Risk Zone Mapping Research Articles

MAPPING OF FLOOD RISK ZONES IN KAFANCHAN TOWN, JEMA’A LOCAL GOVERNMENT AREA, KADUNA STATE, NIGERIA

Floods are defined as water disasters that causes temporal or permanent consequences in an environment. This research focused on the Mapping of Flood Risk Zones in Kafanchan town, Jema’a local government area by the utilization of field survey and GIS in other to identify and map out the flood prone areas in kafanchan. Multicriteria Method was used for evaluation of the flood risk areas by using various criteria like rainfall data, distance to road, drainage density, and slope. Findings revealed that heavy rainfall, inadequate drainage and slope are the major causes of flooding. From the findings the flood risk map identified places with high, moderate and low vulnerability. Results showed that flood disasters have detrimental effect on people and property. The flood tragedy significantly harmed the environment as well as socio-economic sector. In the research area, the threat of flood disaster has not been addressed by Disaster management/response organizations. Findings, however, showed that these initiatives are insufficient to reduce the threat and risk of flooding in the research area. This study helps government and policymakers in zoning to avoid future flood events from causing tremendous havoc in the study area and providing Early Warning Systems for the area.

Optimizing Malaria Control: Granular and Cost-Effective Mosquito Habitat Index in Endemic Areas Through Satellite Imagery

Malaria, classified as a tropical disease under the Sustainable Development Goals (SDGs) indicator 3.3, remains a significant global health challenge. In this study, by taking advantage of multiple spectral composite indexes of multisource satellite imagery to capture various geospatial features relevant to the suitability of marsh mosquito habitat, we introduced the Mosquito Habitat Suitability Index (MHSI) to assess potential Anopheles mosquito breeding sites in terms of the vegetation density, water bodies, environment temperature, and humidity in any particular areas. The MHSI integrates the publicly accessible granular level of the normalized difference vegetation index, water index, land surface temperature, and moisture index from cost-effective low and medium-resolution optical satellite data. We focus on West Papua Province, Indonesia, known for diverse ecological conditions and varying malaria prevalence, as a case study area. From the built index, the risk zone map is then formed with the K-Means algorithm. One key finding is the elevated risk in Fakfak Regency, demanding particular attention, as its high-risk area represents 45% of its total. This research aids localized decision-making to combat malaria's unique challenges in West Papua Province which are relevant for implementation in other regions, contributing to SDG-aligned interventions for malaria eradication by 2030.

Assessing the impact of booster vaccination on diphtheria transmission: Mathematical modeling and risk zone mapping

The COVID-19 pandemic caused significant disruptions in the healthcare system, affecting vaccinations and the management of diphtheria cases. As a consequence of these disruptions, numerous countries have experienced a resurgence or an increase in diphtheria cases. West Java province in Indonesia is identified as one of the high-risk areas for diphtheria, experiencing an upward trend in cases from 2021 to 2023. To analyze the situation, we developed an SIR model, which integrated DPT and booster vaccinations to determine the basic reproduction number, an essential parameter for infectious diseases. Through spatial analysis of geo-referenced data, we identified hotspots and explained diffusion in diphtheria case clusters. The calculation of R0 resulted in an R0 = 1.17, indicating the potential for a diphtheria outbreak in West Java. To control the increasing cases, one possible approach is to raise the booster vaccination coverage from the current 64.84% to 75.15%, as suggested by simulation results. Furthermore, the spatial analysis revealed that hot spot clusters were present in the western, central, and southern regions, posing a high risk not only in densely populated areas but also in rural regions. The diffusion pattern of diphtheria clusters displayed an expansion-contagious pattern. Understanding the rising trend of diphtheria cases and their geographic distribution can offer crucial insights for government and health authorities to manage the number of diphtheria cases and make informed decisions regarding the best prevention and intervention strategies.

