The aim of the research is to analyse the landscape structure changes from the end of the Soviet times in 1974–1986 until 2005 when market economy existed in Lithuania. The changes of landscape structure were observed in 100 sample areas (squares) each of them having 2.5 km2 area and distributed in different landscape types. The changes in sample areas (squares) with determination of land cover structure transformations were observed using topographic photos and ortophoto images at a scale 1:10 000.

The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County, Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R² values, fluctuated according to the scale, season, and regression method used.

Measures to select despeckling filter to remove speckles from TerraSAR-X image have been evaluated for an area having varied land cover classes. Gamma, Lee, Sigma, Mean, Median and Frost filters were applied in different window sizes. These images are visually compared and four parameters viz. Noise Mean Value (NMV), Noise Variance (NV), Mean Square Difference (MSD) and Equivalent Numbers of Looks (ENL) are calculated.

Updating land cover maps from remotely sensed data in a timely manner is important for many areas of scientific research. Unfortunately, traditional classification procedures are very labor intensive and subjective because of the required human interaction. Based on the strategy of updating land cover data only for the changed area, we proposed an integrated, automated approach to update land cover maps without human interaction.

This work presents a classification technique for hyperspectral image analysis when concurrent ground truth is either unavailable or available. The method adopts a principal component analysis (PCA)-based projection pursuit (PP) procedure with an entropy index for dimensionality reduction, followed by a Markov random field (MRF) model-based segmentation. An ordinal optimization approach to PP determines a set of ‘good enough projections’ with high probability, the best among which is chosen with the help of MRF model-based segmentation.

The objective of this paper is to exploit the potential application of an evidential belief function model to landslide susceptibility mapping at Kuala Lumpur city and surrounding areas using geographic information system (GIS). At first, a landslide inventory map was prepared using aerial photographs, high resolution satellite images and field survey. A total 220 landslides were mapped and an inventory map was prepared. Then the landslide inventory was randomly split into a testing dataset 70% (153 landslides) and remaining 30% (67 landslides) data was used for validation purpose.

Population of two cities in Pakistan has already crossed the 10-million figure and for the rest of the areas in the country populations are also increasing rapidly. Urbanization has boosted the use of energy in the cities and so is greenhouse gas (GHG) emissions but the ground situation as to the extent, vulnerability, past trends and future scenarios are not unveiled for the cities of Pakistan. Dearth of data in Pakistan is a huge hindrance to the investigation of energy use and actual GHG emissions.

Most of the plantation impact studies reported in literature normally use either one of the sensitivity-based approach or a hydrological model with few actually comparing the impact results from these different approaches. This paper investigates the impacts of increase or decrease in plantations and climate variability on streamflow using two approaches: the sensitivity-based approach (including a non-parametric model and six Budyko framework based models) and the hydrological modelling approach (using Xinanjiang and SIMHYD models) for three medium sized catchments in Australia.

Sustainable forest management (SFM), an explicit policy objective in Canada, balances social, economic and environmental values. The status and trends of forest-associated species is one indicator of SFM, though it is under utilized due to challenges with indicator selection and data availability. This paper demonstrates and tests an indicator selection methodology which combines Breeding Bird Survey (BBS) data and LandSat land cover data to identify indicator guilds, or groups of indicator species, for a series of forest composition and configuration attributes in the Great Lakes St.

The increasing demand of water has brought tremendous pressure on groundwater resources in the regions were groundwater is prime source of water. The objective of this study was to explore groundwater potential zones in Maheshwaram watershed of Andhra Pradesh, India with semi-arid climatic condition and hard rock granitic terrain. GIS-based modelling was used to integrate remote sensing and geophysical data to delineate groundwater potential zones.

Shallow landslides induced by heavy rainfall events represent one of the most disastrous hazards in mountainous regions because of their high frequency and rapid mobility. Recent advancements in the availability and accessibility of remote sensing data, including topography, land cover and precipitation products, allow landslide hazard assessment to be considered at larger spatial scales. A theoretical framework for a landslide forecasting system was prototyped in this study using several remotely sensed and surface parameters.

The significance of nutrient inputs at the watershed scale is best expressed in terms of in-stream processes, compared to evaluating simple field measurements of nutrient inputs. Modeling tools are necessary to consider the complexity of river networks in the determination of the sources and processes by which nutrients are transported at the watershed scale.