148
6.8 Conclusions
While historically RS has been successfully used to assess and monitor vegetation
condition on a coarse, relative scale, recent advances and new analysis techniques
now enable us to also use RS to identify and track early decline, disturbance, and
stress conditions in vegetative systems. Considering the environmental challenges
currently facing terrestrial systems, this information is critical to inform management, policy, and planning in order to maintain the structure and function of these
systems.
The challenge is for scientists to look beyond traditional approaches to vegetation assessment and target earlier or more subtle decline response resulting from
incipient or chronic environmental stress agents (e.g., climate change, pollution).
Key challenges include linking hyperspectral data to specific stress agents, extending the availability of higher-resolution imagery, and operationalizing near-realtime monitoring of the forest resource (Senf et al. 2017). Scientists must work
closely with land managers to bring these new technologies to application in order
to harness RS’s full potential to inform the management of critical ecological
resources.
References
Aoki M, Yabuki K, Totsuka T (1981) An evaluation of chlorophyll content of leaves based on the
spectral reflectivity in several plants. Res Rep Nat Inst Environ Stud Jpn 66:125–130
Asner GP, Heidebrecht KB (2003) Imaging spectroscopy for desertification studies: comparing AVIRIS and EO-1 Hyperion in Argentina drylands. IEEE Trans Geosci Remote Sens
41(6):1283–1296
Barnes JD (1992) A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environ Experim Bot 32(2):85–100
Blackburn GA (1998) Quantifying chlorophylls and carotenoids at leaf and canopy scales: an
evaluation of some hyperspectral approaches. Remote Sens Environ 66(3):273–285
Blackburn GA (1999) Relationships between spectral reflectance and pigment concentrations in
stacks of deciduous broadleaves. Remote Sens Environ 70:224–237
Boochs F, Kupfer G, Dockter K, Kuhbauh W (1990) Shape of the red edge as vitality indicator for
plants. International Journal of Remote Sensing 11(10):1741–1753
Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ 76(2):156–172
Buschmann C, Nagel E (1993) In vivo spectroscopy and internal optics of leaves as a basis for
remote sensing of vegetation. Int J Remote Sens 14:711–722
Carter GA (1993) Responses of leaf spectral reflectance to plant stress. Am J Bot 80(3):239–243
Carter GA (1994) Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Int
J Remote Sens 15(3):697–703
Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88(4):677–684
Carter GA, Miller RL (1994) Early detection of plant stress by digital imaging within narrow
stress-sensitive wavebands. Remote Sens Environ 50(3):295–302
J. Pontius et al.
6.8 Conclusions
While historically RS has been successfully used to assess and monitor vegetation
condition on a coarse, relative scale, recent advances and new analysis techniques
now enable us to also use RS to identify and track early decline, disturbance, and
stress conditions in vegetative systems. Considering the environmental challenges
currently facing terrestrial systems, this information is critical to inform management, policy, and planning in order to maintain the structure and function of these
systems.
The challenge is for scientists to look beyond traditional approaches to vegetation assessment and target earlier or more subtle decline response resulting from
incipient or chronic environmental stress agents (e.g., climate change, pollution).
Key challenges include linking hyperspectral data to specific stress agents, extending the availability of higher-resolution imagery, and operationalizing near-realtime monitoring of the forest resource (Senf et al. 2017). Scientists must work
closely with land managers to bring these new technologies to application in order
to harness RS’s full potential to inform the management of critical ecological
resources.
References
Aoki M, Yabuki K, Totsuka T (1981) An evaluation of chlorophyll content of leaves based on the
spectral reflectivity in several plants. Res Rep Nat Inst Environ Stud Jpn 66:125–130
Asner GP, Heidebrecht KB (2003) Imaging spectroscopy for desertification studies: comparing AVIRIS and EO-1 Hyperion in Argentina drylands. IEEE Trans Geosci Remote Sens
41(6):1283–1296
Barnes JD (1992) A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environ Experim Bot 32(2):85–100
Blackburn GA (1998) Quantifying chlorophylls and carotenoids at leaf and canopy scales: an
evaluation of some hyperspectral approaches. Remote Sens Environ 66(3):273–285
Blackburn GA (1999) Relationships between spectral reflectance and pigment concentrations in
stacks of deciduous broadleaves. Remote Sens Environ 70:224–237
Boochs F, Kupfer G, Dockter K, Kuhbauh W (1990) Shape of the red edge as vitality indicator for
plants. International Journal of Remote Sensing 11(10):1741–1753
Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ 76(2):156–172
Buschmann C, Nagel E (1993) In vivo spectroscopy and internal optics of leaves as a basis for
remote sensing of vegetation. Int J Remote Sens 14:711–722
Carter GA (1993) Responses of leaf spectral reflectance to plant stress. Am J Bot 80(3):239–243
Carter GA (1994) Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Int
J Remote Sens 15(3):697–703
Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88(4):677–684
Carter GA, Miller RL (1994) Early detection of plant stress by digital imaging within narrow
stress-sensitive wavebands. Remote Sens Environ 50(3):295–302
J. Pontius et al.