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2 pages, 160 KiB

Open AccessEditorial

Editorial for the Special Issue “Disaster Monitoring Using Remote Sensing”

by Jinmu Choi and Duk-Jin Kim

Remote Sens. 2023, 15(3), 751; https://doi.org/10.3390/rs15030751 - 28 Jan 2023

Viewed by 1193

Abstract

Human civilization is rapidly developing thanks to the spread of geospatial technology and artificial intelligence [...] Full article

(This article belongs to the Special Issue Disaster Monitoring Using Remote Sensing)

Research

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19 pages, 10042 KiB

Open AccessArticle

Evaluation of Wildfire Occurrences in Pakistan with Global Gridded Soil Properties Derived from Remotely Sensed Data

by Warda Rafaqat, Mansoor Iqbal, Rida Kanwal and Song Weiguo

Remote Sens. 2022, 14(21), 5503; https://doi.org/10.3390/rs14215503 - 1 Nov 2022

Cited by 6 | Viewed by 3170

Abstract

Wildfires are predicted to occur more frequently and intensely as a result of global warming, posing a greater threat to human society, terrestrial ecosystems, and the atmosphere. Most existing methods for monitoring wildfire occurrences are based either on static topographical information or weather-based [...] Read more.

Wildfires are predicted to occur more frequently and intensely as a result of global warming, posing a greater threat to human society, terrestrial ecosystems, and the atmosphere. Most existing methods for monitoring wildfire occurrences are based either on static topographical information or weather-based indices. This work explored the advantages of a new machine learning-based ‘soil properties’ attribute in monitoring wildfire occurrence in Pakistan. Specifically, we used satellite observations during 2001–2020 to investigate the correlation at different temporal and spatial scales between wildfire properties (fire count, FC) and soil properties and classes (SoilGrids1km) derived from combination with local covariates using machine learning. The correlations were compared to that obtained with the static topographic index elevation to determine whether soil properties, such as soil bulk density, taxonomy, and texture, provide new independent information about wildfires. Finally, soil properties and the topographical indices were combined to establish multivariate linear regression models to estimate FC. Results show that: (1) the temporal variations of FC are negatively correlated with soil properties using the monthly observations at 1° grid and regional scales; and overall opposite annual cycles and interannual variations between and soil properties are observed in Pakistan; (2) compared to the other static variables such as elevation, soil properties shows stronger correlation with the temperate wildfire count in Northern Pakistan but weaker correlation with the wildfire properties in Southern Pakistan; and it is found that combining both types of indices enhances the explained variance for fire attributes in the two regions; (3) In comparison to linear regression models based solely on elevation, multivariate linear regression models based on soil properties offer superior estimates of FC. Full article

(This article belongs to the Special Issue Disaster Monitoring Using Remote Sensing)

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16 pages, 3431 KiB

Open AccessArticle

A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data

by Qian Wang, Lin Zhao, Mali Wang, Jinjia Wu, Wei Zhou, Qipeng Zhang and Meie Deng

Remote Sens. 2022, 14(19), 4981; https://doi.org/10.3390/rs14194981 - 7 Oct 2022

Cited by 11 | Viewed by 3137

Abstract

The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to [...] Read more.

The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought. Full article

(This article belongs to the Special Issue Disaster Monitoring Using Remote Sensing)

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24 pages, 4541 KiB

Open AccessArticle

Flood Vulnerability Assessment and Mapping: A Case Study for Australia’s Hawkesbury-Nepean Catchment

by Imogen Schwarz and Yuriy Kuleshov

Remote Sens. 2022, 14(19), 4894; https://doi.org/10.3390/rs14194894 - 30 Sep 2022

Cited by 15 | Viewed by 8195

Abstract

Floods are one of the most destructive natural hazards to which Australia is exposed. The frequency of extreme rainfall events and consequential floods are projected to increase into the future as a result of anthropogenic climate change. This highlights the need for more [...] Read more.

