Assessment of the probability of occurrence of multiple Environmental hazards in mangrove habitats using remote sensing and geographic information system

Davood Mafi-Gholami, Raymond Ward

Research output: Contribution to journalArticle

Abstract

In mangroves assessing the threat of multiple environmental hazards is important to inform effective management decisions to protect these habitats and to reduce or prevent damage as a result of environmental impacts. In this study, the threat of multiple environmental hazards including drought, reducing surface runoff of upstream catchments, strong winds, extreme temperatures, fishing activities and loss at seaward edges of mangroves in the northern coast of the Persian Gulf and Oman Sea in Iran are assessed and mapped.
In this study a Mann-Kendall (MK) test was employed using MAKESENSE 1.0 to detect trends in standardized precipitation index (SPI) values at a confidence level of 95% and 99%. Based on changes in Z values and implementation of the natural break command in ArcGIS, a map of changes as a result of drought was categorized into four classes of low (code 1), moderate (code 2), high (code 3) and very high (code 4), based on the values of Z | ≥ 1.96 (increasing trend of drought severity). This was used to assess the threat to mangroves.
In order to map changes in surface runoff from upstream catchments during the 30-year period (1986-2016), the time series of changes in runoff coefficient values were evaluated. A 30-year time series of land use / land cover changes (LULC) in upstream catchments of mangroves was also prepared using data from 210 Landsat images. Using the LULC map, the Runoff coefficient of the catchment is calculated from the runoff coefficient for permeable areas (Cper). Cper was calculated from a weighted sum of land use, soil type and slope factors, respectively, the first, second, and third term in the right-hand side of Eq. 1:
(1) C_per=w1(0.02/n)+w2(θ_w/(1-θ_w ))+w3(s/(10+s))
In equation (1), n is the Manning’s roughness coefficient dependent on the LULC, θ_w is the volumetric soil water content at wilting point, and S is the land surface slope in percentage land surface slope in percentage. The value of (θ_w/(1-θ_w )) was calculated using the soil texture map of upstream catchments of mangroves obtained from Iranian Forests, Range and Watershed Management Organization (FRWMO). The values of the coefficients W1, W2 and W3 were considered as 0.4, 0.3 and 0.3 respectively. Using a 30-year time series of runoff coefficients and annual precipitation values, a 30-year time series of surface runoff changes in catchments was prepared. The time series of the changes in surface runoff values was used to create a map of the reduction of catchments using four classes of low (code 1), moderate (code 2), high (code 3) and very high (Code 4) to assess the threat to mangroves. Classification was undertaken using the natural break command in ArcGIS.
Changes in the intensity of fishing activities in mangrove habitats were mapped using the location of the fishing ports and the number of active launches and boats, determined by reviewing the satellite imagery of Google Earth Pro (© DigitalGlobe Inc., © GeoEye Inc.) and visiting the coasts. The coastal waters area was divided into 4×4 km GIS grid cells (598 cells) and in each of the grid cells, the intensity of the fishing activity was calculated and finally a map of the intensity of the fishing activity in coastal waters was prepared.
The map of the Fishing Index (FI) was derived . Using the Fishing Index (FI) map, fishing activity intensity was classified using the natural break command in ArcGIS as low (code 1), moderate (code 2), high (code 3) and very high (Code 4), this was used to assess the threat to the mangroves.
