From UAV to PlanetScope: Upscaling fractional cover of an invasive species Rosa rugosa.

[1]  M. Di Febbraro,et al.  Remote Sensing and Invasive Plants in Coastal Ecosystems: What We Know So Far and Future Prospects , 2023, Land.

[2]  R. Haight,et al.  Hierarchical Governance in Invasive Species Survey Campaigns , 2022, SSRN Electronic Journal.

[3]  Longjun Qin,et al.  How can UAV bridge the gap between ground and satellite observations for quantifying the biomass of desert shrub community? , 2022, ISPRS Journal of Photogrammetry and Remote Sensing.

[4]  K. Sepp,et al.  Multi-source remote sensing data reveals complex topsoil organic carbon dynamics in coastal wetlands , 2022, Ecological Indicators.

[5]  J. Jozwiak,et al.  Multi-source remote sensing recognition of plant communities at the reach scale of the Vistula River, Poland , 2022, Ecological Indicators.

[6]  M. Samways,et al.  Mapping an alien invasive shrub within conservation corridors using super-resolution satellite imagery. , 2022, Journal of environmental management.

[7]  D. A. Moeller,et al.  Deep learning detects invasive plant species across complex landscapes using Worldview‐2 and Planetscope satellite imagery , 2022, Remote Sensing in Ecology and Conservation.

[8]  G. Kemper,et al.  SOIL EROSION CALCULATION USING AERIAL IMAGES BASED DTM IN A CROSS BORDER VINERY REGION , 2022, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[9]  Raul Sampaio de Lima,et al.  The Potential of Optical UAS Data for Predicting Surface Soil Moisture in a Peatland across Time and Sites , 2022, Remote. Sens..

[10]  H. Freitas,et al.  Early detection, herbicide resistance screening, and integrated management of Invasive Plant Species: A review. , 2022, Pest management science.

[11]  W. Nijland,et al.  Improving UAV-SfM time-series accuracy by co-alignment and contributions of ground control or RTK positioning , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[12]  D. Pouliot,et al.  UAV and High Resolution Satellite Mapping of Forage Lichen (Cladonia spp.) in a Rocky Canadian Shield Landscape , 2022, Canadian Journal of Remote Sensing.

[13]  D. Roy,et al.  A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery , 2021 .

[14]  Amy E. Frazier,et al.  A Technical Review of Planet Smallsat Data: Practical Considerations for Processing and Using PlanetScope Imagery , 2021, Remote. Sens..

[15]  N. G. Taylor,et al.  Economic costs of invasive alien species across Europe , 2021, NeoBiota.

[16]  D. Rocchini,et al.  The relationship between species and spectral diversity in grassland communities is mediated by their vertical complexity , 2021, Applied Vegetation Science.

[17]  D. Riaño,et al.  The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem , 2021, Remote Sensing of Environment.

[18]  Jinbao Liu,et al.  Combination of machine learning and VIRS for predicting soil organic matter , 2021, Journal of Soils and Sediments.

[19]  Birgit Kleinschmit,et al.  Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[20]  K. Joyce,et al.  Of Course We Fly Unmanned—We’re Women! , 2021, Drones.

[21]  Stefan Hinz,et al.  Review on Convolutional Neural Networks (CNN) in vegetation remote sensing , 2021, ISPRS Journal of Photogrammetry and Remote Sensing.

[22]  François Jonard,et al.  Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning , 2021, Remote. Sens..

[23]  Kathy Steppe,et al.  Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses , 2021, Remote. Sens..

[24]  Zalán Tobak,et al.  Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data , 2021, Land.

[25]  L. Frate,et al.  Unmanned Aerial Vehicle (UAV)-Based Mapping of Acacia saligna Invasion in the Mediterranean Coast , 2021, Remote. Sens..

[26]  Yanjun Su,et al.  Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations , 2021 .

[27]  Nicholas C. Coops,et al.  lidR: An R package for analysis of Airborne Laser Scanning (ALS) data , 2020 .

[28]  K. Sepp,et al.  A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows , 2020 .

[29]  Dae Geon Lee,et al.  Land Cover Classification Using SegNet with Slope, Aspect, and Multidirectional Shaded Relief Images Derived from Digital Surface Model , 2020, J. Sensors.

[30]  Rubens A. C. Lamparelli,et al.  Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop-Livestock System Using Textural Information from PlanetScope Imagery , 2020, Remote. Sens..

[31]  J. Janssen,et al.  EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats , 2020, Applied Vegetation Science.

[32]  Rudolf Urban,et al.  Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds , 2020, Int. J. Digit. Earth.

