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