In this study, the threat from wind speeds greater than 8 m/s was mapped, this cut off was used as this velocity is considered as potentially damaging to the structure and function of these mangroves. A 30-year time series (1986-2016) of daily wind speed data from synoptic stations adjacent to the mangroves was used. In this study, the Weibull function was used to calculate the probability of wind speeds greater than 8 m/s. Wind speeds greater than 8 m/s were extracted and their average was calculated and multiplied by the probability of occurrence for each of stations during the thirty year period. A risk map of winds speeds greater than 8 m /s was prepared and classified using the natural break command in ArcGIS, the four classes were low (code 1), moderate (code 2), high (code 3) and very high (Code 4), and these were used to assess the threat to mangroves.
Based on previous studies and for the analysis of spatial variations in the occurrence of extreme temperatures, a temperature of 38° C was selected as the threshold temperature for mangrove damage. All daily temperatures equal to and greater than 38° C were extracted from the long-term dataset of daily temperatures for the 30-year period (1986-2016). By dividing the number of days with a temperature equal to and greater than 38° C by the total number of daily temperature records in the 30-year period, the probability of occurrence of temperatures above this threshold was calculated for each of the synoptic stations. At each synoptic station, the mean value of all temperatures equal to and greater than 38°C was calculated and multiplied by the probability of occurrence calculated for that station. Finally, using ArcGIS, a risk map of temperatures equal to and greater than 38 °C was prepared within the coastal areas and classified using the natural breaks command as low (code 1), moderate (code 2), high (Code 3) and very high (code 4) to assess the threat to the mangroves.
In this study, Landsat images of 1986, 2000, and 2016 were used to analyse the rates of changes in the seaward edges of mangroves over the 30-year period (1986-2016). To separate mangroves from surrounding water and coastal land areas and to identify the final borders of the study sites, an NDVI vegetation index was used. In this study, 2701 transects 30 m apart were mapped using the DSAS software to calculate the rate of changes in the seaward edges of mangroves. As with previous studies, the linear regression rate (LRR) method was used to measure the rates of changes in the seaward edges of mangroves. These were used to create a map of rates of regression of mangroves classified as: no regression (code 1), low rate of regression (code 2), moderate rate of regression (code 3) and high rate of regression (code 4) using the natural break command in ArcGIS, to assess the threat to the mangroves.
At this stage, classified hazard maps were combined using ArcGIS and equation (4):
(4) TI=√((a×b×c×d×e×f)/6)
Where TI = Threat Index, a = drought, b = surface runoff, C = wind, d = air temperature, e = fishing activity and f = loss of seaward edges of mangroves. The TI was used to create a threat map for the mangroves at all four sites classified as: low, medium and high using the natural break command in ArcGIS.
The results of this study showed that, considering the severity and probability of occurrence of the hazards, Khamir and Jask mangrove habitats are highly threatened by environmental hazards (Figure 1). Investigating the severity of occurrence of environmental hazards in mangrove habitats shows that Khamir and Jask habitats are considered to have a high to very high threat level from drought, reduced runoff from the catchments and extent of loss, significantly higher than the Tiab and Sirik sites. It is likely that the increase in the severity and risk from these hazards has had adverse effects on the structure and functions of these mangrove habitats as found in other studies conducted in other regions of the world that show that reducing rainfall and increasing the risk of drought reduces the extent of mangrove area and increases their vulnerability to other environmental hazards.
Original languageEnglish
Article number3
Pages (from-to)425-443
JournalJournal of Environmental Studies
Volume44
Issue number3
DOIs
Publication statusPublished - 2019

