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N. Mijani
Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran

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Journal article
Published: 14 September 2020 in Remote Sensing
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Due to the excessive use of natural resources in the contemporary world, the importance of ecological and environmental condition modeling has increased. Wetlands and cities represent the natural and artificial strategic areas that affect ecosystem conditions. Changes in the ecological conditions of these areas have a great impact on the conditions of the global ecosystem. Therefore, modeling spatiotemporal variations of the ecological conditions in these areas is critical. This study was aimed at comparing degrees of variation among surface ecological conditions due to natural and unnatural factors. Consequently, the surface ecological conditions of Gomishan city and Gomishan wetland in Iran were modeled for a period of 30 years, and the spatiotemporal variations were evaluated and compared with each other. To this end, 20 Landsat 5, 7, and 8, and 432 Moderate Resolution Imaging Spectroradiometer (MODIS), monthly land surface temperature (LST) (MOD11C3) and normalized difference vegetation index (NDVI) (MOD13C3) products were utilized. The surface ecological conditions were modeled according to the Remote Sensing-based Ecological Index (RSEI), and the spatiotemporal variation of the RSEI values in the study area (Gomishan city, Gomishan wetland) were evaluated and compared with each other. According to MODIS products, the mean of the LST and NDVI variance values for the study area (Gomishan city, Gomishan wetland) were obtained to be 6.5 °C (2.1, 12.1) and 0.009 (0.005, 0.013), respectively. The highest LST and NDVI temporal variations were found for Gomishan wetland near the Caspian Sea. According to Landsat images, Gomishan wetland and Gomishan city have the highest and lowest temporal variations in surface biophysical characteristics, respectively. The mean RSEI for the study area (Gomishan city, Gomishan wetland) was 0.43 (0.65, 0.29), respectively. Additionally, the mean Coefficient of Variation (CV) of RSEI for the study area (Gomishan city, Gomishan wetland) was 0.10 (0.88, 0.51), respectively. The surface ecological conditions of Gomishan city were worse than those of the Gomishan wetland at all dates. Temporal variations in the surface ecological conditions of Gomishan wetland were greater than those of the study area and Gomishan city. These results can provide useful and effective information for environmental planning and decision-making to improve ecological conditions, protect the environment, and support sustainable ecosystem development.

ACS Style

Salman Qureshi; Seyed Kazem Alavipanah; Maria Konyushkova; Naeim Mijani; Solmaz Fathololomi; Mohammad Karimi Firozjaei; Mehdi Homaee; Saeid Hamzeh; Ata Abdollahi Kakroodi. A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran. Remote Sensing 2020, 12, 2989 .

AMA Style

Salman Qureshi, Seyed Kazem Alavipanah, Maria Konyushkova, Naeim Mijani, Solmaz Fathololomi, Mohammad Karimi Firozjaei, Mehdi Homaee, Saeid Hamzeh, Ata Abdollahi Kakroodi. A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran. Remote Sensing. 2020; 12 (18):2989.

Chicago/Turabian Style

Salman Qureshi; Seyed Kazem Alavipanah; Maria Konyushkova; Naeim Mijani; Solmaz Fathololomi; Mohammad Karimi Firozjaei; Mehdi Homaee; Saeid Hamzeh; Ata Abdollahi Kakroodi. 2020. "A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran." Remote Sensing 12, no. 18: 2989.

Journal article
Published: 02 September 2020 in Remote Sensing
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The surface anthropogenic heat island (SAHI) phenomenon is one of the most important environmental concerns in urban areas. SAHIs play a significant role in quality of urban life. Hence, the quantification of SAHI intensity (SAHII) is of great importance. The impervious surface cover (ISC) can well reflect the degree and extent of anthropogenic activities in an area. Various actual ISC (AISC) datasets are available for different regions of the world. However, the temporal and spatial coverage of available and accessible AISC datasets is limited. This study was aimed to evaluate the spectral indices efficiency to daytime SAHII (DSAHII) quantification. Consequently, 14 cities including Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome in Europe and Dallas, Seattle, Minneapolis, Los Angeles, Chicago, and Phoenix in the USA, were selected. A set of 91 Landsat 8 images, the Landsat provisional surface temperature product, the High Resolution Imperviousness Layer (HRIL), and the National Land Cover Database (NLCD) imperviousness data were used as the AISC datasets for the selected cities. The spectral index-based ISC (SIISC) and land surface temperature (LST) were modelled from the Landsat 8 images. Then, a linear least square model (LLSM) obtained from the LST-AISC feature space was applied to quantify the actual SAHII of the selected cities. Finally, the SAHII of the selected cities was modelled based on the LST-SIISC feature space-derived LLSM. Finally, the values of the coefficient of determination (R2) and the root mean square error (RMSE) between the actual and modelled SAHII were calculated to evaluate and compare the performance of different spectral indices in SAHII quantification. The performance of the spectral indices used in the built LST-SIISC feature space for SAHII quantification differed. The index-based built-up index (IBI) (R2 = 0.98, RMSE = 0.34 °C) and albedo (0.76, 1.39 °C) performed the best and worst performance in SAHII quantification, respectively. Our results indicate that the LST-SIISC feature space is very useful and effective for SAHII quantification. The advantages of the spectral indices used in SAHII quantification include (1) synchronization with the recording of thermal data, (2) simplicity, (3) low cost, (4) accessibility under different spatial and temporal conditions, and (5) scalability.

