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.     

Living with nuclear power: Sense of place,proximity, and risk perceptions in local host communities

Previous research notes that sense of place may intensify, and that levels of public risk perception may decrease with proximity to an established hazardous or stigmatised site. In addition, the literature suggests that sense of place may act either to mediate or moderate community perceptions of risk in such localities. This study comprised a major household survey (n = 1326) and an interview study (n = 39) and was conducted close to the nuclear power stations at Oldbury and Hinkley Point, both in the UK. It investigated the roles of perceptions of place and hazard proximity in considering (a) perceptions of risk and (b) public attitudes towards the building of a new nuclear power station in the nearby area. In addition, a novel scale was developed to measure the perceived contribution of the nearby nuclear power station to sense of place. The results suggest first, that sense of place mediates (but does not moderate) perceptions of risk in very proximate communities, and second, that public attitudes to new build in communities situated very close to established nuclear sites may be largely dependent on the extent to which the existing facility is perceived to contribute towards sense of place. The implications of these results for existing theory are discussed.     

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.     

Aggregating the benefits of environmental improvements: distance-decay functions for use and non-use values

One of the main problems in using environmental cost-benefit analysis is deciding on the relevant population: whose benefits should we count? This is important since aggregate benefits depend on both per-person benefit and the number of beneficiaries. Yet this latter term is often hard to evaluate. Distance-decay functions are one way of addressing this problem. In this paper, we present estimates of distance-decay functions for a particular environmental improvement, namely a reduction in low flow problems on the River Mimram in Southern England. We do this both for users and non-users, in the context of a contingent valuation study of the benefits of improving low flow conditions. We test whether distance-decay effects for mean Willingness to Pay are stronger for a single environmental good (the River Mimram, in this case) than for a more inclusive set (here, all rivers in Thames region which suffer from low flow problems). Finally, we explore the impact on part-whole bias, in terms of the relationship between WTP for an individual site and WTP for a more inclusive group of sites, of allowing for distance-decay effects.     

Can Volunteers Collect Data that are Comparable to Professional Scientists? A Study of Variables Used in Monitoring the Outcomes of Ecosystem Rehabilitation

Having volunteers collect data can be a cost-effective strategy to complement or replace those collected by scientists. The quality of these data is essential where field-collected data are used to monitor progress against predetermined standards because they provide decision makers with confidence that choices they make will not cause more harm than good. The integrity of volunteer-collected data is often doubted. In this study, we made estimates of seven vegetation attributes and a composite measure of six of those seven, to simulate benchmark values. These attributes are routinely recorded as part of rehabilitation projects in Australia and elsewhere in the world. The degree of agreement in data collected by volunteers was compared with those recorded by professional scientists. Combined results showed that scientists collected data that was in closer agreement with benchmarks than those of volunteers, but when data collected by individuals were analyzed, some volunteers collected data that were in similar or closer agreement, than scientists. Both groups’ estimates were in closer agreement for particular attributes than others, suggesting that some attributes are more difficult to estimate than others, or that some are more subjective than others. There are a number of ways in which higher degrees of agreement could be achieved and introducing these will no doubt result in better, more effective programs, to monitor rehabilitation activities. Alternatively, less subjective measures should be sought when developing monitoring protocols. Quality assurance should be part of developing monitoring methods and explicitly budgeted for in project planning to prevent misleading declarations of rehabilitation success.