Department of Remote Sensing (GIS) (2014 - Present)
Geomatic Engineering
, University of New South Wales, Sydney, Australia
Remote Sensing & GIS
Remote Sensing & GIS, Tarbiat Modares University, Tehran, Iran
Range and Watershed Management
Natural Resources Engineering, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, Iran
Ali Shamsoddini is an associate professor at Department of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran, since 2014. He is working on the applications of remotely sensed data, especially optical and LiDAR data in different fields of geosciences including agriculture, forest and natural resources. In the field of remote sensing, data fusion (i.e. image fusion, downscaling, etc.) is one of his interests. Also, he is interested to use different machine learning techniques in remote sensing-related applications. He has a bachelor degree in natural resource management from Agricultural Sciences and Natural Resource Management University of Gorgan, Iran and graduated a master of remote sensing and GIS from Tarbiat Modares University, Tehran, Iran. In 2013, he graduated from University of New South Wales (UNSW), Sydney, Australia with a PhD in remote sensing engineering. After finishing PhD, he was employed as a Postdoc researcher in the school of mining engineering, UNSW, for one and half year.
Downscaling methods seem to be a reasonable solution to solve the problem of having no simultaneous high spatial and temporal satellite data, and it is possible somehow to meet the requirement of having high spatial-temporal resolution satellite data for monitoring the natural phenomena such as evapotranspiration, through these methods. Sentinel-2 satellite launched in 2015 enables to provide 10-m spatial resolution data with a 5-day revisit time; however, its sensor does not acquire data in thermal infrared wavelength. This study aims to generate 10-m daily evapotranspiration maps based on Sentinel-2 and MODIS data fusion for Amir-Kabir Agroindustry farms. For this purpose, STARFM and improved TSHARP methods were applied for downscaling MO
Air pollution is one of the most important consequences of human activities, which not only threatens human health but also negatively affects all elements of the environment, including plants and animals. Tehran, the capital of Iran, and the administrative, political and economic center of the country, is no exception which is constantly struggling with these hazard. So far, many linear and nonlinear models have been applied to model air pollution. In this research, 8 pollutant measurement stations distributed over Tehran were selected according to the availability of their recorded data. In order to provide a model predicting pollutants, spatially and temporally, the combination of spatial and temporal features extracted of remote sensing
Recently, downscaling algorithms have been developed to obtain ET images with high temporal-spatial resolution. The purpose of the present study is to produce daily ET maps with spatial resolution of 30 m for farmlands of Amirkabir Agriculture & Industry. To reach this goal, two different scenarios were used. In the first scenario, SEBAL algorithm input parameters (surface albedo coefficient, normalized difference vegetation index [NDVI], leaf area index [LAI] and land surface temperature [LST]) calculated from MODIS data were downscaled to spatial resolution of Landsat-8, and then actual ET was calculated. In the second scenario, ET data estimated by MODIS data and SEBAL algorithm was downscaled to Landsat-8 spatial resolution. In the firs
< p >Vegetation biophysical and biochemical variables are key inputs to a wide range of modelling approaches for carbon, water, energy cycle, climate and agricultural applications. Leaf Area Index (LAI) is among the most important canopy variables, used by many different physiological and functional plant models. Several approaches have been developed for vegetation properties retrieval from remotely sensed hyperspectral data. Among them, nonparametric machine learning methods have increasingly gained attention?in vegetation variable retrieval due to their flexibility and efficiency while working with data of high dimensionality over the last decades. Although these methods provide reasonable accuracy at relatively high speed, they are main
Precise estimation of the forest structural parameters supports decision makers for sustainable management of the forests. Moreover, timber volume estimation and consequently the economic value of a forest can be derived based on the structural parameter quantization. Mean age and height of the trees are two important parameters for estimating the productivity of the plantations. This research aims to estimate mean height and age of a Pinus radiata plantation using SPOT-5 textural and spectral data derived from multi-spectral and panchromatic images, respectively. The study site for this research consisted of a 5000 ha Pinus radiata plantation from 35◦ 23/35//S to 35◦ 29/58//latitude, and 147◦ 58/48//E to 148◦ 04/02//E longitude, ne
Sustainable management of the forests requires satellite data at a large scale. This research aims to exploit pixel-based image fusion methods including principal component analysis (PCA) transformation, wavelet transformation, PCA/Wavelet transformation to improve the estimation accuracy of the mean height and age of a Pinus radiata plantation using SPOT-5 panchromatic and multi-spectral images at segment level. Therefore, the average height and age of the trees is measured within 61 plots in a Pinus radiata plantation in NSW, Australia. After applying preprocessing on the images, the spectral information including reflectance and vegetation indices along with textural information derived from gray level co-occurrence matrix for four windo
