Journal of Spatial Analysis

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Forest fire is one of the important problems in Iran which is caused by different factors such as human and natural factors. One of these factors is climate conditions that can be created by heat wave and special circulation of atmospheric phenomena. Occurrence of forest fire in north of Iran have different impacts on environment such as destruction of natural. According to the position of Iran in the dry climate zone provides required conditions for this hazard. Unfortunately,every year thousands of hectares of precious green cover is burned. Forest fires have harmful effects on human life directly,or in directly and lead to environmental destruction and pollution, global warming, loss of vegetation, and dry soil erosion. As a result, research on forest fires will become necessary. The study region is Mazandaran province forests located  in north of Iran with area of  23756.4 square Kilometers.The main object of this study is to detect the forest fires using satellite data and associated analysis with synoptic approach based on weather maps.

To detect fire in the study area different satellite data such as  synchronized and geostationary satellite data were used. In this study, MODIS satellite imagery and global algorithm detection of fire to detect fire in the forest and meadows of Mazandaran province were used. The climate data including weather station data and weather map were used. Other data include data of LST and vegetation products of MODIS. In order to downscale the global data an appropriate threshold was defined. In the proposed method,  After geometric correction and radiometric the cloud mask was removed, And then fire potential was identified with different thresholds and tests. Three fire episodes of  Savadkooh 2006, Noor , 2009 , and Behshahr, 2010 were selected for study.

Results showed  a threshold value of 310 ° K for MODIS sensor band 22 is good for a global scale. Cold and small fires are not detected, Therefore Local threshold was used. In addition, surface temperature and vegetation mapping , chlorophyll amount of vegetation were used before and after the fire episode.It became apparent that the amount of chlorophyll was reduced and the temperature was increased after the fire.

   The synoptic maps of the fire day showed a low pressure over the region and mid level systems indicated the advection of warm air over the area. Surface stations showed the increase of temperature and reduction of moisture during the fire days over the long period mean values.

According to the results of the study the ground level data accompanied the upper level images and pressure patterns.

Universal high performance of fire detection algorithm was used  to identify areas of forest fires Using MODIS satellite images and global algorithm modified to suit the characteristics of the study area fire detection. Then three of the fires were identified with this method. The algorithms with MODIS images and weather data together indicated the validity of the study and performance of this algorithm to identify the location of fire in the study region. Therefore the method of this study can be used in other areas to detect forest fires.

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Understanding the changes in extreme precipitation over a region is very important for adaptation strategies to climate change. One of the most important topics in this field is detection and attribution of climate change. Over the past two decades, there has been an increasing interest for scientists, engineers and policy makers to study about the effects of external forcing to the climatic variables and associated natural resources and human systems and whether such effects have surpassed the influence of the climate’s natural internal variability. The definitions used in the 5th assessment report were taken from the IPCC guidance paper on detection and attribution, and were stated as follows: “Detection of change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense without providing a reason for that change. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small.  Attribution is defined as the process of evaluating the relative contributions of multiple causal factors to a change or event with an assignment of statistical confidence”. Detection and attribution of human-induced climate change provide a formal tool to decipher the complex causes of climate change. In this study the optimal fingerprinting detection and attribution have been attempted to investigate the changes in the annual maximum of daily precipitation and the annual maximum of 5-day consecutive precipitation amount over the southwest of Iran.

This is achieved through the use of the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources Project(APHRODITE) dataset as observation, a climate model runs and the standard optimal fingerprint method. To evaluate the response of climate to external forcing and to estimate the internal variability of the climate system from pre-industrial runs, the Norwegian Climate Center’s Earth System Model- NorESM1-M was used. We used up scaling to remap both grid data of observations and simulations to a large pixel. This remapped pixel coverages the area of the southwest of Iran. The optimal finger printing method needs standardized values like probability index(PI) or anomalies as input data, since the magnitude of precipitation varied highly from one region to another. The General Extreme Value distribution (GEV) is used to convert time series of the Rx1day and Rx5day into corresponding time series of PI.  Then we calculated non-overlapping 5-year mean PI time series over the area study. In this research, we applied optimal fingerprinting method by using empirical orthogonal functions.  The implementation of optimal fingerprinting often involves projecting onto k leading EOFs in order to decrease the dimension of the data and improve the estimate of internal climate variability. A residual consistency test used to check if the estimated residuals in regression algorithm are consistent with the assumed internal climate variability. Indeed, as the covariance matrix of internal variability is assumed to be known in these statistical models, it is important to check whether the inferred residuals are consistent with it; such that they are a typical realization of such variability. If this test is passed, the overall statistical model can be considered suitable.

