Thanks for excellent work done! The foundation elements are very well selected and presented.
Here some inputs & thoughts to be considered when further revising the paper:
1. In the field assessment, better to avoid collecting aggregated data if variables of interest can be measured by interval or ratio scale. Aggregation can be always done at the reporting stage. This is a typical case e.g. with seedling/sapling data.
2. For saving time and money, is recommended to avoid measuring too much data. For example it is waste of money to record every single tree height in savanna type if (almost) the same accuracy leve (in terms of volume/biomass/carbon) can be reached through recording some well-selected height sample trees in sample plots and then using localized tree height curves in the data analysis phase.
3. In planning the data collecting protocol, it should be known how the data will be analyzed. There are cases where for example depth of litter has been recorded, but no clear idea how that data is utililized in computing carbon in litter.
4. Database: data should be preferably normalized, even though field forms are not showing normalized data structure. (Maybe too technical topic into this manual?).
5. Database: correct data hierarchy should be in place in order to utilize inheritance, for example a sample tree should inherit properties of plot section, plot, and cluster. This makes writing of data analysis scripts easier when using object-oriented language, as R.
6. Please add examples of computing variance estimators for stratified sampling (both area based, and point sampling methods).
7. Chapter 4.3.1 (manual): It is recommended to include definitions & explanations for terms and variables, e.g. what is a 'tree', 'shrub', 'forest', 'breast height','stump height' etc. in the national context.
8. Somewhere it could be mentioned that “Not to change field protocols during an inventory cycle”. This can cause difficulties in data analysis & reporting phase!
Thanks for excellent work done! The foundation elements are very well selected and presented.
Here some inputs & thoughts to be considered when further revising the paper:
1. In the field assessment, better to avoid collecting aggregated data if variables of interest can be measured by interval or ratio scale. Aggregation can be always done at the reporting stage. This is a typical case e.g. with seedling/sapling data.
2. For saving time and money, is recommended to avoid measuring too much data. For example it is waste of money to record every single tree height in savanna type if (almost) the same accuracy leve (in terms of volume/biomass/carbon) can be reached through recording some well-selected height sample trees in sample plots and then using localized tree height curves in the data analysis phase.
3. In planning the data collecting protocol, it should be known how the data will be analyzed. There are cases where for example depth of litter has been recorded, but no clear idea how that data is utililized in computing carbon in litter.
4. Database: data should be preferably normalized, even though field forms are not showing normalized data structure. (Maybe too technical topic into this manual?).
5. Database: correct data hierarchy should be in place in order to utilize inheritance, for example a sample tree should inherit properties of plot section, plot, and cluster. This makes writing of data analysis scripts easier when using object-oriented language, as R.
6. Please add examples of computing variance estimators for stratified sampling (both area based, and point sampling methods).
7. Chapter 4.3.1 (manual): It is recommended to include definitions & explanations for terms and variables, e.g. what is a 'tree', 'shrub', 'forest', 'breast height','stump height' etc. in the national context.
8. Somewhere it could be mentioned that “Not to change field protocols during an inventory cycle”. This can cause difficulties in data analysis & reporting phase!
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