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Quick and automatic detection of co-seismic landslides with multifeature deep learning model

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Science China earth Sciences 2024年第7期67卷 2311-2325页

作者： Wenchao HUANGFU Haijun QIU Peng CUI Dongdong YANG Ya LIU Bingzhe TANG Zijing LIU Mohib ULLAH College of Urban and environmental Sciences Northwest UniversityXi'an 710127China institute of mountain hazards and environment Chinese Academy of SciencesChengdu 610041China Shaanxi key laboratory of earth surface System and environmental Carrying Capacity Northwest UniversityXi'an 710127China institute of earth surface System and hazards Northwest UniversityXi'an 710127China

Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.

关键词： Co-seismic landslide Automatic detection Deep learning Landslide gain index PlanetScope images

Economic Risk Assessment of Future Debris Flows by Machine Learning Method

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International Journal of Disaster Risk Science 2024年第1期15卷 149-164页

作者： Chenchen Qiu Lijun Su Alessandro Pasuto Giulia Bossi Xueyu Geng School of Engineering University of WarwickCoventry CV47ALUK key laboratory of mountain hazards and earth surface Process Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Research institute for Geo-Hydrological Protection(IRPI-CNR) National Research Council of ItalyPadova 35127Italy

A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the economic risks caused by future debris flows,a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map.We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year.The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact,temporal probability,and annual susceptibility.We employed a hybrid machine learning model-certainty factor-genetic algorithm-support vector classification-to calculate susceptibilities.Simultaneously,a Poisson model was applied for temporal probabilities,while the determination of annual probability of spatial impact relied on statistical results.Additionally,four major elements at risk were selected for the generation of an economic loss map:roads,vegetation-covered land,residential buildings,and farmland.The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values.Therefore,we proposed a physical vulnerability matrix for residential buildings,factoring in impact pressure on buildings and their horizontal distance and vertical distance to debrisflow channels.In this context,an ensemble model(XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings.The results show that residential buildings occupy 76.7% of the total economic risk,while roadcovered areas contribute approximately 6.85%.Vegetation-covered land and farmland collectively represent 16.45% of the entire risk.These findings can provide a scientific support for the effective mitigation of future debris flows.

关键词： Economic risk Future debris fows Gyirong Zangbo Basin Machine learning model Physical vulnerability matrix Southwest Tibet,China

Climate-Vegetation Coverage Interactions in the Hengduan mountains Area, Southeastern Tibetan Plateau, and Their Downstream Effects

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Advances in Atmospheric Sciences 2024年第4期41卷 701-716页

作者： Congxi FANG Jinlei CHEN Chaojun OUYANG Lu WANG Changfeng SUN Quan ZHANG Jun WEN key laboratory of mountain hazards and earth surface processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China State key laboratory of Cryospheric Science Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China College of Urban and environmental Sciences Peking UniversityBeijing 100871China institute of Global environmental Change Xi’an Jiaotong UniversityXi’an 710049China The State key laboratory of Loess and Quaternary Geology Institute of Earth EnvironmentChinese Academy of SciencesXi’an 710061China College of Atmospheric Sciences Chengdu University of Information TechnologyChengdu 610225China

Little is known about the mechanism of climate-vegetation coverage coupled changes in the Tibetan Plateau(TP)region,which is the most climatically sensitive and ecologically fragile region with the highest terrain in the world.This study,using multisource datasets(including satellite data and meteorological observations and reanalysis data)revealed the mutual feedback mechanisms between changes in climate(temperature and precipitation)and vegetation coverage in recent decades in the Hengduan mountains Area(HMA)of the southeastern TP and their influences on climate in the downstream region,the Sichuan Basin(SCB).There is mutual facilitation between rising air temperature and increasing vegetation coverage in the HMA,which is most significant during winter,and then during spring,but insignificant during summer and autumn.Rising temperature significantly enhances local vegetation coverage,and vegetation greening in turn heats the atmosphere via enhancing net heat flux from the surface to the atmosphere.The atmospheric heating anomaly over the HMA thickens the atmospheric column and increases upper air pressure.The high pressure anomaly disperses downstream via the westerly flow,expands across the SCB,and eventually increases the SCB temperature.This effect lasts from winter to the following spring,which may cause the maximum increasing trend of the SCB temperature and vegetation coverage in spring.These results are helpful for estimating future trends in climate and eco-environmental variations in the HMA and SCB under warming scenarios,as well as seasonal forecasting based on the connection between the HMA eco-environment and SCB climate.

关键词： Hengduan mountains Area normalized difference vegetation index climate change net heat flux downstream effects

Impacts of permafrost degradation on streamflow in the northern Himalayas

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Science China earth Sciences 2024年第6期67卷 1990-2000页

作者： Linfeng FAN Fang JI Xingxing KUANG Zhilin GUO Rui ZHANG Chunmiao ZHENG State key laboratory of mountain hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Yangtze institute for Conservation and Development Hohai UniversityNanjing 210024China School of environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Eastern institute for Advanced Study Ningbo 315200China

The Himalayan water tower provides crucial water resources for Asia.Permafrost degradation is deemed to exert important impacts on streamflow in the Himalayan rivers.Yet,the magnitudes of such impacts remain poorly quantified.Here,we established a robust hydrological model that incorporated active layer deepening and ground ice melt for the drainage basin of the largest river in the northern Himalayas-the Yarlung Zangbo River(YZR).We estimated that permafrost degradation led to~0.65 km^(3)/yr decrease in surface runoff and~0.35 km^(3)/yr increase in baseflow and ground ice melt contributed~0.25% to the annual streamflow in the YZR for the period 2001-2022.The“fill-and-spill”mechanism helps explain the seeming contradiction of observed increasing versus decreasing baseflow in different permafrost regions worldwide.We propose that the dilution of riverine dissolved organic carbon(DOC)concentrations by baseflow may lead to the riverine DOC hysteresis patterns.This study not only lays solid scientific basis for water resources management in the Himalayas,but also yields new insights into how to interpret measured river discharge and nutrient flux in permafrost regions over the globe.