Application of NEXRAD precipitation data for assessing the implications of low development practices in an ungauged basin

AbstractHydrologic analysis in watersheds lacking rain gauge stations has been a challenge and even those with stations that do not contain the required amount of data create problems in model verification. So, the study integrates the Next Generation Weather RadarIII precipitation data and the Personal Computer Storm Water Management Model (PCSWMM) for evaluating the model's effectiveness. The study further integrates 100‐year return period precipitation intensity and PCSWMM to generate a one‐dimensional flood risk zone map, which shows the major sub‐catchments under risk zones. Based on the identification of risk zones from PCSWMM, three different low‐impact developments (LIDs), street plants, infiltration trenches, and green roofs are applied independently and uniformly to compare the decrease in flow. Thereafter, the prioritized list of critical sub‐catchments from hydraulic modeling is compared with the compromise programming method, an approach for studying the decrement in flow by increasing LID application (infiltration trench) in the first five critical sub‐catchments, suggesting planners and researchers identify the most critical sub‐catchments and develop future potential strategies.

Current and future land fire risk mapping in the southern region of Sumatra, Indonesia, using CMIP6 data and GIS analysis

Land fires lead to a deterioration of environmental quality and have perilous social and economic aspects. One method for mitigating this hazard is to determine the range of fire risk areas. Thus, this study aimed to analyze the current and potential future fire risk in the Ogan Komering Ilir, southern Sumatra region of Indonesia, using CMIP6 data and geospatial technology. In order to analyze fire risk areas, the Landsat 8 satellite image from 2021 (as a baseline period) was used. Variables like the normalized difference moisture index (NDMI), the normalized difference vegetation index (NDVI), and the land surface temperature (LST) were extracted from the satellite image. Then, the standard precipitation index (SPI) was determined using rainfall data from meteorological stations. For future period prediction (periods 2021–2040 and 2041–2060), this study used future climate data from the CMIP6. By overlaying these variables, current and future fire risk zone maps were produced. The results obtained showed that in the current period, around 726.4 km2 (4.2%) of the area was at high fire risk, then 5,621.3 km2 (32.7%) and 10,835.3 km2 (63.1%) were at moderate and low fire risk zones, respectively. Furthermore, for the future period, this study estimated there was an increase in high fire risk areas of about 14% during 2021–2040 and 30% during 2041–2060. This was recorded in the southern and northern parts of the study area. Fire risk areas were strongly associated with NDMI, NDVI, and LST. The study's findings may be useful for land fire management in fire-prone areas.

Performance measures of frequency ratio, weight of evidence and AHP models for flood risk assessment of Panchganga river basin (PRB), Kolhapur (India)

Flood events are the most common and frequent natural disasters causing enormous loss to property and life worldwide. In this study, the Panchganga River Basin (PRB) was selected for flood susceptibility mapping using the frequency ratio (FR), weight of evidence (WoE) and Analytical Hierarchy Process (AHP) model with remote sensing and GIS based approach. For this, a flood inventory with 224 historic flood locations was prepared using Google Earth Engine. About 75% of flood locations were randomly selected for training and 25% for testing purposes. The flood susceptibility map prepared using FR, WoE and AHP model was classified into five zones such as very high, high, moderate, low risk and safe zone using ArcGIS software. The receiver operating characteristic (ROC) curves were plotted with area under curve (AUC) of 89.27%, 87.34% and 80.60% for FR, WoE and AHP models respectively. The present study will help people near Panchganga river to move to a safer place during flooding. The flood risk zone maps are extremely helpful in infrastructural developments, town planning and civil protection.

