SP-PAM APPLICATION
Project co-funded by NSF „Application of information technologies for development of forest ecosystem models as an approach for DGVMs progress“ of COST Action 0805 Terrabites
SPPAM main purposes are :
- Finding sections of stress periods for each species for which data is available.
- Providing statistical information about the surveyed tree species.
Visit SP-PAM Application website
THE SP-PAM APPLICATIONFUNCTIONALITY
1.Polynomial approximation of series and selection of best approximation:
- Automatic generation of approximate polynomial of the greatest extent possible.
- Calculation of growth index (It) as the ratio between the measured and the estimated value for each year and row.
- Finding an average model row from It for individual localities per species (standardization of dendrohronological rows).
- Rows with R2 > 0.45 and correlation index r ≥ 0.1are present in the analysis;
2. Calculation of EPS and rejection of localities with EPS < 80%;
3. Multiple regression analysis of model index rows of widths, temperature and precipitation with intention to reveal the limiting factors for growth, type and localities;
4. Identification of stress periods (SP) for individual species and localities – periods with It < 1 and of stress sections – In;
5. Characteristic of identified In to find the most reliable stress-sections (intervals of years for various localities and types)– calculation of indicators of In: duration (D), amplitude (A) and frequency (F) – average and extreme values, coverage (Cov.) and cardinality (Card.) of stress sections. Stress sections with Cov. ≥ 50% take part in the analysis;
6. Polynomial approximation of climate data for temperatures and rainfall, and selection of the best approximation. Calculation of Itm, Ip;
7. Finding the average temperatures and rainfall for 30 – year periods, and the confidence interval of the average climatic and biological climate years by localities. Calculating of av.T, dT, av. P and dP;
8. Determination of adverse climatic and biological climatic years – AHD, AHW, ACD, ACW in both regimes and different combinations in which one of the regimes is normal according to the dT and dP;
9. Parallel analysis of stress periods and climate data. Comparative analysis of the periods of stress sections with adverse years;
10. Storing the results in a database;
11. Adding optional features to perform separate analysis;
12. Adding features for a graphical representation of the analysis results.