ACS Style

Mohammad Karimi Firozjaei; Solmaz Fathololoumi; Naeim Mijani; Majid Kiavarz; Salman Qureshi; Mehdi Homaee; Seyed Kazem Alavipanah. Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology. Remote Sensing 2020, 12, 2854 .

AMA Style

Mohammad Karimi Firozjaei, Solmaz Fathololoumi, Naeim Mijani, Majid Kiavarz, Salman Qureshi, Mehdi Homaee, Seyed Kazem Alavipanah. Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology. Remote Sensing. 2020; 12 (17):2854.

Chicago/Turabian Style

Mohammad Karimi Firozjaei; Solmaz Fathololoumi; Naeim Mijani; Majid Kiavarz; Salman Qureshi; Mehdi Homaee; Seyed Kazem Alavipanah. 2020. "Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology." Remote Sensing 12, no. 17: 2854.

Journal article
Published: 28 May 2020 in Ecological Indicators
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A harmful effect of anthropogenic activities in urban environments is the increases of thermal discomfort and subsequently, a negative effect on humans’ mental and physical performance. Therefore, it is of high importance to detect, monitor, and predict thermal discomfort, especially its temporal and spatial patterns in cities. The objective of this study is to propose a new method for modeling outdoor thermal comfort based on remote sensing and climatic datasets. To do so, several datasets were utilized, including those from Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Digital Elevation Model (DEM) from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and climatic datasets from local meteorological stations. The method was experimented in the city of Tehran, Iran. For modeling outdoor thermal comfort, the Least Squares Adjustment (LSA) model was presented based on the Principle Component Analysis (PCA). In this model, the Principle Components (PCs) of the environmental and surface biophysical parameters were considered as independent variables and Discomfort Index (DI) as dependent variable. Finally, by determining the optimal values of the adjustment coefficients for each independent variable, maps of outdoor thermal comfort at different timestamps were produced and analyzed. The results of the modeling showed that correlation coefficient and Root Mean Square Error (RMSE) between the modeled and observed outdoor thermal comfort values at the meteorological stations for the training data sets were 0.86 and 1.80, for the testing data set were 0.89 and 2.04, respectively, while it was 0.85 and 1.15 for the self-deployed devices. The average values of DI in warm season of year was 8.5 °C higher than the cold season of the year. Further, in both warm and cold seasons of year the mean value of DI for bare land was found higher than other land covers, whereas that of water bodies lower than others. Our findings suggest that efficiency can be achieved for modeling outdoor thermal comfort using LSA with remote sensing and climatic datasets.

ACS Style

Naeim Mijani; Seyed Kazem Alavipanah; Mohammad Karimi Firozjaei; Jamal Jokar Arsanjani; Saeid Hamzeh; Qihao Weng. Modeling outdoor thermal comfort using satellite imagery: A principle component analysis-based approach. Ecological Indicators 2020, 117, 106555 .

AMA Style

Naeim Mijani, Seyed Kazem Alavipanah, Mohammad Karimi Firozjaei, Jamal Jokar Arsanjani, Saeid Hamzeh, Qihao Weng. Modeling outdoor thermal comfort using satellite imagery: A principle component analysis-based approach. Ecological Indicators. 2020; 117 ():106555.

Chicago/Turabian Style

Naeim Mijani; Seyed Kazem Alavipanah; Mohammad Karimi Firozjaei; Jamal Jokar Arsanjani; Saeid Hamzeh; Qihao Weng. 2020. "Modeling outdoor thermal comfort using satellite imagery: A principle component analysis-based approach." Ecological Indicators 117, no. : 106555.

