Delivering Systematic and Repeatable Area-Based Conservation Assessments: From Global to Local Scales
Abstract
:1. Introduction
2. Methods: Developing a New Back-End Approach for Area-Based Planning
2.1. Pre-Processing
2.2. Processing
- The global CEP dataset is created in one-degree tiles and then aggregated into a 10 × 10-degree grid for a total of 648 tiles covering the globe.
- The relevant GRASS function is run in parallel on each single tile, where processing in a single tile is assigned to a different core of a multi-core server. With the aim of making the best use of available memory, the number of cores used for parallelization is optimized for each thematic dataset and is inversely proportional to the resolution of the raster to be processed (the higher the resolution, the more memory is allocated for each process).
- Output statistics for each tile are merged into a single CSV file, which is subsequently imported as a table in the Postgres SQL database.
2.3. Post-Processing
- Two general SQL functions for the post-processing of raw statistics (one for categorical and one for continuous raster datasets, respectively) have been developed. The appropriate function is run using as input the raw statistics table of each dataset and producing in output an intermediate table, where statistics are rebuilt for each individual object through the aggregation of all relevant, unique elements using the qid and cid codes.
- For each indicator, a specific script, tailored for the needs of the corresponding metric, processes the intermediate table and returns the final metric for each of the relevant reporting levels, ready for distribution via REST and web services.
3. Results: Implementing the Back End
- Compute flat CEP as described in Section 2.1.
- Export flat CEP as raster tiles (10 × 10 degrees wide; 30 m spatial resolution) and import them into GRASS DB.
- In GRASS, compute areas by categories using the latest available version of Copernicus Land Cover (100 m spatial resolution). The result is a non-spatial table with coverage values in km2 of all categories of land cover for each cid of CEP.
- Import the non-spatial table in Postgres.
- Aggregate again the cids accordingly through
- Selection of cids satisfying the criteria included in the original question (iso3 = ‘IDN’ AND is_protected IS true AND lc_class = ‘cropland’)
- Sum up of coverages for selected cids
- The final metric is the area in km2 of croplands within protected areas of Indonesia.
3.1. Global Indicators to Support International Biodiversity Policy Reporting
3.2. Spatial Conservation Planning for Renewable Energy in the EU
4. Discussion: The Future of DOPA Back End and Its Applications
4.1. Achieving Technical Efficiency through Innovation
4.2. Integrated System Design and Governance
4.3. Developing Indicators for Area-Based Conservation 2030–2050
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Phase | Inputs | Tools | Outputs |
---|---|---|---|
Pre-processing | Baseline datasets (=reporting levels) Administrative layers Ecosystems
| PostGIS flattening scripts for multi-overlapping vectors. GRASS-GIS: Full workflow, organized as a series of scripts, for the estimation of Total Carbon Stocks as the sum of 5 different carbon ‘pools’. The procedure is based on methodologies published in [24,25] | Pre-processed relational database that includes
species richness maps: cardinality of the array) Five global carbon datasets: |
Thematic datasets Conservation sites
Global landcover and carbon datasets | |||
Processing | Pre-processing outputs + global thematic datasets | Postgres SQL-only scripts for base protection coverages (country/land/marine/ecoregion/protection).
| GLOBAL output tables Global raw intersections of base/thematic datasets (coverages cover the whole global surface) |
Post-processing | processing outputs |
| Country/ecoregion/protected area indicator tables (i.e., standard DOPA outputs) On-demand aggregations (e.g., specific reporting for a region or a site, a subset of species assessment, etc.) |
Indicator Relevant to KM-GBF and SDGs | Global | Regional—Europe | National—Italy |
---|---|---|---|
Terrestrial Protected Area Coverage | 21,295,150 km2 (14.45%) | 1,086,134 km2 (26.23%) | 65,057 km2 (21.57%) |
Marine (EEZ) Protected Area Coverage | 29,245,536 km2 (8.06%) | 654,856 km2 (11.21%) | 57,452 km2 (10.71%) |
Ecoregions representation | Number of ecoregions with these coverages: 0–17%: 644 17–30%: 180 30–50%: 137 More than 50%: 136 | Number of ecoregions with these coverages: 0–17%: 16 17–30%: 16 30–50%: 20 More than 50%: 16 | Number of ecoregions with these coverages: 0–17%: 4 17–30%: 4 30–50%: 5 More than 50%: 2 |
Protection of key biodiversity areas | KBA fully protected: 3278 KBA partially protected: 6780 KBA not protected: 5890 | KBA fully protected: 1266 KBA partially protected: 1972 KBA not protected: 108 | KBA fully protected: 41 KBA partially protected: 127 KBA not protected: 4 |
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Juffe-Bignoli, D.; Mandrici, A.; Delli, G.; Niamir, A.; Dubois, G. Delivering Systematic and Repeatable Area-Based Conservation Assessments: From Global to Local Scales. Land 2024, 13, 1506. https://doi.org/10.3390/land13091506
Juffe-Bignoli D, Mandrici A, Delli G, Niamir A, Dubois G. Delivering Systematic and Repeatable Area-Based Conservation Assessments: From Global to Local Scales. Land. 2024; 13(9):1506. https://doi.org/10.3390/land13091506
Chicago/Turabian StyleJuffe-Bignoli, Diego, Andrea Mandrici, Giacomo Delli, Aidin Niamir, and Grégoire Dubois. 2024. "Delivering Systematic and Repeatable Area-Based Conservation Assessments: From Global to Local Scales" Land 13, no. 9: 1506. https://doi.org/10.3390/land13091506