Floods are one of the most destructive natural hazards to which Australia is exposed. The frequency of extreme rainfall events and consequential floods are projected to increase into the future as a result of anthropogenic climate change. This highlights the need for more holistic risk assessments of flood affected regions. Flood risk assessments (FRAs) are used to inform decision makers and stakeholders when creating mitigation and adaptation strategies for at-risk communities. When assessing flood risk, previous FRAs from Australia’s most flood prone regions were generally focused on the flood hazard itself, and rarely considering flood vulnerability (FV). This study assessed FV in one of Australia’s most flood prone regions—the Hawkesbury-Nepean catchment, and investigated indicator-based approaches as a proxy method for Australian FV assessment instead of hydrological modelling. Four indicators were selected with the intention of representing environmental and socio-economic characteristics: elevation, degree of slope, index of relative socio-economic disadvantage (IRSD), and hydrologic soil groups (HSGs). It was found that combination of low elevation, low degree of slope, low IRSD score, and very-low infiltration soils resulted in very high levels of vulnerability. FV was shown to be at its highest in the Hawkesbury-Nepean valley flood plain region on the outskirts of Greater Western Sydney, particularly in Blacktown, Penrith, and Liverpool. This actionable risk data which resulted from the final FV index supported the practicality and serviceability of the proxy indicator-based approach. The developed methodology for FV assessment is replicable and has the potential to help inform decision makers of flood-prone communities in Australia, particularly in data scarce areas. Full article

(This article belongs to the Special Issue Disaster Monitoring Using Remote Sensing)

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17 pages, 10665 KiB

Open AccessCommunication

Active Fault Trace Identification Using a LiDAR High-Resolution DEM: A Case Study of the Central Yangsan Fault, Korea

by Sangmin Ha, Moon Son and Yeong Bae Seong

Remote Sens. 2022, 14(19), 4838; https://doi.org/10.3390/rs14194838 - 28 Sep 2022

Cited by 17 | Viewed by 3461

Abstract

Korea has been recognized as an earthquake-safe zone, but over recent decades, several earthquakes, at a medium scale or higher, have occurred in succession in and around the major fault zones, hence there is a need for studying active faults to mitigate earthquake [...] Read more.

Korea has been recognized as an earthquake-safe zone, but over recent decades, several earthquakes, at a medium scale or higher, have occurred in succession in and around the major fault zones, hence there is a need for studying active faults to mitigate earthquake risks. In Korea, research on active faults has been challenging owing to urbanization, high precipitation, and erosion rates, and relatively low earthquake activity compared to the countries on plate boundaries. To overcome these difficulties, the use of aerial light detection and ranging (LiDAR) techniques providing high-resolution images and digital elevation models (DEM) that filter vegetation cover has been introduced. Multiple active fault outcrops have been reported along the Yangsan Fault, which is in the southeastern area of the Korean Peninsula. This study aimed to detect active faults by performing a detailed topographic analysis of aerial LiDAR images in the central segment of the Yangsan Fault. The aerial LiDAR image covered an area of 4.5 km by 15 km and had an average ground point density of 3.5 points per m², which produced high-resolution images and DEMs at greater than 20 cm. Using LiDAR images and DEMs, we identified a 2–4 m high fault scarp and 50–150 m deflected streams with dextral offset. Based on the image analysis, we further conducted a trench field investigation and successfully located the active fault that cut the Quaternary deposits. The N–S to NNE-striking fault surfaces cut unconsolidated deposits comprising nine units, and the observed slickenlines indicated dextral reverse strike-slip. The optically stimulated luminescence (OSL) age dating results of the unconsolidated deposits indicate that the last earthquake occurred 3200 years ago, which is one of the most recent along the Yangsan Fault. Full article

(This article belongs to the Special Issue Disaster Monitoring Using Remote Sensing)

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13 pages, 3585 KiB

Open AccessArticle

Spatial Cross-Correlation of GOSAT CO₂ Concentration with Repeated Heat Wave-Induced Photosynthetic Inhibition in Europe from 2009 to 2017

by Young-Seok Hwang, Stephan Schlüter and Jung-Sup Um

Remote Sens. 2022, 14(18), 4536; https://doi.org/10.3390/rs14184536 - 11 Sep 2022

Cited by 3 | Viewed by 1784

Abstract

In recent decades, European countries have faced repeated heat waves. Traditionally, atmospheric CO₂ concentration linked to repeated heat wave-induced photosynthetic inhibition has been explored based on local-specific in-situ observations. However, previous research based on field surveys has limitations in exploring area-wide atmospheric [...] Read more.

