Pakistan is an agricultural country with an increasing interest for hydropower. Water management problems such as sedimentation and evaporation have been of high concern for surface water reservoirs for many years. Therefore, groundwater storage through subsurface dams could be promising, especially considering the monsoon rainfall and seasonal river flows in Pakistan. The paper aims to develop and test a methodology to locate suitable sites for construction of subsurface dams using spatial multi-criteria analysis (SMCA) in the northern parts of Pakistan.

The role of vegetation analysis by remote sensing (RS) and Geographical Information System (GIS) technology to support plans for sustainable development is discussed through a proposal of an index of ecosystem “integrity” or of regeneration capacity. The index is based on the vegetation “mass” of a given land cover type and the capacity that such mass has to contrast soil erosion. The index is therefore taking into account the effects of human activities and not merely the state of ecosystems in terms of their components.

Water resources are influenced by various factors such as weather, topography, geology, and environment. Therefore, there are many difficulties in evaluating and analyzing water resources for the future under climate change. In this paper, we consider climate, land cover and water demand as the most critical factors affecting change in future water resources. We subsequently introduce the procedures and methods employed to quantitatively evaluate the influence of each factor on the change in future water resources.

Understanding factors that drive the choice of breeding habitats for birds is important for species management and conservation. We addressed this question in the case of the European Roller Coracias garrulus, an endangered species listed in Annex I of the European Union directive for bird conservation. For secondary hole-nesting birds, such as rollers, the breeding micro- and macrohabitat selection may rely on the location of the nest hole in a tree and its immediate surrounding environment as well as the larger scale foraging habitat.

Surface albedo determines radiative forcing and is a key parameter for driving Earth’s climate. Better characterization of surface albedo for individual land cover types can reduce the uncertainty in estimating changes to Earth’s radiation balance due to land cover change. This paper presents new albedo look-up maps (LUM) using a multi-scale hierarchical approach based on MODIS BRDF/albedo products and Landsat imagery. Ten years (2001-2011) of MODIS BRDF/albedo products were used to generate global albedo climatology.

Rapid development of shrimp farming may lead to unrecognized and undesirable changes of land cover/land use patterns in coastal areas. Of special concern is the loss of mangrove forest in coastal areas such as Quang Ninh, Vietnam, which is adjacent to the World Heritage-listed Ha Long Bay. Understanding the status and changes of land cover/land use for coastal shrimp farms and mangrove forests can support environmental protection and decision-making for sustainable development in coastal areas.

The characteristics and spatial distribution of fuels are critical for assessing fire hazard, fuel consumption, greenhouse gas emissions and other fire effects. However, fuel maps are difficult to generate and update, because many regions of the world lack fuel descriptions or adequate mapped vegetation attributes to assign these fuelbeds spatially across the landscape. This paper presents a process to generate fuel maps for large areas using remotely sensed information and ancillary fuel characteristic data.

In this study, the Breaks for Additive Seasonal and Trend (BFAST), a recently introduced trend analysis technique, was employed for the detection of fire-induced changes in a Mediterranean ecosystem. BFAST enables the decomposition of time series into trend, seasonal and noise components, resulting in the detection of gradual and rapid land cover changes. Normalised Difference Vegetation Index (NDVI) time series derived from the MODIS and VEGETATION (VGT) standard products were analysed.

This study examined the effect of two different forest fires 19 and 23 years ago on the Mediterranean island of Thasos. An object-based classification scheme was developed to map the major land-cover types using multi-temporal Système Pour l’Observation de la Terre (SPOT) and European Remote-Sensing (ERS) (C-band VV) images covering the time period from 1993 to 2007. The developed scheme mapped the post-fire land-cover types accurately: 0.84 Kappa coefficient and 90.5% overall accuracy.

Land cover distribution is one of the factors that influence fire behaviour and its consequences in the landscape. The relation between land cover type and fire was investigated at a broad scale, in order to analyse land cover differences in fire proneness. The selection ratio for nine different land cover categories was calculated for the fire perimeters mapped in Southern Europe between 2000 and 2008. The results obtained were then compared per country and region. The fire proneness of topographic classes and its potential association with land cover types were also assessed.

The objective of this study was to quantify the impacts of land use/land cover (LULC) change on the hydrology of the Jedeb, an agricultural dominated mesoscale catchment, in the Abay/Upper Blue Nile basin, Ethiopia. Two methods have been used. First, the trends of certain daily flow variability parameters were evaluated to detect statistical significance of the change of the hydrologic response. Second, a conceptual monthly hydrological model was used to detect changes in the model parameters over different periods to infer LULC change.

Dense multi-temporal stacks of Landsat imagery have most commonly been exploited to identify land cover and land use changes (LCLUC) based on detection of abrupt changes in continuous value spectral indices. In this study, a discrete classification approach to LCLUC identification based on stable training sites is tested on a nine-date, 4-year Landsat-7 ETM + time sequence for a study area in Ghana that is prone to cloud cover.