Members’ Publication

Densely populated floodplains downstream of Asia’s mountain ranges depend heavily on mountain water resources, in particular for irrigation. An intensive and complex multi-cropping irrigated agricultural system has developed here to optimize the use of these mountain water resources in conjunction with monsoonal rainfall. Snow and glacier melt thereby modulate the seasonal pattern of river flows and, together with groundwater, provide water when rainfall is scarce. Climate change is expected to weaken this modulating effect, with potentially strong effects on food production in one of the world’s breadbaskets. Here we quantify the space-, time- and crop-specific dependence of agriculture in the Indo-Gangetic Plains on mountain water resources, using a coupled state-of-the-art, high-resolution, cryosphere–hydrology–crop model. We show that dependence varies strongly in space and time and is highest in the Indus basin, where in the pre-monsoon season up to 60% of the total irrigation withdrawals originate from mountain snow and glacier melt, and that it contributes an additional 11% to total crop production. Although dependence in the floodplains of the Ganges is comparatively lower, meltwater is still essential during the dry season, in particular for crops such as sugar cane. The dependency on meltwater in the Brahmaputra is negligible. In total, 129 million farmers in the Indus and Ganges substantially depend on snow and glacier melt for their livelihoods. Snow and glacier melt provides enough water to grow food crops to sustain a balanced diet for 38 million people. These findings provide important information for agricultural and climate change adaptation policies in a climate change hot spot where shifts in water availability and demand are projected as a result of climate change and socio-economic growth.

In this article, we build our theoretical arguments on an empirical account of a state-implemented housing project in the periphery of Dhaka. Thus, we elaborate on a set of bureaucratic acts, the existing power relations, and group interests that influence planning practices and condition people’s access to public resources. Analyzing the process of project implementation, we explain the various resources and strategies that those in relatively powerful positions activate in order to considerably influence planning practice and public resource distribution. We specifically analyze how the strategies and discourses employed to bring the project forward influence the emerging spatialities and issues of socio-spatial justice and inequality at Dhaka’s urban fringe. This article thus provides empirical evidence explaining the impossibility of rigid statutory planning. Finally, we reflect on what urban planning needs to acknowledge in order for positive change.

Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques.