Four Dimensional Mapping of Vegetation Moisture Content Using Dual-Wavelength Terrestrial Laser Scanning
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[1] S. Ustin,et al. Water content estimation in vegetation with MODIS reflectance data and model inversion methods , 2003 .
[2] T. Jackson,et al. Remote sensing of vegetation water content from equivalent water thickness using satellite imagery , 2008 .
[3] F. Baret,et al. PROSPECT: A model of leaf optical properties spectra , 1990 .
[4] Robin L. Chazdon,et al. PHOTOSYNTHETIC LIGHT ENVIRONMENTS IN A LOWLAND TROPICAL RAIN FOREST IN COSTA RICA , 1984 .
[5] David Riaño,et al. Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona , 2008 .
[6] B. Rock,et al. Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .
[7] J. Peñuelas,et al. The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .
[8] R. Fensholt,et al. Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment , 2003 .
[9] J. Eitel,et al. Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner , 2010 .
[10] Andrew K. Skidmore,et al. Effects of Canopy Structural Variables on Retrieval of Leaf Dry Matter Content and Specific Leaf Area From Remotely Sensed Data , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[11] M. G. Ryan,et al. Evaluating theories of drought-induced vegetation mortality using a multimodel-experiment framework. , 2013, The New phytologist.
[12] A. Kuusk. A two-layer canopy reflectance model , 2001 .
[13] S. Tarantola,et al. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .
[14] F. M. Danson,et al. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors , 1995 .
[15] D. Smart,et al. Evaluation of Hyperspectral Reflectance Indexes to Detect Grapevine Water Status in Vineyards , 2007, American Journal of Enology and Viticulture.
[16] F. Mark Danson,et al. Angular Reflectance of Leaves With a Dual-Wavelength Terrestrial Lidar and Its Implications for Leaf-Bark Separation and Leaf Moisture Estimation , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[17] S. Seneviratne,et al. Concurrent 2018 Hot Extremes Across Northern Hemisphere Due to Human‐Induced Climate Change , 2019, Earth's future.
[18] T. Jackson,et al. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands , 2005 .
[19] D. Riaño,et al. Estimation of live fuel moisture content from MODIS images for fire risk assessment , 2008 .
[20] C. Tucker. Remote sensing of leaf water content in the near infrared , 1980 .
[21] C. Herrera,et al. Epigenetic correlates of plant phenotypic plasticity: DNA methylation differs between prickly and nonprickly leaves in heterophyllous Ilex aquifolium (Aquifoliaceae) trees , 2013 .
[22] S. Tarantola,et al. Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .
[23] Pablo J. Zarco-Tejada,et al. Detection of water stress in an olive orchard with thermal remote sensing imagery , 2006 .
[24] Nate G. McDowell,et al. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene , 2015 .
[25] F. M. Danson,et al. A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving t , 2013 .
[26] B. Gao. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .
[27] Heather McNairn,et al. Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies , 2003 .
[28] T. Sharkey,et al. Stomatal conductance and photosynthesis , 1982 .
[29] Lu Li,et al. Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties , 2015, Remote. Sens..
[30] Sylvia Dayau,et al. Significance and limits in the use of predawn leaf water potential for tree irrigation , 1999, Plant and Soil.
[31] Teemu Hakala,et al. Uncertainty in multispectral lidar signals caused by incidence angle effects , 2018, Interface Focus.
[32] D. Meier,et al. Photosynthetic activity, chloroplast ultrastructure, and leaf characteristics of high-light and low-light plants and of sun and shade leaves , 1981, Photosynthesis Research.
[33] S. Jansen,et al. The correlations and sequence of plant stomatal, hydraulic, and wilting responses to drought , 2016, Proceedings of the National Academy of Sciences.
[34] D. Roberts,et al. Deriving Water Content of Chaparral Vegetation from AVIRIS Data , 2000 .
[35] Kevin W Eliceiri,et al. NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.
