UAS, sensors, and data processing in agroforestry: a review towards practical applications
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Luís Pádua | T. Adão | E. Peres | J. Sousa | Jonás Hruska | R. Morais | Jakub Vanko | L. Pádua | Emanuel Peres
[1] L. Steels,et al. Topological order in the Haldane model with spin-spin on-site interactions , 2017, 1707.03480.
[2] Arthur P. Cracknell,et al. UAVs: regulations and law enforcement , 2017 .
[3] C. Schmullius,et al. Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective , 2017 .
[4] Wolfgang Schwanghart,et al. Local-scale flood mapping on vegetated floodplains from radiometrically calibrated airborne LiDAR data , 2016 .
[5] Jing Liu,et al. LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid , 2016 .
[6] L. Caturegli,et al. Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses , 2016, PloS one.
[7] Diego Patino,et al. Multispectral mapping in agriculture: Terrain mosaic using an autonomous quadcopter UAV , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).
[8] S. Nebiker,et al. LIGHT-WEIGHT MULTISPECTRAL UAV SENSORS AND THEIR CAPABILITIES FOR PREDICTING GRAIN YIELD AND DETECTING PLANT DISEASES , 2016 .
[9] Dirk Hoffmeister,et al. A Comparison of UAV- and TLS-derived Plant Height for Crop Monitoring: Using Polygon Grids for the Analysis of Crop Surface Models (CSMs) , 2016 .
[10] Pablo J. Zarco-Tejada,et al. Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery , 2016, Remote. Sens..
[11] L. Wallace,et al. Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds , 2016 .
[12] Bilal Alsalam,et al. Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision , 2016, 2016 IEEE Aerospace Conference.
[13] Michael Sommer,et al. UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes—A Case Study from the CarboZALF Experimental Area , 2016, Sensors.
[14] Pablo J. Zarco-Tejada,et al. Airborne Thermal Imagery to Detect the Seasonal Evolution of Crop Water Status in Peach, Nectarine and Saturn Peach Orchards , 2016, Remote. Sens..
[15] Qi Zhang,et al. Research on UAV Remote Sensing Image Mosaic Method Based on SIFT , 2015 .
[16] Adam J. Mathews. A Practical UAV Remote Sensing Methodology to Generate Multispectral Orthophotos for Vineyards: Estimation of Spectral Reflectance Using Compact Digital Cameras , 2015, Int. J. Appl. Geospat. Res..
[17] K. Karantzalos,et al. LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS , 2015 .
[18] R. Quiroz. Remote sensing as a monitoring tool for cropping area determination in smallholder agriculture in Tanzania and Uganda , 2015 .
[19] J. Liénard,et al. 3D Tree Dimensionality Assessment Using Photogrammetry and Small Unmanned Aerial Vehicles , 2015, bioRxiv.
[20] Simon Bennertz,et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley , 2015, Int. J. Appl. Earth Obs. Geoinformation.
[21] Jorge Torres-Sánchez,et al. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops , 2015, Comput. Electron. Agric..
[22] Lorenzo Comba,et al. Vineyard detection from unmanned aerial systems images , 2015, Comput. Electron. Agric..
[23] Pablo J. Zarco-Tejada,et al. Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas , 2015, Remote. Sens..
[24] V. Klemas,et al. Coastal and Environmental Remote Sensing from Unmanned Aerial Vehicles: An Overview , 2015 .
[25] Pablo J. Zarco-Tejada,et al. High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..
[26] Marco Dubbini,et al. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..
[27] Gonzalo Pajares,et al. Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) , 2015 .
[28] Piero Toscano,et al. Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture , 2015, Remote. Sens..
[29] Henrique Lorenzo,et al. Aerial thermography from low-cost UAV for the generation of thermographic digital terrain models , 2015 .
[30] Miguel Ángel Moreno,et al. Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques , 2015, American Journal of Enology and Viticulture.
[31] Fei Li,et al. Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain , 2014, Int. J. Appl. Earth Obs. Geoinformation.
[32] Enric Pastor,et al. UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas , 2014, Remote. Sens..
[33] Juha Suomalainen,et al. A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles , 2014, Remote. Sens..
