Investment in Disaster Risk Management in Europe Makes Economic Sense

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousã district in Portugal. We used ten multispectral bands with a spatial resolution of 10 m, and four vegetation indices, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying principal component analysis (PCA) on the 10 S2A bands, four texture features, including mean (ME), homogeneity (HO), correlation (CO), and entropy (EN), were derived for the first three principal components. Textures were obtained using the Gray-Level Co-Occurrence Matrix (GLCM). As a result, 26 independent variables were extracted from S2. After defining the land use classes using an object-based approach, the Random Forest (RF) classifier was applied. The map accuracy was evaluated by the confusion matrix, using the metrics of overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa coefficient (Kappa). The described classification methodology showed a high OA of 90.5% and kappa of 89% for vegetation mapping. Using GLCM texture features and vegetation indices increased the accuracy by up to 2%; however, classification using GLCM texture features and spectral bands achieved the highest OA (92%), indicating the texture features′ capability in detecting the variability of forest species at stand level. The ME and CO showed the highest contribution to the classification accuracy among the GLCM textures. GNDVI outperformed other vegetation indices in variable importance. Moreover, using only S2A spectral bands, especially bands 11, 12, and 2, showed a high potential to classify the map with an OA of 88%. This study showed that adding at least one GLCM texture feature and at least one vegetation index into the S2A spectral bands may effectively increase the accuracy metrics and tree species discrimination.

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Kneuts¹,

Asbroeck²,

Boxstael³

et al. 2022

Tijdschr Geneesk

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Disasters on the border of our abilities Disasters do not take into account geopolitical boundaries. Each country has its own structures and legislation to deal with. When a disaster occurs at or around national borders and cooperation is necessary, the existing structures and legislation of the countries involved must be able to reconcile. In this article, the current situation between neighboring countries Belgium and the Netherlands is discussed. The legislation and structures for disaster management per country, as well as the possible obstacles in case of cooperation are described. Several agreements have already been made at a regional, provincial and national level. A legal framework has also been created at European level to facilitate international aid. However, there are still several obstacles to overcome. The disaster management of neighboring countries does not seem to be well known to aid workers, the communication between countries is not yet optimal and international exercises are hardly carried out. Therefore, a couple of recommendations to improve international cooperation in the border areas are given.

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Investment in Disaster Risk Management in Europe Makes Economic Sense

Cited by 3 publications

References 41 publications

Understanding the Economic Efficiency of Spending on Fire and Rescue Services: A Literature Review on the Causal Impacts of Fire Suppression Activities

Understanding the Economic Efficiency of Spending on Fire and Rescue Services: A Literature Review on the Causal Impacts of Fire Suppression Activities

Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal

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