Results obtained for response to anthropogenic and natural forcing combined forcing (ALL) for Rx1day and Rx5day show that scaling factors are significantly greater than zero and consistent with unit. These results indicate that the simulated ALL response is consistent with Rx1day observed changes. Also, it is found that the changes in observed extreme precipitation during 1951-2005 lie outside the range that is expected from natural internal variability of climate alone and greenhouse gasses alone, based on NorESM1-M climate model. Such changes are consistent with those expected from anthropogenic forcing alone. The detection results are sensitive to EOFs. We estimate the anthropogenic and natural forcing combined attributable change in PI over 1951–2005 to be 1.64% [0.18%, 3.1%, >90% confidence interval] for RX1day and 2.5% [1%,4%] for RX5day.

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Investigating the threats of mangrove forests
with the help of remotely sensed data
 
Behzad Rayegani: Assistant Professor of College of Environment, Department of Environment
 
 
Mangroves are a group of trees and shrubs that live in the coastal intertidal zone. Mangrove forests are very important because they are known as natural heritage and crucial in protecting coastal ecosystems. Mangrove forests stabilize the coastline, reducing erosion from storm surges, currents, waves, and tides. The intricate root system of mangroves also makes these forests attractive to fish and other organisms seeking food and shelter from predators. So, they are ideal places to support the elements of seafood networks. However, these forests are in danger of degradation because of rapid population growth, poor planning and unsustainable economic development. In the process of regenerating an ecosystem, it is necessary to identify the precursors of the threat, to consider the means to eliminate these threats. Therefore, identifying the threatening factors of the mangrove forest ecosystem is the first step in the restoration and protection of the ecosystem.
This study aims to investigate the change and the destruction in Mangrove forests and to identify threatening forces in the Hara Protected Area. Remote sensing is now widely used in studies of ecosystem changes because its information is available for the past, and there are many highly-developed techniques for change detection through remote sensing. Therefore, in order to identify the threatening factors of mangrove forests, remote sensing techniques were used to identify changed areas during a 15-year period. Images of ETM+ and OLI sensor from 2001 to 2015 were collected in the Hara Protected Area (Khorekhoran International Wetland). Given that we have used the multiple-date remote sensor data in this study, it was necessary to use absolute atmospheric correction methods for radiometric harmonization of data. So, with the aid of the ERDAS IMAGINE 2014 software, the Atmospheric and Topographic Correction (ATCOR) model was applied to all data. Subsequently, due to the difference in radiometric resolution of the OLI sensor with the ETM+ sensor, the output of ATCOR of both sensors was stretched into 8-bit data in order to eliminate the existing divergence in radiometric resolution. Also, based on spatial information, one of the image of OLI sensor at the current time was corrected geometrically, and then other images were registered to this image to eliminate geometric errors. There are many ways to detect changes with the help of remote sensing data, but we used two widely used techniques in this study: 1) post-classification comparison; 2) Change detection techniques of Algebra. Totally four different change detection methods were applied to these images. Change detection techniques of Algebra image method include image difference, image ratio, regression and post-classification comparison were used. At first, with the knowledge of the studied area, by combining the two supervised and unsupervised classification (hybrid method), the pixels that were known as mangrove forests were identified in both time periods of study. Then pixels with decreasing trend were determined by post-classification comparison method. From the image of the mangrove forests with the logic of Boolean (OR), a mask of mangrove was obtained, which showed the areas of mangroves during the two periods. This mask was used to make the second group of methods for determining changes (Algebra method) applied to the data. By doing this, in all algebra methods, the histogram showed the normal distribution. Finally, the vegetation spectral indices were applied to the data and their coefficient of variation was obtained in the Boolean mask area. Among these indices, NDVI showed better performance, so the algebra operation was used for this index. Accordingly, areas with decrease, increase and no change trends were visited and then overall accuracy and kappa coefficients were determined.
The results showed that the method of post classification comparison has the highest accuracy in the monitoring of vegetation changes in mangrove forests. This method with a total accuracy of over 93% and a kappa of more than 0.9 showed the highest accuracy in the detection methods of the changes, therefore, in the final examination and prioritization of the regions, this method was used. The surveys showed that the smuggling of fuel due to pour gasoline into the water and camel grazing are the most important destructive factors in the mangrove forest. After determining the rate degradation in four regions, these regions were ranked in order to carry out reclamation and restoration projects.
In the case of intelligent use of the capabilities of remote sensing, one can easily identify the threatening factors of an ecosystem. In the case of mangroves, the only limiting factor is tidal conditions. It is therefore recommended that, as in this study, images are chosen to determine the changes that are in a same tidal state
 