关键词： Permafrost degradation Hydrology Streamflow surface runoff Groundwater Himalayas

Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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Computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China key Lab of mountain hazards and surface processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and Information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram

Deep learning for cross-region streamflow and flood forecasting at a global scale

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The Innovation 2024年第3期5卷 99-109页

作者： Binlan Zhang Chaojun Ouyang Peng Cui Qingsong Xu Dongpo Wang Fei Zhang Zhong Li Linfeng Fan Marco Lovati Yanling Liu Qianqian Zhang State key laboratory of mountain hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China State key laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu 610059China Data Science in earth Observation Technical University of Munich80333 MunichGermany State key laboratory of Loess and Quaternary Geology Institute of Earth EnvironmentChinese Academy of SciencesXi’an 710061China Department of Civil Engineering McMaster UniversityHamiltonON L8S 4L8Canada Chengdu University of Information Technology Chengdu 610225China

Streamflow and flood forecasting remains one of the long-standing challenges in hydrology.Traditional physically based models are hampered by sparse parameters and complex calibration procedures particularly in ungauged catchments.We propose a novel hybrid deep learning model termed encoder-decoder double-layer long short-term memory(ED-DLSTM)to address streamflow forecasting at global scale for all(gauged and ungauged)catchments.Using historical datasets,ED-DLSTM yields a mean Nash-Sutcliffe efficiency coefficient(NSE)of 0.75 across more than 2,000 catchments from the United States,Canada,Central Europe,and the United Kingdom,highlighting improvements by the state-of-the-art machine learning over traditional hydrologic models.Moreover,ED-DLSTM is applied to 160 ungauged catchments in Chile and 76.9%of catchments obtain NSE>0 in the best situation.The interpretability of cross-region capacities of ED-DLSTM are established through the cell state induced by adding a spatial attribute encoding module,which can spontaneously form hydrological regionalization effects after performing spatial coding for different catchments.The study demonstrates the potential of deep leaning methods to overcome the ubiquitous lack of hydrologic information and deficiencies in physical model structure and parameterization.

关键词： forecasting flood flow

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas:A case study in the Hengduan mountains

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Journal of mountain Science 2024年第5期21卷 1522-1533页

作者： HU Xudong SHEN Yitong HU Kaiheng XU Wennian LIU Daxiang HE Songtang GAO Jiazhen WEI Li LIU Shuang Hubei Provincial Engineering Research Center of Slope Habitat Construction Technique Using Cement-based Materials(China Three Gorges University) Yichang 443002China Hubei key laboratory of Disaster Prevention and Mitigation(China Three Gorges University) Yichang 443002China College of Civil Engineering and Architecture China Three Gorges UniversityYichang 443002China Shanghai Investigation Design&Research Institute Co.Ltd.Shanghai 200335China key laboratory of mountain hazards and earth surface processes Chinese Academy of SciencesChengdu 610041China institute of mountain hazards and environment Chinese Academy of SciencesChengdu 610041China Wuhan Polytechnic Wuhan 430074China

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.

关键词： debris-flow prone areas causal factors GIS-based method spatial heterogeneity Hengduan mountains

Characterizing the behavior of surge-type glaciers in the Puruogangri Ice Field, Tibetan Plateau

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Journal of Geographical Sciences 2024年第6期34卷 1174-1194页

作者： ZHOU Sugang WANG Ninglian LI Zhijie YAO Xiaojun ZHANG Yuje QIN Gexia Shaanxi key laboratory of earth surface System and environmental Carrying Capacity College of Urban and Environmental SciencesNorthwest UniversityXi'an 710127China institute of earth surface System and hazards College of Urban and Environmental SciencesNorthwest UniversityXi'an 710127China College of Geography and environmental Science Northwest Normal UniversityLanzhou 730070China

The Puruogangri Ice Field(PIF), classified as an ultra-continental glacier, is considered extremely stable. However, several glaciers in this area have recently experienced surge events with significant instability and information on surge-type glaciers(STGs) in this region remains scarce. In this study, we identified six STGs and reported the observed characteristics of their surging behavior in the region by mapping glacier boundaries, surface flow velocity information, and glacier surface elevation changes using recent Landsat satellite imagery and shuttle radar topography mission(SRTM), TanDEM, and ASTER digital elevation model(AST14DEM) data. These data provide valuable insights into recent glacial processes, flow instability, and rapid glacial movement. During the active phase of the glaciers,all exhibited frontal advances and changes in surface elevation. Owing to limitations in the satellite imagery, flow velocity profiles were only available for glaciers N1(G089071E33998N),NE1(G089128E33943N), and SE3(G089278E33913N) during the active phase. However,these results effectively reflect the velocity variations in both glaciers before, during, and after the surge. Based on the characteristics of the STG, scientific expeditions, and meteorological data, we believe that the surge in PIF was largely influenced by glacier meltwater and changes in subglacial drainage systems.

关键词： glacier surge glacier surface elevation changes glacier surface velocity Puruogangri Ice Field

Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

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Advances in Atmospheric Sciences 2024年第3期41卷 420-434页

作者： Dezhen YIN Fang LI Yaqiong LU Xiaodong ZENG Zhongda LIN Yanqing ZHOU International Center for Climate and environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China institute of mountain hazards and environment Chinese Academy of SciencesChengdu 610041China State key laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State key laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

关键词： global gridded crop model historical crop yield China multi-model evaluation