[36] J. Mills,et al. Three dimensional mapping of forest canopy equivalent water thickness using dual-wavelength terrestrial laser scanning , 2019, Agricultural and Forest Meteorology.
[37] Andrew K. Skidmore,et al. Impact of Vertical Canopy Position on Leaf Spectral Properties and Traits across Multiple Species , 2018, Remote. Sens..
[38] Jingcheng Zhang,et al. Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects , 2014 .
[39] K. Hikosaka,et al. Leaf canopy as a dynamic system: ecophysiology and optimality in leaf turnover. , 2004, Annals of botany.
[40] D. Lüthi,et al. The role of increasing temperature variability in European summer heatwaves , 2004, Nature.
[41] F. M. Danson,et al. High-spectral resolution data for determining leaf water content , 1992 .
[42] Pingheng Li,et al. Canopy vertical heterogeneity plays a critical role in reflectance simulation , 2013 .
[43] P. Stott,et al. Human contribution to the European heatwave of 2003 , 2004, Nature.
[44] J. Mills,et al. Estimation of vegetation water content at leaf and canopy level using dual-wavelength commercial terrestrial laser scanners , 2018, Interface Focus.
[45] Jennifer Pontius,et al. Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty , 2014 .
[46] Juha Hyyppä,et al. The potential of dual-wavelength terrestrial lidar in early detection of Ips typographus (L.) infestation – Leaf water content as a proxy , 2019, Remote Sensing of Environment.
[47] P. Uhe,et al. The day the 2003 European heatwave record was broken. , 2019, The Lancet. Planetary health.
[48] Michael E. Schaepman,et al. Estimating canopy water content using hyperspectral remote sensing data , 2010, Int. J. Appl. Earth Obs. Geoinformation.
[49] Claudia M. Castaneda,et al. Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods , 1998 .
[50] M. Vastaranta,et al. Can Leaf Water Content Be Estimated Using Multispectral Terrestrial Laser Scanning? A Case Study With Norway Spruce Seedlings , 2018, Front. Plant Sci..
[51] Andrew K. Skidmore,et al. Canopy leaf water content estimated using terrestrial LiDAR , 2017 .
[52] Rachel Gaulton,et al. The potential of dual-wavelength laser scanning for estimating vegetation moisture content , 2013 .
[53] Vicky Buchanan-Wollaston,et al. The molecular biology of leaf senescence , 1997 .
[54] K. G. McNaughton,et al. Stomatal Control of Transpiration: Scaling Up from Leaf to Region , 1986 .
[55] F. Baret,et al. Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .
[56] K. Itten,et al. Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties , 2004 .
[57] M. Werger,et al. Maximizing daily canopy photosynthesis with respect to the leaf nitrogen allocation pattern in the canopy , 1987, Oecologia.
[58] Roberta E. Martin,et al. Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. , 2017, The New phytologist.
[59] J. Mills,et al. THE POTENTIAL OF DUAL-WAVELENGTH TERRESTRIAL LASER SCANNING IN 3D CANOPY FUEL MOISTURE CONTENT MAPPING , 2019, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
[60] K. Tansey,et al. Field spectroscopy and radiative transfer modelling to assess impacts of petroleum pollution on biophysical and biochemical parameters of the Amazon rainforest , 2017, Environmental Earth Sciences.
[61] Assaf Anyamba,et al. Global Trends in Seasonality of Normalized Difference Vegetation Index (NDVI), 1982-2011 , 2013, Remote. Sens..
[62] E. Fischer,et al. A Review of the European Summer Heat Wave of 2003 , 2010 .
[63] John D. Aber,et al. FOLIAGE-HEIGHT PROFILES AND SUCCESSION IN NORTHERN HARDWOOD FORESTS' , 1979 .
[64] P. Reich,et al. Canopy structure and vertical patterns of photosynthesis and related leaf traits in a deciduous forest , 1993, Oecologia.