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environmental hazard
mangrove
remote sensing
habitat
fishing
runoff
catchment
drought
time series
temperature
wind velocity
geographic information system
code
land use
land cover
hazard
Landsat
coastal water
land surface

Keywords

  • Environmental hazards
  • assessment of the probabilty of occcurence
  • mangrove
  • Hormozgan Province

Cite this

@article{6783da07e4a04dbdbe23f9c9260c6409,
title = "Assessment of the probability of occurrence of multiple Environmental hazards in mangrove habitats using remote sensing and geographic information system",
abstract = "In mangroves assessing the threat of multiple environmental hazards is important to inform effective management decisions to protect these habitats and to reduce or prevent damage as a result of environmental impacts. In this study, the threat of multiple environmental hazards including drought, reducing surface runoff of upstream catchments, strong winds, extreme temperatures, fishing activities and loss at seaward edges of mangroves in the northern coast of the Persian Gulf and Oman Sea in Iran are assessed and mapped.In this study a Mann-Kendall (MK) test was employed using MAKESENSE 1.0 to detect trends in standardized precipitation index (SPI) values at a confidence level of 95{\%} and 99{\%}. Based on changes in Z values and implementation of the natural break command in ArcGIS, a map of changes as a result of drought was categorized into four classes of low (code 1), moderate (code 2), high (code 3) and very high (code 4), based on the values of Z | ≥ 1.96 (increasing trend of drought severity). This was used to assess the threat to mangroves.In order to map changes in surface runoff from upstream catchments during the 30-year period (1986-2016), the time series of changes in runoff coefficient values were evaluated. A 30-year time series of land use / land cover changes (LULC) in upstream catchments of mangroves was also prepared using data from 210 Landsat images. Using the LULC map, the Runoff coefficient of the catchment is calculated from the runoff coefficient for permeable areas (Cper). Cper was calculated from a weighted sum of land use, soil type and slope factors, respectively, the first, second, and third term in the right-hand side of Eq. 1:(1) C_per=w1(0.02/n)+w2(θ_w/(1-θ_w ))+w3(s/(10+s))In equation (1), n is the Manning’s roughness coefficient dependent on the LULC, θ_w is the volumetric soil water content at wilting point, and S is the land surface slope in percentage land surface slope in percentage. The value of (θ_w/(1-θ_w )) was calculated using the soil texture map of upstream catchments of mangroves obtained from Iranian Forests, Range and Watershed Management Organization (FRWMO). The values of the coefficients W1, W2 and W3 were considered as 0.4, 0.3 and 0.3 respectively. Using a 30-year time series of runoff coefficients and annual precipitation values, a 30-year time series of surface runoff changes in catchments was prepared. The time series of the changes in surface runoff values was used to create a map of the reduction of catchments using four classes of low (code 1), moderate (code 2), high (code 3) and very high (Code 4) to assess the threat to mangroves. Classification was undertaken using the natural break command in ArcGIS.Changes in the intensity of fishing activities in mangrove habitats were mapped using the location of the fishing ports and the number of active launches and boats, determined by reviewing the satellite imagery of Google Earth Pro ({\circledC} DigitalGlobe Inc., {\circledC} GeoEye Inc.) and visiting the coasts. The coastal waters area was divided into 4×4 km GIS grid cells (598 cells) and in each of the grid cells, the intensity of the fishing activity was calculated and finally a map of the intensity of the fishing activity in coastal waters was prepared.The map of the Fishing Index (FI) was derived . Using the Fishing Index (FI) map, fishing activity intensity was classified using the natural break command in ArcGIS as low (code 1), moderate (code 2), high (code 3) and very high (Code 4), this was used to assess the threat to the mangroves.In this study, the threat from wind speeds greater than 8 m/s was mapped, this cut off was used as this velocity is considered as potentially damaging to the structure and function of these