[34] P. Zarco-Tejada,et al. Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle , 2014, Precision Agriculture.
[35] Simon Bennertz,et al. Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..
[36] Mohammadreza Aghaei,et al. Light Unmanned Aerial Vehicles (UAVs) for Cooperative Inspection of PV Plants , 2014, IEEE Journal of Photovoltaics.
[37] I. Colomina,et al. Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .
[38] Jochen Teizer,et al. Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system , 2014 .
[39] Arko Lucieer,et al. Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[40] F. López-Granados,et al. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV , 2014 .
[41] F. Nex,et al. UAV for 3D mapping applications: a review , 2014 .
[42] F. López-Granados,et al. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat , 2014, Precision Agriculture.
[43] Pablo J. Zarco-Tejada,et al. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices , 2013 .
[44] Juha Hyyppä,et al. Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography , 2013, Remote. Sens..
[45] Jinqiang Cui,et al. UAV LiDAR for below-canopy forest surveys , 2013 .
[46] Heikki Saari,et al. Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV , 2013, Remote Sensing.
[47] Gaurav Tiwari,et al. UAV systems for parameter identification in agriculture , 2013, 2013 IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS).
[48] Heikki Saari,et al. Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture , 2013, Remote. Sens..
[49] F. López-Granados,et al. Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images , 2013, PloS one.
[50] Luke Wallace,et al. Assessing the stability of canopy maps produced from UAV-LiDAR data , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.
[51] E. Fereres,et al. Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard , 2013, Precision Agriculture.
[52] Adam J. Mathews,et al. Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud , 2013, Remote. Sens..
[53] Karen Anderson,et al. Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .
[54] Yukio Kosugi,et al. Characterization of Rice Paddies by a UAV-Mounted Miniature Hyperspectral Sensor System , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[55] Martin Israel. A UAV-BASED ROE DEER FAWN DETECTION SYSTEM , 2012 .
[56] J. Baluja,et al. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV) , 2012, Irrigation Science.
[57] J. Kovacs,et al. The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.
[58] Heikki Saari,et al. Hyperspectral reflectance signatures and point clouds for precision agriculture by light weight UAV imaging system , 2012 .
[59] Vincent G. Ambrosia,et al. Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use , 2012, Remote. Sens..
[60] Arko Lucieer,et al. Development of a UAV-LiDAR System with Application to Forest Inventory , 2012, Remote. Sens..
[61] Arko Lucieer,et al. An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds , 2012, Remote. Sens..
[62] Stephan Getzin,et al. Assessing biodiversity in forests using very high‐resolution images and unmanned aerial vehicles , 2012 .
[63] P. Zarco-Tejada,et al. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .
[64] International Journal of Remote Sensing , 2012 .
[65] A. Matese,et al. A flexible unmanned aerial vehicle for precision agriculture , 2012, Precision Agriculture.
[66] Heikki Saari,et al. Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications , 2011, Remote Sensing.
[67] Francis Y. Enomoto,et al. The Ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006–2010) , 2011 .
[68] Ryan R. Jensen,et al. Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities , 2011 .
[69] Takeshi Motohka,et al. Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology , 2010, Remote. Sens..
[70] A. Skidmore,et al. Erasmus Mundus - External cooperation window as a framework for higher education cooperation in the middle east region: opportunities and challenges , 2010 .
[71] Reg Austin,et al. Unmanned Aircraft Systems: Uavs Design, Development and Deployment , 2010 .
[72] Chris Jenks,et al. Law from Above: Unmanned Aerial Systems, Use of Force, and the Law of Armed Conflict , 2010 .
[73] Pablo J. Zarco-Tejada,et al. Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[74] G. Meyer,et al. Verification of color vegetation indices for automated crop imaging applications , 2008 .
[75] Chi-Hua Huang,et al. Low-Altitude Digital Photogrammetry Technique to Assess Ephemeral Gully Erosion , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.
[76] Jeffrey M. Sullivan,et al. Evolution or revolution? the rise of UAVs , 2006, IEEE Technology and Society Magazine.
[77] Hanqiu Xu. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .
[78] N. D. Tillett,et al. Automated Crop and Weed Monitoring in Widely Spaced Cereals , 2006, Precision Agriculture.
