Crop rotation is one of the important decisions made independently by numerous farm managers, and is a critical variable in models of crop growth and soil carbon. In Iowa and much of the Midwestern United States (US), the typical management decision is to rotate corn and soybean crops for a single field; therefore, the land-cover changes each year even though the total area of agricultural land-use remains the same. The price for corn increased from 2001 to 2010, which increased corn production in Iowa.

Although protected areas in Africa contain possibly the highest repositories of carbon and thus can play a role in mitigating the effects of climate change through carbon sequestration, they are threatened due to increasing levels of deforestation and forest degradation (DFD). However, little information is available on the on-site causes of DFD in these areas. This paper estimates the levels of DFD and identifies the drivers in the Ankasa Conservation Area (ACA) in Ghana as a case study. A survey was used to identify both direct and underlying factors that promote the DFD.

Research increasingly suggests that moderate to vigorous physical activity (MVPA) is essential to children's health. However, little is known about the extent to which and when different urban environments influence the extent to which children engage in MVPA. To this end, this study explores the relationship between children's MVPA and urban land use and land cover (LULC) for several temporal subdivisions of children's weekly routines (before school, after school and weekends).

A number of classification techniques to generate land cover maps from satellite imagery have been proposed but supervised classification with manual selection and delineation of training samples (TSs) continues to be the preferred technique. The current practices of field visits and manual delineation of TSs by visual recognition are highly demanding on both resources and time, with limited utility.

Oil exploration and production activities (OEPA) and other extractive endeavors can create large-scale and permanent landscape alterations through the establishment of infrastructure features such as roads, well pads, pipelines and production facilities. These structures can lead to or increase landscape fragmentation and degradation, reduce biodiversity, disrupt important ecosystem services and attract informal settlements that further alter the landscape, deplete area resources and lead to social conflict.

Land use is a key driver of global environmental change. Unless major shifts in consumptive behaviours occur, land-based production will have to increase drastically to meet future demands for food and other commodities. One approach to better understand the drivers and impacts of agricultural intensification is the identification of global, archetypical patterns of land systems. Current approaches focus on broad-scale representations of dominant land cover with limited consideration of land-use intensity.

Wetlands provide vital ecosystem services such as water purification, flood control, and climate moderation among others, which enhance environmental quality, promote public health, and contribute to risk reduction. The biggest threat to wetlands is posed by human activities which transform wetlands, often for short-term consumptive benefits. This paper aimed to classify and map recent land cover and provide a multi-temporal analysis of changes from 2002 to 2014 in the Nakivubo wetland through which wastewater from Kampala city drains to Lake Victoria in Uganda.

The present study compares the effectiveness of two common preclassification change detection (CD) methods that use two-dimensional data space of spectral-textural (S-T) change information. The methods are principal component analysis (PCA) and change vector analysis (CVA) in the Gorgan Township area, Golestn Province, Iran. A series of texture-based information was calculated mainly to separate those land use/land cover (LULC) conversions that are spectrally indistinguishable and also to provide a basis for automatic classification of S-T data space.

We examined the utility of nutrient criteria derived solely from total phosphorus (TP) concentrations in streams (regression models and percentile distributions) and evaluated their ecological relevance to diatom and algal biomass responses. We used a variety of statistics to characterize ecological responses and to develop concentration-based nutrient criteria (derived from ecological effects) for streams in Connecticut, USA, where urbanization is the primary cause of watershed alteration.

A complete, spatially explicit dataset illustrating the 21st century mining footprint for the conterminous United States does not exist. To address this need, we developed a semi‐automated procedure to map the country's mining footprint (30‐m pixel) and establish a baseline to monitor changes in mine extent over time. The process uses mine seed points derived from the U.S. Energy Information Administration (EIA), U.S.

Ashfall and pyroclastic flows from the large eruption of June 1991 destroyed much of the vegetation on the flanks of Mt. Pinatubo. Subsequent vegetation recovery has helped stabilize slopes and reduce debris flow hazard. In this project, visible and near-infrared (VNIR) satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (ASTER) captured at a similar time of year in 2001, 2004 and 2008 were used to quantify vegetation recovery within 22 upland watersheds on the mountain, 10–16 years after the eruption took place.

It is challenging to perform spatial geochemical modelling due to the spatial heterogeneity features of geochemical variables. Meanwhile, high quality geochemical maps are needed for better environmental management. Soil organic C (SOC) distribution maps are required for improvements in soil management and for the estimation of C stocks at regional scales. This study investigates the use of a geographically weighted regression (GWR) method for the spatial modelling of SOC in Ireland. A total of 1310 samples of SOC data were extracted from the National Soil Database of Ireland.