[1] This paper presents a methodological procedure based on remote sensing and image analysis techniques designed to map and quantify water stocks in small irrigation reservoirs over vast, user-defined regions. Because the method is based on unsupervised pixel classification schemes, it is analytically transparent and entirely replicable and can therefore be used in most settings as a tool for integrated water resource management, planning, or policy making, with benefits to irrigation, land use, agriculture, and water-related social issues. Satellite images of semiarid south India are used here to quantify fluctuating water volumes in $2500 reservoirs. In this pilot study, the detection of temporal trends and spatial discontinuities in land use at successive dates within reservoir beds is a proxy for assessing the performance of reservoirs and for formulating hypotheses on the environmental, socioeconomic, or anthropological reasons behind the inferred levels of infrastructural maintenance or disuse. The synoptic approach paves the way for future efforts as better ground truth data become available.Citation: Mialhe, F., Y. Gunnell, and C. Mering (2008), Synoptic assessment of water resource variability in reservoirs by remote sensing: General approach and application to the runoff harvesting systems of south India, Water Resour. Res., 44, W05411,

We first provide a critical review of statistical procedures employed in the literature for testing uncertainty in digital terrain analysis, then focus on several aspects of spatial autocorrelation that have been neglected in the analysis of gridded elevation data. When applied to first derivatives of elevation such as topographic slope, a spatial approach using Moran’s I and the LISA (Local Indicator of Spatial Association) allows: (1) georeferenced data patterns to be generated; (2) error hot- and coldspots to be located; and (3) error propagation during DEM manipulation to be evaluated. In a worked example focusing on the Wasatch mountain front, Utah, we analyse the relative advantages of six DEMs resulting from different acquisition modes (airborne, optical, radar, or composite): the LiDAR (2 m), CODEM (5 m), NED10 (10 m), ASTER DEM (15 m) and GDEM (30 m), and SRTM (90 m). The example shows that (apart from the LiDAR) the NED10, which is generated from composite data sources, is the least error-ridden DEM for that region. Knowing error magnitudes and where errors are located determines where corrections to elevation are required in order to minimize error accumulation or propagation, and clarifies how they might affect expert judgement in environmental decisions. Ground resolution issues can subsequently be addressed with greater confidence by resampling the preferred grid to terrain resolutions suited to the landscape attributes of interest. Source product testing is an essential yet often neglected part of DEM analysis, with many practical applications in hydrological modelling, for predictions of slope- to catchment-scale mass sediment flux, or for the assessment of slope stability thresholds.

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This study documents the event chronology and causes of land-use change in a deltaic region of the Philippines since the beginnings of aquaculture in the late nineteenth century. Satellite images and topographic maps spanning the period 1972–2013 were processed to map fishponds and the natural habitats over which they have encroached. Historical archives were consulted and interviews were conducted to understand the historical exploitation of local natural resources and the reasons behind the recorded land changes. Results showed that aquaculture developed in the late nineteenth century and expanded subsequently across the landscape under a succession of forcing factors. The global market, for example, played an early role but a number of land-use changes were also a direct response to changing environmental constraints and natural hazards. These cumulative events have promoted continuous gain in favor of aquaculture, to the detriment of other land-use options. (Résumé d'auteur

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A new method of land cover mapping from satellite images using granulometric analysis is presented here. Discontinuous landscapes such as steppian bushes of semi arid regions and recently growing urban settlements are especially concerned by this study. Spatial organisations of the land cover are quantified by means of the size distribution analysis of the land cover units extracted from high resolution remotely sensed images. A granulometric map is built by automatic classification of every pixel of the image according to the granulometric density inside a sliding neighbourhood. Granulometric mapping brings some advantages over traditional thematic mapping by remote sensing by focusing on fine spatial events and small changes in one peculiar category of the landscape.