 
Keywords: Remotely Sensed change detection, Image Algebra Change Detection, Post-classification comparison, Determination of thresholds
 
 
 
 

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Real-time detection of forest fire using NOAA/AVHRR data
Study area :(Kayamaki Wildlife Refuge)
 
Extended Abstract
Introduction
Land and forest fires are one of the most common problems in the world that cause various disturbances in forest and land efficiency. Real-time fire detection is crucial to prevent large-scale casualties. In order to identify early fire in areas where there is a high risk of fire, it is necessary to monitor these areas regularly. Forest monitoring is a technique used to detect fires in the past using traditional techniques such as surveillance, helicopter and aircraft. Today, satellite imagery is one of the most imperative and effective tools for detecting active fires in the world.
Materials and Methods
In this study, NOAA/AVHRR images were used for fire detection and MODIS products were applied for evaluation and validation.
Fire Detection Algorithms
There are several algorithms for detecting fires using satellite imagery. In this study, 3 algorithms of Giglio, extended and IGPP were used. The selection of these algorithms was due to the extensive background research in most of the previous studies that used them and the results of these algorithms, especially the IGPP, were far more than other algorithms.
Giglio Algorithm
Giglio et al., (1999) criticized Arino and Melinott (1993) threshold as too high for certain regions of the world such as tropical rain forests, temperate climates and marshes where the air temperature for small fires (100 m³) is usually between 308 and 314 degrees Kelvin. They believed that the smaller fires were not fully recognized by Arino and Melinott (1993) thresholds. They concluded that in suburban forests 60% of fires had temperatures below 320K of which 70% were in rainforests and 85% happened in the Savanna. Thus, the threshold cannot be applied on a large scale and it is only applicable for a regional scale.
IGBP Algorithm
The IGBP fire detection algorithm is implemented in two steps. The first step is the threshold test in which a pixel in micrometers (11.03 μm) minus the band 4 is greater than 8 degrees Kelvin, the desired pixel being considered as a potential fire pixel. Band 3 (3.9 μm) exceeds 311 K, and band 3 illumination temperature is 3.9.
Developed Algorithm
This algorithm is used to detect small and large fires (both at night and day).
 