mangroves. A 30-year time series (1986-2016) of daily wind speed data from synoptic stations adjacent to the mangroves was used. In this study, the Weibull function was used to calculate the probability of wind speeds greater than 8 m/s. Wind speeds greater than 8 m/s were extracted and their average was calculated and multiplied by the probability of occurrence for each of stations during the thirty year period. A risk map of winds speeds greater than 8 m /s was prepared and classified using the natural break command in ArcGIS, the four classes were low (code 1), moderate (code 2), high (code 3) and very high (Code 4), and these were used to assess the threat to mangroves. Based on previous studies and for the analysis of spatial variations in the occurrence of extreme temperatures, a temperature of 38° C was selected as the threshold temperature for mangrove damage. All daily temperatures equal to and greater than 38° C were extracted from the long-term dataset of daily temperatures for the 30-year period (1986-2016). By dividing the number of days with a temperature equal to and greater than 38° C by the total number of daily temperature records in the 30-year period, the probability of occurrence of temperatures above this threshold was calculated for each of the synoptic stations. At each synoptic station, the mean value of all temperatures equal to and greater than 38°C was calculated and multiplied by the probability of occurrence calculated for that station. Finally, using ArcGIS, a risk map of temperatures equal to and greater than 38 °C was prepared within the coastal areas and classified using the natural breaks command as low (code 1), moderate (code 2), high (Code 3) and very high (code 4) to assess the threat to the mangroves.In this study, Landsat images of 1986, 2000, and 2016 were used to analyse the rates of changes in the seaward edges of mangroves over the 30-year period (1986-2016). To separate mangroves from surrounding water and coastal land areas and to identify the final borders of the study sites, an NDVI vegetation index was used. In this study, 2701 transects 30 m apart were mapped using the DSAS software to calculate the rate of changes in the seaward edges of mangroves. As with previous studies, the linear regression rate (LRR) method was used to measure the rates of changes in the seaward edges of mangroves. These were used to create a map of rates of regression of mangroves classified as: no regression (code 1), low rate of regression (code 2), moderate rate of regression (code 3) and high rate of regression (code 4) using the natural break command in ArcGIS, to assess the threat to the mangroves.At this stage, classified hazard maps were combined using ArcGIS and equation (4):(4) TI=√((a×b×c×d×e×f)/6)Where TI = Threat Index, a = drought, b = surface runoff, C = wind, d = air temperature, e = fishing activity and f = loss of seaward edges of mangroves. The TI was used to create a threat map for the mangroves at all four sites classified as: low, medium and high using the natural break command in ArcGIS.The results of this study showed that, considering the severity and probability of occurrence of the hazards, Khamir and Jask mangrove habitats are highly threatened by environmental hazards (Figure 1). Investigating the severity of occurrence of environmental hazards in mangrove habitats shows that Khamir and Jask habitats are considered to have a high to very high threat level from drought, reduced runoff from the catchments and extent of loss, significantly higher than the Tiab and Sirik sites. It is likely that the increase in the severity and risk from these hazards has had adverse effects on the structure and functions of these mangrove habitats as found in other studies conducted in other regions of the world that show that reducing rainfall and increasing the risk of drought reduces the extent of mangrove area and increases their vulnerability to other environmental hazards.",
keywords = "Environmental hazards, assessment of the probabilty of occcurence, mangrove, Hormozgan Province",
author = "Davood Mafi-Gholami and Raymond Ward",
year = "2019",
doi = "10.22059/JES.2019.259330.1007675",
language = "English",
volume = "44",
pages = "425--443",
journal = "Journal of Environmental Studies",
issn = "2345-6922",
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TY - JOUR

