Phytodiversity is affected both by natural and anthropogenic factors and in Island ecosystems these impacts can devastate or reduce diversity, if the native vegetation is lost. In addition to rich species richness and diversity, Island systems are the sites of high endemism and any threat to these ecosystems will consequently lead to loss and extinction of species.

In any watershed, many factors influence land use/cover change (LUCC). The nonlinear relationships between these factors and LUCC are very complicated and make it difficult to build a model that is capable of accurately simulating the range of physical processes. The aim of this study was: (1) to simplify the structure of a simulation model and improve its simulation speed, and (2) evaluate the impact of land use/cover change on surface runoff and evapotranspiration.

In this work, a high resolution wind atlas for Nakhon Si Thammarat and Songkhla provinces in southern Thailand was developed using combined mesoscale, MC2, and microscale, MsMicro, modeling techniques. The model inputs consist of long-term statistical climate data, i.e. the NCEP/NCAR database, high resolution topography and land cover data. The 200 m resolution wind resource maps were validated with observed mean wind speeds from 10 met stations located along the coastlines of the territory studied.

Koa (Acacia koa) forests are found across broad environmental gradients in the Hawaiian Islands. Previous studies have identified important environmental factors controlling stand structure and productivity at the plot level, but these have not been applied at the landscape level because of small-scale spatial variability.

Moderate-grained data may not always represent landscape structure in adequate detail which could cause misleading results. Certain metrics have been shown to be predictable with changes in scale; however, no studies have verified such predictions using independent fine-grained data.Our objective was to use independently derived land cover datasets to assess relationships between metrics based on fine- and moderate-grained data for a range of analysis extents.

Predictive models are frequently used to define the most suitable areas for species protection or reintroduction. Land-cover variables can be used in different ways for distribution modelling. The surface area of a set of land-cover classes is often used, each land-cover presence/absence or the distance to them from any point of the study area can be preferred; multiple types of land-cover variables may be combined to produce a single model.

The remote sensing community has recently adopted land-cover map updating methodologies using spectral image differencing, change masking and concatenation procedures to monitor land change accurately and consistently. Unfortunately, map updating requires costly, time-consuming manual image interpretation to achieve accurate spectral threshold placement for land-change masking. The purpose of this study is to minimize time and costs associated with manual image interpretation of change thresholds by developing a new, semi-automated method using support vector machines (SVM).

In this study, we show the novel applications of model-based scattering power decomposition analyses in synthetic aperture radar (SAR) polarimetry for man-made target detection on the sea surface. Model-based decomposition technique is primarily used for land-cover classification mainly because microwave scattering from land is composed of various scattering mechanisms.

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.

Soil erosion caused by low vegetation cover associated with agricultural land use in the catchment is blamed for the eutrophication of Lake Victoria. Above-ground biomass as an indicator of vegetation cover and biodiversity was assessed using the Normalized Difference Vegetation Index, estimation of tree density and biomass with the aim of assessing the extent to which vegetation covers the soil surface. Tree density is significantly different between agricultural and semi-natural systems with an average of 96 and 90 trees ha⁻¹ observed in Rakai and Mayuge respectively.

Climate change is affecting biodiversity worldwide inducing species to either “move, adapt or die”. In this paper we propose a conceptual framework for analysing range shifts, namely a catalogue of the possible patterns of change in the distribution of a species along elevational or other environmental gradients and an improved quantitative methodology to identify and objectively describe these patterns.