•Propose HADeenNet for landslide detection from different high spatial resolution images.•Outperform typical frameworks for landslide detection from images of different sensors.•Develop an attention ...module to synthesize features learned from a hierarchical network.•Get rid of unbalanced sample distribution by improving the pipeline in generating samples.
Efficient landslide mapping from high spatial resolution images is important in many practical applications, such as emergency response. Numerous studies and methods have been published on this subject; however, these methods are difficult to apply in the real world because they are mainly based on remotely sensed landslides from a single sensor with a specific spatial resolution. Additionally, models built within deep learning frameworks tend to adopt similar encoder-decoder network structures, wherein many landslide features are easily filtered out by continuous convolutions. In this paper, we propose a hierarchical deconvolution network to detect landslides. The model enlarges input feature maps by a deconvolution operation and convolutes the enlarged feature maps to learn to detect landslides. Moreover, the hierarchical structure enables the proposed network to better synthesize landslide features at a higher spatial resolution. An attention module is also proposed to enhance multi-scale landslide features. Our model is trained on four large earthquake-triggered landslide areas and one publicly released landslide dataset, where each dataset consists of hundreds to thousands of landslides. To mimic practical applications, the trained model is evaluated over three areas that recently experienced landslides. The performance of our proposed model is compared with six widely used frameworks, and it achieved a 21% higher F1-measure and at least 10% higher IOU using each of the evaluation landslide datasets. Additionally, the effectiveness of our model in maintaining landslide features, especially small landslides, is verified through comparisons with other commonly used frameworks, which demonstrate a strong potential use for practical cases.
China is a country that is significantly affected by and sensitive to global climate change. Floods are one of the major natural disasters in China, and they occur with high frequency and wide impact ...in the country, causing serious losses. Since the 1990s, they have become more frequent. China has made remarkable achievements in flood risk management, but the problems and challenges of this in the context of climate change and urbanization are still serious and require in-depth analysis and targeted adaptations. During the summer of 2020, southern China suffered from catastrophic flooding; however, the losses from this flooding were much lower than those of previous major floods. Herein, the flood disasters of the Yangtze River Basin in China in 1998 and 2020 are compared and analyzed from atmospheric, hydrological, socioeconomic, and disaster-loss perspectives and the reasons behind the observed differences are examined and discussed. The findings indicate that risk-management capabilities, such as engineering defense capabilities, environmental recovery capabilities, forecasting and early-warning capabilities, and emergency response capabilities, have achieved remarkable results. The results show that disaster loss has been largely reduced because of China's achievements in disaster risk reduction measures. The problems and challenges faced by China's flood risk management are analyzed, and detailed watershed comprehensive flood risk management recommendations are put forward to reduce the losses caused by flooding.
In the United States, Canada, the United Kingdom, and other countries with advanced pipeline management, some organizations are responsible for pipeline safety protection management for underground ...hazardous materials. The security and maintenance of a hazardous material pipeline are serious considerations for urban safety, because the materials transported by underground pipelines contain hazardous goods, such as the flammable or explosive particles of solids, liquids, and gases. Damage to a pipeline by external forces often leads to secondary disasters, such as the leakage of hazardous materials, fires, explosions, and environmental pollution. Such events seriously affect the safety of individuals and their property.
Accordingly, this study used seismic scenario analysis with a spatial grid to evaluate earthquake damage to an underground pipeline in an urban area. Damage to underground pipelines was classified, pipeline disaster management procedures were discussed, and improvement measures were proposed, such as establishing a geographic information platform and conducting disaster impact assessments for hazardous material pipelines. Underground hazardous material pipelines were assessed in scenarios including earthquakes. Such assessments are intended to provide disaster reduction plans and disaster prevention drills to improve pipeline safety as well as the planning for pipeline materials to aid seismic resistance.
•Consequences of Natech incidents are more serious then general pipeline incidents.•Pipeline earthquake impact is dependent on the ground motion distribution.•Master the higher risk areas of pipeline damages and its disaster mitigation measures.
