estimated GPP consistent with satellite-based EVI, and hence were able to map
rooting depths at regional scales across the Amazon with satellite data.
Combined remote sensing and in situ tower flux measurements have also
yielded close relationships with water fluxes (Glenn et al. 2007, 2011). Guerschman et al. (2009) developed an algorithm for estimating monthly actual evapotranspiration (AET) across varying sites in Australia. They used EVI and GVMI
from MODIS data, scaled to Priestley-Taylor potential evapotranspiration. The
EVI provided information on LAI while GVMI provided information on surface
water, bare soil and vegetation water content. Yang et al. (2006) derived continental-scale estimates of evapotranspiration (ET) by combining MODIS data with
eddy covariance flux tower measurements using an inductive machine learning
technique called support vector machines (SVM). EVI was found to be the most
important explanatory factor in their fairly accurate estimates of ET (root mean
square of 0.62 mm d
-1 ). ET measurements at regional scales, from 9 flux towers
established in riparian plant communities on the Middle Rio Grande, Upper San
Pedro River, and Lower Colorado River were also found to correlate strongly with
EVI values and the inclusion of maximum daily air temperatures (T a ) measured at
the tower sites further improved this relationship (r
2 = 0.74) (Nagler et al. 2005a).
Other ET studies at flux tower sites in semiarid riparian and upland grass and shrub
plant communities were also found strongly correlated with MODIS EVI
(r = 0.80–0.94) (Nagler et al. 2005b, 2007).
The AVHRR-NDVI has a long history of vegetation drought and climate
variability studies. Anyamba and Tucker (2005) demonstrated the strong correspondence of 20+ year NDVI trends and anomalies with rainfall in the Sahel
(Fig. 1.14), and Breshears et al. (2005) showed large scale, drought-induced
vegetation mortality over the western U.S. with AVHRR- NDVI satellite data.
Many ecologists are concerned of the potential impacts on biodiversity and forest
ecosystem services resulting from major shifts in climate and wish to develop
predictive relationships between tree species richness and forest productivity under
current climate conditions (Turner et al. 2003). Waring et al. (2006) found a good
relationship between EVI, as a surrogate of productivity, and tree species richness
measured across the forest eco-regions of the conterminous USA (Fig. 1.15). They
used phenology metrics of growing season EVI values from MODIS in developing
their species diversity relationships and found climate- independent satellite
methods to be more useful in assessing tree biodiversity than models requiring
climate data owing to the problem in extrapolating such data accurately.
Various studies have found much utility in VI products for landscape disturbance mapping and impacts of invasive species. As an example, Jin and Sader
(2005) successfully used MODIS NDVI to detect and quantify forest disturbances
in northern Maine. The MODIS-based Global Disturbance Index (MGDI) was
designed to provide information on the timing, location, and extent of large scale
disturbances on ecosystems, involving fires, hurricanes, pests, and woody plant
species mortality (Mildrexler et al. 2009). Large scale disturbance events have
major impacts on the global carbon cycle and can result in sudden and large pulses
1 Indices of Vegetation Activity
25
rooting depths at regional scales across the Amazon with satellite data.
Combined remote sensing and in situ tower flux measurements have also
yielded close relationships with water fluxes (Glenn et al. 2007, 2011). Guerschman et al. (2009) developed an algorithm for estimating monthly actual evapotranspiration (AET) across varying sites in Australia. They used EVI and GVMI
from MODIS data, scaled to Priestley-Taylor potential evapotranspiration. The
EVI provided information on LAI while GVMI provided information on surface
water, bare soil and vegetation water content. Yang et al. (2006) derived continental-scale estimates of evapotranspiration (ET) by combining MODIS data with
eddy covariance flux tower measurements using an inductive machine learning
technique called support vector machines (SVM). EVI was found to be the most
important explanatory factor in their fairly accurate estimates of ET (root mean
square of 0.62 mm d
-1 ). ET measurements at regional scales, from 9 flux towers
established in riparian plant communities on the Middle Rio Grande, Upper San
Pedro River, and Lower Colorado River were also found to correlate strongly with
EVI values and the inclusion of maximum daily air temperatures (T a ) measured at
the tower sites further improved this relationship (r
2 = 0.74) (Nagler et al. 2005a).
Other ET studies at flux tower sites in semiarid riparian and upland grass and shrub
plant communities were also found strongly correlated with MODIS EVI
(r = 0.80–0.94) (Nagler et al. 2005b, 2007).
The AVHRR-NDVI has a long history of vegetation drought and climate
variability studies. Anyamba and Tucker (2005) demonstrated the strong correspondence of 20+ year NDVI trends and anomalies with rainfall in the Sahel
(Fig. 1.14), and Breshears et al. (2005) showed large scale, drought-induced
vegetation mortality over the western U.S. with AVHRR- NDVI satellite data.
Many ecologists are concerned of the potential impacts on biodiversity and forest
ecosystem services resulting from major shifts in climate and wish to develop
predictive relationships between tree species richness and forest productivity under
current climate conditions (Turner et al. 2003). Waring et al. (2006) found a good
relationship between EVI, as a surrogate of productivity, and tree species richness
measured across the forest eco-regions of the conterminous USA (Fig. 1.15). They
used phenology metrics of growing season EVI values from MODIS in developing
their species diversity relationships and found climate- independent satellite
methods to be more useful in assessing tree biodiversity than models requiring
climate data owing to the problem in extrapolating such data accurately.
Various studies have found much utility in VI products for landscape disturbance mapping and impacts of invasive species. As an example, Jin and Sader
(2005) successfully used MODIS NDVI to detect and quantify forest disturbances
in northern Maine. The MODIS-based Global Disturbance Index (MGDI) was
designed to provide information on the timing, location, and extent of large scale
disturbances on ecosystems, involving fires, hurricanes, pests, and woody plant
species mortality (Mildrexler et al. 2009). Large scale disturbance events have
major impacts on the global carbon cycle and can result in sudden and large pulses
1 Indices of Vegetation Activity
25