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Elevation map and annual precipitation and annual mean temperature in the study area. (a) Elevation map (unit: m), (b) annual precipitation (mm) and annual mean temperature (°C).
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Flooding is extremely dangerous when a river overflows to inundate an urban area. From 1995 to 2016, North Korea (NK) experienced extensive damage to life and property almost every year due to a levee breach resulting from typhoons and heavy rainfall during the summer monsoon season. Recently, Hoeryeong City (2016) experienced heavy rain during Typ...
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Citations
... North Korea is a highly mountainous country in north-east Asia, which was historically covered by large tracts of forest and has relatively small areas of lowland suitable for supporting large-scale agriculture . Deforestation in North Korea is of particular concern because the country's forests provide important fuel resources for heating and cooking for many of its poorest citizens (Kim et al. 2020); perform essential functions for watershed management in a country with a temperate monsoon climate and prone to both droughts and flooding (Lim and Lee 2018); and provide habitat for a diverse range of native species (Jo et al. 2018). Protected areas cover just 2.4% of North Korea's total land area (McCarthy et al. 2021). ...
... These reports also typically concerned changes that had occurred during an individual's own lifetime, rather than over successive generations (Soga and Gaston 2018). Forest cover change in North Korea likely also has direct consequences for the country's human population, especially in rural areas (Lim and Lee 2018;Kim et al. 2020). Each of these factors may increase the likelihood of accurate recall of information within this context. ...
Satellite-based remote sensing approaches provide a cost-efficient means to collect information on the world’s forests and to repeatedly survey large, or inaccessible, forest areas. However, it may not always be possible to ground truth–associated findings using direct ecological field surveys conducted by trained forest scientists. Local ecological knowledge (LEK) is an alternative form of data which could be used to complement, interpret and verify information from satellite data. Using a case study on the Democratic People’s Republic of Korea (North Korea), we evaluate the potential for integrating remote sensing and LEK data, gathered with non-specialist former residents, to understand patterns and drivers of forest cover change. LEK reports often concurred with, or provided key information to enable interpretation of, satellite data. This revealed that between 1986 and 2021, North Korea experienced high, but uneven, rates of deforestation. There was a pronounced northwards deforestation shift in the mid-1990s, coinciding with a period of extreme hardship and famine (the “Arduous March”), and associated with clearance of trees in more forested northern provinces as an economic and fuel resource, and conversion of forest to agricultural cropland. Loss of forest cover in North Korea has continued and recently accelerated, to a rate of > 200 km² per annum between 2019 and 2021. This increases the vulnerability of North Korean socio-ecological systems to future environmental change and is an obstacle to the recovery of threatened species across the Korean Peninsula. We recommend that LEK- and remote sensing–based approaches are considered within a suite of complementary techniques to analyse forest changes where ecological field surveys cannot be conducted.
... Also, these algorithms are less sensitive to multidimensional phenomena, therefore they are suitable methods for classifying multispectral and hyperspectral data (Mendyl et al. 2024;Başakın et al. 2022;Uncuoglu et al. 2022;Citakoglu 2021;Citakoglu and Coşkun 2022;Demir and Citakoglu 2023;Zouzou and Citakoglu 2023;Islam and Chowdhury 2024;Asiri et al. 2024). Today, the combination of SVM and RF algorithms with remote sensing data and geographic information system (GIS) are the most widely used methods for preparing flood maps (Islam and Chowdhury 2024;Asiri et al. 2024;Brivio et al. 2002;Katiyar et al. 2021;Psomiadis et al. 2019;Rahman et al. 2021;Groeve 2010;Jain et al. 2005;Elhag and Abdurahman 2020;Tao et al. 2024;Tripathi et al. 2021;Qiu et al. 2021;Nanda et al. 2022;Chormanski et al. 2011;Lim and Lee 2018;Cao et al. 2019). ...
Floods in Iran cause a lot of damage in different places every year. The 2019 floods of the Gorgan and Atrak rivers basins in the north of Iran were one of the most destructive events in this country. Therefore, investigating the flood hazard of these areas is very necessary to manage probable future floods. For this purpose, in the present study, the capability of Random Forest (RF) and Support Vector Machine (SVM) algorithms was investigated in combination with Sentinel series and Landsat-8 images to prepare the 2019 flood map. Then, the flood hazard map of these areas was prepared using the new hybrid Fuzzy Best Worse Model-Weighted Multi-Criteria Analysis (FBWM-WMCA) model. According to the results of the FBWM-WMCA model, 38.58%, 50.18%, 11.10%, and 0.14% of the Gorgan river basin and 45.11%, 49.96%, 4.17%, and 0.076% of the Atrak river basin are in high, medium, low, and no hazards, respectively. The highest flood hazard areas in Gorgan and Atrak rivers basins in the north, northwest, west, and east, and south and southwest are mostly at medium flood hazard. Also, the results of RF and SVM algorithms with an overall accuracy of more than 85% for Sentinel-1, Sentinel-2, and Landsat-8 images and 80% for Sentinel-3 images indicate that the flooding is related to the western, southwestern, and northern regions including agricultural, bare lands and built up. According to the obtained results and the efficiency of the FBWM-WMCA model, the Gorgan and Atrak rivers basins need proper planning for flood hazard management.
