Sub-pixel mapping is a process to provide the spatial distributions of land cover classes with finer spatial resolution than the size of a remotely sensed image pixel. Traditional Markov random field-based sub-pixel mapping (MRF_SPM) adopts a fixed smoothing parameter estimated based on the entire image to balance the spatial and spectral energies. However, the spectra of the remotely sensed pixels are always spatially variable.

Super-resolution mapping (SRM) is a recently developed research task in the field of remotely sensed information processing. It provides the ability to obtain land-cover maps at a finer scale using relatively low-resolution images. Existing algorithms based on indicator geostatistics and downscaling cokriging offer an SRM approach using spatial structure models derived from real data. In this article, a novel SRM method is developed based on a sequentially produced with local indicator variogram (SLIV) SRM model.

Land cover and land cover change, both in time and space, were analyzed in the Gediz Delta (Turkey) and the Rhone Delta (Camargue, France) to determine the evolution of two great deltas in the Mediterranean basin. Geographical Information Systems and remote sensing were used to estimate the impact of land use changes on habitats over a 35 year period from 1975 to 2010.

Low-lying coastal areas are more vulnerable to the impacts of climate change as they are highly prone for inundation to SLR (Sea-Level Rise). This study presents an appraisal of the impacts of SLR on the coastal natural resources and its dependent social communities in the low-lying area of Vellar–Coleroon estuarine region of the Tamil Nadu coast, India. Digital Elevation Model (DEM) derived from SRTM 90M (Shuttle Radar Topographic Mission) data, along with GIS (Geographic Information System) techniques are used to identify an area of inundation in the study site.

Applications of microwave remote-sensing data in land data assimilation are a topic of current interest and importance due to their high temporal and spatial resolution and availability. However, there have been few studies on land surface sub-grid scale heterogeneity and calculating microwave wetland surface emissivity when directly assimilating gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data to estimate soil moisture.

The Grain for Green Project (GGP) is the largest land retirement/afforestation program in China; it was primarily initiated to reduce the soil erosion and improve the ecological conditions in the Loess Plateau in 1999. If effective, this massive regional effort will induce significant improvement in the vegetation conditions. At this time, the effectiveness of the GGP has not been well documented.

Pathogen contamination of waterbodies, which is often identified by the presence of pathogen indicators such as Escherichia coli, is a major water quality concern in the United States. Reducing in-stream pathogen contamination requires an understanding of the combined impacts of land cover, climatic conditions, and anthropogenic activities at the watershed scale. In this study these factors are considered by assessing linear relationships between in-stream E. coli water quality data, watershed indexes, and rainfall for the Squaw Creek Watershed, IA, USA.

In order for carbon credits awarded for reducing emissions from deforestation and degradation of forests (REDD) to be effective, they need to be competitive with alternative land uses. In the case of Southeast Asia, oil palm cultivation is one of the most lucrative possible land uses. Existing mechanisms for awarding certified emission reductions (CERs) might not be adequately flexible to changing commodity prices or to meet the needs of landowners who heavily discount future returns from their land.

The iterative and convergent nature of ensemble learning algorithms provides potential for improving classification of complex landscapes. This study performs land-cover classification in a heterogeneous Massachusetts landscape by comparing three ensemble learning techniques (bagging, boosting, and random forests) and a non-ensemble learning algorithm (classification trees) using multiple criteria related to algorithm and training data characteristics.

We tested how accurately image data from the Advanced Land Imager (ALI) sensor vs. the Landsat Thematic Mapper (TM) predict the land cover of Lonicera maackii in the forest understory, taking advantage of this invasive shrub's extended leaf retention in the fall when the canopy is leafless. Percent cover of L. maackii in 20 woodlots in southwestern Ohio was regressed on values for spectral vegetation indices (SVIs) derived for each image. The land cover of L. maackii was best explained by the Simple Ratio (SR) using TM data (R ² = 0.537). The regression results for SVIs from TM vs.

We use the MEGAN (Model of emissions of Gases and Aerosols from Nature) module embedded within the global three-dimensional Goddard Earth Observing System chemical transport model (GEOS-Chem) to simulate the interannual variations in biogenic volatile organic compound (BVOC) emissions and concentrations of ozone and secondary organic aerosols (SOA) in China over years 2001–2006.

An urban net all-wave radiation parameterization scheme is evaluated using annual datasets for 2010 recorded at a Beijing urban observation site. The statistical relationship between observed data and simulation data of net radiation has a correlation coefficient of 0.98 and model efficiency of 0.93. Therefore, it can be used to simulate the radiation balance of Beijing. This study analyzes the variation in the radiation balance for different underlying surfaces. To simulate radiation balance differences, we set four pure land-cover types (forest, grass, roads, and buildings).