[79] John R. Miller,et al. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy , 2005 .
[80] Paul E. Gessler,et al. Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling , 2005 .
[81] Y. Cohen,et al. Estimation of leaf water potential by thermal imagery and spatial analysis. , 2005, Journal of experimental botany.
[82] James A. Brass,et al. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support , 2004 .
[83] Christian Germain,et al. Row detection in high resolution remote sensing images of vine fields , 2003 .
[84] M. Fladeland,et al. Remote sensing for biodiversity science and conservation , 2003 .
[85] John R. Miller,et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .
[86] A. Gitelson,et al. Novel algorithms for remote estimation of vegetation fraction , 2002 .
[87] E. Chuvieco,et al. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination , 2002 .
[88] S. Christensen,et al. Colour and shape analysis techniques for weed detection in cereal fields , 2000 .
[89] P. Thenkabail,et al. Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .
[90] Hamlyn G. Jones,et al. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling , 1999 .
[91] Alan H. Strahler,et al. The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..
[92] A. Gitelson,et al. Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .
[93] B. Gao. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .
[94] G. Rondeaux,et al. Optimization of soil-adjusted vegetation indices , 1996 .
[95] Dorothy K. Hall,et al. A snow index for the Landsat Thematic Mapper and Moderate Resolution Imaging Spectroradiometer , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.
[96] A. Huete,et al. A Modified Soil Adjusted Vegetation Index , 1994 .
[97] G. Meyer,et al. Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .
[98] C. Field,et al. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .
[99] V. Caselles,et al. Mapping burns and natural reforestation using thematic Mapper data , 1991 .
[100] A. Huete. A soil-adjusted vegetation index (SAVI) , 1988 .
[101] C. Tucker. Red and photographic infrared linear combinations for monitoring vegetation , 1979 .
[102] C. Jordan. Derivation of leaf-area index from quality of light on the forest floor , 1969 .
[103] Hoam Chung,et al. Estimation of crop water stress in a nectarine orchard using high-resolution imagery from unmanned aerial vehicle (UAV) , 2015 .
[104] J. Bendig. Unmanned aerial vehicles (UAVs) for multi-temporal crop surface modelling. A new method for plant height and biomass estimation based on RGB-imaging , 2015 .
[105] Troy S. Bruggemann,et al. Sensors for missions , 2015 .
[106] D. Ryu,et al. Automated detection and segmentation of vine rows using high resolution UAS imagery in a commercial vineyard , 2015 .
[107] Apostolos Tsagaris,et al. The Use of Unmanned Aerial Systems (UAS) in Agriculture , 2015, HAICTA.
[108] Catur Aries Rokhmana,et al. The Potential of UAV-based Remote Sensing for Supporting Precision Agriculture in Indonesia☆ , 2015 .
[109] Wilhelm Claupein,et al. Enhancement of micro Unmanned Aerial Vehicles for agricultural aerial sensor systems , 2013 .
[110] C. Watson,et al. Development of an Unmanned Aerial Vehicle (UAV) for hyper-resolution vineyard mapping based on visible, multispectral and thermal imagery , 2011 .
[111] Thomas Blaschke,et al. Object based image analysis for remote sensing , 2010 .
[112] S. Nebiker,et al. A Light-weight Multispectral Sensor for Micro UAV - Opportunities for Very High Resolution Airborne Remote Sensing , 2008 .
[113] Pablo J. Zarco-Tejada,et al. Using hyperspectral remote sensing to map grape quality in 'Tempranillo' vineyards affected by iron deficiency chlorosis , 2007 .
[114] John A. Richards,et al. Remote Sensing Digital Image Analysis: An Introduction , 1999 .
[115] S. Idso,et al. Normalizing the stress-degree-day parameter for environmental variability☆ , 1981 .
[116] N. Otsu. A Threshold Selection Method from Gray-Level Histograms , 1979, IEEE Trans. Syst. Man Cybern..
[117] J. A. Schell,et al. Monitoring vegetation systems in the great plains with ERTS , 1973 .
[118] Aníbal Ollero,et al. Journal of Intelligent & Robotic Systems manuscript No. (will be inserted by the editor) An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement , 2022 .