radiation is the predominant factor driving the correlation between LST and NDVI,
whereas other biophysical variables play a lesser role. Air temperature is the primary
factor in midsummer. They conclude that there is a need to use empirical LST–NDVI
relationships with caution to restrict their application to drought monitoring to areas
and periods where negative correlations are observed, primarily to conditions when
water—and not energy—is the primary factor limiting vegetation growth.
3.7 DESERT OR ARID ENVIRONMENTS
Desert or arid environments occupy a significant portion of Earth’s surface, and with
the prospect of the spatial extent of arid lands possibly increasing due to global
climate change, they are important areas for analysis in landscape ecology. In
association with drought monitoring, TIR data have been used to study surface temperature characteristics over desert or arid environments. For example, Shamsipour
et al. (2011) used AVHRR and meteorological data to assess drought events in the
semiarid central plains of Iran. Drought recognition is based on the analysis of the
standard precipitation index (SPI) derived from meteorological variables and NDVI
obtained from AVHRR data. These variables include the vegetation condition index
(VCI), LST, land surface moisture (LSM), temperature condition index (TCI), and
vegetation health index (VHI). Analysis was restricted to the spring season from 1998
to 2004. Results show that indices derived from the AVHRR thermal band have a
higher sensitivity to drought conditions than indices derived from the visible bands.
Indices derived from reflective bands such as NDVI and VCI appear to be better
correlated to meteorological parameters than thermal band indices such as TCI.
Indices that are calculated from both the reflective and TIR bands like LSM and VHI
do not seem to be a reliable measure of drought conditions in the study area.
Research by Qin et al. (2001, 2002, 2005) also used thermal data from the AVHRR to
estimate LST and the variation of this parameter over the Israel–Sinai peninsula. As they
note, the retrieval of LST from AVHRR data with two channels in the 10.5–11.3-mm
bandwidth is usually derived using the split-window algorithm technique. In order to
assess the spatial distribution of LST over the study area, they used a modified version of
the split-window technique that only requires input on emissivity and transmittance, as
opposed to other versions of this technique that require atmospheric parameters that are
generally difficult to estimate due to absence of in situ atmospheric profile data.
8 An
LST image in combination with a pseudocolor image generated from AVHRR reflective
8 Extensive work has gone into the development of algorithms to estimate LST from AVHRR channels 4
and 5. The primary approach is the so-called split-window technique that uses the difference in brightness
temperatures between AVHRR channels 4 and 5 to correct for atmospheric effects on sea surface and land
surface temperatures. The split-window method corrects for atmospheric effects based on differential
absorption in adjacent infrared bands in deriving LST from satellite data. The split-window technique works
independently of other data sources and takes advantage of the differential effect of the atmosphere on the
radiometric signal across the atmosphic window region (Czajkowski et al., 2004.). Two other informative
sources on the split-window technique and retrieval of LST from satellite data are Wan and Dozier (1996)
and Dash et al. (2001).
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THERMAL INFRARED REMOTE SENSING FOR ANALYSIS OF LANDSCAPE
whereas other biophysical variables play a lesser role. Air temperature is the primary
factor in midsummer. They conclude that there is a need to use empirical LST–NDVI
relationships with caution to restrict their application to drought monitoring to areas
and periods where negative correlations are observed, primarily to conditions when
water—and not energy—is the primary factor limiting vegetation growth.
3.7 DESERT OR ARID ENVIRONMENTS
Desert or arid environments occupy a significant portion of Earth’s surface, and with
the prospect of the spatial extent of arid lands possibly increasing due to global
climate change, they are important areas for analysis in landscape ecology. In
association with drought monitoring, TIR data have been used to study surface temperature characteristics over desert or arid environments. For example, Shamsipour
et al. (2011) used AVHRR and meteorological data to assess drought events in the
semiarid central plains of Iran. Drought recognition is based on the analysis of the
standard precipitation index (SPI) derived from meteorological variables and NDVI
obtained from AVHRR data. These variables include the vegetation condition index
(VCI), LST, land surface moisture (LSM), temperature condition index (TCI), and
vegetation health index (VHI). Analysis was restricted to the spring season from 1998
to 2004. Results show that indices derived from the AVHRR thermal band have a
higher sensitivity to drought conditions than indices derived from the visible bands.
Indices derived from reflective bands such as NDVI and VCI appear to be better
correlated to meteorological parameters than thermal band indices such as TCI.
Indices that are calculated from both the reflective and TIR bands like LSM and VHI
do not seem to be a reliable measure of drought conditions in the study area.
Research by Qin et al. (2001, 2002, 2005) also used thermal data from the AVHRR to
estimate LST and the variation of this parameter over the Israel–Sinai peninsula. As they
note, the retrieval of LST from AVHRR data with two channels in the 10.5–11.3-mm
bandwidth is usually derived using the split-window algorithm technique. In order to
assess the spatial distribution of LST over the study area, they used a modified version of
the split-window technique that only requires input on emissivity and transmittance, as
opposed to other versions of this technique that require atmospheric parameters that are
generally difficult to estimate due to absence of in situ atmospheric profile data.
8 An
LST image in combination with a pseudocolor image generated from AVHRR reflective
8 Extensive work has gone into the development of algorithms to estimate LST from AVHRR channels 4
and 5. The primary approach is the so-called split-window technique that uses the difference in brightness
temperatures between AVHRR channels 4 and 5 to correct for atmospheric effects on sea surface and land
surface temperatures. The split-window method corrects for atmospheric effects based on differential
absorption in adjacent infrared bands in deriving LST from satellite data. The split-window technique works
independently of other data sources and takes advantage of the differential effect of the atmosphere on the
radiometric signal across the atmosphic window region (Czajkowski et al., 2004.). Two other informative
sources on the split-window technique and retrieval of LST from satellite data are Wan and Dozier (1996)
and Dash et al. (2001).
46
THERMAL INFRARED REMOTE SENSING FOR ANALYSIS OF LANDSCAPE