Application of multiplatform, multispectral remote sensors for mapping intertidal macroalgae: A comparative approach
暂无分享,去创建一个
[1] N. Campbell,et al. Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification , 1992 .
[2] John A. Richards,et al. Efficient maximum likelihood classification for imaging spectrometer data sets , 1994, IEEE Trans. Geosci. Remote. Sens..
[3] Robert A. Schowengerdt,et al. A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification , 1995, IEEE Trans. Geosci. Remote. Sens..
[4] S. K. McFeeters. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .
[5] D. Stengel,et al. Morphology and in situ growth rates of plants of Ascophyllum nodosum (Phaeophyta) from different shore levels and responses of plants to vertical transplantation , 1997 .
[6] D. Stengel,et al. Seasonal variation in the pigment content and photosynthesis of different thallus regions of Ascophyllum nodosum (Fucales, Phaeophyta) in relation to position in the canopy , 1998 .
[7] E. Milton,et al. The use of the empirical line method to calibrate remotely sensed data to reflectance , 1999 .
[8] A. Jacobsen. SPECTRAL IDENTIFICATION OF DANISH GRASSLAND CLASSES RELATED TO MANAGEMENT AND PLANT SPECIES COMPOSITION , 1999 .
[9] Giles M. Foody,et al. Status of land cover classification accuracy assessment , 2002 .
[10] Lynn M. Resler. Remote Sensing and Image Analysis: 4th Edition. T.M. Lillesand and R.W. Kiefer. John Wiley and Sons, New York, 2000. 736 pp. ISBN: 0471255157 , 2002 .
[11] J. Kerr,et al. From space to species: ecological applications for remote sensing , 2003 .
[12] Touria Bajjouk,et al. Application of airborne imaging spectrometry system data to intertidal seaweed classification and mapping , 1996, Hydrobiologia.
[13] Using high spatial resolution hyperspectral imagery to map intertidal habitat structure in Hood Canal, Washington, U.S.A. , 2004 .
[14] Robert L. Vadas, Sr.,et al. Biomass and Productivity of Intertidal Rockweeds (Ascophyllum nodosum LeJolis) in Cobscook Bay , 2004 .
[15] S. Franklin,et al. Remote sensing for large-area habitat mapping , 2005 .
[16] Kidiyo Kpalma,et al. An automatic image registration for applications in remote sensing , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[17] S. Silvestri,et al. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing , 2006 .
[18] H. Su,et al. Evaluation of eelgrass beds mapping using a high-resolution airborne multispectral scanner , 2006 .
[19] Hanqiu Xu. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .
[20] T. Kutser,et al. Spectral library of macroalgae and benthic substrates in Estonian coastal waters , 2006, Proceedings of the Estonian Academy of Sciences. Biology. Ecology.
[21] M. Stekoll,et al. A remote sensing approach to estimating harvestable kelp biomass , 2006, Journal of Applied Phycology.
[22] Mark P. Johnson,et al. Limpet grazing and loss of Ascophyllum nodosum canopies on decadal time scales , 2007 .
[23] Benjamin D. Hennig,et al. Hyperspectral remote sensing and analysis of intertidal zones: A contribution to monitor coastal biodiversity , 2007 .
[24] L. Airoldi,et al. Recovering a lost baseline: missing kelp forests from a metropolitan coast , 2008 .
[25] D. Siegel,et al. Scaling giant kelp field measurements to regional scales using satellite observations , 2010 .
[26] George P. Petropoulos,et al. A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping , 2010, Sensors.
[27] A. Rango,et al. Image Processing and Classification Procedures for Analysis of Sub-decimeter Imagery Acquired with an Unmanned Aircraft over Arid Rangelands , 2011 .
[28] Stuart R. Phinn,et al. Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007 , 2011, Remote. Sens..
[29] Yangquan Chen,et al. Using a multispectral autonomous unmanned aerial remote sensing platform (AggieAir) for riparian and wetlands applications , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.
[30] Juan Freire,et al. Remote sensing with SPOT-4 for mapping kelp forests in turbid waters on the south European Atlantic shelf , 2011 .
[31] J. Hicke,et al. Evaluating the potential of multispectral imagery to map multiple stages of tree mortality , 2011 .
[32] A. Lechner,et al. CHARACTERISING UPLAND SWAMPS USING OBJECT-BASED CLASSIFICATION METHODS AND HYPER-SPATIAL RESOLUTION IMAGERY DERIVED FROM AN UNMANNED AERIAL VEHICLE , 2012 .
[33] T. Kutser,et al. Assessment of AHS (Airborne Hyperspectral Scanner) sensor to map macroalgal communities on the Ría de vigo and Ría de Aldán coast (NW Spain) , 2012 .
[34] Natascha Oppelt,et al. Hyperspectral classification approaches for intertidal macroalgae habitat mapping: a case study in Heligoland , 2012 .
[35] Karen Anderson,et al. Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .
