Chapter 20 Remote Sensing of African Lakes: a Review

Chapter 20 Remote Sensing of African Lakes: A Review Thomas J. Ballatore, Shane R. Bradt, Lydia Olaka, Andrés Cózar and Steven A. Loiselle Abstract The optical complexity and small size of inland waters make the application of remote sensing more challenging than for the open ocean. However, in Africa, where in situ monitoring of important water bodies is financially, institu- tionally and spatially constrained, there is strong demand for remote sensing to fill the critical information gap. Here we review a wide range of applications of both passive and active remote sensing to African lakes. The applications fall into five main categories: (1) visible, NIR, thermal and microwave sensing of lake area; (2) altimetric and gravimetric sensing of lake level; (3) thermal sensing of lake surface temperature; (4) visible, NIR and microwave sensing of macrophytes; and (5) optical sensing of trophic conditions including chlorophyll-a and euphotic depth. Sensors used include Landsat MSS, TM and ETM , MERIS, MODIS, SeaWiFS, AVHRR,Meteosat, TOPEX/Poseidon, Jason-1, OSTM/Jason-2,+ ERS-1, ERS-2, En- visat, GFO, ICESat, ALOS-PALSAR and GRACE. The majority of studies have been applied to the “great” lakes such as Chad, Malawi, Tanganyika and Victoria; however, there is a growing body of literature on smaller lakes. We examine the possibilities that remote sensing offers to monitoring and management of African lakes as well as the potential limitations of the technology using Lake Victoria as an illustrative case. T. J. Ballatore (!) S. R. Bradt L. Olaka Lake Basin Action Network,· Moriyama,· Japan e-mail: [email protected] S. R. Bradt University of New Hampshire, Durham, NH, USA L. Olaka University of Nairobi, Nairobi, Kenya A. Cózar Universidad de Cádiz, Puerto Real, Spain S. A. Loiselle Università degli Studi di Siena, Siena, Italy V. Barale, M. Gade (eds.), Remote Sensing of the African Seas, 403 DOI 10.1007/978-94-017-8008-7_20, © Springer Science+Business Media Dordrecht 2014 404 T. J. Ballatore et al. 20.1 Introduction The idea of remote sensing—the art and science of obtaining information about an object without being in physical contact with the object (Jensen 2007)—is in many ways the holy grail of lake management. The potential to look wide and often at conditions in a given lake has spurred much interest, resulting in a burgeoning body of research and applications including recent work by Gower et al. (2005), Ruiz- Verdú et al. (2008), Chipman et al. (2009), Gitelson et al. (2011), Bradt (2012) and Matthews et al. (2012). The potential offered by remote sensing is particularly attractive in Africa where administrative capacity to carry out field-based monitoring is limited. Lake managers in Africa are confronted with a task of monitoring lakes that would challenge even the most well-funded monitoring programs. Here, we review the applications of remote sensing to African lakes as described in the scientific literature. The applications are surprisingly broad: spatially, they include 29 lakes that cover all corners of the continent (see Fig. 20.1); thematically, they range from passive to active sensors looking at both water quantity and quality. Overall, we find it convenient to divide the applications into two broad categories: those that look at the amount of water in a given lake and those that look at the properties of that water. The former category include studies that measure lake area through (1) visible and near-infrared (NIR) sensing of lake extent/shoreline, (2) thermal sensing of inundated areas, and (3) microwave sensing of water under cloud cover. They also include studies that examine lake level through (4) altimetric and (5) gravimetric observations. The latter category includes work on strictly physical aspects such as (6) thermal sensing for lake surface temperature as well as biological aspects such as (7) visible and NIR sensing of macrophytes and (8) microwave sensing of macrophytes and perhaps the most widely known application—(9) optical sensing of trophic conditions through measurement of chlorophyll-a, euphotic depth and a range of related indicators. Few of the studies reviewed here fall solely into one of the above nine categories; most papers make use of a combination of techniques. For example, a paper using altimetric observations to estimate changes in lake level might also rely on visible and NIR imagery to confirm lake area (and hence, level) changes. More importantly, it must be noted that remote sensing does not completely obvi- ate the need for field observations and traditional sampling programs. In fact, most of the methods described here still require, or greatly benefit from, calibration with actual measurements taken in the field concurrent with the remotely-sensed images. Nevertheless, as the literature demonstrates, and as we explore in the discussion, remote sensing can indeed provide a wealth of information to greatly aid the work of those concerned with lake management in local communities, national governments and international organizations throughout Africa. 