1. For north temperate lakes, the well-studied empirical relationship between phosphorus (as measured by total phosphorus, TP), the most commonly limiting nutrient and algal biomass (as measured by chlorophyll a, CHL) has been found to vary across a wide range of landscape settings. Variation in the parameters of these TP-CHL regressions has been attributed to such lake variables as nitrogen/phosphorus ratios, organic carbon and alkalinity, all of which are strongly related to catchment characteristics (e.g. natural land cover and human land use).

This study aims to assess CO₂ levels in China, based on the dry air column-averaged mixing ratios of CO₂ measured by the Scanning Imaging Absorption spectrometer for Atmospheric CartograpHY (SCIAMACHY) instrument on board ENVISAT and emission inventories. A land cover and statistical data set, the Regional emission inventory in Asia (REAS), was also used in this study. The results show that the spatial variations of the CO₂ concentration in the whole of China are obvious, and with clear seasonal fluctuation during 2003–2005.

While crop rotation patterns can be complex with multiple crops rotated over several years, the most common rotation practice in the Central United States is biannual rotation between corn and soybeans. We analyzed the changes in crop rotation patterns from 2003 to 2010 using the Cropland Data Layer (CDL), which provides remotely sensed land cover layers for agricultural crops in the Central United States.

In this study, a multi-scale approach was used for classifying land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that they are located within) from coarser segmentations of the same scene, and this set of super-object information was used as additional input data for image classification.

The integrated remote sensing (RS) and geographic information system (GIS) approach was utilized in this study to classify land use and land cover (LULC), detect changes based over time, and identify transition trends in the Marikina sub-watershed, Laguna de Bay watershed, Philippines. Landsat 5 Thematic Mapper (TM) imageries acquired in 1999 and 2006 were pre-processed and classified using a supervised classification technique with maximum likelihood classifier algorithm in RS and were used to develop maps of the sub-watershed and sub-subwatershed levels in a GIS platform.

The impacts of deforestation and land cover change upon underlying soils were examined on one hillside in central Armenia. Soil characteristics in three land cover areas—forest, coppice, and pasture—were recorded and soil samples were analyzed. Deforestation and land cover change were found to increase erosion rates. From soil horizon and structural characteristics, it can be estimated that 40 cm of soil have been lost in the pasture and 20 cm have been lost in the coppice compared to the forest. Soil organic carbon was also affected by deforestation and land cover change.

The Pampean region covers a large surface in central Argentina, but despite the extensive agricultural activities and the high nutrient levels recorded in streams of the region, few authors have analysed the influence of land use on water quality. Here, we evaluated the relationships among catchment attributes (size, morphometry and land cover) and water chemistry in 23 Pampean streams in different seasons (autumn, spring and summer) and at three spatial scales: whole catchment and two scales of riparian buffers (200 and 500 m adjacent to both stream margins).

After the industrial revolution, urban growth has been increasing, especially with technological advances. Urbanization is accelerating environmental pollution and also affects climate significantly because of land use or land cover changes. In this study, the Hydrological Simulation Program-Fortran (HSPF) model developed by the United States Environment Protection Agency (USEPA) is used for modeling the impervious areas of Eskişehir which is located in the Porsuk Stream Watershed in Inner Anatolia, Turkey.

Identifying the relative importance of climatic and other environmental controls on the interannual variability and trends in global land surface phenology and greenness is challenging. Firstly, quantifications of land surface phenology and greenness dynamics are impaired by differences between satellite data sets and phenology detection methods. Secondly, dynamic global vegetation models (DGVMs) that can be used to diagnose controls still reveal structural limitations and contrasting sensitivities to environmental drivers.

Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors.

As part of the development of the 2011 National Land Cover Database (NLCD) tree canopy cover layer, a pilot project was launched to test the use of high-resolution photography coupled with extensive ancillary data to map the distribution of tree canopy cover over four study regions in the conterminous US. Two stochastic modeling techniques, random forests (RF) and stochastic gradient boosting (SGB), are compared.

The increasing demand of water has brought tremendous pressure on groundwater resources in the regions were groundwater is prime source of water. The objective of this study was to explore groundwater potential zones in Maheshwaram watershed of Andhra Pradesh, India with semi-arid climatic condition and hard rock granitic terrain. GIS-based modelling was used to integrate remote sensing and geophysical data to delineate groundwater potential zones.