In this study we explored the potential of open source data mining software support to classify freely available Landsat image. The study identified several major classes that can be distinguished using Landsat data of 30 m spatial resolution.

With the availability of very high resolution multispectral imagery, it is possible to identify small features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult task. This paper demonstrates the potential of 8 bands capability of World View 2 satellite for better automated feature extraction and discrimination studies.

Golf courses represent a common type of anthropogenically modified habitat in suburban environments. Golf courses may provide suitable habitat for semi-aquatic animals in suburban areas, yet studies comparing animal abundances in golf course ponds with other pond types in suburban environments are somewhat limited. In this study, we compared turtle abundances in golf course ponds with ponds found in residential areas and ponds found in rural (farm) areas and examined the relationship between turtle abundance and residential land-cover within individual golf courses.

Rainfall-runoff modelling is widely used for runoff estimation at the catchment scale. However, its simulation capability is sometimes influenced because of rapid land cover changes occurring in catchments. This paper investigates whether modification of a rainfall-runoff model, Xinanjiang, by the incorporation of dynamic remote sensing data (MODIS leaf area index (LAI) and albedo) can improve runoff estimates for four south-east Australian catchments which experienced severe bushfire impacts.

This study provides a spatially comprehensive assessment of sustainable agricultural residue removal potential across the United States for bioenergy production. Earlier assessments determining the quantity of agricultural residue that could be sustainably removed for bioenergy production at the regional and national scale faced a number of computational limitations. These limitations included the number of environmental factors, the number of land management scenarios, and the spatial fidelity and spatial extent of the assessment.

To evaluate the status of biodiversity and to determine how current conservation efforts can be improved, biodiversity monitoring is crucial. An important aspect of data quality lies in its spatial resolution. It is unclear how finer scale land cover and land value information might further benefit biodiversity conservation. This paper aimed to assess the impacts of scale by modelling the conservation of endangered European wetland species and their corresponding habitats. Fine-scale datasets were derived by integrating existing geographical, biophysical and economic data.

Implementation of the EU Water Framework Directive (WFD) has drawn much attention to hydromorphological alterations of surface waters. The Lake Habitat Survey (LHS) protocol provides a method for characterising and assessing the physical habitats of lakes and reservoirs. Two metrics were developed based on this method: the Lake Habitat Modification Score (LHMS) and the Lake Habitat Quality Assessment (LHQA), as measures of lake modification and habitat value, respectively. However, the use of these metrics to predict measures of ecological quality remains largely untested.

With recent technological advances in remote sensing sensors and systems, very high-dimensional hyperspectral data are available for a better discrimination among different complex land-cover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or ‘curse of dimensionality’ in hyperspectral data sets. Moreover, these high numbers of bands are usually highly correlated.

This work addresses the estimation of net surface longwave radiation (NSLR) from net surface shortwave radiation (NSSR) by analysing the Surface Radiation Budget Network (SURFRAD) radiation data under cloudy conditions. A general model is developed to estimate NSLR from the NSSR for cloudy skies with a root mean square error (RMSE) of 23.16 W m⁻² compared with in situ data. The model is applied to AmeriFlux data. The results show that the mean error and RMSE are –2.31 W m⁻² and 29.25 W m⁻², respectively, compared with the measurement of AmeriFlux.

The Maubesi Nature Reserve (MNR) is a protected lowland area in eastern Indonesia that mainly consists of mangrove forest. The objective of this paper was to demonstrate a simple technique to visualize and quantify the change in mangrove area using a 3-year dataset of Landsat TM images acquired in 1989, 2003 and 2009. The normalized difference vegetation index (NDVI) was calculated to determine high and low vegetation biomass in each image.

Land surface change caused by fires and succession is confounded by many site-specific factors and requires further study. The objective of this study was to reveal the spatially explicit land surface change by minimizing the confounding factors of weather variability, seasonal offset, topography, land cover, and drainage. In a pilot study of the Yukon River Basin of interior Alaska, we retrieved Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) from a postfire Landsat image acquired on August 5th, 2004.

Land cover generated from satellite images is widely used in many real-world applications such as natural resource management, forest type mapping, hydrological modeling, crop monitoring, regional planning, transportation planning, public information services, and so on. Moreover, land cover data are one of the primary inputs to many geospatial models.