T1 - Assessment of the probability of occurrence of multiple Environmental hazards in mangrove habitats using remote sensing and geographic information system

AU - Mafi-Gholami, Davood

AU - Ward, Raymond

PY - 2019

Y1 - 2019

N2 - In mangroves assessing the threat of multiple environmental hazards is important to inform effective management decisions to protect these habitats and to reduce or prevent damage as a result of environmental impacts. In this study, the threat of multiple environmental hazards including drought, reducing surface runoff of upstream catchments, strong winds, extreme temperatures, fishing activities and loss at seaward edges of mangroves in the northern coast of the Persian Gulf and Oman Sea in Iran are assessed and mapped.In this study a Mann-Kendall (MK) test was employed using MAKESENSE 1.0 to detect trends in standardized precipitation index (SPI) values at a confidence level of 95% and 99%. Based on changes in Z values and implementation of the natural break command in ArcGIS, a map of changes as a result of drought was categorized into four classes of low (code 1), moderate (code 2), high (code 3) and very high (code 4), based on the values of Z | ≥ 1.96 (increasing trend of drought severity). This was used to assess the threat to mangroves.In order to map changes in surface runoff from upstream catchments during the 30-year period (1986-2016), the time series of changes in runoff coefficient values were evaluated. A 30-year time series of land use / land cover changes (LULC) in upstream catchments of mangroves was also prepared using data from 210 Landsat images. Using the LULC map, the Runoff coefficient of the catchment is calculated from the runoff coefficient for permeable areas (Cper). Cper was calculated from a weighted sum of land use, soil type and slope factors, respectively, the first, second, and third term in the right-hand side of Eq. 1:(1) C_per=w1(0.02/n)+w2(θ_w/(1-θ_w ))+w3(s/(10+s))In equation (1), n is the Manning’s roughness coefficient dependent on the LULC, θ_w is the volumetric soil water content at wilting point, and S is the land surface slope in percentage land surface slope in percentage. The value of (θ_w/(1-θ_w )) was calculated using the soil texture map of upstream catchments of mangroves obtained from Iranian Forests, Range and Watershed Management Organization (FRWMO). The values of the coefficients W1, W2 and W3 were considered as 0.4, 0.3 and 0.3 respectively. Using a 30-year time series of runoff coefficients and annual precipitation values, a 30-year time series of surface runoff changes in catchments was prepared. The time series of the changes in surface runoff values was used to create a map of the reduction of catchments using four classes of low (code 1), moderate (code 2), high (code 3) and very high (Code 4) to assess the threat to mangroves. Classification was undertaken using the natural break command in ArcGIS.Changes in the intensity of fishing activities in mangrove habitats were mapped using the location of the fishing ports and the number of active launches and boats, determined by reviewing the satellite imagery of Google Earth Pro (© DigitalGlobe Inc., © GeoEye Inc.) and visiting the coasts. The coastal waters area was divided into 4×4 km GIS grid cells (598 cells) and in each of the grid cells, the intensity of the fishing activity was calculated and finally a map of the intensity of the fishing activity in coastal waters was prepared.The map of the Fishing Index (FI) was derived . Using the Fishing Index (FI) map, fishing activity intensity was classified using the natural break command in ArcGIS as low (code 1), moderate (code 2), high (code 3) and very high (Code 4), this was used to assess the threat to the mangroves.In this study, the threat from wind speeds greater than 8 m/s was mapped, this cut off was used as this velocity is considered as potentially damaging to the structure and function of these mangroves. A 30-year time series (1986-2016) of daily wind speed data from synoptic stations adjacent to the mangroves was used. In this study, the Weibull function was used to calculate the probability of wind speeds greater than 8 m/s. Wind speeds greater than 8 m/s were extracted and their average was calculated and multiplied by the probability of occurrence for each of stations during the thirty year period. A risk map of winds speeds greater than 8 m /s was prepared and classified using the natural break command in ArcGIS, the four classes were low (code 1), moderate (code 2), high (code 3) and very high (Code 4), and these were used to assess the threat to mangroves. Based on previous studies and for the analysis of spatial variations in the occurrence of extreme temperatures, a temperature of 38° C was selected as the threshold temperature for mangrove damage. All daily temperatures equal to and greater than 38° C were extracted from the long-term dataset of daily temperatures for the 30-year period (1986-2016). By dividing the number of days with a temperature equal to and greater than 38° C by the total number of daily temperature records in the 30-year period, the probability of occurrence of temperatures above this threshold was calculated for each of the synoptic stations. At each synoptic station, the mean value of all temperatures equal to and greater than 38°C was calculated and multiplied by the probability of occurrence calculated for that station. Finally, using ArcGIS, a risk map of temperatures equal to and greater than 38 °C was prepared within the coastal areas and classified using the natural breaks command as low (code 1), moderate (code 2), high (Code 3) and very high (code 4) to assess the threat to the mangroves.In this study, Landsat images of 1986, 2000, and 2016 were used to analyse the rates of changes in the seaward edges of mangroves over the 30-year period (1986-2016). To separate mangroves from surrounding water and coastal land areas and to identify the final borders of the study sites, an NDVI vegetation index was used. In this study, 2701 transects 30 m apart were mapped using the DSAS software to calculate the rate of changes in the seaward edges of mangroves. As with previous studies, the linear regression rate (LRR) method was used to measure the rates of changes in the seaward edges of mangroves. These were used to create a map of rates of regression of mangroves classified as: no regression (code 1), low rate of regression (code 2), moderate rate of regression (code 3) and high rate of regression (code 4) using the natural break command in ArcGIS, to assess the threat to the mangroves.At this stage, classified hazard maps were combined using ArcGIS and equation (4):(4) TI=√((a×b×c×d×e×f)/6)Where TI = Threat Index, a = drought, b = surface runoff, C = wind, d = air temperature, e = fishing activity and f = loss of seaward edges of mangroves. The TI was used to create a threat map for the mangroves at all four sites classified as: low, medium and high using the natural break command in ArcGIS.The results of this study showed that, considering the severity and probability of occurrence of the hazards, Khamir and Jask mangrove habitats are highly threatened by environmental hazards (Figure 1). Investigating the severity of occurrence of environmental hazards in mangrove habitats shows that Khamir and Jask habitats are considered to have a high to very high threat level from drought, reduced runoff from the catchments and extent of loss, significantly higher than the Tiab and Sirik sites. It is likely that the increase in the severity and risk from these hazards has had adverse effects on the structure and functions of these mangrove habitats as found in other studies conducted in other regions of the world that show that reducing rainfall and increasing the risk of drought reduces the extent of mangrove area and increases their vulnerability to other environmental hazards.