Interpretation of the Results
After selecting fire detection algorithms, pre-processing (geometric, radiometric and atmospheric corrections), processing (applying fire relationships and fire formulas for fire detection) and post-processing (evaluating and validating the results), the fires were identified by the fire algorithms (images). Final results of fires identified for 2016 and 2017 (for 4 days) by fire algorithms indicate that fires identified by Giglio algorithm were 22 cases, those by IGPP algorithm were 27 cases and the ones by the developed algorithm were 15 cases. For this reason, the IGPP algorithm can be taken as the most appropriate algorithm in this study for fire detection using satellite imagery.
Evaluation of fires identified through MODIS products
To evaluate identified fires, after recognizing them with relevant algorithms, we used MODIS products for their evaluation (due to the lack of ground data on the days studied for evaluation). MODIS products were obtained from sites where the location of each fire was reported. For the evaluation of identified fires based on fire detection algorithms with MODIS products, 10 fire occurrences were used. The evaluation results express that out of 10 fires only 7 fires were recognized by the algorithms of MODIS products. 5 fire events were identified by Giglio algorithm (from 7 fires), 6 fires from IGBP (out of 7 fires), and 3 fire events from 7 extended algorithm were selected as fire pixels.
Comparison of the implications of the fire algorithms
The implications of fire occurrence algorithms indicate that the IGBP algorithm with 6 fires (out of 7 tested fires with error rate of 14% and with the number of fires detected (86%)), Giglio algorithm with 5 fires (out of 7 tested fires, with error rate of 28% and with the number of fires (72%)) and the developed algorithm with 3 fires (out of 7 fires tested with an error rate of 57% and with fire rate of 43%) have been identified. Therefore, it is concluded that the IGBP is the most appropriate algorithm for real-time fire detection, followed by Giglio and the developed algorithm in second and third orders, respectively.
Keywords:Real Time Fire Detection, Fire Algorithms, NOAA/AVHRR, Kiamaki Wildlife Refuge.
 

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 Detection and Attribution of Changing in Seasonal variability cause of climate change (Case study: Hillsides of Central Southern Alborz Mountains)
Abstract
One of the most important challenges for the human communities is Global Warming. This vital problem affected by Climate Change and corresponding effects. Thus this article attempted to assess the trend of real climate variables from synoptic stations. Daily precipitation, Daily Maximum Temperature and Daily Minimum Temperature have been selected for the Hillsides of Southern Central Alborz Mountains and have been tried to prove climate change and attribute the related forcing such as Greenhouse Gases. The Capital of Iran located in this region and this region has a special occasion, because at least a quarter of Iranian population live in these provinces (Tehran and Alborz) and four big dams located in this region. The Intergovernmental Panel on Climate Change’s defines ‘‘detection’’ of climate change as ‘‘the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change,’’ while ‘‘attribution’’ is defined as the process of evaluating the relative contribution of multiple causal factors to a change or event with an assignment of statistical confidence. Regional D&A studies provide an insight to local changes in natural systems and may help in planning and developing robust adaptation strategies. Previously, formal detection and attribution have been used to investigate the nature of changes in various climatological variables such as air temperature, surface specific humidity, ocean heat, sea level pressure, continental river runoff, global land precipitation and precipitation extremes. However, almost all of these studies deal with climatological or meteorological variables at the global or continental scale. Studies which have attempted to formally detect and attribute regional hydrometeorological changes to anthropogenic effects are rare. Regional-scale D&A analysis is more difficult because the detection of anthropogenic ‘‘signal’’ in natural internal climate variability ‘‘noise’’ is determined by the signal-to-noise ratio which is proportional to the spatial scale of analysis, especially for real observation data. For overcoming this issue interpolation method (IDW) has been applied to transfer point data to area (gridded) data. The point data gathered from 3 synoptic stations (Mehrabad, Karaj and Abali). Then transferred data have been Standard and Averaged for 3 years. Standard values of annual and seasonal amounts have been computed for individual stations as the average of the standard values of annual and seasonal amounts available 3 years anomaly values. Estimates of annual or seasonal variables anomalies were obtained by averaging the annual or seasonal by 12 or 3 respectively. For detecting and attributing 3 simulation signals (ALL, GHG and NAT) selected from Canadian General Circulation Model (CanESM2.0) of CMIP5 archive subcategories. Space–time series of observations and model simulated variables responses to external forcings (the “signals”) first have been compared qualitatively by computing correlation coefficients between observations and simulations. This simple method does not optimize the signal-to-noise ratio nor provide a quantitative measure of the magnitude of model simulated response relative to that in the observations. Nevertheless, it provides an easy-to-understand view of the similarity between observed and model-simulated changes. Optimal detection and attribution analysis very often requires a reduction of dimensionality. This is typically done by projecting both observations and simulations onto leading empirical orthogonal functions (EOFs) of internal variability and using the residual consistency check to determine the number of EOFs to be retained in the analysis. To produce internal variability for residual test and consistency, Pi-Ctrl Runs have been used. The Preindustrial simulations have high volume, this subject complicates calculation therefore Experimental Orthogonal Functions (EOFs) have been used to reduce the Pi-Ctrl simulations volume and provide situations for Optimal Fingerprint. Optimal Fingerprint method is the best method for Detection and Attribution. Results have been obtained by this manner indicated Global Warming affected the study region by affecting on mean cumulative winter precipitation (0.88), mean spring minimum temperature (0.78) and mean summer maximum temperature (0.76). These numbers are the beta coefficient that named scaling factor. Although the scaling factor for the mean spring minimum temperature affected from GHG signal obtained (0.73), but the GHG forcing alone didn’t have a significant effect on the precipitation and maximum temperature. Also, NAT signal didn’t have significant effect on the region alone, too. The obtained results of this study indicate the earlier studies, such as Wan et al, 2014.
 
