NPCi_2018

This dataset contains high-resolution spatial mapping of semi-natural habitats (SNHs) across Europe, designed to support the development of the Natural Pest Control Index (NPCi). Derived from Copernicus High-Resolution Layers (HRLs), with resolutions ranging from 5m to 10m, the dataset categorizes landscapes into key SNH types, including Woody Areal - Edge (WAe), Woody Areal - Interior (WAi), Woody Linear (WL), and Herbaceous Areal (HA).

The enhanced 50m spatial resolution of the NPCi model enables detailed assessments of landscapes' potential to support beneficial flying predators, such as predatory flies and parasitic wasps. This map play a crucial role in informing agricultural and environmental policies, including the EU Green Deal, by pinpointing areas with high and low natural pest control potential.

Applications of this dataset span ecosystem services modeling, environmental policy development, and agricultural planning. By facilitating precise habitat characterization and optimization of land use strategies, it promotes sustainable agricultural practices and supports informed decision-making at local, regional, and global scales.

This dataset includes the following raster files:
- NFIELD_2017.tif: A raster file representing nitrogen field levels for 2017, derived from the CAPRI model. This spatially explicit dataset provides insights into nitrogen usage or presence across agricultural areas in Europe.

- NPCi_2018.tif: This raster contains the Natural Pest Control Index (NPCi) for 2018, an ecosystem service indicator that highlights the potential for natural pest control services within agricultural landscapes. The index is based on semi-natural habitat configurations and overall landscape complexity.

- SNH_2018.tif tif: A raster file mapping Semi-Natural Habitats (SNHs) in Europe as of 2018. It includes critical landscape features such as hedgerows, woodlands, and grasslands. This file classifies SNHs into four types: Woody Areal - Edge (WAe), Woody Areal - Interior (WAi), Woody Linear (WL), and Herbaceous Areal (HA). It serves as a foundation for assessing habitat-based ecosystem services, including biodiversity support and pest control.

- TCD10+WVM2018_MSPA.tif: A raster file combining Tree Cover Density (TCD) and Woody Vegetation Mask (WVM) data layers, processed using Morphological Spatial Pattern Analysis (MSPA). This layer provides insights into the spatial patterns and connectivity of woody vegetation across Europe.

- extGL_2018_010m_eu_v1_comp.tif: This raster layer represents an extended grassland dataset for 2018 at a 10-meter resolution. It highlights grassland distribution across Europe and supports analyses of their contributions to ecosystem services and biodiversity.

The dataset includes three Python scripts designed to process and analyse the data layers:
- NPCi_script.py: Processes spatial data to compute the Natural Pest Control Index (NPCi) by integrating layers such as Morphological Spatial Pattern Analysis (MSPA), extensive grasslands (extGL), and CORINE data. The script performs reclassification, resampling, and geospatial analyses to generate the final NPCi outputs.

- SNH_script.py: Generates the Semi-Natural Habitat (SNH) layer by combining MSPA, extensive grasslands, and CORINE land cover data. The script applies spatial masking and reclassification to create a raster layer supporting ecological analysis.

- WVM_TCD_script.py: Processes input raster layers for Woody Vegetation Mask (WVM) and Tree Cover Density (TCD). It resamples, tiles, and merges these layers using specific rules to produce a combined output raster for further analysis.