20 Remote Sensing of African Lakes: A Review 405 Fig. 20.1 Locator map of the lakes covered in the remote sensing literature reviewed here 20.2 Lake Area 20.2.1 Visible and NIR Applications The one of the earliest and simplest applications of remote sensing in lake management is the use of visible and near-infrared (NIR) images to identify changes in a lake’s extent. The wide field captured in a single image by remote sensors is especially useful for lakes in remote regions where field surveys of the shoreline are impractical because of distance or inaccessibility. The technique has been used mainly for relatively shallow lakes in endorheic drainage basins where changes in 406 T. J. Ballatore et al. water balance are strongly reflected in changes in lake extent. The strong contrast between light reflected from land vs. water, along with the complete and fine spatial coverage offered by sensors such as the Multi Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM ), onboard the Land- sat satellite series, mean that this technique is not limited to+ large lakes and can be applied, in theory at least, to lakes of 1 km2 or less. Lake Chad has drawn much attention in the literature given its dramatic decrease in extent. Mohler et al. (1989) digitized photographs taken with onboard, handheld cameras during manned space flights (Gemini, Skylab, and the Space Shuttle) dating from 1966 to 1985 to document an approximate 90 % decrease in lake extent. Two main cameras were used: a 70 mm Hasselblad and a 230–460 mm Large Format Camera (LFC) and, except for one black-and-white photo and one color-infrared photo (both taken with the LFC), the images were captured on natural color film. After digitization, spatial resolution was reported as 11 m for LFC and 29 m for the Hasselblad. Water and land were classified with a maximum likelihood algorithm. It is important to note that the 1966 Gemini image, which showed an extent of 22,772 km2, pre-dated the era of non-manned satellite observing systems which began with Landsat MSS in 1972, a time when the lake had already undergone most of the areal change. Wald (1990), building on the work of Mohler et al. (1989), used images from Meteosat in the visible and NIR range (0.4–1.1 µm) to document the lake’s further shrinkage to 500 km2 in April 1989. Schneider et al. (1985) used the NIR Band 7 (0.8–1.1 µm) from Landsat MSS (spatial resolution 80 m) to document the lake area changes from 1972 to 1982. They also used the∼ visible and NIR Channels 1 and 2 (0.55–0.68 and 0.725–1.1 µm) of the Advanced Very High Resolution Radiometer (AVHRR), onboard the NOAA- 7 satellite, (spatial resolution 1.1 km2) to develop a simple Normalized Difference Vegetation Index (NDVI) to detail open water and vegetation along transects of key portions of the lake from November 1981 to November 1982. Despite its lower spatial resolution, the higher temporal resolution of AVHRR (daily vs. 18 days for MSS) was an advantage in mapping the rapidly changing water area and vegetation. Birkett (2000) used the AVHRR’s NIR Channel 2 (0.725–1.1 µm) to map lake extent (inundated area) over a 4-yr period starting in 1995. A simple histogram technique of the Digital Number (DN) values allow classification of inundated areas with an overall relative error of 5 % (calculated from results of different algorithms given the lack of field data) with∼ lower errors during peak flows and higher errors during other times, due in part to the presence of pools smaller than the AVHRR spatial resolution. The paper also made extensive use of satellite altimetry data. Turada (2008) used a selection of visible and NIR bands from the Medium Reso- lution Imaging Spectrometer (MERIS), onboard the European Space Agency (ESA) satellite Envisat, (300 m spatial resolution) to look at inundation and land cover change in the Lake Chad basin through Spectral Mixture Analysis (endmembers: soil, vegetation and water). The MERIS-based classification was validated with data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), a high resolution imaging instrument that is flying on the US National Aeronautics and Space Administration (NASA) orbital platform named Terra, (15 m 20 Remote Sensing of African Lakes: A Review 407 spatial resolution; visible and NIR) as well as from IKONOS (in the 4 m spatial resolution version; visible and NIR) and showed that despite the relatively low spatial resolution, MERIS was able to detect changes in lake extent (kappa coefficients, as per Cohen 1960, ranging from 0.92 to 0.95).

Load more