effects of stress on stomatal conductance, which impacted the VI method, and midday
depression of ET, which impacted the thermal-band methods because they assume a
constant relationship between ET and net radiation over the daylight hours. These
results illustrate the need to combine and compare both ground and remote sensing
method in scaling point measurements to larger landscape units and to exercise
caution in accepting remote sensing results if not validated by ground data.
The case studies in this chapter show that multiscale remote sensing methods are
useful in change detection and landscape monitoring programs, but they should be
combined with ground data for validation, and multiple independent methods for
measuring biophysical variables by both ground and remote sensing methods should
be employed to discover sources of error and uncertainty that will affect interpretation
of results. No single scale or method of measurement was suitable for monitoring
riparian vegetation in these studies.
REFERENCES
Abraha, M. G., and Savage, M. J. 2012. Energy and mass exchange over incomplete vegetation
cover. Critical Reviews in Plant Sciences 31:321–341.
Allen, R. G., Pereira, L., Rais, D., and Smith, M. 1998. Crop Evapotranspiration—Guidelines
for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper No. 56, Food
and Agricultural Organization of the United Nations, Rome.
Allen, R. G., Pereira, L. S., Howell, T. A., and Jensen, M. E. 2011. Evapotranspiration
information reporting: I. Factors governing measurement accuracy. Agricultural Water
Management 98:899–920.
Anderson, N. T., and Marchisio, G. B. 2012. WorldView-2 and the evolution of the DigitalGlobe remote sensing satellite constellation: Introductory paper for the special session on
WorldView-2. In S. S. Shen and P. E. Lewis (Eds.), Algorithms and Technologies for
Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. SPIE Digital Library,
http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1354542.
Baugh, W. M., and Groeneveld, D. P. 2006. Broadband vegetation index performance evaluated
for a low-cover environment. International Journal of Remote Sensing 27:4715–4730.
Bean, D. W., Dudley, T. D., and Keller, J. C. 2007a. Seasonal timing of diapause induction
limits the effective range of Diorhabda elongata deserticola (Coleoptera: Chrysomelidae)
as a biological control agent for tamarisk (Tamarix spp.). Environmental Entomology,
36:15–25.
Bean, D. W., Wang, T., Bartelt, R. J., and Zilkowski, B. W. 2007b. Diapause in the leaf beetle
Diorhabda elongata (Coleoptera: Chrysomelidae), a biological control agent for tamarisk
(Tamarix spp.). Environmental Entomolology 36:531–540.
Brouwer, C., and Heibloem, M. 1986. Irrigation Water Management Training Manual No. 3.
FAO: Rome.
Chew, M. K. 2009. The monstering of Tamarisk: How scientists made a plant into a problem.
Journal of the History of Biology 42:231–266.
Dale, V. H., Joyce, L. A., McNulty, S., Neilson, R. P., Ayres, M. P., Flannigan, M. D., Hanson,
P. J., Irland, L. C., Lugo, A. E., Peterson, C. J., Simberloff, D., Swanson, F. J., Stocks, B. J.,
and Wotton, B. M. 2001. Climate change and forest disturbances. BioScience 51:734–734.
102
CHANGE DETECTION USING VEGETATION INDICES AND MULTIPLATFORM
depression of ET, which impacted the thermal-band methods because they assume a
constant relationship between ET and net radiation over the daylight hours. These
results illustrate the need to combine and compare both ground and remote sensing
method in scaling point measurements to larger landscape units and to exercise
caution in accepting remote sensing results if not validated by ground data.
The case studies in this chapter show that multiscale remote sensing methods are
useful in change detection and landscape monitoring programs, but they should be
combined with ground data for validation, and multiple independent methods for
measuring biophysical variables by both ground and remote sensing methods should
be employed to discover sources of error and uncertainty that will affect interpretation
of results. No single scale or method of measurement was suitable for monitoring
riparian vegetation in these studies.
REFERENCES
Abraha, M. G., and Savage, M. J. 2012. Energy and mass exchange over incomplete vegetation
cover. Critical Reviews in Plant Sciences 31:321–341.
Allen, R. G., Pereira, L., Rais, D., and Smith, M. 1998. Crop Evapotranspiration—Guidelines
for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper No. 56, Food
and Agricultural Organization of the United Nations, Rome.
Allen, R. G., Pereira, L. S., Howell, T. A., and Jensen, M. E. 2011. Evapotranspiration
information reporting: I. Factors governing measurement accuracy. Agricultural Water
Management 98:899–920.
Anderson, N. T., and Marchisio, G. B. 2012. WorldView-2 and the evolution of the DigitalGlobe remote sensing satellite constellation: Introductory paper for the special session on
WorldView-2. In S. S. Shen and P. E. Lewis (Eds.), Algorithms and Technologies for
Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. SPIE Digital Library,
http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1354542.
Baugh, W. M., and Groeneveld, D. P. 2006. Broadband vegetation index performance evaluated
for a low-cover environment. International Journal of Remote Sensing 27:4715–4730.
Bean, D. W., Dudley, T. D., and Keller, J. C. 2007a. Seasonal timing of diapause induction
limits the effective range of Diorhabda elongata deserticola (Coleoptera: Chrysomelidae)
as a biological control agent for tamarisk (Tamarix spp.). Environmental Entomology,
36:15–25.
Bean, D. W., Wang, T., Bartelt, R. J., and Zilkowski, B. W. 2007b. Diapause in the leaf beetle
Diorhabda elongata (Coleoptera: Chrysomelidae), a biological control agent for tamarisk
(Tamarix spp.). Environmental Entomolology 36:531–540.
Brouwer, C., and Heibloem, M. 1986. Irrigation Water Management Training Manual No. 3.
FAO: Rome.
Chew, M. K. 2009. The monstering of Tamarisk: How scientists made a plant into a problem.
Journal of the History of Biology 42:231–266.
Dale, V. H., Joyce, L. A., McNulty, S., Neilson, R. P., Ayres, M. P., Flannigan, M. D., Hanson,
P. J., Irland, L. C., Lugo, A. E., Peterson, C. J., Simberloff, D., Swanson, F. J., Stocks, B. J.,
and Wotton, B. M. 2001. Climate change and forest disturbances. BioScience 51:734–734.
102
CHANGE DETECTION USING VEGETATION INDICES AND MULTIPLATFORM