Graphical Abstract
... Nevertheless, they need field observation data to make high-accuracy predictions (Al-Areeq et al., 2021a,b;Al-Zahrani et al., 2016;Sharif et al., 2014). Several models have been created and proposed to aid in flood prediction (de Musso et al., 2018;Khosravi et al., 2018;Lim and Lee, 2018;Tong et al., 2018), though in hilly locations, these models were unable to predict flash floods (Bui et al., 2019b). On the other side, regression models are created in conventional analysis to anticipate discharge based on field observations of past data (Costache & Zaharia, 2017;Tehrany et al., 2014). ...
... A lot of researches have used various methods to prepare flood susceptibility mapping over the last several decades. Among these methods are the analytical hierarchy process (AHP) (Mokhtari et al. 2023), artificial neural networks (Kia et al. 2012), random forest (Esfandiari et al. 2020), frequency ratio models (Lee et al. 2012;Khosravi et al. 2016;Rahmati et al. 2016), fuzzy logic and genetic algorithms (Hong et al. 2018a), variable fuzzy theory (Guo et al. 2014), hydrological forecasting systems (Bhaskar et al. 2009;Hostache et al. 2018), adaptive neuro-fuzzy interface systems (Barneveld et al. 2008;Speight et al. 2021), logistic regression (Lim & Lee 2018), weight of evidence (Hong et al. 2018b), analytic network process (Yariyan et al. 2020), statistical index (Cao et al. 2016), and Shannon's entropy (Arora et al. 2021). ...
Effective disaster management heavily relies on accurate flood susceptibility mapping. The fuzzy analytic hierarchy process (FAHP) is adept at considering the imprecise nature of decision-making criteria. This study assesses FAHP's effectiveness in flood susceptibility mapping, comparing it with the conventional analytic hierarchy process (AHP). By using Geographic Information System-analyzed remotely sensed data, the research systematically evaluates flood risk southeast of Algiers. Various datasets, including DEM, slope, precipitation, and land use maps, were collected via remote sensing. A linear fuzzy membership function transformed the data into fuzzy values. AHP determined the importance of each dataset, with calculated weights multiplied by corresponding fuzzy values. Fuzzy analysis combined these characteristics into a five-category flood risk map, verified with Google Earth and satellite images. Results indicate a high potential for flood hazard mapping, categorizing 30% of frequently flooded regions as high risk. Maps reveal north basin areas are more flood prone due to excessive precipitation, and urban areas in floodplains are vulnerable. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) assessments demonstrate AHP and FAHP's effectiveness. AUC values of 88.40 and 92% indicate that both models accurately predict flood-prone areas. FAHP excels, reducing subjectivity and ambiguity in human judgments.
... Optical satellite photogrammetry is an effective means to obtain global geospatial data. Through advances in satellite remote sensing technology, optical mapping satellite are widely used in various fields such as topographic mapping (Baltsavias et al., 2009;Zhang & Gruen, 2006), city three-dimensional modelling (Gong et al., 2021;Poli et al., 2015), disaster monitoring (Lim & Seock Lee, 2018), bathymetric mapping (Cao et al., 2019Chen et al., 2019), etc. According to the way of acquiring stereo images, optical photogrammetry satellite are divided into agile imaging class and thematic mapping class. ...
The GaoFen-14 (GF-14) satellite is China’s most recent high-resolution earth observation satellite system. It is equipped with a two linear-array stereo camera and is intend for topographic
mapping at 1:10,000-scale without ground control points (GCPs). The technical parameters of payloads will change once the satellite enters orbit. As a result, strict on-orbit
geometric calibration is necessary. This study performs the on-orbit geometric calibration of the GF-14 stereo camera using ground calibration data. The exterior orientation errors are
corrected by a comprehensive bias matrix, internal orientation errors are described by a fifth order polynomial. The results of Yinchuan calibration field show that the planar accuracy is
better than 286 m (RMS) before calibration, and improved to 2.11 m (front camera) and 1.51 m (back camera) by external orientation, and further improved to 1.46 m (front camera) and 0.99
m (back camera) after internal calibration. The front intersection accuracy reaches 0.67 m in plane (RMS) and 1.10 m in elevation (RMS), respectively. Checking by multiple global check
points (CKPs), the planar and elevation RMS reaches 2.38 m (5.09 m in CE90) and 2.08 m (3.43 m in LE90), respectively.