[36] T. Kutser,et al. Assessment of the hyperspectral sensor CASI-2 for macroalgal discrimination on the Ría de Vigo coast (NW Spain) using field spectroscopy and modelled spectral libraries , 2013 .
[37] Jan G. P. W. Clevers,et al. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[38] I. Colomina,et al. Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .
[39] Jonne Kotta,et al. In-air spectral signatures of the Baltic Sea macrophytes and their statistical separability , 2014 .
[40] Piero Toscano,et al. Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture , 2015, Remote. Sens..
[41] E. Malta,et al. European seaweeds under pressure: Consequences for communities and ecosystem functioning , 2015 .
[42] Brandon J. Russell,et al. Hyperspectral discrimination of floating mats of seagrass wrack and the macroalgae Sargassum in coastal waters of Greater Florida Bay using airborne remote sensing , 2015 .
[43] Marco Dubbini,et al. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..
[44] P. Thenkabail,et al. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation , 2015 .
[45] D. Siegel,et al. Remote monitoring of giant kelp biomass and physiological condition: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) mission , 2015 .
[46] Brendan F. Kohrn,et al. Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology , 2016, Applications in Plant Sciences.
[47] Antoine M. Dujon,et al. Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales , 2016 .
[48] S. Wich,et al. A preliminary assessment of using conservation drones for Sumatran orang-utan (Pongo abelii) distribution and density , 2016 .
[49] I. Jonsen,et al. An Economical Custom-Built Drone for Assessing Whale Health , 2017, Front. Mar. Sci..
[50] Sammy A. Perdomo,et al. RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields , 2018, Precision Agriculture.
[51] Blake M. Allan,et al. Applications of unmanned aerial vehicles in intertidal reef monitoring , 2017, Scientific Reports.
[52] Raul Morais,et al. Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..
[53] Giorgos Mallinis,et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring , 2018, Remote. Sens..
[54] Jorge Torres-Sánchez,et al. An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery , 2018, Remote. Sens..
[55] J. Shutler,et al. Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone , 2018 .
[56] Doreen S. Boyd,et al. UAVs in pursuit of plant conservation - Real world experiences , 2017, Ecol. Informatics.
[57] Jennifer J. Swenson,et al. Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments , 2018, Remote. Sens..
[58] Matthew F. McCabe,et al. Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects , 2018, Remote. Sens..
[59] Yong He,et al. Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery , 2018, Biosystems Engineering.
[60] Daniele Ventura,et al. Mapping and Classification of Ecologically Sensitive Marine Habitats Using Unmanned Aerial Vehicle (UAV) Imagery and Object-Based Image Analysis (OBIA) , 2018, Remote. Sens..
[61] Elisabeth A. Addink,et al. Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images , 2018, Remote. Sens..
[62] Christophe Delacourt,et al. Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications , 2018, Remote. Sens..
[63] Jonathan P. Dash,et al. UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health , 2018, Remote. Sens..
[64] Christophe Sannier,et al. The effects of imperfect reference data on remote sensing-assisted estimators of land cover class proportions , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[65] I. Tittley,et al. A comparison of multispectral aerial and satellite imagery for mapping intertidal seaweed communities , 2018 .
[66] Junichi Kurihara,et al. A novel approach for vegetation classification using UAV-based hyperspectral imaging , 2018, Comput. Electron. Agric..
[67] B. Kelaher,et al. The potential for unmanned aerial vehicles (UAVs) to conduct marine fauna surveys in place of manned aircraft , 2018 .
[68] Louise Willemen,et al. Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency , 2018, Remote. Sens..
[69] S. Longmore,et al. Successful observation of orangutans in the wild with thermal-equipped drones , 2019, Journal of Unmanned Vehicle Systems.
[70] Isla H Myers-Smith,et al. Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes , 2018, bioRxiv.
[71] Kyle C. Cavanaugh,et al. Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery , 2019, Remote. Sens..
[72] J. Paneque-Gálvez,et al. The Global Emergence of Community Drones (2012–2017) , 2019, Drones.
[73] A. Pellegrinelli,et al. Multispectral UAV monitoring of submerged seaweed in shallow water , 2019, Applied Geomatics.
[74] J. Conn,et al. High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery , 2019, PLoS neglected tropical diseases.
[75] L. Lautz,et al. Sub-metre mapping of surface soil moisture in proglacial valleys of the tropical Andes using a multispectral unmanned aerial vehicle , 2019, Remote Sensing of Environment.
[76] Timothy McCarthy,et al. Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques , 2019, Remote. Sens..
[77] Ian Hawes,et al. Unmanned Aerial Vehicles (UAVs) for Monitoring Macroalgal Biodiversity: Comparison of RGB and Multispectral Imaging Sensors for Biodiversity Assessments , 2019, Remote. Sens..
[78] David W Johnston,et al. Unoccupied Aircraft Systems in Marine Science and Conservation. , 2019, Annual review of marine science.
[79] D. Siegel,et al. Three decades of variability in California's giant kelp forests from the Landsat satellites , 2020 .