Accurate crop type information is of paramount importance for decision makers. This paper focuses on refining rapeseed field detection. This goal is achieved by creating high accuracy rapeseed maps using Sentinel-1 (S1) time series and secondly, by developing different solutions for mapping the rapeseed fields when there are constraints in ground samples collection. Proposed solutions include transferring a model developed over one year to other years with no re-training, and developing models with limited training samples. The research evaluates the performance of Random Forest (RF) and three deep learning (DL) algorithms: Long Short-Term Memory Fully Convolutional Network (LSTM-FCN), InceptionTime, and Multi-layer Perceptron (MLP). All four algorithms were used to classify the S1 time series with a large number of ground samples from the same years for training and testing. Smaller sample sizes were then tested for the training phase (100, 300, 500 and 1000 samples in a study site of 800 km2). Model transferability is tested across years. The impact of S1 image count on transfer accuracy is examined. Additionally, the effect of the phenological shift in the rapeseed growth cycle of 15 and 30 days between the training and test years was also investigated. The findings demonstrate strong model performance when training and testing occur in the same year (F1-score up to 95%). Within sample sizes of 300 to 1000, RF and InceptionTime stand out with high accuracy (F1-score>90%). When employing different years for training and testing with ample sample sizes, all four algorithms correctly classified rapeseed (F1-score between 85.5% and 92.7%). In cases of a reduced number of images, the performance of InceptionTime and LSTM-FCN decreased (16% decrease in the F1-score), while RF and MLP maintain their performance. Notably, RF outperforms DL algorithms with an F1 score of 89.1%. In the context of a phenological shift, only InceptionTime and LSTM-FCN demonstrated strong performance (F1-score between 87.7% and 92.6%).

The Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station has acquired more than 35 billion shots globally in the period between April 2019 and August 2021. The acquired shots could offer a significant database for the measure and monitoring of inland water levels over the Earth’s surface. Nonetheless, previous and current studies have shown that the provided GEDI elevation estimates are significantly less accurate than any available radar or LiDAR altimeter. Indeed, our analysis of GEDI’s altimetric capabilities to retrieve water levels over the five North American Great Lakes presented estimates with a bias that ranged between 0.26 and 0.35 m and a root mean squared error (RMSE) ranging between 0.54 and 0.68 m. Therefore, our objective in this study is to post-process the original GEDI water level estimates from an error model taking instrumental, atmospheric, and lakes surface state factors as proxies, which affect the physical shape of the waveforms, hence introducing uncertainties on the elevation estimates. The first tested model, namely a random forest regressor ($R{F}_{ICW}$) with the instrumental, atmospheric, and water surface state factors as inputs, was validated temporally (trained on a given year and validated on another) and spatially (trained on a given lake and validated on the remaining four). The results showed a significant decrease in elevation estimation errors both temporally and spatially. The temporally validated models showed an RMSE on the corrected elevation estimates of 0.18 m. Concerning the spatially validated model, the results varied based on the lake data used for training. Indeed, the most accurate spatially validated model showed an RMSE of 0.17 m, while the least accurate model showed an RMSE of 0.26 m. Finally, given that an elevation correction model using all the factors (instrumental, atmospheric, and water surface state factors) presents a best-case scenario, as water surface state factors are only available over a selected number of lakes globally, three additional models based on random forest were tested. The first,$R{F}_I$, uses only instrumental factors as correction factors, $R{F}_{IC}$ uses both instrumental and atmospheric factors, while the third, $R{F}_{IW}$, uses instrumental and water surface state factors. The temporal validation of these models showed that the model using instrumental factors, while less accurate than the remaining two models, was capable of correcting the original GEDI elevation estimates by a factor of two across the five lakes. On the other hand, the $R{F}_{IC}$ model was the most accurate between the three, with a slight degradation in comparison to the full model. Indeed, the $R{F}_{IC}$ model showed an RMSE on the estimation of water levels of 0.21 m.

