip Plants Based On Visible Bands From UAV Images,
ISPRS21(B1-2021: 173-181).
DOI Link 2201
BibRef

Nyawacha, S.O., Meta, V., Osio, A.,
Spatial Temporal Mapping of Spread of Water Hyacinth In Winum Gulf, Lake Victoria,
ISPRS21(B3-2021: 341-346).
DOI Link 2201
BibRef

Asad, M.H.[Muhammad Hamza], Bais, A.[Abdul],
Weed Density Estimation Using Semantic Segmentation,
PSIVT19(162-171).
Springer DOI 2003
BibRef

Baidar, T., Shrestha, A.B., Ranjit, R., Adhikari, R., Ghimire, S., Shrestha, N.,
Impact Assessment of Mikania Micrantha On Land Cover And Maxent Modeling to Predict Its Potential Invasion Sites,
Hannover17(305-310).
DOI Link 1805
BibRef

Martínez-Sánchez, J., González-de Santos, L.M., Novo, A., González-Jorge, H.,
UAV and Satellite Imagery Applied to Alien Species Mapping in NW Spain,
UAV-g19(455-459).
DOI Link 1912
BibRef

Mudereri, B.T., Dube, T., Adel-Rahman, E.M., Niassy, S., Kimathi, E., Khan, Z., Landmann, T.,
A Comparative Analysis of Planetscope and Sentinel Sentinel-2 Space-borne Sensors in Mapping Striga Weed Using Guided Regularised Random Forest Classification Ensemble,
IWIDF19(701-708).
DOI Link 1912
BibRef

Förster, M., Schmidt, T., Wolf, R., Kleinschmit, B., Fassnacht, F.E., Cabezas, J., Kattenborn, T.,
Detecting the spread of invasive species in central Chile with a Sentinel-2 time-series,
MultiTemp17(1-4)
IEEE DOI 1712
geophysical image processing, hyperspectral imaging, image segmentation, land cover, least squares approximations, time-series BibRef

Marshall, V., Lewis, M., Ostendorf, B.,
Do Additional Bands (coastal, Nir-2, Red-edge And Yellow) In Worldview-2 Multispectral Imagery Improve Discrimination Of An Invasive Tussock, Buffel Grass (cenchrus Ciliaris)?,
ISPRS12(XXXIX-B8:277-281).
DOI Link 1209
P>

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Soybean Crop Analysis, Beans, Production, Detection, Health, Change .


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