In recent decades, European countries have faced repeated heat waves. Traditionally, atmospheric CO₂ concentration linked to repeated heat wave-induced photosynthetic inhibition has been explored based on local-specific in-situ observations. However, previous research based on field surveys has limitations in exploring area-wide atmospheric CO₂ concentrations linked to repeated heat wave-induced photosynthetic inhibition. The present study aimed to evaluate the spatial cross-correlation of Greenhouse gases Observing SATellite (GOSAT) CO₂ concentrations with repeated heat wave-induced photosynthetic inhibition in Europe from 2009 to 2017 by applying geographically weighted regression (GWR). The local standardized coefficient of a fraction of photosynthetically active radiation (FPAR: −0.24) and the normalized difference vegetation index (NDVI: −0.22) indicate that photosynthetic inhibition increases atmospheric CO₂ in Europe. Furthermore, from 2009 to 2017, the heat waves in Europe contributed to CO₂ emissions (27.2–32.1%) induced by photosynthetic inhibition. This study provides realistic evidence to justify repeated heat wave-induced photosynthetic inhibition as a fundamental factor in mitigating carbon emissions in Europe. Full article

(This article belongs to the Special Issue Disaster Monitoring Using Remote Sensing)

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30 pages, 16731 KiB

Open AccessArticle

Extraction of Landslide Information Based on Object-Oriented Approach and Cause Analysis in Shuicheng, China

by Yunfei Han, Ping Wang, Yongguo Zheng, Muhammad Yasir, Chunmei Xu, Shah Nazir, Md Sakaouth Hossain, Saleem Ullah and Sulaiman Khan

Remote Sens. 2022, 14(3), 502; https://doi.org/10.3390/rs14030502 - 21 Jan 2022

Cited by 13 | Viewed by 3104

Abstract

In China, landslides are abundant, widespread, and regular, destroying villages and agriculture and sometimes posing a threat to people’s lives. The question of how to rapidly detect and attain landslide data is a significant topic of research, yet traditional measurement using medium-resolution remote [...] Read more.

In China, landslides are abundant, widespread, and regular, destroying villages and agriculture and sometimes posing a threat to people’s lives. The question of how to rapidly detect and attain landslide data is a significant topic of research, yet traditional measurement using medium-resolution remote sensing data is problematic. Object-oriented categorization is utilized in this research to extract landside data from high-resolution GF-1 and Sentinel-2 data. Data preprocessing begins with orthophoto correction, image matching, and data fusion, followed by band enhancement, which comprises band synthesis, principal component analysis, and filtering, and finally landside extraction using an object-oriented technique. The impact of geology, lithology, rainfall, and human activities on the occurrence of landslides in the study area is explored utilizing DEM data, visualization tools, remote sensing interpretation map, and other associated data. The studies are conducted in Shuicheng County, Guizhou Province, China, with a segmentation scale of 25 pixels and 14 classification feature parameters. Following that, the landslide mass is extracted and categorization findings of nearby characteristics are acquired. Finally, the destructiveness of the landslide is determined by comparing the results of object-oriented classification before and after the landslide. With a Kappa coefficient of 0.76 and a landslide extraction accuracy of 79.8%, the overall classification accuracy is 87%. Combined with the geological structure, rock lithology, spatial location, landslide occurrence process, elevation of the study area, precipitation and the impact of human activities, the causes of the landslide are discussed and analyzed. The early warning of other unknown landslides can be obtained by analyzing the features of the aforementioned components. Full article

(This article belongs to the Special Issue Disaster Monitoring Using Remote Sensing)

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16 pages, 33164 KiB

Open AccessArticle

Characteristics of Spatiotemporal Changes in the Occurrence of Forest Fires

by Taehee Kim, Suyeon Hwang and Jinmu Choi

Remote Sens. 2021, 13(23), 4940; https://doi.org/10.3390/rs13234940 - 4 Dec 2021

Cited by 8 | Viewed by 2784

Abstract

The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire [...] Read more.

The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire damage ledger information with VIIRS-based forest fire occurrence data. Then, detrended fluctuation analysis and the local indicator of temporal burstiness were applied. In the results, the forest fire occurrence follows a self-organized criticality mechanism, and the temporal irregularities of fire occurrences exist. When the forest fire occurrence time series in Gyeonggi-do Province, which had the highest value of the local indicator of temporal burstiness, was checked, it was found that the frequency of forest fires was increasing at intervals of about 10 years. In addition, when the frequencies of forest fires and the spatial distribution of the local indicators of forest fire occurrences were compared, it was found that there were spatial differences in the occurrence of forest fires. This study is meaningful in that it analyzed the time series characteristics of the distribution of forest fires in Korea to understand that forest fire occurrences have long-term temporal correlations and identified areas where the temporal irregularities of forest fire occurrences are remarkable with the local indicators of temporal burstiness. Full article

(This article belongs to the Special Issue Disaster Monitoring Using Remote Sensing)

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