Key words: Climate change, Detection, Attribution, Optimal Fingerprint, Hillsides of Central Southern Alborz Mountains
 

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The growth of settlements and the increase of human activities in the floodplains, especially the banks of rivers and flood-prone places, have increased the amount of capital caused by this risk. Therefore, it is very important to determine the extent of the watershed in order to increase risk reduction planning, preparedness and response and reopening of this risk. The present study uses the common pattern of the machine and the classification of Sentinel 2 images to produce land cover maps, in order to construct sandy areas and determine land issues affected by the flood of March 2018 in Aqqla city. Also, in order to check and increase the accuracy of the algorithms, three software indices of vegetation cover (NDVI), water areas (MNDWI) and built-up land (NDBI) were used using images. The different sets of setting of each algorithm were evaluated by cross-validation method in order to determine their effect on the accuracy of the results and prevent the optimistic acquisition of spatial correlation from the training and test samples. The results show that the combination of different indices in order to increase the overall accuracy of the algorithms and to produce land cover maps, the forest algorithm is used with an accuracy of 83.08% due to the use of the collection method of higher accuracy and generalizability than compared to. Other algorithms of support vector machine and neural network with accuracy of 79.11% and 75.44% of attention respectively. After determining the most accurate algorithm, the map of flood zones was produced using the forest algorithm in two classes of irrigated and non-irrigated lands, and the overall accuracy of the algorithm in the most optimal models and by combining vegetation indices (MNDWI) was 93.40%. Then, with overlapping maps of land cover and flood plains, the surface of built-up land, agricultural land and green space covered by flood was 4.2008 and 41.0772 square kilometers, respectively.
 

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Investigation of the effects of Covid-19 pandemic on UHI in residential, industrial and green spaces of Tehran

 Abstract
Rapid urbanization in recent decades has been a major driver of ecosystems and environmental degradation, including changes in agricultural land use and forests. Urbanization is rapidly transforming ecosystems into buildings that increase heat storage capacity. Loss of vegetation and increase in built-up areas may ultimately affect climate variability and lead to the creation of urban heat islands. The occurrence of natural disasters such as flood, earthquake … is one of the most effecting factors on the changes in intensity of urban heat islands. So far, a lot of research has been done on how it is affected by various types of natural disasters such as floods, earthquakes, droughts and tsunamis.
Two major environmental challenges for many cities are preventing flooding after heavy rains and minimizing urban temperature rise due to the effects of heat islands. There is a close relationship between these two phenomena, because with increasing air temperature, the intensity of precipitation increases. Drought is also a phenomenon that is affected by rainfall, temperature, evapotranspiration, water and soil conditions. One of the major differences between drought and other natural disasters is that they occur over a longer period of time and gradually than others that occur suddenly. Another natural disaster is the tsunami, which increases the area of water by turning wetlands into lakes, thereby increasing the index of normal water differences, which has a strong negative relationship with surface temperature. Ecosystems in urban areas play a role in reducing the impact of urban heat islands. This is because plants and trees regulate the temperature of their foliage by evaporation and transpiration, which leads to a decrease in air temperature.
Applying the locked down of the Covid-19 pandemic since the spring of 2020 has led to the global restoration of climatic elements such as air quality and temperature. In this study, the effects of Covid-19 locked down on the intensity of urban heat islands due to the limitations in industrial activities such as factories and power plants and the application of new laws to reduce traffic in Tehran were investigated. In this regard, the Landsat-8 satellite taken from a part of Tehran city has been used.