Natural disasters result in increasing economic losses worldwide. Existing loss databases primarily capture insured damages and therefore often overlook uninsured assets and public compensation efforts. This study examines the role of public-sector compensation in disaster recovery, using the multi-hazard 2020 Storm Gloria in Catalonia as a case study. By systematically collecting, classifying and analyzing public compensation data related to rebuilding and restoring the direct tangible damages, we provide new insights into financial aid distribution for disaster recovery. In addition, an analysis of single major hazards is performed to understand the event's frequency, as well as its temporal and spatial distribution. Finally, the relationship between the observed losses and the return period of the triggering hazards is evaluated. The findings reveal that fluvial and coastal hazards caused over 80 % of recorded damages, while meteorological and slope hazards contributed the remainder. Concerning the affected elements, infrastructure sustained the highest losses, followed by economic and social sectors. Rebuilding and reconstruction costs for Storm Gloria were split evenly between fully public and public-private partnerships efforts. Public funding prioritized community assets and critical infrastructure, using hazard-dependent cost assessments and standardized government procedures. Additionally, the study identifies potential multi-hazard municipalities where overlapping hazards intensified damages, highlighting the need for comprehensive disaster documentation. Results also indicate that fully public compensations lack a direct correlation with hazard probability, reflecting prioritization based on recovery needs rather than hazard frequency. The research underscores the critical role of public intervention in disaster risk management and calls for enhanced data standardization to improve loss estimation methodologies in multi-hazard scenarios. Finally, this study contributes to improving our understanding of disaster loss assessment and provides a framework for future evaluations of government interventions in post-disaster recovery.

Many extreme hazardous events do not occur in isolation, but rather interact with other events, as they occur simultaneously, cascadingly, or consecutively. Hazard interrelations are critical components in hazard assessments in today’s complex and interconnected world. One event may directly or indirectly affect other hazard events through various types of spatiotemporal connections within the geophysical environment. These interrelations can greatly amplify or create new emerging impacts, often in ways that are underestimated by traditional methods. Especially considering the large volume of work done within the area of multi-hazards, there is still a lack of applying these ideas in practical disaster risk management. Drawing on the state-of-the-art conceptual models and empirical insights, as well as iterative feedback from practitioners, we propose a practical classification aid for these multi-hazard interrelations. Guided by the division into type, spatial and temporal assessment of interrelations, we propose a TST (Type–Space–Time) classification showing examples for all the different connection possibilities. We present 36 combinations of hazard interrelations and provide examples of historical multi-hazard events for each of them, including a practical framework that supports preparatory measures, response strategies, and planning decisions. It aims to provide a practical approach that aids in defining methods for assessing the frequency and intensity of multi-hazard events, the dynamic exposure and vulnerability, and combining these in multi-hazard loss assessment for specific multi-hazard scenarios, as well as in risk assessment, including the probability of such scenarios occurring. We also provide conceptual models on how to assess the impact of multi-hazards for various combinations of hazard interrelations.

Climate change will exacerbate environmental threats, which will, in turn, affect agricultural production patterns. This study addresses the feasibility of using Sentinel-2 (S2) optical remote sensing data to map the consequences of heatwaves on vineyard plots. The proposed method to map damaged and undamaged vineyards is based on the use of an inter-annual (C1) and an intra-annual (C2) criterion derived from the Normalized Difference Vegetation Index (NDVI) calculated on S2 data. While the inter-annual criterion compares the NDVI of the heatwave year to the average NDVI of the previous years with no heatwave, the intra-annual criterion compares the NDVI values before and after the heatwave in the same year. Predictions from either criteria or both combined were tested against two datasets collected during two field surveys performed in 2020 and 2021, with different ways of recording the damage caused on the grapevine by the 2019 heatwave in southeastern France. Results showed that within the reference vineyard plots with heat damage, 46 %, 62 % and 40 % of the S2 pixels were correctly predicted as damaged using C1, C2 and their combination, respectively. Within undamaged plots, 91 %, 88 % and 99 % of the S2 pixels were correctly predicted as undamaged using C1, C2 and the combination, respectively. Results also showed that only severe leaf damage was detected using the S2 NDVI. The combination of C1 and C2 provides the most accurate detection of heatwave consequences on vineyards.