Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms

Shahfahad, .; Talukdar, Swapan; Das, Tanmoy; Naikoo, Mohd Waseem; Rihan, Mohd; Rahman, Atiqur

doi:10.1002/9781119788157.ch9

Cited by 20 publications

(11 citation statements)

References 37 publications

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“…The monthly distributions of Z and β for the cells and hydrological variables are illustrated in Figure 3, with the p-values derived from the seasonal MK test marked on each plot (green cell border) as an indicator to assess clusters with notable trends. Previous studies on hydrological trends have conventionally set a significance threshold of p (at which trends are deemed significant) at 0.01 [40]. For T mean , statistically significant increasing trends were observed for all cells, with less marked trends in the inland mountainous areas (Z between 7.5 and 8.25) and more marked trends in the inland flat and hilly areas (Z between 9.75 and 10.5).…”

Section: Seasonal Mk Analysismentioning

confidence: 98%

“…The monthly distributions of Z and β for the cells and hydrological variables are illustrated in Figure 3, with the p-values derived from the seasonal MK test marked on each plot (green cell border) as an indicator to assess clusters with notable trends. Previous studies on hydrological trends have conventionally set a significance threshold of p (at which trends are deemed significant) at 0.01 [40]. The monthly distributions of Z and β for the cells and hydrological variables are illustrated in Figure 3, with the p-values derived from the seasonal MK test marked on each plot (green cell border) as an indicator to assess clusters with notable trends.…”

Section: Seasonal Mk Analysismentioning

confidence: 99%

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Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach

Di Nunno,

Diodato,

Bellocchi

et al. 2024

Climate

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Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of climate change, focusing specifically on reference evapotranspiration (ETo) and its associated hydrological variables. The seasonal Mann–Kendall (MK) test was used to assess trends in gridded data. The K-means algorithm was then applied to divide Lazio into four homogeneous regions (clusters), each characterized by distinct trends in hydrological variables. The analysis revealed statistically significant increasing trends (p ≤ 0.01) in temperature, solar radiation, and ETo, with more marked effects observed in the coastal and hilly clusters. In contrast, statistically significant decreasing trends (p ≤ 0.01) were observed for relative humidity, while no statistically significant trends (p > 0.01) were observed for precipitation. This study’s methodology, combining trend analysis and clustering, provides a comprehensive view of ETo dynamics in Lazio, aiding in pattern recognition and identifying regions with similar trends.

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Section: Seasonal Mk Analysismentioning

confidence: 98%

“…The monthly distributions of Z and β for the cells and hydrological variables are illustrated in Figure 3, with the p-values derived from the seasonal MK test marked on each plot (green cell border) as an indicator to assess clusters with notable trends. Previous studies on hydrological trends have conventionally set a significance threshold of p (at which trends are deemed significant) at 0.01 [40]. The monthly distributions of Z and β for the cells and hydrological variables are illustrated in Figure 3, with the p-values derived from the seasonal MK test marked on each plot (green cell border) as an indicator to assess clusters with notable trends.…”

Section: Seasonal Mk Analysismentioning

confidence: 99%

Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach

Di Nunno,

Diodato,

Bellocchi

et al. 2024

Climate

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“…Analysing such influencing variables various studies have utilized various statistical, physical and machine learning approaches to study fire behaviours, extents, patterns and susceptibility modelling. These approaches include logistic regression (LR) [ 35 ], multiple linear regression [ 36 ], frequency ratio method [ 37 ], analytical hierarchical process [ 38 , 39 ], fuzzy AHP [ 29 ], Generalized Linear Model (GLM) [ [40] , [41] , [42] , [43] ] and machine learning approaches [ 44 , 45 ]. While methods like WLC (Weighted Linear Composition Method) offer convenience to solve complex multi-criteria decision-making problems [ 46 ] and the Analytical Hierarchical Process (AHP) has been used in the GIS-MCDA process, but they are based on hypothesis making them inaccurate [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Forest fire vulnerability in Nepal's Chure region: Investigating the influencing factors using generalized linear model

Joshi,

Adhikari,

Bhattarai

et al. 2024

Heliyon

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“…Another direction evaluates LST and its association with land use types, such as vegetation, water bodies, and built-up areas. These studies are crucial for understanding urban heat dynamics and evaluating the effects of landcover changes [32,33]. Exploration of the relationship between urban temperature and land use type began as early as the 1990s in the United States [34].…”

Section: Introductionmentioning

confidence: 99%

How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data

Wu,

Zhang,

et al. 2023

Sustainability

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Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future.

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Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms

Cited by 20 publications

References 37 publications

Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach

Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach

Forest fire vulnerability in Nepal's Chure region: Investigating the influencing factors using generalized linear model

How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data

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