Changes in land-cover within high nature value farmlands inside and outside Natura 2000 sites in Europe: A preliminary assessment.
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ABSTRACT:
SUBMITTER: Anderson E
PROVIDER: S-EPMC7568739 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Urban growth is a major threat to biodiversity conservation at the global scale. Its impacts are expected to be especially detrimental when it sprawls into the landscape and reaches sites of high conservation value due to the species and ecosystems they host, such as protected areas. I analyzed the degree of urbanization (i.e., urban cover and growth rate) from 2006 to 2015 in protected sites in the Natura 2000 network, which, according to the Habitats and Birds Directives, harbor species and habitats of high conservation concern in Europe. I used data on the degree of land imperviousness from COPERNICUS to calculate and compare urban covers and growth rates inside and outside Natura 2000. I also analyzed the relationships of urban cover and growth rates with a set of characteristics of Natura sites. Urban cover inside Natura 2000 was 10 times lower than outside (0.4% vs. 4%) throughout the European Union. However, the rates of urban growth were slightly higher inside than outside Natura 2000 (4.8% vs. 3.9%), which indicates an incipient urban sprawl inside the network. In general, Natura sites affected most by urbanization were those surrounded by densely populated areas (i.e., urban clusters) that had a low number of species or habitats of conservation concern, albeit some member states had high urban cover or growth rate or both in protected sites with a large number of species or habitats of high conservation value. Small Natura sites had more urban cover than large sites, but urban growth rates were highest in large Natura sites. Natura 2000 is protected against urbanization to some extent, but there is room for improvement. Member states must enact stricter legal protection and control law enforcement to halt urban sprawl into protected areas under the greatest pressure from urban sprawl (i.e., close to urban clusters). Such actions are particularly needed in Natura sites with high urban cover and growth rates and areas where urbanization is affecting small Natura sites of high conservation value, which are especially vulnerable and concentrated in the Mediterranean region.
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Project description:A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with "urbanization" showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.
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Project description:Land cover change is a dynamic phenomenon driven by synergetic biophysical and socioeconomic effects. It involves massive transitions from natural to less natural habitats and thereby threatens ecosystems and the services they provide. To retain intact ecosystems and reduce land cover change to a minimum of natural transition processes, a dense network of protected areas has been established across Europe. However, even protected areas and in particular the zones around protected areas have been shown to undergo land cover changes. The aim of our study was to compare land cover changes in protected areas, non-protected areas, and 1 km buffer zones around protected areas and analyse their relationship to climatic and socioeconomic factors across Europe between 2000 and 2012 based on earth observation data. We investigated land cover flows describing major change processes: urbanisation, afforestation, deforestation, intensification of agriculture, extensification of agriculture, and formation of water bodies. Based on boosted regression trees, we modelled correlations between land cover flows and climatic and socioeconomic factors. The results show that land cover changes were most frequent in 1 km buffer zones around protected areas (3.0% of all buffer areas affected). Overall, land cover changes within protected areas were less frequent than outside, although they still amounted to 18,800 km2 (1.5% of all protected areas) from 2000 to 2012. In some parts of Europe, urbanisation and intensification of agriculture still accounted for up to 25% of land cover changes within protected areas. Modelling revealed meaningful relationships between land cover changes and a combination of influencing factors. Demographic factors (accessibility to cities and population density) were most important for coarse-scale patterns of land cover changes, whereas fine-scale patterns were most related to longitude (representing the general east/west economic gradient) and latitude (representing the north/south climatic gradient).
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Project description:Woody vegetation in farmland acts as a carbon sink and provides ecosystem services for local people, but no macro-scale assessments of the impact of management and climate on woody cover exists for drylands. Here we make use of very high spatial resolution satellite imagery to derive wall-to-wall woody cover patterns in tropical West African drylands. Our study reveals a consistently high woody cover in farmlands along all semi-arid and sub-humid rainfall zones (16%), on average only 6% lower than in savannas. In semi-arid Sahel, farmland management increases woody cover to a greater level (12%) than found in neighbouring savannas (6%), whereas farmlands in sub-humid zones have a reduced woody cover (20%) as compared to savannas (30%). In the region as a whole, rainfall, terrain and soil are the most important (80%) determinants of woody cover, while management factors play a smaller (20%) role. We conclude that agricultural expansion cannot generally be claimed to cause woody cover losses, and that observations in Sahel contradict simplistic ideas of a high negative correlation between population density and woody cover.
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