1012
November 2014
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
NDVI standard normal deviate values show regions with
higher vegetation productivity, while lower NDVI standard
normal deviate values show areas with lower vegetation
productivity. Areas that display greater departures from the
mean NDVI are, in theory, areas with more heterogeneous
vegetation cover, as identified by the mean-variance plots.
In general, the patterns in Figure 7 reveal higher vegetation
productivity in the seasonally-inundated parts of both the
eastern Caprivi Strip of Namibia and of Mamili National Park
in the south-western part of CRB, as well as in much of the
Chobe Forest Reserve and the interior parts of Chobe National
Park. Consistently low vegetation productivity areas in CRB
are in the communal lands of both countries and particularly
in areas adjacent to Lake Liambezi on the Namibian side of
the river where population densities are relatively high (see
Pricope et al. 2014 for more details).
Semi-arid regions in Africa will be among the most
impacted as climate variability and change continue to
introduce uncertainty in daily and seasonal decision-making
for rural, mostly subsistence-based households. Thus, it is
critical that we continue to monitor changes and employ a wide
Figure 7. Spatial patterns of growing season standardized NDVI values for CRB for four years of highest vegetation variability, in terms of
mean and standard deviation.
(Source: Pricope et al., 2015)
array of available geospatial technologies to provide support
for mitigating and adapting to these changes currently and in
the future. The Socio-Environmental Analysis Lab (SEAL) at
UNCW is an interdisciplinary lab engaged in on-going research
on human-environment interactions in southern and eastern
Africa, Nepal and theUnited States. We invite collaborators and
prospective graduate students interested in working on human-
environment interactions from a variety of perspectives. We
also work collaboratively with the American Climber Science
Program (ACSP), an integrated research effort that combines
remote sensing with extensive data-collection from a variety of
scientific partners. More information can be found about the
ACSP at
and we invite collaborators
in all disciplines. Through a series of PE&RS Highlight Articles
(All et al., 2014; Cole et al., 2014; Schmitt et al., 2014), we
are periodically sharing our work in diverse environments
from tropical rainforests to high mountain glaciers in Central
America, the Himalayas, Andes and Africa.