Forest Fire Risk Zone Mapping in Mizoram Using RS and GIS

In recent years, India’s Northeastern territory has been plagued by wildfires. Mizoram, as per FSI, has been among the most affected regions, with approximately 20,744-recorded wildfires. Forest fire directly or indirectly affects human health, climate change, and the environment. The forest fire risk zonation is generated utilizing remote sensing data, and Geographic Information System based on specified physical and socioeconomic factors. As part of the current study, a Geographical Information System is employed to determine forest fire risk based on predetermined physical and socioeconomic parameters. This study used three distinct models of fire risk zonation index namely FRI (Fire Risk Index), HFI (Hybrid Fire Index), and SFI (Structural Fire Index) to identify the zones in the study area under the risk of forest fires. The Indian state of Mizoram is categorized into five distinct hazard zones based on the probability of wildfire incidents. Fire alerts generated using risk models, and real-time hotspot datasets (forest fire spots) received from MODIS and USGS have been validated. According to the study’s findings, 18.84 km2 of the study area is at low risk of forest fire, 11.072 km2 is at moderate risk, and 5.38 km2 is at high risk. The probability from each metric varies since the input and weightage of the different parameters vary from one another. SFI, therefore, therefore predicts a lower frequency of high-risk wildfire-prone zones than HFI and FRI. In this research, the coefficient of discrimination (R2) is employed to assess the reliability of the projected fire indices being compared with real-time hot spots. Here, FRI possesses the highest accuracy (R2=0.892), HFI has moderate accuracy (R2=0.676), and FRI has the lowest accuracy (R2=0.629).

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions.

Geospatial analysis of flood emergency evacuation sites in Accra.

This research is useful as mapping could increase awareness of evacuation sites to speed up rapid response to immediate impacts of flood events. The findings of the study will facilitate future decisions for demarcating potential flood emergency evacuation sites. The overall aim of this study is to contribute to literature, policy, spatial analysis and the practice of flood risk management in the study area. The research findings can also be applied to other flood-prone areas with similar contexts as the study site.

Effects of Forest Fire Prevention Policies on Probability and Drivers of Forest Fires in the Boreal Forests of China during Different Periods

Fire prevention policies during different periods may lead to changes in the drivers of forest fires. Here, we use historical fire data and apply the boosted regression tree (BRT) model to analyze the spatial patterns and drivers of forest fires in the boreal forests of China from 1981 to 2020 (40 years). We divided the fire data into four periods using the old and new Chinese Forest Fire Regulations as a dividing line. Our objectives here were: to explore the influence of key historical events on the drivers of forest fires in northern China, establish a probability model of forest fire occurrence, and draw a probability map of forest fire occurrence and a fire risk zone map, so as to interpret the differences in the drivers of forest fires and fire risk changes over different periods. The results show that: (1) The model results from 1981 to 2020 (all years) did not improve between 2009 and 2020 (the most recent period), indicating the importance of choosing the appropriate modeling time series length and incorporating key historical events in future forest fire modeling; (2) Climate factors are a dominant factor affecting the occurrence of forest fires during different periods. In contrast with previous research, we found that here, it is particularly important to pay attention to the relevant indicators of the autumn fire prevention period (average surface temperature, sunshine hours) in the year before the fire occurrence. In addition, the altitude and the location of watchtowers were considered to have a significant effect on the occurrence of forest fires in the study area. (3) The medium and high fire risk areas in our three chosen time periods (1981–14 March 1988; 15 March 1988–2008; 2009–2020) have changed significantly. Fire risks were higher in the east and southeast areas of the study area in all periods. The northern primeval forest area had fewer medium-risk areas before the new and old regulations were formulated, but the medium-risk areas increased significantly after the old regulations were revised. Our study will help understand the drivers and fire risk distribution of forest fires in the boreal forests of China under the influence of history and will help decision-makers optimize fire management strategies to reduce potential fire risks.

Dengue risk zone mapping of Thiruvananthapuram district, India: a comparison of the AHP and F-AHP methods.

