The use of NDVI and its Derivatives for Monitoring Lake Victoria’s Water Level and Drought Conditions

Normalized Difference Vegetation Index (NDVI), which is a measure of vegetation vigour, and lake water levels respond variably to precipitation and its deficiency. For a given lake catchment, NDVI may have the ability to depict localized natural variability in water levels in response to weather patterns. This information may be used to decipher natural from unnatural variations of a given lake’s surface. This study evaluates the potential of using NDVI and its associated derivatives (VCI (vegetation condition index), SVI (standardised vegetation index), AINDVI (annually integrated NDVI), green vegetation function (F g ), and NDVIA (NDVI anomaly)) to depict Lake Victoria’s water levels. Thirty years of monthly mean water levels and a portion of the Global Inventory Modelling and Mapping Studies (GIMMS) AVHRR (Advanced Very High Resolution Radiometer) NDVI datasets were used. Their aggregate data structures and temporal co-variabilities were analysed using GIS/spatial analysis tools. Locally, NDVI was found to be more sensitive to drought (i.e., responded more strongly to reduced precipitation) than to water levels. It showed a good ability to depict water levels one-month in advance, especially in moderate to low precipitation years. SVI and SWL (standardized water levels) used in association with AINDVI and AMWLA (annual mean water levels anomaly) readily identified high precipitation years, which are also when NDVI has a low ability to depict water levels. NDVI also appears to be able to highlight unnatural variations in water levels. We propose an iterative approach for the better use of NDVI, which may be useful in developing an early warning mechanisms for the management of lake Victoria and other Lakes with similar characteristics

Generation of Up to Date Land Cover Maps for Central Asia

Human activity and climate variability has always changed the Earth’s surface and both will mainly contribute to future alteration in land cover and land use changes. In this chapte we demonstrate a land cover and land use classification approach for Central Asia addressing regional characteristics of the study area. With the aim of regional classification map for Central Asia a specific classification scheme based on the Land Cover Classification System (LCCS) of the Food and Agriculture Organisation of the United Nations Environment Programme (FAO-UNEP) was developed. The classification was performed by using a supervised classification method applied on metrics, which were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data with 250 m spatial resolution. The metrics wer derived from annual time-series of red and nearinfrared reflectance as well as from Normalized Difference Vegetation Index (NDVI) and thus reflect the temporal behavior of different land cover types. Reference data required for a supervised classification approach were collected from several high resolution satellite imagery distributed all over the study area. The overall accuracy results for performed classification of the year 2001 and 2009 are 91.2 and 91.3 %. The comparison of both classification maps shows significant alterations for different classes. Water bodies such as Shardara Water Reservoir and Aral Sea have changed in their extent. Whereby, the size of the Shardara Water Reservoir is very dynamic from year to year due to water management and the eastern lobe of southern Aral Sea has decreased because of the lack of inflow from Amu Darja. Furthermore, some large scale changes were detected in sparsely vegetated areas in Turkmenistan, where spring precipitation mainly affects the vegetation density. In the north of Kazakhstan significant forest losses caused by forest fires and logging were detected. The presented classification approach is a suitable tool for monitoring land cover and land use in Central Asia. Such independent information is important for accurate assessment of water and land recourses

Water supply and ancient society in the Lake Balkhash Basin: Runoff variability along the historical Silk Road

Expansion of agricultural practices from the Fertile Crescent to China during the mid and late Holocene are believed to have shaped the early network of Silk Road routes and possibly regulated the dynamics of trade and exchange in the urban oases along the Silk Road throughout its existence. While the impacts of climate change on the Silk Road are more or less documented for the medieval period, they remain poorly understood for early history of the Silk Road, especially in Central Asia. We analyze hydroclimatic proxies derived from fluvial stratigraphy, geochronology, and tree-ring records that acted on various time scales in the Lake Balkhash Basin to learn how changes in water supply could have influenced the early farmers in the Semirechye region of southern Kazakhstan. Our approach aims to identify short-term and long-term variability of regional runoff and to compare the hydrological data with cultural dynamics coupled with the archaeological settlement pattern and agricultural production. The reconstructed runoff variability underscore the contribution of winter precipitation driven by the interaction between the Arctic oscillation and the Siberian High-Pressure System, to Central Asian river discharge. We show that Saka people of the Iron Age employed extensive ravine agriculture on the alluvial fans of the Tian Shan piedmont, where floodwater farming peaked between 400 BC and 200 BC. The early Silk Road farmers on the alluvial fans favored periods of reduced flood flows, river stability and glacier retreat in the Tian Shan Mountains. Moreover, they were able to apply simple flow control structures to lead water across the fan surface. It is very unlikely that changes in water supply ever significantly constricted agricultural expansion in this region