Clijsters et al [74] designed a high-speed and real-time molten pool monitoring system, consisting of four modules, namely optical set-up, data processing, reference data and quality estimation.
The methods that adopt the usage of the bitstream data for quality estimation avoid the computational complexity of processing the full video data, as full decoding of the input video is not usually required in the case of bitstream-based methods.
The accuracy of predictions provided by these models depends on the data quality, appropriate estimation of model parameters, and the validation undertaken.
Essentially, the choice of the features for extraction from bitstream or pixel domain depends on the design requirements of a method, the availability of a certain type of data for quality estimation, and the encoding scheme.
The estimation accuracy of a method along the spatial lags may be affected by a) spatial distance (between estimation and data points), and b) data quality (whether estimates are based on PM2.5 measurements or on auxiliary information such as AOD).
Baseline vectors and spatial adjustments of the GPS network were calculated after study of data processing and quality estimation methods.
While subject to certain shortcomings in data quality and quantitative estimations of the magnitude of the spatial effects cannot be made, results show that most spatial characteristics of urban green spaces do influence their ecosystem service value as embedded in land value, except for the shape configuration characteristic for which the study yields no result.
The utility of MODIS data in remote water quality estimation lies in intra-annual monitoring of lake water clarity in inaccessible, large lakes, whereas Landsat is more appropriate for inter-annual, regional trend analyses of lakes ≥ 8 ha.
Before the analysis, some exploratory tests were performed to validate both the good data quality and the variance estimation.