Dengue fever, which is spread by Aedes mosquitoes, has claimed many lives in Kerala, with the Thiruvananthapuram district bearing the brunt of the toll. This study aims to demarcate the dengue risk zones in Thiruvananthapuram district using the analytical hierarchy process (AHP) and the fuzzy-AHP (F-AHP) methods. For the risk modelling, geo-environmental factors (normalized difference vegetation index, land surface temperature, topographic wetness index, land use/land cover types, elevation, normalized difference built-up index) and demographic factors (household density, population density) have been utilized. The ArcGIS 10.8 and ERDAS Imagine 8.4 software tools have been used to derive the risk zone maps. The area of the risk maps is classified into five zones. The dengue risk zone maps were validated using dengue case data collected from the Integrated Disease Surveillance Programme portal. From the receiver operating characteristic (ROC) curve analysis and the area under the ROC curve (AUC) values, it is proved that the F-AHP method (AUC value of 0.971) has comparatively more prediction capability than the AHP method (AUC value of 0.954) in demarcating the dengue risk zones. Also, based on the comparison of the risk zone map with actual case data, it was confirmed that around 82.87% of the dengue cases occurred in the very high and high-risk zones, thus proving the efficacy of the model. According to the dengue risk map prepared using the F-AHP model, 9.09% of the area of Thiruvananthapuram district is categorized as very high risk. The prepared dengue risk maps will be helpful for decision-makers, staff with the health, and disaster management departments in adopting effective measures to prevent the risks of dengue spread and thereby minimize loss of life.

Mapping the forest fire risk zones using artificial intelligence with risk factors data.

Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires, and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of 11 human-induced factors, a natural factor, and temperature, which is one of the risk factors that determine the conditions for the occurrence of forest fires. Moreover, k-means clustering algorithm, which is an artificial intelligence method, was employed in preparation of the fire risk zone map. Turkey was selected as the study area because there are social and economic variations among its regions. Thus, the regional forest directorates in Turkey were separated into four clusters as extreme-risk zone, high-risk zone, moderate-risk zone, and low-risk zone. Also, a map presenting these risk zones were provided. The map reveals that, in general, the western and southwestern coastal areas of Turkey are at high risk of forest fires. On the other hand, the fire risk is relatively low in the northern, central, and eastern areas.

High Altitude Rangelands Invasion by Non-Palatable Plant Species in the Perception of Yak Herders

The biological and socio-ecological dimensions of alpine rangelands degradation by invasive species have been considered in a field survey conducted in two yaks (Bos grunniens) rearing areas i.e., Langtang National Park (LNP) and Kanchanjunga Conservation Area (KCA). Data were collected by herder’s interview using the well-prepared and pretested set of questionnaires (60), followed by a focus group discussion (FGD) and key informants survey (KIS) each five at each site by following a set of checklists. There was a common perception about the invasive species among the herders in the study sites. The Rumex nepalensis was the most invasive species reported (75% of the respondents) at altitudes up to 3000 m in the KCA, while it was additionally with Eupatorium adenophorum (60%) in the LNP, while the Lyonia and Juniperus were the common invasive species in both sites. Altogether, twelve plant species were reported as invasive and non-palatable species from various botanical groups were indifferent to the changes made by invasive species in rangelands soil characteristics but gave well insight into the declining productivity of grasslands and herbage productivity and quality. Herders established bush clearance and slash and burn agriculture in rangelands as traditional adaptation measures to control the invasive species. Mapping of risk zones of invasive and alien species in the alpine rangelands is necessary across the alpine rangelands of Nepal and a long-term monitoring framework is desirable to confirm the herder's information on invasive alpine species of Nepalese Himalayas.

Assessment of Flood Risk and Mapping of Flood Risk Zones in Yenagoa, Bayelsa State, Nigeria

In recent years, most Nigerian cities have experienced several major flood episodes with serious catastrophes. Hence, this study was carried out to assess and map flood risk zones in Yenagoa. The Geographic Information System (GIS) and remote sensing technologies were used in generating and analyzing data to identify the different flood risk zones in Yenagoa. Environmental indices such as flood heights, land elevation and land cover images were analyzed using geo-statistical tools such as map overlay operations to produce a flood risk map. The flood risk map produced and the ground truthing carried out confirmed that the highest flood risk are located within residential areas where about 32% of the built-up area was vulnerable to moderate or high risk of flooding. The study revealed that both natural and anthropogenic factors are responsible for the severe flooding experienced in Yenagoa, especially due to heavy precipitation, elevation of the area, poor waste disposal, and inadequate urban development control amongst other factors. The city was classified into three flood risk zones-high, moderate and low, with about 48% of the area being either at moderate or high risk of flooding. It is therefore recommended that the Ministry of Physical and Urban Development should carryout proper urban development control, which will prevent building on flood plains, wetlands and natural drainage channels.

