S. Gunasinghe

Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data

The overarching goal of this study was to develop a comprehensive methodology for mapping natural and human‐made wetlands using fine resolution Landsat enhanced thematic mapper plus (ETM+), space shuttle radar topographic mission digital elevation model (SRTM DEM) data and secondary data. First, automated methods were investigated in order to rapidly delineate wetlands; this involved using: (a) algorithms on SRTM DEM data, (b) thresholds of SRTM‐derived slopes, (c) thresholds of ETM+ spectral indices and wavebands and (d) automated classification techniques using ETM+ data. These algorithms and thresholds using SRTM DEM data either over‐estimated or under‐estimated stream densities (S d) and stream frequencies (S f), often generating spurious (non‐existent) streams and/or, at many times, providing glaring inconsistencies in the precise physical location of the streams. The best of the ETM+‐derived indices and wavebands either had low overall mapping accuracies and/or high levels of errors of omissions and/or errors of commissions. Second, given the failure of automated approaches, semi‐automated approaches were investigated; this involved the: (a) enhancement of images through ratios to highlight wetlands from non‐wetlands, (b) display of enhanced images in red, green, blue (RGB) false colour composites (FCCs) to highlight wetland boundaries, (c) digitizing the enhanced and displayed images to delineate wetlands from non‐wetlands and (d) classification of the delineated wetland areas into various wetland classes. The best FCC RGB displays of ETM+ bands for separating wetlands from other land units were: (a) ETM+4/ETM+7, ETM+4/ETM+3, ETM+4/ETM+2, (b) ETM+4, ETM+3, ETM+5 and (c) ETM+3, ETM+2, ETM+1. In addition, the SRTM slope threshold of less than 1% was very useful in delineating higher‐order wetland boundaries. The wetlands were delineated using the semi‐automated methods with an accuracy of 96% as determined using field‐plot data. The methodology was evaluated for the Ruhuna river basin in Sri Lanka, which has a diverse landscape ranging from sea shore to hilly areas, low to very steep slopes (0° to 50°), arid to semi‐arid zones and rain fed to irrigated lands. Twenty‐four per cent (145 733 ha) of the total basin area was wetlands as a result of a high proportion of human‐made irrigated areas, mainly under rice cropping. The wetland classes consisted of irrigated areas, lagoons, mangroves, natural vegetation, permanent marshes, salt pans, lagoons, seasonal wetlands and water bodies. The overall accuracies of wetland classes varied between 87% and 94% (K hat = 0.83 to 0.92) with errors of omission less than 13% and errors of commission less than 1%.

An assessment of crop water productivity in the Indus and Ganges River Basins: current status and scope for improvement

The Indus and Ganges River Basin, being the most populous in the world, is under extreme pressure to sustain food security. Production resources including water are being exploited to various levels from underdevelopment to heavy overexploitation. This report provides a bird’s eye view of the basin and focuses on the nexus between agricultural production and water consumption, making it possible to pinpoint the areas with high/low water productivity and identify the factors behind this, which helps to promote informed decision making in light of environmental sustainability.

Establishing the best spectral bands and timing of imagery for land use – land cover (LULC) class separability using Landsat ETM+ and Terra MODIS data

The main goals of this study were to (i) establish Landsat enhanced thematic mapper plus (ETM+) and moderate resolution imaging spectroradiometer (MODIS) spectral bands best suited for land use – land cover (LULC) class separability, and (ii) study the role of the timing of imagery best suited for LULC class mapping. The study was carried out in the lower portion of the Uda Walawe River basin of southern Sri Lanka. The expansion of irrigated agriculture in this basin has resulted in several distinct changes in the LULC classes and their distribution. The area is dominated by agriculture, plantations, chena (slash and burn) lands with various types of natural vegetation such as degraded forests and scrubland, and wetlands with recently developed irrigation canals and tanks. The results showed that the two shortwave-infrared (SWIR) bands of Landsat ETM+ (bands centered at 1.650 and 2.220 µm) and MODIS (2.130 and 1.640 µm) and the thermal band (11.450 µm) of Landsat ETM+ were most sensitive in separating an overwhelming proportion of the 15 LULC classes studied. However, other bands, though not as powerful as thermal or SWIR bands, by themselves, often play a vital role in separating certain specific LULC classes that are not easily separable by thermal and (or) SWIR bands. The MODIS monthly time series showed that the timing of the imagery was crucial in the separability of LULC classes. An overwhelming proportion of the classes were separated from one another using the data for the two wettest months (November and December) and the driest month (July). All 15 LULC classes were separable using the three wettest months (November, December, and January) and the two driest months (June and July).

The spatial data and knowledge gateways at the International Water Management Institute (IWMI)

In this paper we discuss spatial data and knowledge base (SDKB) gateway portals developed by the International Water Management Institute (IWMI). Our vision is to generate and/or facilitate easy and free access to state-of-art SDKB of excellence globally. Our mission is to make SDKB accessible online, globally, for free. The IWMI data storehouse pathway (IWMIDSP; http://www.iwmidsp.org) is a pathfinder global public good (GPG) portal on remote sensing and GIS (RS/GIS) data and products with specific emphasis on river basin data, but also storing valuable data on Nations, Regions, and the World. A number of other specialty GPG portals have also been released. These include Global map of irrigated area (http://www.iwmigiam.org), Drought monitoring system for southwest Asia (http://dms.iwmi.org), Tsunami satellite sensor data catalogue (http://tsdc.iwmi.org), and Knowledge base system (KBS) for Sri Lanka (http://www.iwmikbs.org). The IWMIDSP has been the backbone of several other projects such as global irrigated area mapping, drought monitoring system, wetlands, and knowledge base systems. A discussion on these pathfinder web portals follow.