Accurate large-scale building detection is significant in monitoring urban development, map updating, change detection, and digital city establishment. However, due to the complicated details of ...background objects in high spatial resolution remotely sensed images, the models proposed in building detection are still not performing satisfactorily. Particularly, such issue lies in the small buildings, which are easily to be omitted, and the pixels in the bounding area of each building instance can be especially confusing with the background objects. Aiming to deal with such problem, we propose Res2-Unet to employ multi-scale learning at a granular level, rather than the commonly used layer-wise feature learning, to enlarge the scale of receptive fields of each bottleneck layer. It replaces the widely used 3 <inline-formula><tex-math notation="LaTeX"> \times </tex-math></inline-formula> 3 convolution on n-channel feature maps with a set of smaller groups, which are organized in a hierarchical structure to enlarge the scale-variability. The general framework is an end-to-end learning network, taking a typical semantic segmentation network structure with encoders to encode the input image into feature maps and decoders to decode the feature maps into binary segmented result image. Moreover, to enhance the building boundary generation ability of our model, a boundary loss function is proposed to improve the detection performance. The proposed framework is evaluated on three public datasets, Massachusetts building dataset, WHU East Asia Satellite dataset and WHU Aerial building dataset. It is compared with the published performances and has achieved the state-of-the-art accuracies. That verifies the robustness of the proposed framework.
Drought and flood abrupt alternations (DFAA) are new challenges under climate change with particular emphasis on its affects related to agriculture. However, current regional DFAA analysis research ...rarely investigates agricultural DFAA with special regards to agricultural elements. In this work, a method based on a daily scale index named the standardized antecedent precipitation evapotranspiration index (SAPEI) and crop characteristics was established to investigate the characteristics of agricultural DFAA during cotton growth stages in the middle-and-lower Yangtze River (MLRYR) during 1961–2020. Additionally, the influence of DFAA on cotton climatic yield in response to flooding and drought was examined by multiple regression. The results demonstrate that the SAPEI efficiently described the relations between cotton climatic yield and the intensities of cotton drought and flood and well characterized cotton DFAA events, especially for short-term events. The most recent decade over the past six decades has seen the most frequent cotton DFAA events, and the only significant trend (p < 0.05) of cotton DFAA frequency was an upward trend in Jiangsu Province. In addition, the middle growth stage of cotton was the most DFAA-affected period within a year. Cotton drought-flood alternations (DF) were more common than flood-drought alternations (FD). The most DF-prone and FD-prone regions differed greatly, but the northeastern MLRYR was the most DFAA-prone region. In all provinces, the cotton DFAA frequency was significantly and positively related to the cotton drought frequency. Finally, the relations between cotton climatic yield and the intensities of drought and flood were much less significant in the years with more DFAA events than in other years, indicating an obvious negative interaction between drought and flood in cotton DFAA events. This finding, at the regional scale, confirmed previous field-scale conclusions on cotton responses to DFAA stress. In summary, this work provides references for agricultural water management in adapting to climate change.
•A method for assessing agricultural drought and flood abrupt alternation was proposed.•A near-term high risk of cotton drought and flood abrupt alternation was detected.•More droughts probably bring more agricultural drought and flood abrupt alternations.•Negative interaction between cotton drought and flood was found at a regional scale.
•Integrating SLIDE model within CAESAR-Lisflood.•Quantitatively modeling the disaster chain mechanism over landscape evolution.•Predicting landslide dynamic susceptibility under extreme ...rainfall.•Providing effective decision-support for digital disaster reduction management.
