151
Kolb T, Teulon D (1991) Relationship between sugar maple budburst phenology and pear thrips
damage. Can J For Res 21(7):1043–1048
Koltunov A, Ramirez C, Ustin SL (2015) eDaRT: the ecosystem disturbance and recovery tracking
system prototype supporting ecosystem management in California. NASA Carbon Cycle and
Ecosystems Joint Science Workshop College Park, MD, April, 19–24
Kosiba AM, Meigs GW, Duncan J, Pontius J, Keeton WS, Tait E (2018) Spatiotemporal patterns of
forest damage in the Northeastern United States: 2000–2016. Forest Ecology and Management
430:94–104.
Lausch A, Heurich M, Gordalla D, Dobner HJ, Gwillym-Margianto S, Salbach C (2013)
Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral
remote-sensing techniques at different scales. For Ecol Manag 308:76–89
Lausch A, Erasmi S, King D, Magdon P, Heurich M (2017) Understanding forest health with
remote sensing-part II—a review of approaches and data models. Remote Sens 9(2):129
Lillesand TM, Kiefer RW (1994) Remote sensing and photo interpretation, 3rd edn. John Wiley&
Sons, New York
Maccioni A, Agati G, Mazzinghi P (2001) New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J Photochem Photobiol 61(1,2):52–61
Martin K, Norris A, Drever M (2006) Effects of bark beetle outbreaks on avian biodiversity in
the British Columbia interior: implications for critical habitat management. J Ecosyst Manag
7(3):10–24
McBride MF, Lambert KF, Huff ES, Theoharides KA, Field P, Thompson JR (2017) Increasing
the effectiveness of participatory scenario development through codesign. Ecol Soc 22(3):16
McConnell TJ (1999) Aerial sketch mapping surveys, the past, present and future. In: Paper from
the north American science symposium, toward a unified framework for inventorying and monitoring forest ecosystem resources, Guadalajara, Mexico
McMurtrey J III, Chappelle EW, Kim M, Meisinger J (1994) Distinguishing nitrogen fertilization
levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance
measurements. Remote Sens Environ 47(1):36–44
Meroni M, Rossini M, Picchi V, Panigada C, Cogliati S, Nali C, Colombo R (2008) Assessing
steady-state fluorescence and PRI from hyperspectral proximal sensing as early indicators of
plant stress: the case of ozone exposure. Sensors 8(3):1740–1754. https://doi.org/10.3390/
s8031740
Merzlyak MN, Gitelson AA, Chivkunova OB, Rakitin VY (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol Plant 106(1):135–
141. https://doi.org/10.1034/j.1399-3054.1999.106119.x
Mohammed GH, Binder WD, Gillies SL (1995) Chlorophyll fluorescence - a review of its practical
forestry applications and instrumentation. Scand J For Res 10(4):383–410
Mumford R (2017) New approaches for the early detection of tree health pests and pathogens.
Impact 1(7):47–49
Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995a) The interpretation of spectral vegetation
indexes. IEEE Trans Geosci Remote Sens 33(2):481–486
Myneni RB, Maggion S, Iaquinta J, Privette JL, Gobron N, Pinty B, Kimes DS, Verstraete MM,
Williams DL (1995b) Optical remote sensing of vegetation: modeling, caveats, and algorithms.
Remote Sens Environ 51:169–188
Norman SP, Hargrove WW, Spruce JP, Christie WM, Schroeder SW (2013) Highlights of satellitebased forest change recognition and tracking using the ForWarn System. Gen Tech Rep SRSGTR- 180 Asheville, NC: USDA-Forest Service, Southern Research Station, 30 p, 180:1–30
Pearlman JS, Barry PS, Segal CC, Shepanski J, Beiso D, Carman SL (2003) Hyperion, a spacebased imaging spectrometer. IEEE Trans Geosci Remote Sens 41:1160–1173
Pearson L, Miller LD (1972) Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grasslands, Colorado. In: Proceedings of
the 8th international symposium on remote sensing of the environment, Ann Arbor, MI, 1972.
