Video observation is one out of more observational data collection techniques within ethnographic data collection methods, ranging from non-participant video observation to participant video observation [ 52- 54].
In traditional statistical methods, thirty or more observations (or data points) are typically needed for each variable, in order to gain meaningful insights and analyze the results.
More Chinese Earth observation data are expected to be used in international monitoring.
For participants who had a gap of 28 days or more since the last observation, data from the first record after the gap were selected.
Although several cities in China have accumulated annual light climate observation data, more long-term and large-scale light climate observation data are required to determine the changes in China's daylighting climate.
Variables of interest such as biomass or carbon stock are assessed on a small sample of field plots, and these data are combined with the more densely-sampled earth observation data using statistical estimation procedures in order to generate estimates.
With this interface, the user can more intuitively access the necessary observation data on natural phenomena of interest, to be reliably selected by the time information at which the phenomenon had occurred, avoiding choosing the wrong data in operations.
Of the other two methods, maximum likelihood estimation (MLE) is more commonly used when the observation data set is uncensored (no observation concentration less than the laboratory detection limit) and least absolute deviation (LAD) when model residuals are not normally distributed, both of which were not the case with this study [32].
Meanwhile, the fitting result should be compared with continuous observation data in more than one month, so that the curve fitting scheme of the power output characteristic can be obtained in terms of the reliability, stability, and the error value of the fitting term.
Observation data that contain more than 20% missing days from more than 1 month in a year or that have more than 10% missing days in a year are removed from the dataset.