AB - In mangroves assessing the threat of multiple environmental hazards is important to inform effective management decisions to protect these habitats and to reduce or prevent damage as a result of environmental impacts. In this study, the threat of multiple environmental hazards including drought, reducing surface runoff of upstream catchments, strong winds, extreme temperatures, fishing activities and loss at seaward edges of mangroves in the northern coast of the Persian Gulf and Oman Sea in Iran are assessed and mapped.In this study a Mann-Kendall (MK) test was employed using MAKESENSE 1.0 to detect trends in standardized precipitation index (SPI) values at a confidence level of 95% and 99%. Based on changes in Z values and implementation of the natural break command in ArcGIS, a map of changes as a result of drought was categorized into four classes of low (code 1), moderate (code 2), high (code 3) and very high (code 4), based on the values of Z | ≥ 1.96 (increasing trend of drought severity). This was used to assess the threat to mangroves.In order to map changes in surface runoff from upstream catchments during the 30-year period (1986-2016), the time series of changes in runoff coefficient values were evaluated. A 30-year time series of land use / land cover changes (LULC) in upstream catchments of mangroves was also prepared using data from 210 Landsat images. Using the LULC map, the Runoff coefficient of the catchment is calculated from the runoff coefficient for permeable areas (Cper). Cper was calculated from a weighted sum of land use, soil type and slope factors, respectively, the first, second, and third term in the right-hand side of Eq. 1:(1) C_per=w1(0.02/n)+w2(θ_w/(1-θ_w ))+w3(s/(10+s))In equation (1), n is the Manning’s roughness coefficient dependent on the LULC, θ_w is the volumetric soil water content at wilting point, and S is the land surface slope in percentage land surface slope in percentage. The value of (θ_w/(1-θ_w )) was calculated using the soil texture map of upstream catchments of mangroves obtained from Iranian Forests, Range and Watershed Management Organization (FRWMO). The values of the coefficients W1, W2 and W3 were considered as 0.4, 0.3 and 0.3 respectively. Using a 30-year time series of runoff coefficients and annual precipitation values, a 30-year time series of surface runoff changes in catchments was prepared. The time series of the changes in surface runoff values was used to create a map of the reduction of catchments using four classes of low (code 1), moderate (code 2), high (code 3) and very high (Code 4) to assess the threat to mangroves. Classification was undertaken using the natural break command in ArcGIS.Changes in the intensity of fishing activities in mangrove habitats were mapped using the location of the fishing ports and the number of active launches and boats, determined by reviewing the satellite imagery of Google Earth Pro (© DigitalGlobe Inc., © GeoEye Inc.) and visiting the coasts. The coastal waters area was divided into 4×4 km GIS grid cells (598 cells) and in each of the grid cells, the intensity of the fishing activity was calculated and finally a map of the intensity of the fishing activity in coastal waters was prepared.The map of the Fishing Index (FI) was derived . Using the Fishing Index (FI) map, fishing activity intensity was classified using the natural break command in ArcGIS as low (code 1), moderate (code 2), high (code 3) and very high (Code 4), this was used to assess the threat to the mangroves.In this study, the threat from wind speeds greater than 8 m/s was mapped, this cut off was used as this velocity is considered as potentially damaging to the structure and function of these mangroves. A 30-year time series (1986-2016) of daily wind speed data from synoptic stations adjacent to the mangroves was used. In this study, the Weibull function was used to calculate the probability of wind speeds greater than 8 m/s. Wind speeds greater than 8 m/s were extracted and their average