The interaction mechanism between the dynamically changing environment and the subsequent natural hazards in the years following an earthquake has been studied based on a multi-temporal remote sensing dataset and continually measured data. However, how to quantitatively model this interaction mechanism has not been fully addressed. In this study, we first modeled the interaction mechanism between the ‘rainfall-landslide-flash flood’ disaster chain and the dynamically changing environment in mountainous areas by incorporating the SLope-Infiltration-Distributed Equilibrium (SLIDE) model within the landscape evolution model (CAESAR-Lisflood) and then applied the integrated CAESAR-Lisflood model in a Wenchuan earthquake-stricken area. The results demonstrated that the landslide susceptibility under extreme rainfall can be predicted effectively based on the integrated CAESAR-Lisflood model and that new/enlarged landslides occur more easily in mountain valleys, near the valley outlet, and in the main steep gullies. Most of the high landslide susceptibility areas are not located in coseismic landslide areas with high vegetation recovery. The landslide legacy effects had a significant influence on the landscape erosion and deposition processes and a great effect on the spatial distribution pattern of the material redistribution in the basin, which in turn affected the subsequent disaster occurrence. The integrated CAESAR-Lisflood model compensates for the effect of the “rainfall-landslide-flash flood” disaster chain on the process of erosion and deposition, improves the model’s applicability in earthquake-stricken areas, and provides scientific information for regional disaster management and reduction.
Extreme persistent precipitation is becoming increasingly common globally, and persistent extreme regional precipitation events (PERPEs) pose a particularly serious threat to humans and environmental ...systems. This study investigated total precipitation (TP), daytime precipitation (DP), and night‐time precipitation (NP) in southwest China (SWC) during 1961–2019. The occurrence of TP, DP, and NP was analysed under the conditions of persistence over 1–5 days, temporal persistence over >2 days, and temporal nonpersistence over ≤2 days, and spatiotemporal overlap for at least 3 days (i.e., a PERPE) using the objective identification technique for regional extreme events. The results indicated the following. (a) Annual maximum daily precipitation ≥200 (100) mm in terms of TP (NP) was recorded mainly in the Sichuan Basin (SCB) and eastern parts of the Yunnan–Guizhou Plateau (YGP), and it occurred mostly in summer. For DP, annual maximum daily precipitation of ≥100 mm was recorded throughout SWC, and it occurred mainly in winter in the Henduan Mountains, summer in the SCB, and spring in western parts of the YGP. (b) The magnitudes of NP and DP differed for events persisting for 1–5 days. The average amount of precipitation with temporal persistence increased during 1961–2019 for TP, DP, and NP. However, the frequency of occurrence of events with temporal persistence increased more in comparison with that of events with temporal nonpersistence. Overall, the frequency of occurrence of events with temporal persistence or temporal nonpersistence in the western SCB and eastern YGP increased more for TP and NP than for DP. (c) In the study period, 22, 44, and 4 PERPEs were detected for TP, NP, and DP, respectively, occurring mainly in the SCB, indicating that TP was dominated by night‐time PERPEs. These findings improve understanding of PERPEs changes in SWC and provide scientific support for implementation of measures intended to prevent and mitigate extreme flood events and waterlogging.
Annual maximum daily precipitation of ≥200 (100) mm in terms of TP (NP) was recorded mainly in the Sichuan Basin. The persisting for 1–5, >2, or ≤2 days mainly occurred in the western SCB and eastern parts of the YGP for TP (NP) than DP. The PERPEs of TP was dominated by night‐time PERPEs.
•The influence of meteorological index selection is nonnegligible•The standardized precipitation and evapotranspiration index is generally the best performer.•Relative performances of the indices ...vary with calculation periods.•The crops are most vulnerable to flooding during their middle growth stages.•Oilseed rape and Anhui were the crop and region most affected by flooding.