Materials and Methods
In order to investigate the effects of locked down in the spring of 2020 on the intensity of urban heat islands; the status of UHI maps in Tehran during the same period of locked down in three years before and one year after has been studied. The proposed method in this paper consists of two main steps. The first step is to generate UHI maps using land surface temperature (LST), normalized difference vegetation index (NDVI) and land use / land cover map analysis. In the second step, in order to analyze the behavioral changes in the intensity of urban heat islands during locked down and compare it with previous and subsequent years, changes in the intensity of UHIs are monitored.
UHI maps consist of three classes of high, medium and low intensities urban heat islands, which are based on performing the rule based analysis on land surface temperature characteristics and normal vegetation difference index derived from Landsat-8 satellite images as well as land use / land cover map. LULC maps are produced by support vector machine classification method consisting of three classes of soil, building and vegetation. In order to calculate the spectral features used in the rule based analysis, atmospheric and radiometric corrections must first be made on the red, near-infrared, and thermal spectral bands of the image captured by the Landsat-8 satellite. Then, vegetation spectral indices including NDVI and PV indices are generated.

Disscussion of Results
The capability of the proposed algorithm in this paper is first evaluated in the whole area covered by satellite images taken from the city of Tehran, and then in three areas including residential, industrial and green spaces. The data used in this article are images taken by the OLI sensor of Landsat-8 satellite in the spring of 2017-2021.
In the first step of the proposed method, maps of urban heat islands are generated based on multi-temporal satellite images of Landsat-8 taken in the years 2017to 2021 in the MATLAB programming software. Then, by comparing pairs of UHI maps in each of the residential, industrial and green space study areas, the trend of changes in the intensity of UHI is analyzed and the effects of locked down application in 2020 are evaluated.
The results of changes detection in urban heat islands in the period under consideration in this study showed that the percentage of areas that are in the class of high UHI in 2020 due to locked down of pandemic Covid-19 compared to the average of three years before that is 55.71%, has a decrease of 17.61%. The percentage of areas in the class of medium UHI intensity in 2020 due to locked down compared to the average of three years ago, which is 39%, increased by 4.8%, and in 2021 this amount again has decreased to less than the average. Also, the percentage of low intensity UHI class in 1399 compared to the average of three years ago, which is 5.3%, has increased by 12.8%.

Conclusion
In this study, the effect of locked down application due to the Covid-19 virus pandemic, which was applied in Iran in the spring of 2020 is investigated on the intensity of  urban heat islands in a part of Tehran city and three selected areas with residential, industrial and green space. Detection of changes in the intensity of urban heat islands was done based on the post-classification method and on the UHI classification maps related to the years 2017 to 2021. In order to produce UHI maps, in addition to the land surface temperature, the amount of vegetation index and the type of land use / land cover class were also used in the form of a set of classification rules.
Comparing the results of the study areas of residential, industrial and green spaces, it is important to note that the rate of reduction of the area of UHI with high intensity in the residential area is 5.25% more than the industrial area and 6.1% more than the green space. However, the reduction of locked down restrictions in 2021 had the greatest effect on the return of the area of ​​the high UHI class and caused the area of ​​this class to increase by 23% compared to 2020. These results indicate the fact that restrictions on the activities of industrial units such as factories and power plants and the application of new laws to reduce traffic, despite the same weather conditions in an area have been able to significantly reduce the severity of urban heat islands.

 Keywords: Urban Heat Islands, Land Surface Temperature, Vegetation Index, Change Detection, Covid-19