408
T. J. Ballatore et al.
Also for Lake Chad, Leblanc et al. (2011) used Meteosat 30-day Tmax data
(10.5–12.5 μm) to thermally sense water under vegetative cover, validated by field
surveys and AVHRR data over the period of 1986–2001. While they note that the
thermal technique was difficult to apply during the rainy season because the high
water content of non-lake areas produced a signature similar to that of open water,
the high-temporal resolution (30 min) of the geosynchronous Meteosat meant that
cloud cover was not a significant handicap.
20.2.3 Microwave Applications
In areas were cloud cover is more persistent, the visible, NIR and thermal techniques
are less useful. Rebelo (2010) describes the application of the Phased Array type
L-band Synthetic Aperture Radar (PALSAR), orbiting on the Advanced Land Observing Satellite (ALOS) of the Japan Aerospace Exploration Agency’s (JAXA), an
active microwave sensor, to identify surface changes in Lake Urema and surrounding wetlands from December 2006 to February 2008. The ALOS-PALSAR images
(∼ 12.5 m spatial resolution) were smoothed with a 5 × 5 pixel median filter and
classified using a PCA method to generate input for a supervised classification.
20.3 Lake Level
20.3.1 Altimetric Sensing (Microwave)
If bathymetry is known, lake level can be inferred from the shoreline changes discussed above; however, this is not always possible. Furthermore, cloud cover can
greatly limit the time series data derived from visible, NIR and thermal products.
Active altimetric sensing with microwaves was originally developed for ocean topographic mapping but has been successfully applied to lakes. Under certain conditions,
lake level can be estimated to within a few cm of actual values. Additionally, cloud
cover is not an issue. Given the important role that lake level plays in human use of
lakes, this method has seen wide use. Unfortunately, the nature of the narrow active
signal sent from a given sensor means that only lakes directly in the sensor path can
be covered.
Use of altimetric data has been fostered by the development of online databases
that process the raw data from various sensors and provide “finished” products. The
main ones referred to in the literature below are (1) ESA’s River and Lake website
(ESA 2013), which uses data from Envisat, Jason-1 and Jason-2; (2) the Laboratoire d’Etude en Géophysique et Océanographie Spatiale (LEGOS) Hydroweb, a
surface monitoring by satellite altimetry website (LEGOS 2013), which uses data
from the satellites Envisat, European Remote Sensing 1 and 2 (ERS-1 and ERS-2),
Geosat Follow-On (GFO), Jason-1, Jason-2 and TOPEX/Poseidon (Cretaux et al.
(2011); and (3) the US Department of Agriculture (USDA) Global Reservoir and
Lake Monitor (USDA 2013) which uses data from Envisat, GFO, Jason-1, Jason-2
and TOPEX/Poseidon.
T. J. Ballatore et al.
Also for Lake Chad, Leblanc et al. (2011) used Meteosat 30-day Tmax data
(10.5–12.5 μm) to thermally sense water under vegetative cover, validated by field
surveys and AVHRR data over the period of 1986–2001. While they note that the
thermal technique was difficult to apply during the rainy season because the high
water content of non-lake areas produced a signature similar to that of open water,
the high-temporal resolution (30 min) of the geosynchronous Meteosat meant that
cloud cover was not a significant handicap.
20.2.3 Microwave Applications
In areas were cloud cover is more persistent, the visible, NIR and thermal techniques
are less useful. Rebelo (2010) describes the application of the Phased Array type
L-band Synthetic Aperture Radar (PALSAR), orbiting on the Advanced Land Observing Satellite (ALOS) of the Japan Aerospace Exploration Agency’s (JAXA), an
active microwave sensor, to identify surface changes in Lake Urema and surrounding wetlands from December 2006 to February 2008. The ALOS-PALSAR images
(∼ 12.5 m spatial resolution) were smoothed with a 5 × 5 pixel median filter and
classified using a PCA method to generate input for a supervised classification.
20.3 Lake Level
20.3.1 Altimetric Sensing (Microwave)
If bathymetry is known, lake level can be inferred from the shoreline changes discussed above; however, this is not always possible. Furthermore, cloud cover can
greatly limit the time series data derived from visible, NIR and thermal products.
Active altimetric sensing with microwaves was originally developed for ocean topographic mapping but has been successfully applied to lakes. Under certain conditions,
lake level can be estimated to within a few cm of actual values. Additionally, cloud
cover is not an issue. Given the important role that lake level plays in human use of
lakes, this method has seen wide use. Unfortunately, the nature of the narrow active
signal sent from a given sensor means that only lakes directly in the sensor path can
be covered.
Use of altimetric data has been fostered by the development of online databases
that process the raw data from various sensors and provide “finished” products. The
main ones referred to in the literature below are (1) ESA’s River and Lake website
(ESA 2013), which uses data from Envisat, Jason-1 and Jason-2; (2) the Laboratoire d’Etude en Géophysique et Océanographie Spatiale (LEGOS) Hydroweb, a
surface monitoring by satellite altimetry website (LEGOS 2013), which uses data
from the satellites Envisat, European Remote Sensing 1 and 2 (ERS-1 and ERS-2),
Geosat Follow-On (GFO), Jason-1, Jason-2 and TOPEX/Poseidon (Cretaux et al.
(2011); and (3) the US Department of Agriculture (USDA) Global Reservoir and
Lake Monitor (USDA 2013) which uses data from Envisat, GFO, Jason-1, Jason-2
and TOPEX/Poseidon.