150
Gao BC (1996) Ndwi - a normalized difference water index for remote sensing of vegetation liquid
water from space. Remote Sens Environ 58(3):257–266
Genovesi P, Carboneras C, Vila M, Walton P (2015) EU adopts innovative legislation on invasive species: a step towards a global response to biological invasions? Biol Invasions
17(5):1307–1311
Gitelson A, Merzlyak MN (1994) Quantitative estimation of chlorophyll-a using reflectance
spectra: experiments with autumn chestnut and maple leaves. J Photochem Photobiol B Biol
22(3):247–252
Gitelson AA, Merzlyak MN (1996) Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol 148(3–4):494–500
Gitelson AA, Buschmann C, Lichtenthaler HK (1999) The chlorophyll fluorescence ratio
F-735/F-700 as an accurate measure of the chlorophyll content in plants. Remote Sens Environ
69(3):296–302
Gitelson AA, Merzlyak MN, Chivkunova OB (2001) Optical properties and non-destructive estimation of anthocyanin content in plant leaves. Photochem Photobiol 74(1):38–45
Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation
indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90(3):337–352. https://doi.
org/10.1016/j.rse.2003.12.013
Hanavan RP, Pontius J, Hallett R (2015) A 10-year assessment of hemlock decline in the Catskill
Mountain region of New York State using hyperspectral remote sensing techniques. J Econ
Entomol 108(1):339–349
Hardisky MA, Klemas V, Smart RM (1983) The influence of soil salinity, growth form, and leaf
moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm Eng Remote
Sens 49(1):77–83
Harwood T, Tomlinson I, Potter C, Knight J (2011) Dutch elm disease revisited: past, present and
future management in Great Britain. Plant Pathol 60(3):545–555
Hennon PE, D'Amore DV, Schaberg PG, Wittwer DT, Shanley CS (2012) Shifting climate, altered
niche, and a dynamic conservation strategy for yellow-cedar in the North Pacific coastal rainforest. Bioscience 62(2):147–158
Horler D, DOCKRAY M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote
Sens 4(2):273–288
Huete AR (1988) A soil adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309.
https://doi.org/10.1016/0034-4257(88)90106-x
Huete A, Justice C, Liu H (1994) Development of vegetation and soil indices for MODISEOS. Remote Sens Environ 49(3):224–234
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of
Environment 83(1–2):195–213
Huggett BA, Schaberg PG, Hawley GJ, Eagar C (2007) Long-term calcium addition increases
growth release, wound closure, and health of sugar maple (Acer saccharum) trees at the
Hubbard Brook Experimental Forest. Can J For Res 37(9):1692–1700
Hunt ER, Rock BN (1989) Detection of changes in leaf water content using near infrared and middle infrared reflectances. Remote Sens Environ 30(1):43–54. https://doi.
org/10.1016/0034-4257(89)90046-1
Johnson EW, Ross J (2008) Quantifying error in aerial survey data. Aust For 71(3):216–222
Johnson E, Wittwer D (2008) Aerial detection surveys in the United States. Aust For 71(3):212–215
Jordan CF (1969) Derivation of leaf area index from quality of light on the forest floor. Ecology
(Washington DC) 50(4):663–666. https://doi.org/10.2307/1936256
Kaufman YJ, Tanre D (1992) Atmospherically resistant vegetation index (ARVI) for EOSMODIS. IEEE Trans Geosci Remote Sens 30(2):261–270
Kokaly RF, Despain DG, Clark RN, Livo KE (2003) Mapping vegetation in yellowstone national
park using spectral feature analysis of AVIRIS data. Remote Sens Environ 84(3):437–456
J. Pontius et al.
Gao BC (1996) Ndwi - a normalized difference water index for remote sensing of vegetation liquid
water from space. Remote Sens Environ 58(3):257–266
Genovesi P, Carboneras C, Vila M, Walton P (2015) EU adopts innovative legislation on invasive species: a step towards a global response to biological invasions? Biol Invasions
17(5):1307–1311
Gitelson A, Merzlyak MN (1994) Quantitative estimation of chlorophyll-a using reflectance
spectra: experiments with autumn chestnut and maple leaves. J Photochem Photobiol B Biol
22(3):247–252
Gitelson AA, Merzlyak MN (1996) Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol 148(3–4):494–500
Gitelson AA, Buschmann C, Lichtenthaler HK (1999) The chlorophyll fluorescence ratio
F-735/F-700 as an accurate measure of the chlorophyll content in plants. Remote Sens Environ
69(3):296–302
Gitelson AA, Merzlyak MN, Chivkunova OB (2001) Optical properties and non-destructive estimation of anthocyanin content in plant leaves. Photochem Photobiol 74(1):38–45
Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation
indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90(3):337–352. https://doi.
org/10.1016/j.rse.2003.12.013
Hanavan RP, Pontius J, Hallett R (2015) A 10-year assessment of hemlock decline in the Catskill
Mountain region of New York State using hyperspectral remote sensing techniques. J Econ
Entomol 108(1):339–349
Hardisky MA, Klemas V, Smart RM (1983) The influence of soil salinity, growth form, and leaf
moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm Eng Remote
Sens 49(1):77–83
Harwood T, Tomlinson I, Potter C, Knight J (2011) Dutch elm disease revisited: past, present and
future management in Great Britain. Plant Pathol 60(3):545–555
Hennon PE, D'Amore DV, Schaberg PG, Wittwer DT, Shanley CS (2012) Shifting climate, altered
niche, and a dynamic conservation strategy for yellow-cedar in the North Pacific coastal rainforest. Bioscience 62(2):147–158
Horler D, DOCKRAY M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote
Sens 4(2):273–288
Huete AR (1988) A soil adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309.
https://doi.org/10.1016/0034-4257(88)90106-x
Huete A, Justice C, Liu H (1994) Development of vegetation and soil indices for MODISEOS. Remote Sens Environ 49(3):224–234
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of
Environment 83(1–2):195–213
Huggett BA, Schaberg PG, Hawley GJ, Eagar C (2007) Long-term calcium addition increases
growth release, wound closure, and health of sugar maple (Acer saccharum) trees at the
Hubbard Brook Experimental Forest. Can J For Res 37(9):1692–1700
Hunt ER, Rock BN (1989) Detection of changes in leaf water content using near infrared and middle infrared reflectances. Remote Sens Environ 30(1):43–54. https://doi.
org/10.1016/0034-4257(89)90046-1
Johnson EW, Ross J (2008) Quantifying error in aerial survey data. Aust For 71(3):216–222
Johnson E, Wittwer D (2008) Aerial detection surveys in the United States. Aust For 71(3):212–215
Jordan CF (1969) Derivation of leaf area index from quality of light on the forest floor. Ecology
(Washington DC) 50(4):663–666. https://doi.org/10.2307/1936256
Kaufman YJ, Tanre D (1992) Atmospherically resistant vegetation index (ARVI) for EOSMODIS. IEEE Trans Geosci Remote Sens 30(2):261–270
Kokaly RF, Despain DG, Clark RN, Livo KE (2003) Mapping vegetation in yellowstone national
park using spectral feature analysis of AVIRIS data. Remote Sens Environ 84(3):437–456
J. Pontius et al.