ERIM, Ann Arbor, pp 1357–1381
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…
Kolb T, Teulon D (1991) Relationship between sugar maple budburst phenology and pear thrips
damage. Can J For Res 21(7):1043–1048
Koltunov A, Ramirez C, Ustin SL (2015) eDaRT: the ecosystem disturbance and recovery tracking
system prototype supporting ecosystem management in California. NASA Carbon Cycle and
Ecosystems Joint Science Workshop College Park, MD, April, 19–24
Kosiba AM, Meigs GW, Duncan J, Pontius J, Keeton WS, Tait E (2018) Spatiotemporal patterns of
forest damage in the Northeastern United States: 2000–2016. Forest Ecology and Management
430:94–104.
Lausch A, Heurich M, Gordalla D, Dobner HJ, Gwillym-Margianto S, Salbach C (2013)
Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral
remote-sensing techniques at different scales. For Ecol Manag 308:76–89
Lausch A, Erasmi S, King D, Magdon P, Heurich M (2017) Understanding forest health with
remote sensing-part II—a review of approaches and data models. Remote Sens 9(2):129
Lillesand TM, Kiefer RW (1994) Remote sensing and photo interpretation, 3rd edn. John Wiley&
Sons, New York
Maccioni A, Agati G, Mazzinghi P (2001) New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J Photochem Photobiol 61(1,2):52–61
Martin K, Norris A, Drever M (2006) Effects of bark beetle outbreaks on avian biodiversity in
the British Columbia interior: implications for critical habitat management. J Ecosyst Manag
7(3):10–24
McBride MF, Lambert KF, Huff ES, Theoharides KA, Field P, Thompson JR (2017) Increasing
the effectiveness of participatory scenario development through codesign. Ecol Soc 22(3):16
McConnell TJ (1999) Aerial sketch mapping surveys, the past, present and future. In: Paper from
the north American science symposium, toward a unified framework for inventorying and monitoring forest ecosystem resources, Guadalajara, Mexico
McMurtrey J III, Chappelle EW, Kim M, Meisinger J (1994) Distinguishing nitrogen fertilization
levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance
measurements. Remote Sens Environ 47(1):36–44
Meroni M, Rossini M, Picchi V, Panigada C, Cogliati S, Nali C, Colombo R (2008) Assessing
steady-state fluorescence and PRI from hyperspectral proximal sensing as early indicators of
plant stress: the case of ozone exposure. Sensors 8(3):1740–1754. https://doi.org/10.3390/
s8031740
Merzlyak MN, Gitelson AA, Chivkunova OB, Rakitin VY (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol Plant 106(1):135–
141. https://doi.org/10.1034/j.1399-3054.1999.106119.x
Mohammed GH, Binder WD, Gillies SL (1995) Chlorophyll fluorescence - a review of its practical
forestry applications and instrumentation. Scand J For Res 10(4):383–410
Mumford R (2017) New approaches for the early detection of tree health pests and pathogens.
Impact 1(7):47–49
Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995a) The interpretation of spectral vegetation
indexes. IEEE Trans Geosci Remote Sens 33(2):481–486
Myneni RB, Maggion S, Iaquinta J, Privette JL, Gobron N, Pinty B, Kimes DS, Verstraete MM,
Williams DL (1995b) Optical remote sensing of vegetation: modeling, caveats, and algorithms.
Remote Sens Environ 51:169–188
Norman SP, Hargrove WW, Spruce JP, Christie WM, Schroeder SW (2013) Highlights of satellitebased forest change recognition and tracking using the ForWarn System. Gen Tech Rep SRSGTR- 180 Asheville, NC: USDA-Forest Service, Southern Research Station, 30 p, 180:1–30
Pearlman JS, Barry PS, Segal CC, Shepanski J, Beiso D, Carman SL (2003) Hyperion, a spacebased imaging spectrometer. IEEE Trans Geosci Remote Sens 41:1160–1173
Pearson L, Miller LD (1972) Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grasslands, Colorado. In: Proceedings of
the 8th international symposium on remote sensing of the environment, Ann Arbor, MI, 1972.
ERIM, Ann Arbor, pp 1357–1381
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…