... Bathrellos et al. (2017) employed GIS and multi-criteria analysis techniques to manage and assess factors associated with geological hazard risk, resulting in the generation of an initial map of hazardous zones. Lim and Lee (2018) employed optical and radar remote sensing data to develop a flood risk zoning model. Kim et al. (2020) conducted studies demonstrating that an improved digital elevation model generated using advanced remote sensing techniques yielded more accurate flood maps, depicting more reasonable flood patterns. ...
Urban flood risk assessment plays a crucial role in disaster risk reduction and preparedness. It is essential to mitigate flood risks and establish a comprehensive analysis of factors influencing flood risk, as well as classify risk levels, in order to provide a clear model for risk assessment. This article aims to propose an efficient assessment method that can classify urban flood risk levels and assist cities in flood risk management, particularly in identifying high-risk areas. The study area chosen for this method is the municipal district of Fuzhou City, located in Fujian Province, China. The proposed method utilizes the Urban Flood Risk Assessment Index, which is developed based on the risk-vulnerability framework. It integrates the combinatorial empowerment method, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and the K-means algorithm to cluster the quantitative risk factors, enabling a comprehensive analysis of the risk results. The findings demonstrate that areas characterized by intense extreme rainfall, lower elevation, gradual slope, high runoff coefficient, and high population density tend to exhibit higher flood risk. Moreover, the dominant factors contributing to high risk in different regions vary. The results obtained from this method align well with the distribution of historical flood points, indicating the effectiveness of the risk map prepared using this approach. In comparison to the results obtained from the single clustering method and the TOPSIS method used in traditional risk assessment, the proposed method can successfully identify high-risk urban flood areas. Consequently, this method offers a valuable scientific basis for urban flood prevention and control planning.
... Flooding in the floodplains of Indian rivers is a common occurrence that does not cause alarm until it is linked to major socioeconomic implications (Lim and Lee 2018;Syifa et al. 2019). As a result, public perception is an essential factor in determining flood risk. ...
Water is an important natural resource for human survival, and it is the
base for all vital activities. Water needs rise along with population growth, but water
supplies largely remain stable. There is not a shortage of water to fulfill our needs;
rather, there is a problem with water management. Water supplies are being depleted
for a variety of reasons, including population expansion, industrialization, deforestation,
a lack of rainfall, and changes in land use and land cover (LULC). The four
elements of hydrological processes that are most likely to be impacted by changes in
LULC in terms of their quantity and pattern are surface runoff, base flow, interflow,
and evapotranspiration. Information on current patterns of land use and temporal
land use changes is a fundamental necessity for the effective use of land. Accurate,
useful, and current data on land usage are crucial in this dynamic context. Due to
changes in the features of the land surface, the LULC alterations could have an impact
on infiltration or percolation. To allocate resources for planning and management,
it is required to identify the land use change in the past and current accessible land
use. Remote sensing and a Geographic Information System (GIS) are good options
for properly monitoring LULC and its effects on water quality and water pollution.
Therefore, the purpose of this research is to investigate how LULC changes affect
water resources and how they are managed, as well as how well remote sensing and
GIS technologies function as monitoring tools. Therefore, the purpose of this research
is to investigate the impact of LULC on water resources and their management as well
as how well remote sensing and GIS technologies function as monitoring tools. This
paper concludes that water resource monitoring and management is important for
the human being. Through satellite imageries, we can timely diagnose and predict various attributes of water that will be helpful in the management of surface and
groundwater resources.
... This approach was separated by the statistical models and machine learning. Statistical models such as logistic regression (Al-Juaidi et al. 2018, Lim andLee 2018), frequency ratio (Ghosh et al. 2022a), weight of evidence (Hong et al. 2018), fuzzy logic (Sahana and Patel 2019), and AHP (Swain et al. 2020) have shown promise, but in the context of climate change and urbanizations, inundation is becoming ever more complicated and non-linear; therefore, statistical models cannot achieve adequate levels of accuracy in their predictions. The machine learning approach has been receiving a great deal of attention within the scientific community. ...