was calculated and multiplied by the probability of occurrence for each of stations during the thirty year period. A risk map of winds speeds greater than 8 m /s was prepared and classified using the natural break command in ArcGIS, the four classes were low (code 1), moderate (code 2), high (code 3) and very high (Code 4), and these were used to assess the threat to mangroves. Based on previous studies and for the analysis of spatial variations in the occurrence of extreme temperatures, a temperature of 38° C was selected as the threshold temperature for mangrove damage. All daily temperatures equal to and greater than 38° C were extracted from the long-term dataset of daily temperatures for the 30-year period (1986-2016). By dividing the number of days with a temperature equal to and greater than 38° C by the total number of daily temperature records in the 30-year period, the probability of occurrence of temperatures above this threshold was calculated for each of the synoptic stations. At each synoptic station, the mean value of all temperatures equal to and greater than 38°C was calculated and multiplied by the probability of occurrence calculated for that station. Finally, using ArcGIS, a risk map of temperatures equal to and greater than 38 °C was prepared within the coastal areas and classified using the natural breaks command as low (code 1), moderate (code 2), high (Code 3) and very high (code 4) to assess the threat to the mangroves.In this study, Landsat images of 1986, 2000, and 2016 were used to analyse the rates of changes in the seaward edges of mangroves over the 30-year period (1986-2016). To separate mangroves from surrounding water and coastal land areas and to identify the final borders of the study sites, an NDVI vegetation index was used. In this study, 2701 transects 30 m apart were mapped using the DSAS software to calculate the rate of changes in the seaward edges of mangroves. As with previous studies, the linear regression rate (LRR) method was used to measure the rates of changes in the seaward edges of mangroves. These were used to create a map of rates of regression of mangroves classified as: no regression (code 1), low rate of regression (code 2), moderate rate of regression (code 3) and high rate of regression (code 4) using the natural break command in ArcGIS, to assess the threat to the mangroves.At this stage, classified hazard maps were combined using ArcGIS and equation (4):(4) TI=√((a×b×c×d×e×f)/6)Where TI = Threat Index, a = drought, b = surface runoff, C = wind, d = air temperature, e = fishing activity and f = loss of seaward edges of mangroves. The TI was used to create a threat map for the mangroves at all four sites classified as: low, medium and high using the natural break command in ArcGIS.The results of this study showed that, considering the severity and probability of occurrence of the hazards, Khamir and Jask mangrove habitats are highly threatened by environmental hazards (Figure 1). Investigating the severity of occurrence of environmental hazards in mangrove habitats shows that Khamir and Jask habitats are considered to have a high to very high threat level from drought, reduced runoff from the catchments and extent of loss, significantly higher than the Tiab and Sirik sites. It is likely that the increase in the severity and risk from these hazards has had adverse effects on the structure and functions of these mangrove habitats as found in other studies conducted in other regions of the world that show that reducing rainfall and increasing the risk of drought reduces the extent of mangrove area and increases their vulnerability to other environmental hazards.

KW - Environmental hazards

KW - assessment of the probabilty of occcurence

KW - mangrove

KW - Hormozgan Province

U2 - 10.22059/JES.2019.259330.1007675

DO - 10.22059/JES.2019.259330.1007675

M3 - Article

VL - 44

SP - 425

EP - 443

JO - Journal of Environmental Studies

JF - Journal of Environmental Studies

SN - 2345-6922

IS - 3

M1 - 3

ER -