Multiscale satellite and spatial information and analysis framework in support of a large-area forest monitoring and inventory update

Many countries undertake a national forest inventory to enable statistically valid monitoring in support of national and international reporting of forest conditions and change. Canada’s National Forest Inventory (NFI) program is designed to operate on a 10-year remeasurement cycle, with an interim report produced at the 5-year mid-point. The NFI is a sample-based inventory, with approximately 18,850 2 ×2-km photo plots across the country, distributed on a 20×20-km grid of sample points; these photo plots are the primary data source for the NFI. Capacity to provide annual monitoring information is required to keep policy and decision makers apprised of current forest conditions. In this study, we implemented a multistage monitoring framework and used a Moderate Resolution Imaging Spectroradiometer (MODIS) change product to successfully identify 78% of the changes in forest cover area that were captured with a Landsat change detection approach. Of the NFI photo plots that were identified by both the Landsat and MODIS approaches as having changes in forest cover, the proportion of change area within the plots was similar (R ²?=?0.78). Approximately 70% of the Landsat-derived change events occupied less than 40% of a single MODIS pixel, and more than 90% of the change events of this size were successfully detected with the MODIS product. Finally, MODIS estimates of the proportion of forest cover change at the NFI photo plot level were comparable to change estimates for the ecoregions as a whole (R ²?=?0.95). High-temporal, low-spatial resolution imagery such as MODIS, in combination with other remotely sensed data sources, can provide information on disturbance events within a national forest inventory remeasurement cycle, thereby satisfying the interim information needs of policy and decision makers as well as the requirements of national and international reporting commitments.     

Magnesium hydroxide bulk and colloid-associated 152Eu in an alkaline environment: colloid characterisation and sorption properties in the presence and absence of carbonate

The distribution of 152Eu between magnesium hydroxide bulk, colloids and solution has been assessed under alkaline conditions, such as those in nuclear fuel storage ponds. The colloidal phase has been characterised by two complementary methods: coupled ultrafiltration-ICP-AES and scanning electron microscopy. The quantity and the size distribution of the colloidal phase is strongly ionic strength-dependent. A decrease of the quantity of colloids, in particular the larger size ranges, has been observed with increasing ionic strength. Small colloids (1 kDa-10 kDa fraction) are predominant at all ionic strengths. The morphology of colloids, observed by field-emission gun scanning electron microscopy, appears to change from hexagonal prismatic (characteristic to the mineral) to spherical (energetically more favourable) as size decreases. The distribution of 152Eu between the solid and liquid/colloidal phases has been investigated at carbonate concentrations ranging from 0 to 10(-2) M by coupled ultrafiltration and gamma-spectrometry. Mg(OH)2 bulk appears to be a very strong sorbent for 152Eu, since complete sorption onto the bulk happens for carbonate concentrations as high as 10(-3) M. Scavenging of 152Eu by Mg(OH)2 colloids is negligible in the presence of Mg(OH)2 bulk. The distribution of 152Eu between liquid and colloidal phases has been investigated in the absence of bulk at various carbonate concentrations. A significant uptake of 152Eu by the colloids in solution has been observed, which decreases with increasing carbonate concentration. 152Eu appears to be mainly associated to the smallest colloids (1 kDa-10 kDa fraction). There is a strong correlation between the sorption properties and the surface area of the colloids.     

Accuracy assessment of land surface temperature retrievals from Landsat 7 ETM + in the Dry Valleys of Antarctica using iButton temperature loggers and weather station data

The McMurdo Dry Valleys of Antarctica are the largest snow/ice-free regions on this vast continent, comprising 1 % of the land mass. Due to harsh environmental conditions, the valleys are bereft of any vegetation. Land surface temperature is a key determinate of microclimate and a driver for sensible and latent heat fluxes of the surface. The Dry Valleys have been the focus of ecological studies as they arguably provide the simplest trophic structure suitable for modelling. In this paper, we employ a validation method for land surface temperatures obtained from Landsat 7 ETM + imagery and compared with in situ land surface temperature data collected from four transects totalling 45 iButtons. A single meteorological station was used to obtain a better understanding of daily and seasonal cycles in land surface temperatures. Results show a good agreement between the iButton and the Landsat 7 ETM + product for clear sky cases. We conclude that Landsat 7 ETM + derived land surface temperatures can be used at broad spatial scales for ecological and meteorological research.     

Productivity equation for reclaiming surface mines

This article addresses the development of an agricultural productivity equation for predicting new soil (neo-sol) plant growth potential in Clay County, Minnesota, USA. Soil factors examined in the study include percent organic matter, percent slope, percent rock fragments, hydraulic conductivity, electrical conductivity, pH, topographic position, available water-holding capacity, bulk density, and percent clay. Squared terms and two-factor interaction terms were also examined as possible regressors. A best equation was selected that had a multiple coefficient of determination of 0.7399 and has five significant regressors and intercept withP.0001. The regressors are hydraulic conductivity, percent slope squared, bulk density times percent rock fragments, electrical conductivity times percent rock fragments, and electrical conductivity times percent organic matter. The regressors predict soil suitability for a general crop model. The crops included in the model are wheat, oats, barley, soybeans, sugar beets, sunflowers, and grasses/legumes.     

‘The good district’: Newbury as a seed‐bed for success

This study analyses the behaviour of high technology and new firms in the Newbury District of Berkshire in terms of employment, location, genealogy, and local economic impact. It finds that encouraged by good communications networks and a ‘resident electronics establishment’, the direct, as opposed to the indirect, employment consequences are generally encouraging, but not widely replicable. Moreover, the general picture of employment growth masks important differences in labour demand across occupations, particularly with respect to ‘traditional male’ skilled manual workers.     

WEST AFRICA: VOLTA DISCHARGE DATA QUALITY ASSESSMENT AND USE1

ABSTRACT: Water resource management in West Africa is often a complicated process due to inadequate resources, climatic extremes, and insufficient hydrological information. Insufficient data hinder sustainable watershed management practices, one of the top priorities in the Volta River Basin. This research properly fills in missing data by modeling the hydrological distribution in the Volta River Basin. On average, discharge gages across the basin are missing 20 percent of their monthly data over 20 years. Two methods were used to supplement missing data: a statistically linear model and a conceptual hydrological model. A linear equation, developed from the regression of precipitation and runoff, was used to evaluate the quality of existing data. The hydrological model separates the system into root and groundwater zones. Measured values were used to calibrate the hydrological model and to validate the statistical model. The quality of existing data was analyzed and organized for usability. Accuracy of the hydrological model was also evaluated for its effectiveness using R² and standard error. It was found that the hydrological model was an improvement from the linear model on a monthly basis; R² values improved by as much as 0.5 and monthly error decreased. Monthly predictions of the hydrological model were used to fill gaps of measured data sets.