Flooding is a worldwide destructive disaster that severely restricts agricultural production. Meteorological indices are common tools for regionally assessing the impact of flooding on crop yields, but their performances are rarely compared. In this work, six convenient meteorological indices, namely, precipitation (P), standardized precipitation anomaly (PA), China Z index (CZI), standardized precipitation index (SPI), standardized precipitation and evapotranspiration index (SPEI), and standardized antecedent precipitation and evapotranspiration index (SAPEI), were employed to establish correlations between flooding intensity and crop meteorological yield (FL-Ym correlation). Four major crops in the middle-lower reach of the Yangtze River (cotton, oilseed rape, wheat, and maize) were selected as the study crops. The results indicated that the flooding intensities quantified by the SPI, SPEI, and CZI had strong interconnections, whereas those quantified by the P and SAPEI were less related to the others. In the cases with stronger negative FL-Ym correlations, different indices were more likely to yield consistent identifications. In terms of the districts witnessing significant FL-Ym correlations, in only 26%, 41%, 44%, and 75% of them (respectively corresponding to cotton, wheat, maize, and oilseed rape), the correlations were consistently identified as significant by the majority of the indices, demonstrating the nonnegligible influence of index selection. The relative performances of the examined indices varied with the employed calculation periods (whole growth period and single critical stage), whereas the SPEI was generally the best performer. The SAPEI performed best in assessing the flooding impact during crop critical growth stages, in sharp contrast with its mediocre performance over the whole crop growth period. According to the results of multiple indices, flooding during the middle stages of the study crops exerted the greatest negative impact. Additionally, oilseed rape and Anhui Province were identified as the crop and the region most affected by flooding. This work can provide support for flooding disaster assessment and agricultural water management.
Establishing the National Comprehensive Disaster-Reduction Demonstration Community (NCDDC) is crucial for enhancing comprehensive disaster risk reduction at the grassroots level in China. Studying ...the distribution characteristics and influencing factors of NCDDCs can guide future NCDDC layout optimization and related policy adjustments. Using the standard deviation ellipse, nearest neighbor index, kernel density, spatial autocorrelation, and Geodetector, we analyzed the spatiotemporal distribution characteristics of NCDDCs in China from 2008 to 2021 and detected their influencing factors. The findings are as follows: (1) NCDDCs exhibit an uneven distribution at different scales, including spatial, urban–rural, and county scales. (2) The spatial distribution of NCDDCs mainly follows a northwest–southeast pattern during 2008–2014 and shows a northeast–southwest trend after 2014. (3) The positive spatial correlation and spatial agglomeration of NCDDCs increase annually. (4) NCDDCs show a concentrated and contiguous distribution pattern in 2021, based on “core density zone–ring-core decreasing area–ring-core expansion group–Ɔ-shaped area–belt-shaped area”. (5) The main factors affecting the NCDDC distribution are hospital density, road density, GDP density, and population density, with factors’ interactions exhibiting bilinear and nonlinear enhancement effects. This study reveals the NCDDC spatiotemporal distribution characteristics and its influence mechanism, providing a scientific basis for future NCDDC layout optimization and related policy adjustments.
The 2023 Türkiye–Syria earthquake was reported as the largest earthquake of Mw7.8, resulting in over 50,783 and 7259 deaths in Turkey and Syria, respectively. It has also damaged numerous residential ...buildings and other essential infrastructures, thus rendering more than 850,000 children and 356,000 pregnant women homeless, forcing them into displacement and its dire consequences, such as inadequate temporary shelters, a lack of access to safe drinking water, sanitation and hygiene (WASH), necessary for disease prevention, health promotion and maintenance. The disaster has disproportionately affected the Syrian refugee community in Turkey as it has fuelled disparities and discrimination, exacerbating the response to the disaster and forcing refugees to return to Syria due to dire living conditions. Minimizing the effects of the disaster on the communities is therefore essential. There is a need to strengthen health system resilience and emergency response to natural disasters to reduce and prevent the aftermath. Disaster preparedness plans should include regulations that ensure that local buildings and infrastructure are disaster‐resistant. Furthermore, it is vital to highlight the importance of funding and appropriate resource allocation for disaster risk reduction. These include improving plans and logistics for recovery efforts, adequate preparation of temporary shelters and evacuation centres and allocating necessities such as food and water. Investment in proper search and rescue response, a special workforce for response and the rebuilding of important infrastructure are crucial. Finally, response to disasters must be inclusive and prioritize vulnerable populations, such as children, the aged women and refugees.
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