Azar Faryabi; Hamid Reza Matinfar; Seyyed Kazem Alavi Panah; Ali Akbar Norouzi
Introduction: A dust aerosol index (DAI) algorithm based on measurements in deep blue (412 nm), blue (440 nm), and shortwave IR (2130 nm) wavelengths using Moderate Resolution Imaging Spectroradiometer (MODIS) observations has been developed. Measurements made in the short-wavelength segment, such as ...
Introduction: A dust aerosol index (DAI) algorithm based on measurements in deep blue (412 nm), blue (440 nm), and shortwave IR (2130 nm) wavelengths using Moderate Resolution Imaging Spectroradiometer (MODIS) observations has been developed. Measurements made in the short-wavelength segment, such as the deep blue or ultraviolet section, are well-detectable in the desert area. Using short-range waves, the visual retention of fine-grained mass data, especially in desert areas, was carefully monitored. The western and southwestern Iran are always exposed to dusty systems due to its vicinity to the deserts of neighboring countries. With regard to the fact that most of the spectral indices proposed for the identification of dust have been tested and implemented based on satellite indicators for desert areas, these indicators and their related thresholds for complex topography areas need more accurate analyses. Therefore, in the western and southwestern Iran, which are mountainous with a diverse vegetation, it is necessary to test and evaluate dust detection methods. Material and methods: The study area included Khuzestan, Ilam and Kermanshah provinces, which is about 107307 square kilometers. In this study, MODIS L1B data from the Aqua satellite was used for dusty days on May 18 and June 25, 2013 and 2015. Before performing spectral calculations on various products, the data of this sensor was preprocessed, which included geometric correction of images, mask cloud and water masks with ENVI and the conversion tool module. After preprocessing (georges, separating the study area, and water mask, and cloud cover) the satellite data, the retrieved spatial radiance of TOA was normalized using satellite data considering the sun's conditions for each wavelength. Results and discussion: In general, it was found that all AOD maps generated from the direct method showed a very good spatial distribution of the local aerosol pattern compared to other methods. As expected, the retrieved AOD map from the L1B spectrum showed that the spatial distribution of the local AOD was very clear. The DAI index algorithm simulates the high-spectral dependence of the atmosphere in the blue wavelength for different surface and atmosphere conditions with a fully tested copy of the radiation-transfer code of -6 S, which is a trusted tool for measuring particle pumping over the oceans, different surfaces of the earth, and clouds. Conclusion: Unlike some of the dust detection algorithms that are carried out using measurements in the infrared thermal band, the advantage of this algorithm to detect dust is the use of spectral scattering, reflection of the surface, and absorption of dust in the air. The advantage of using measurments in the blue wavelength (410 to 490 nm) is to recover the optical properties of the aerosol.
Azar Fariabi; Hamidreza Matinfar
Volume 15, Issue 3 , October 2017, , Pages 187-202
Soil quality is considered to be one of the important indicators of sustainable agriculture and the environment. Based on sustainable agriculture goals and environmental protection, soil quality is defined the capacity of a specific kind of soil in sustaining plant and animal productivity, ...
Soil quality is considered to be one of the important indicators of sustainable agriculture and the environment. Based on sustainable agriculture goals and environmental protection, soil quality is defined the capacity of a specific kind of soil in sustaining plant and animal productivity, maintaining or enhancing water and air quality, and supporting human health and habitation”. The main objective of this study is integrating AHP and fuzzy logic system to assess soil quality based on physical, chemical soil properties and their topographical characteristics.
Materials and Methods:
The study carried out in Telobin area located in northeast Shahrood County, Iran. The thermal regime of the study area is Mesic and its moisture regime is Xeric. Soil were sampled at 36 locations across study area describing all soil variability. Soil samples were analyzed for its physical and chemical soil properties and incorporated to topographical characteristics for further analysis. The map of each soil parameter and topographic index was created using the Inverse Distance Weighting Model. Thereafter, map of soil quality regarding physical, chemical and topographical indicators created by using integrated fuzzy and AHP approaches. AHP Technique was used for weighting all above mentioned indicators.
Results and discussion:
In the term of soil quality the results show that, 3.01% was classified in high quality, 49.57% (2099.87 ha) was classified in poor quality, 44.33% (1877.33 ha) was classified in average quality and 3.5% (50/131 hectares) was classified in good quality. Soil quality was determined by using all indicators, but there are always a few important indicators with a higher weight as the key indicators. In this study soil depth index from physical indicators, organic carbon index from chemical index and slope index from topographic have higher weight. Therefor it was found that using hierarchical analysis-fuzzy logic method for the soil in studied area to determine the quality is well-established. Field observations of the region show that in areas with moderate soil quality, its use is forested and pasture. In areas with good soil quality, the amount of organic carbon and potassium is high and PH is in the range of 7-6, which the absorption of nutrients is high in this areas but in areas where the soil quality is poor or very poor, the amount of organic carbon is low or negligible and the slopes of the area are more than 30%.
The results of this study show that the organic carbon has the highest impact on the quality of soil in the studied area and, about the term of soil quality, most of the area has poor quality. Therefore, it can be argue that the use of the combination of fuzzy and AHP methods in GIS can categorize the status of soil quality to the quantitative levels in different groups. Using the fuzzy technique and opinion of experts can make a database for us. In general, the fuzzy logic approach is considered as a very suitable tool for modeling the physical, chemical, and topographic quality of the area that is considered as an input parameters.