Forest Fire Risk Zone Mapping of Aalital Rural Municipality, Dadeldhura District, Nepal

Forest fire is one of the leading causes of forest and wildlife loss. The objective of this study was to use satellite imagery and Geographic Information System techniques to assess the forest fire risk zonation map of the Aalital rural municipality. This rural municipality is a part of the Sudurpaschim province, Nepal; is prone to forest fires. Four fire risk zones were established in the study area i.e. very high, high, medium, and low-risk zone. Thematic layers were derived from topographic maps and satellite imageries. For the delineation of fire risk zones, a multi-parametric weighted index model i.e. the FRI (Fire Risk Index) method was adopted. The fire incidence data provided by MODIS were used to validate the resulting forest fire risk zone map. About 25.17% of the total study area lies under the very high-risk zone followed by 46.51% under high risk, 25.68% under medium risk, and 2.62% under the low-risk zone. It can be inferred that the majority of the area is at high risk of forest fire. This map of fire risk zone can help in disaster and forest management as valuable data to prepare effective measures for appropriate fire risk management in the area.

Flooding study in the desert climate zone: case study of M’zab valley (Algeria)

The aim of this research is the modelling and mapping of flood risk zones in the M’zab valley of Ghardaia city by combining the hazard map and the flood vulnerability map. The undertaken research utilizes the hydrologic engineering centre-hydrologic modelling system as the modelling tool to simulate the runoff in the watershed and hydrologic engineering centre – river analysis system to model the water levels of different river locations based on steady flow analysis and to define flood-prone areas. The flood risk map allows the identification of five classes ranging from a low to a very high risk of flooding. Thus, the low to very low-risk areas extend over roughly 70.58% of the total surface area of Ghardaia city. These areas, which are located in the southern part of the alluvial plain, are considered as low vulnerability regions, while areas with high-risk cover more than 20% of the city.

Analyzing the Forest Fire Risk of a Tropical Deciduous Forest in Southern Western Ghats Region in Wayanad Wildlife Sanctuary, Kerala, India

In recent years, the forest fire has increased at an alarming rate in India. It has become a common incident in many areas of Wayanad Wildlife Sanctuary situated along the North-Eastern part of Kerala, lying in the Western Ghats. The forest fire incidents happen mainly in the pre-monsoon period ranging from February to April. Using various forest risk parameters, the study analyses the forest fire risk zone in the Wayanad Wildlife Sanctuary. A weighted index method using GIS and Remote sensing was acquired to obtain and distinguish the forest fire-risk map by including the risk variables such as vegetation, topographic factors, land surface temperature and proximity to the road and settlements. The forest fire risk zone map integrates the observed variable with the help of the GIS platform. Out of the study area's total of 412 sq km, 24.65 % falls in very high and 33.39 % comprises the high fire risk zones. Validation of the forest fire risk zone map with the fire locations illustrates that most fire locations (72 %) fall under high and high-risk zones. The result of the study could help the decision-makers to implement fire management plans and strategies.