Journal article
Published: 08 September 2019 in Remote Sensing
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Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

ACS Style

Mohammad Karimi Firozjaei; Seyed Kazem Alavipanah; Hua Liu; Amir Sedighi; Naeim Mijani; Majid Kiavarz; Qihao Weng. A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations. Remote Sensing 2019, 11, 2094 .

AMA Style

Mohammad Karimi Firozjaei, Seyed Kazem Alavipanah, Hua Liu, Amir Sedighi, Naeim Mijani, Majid Kiavarz, Qihao Weng. A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations. Remote Sensing. 2019; 11 (18):2094.

Chicago/Turabian Style

Mohammad Karimi Firozjaei; Seyed Kazem Alavipanah; Hua Liu; Amir Sedighi; Naeim Mijani; Majid Kiavarz; Qihao Weng. 2019. "A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations." Remote Sensing 11, no. 18: 2094.

Journal article
Published: 28 April 2019 in Ecological Indicators
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One of the most important signs of decreasing quality of life in urban environments is the reduction of thermal comfort. Heat discomfort has a negative impact on physical and mental performance of humans. Hence, it is of outmost importance to monitor thermal comfort patterns in cities and study its effect on people. The main objective of this study is to present a spatial multi-criteria decision analysis (MCDA) model for modeling thermal comfort for Tehran as a case study. For doing so, the reflectance and thermal information extracted from Landsat-8 satellite images, ASTER digital elevation model, MOD07 water vapor, and meteorological/climatic datasets were used. Several indicators including the downward shortwave radiation (SWD) and longwave radiation (LWD) to surface, upward longwave radiation (LWU) from the surface, brightness, greenness and wetness of the surface were derived. An Ordered Weighted Averaging (OWA) method was adapted considering different mental circumstances e.g., extremely pessimistic, pessimistic, neutral, optimistic and extremely optimistic. Our findings determine the geographical variation of thermal comfort across our study area e.g., the cold periods of the year are spread in the west and north-west side and the warm periods of the year on the west and north-west, while the central, northern, and eastern regions have a more favorable thermal comfort than other regions. The areal percentage of very suitable thermal comfort category for very pessimistic, pessimistic, neutral, optimistic, and very optimistic during the warm period of the year was 2.7, 5.1, 4.4, 13.4 and 1.18, respectively and in the cold period of the year was 9.1, 13.3, 18.3, 28.9 and 33.9, respectively. In both warm and cold periods with increasing degree of optimism, the area of favorable thermal comfort classes increases, while the area of unfavorable thermal comfort categories decreases. Our results and conclusions drawn from our proposed approach are useful for urban planners and public health researcher for monitoring quality of life in cities.

ACS Style

Naeim Mijani; Seyed Kazem Alavipanah; Saeid Hamzeh; Mohammad Karimi Firozjaei; Jamal Jokar Arsanjani. Modeling thermal comfort in different condition of mind using satellite images: An Ordered Weighted Averaging approach and a case study. Ecological Indicators 2019, 104, 1 -12.

AMA Style

Naeim Mijani, Seyed Kazem Alavipanah, Saeid Hamzeh, Mohammad Karimi Firozjaei, Jamal Jokar Arsanjani. Modeling thermal comfort in different condition of mind using satellite images: An Ordered Weighted Averaging approach and a case study. Ecological Indicators. 2019; 104 ():1-12.

Chicago/Turabian Style

Naeim Mijani; Seyed Kazem Alavipanah; Saeid Hamzeh; Mohammad Karimi Firozjaei; Jamal Jokar Arsanjani. 2019. "Modeling thermal comfort in different condition of mind using satellite images: An Ordered Weighted Averaging approach and a case study." Ecological Indicators 104, no. : 1-12.

Journal article
Published: 27 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR) and Quality Sum (QS). The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P) and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzy-gamma method with a minor difference is in the second order.

ACS Style

N. Mijani; N. Neysani Samani. COMPARISON of FUZZY-BASED MODELS in LANDSLIDE HAZARD MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-4/W4, 407 -416.

AMA Style

N. Mijani, N. Neysani Samani. COMPARISON of FUZZY-BASED MODELS in LANDSLIDE HAZARD MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-4/W4 ():407-416.

Chicago/Turabian Style

N. Mijani; N. Neysani Samani. 2017. "COMPARISON of FUZZY-BASED MODELS in LANDSLIDE HAZARD MAPPING." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4, no. : 407-416.