Static Fire Risk Index for the Forest Resources of Karnataka

Konkathi, Preethi; Shetty, Amba; Kolluru, Venkatesh; Yathish, P.H; Umesh, Pruthviraj

doi:10.1109/igarss.2019.8898522

Cited by 5 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Independent variables were grouped into four categories: topography, vegetation, anthropogenic factors, and climate. Specific variables within categories were selected based on previous studies on forest fire occurrence [24,[34][35][36][37][38][39]. Detailed information on preselected variables is presented in Table 1.…”

Section: Independent Variablesmentioning

confidence: 99%

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Milanović

Marković

Pamučar

et al. 2020

Forests

View full text Add to dashboard Cite

Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country’s area.

show abstract

Section: Independent Variablesmentioning

confidence: 99%

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Milanović

Marković

Pamučar

et al. 2020

Forests

View full text Add to dashboard Cite

show abstract

“…Variables that appear in the literature are obtained according to the expert's analysis. According to the literature and the study area, each factor has been split into five levels [38]: extremely low, low, moderate, high, extremely high [27,[39][40][41]. Then, we use the analytic hierarchy process to assign appropriate weights to the eight factors.…”

Section: Methodsmentioning

confidence: 99%

GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing

et al. 2021

View full text Add to dashboard Cite

Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.

show abstract

“…In another study, Konkathi et al (2019) developed a static fire risk index with topographic ruggedness as one of the parameters influencing forest fires. MODIS land cover type yearly L3 global 500m SIN grid (MCD 12 Q1) was used to compute fuel type based on historical fire data and STRM DEM was used to compute slope index, elevation index, aspect index, and TRI.…”

Section: Topographic Ruggedness Applicationsmentioning

confidence: 99%

Application of Geographic Information Systems in Analyzing Topographic Roughness for Nyanga District in Zimbabwe

Chipatiso

2023

IJDSBDA

View full text Add to dashboard Cite

The Topographic Ruggedness Index (TRI) is one of the essential measurement in topographic analysis, as well as in explaining biodiversity and geo-diversity. This study demonstrate how to utilize Geographic Information Systems (GIS) to generate TRI for Nyanga District using Digital Elevation Model (DEM). Though the TRI strongly depends on a local scale slope derived from an average adjacent neighbor slope algorithm, and selection of different lag distances in the computation of spatial variability, the measurement demonstrated that areas of higher altitude have higher ruggedness index. The TRI model was generated by computing focal statistics to determine focal sum of DEM and of the square of DEM (DEM²). The final TRI was computed using Math Algebra (Raster Calculator) to generate layers of different ruggedness suitable for topographic and biodiversity analysis. The algorithm presented could also be used for smaller areas with high quality data and corrected DEMs. Despite widespread adoption, ruggedness metrics require thorough testing using both artificial landscapes and real world applications.

show abstract

Static Fire Risk Index for the Forest Resources of Karnataka

Cited by 5 publications

References 6 publications

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing

Application of Geographic Information Systems in Analyzing Topographic Roughness for Nyanga District in Zimbabwe

Contact Info

Product

Resources

About