Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+. Though broad scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass (AGB) at spatial grains ranging from 5 to 250m (0.025–6.25 ha), and we evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that the spatial sampling error in AGB is large for standard plot sizes, averaging 46.3% for 0.1 ha subplots and 16.6% for 1 ha subplots. Topographically heterogeneous sites showed positive spatial autocorrelation in AGB at scales of 100m and above; at smaller scales, most study sites showed negative or nonexistent spatial autocorrelation in AGB. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGB leads to a substantial “dilution” bias in calibration parameters, a bias that cannot be removed with current statistical methods. Overall, our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise

Automated Real-Time Vision Quality Inspection Monitoring System

The requirement of product quality inspection in industries for product standardized leads to a development of the quality inspection system. The problem is related to a manual inspection that is done by a human as an inspector. This paper presents an automated real-time vision quality inspection monitoring system as a problem solver to a manual inspection that is tedious and time-consuming task as well as reducing cost especially in small and medium enterprise industries (SME). For the proposed system, soft drink is used as the test product for quality inspection. The system uses computer-network to inspect two quality inspections which are color concentration and water level. The analysis includes pre-processing, color concentration using the histogram and quadratic distance and level inspection using coordinate vertical and horizontal reference levels. The similarities of both experimental and simulation results are obtained for both parameters which are 100% accuracy using 205 samples

Individuals who do and do not perceive difficulties adhering to a diet for diabetes mellitus, their quality of life and glycaemic control

Opinion regarding the successful management of insulin dependent diabetes mellitus (IDDM) has identified nutrition as a key player. Whilst important, diet has also been highlighted as one of the most difficult aspects of the regimen, by both individuals with IDDM and health workers. Current dietetic recommendations for the nutritional management of individuals with IDDM include, the normalisation of plasma glucose and the promotion of patient well being. This study aimed to determine if any significant difference in quality of life (QOL) and glycaemic control existed between groups of individuals with IDDM, who perceive their diet difficult to adhere to and those who perceive adherence easy