[33]  Nadhir Al-Ansari,et al.  Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment , 2020, International journal of environmental research and public health.

[34]  Emanuela W. A. Weidlich,et al.  Controlling invasive plant species in ecological restoration: A global review , 2020, Journal of Applied Ecology.

[35]  Taixia Wu,et al.  Fractional evergreen forest cover mapping by MODIS time-series FEVC-CV methods at sub-pixel scales , 2020 .

[36]  Gregory P. Asner,et al.  Challenges in Estimating Tropical Forest Canopy Height from Planet Dove Imagery , 2020, Remote. Sens..

[37]  K. Sepp,et al.  Fine scale plant community assessment in coastal meadows using UAV based multispectral data , 2020, Ecological Indicators.

[38]  K. Bradshaw,et al.  Detecting plant species in the field with deep learning and drone technology , 2020, Methods in Ecology and Evolution.

[39]  B. Nakileza,et al.  Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda , 2020, Geoenvironmental Disasters.

[40]  Jean-Michel Guldmann,et al.  Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics? , 2020 .

[41]  R. McRoberts,et al.  Near-real time forest change detection using PlanetScope imagery , 2020, European Journal of Remote Sensing.

[42]  J. Cherrie,et al.  Machine Learning and Deep Learning , 2019, International Journal of Innovative Technology and Exploring Engineering.

[43]  Shaopeng Wang,et al.  High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features , 2019, Remote. Sens..

[44]  Michael Förster,et al.  UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data , 2019, Remote Sensing of Environment.

[45]  Anita Simic Milas,et al.  Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers , 2019, Remote. Sens..

[46]  Renaud Mathieu,et al.  Estimating above ground biomass as an indicator of carbon storage in vegetated wetlands of the grassland biome of South Africa , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[47]  Miska Luoto,et al.  Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data , 2019, Remote Sensing of Environment.

[48]  J. Couwenberg,et al.  Multisensor data to derive peatland vegetation communities using a fixed-wing unmanned aerial vehicle , 2019, International Journal of Remote Sensing.

[49]  Klara Dolos,et al.  How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing , 2019, Remote Sensing in Ecology and Conservation.

[50]  Sandra Eckert,et al.  Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem , 2019, Ecology and evolution.

[51]  Hang Zhou,et al.  Deep learning based multi-temporal crop classification , 2019, Remote Sensing of Environment.

[52]  S. Kunttu,et al.  New records of the invasive alien Rosa rugosa (Rosaceae) in the Archipelago Sea National Park, SW Finland , 2019 .

[53]  Alberto Gonzalez-Sanchez,et al.  Estimation of vegetation fraction using RGB and multispectral images from UAV , 2018, International Journal of Remote Sensing.

[54]  Jana Müllerová,et al.  Using Single- and Multi-Date UAV and Satellite Imagery to Accurately Monitor Invasive Knotweed Species , 2018, Remote. Sens..

[55]  Dong Liang,et al.  Fusion of Unmanned Aerial Vehicle Panchromatic and Hyperspectral Images Combining Joint Skewness-Kurtosis Figures and a Non-Subsampled Contourlet Transform , 2018, Sensors.

[56]  K. Esler,et al.  The impact of data precision on the effectiveness of alien plant control programmes: a case study from a protected area , 2018, Biological Invasions.

[57]  M. Breed,et al.  Invasive Rosa rugosa populations outperform native populations, but some populations have greater invasive potential than others , 2018, Scientific Reports.

[58]  Jeannine Cavender-Bares,et al.  The spatial sensitivity of the spectral diversity-biodiversity relationship: an experimental test in a prairie grassland. , 2018, Ecological applications : a publication of the Ecological Society of America.

[59]  Andrei Dornik,et al.  Classification of Soil Types Using Geographic Object-Based Image Analysis and Random Forests , 2017, Pedosphere.

[60]  Carolina Sampedro,et al.  Remote Sensing of Invasive Species in the Galapagos Islands: Comparison of Pixel-Based, Principal Component, and Object-Oriented Image Classification Approaches , 2018 .

[61]  Luís Torgo,et al.  SMOGN: a Pre-processing Approach for Imbalanced Regression , 2017, LIDTA@PKDD/ECML.

[62]  Jan Thiele,et al.  Open-Source Processing and Analysis of Aerial Imagery Acquired with a Low-Cost Unmanned Aerial System to Support Invasive Plant Management , 2017, Front. Environ. Sci..

[63]  Madodomzi Mafanya,et al.  Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study , 2017 .