Accurate prediction of streamflow plays an important role in water resource management and the continuous assessment of inundation susceptibility in the context of climate change plays a key role in facilitating the construction of appropriate strategies for sustainable development. So far, few studies into inundation susceptibility have explicitly incorporated the effects of climate change into their methodologies. This study aimed to assess inundation susceptibility for Vinh Phuc province in Vietnam, from 2000 to 2020, using machine learning and remote sensing. The algorithms used were support vector machine, catboost, and extratrees. A geo-spatial database of 206 inundation points and 11 conditioning factors (namely elevation, slope, curvature, aspect, distance to river, distance to road, NDVI, NDBI, rainfall, soil type, and TWI) from 2000 to 2020 was developed to be used as the input data. RMSE, MAE, AUC, and R² were used to assess the fit of the models. The results showed that all the proposed models were a good fit, with AUC values of 0.95 and over. In general, the total area marked as very low risk or low risk has increased, with the high risk and very high-risk areas having decreased over the period studied. This change was mainly concentrated in the city of Vinh Yen where there has been strong urban growth. The models proposed in this study are a promising toolkit to assess inundation susceptibility continuously and can support decision makers involved in sustainable development. Our results highlight the benefits and consequences of planned and unplanned development. Properly planned can reduce the flood risk, while unplanned development can increase the risk. Therefore, by applying the theoretical framework in this study, decision makers or planners can build the most appropriate strategies for flood control in the context of climate change. Our approach in this study represents a theoretical framework for future research not only on inundation management but also natural hazard management, in regions around the world.
... Flooding in the floodplains of Indian rivers is a common occurrence that does not cause alarm until it is linked to major socioeconomic implications (Lim and Lee 2018;Syifa et al. 2019). As a result, public perception is an essential factor in determining flood risk. ...
Water is an important natural resource for human survival, and it is the base for all vital activities. Water needs rise along with population growth, but water supplies largely remain stable. There is not a shortage of water to fulfill our needs; rather, there is a problem with water management. Water supplies are being depleted for a variety of reasons, including population expansion, industrialization, deforestation, a lack of rainfall, and changes in land use and land cover (LULC). The four elements of hydrological processes that are most likely to be impacted by changes in LULC in terms of their quantity and pattern are surface runoff, base flow, interflow, and evapotranspiration. Information on current patterns of land use and temporal land use changes is a fundamental necessity for the effective use of land. Accurate, useful, and current data on land usage are crucial in this dynamic context. Due to changes in the features of the land surface, the LULC alterations could have an impact on infiltration or percolation. To allocate resources for planning and management, it is required to identify the land use change in the past and current accessible land use. Remote sensing and a Geographic Information System (GIS) are good options for properly monitoring LULC and its effects on water quality and water pollution. Therefore, the purpose of this research is to investigate how LULC changes affect water resources and how they are managed, as well as how well remote sensing and GIS technologies function as monitoring tools. Therefore, the purpose of this research is to investigate the impact of LULC on water resources and their management as well as how well remote sensing and GIS technologies function as monitoring tools. This paper concludes that water resource monitoring and management is important for the human being. Through satellite imageries, we can timely diagnose and predict various attributes of water that will be helpful in the management of surface and groundwater resources.KeywordsLULCWater resourcesRemote sensingGeographical information system
... One widely adopted approach in this category is SVM [35]. Other traditional machine learning algorithms include decision tree classifiers [6], random forests [7], hidden Markov trees [8], and LR [9]. ...
Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer deep neural network that learns abstract semantic features of the input data. This research introduces a novel weakly supervised approach for pixel-wise flood mapping by leveraging multi-temporal remote sensing imagery and image processing techniques (e.g., Normalized Difference Water Index and edge detection) to create weakly labeled data. Using these weakly labeled data, a bi-temporal U-Net model is then proposed and trained for flood detection without the need for time-consuming and labor-intensive human annotations. Using floods from Hurricanes Florence and Harvey as case studies, we evaluated the performance of the proposed bi-temporal U-Net model and baseline models, such as decision tree, random forest, gradient boost, and adaptive boosting classifiers. To assess the effectiveness of our approach, we conducted a comprehensive assessment that (1) covered multiple test sites with varying degrees of urbanization, and (2) utilized both bi-temporal (i.e., pre- and post-flood) and uni-temporal (i.e., only post-flood) input. The experimental results showed that the proposed framework of weakly labeled data generation and the bi-temporal U-Net could produce near real-time urban flood maps with consistently high precision, recall, f1 score, IoU score, and overall accuracy compared with baseline machine learning algorithms.
