Enriched Time-Series Dataset of Polish Forest Composition (2009-2019)

VERSION 1.1
Visualisation files generated using  DOI IDhttps://doi.org/10.34808/p0nz-dv57 
Visualizations (7 PNG files, 2.67 MB, 300 DPI):1. temporal_trends.png (212 KB) - Area/volume trends 2009-20192. diversity_indices.png (429 KB) - 4-panel biodiversity metrics3. age_structure.png (241 KB) - Age distribution4. composition_trends.png (177 KB) - Coniferous vs deciduous5. growth_productivity.png (390 KB) - MAI, CAI, volume/ha6. species_comparison.png (168 KB) - Species distribution (2019)7. comprehensive_dashboard.png (654 KB) - 9-panel overview This dataset contains a Python tool for processing forest inventory data and calculating standard forestry and ecological metrics. The tool reads Excel spreadsheets containing forest inventory data and outputs organized datasets with derived variables. All formulas based on published literature. See code documentation for specific citations. The related dataset https://doi.org/10.34808/jks7-0274   provides the processing tool for users with their own inventory data or custom analysis requirements., whereas this dataset provides processed data for immediate use.
Input processing:

Reads XLSX format inventory file
Standardizes species names to forestry codes
Parses age class categories
Classifies tree types and age categories

Metric calculation:

Biodiversity indices: Shannon Index (H' = -Σ(pi × ln(pi))), Simpson Index (D = 1 - Σ(pi²)), Pielou's Evenness (J' = H'/ln(S)), Species Richness
Age structure: Weighted mean age, age class distribution percentages, coefficient of variation
Composition: Coniferous/deciduous ratios by area and volume, dominant species identification
Growth: Mean Annual Increment (Volume/(Area × Age)), Current Annual Increment (ΔVolume/Area)
Temporal: Year-over-year changes in area and volume

Data provenance tracking:

Tags each derived value with source type
Categories: RAW, RAW_SUMMED, CALCULATED, COUNTED, DETERMINED, ESTIMATED
Generates metadata file explaining categories

Implementation Details:

Language: Python 3.7+
Dependencies: pandas (≥1.3.0), numpy (≥1.21.0), scipy (≥1.7.0), openpyxl (≥3.0.0)
Lines of code: ~680
Processing time: <10 seconds for 1,260 records

Input Requirements:

Excel file (.xlsx) with columns:
Species (text)
Year (integer)
Tree_Type (text)
Category (text, e.g., "Age_41-60")
Volume_m3 (float)
Area_ha (float)

Documentation:

README: Installation, usage, API reference
Usage examples: 16 practical scenarios
Validation report: Detailed test results

Limitations:
- Designed for hierarchical age-class inventory data- Assumes standard forestry category naming conventions- Growth metrics require age class information- MAI calculation depends on age class midpoint estimates