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%.

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Lidar (light detection and ranging) remote sensing technology provides promising tools for 3D characterization of the earth’s surface.  In ecosystem studies, lidar derived structural parameters relating to vegetation and terrain have been extensively used in many applications and are rapidly expanding.  Yet, most of the lidar applications have focused on tall, woody vegetation in forested environments and less research attention is given to non-forest, short stature vegetation dominated ecosystems.  Similar to the lidar developments in forestry, novel methodological approaches and algorithm developments will be necessary to improve estimates of structural and biophysical properties (i.e. biomass and carbon storage) in non-forested short stature environments.  Under changing climate scenarios, the latter is particularly useful to improve our understanding of their future role as terrestrial carbon sinks.  In an attempt to identify research gaps in airborne lidar remote sensing application in short stature vegetation studies, in this review article we provide a comprehensive overview on the current state of airborne lidar applications.  Our focus is mainly on the levels of accuracies and errors reported, as well as the potentials and limitations of the methods applied in these studies.  We also provide insights into future research needs and applications in these environments.