Based on the Regional Integrated Environmental Model System (RIEMS 2.0) developed by START TEA-COM RRC and Department of Atmospheric Science of Nanjing University, a dataset of observation and remote sensing over the Heihe River Basin (HRB) was used to recalibrate the model’s parameters, including topography elevation, land cover type, saturated soil water potential, saturated soil hydraulic conductivity, field moisture capacity, wilting point moisture, soil porosity, and parameter b of soil hydraulic conductivity, to build a high-resolution regional climate model for the HRB.

The Soil and Water Assessment Tool (SWAT) hydrological/water quality model divides a watershed into hydrological response units (HRUs) based on unique land cover, soil type, and slope. HRUs are a set of discontinuous land masses that are spatially located in the watershed but their responses are not tied to any particular field. Field_SWAT, a simple graphical user interface (GUI)-driven tool, was developed to map SWAT simulations from the HRU layer to a user-defined field boundaries layer.

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.

Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied.

Latin American subtropical dry ecosystems have experienced significant human impact for more than a century, mainly in the form of extensive livestock grazing, forest products extraction, and agriculture expansion. We assessed the regional-scale effect of land use and land cover (LULC) on patterns of richness distribution of trees, birds, amphibians, and mammals in the Northern Argentine Dry Chaco (NADC) over c. 19 million hectares. Using species distribution models in a hierarchical framework, we modeled the distributions of 138 species.

The arctic forest-tundra ecotone (FTE) represents a major transition zone between contrasting ecosystems, which can be strongly affected by climatic and biotic factors. Expected northward expansion and encroachment on arctic tundra in response to climate warming may be counteracted by natural and anthropogenic processes such as defoliating insect outbreaks and grazing/browsing regimes.

Patterns of land-use and land-cover change are usually grouped into one of two categories defined by the dominant trend: (1) deforestation resulting from expanding agriculture and (2) forest expansion, usually related to the abandonment of marginal lands. At regional scale, however, both processes can occur simultaneously even in the absence of net change. Given the focus on net change, such redistribution of agricultural and natural and seminatural lands has been generally overlooked.