Forest Fire Risk Zone Mapping of Eravikulam National Park in India

Forest fire is one of the most common natural hazards occurring in the Western Ghats region of Kerala and is one of the reasons for forest degradation. This natural disaster causes considerable damage to the biodiversity of this region during the dry fire season. The area selected for the present study, Eravikulam National Park, which is predominantly of grassland vegetation, is also prone to forest fires. This study aims to delineate the forest fire risk zones in Eravikulam National Park using remote sensing (RS) data and geographic information system (GIS) techniques. In the present study, methods such as Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) were used to derive the weights, and the results were compared. We have used seven factors, i.e. land cover types, normalized difference vegetation index, normalized difference water index, slope angle, slope aspect, distance from the settlement, and distance from the road to prepare the fire risk zone map. The area of the prepared risk zone maps is divided into three zones, namely low, moderate, and high. From the study, it was found that the fire occurring in this area is due to natural as well as anthropogenic factors. The prepared forest fire risk zone maps are validated using the fire incidence data for the period from January 2003 to June 2019 collected from the records of the Forest Survey of India. The investigation revealed that 72% and 24% of the fire incidences occurred in the high risk zone of the maps prepared using the AHP and FR methods, respectively, which ascertained the superiority of the AHP method over the FR method for forest fire risk zone mapping. The receiver operating characteristic (ROC) curve analysis gives an area under the ROC curve (AUC) value of 0.767 and 0.567 for the AHP and FR methods, respectively. The risk zone maps will be useful for staff of the forest department, planners, and officials of the disaster management department to take effective preventive and mitigation measures.

Identification and modelling of forest fire severity and risk zones in the Cross – Niger transition forest with remotely sensed satellite data

Forest fires are a serious environmental hazard within the forest ecosystem, which can be studied with Remote Sensing and GIS. The aim of the study is to identify and model forest fires severity and risk zones within the Cross – Niger transition forest. To achieve this aim, remotely sensed data, such as Landsat – 8 OLI (2020) and ASTER DEM were used to produce land cover maps and topography parameters such as aspect, elevation and slope. The topographic maps and the Google Earth imagery were used to extract human settlements and road networks. The final forest fire risk zone (FFRZ) map was prepared by integrating the different parameters such as Land cover, aspect, elevation, slope, proximities to roads and settlements in the ArcGIS environment. The FFRZ was categorized into three categories as low, moderate and high risk zones, based on their fire susceptibility. The category of low, moderate and high FFRZ were represented as 2731.7 km2 (12.5%), 17997.69 km2 (82.59 %) and 1061.63 km2 (4.87%) respectively. The study shows that Remote Sensing and GIS are excellent tools for modelling forest fire risk zones, hence proving that fires are anthropogenic in origin.

Analysis and interpretation of forest fire data of Sikkim

Forest ecosystems are depleting and heading towards degradation which would adversely affect the world's socio-economic harmony. Various disasters disturb the cordial relationship of the flora and fauna and impose imbalance in the ecology as a whole; forest fire is one of its kind. India has witnessed a 125% rise in forest fire occurrences between the years 2015 and 2017. This paper presents a study of various factors and the analysis of forest fire in Sikkim. The period of 10 years, forest fire incidences, i.e., from the year 2004 to the year 2014 have been considered for the study. The forest fire data was collected from Forest and Environment Department, Government of Sikkim, and preliminary processing was performed to check for anomalies. The study observed that there has been an increased forest fire incidence over the years and highest being in the year 2009. These fire incidences have damaged a total area of 5,047.16 ha of land damaging various flora and fauna. It was observed that the maximum forest fire cases are below an altitude of 1500m, during winter months (December to February extending to March) and in sub-tropical Sal (Shorea robusta) forest. West district of Sikkim recorded the highest number of forest fire incidences and area covered followed by south and east districts; the north district was least affected. As per the visual interpretation of forest fire incidence data and literature review, the main factors responsible for forest fire in Sikkim are low rainfall, dry winter season, and type of vegetation. Also, a linear regression was performed between weather factors like average temperature (°C), relative humidity (%), and wind velocity (Km/h) on incidences of forest fire between the year 2009-2014 (n=389). It was found that the average temperature (r=0.37, Slope=9.59 and SD= ±12.00) and relative humidity (r=-0.6, Slope=-4.52, and SD=±2.68) plays a moderate linear relationship in influencing the incidences of forest fires. However, wind velocity showed almost a flat curve indicating its minimal role in influencing forest fire incidences. Parameter modelling and preparation of forest fire risk zone map would be an effective tool in preventing and managing forest fire in Sikkim.