Urban physical growth is affected by different parameters including environmental, neighborhood and socio-economic factors; however, socio-economic variables are often ignored due to the lack of socio-economic information, especially in developing countries, when the urban physical growth analysis and modeling is the aim. Accordingly, there is not many studies conducted to develop GIS-based socio-economic layers to be used along with common data, such as slope, distance to the roads and so on, in urban physical growth modeling. Therefore, this study aims to introduce an efficient method to generate GIS-based socio-economic layers to be exploited along with the information layers extracted from Landsat images and field-collected data for phy
Sustainable management of the forests requires satellite data at a large scale. This research aims to exploit pixel-based image fusion methods including principal component analysis (PCA) transformation, wavelet transformation, PCA/Wavelet transformation to improve the estimation accuracy of the mean height and age of a Pinus radiata plantation using SPOT-5 panchromatic and multi-spectral images at segment level. Therefore, the average height and age of the trees is measured within 61 plots in a Pinus radiata plantation in NSW, Australia. After applying pre-processing on the images, the spectral information including reflectance and vegetation indices along with textural information derived from gray level co-occurrence matrix for four wind
Land surface temperature (LST) is one of the most important variables required in environmental and climatological studies. In order to calculate LST, accurate emissivity is needed. Recently, several methods have been developed to calculate LAST and emissivity. Some of these methods estimate LST based on a pre-known emissivity, while the others calculate LST and emissivity, simultaneously. LST mapping in urban areas can be difficult due to the high variation of the land cover and the formation of mixed pixels. Accordingly, the LST calculation based on the emissivity derived from a single method can be erroneous, especially using a low spatial resolution image in the urban areas. Integration of the emissivity values derived from different me
The metropolitan regions, especially in developing countries, have experienced rapid population growth due to the absorption of economic immigrants, which have had destructive effects on change in land use environment in the past decades. The current planning process of land use makes it necessary to identify the future pattern of land use on the basis of appropriate criteria with the natural, economic and social environment. Changes in land use occur in a dynamic and complex process due to the mutual effect of natural, social and economic factors and the impact of each factor in different time and scales. Simulation as an efficient way to understand these changes and assess the potential impact of land use changes on the ecology system and
The usefulness of red edge bands, and vegetation indices based on red edge bands, for vegetation health monitoring has already been demonstrated. There are some satellites such as WorldView-2 and Sentinel-2 acquiring images in red edge band data; while, the former data can be expensive and often lack consistent global coverage, the latter does not have a long term archive and consequently cannot be used for a long term time series analysis. This study tests the ability to predict red edge band and red edge-based vegetation indices through freely available Landsat Thematic Mapper data for an Australian Eucalyptus-dominated vegetation cover within and around a mine site. Two modelling strategies including multiple-linear regres
Differentiating agricultural areas which are not covered by vegetation from bare lands as well as identifying bare lands from urban areas in medium spatial resolution images, e.g. Landsat imagery, are usually difficult and erroneous tasks which lead to the inaccurate classification results. Therefore, this study aims to present a new approach to increase the accuracy of the classification. For this purpose, different scenarios were applied based on different input attributes. The input attributes comprised of spectral bands, textural attributes, i.e. grey level co-occurrence matrix (GLCM), and two types of indices including spatial and thermal attributes proposed in this study. Three classification methods, maximum likelihood (ML), artifici
Satellites acquire data in low, medium, and high spatial resolutions. Freely-available high temporal resolution images are often acquired in medium (or low) spatial resolution and high spatial resolution images usually suffer from a low temporal resolution or from high costs. Moreover, high spatial resolution images are prevented to use in modeling of processes such as evapotranspiration due to the lack of thermal bands. Evapotranspiration mapping with a high spatial and temporal resolutions have been always one of the main subjects in the field of remote sensing. Daily evapotranspiration mapping with a 30 meter spatial resolution is the aim of current study. The case study of the research is Amir-Kabir agro-industrial farms. For this purpo
Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2. 5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected
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