[64]  P. Genovesi,et al.  Plant invasion science in protected areas: progress and priorities , 2017, Biological Invasions.

[65]  Lennart Nilsen,et al.  Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation , 2016, Remote. Sens..

[66]  K. Sepp,et al.  Importance of Microtopography in Determining Plant Community Distribution in Baltic Coastal Wetlands , 2016, Journal of Coastal Research.

[67]  María Soledad Mieza,et al.  Delineation of site-specific management units for operational applications using the topographic position index in La Pampa, Argentina , 2016, Comput. Electron. Agric..

[68]  Q. Meng,et al.  Assessing Net Primary Production in Montane Wetlands from Proximal, Airborne, and Satellite Remote Sensing , 2016 .

[69]  Mark W. Smith,et al.  Structure from motion photogrammetry in physical geography , 2016 .

[70]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[71]  Bernd Bischl,et al.  mlr: Machine Learning in R , 2016, J. Mach. Learn. Res..

[72]  Sile Wang,et al.  Greenness identification based on HSV decision tree , 2015 .

[73]  T. Knight,et al.  Early Successional Microhabitats Allow the Persistence of Endangered Plants in Coastal Sand Dunes , 2015, PloS one.

[74]  K. Sepp,et al.  Recent rates of sedimentation on irregularly flooded Boreal Baltic coastal wetlands: responses to recent changes in sea level , 2014 .

[75]  Huawei Wan,et al.  Monitoring the Invasion of Spartina alterniflora Using Very High Resolution Unmanned Aerial Vehicle Imagery in Beihai, Guangxi (China) , 2014, TheScientificWorldJournal.

[76]  Bin Xu,et al.  Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China , 2014, Remote. Sens..

[77]  J. Stenlid,et al.  Root-Associated Fungi of Rosa rugosa Grown on the Frontal Dunes of the Baltic Sea Coast in Lithuania , 2014, Microbial Ecology.

[78]  Kalev Sepp,et al.  The use of medium point density LiDAR elevation data to determine plant community types in Baltic coastal wetlands , 2013 .

[79]  Dongmei Chen,et al.  Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .

[80]  Philippe De Maeyer,et al.  Application of the topographic position index to heterogeneous landscapes , 2013 .

[81]  J. S. Pedersen,et al.  Multiple introductions and no loss of genetic diversity: invasion history of Japanese Rose, Rosa rugosa, in Europe , 2013, Biological Invasions.

[82]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[83]  L. Kooistra,et al.  Mapping invasive woody species in coastal dunes in the Netherlands: a remote sensing approach using LIDAR and high‐resolution aerial photographs , 2012 .

[84]  Andrew K. Skidmore,et al.  Estimation of grassland biomass and nitrogen using MERIS data , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[85]  Shaun R. Coutts,et al.  Modeling population dynamics, landscape structure, and management decisions for controlling the spread of invasive plants , 2012, Annals of the New York Academy of Sciences.

[86]  W. Junk,et al.  Pasture clearing from invasive woody plants in the Pantanal: a tool for sustainable management or environmental destruction? , 2012, Wetlands Ecology and Management.

[87]  Bin Zhao,et al.  A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants , 2011, Ecol. Informatics.

[88]  Xavier P. Burgos-Artizzu,et al.  utomatic segmentation of relevant textures in agricultural images , 2010 .

[89]  A. Acosta,et al.  Are some communities of the coastal dune zonation more susceptible to alien plant invasion , 2010 .

[90]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[91]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[92]  N. Jordan,et al.  Soil modification by invasive plants: effects on native and invasive species of mixed-grass prairies , 2008, Biological Invasions.

[93]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[94]  L. Vescovo,et al.  Determination of green herbage ratio in grasslands using spectral reflectance. Methods and ground measurements , 2007 .

[95]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[96]  T. Blaschke,et al.  Automated classification of landform elements using object-based image analysis , 2006 .

[97]  H. H. Bruun Prospects for Biocontrol of Invasive Rosa rugosa , 2006, BioControl.

[98]  J. G. White,et al.  Aerial Color Infrared Photography for Determining Early In‐Season Nitrogen Requirements in Corn , 2005 .

[99]  A. Gitelson Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation. , 2004, Journal of plant physiology.

[100]  Linda M. Miller,et al.  Yellow Bush Lupine Invasion in Northern California Coastal Dunes I. Ecological Impacts and Manual Restoration Techniques , 1998 .

[101]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[102]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[103]  J. Everitt,et al.  Using spectral vegetation indices to estimate rangeland productivity , 1992 .

[104]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[105]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .