This study uses a series of Landsat images to map the main land-cover types on the Mediterranean island of Lesvos, Greece. We compare a single-year maximum likelihood classification (MLC) with a multi-temporal maximum likelihood classification (MTMLC) approach, with time-series class labels modelled using a first-order hidden Markov model comprising continuous and discrete variables. A rigorous validation scheme shows statistically significant higher accuracy figures for the multi-temporal approach.

In the present work a computation of wind shear coefficients (WSCs) based on 1-h measured wind data has been performed by three stations located over coastal sites in Southern Italy, i.e., Brindisi (BR), Portoscuso (PS) and Termini Imerese (TI). Wind observations have been collected through a 6-year period (January 1, 1997 to December 31, 2002) by wind mast recording at the same two sensor heights (i.e., 10 and 50m AGL), thus enabling a proper wind profile analysis. WSC overall mean values were found to be 0.271 at BR, 0.232 at PS, and 0.150 at TI.

This research article addresses the problem of land-cover classification from the multi-spectral remotely sensed images using a novel self-training based semi-supervised learning (SSL) technique. The proposed system, instead of using a single classifier, builds an ensemble of classifiers with the hope that the ensemble system will have a lesser generalization error than any of its members.

Riparian systems have become increasingly susceptible to both natural and human disturbances as cumulative pressures from changing land use and climate alter the hydrological regimes. This article introduces a landscape dynamics monitoring protocol that incorporates riparian structural classes into the land-cover classification scheme and examines riparian change within the context of surrounding land-cover change.

Automatic information extraction from optical remote sensing images is still a challenge for large-scale remote sensing applications. For instance, artificial sample collection cannot achieve an automatic remote sensing imagery classification. Based on this, this paper resorts to the technologies of change detection and transfer learning, and further proposes a prior knowledge-based automatic hierarchical classification approach for detailed land cover updating. To establish this method, an automatic sample collection scheme for object-oriented classification is presented.

Land use and land cover change, perhaps the most significant anthropogenic disturbance to the environment, mainly due to rapid urbanization/industrialization and large scale agricultural activities. In this paper, an attempt has been made to appraise land use/land cover changes over a century (1914–2007) in the Neyyar River Basin (L=56 km; Area = 483.4 km²) in southern Kerala – a biodiversity hot spot in Peninsular India.

How animal populations respond to habitat manipulations is a central theme in ecology. In recent years, the role that vegetation heterogeneity plays in regulating arthropod populations has received particular attention in both conservation science and agricultural ecology. Numerous observational studies have demonstrated that herbivores and their natural enemies are sensitive to vegetation heterogeneity, but the individual effects of percentage land cover, degree of fragmentation and patch size remain little understood.

The origin, function, and fate of dissolved organic carbon (DOC) in stream flow are not well-understood. This study explores the environmental factors controlling the spatial and temporal variation of DOC in terrestrial ecosystems of a watershed southeast of Boston, Massachusetts. Our hypothesis is that environmental factors: stream flow, land cover type, vegetation density, and drainage area are significant in estimating DOC export from an urban watershed.

Landscape management could contribute to sustainable pest control. Landscape composition, in particular, could either directly impact a pest abundance by affecting its dispersal, mortality or reproduction, or indirectly by affecting its natural enemies. We performed an analysis of the scientific literature to assess how the proportion of different land covers at the landscape level is related to the abundance of pests or to their control by natural enemies. Of 72 independent case studies, 45 reported an effect of landscape composition.

Land use/land cover changes in a lake drainage basin reflect changes in the magnitude of the water balance components and rate of sediment deposition in a lake as a reflection of the fundamental linkage between what happens in a lake considered against what is happening in the drainage basin of the lake. The objective of this study was to quantify the spatio‐temporal land cover/land use changes across the Lake Hayq closed drainage basin in north‐east Ethiopia over a 50‐years period, using multitemporal remote sensing and geospatial data.

The Cerrado is the second largest Brazilian biome and contains the headwaters of three major hydrological basins in Brazil. In spite of the biological and ecological relevance of this biome, there is little information about how land use changes affect the chemistry of low-order streams in the Cerrado. To evaluate these effects streams that drain areas under natural, rural, and urban land cover were sampled near Brasília, Brazil. Water samples were collected between September 2004 and December 2006. Chemical concentrations generally followed the pattern of Urban > Rural > Natural.

This work aimed to understand the distribution of five bacterial pathogens in O’ahu coastal streams and relate their presence to microbial indicator concentrations, land cover of the surrounding watersheds, and physical–chemical measures of stream water quality. Twenty-two streams were sampled four times (in December and March, before sunrise and at high noon) to capture seasonal and time of day variation. Salmonella, Campylobacter, Staphylococcus aureus, Vibrio vulnificus, and V. parahaemolyticus were widespread —12 of 22 O’ahu streams had all five pathogens.