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Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that have investigated the application of the Interferometric Synthetic Aperture Radar (InSAR) coherence products for wetland studies, especially over large areas. Therefore, in this study, coherence products over the entire province of Alberta, Canada (~661,000 km2) were generated using the Sentinel-1 data acquired from 2017 to 2020. Then, these products along with large amount of wetland reference samples were employed to assess the separability of different wetland types and their trends over time. Overall, our analyses showed that coherence can be considered as an added value feature for wetland classification and monitoring. The Treed Bog and Shallow Open Water classes showed the highest and lowest coherence values, respectively. The Treed Wetland and Open Wetland classes were easily distinguishable. When analyzing the wetland subclasses, it was observed that the Treed Bog and Shallow Open Water classes can be easily discriminated from other subclasses. However, there were overlaps between the signatures of the other wetland subclasses, although there were still some dates where these classes were also distinguishable. The analysis of multi-temporal coherence products also showed that the coherence products generated in spring/fall (e.g., May and October) and summer (e.g., July) seasons had the highest and lowest coherence values, respectively. It was also observed that wetland classes preserved coherence during the leaf-off season (15 August–15 October) while they had relatively lower coherence during the leaf-on season (i.e., 15 May–15 August). Finally, several suggestions for future studies were provided.
Meisam Amani; Valentin Poncos; Brian Brisco; Fatemeh Foroughnia; Evan R. DeLancey; Sadegh Ranjbar. InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data. Remote Sensing 2021, 13, 3315 .
AMA StyleMeisam Amani, Valentin Poncos, Brian Brisco, Fatemeh Foroughnia, Evan R. DeLancey, Sadegh Ranjbar. InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data. Remote Sensing. 2021; 13 (16):3315.
Chicago/Turabian StyleMeisam Amani; Valentin Poncos; Brian Brisco; Fatemeh Foroughnia; Evan R. DeLancey; Sadegh Ranjbar. 2021. "InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data." Remote Sensing 13, no. 16: 3315.
AbstractThe soil moisture changes (M_v) have a significant influence on forestry, hydrology, meteorology, agriculture, and climate change. Interferometric Synthetic Aperture Radar (InSAR), as a potential remote sensing tool for change detection, was relatively less investigated for monitoring this parameter. DInSAR phase () is sensitive to the changes in soil moisture (M_v) and, thus, can be potentially used for monitoring M_v. In this study, the relations between and M_v over wheat, canola, corn, soybean, weed, peas, and bare fields were investigated using an empirical regression technique. To this end, dual-polarimetric C-band Sentinel-1A and quad-polarimetric L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne datasets were employed. The regression model showed the coefficient of determination (R2) of 40% to 56% and RMSE of 4.3 vol.% to 6.1 vol.% between the measured and estimated M_v for different crop types when the temporal baseline (T) was very short. As expected, higher accuracies were obtained using UAVSAR given its very short T and its longer wavelength with R2 of 47% to 59% and RMSE of 4.1 vol.% to 6.7 vol.% for different crop types. However, using the Sentinel-1 data with the long T and shorter wavelength (5.6 cm), the accuracies of M_v estimations decreased significantly. The results of this study demonstrated that using the information from Sentinel-1 data is a promising approach for monitoring M_v at an early growing season or before the crop starts growing, but using L-band SAR data and lower temporal baselines are recommended once the biomass increases.
Sadegh Ranjbar; Mehdi Akhoondzadeh; Brian Brisco; Meisam Amani; Mehdi Hosseini. Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 7179 -7197.
AMA StyleSadegh Ranjbar, Mehdi Akhoondzadeh, Brian Brisco, Meisam Amani, Mehdi Hosseini. Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):7179-7197.
Chicago/Turabian StyleSadegh Ranjbar; Mehdi Akhoondzadeh; Brian Brisco; Meisam Amani; Mehdi Hosseini. 2021. "Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 7179-7197.
Mangroves are among the most productive ecosystems in existence, with many ecological benefits. Therefore, generating accurate thematic maps from mangrove ecosystems is crucial for protecting, conserving, and reforestation planning for these valuable natural resources. In this paper, Sentinel-1 and Sentinel-2 satellite images were used in synergy to produce a detailed mangrove ecosystem map of the Hara protected area, Qeshm, Iran, at 10 m spatial resolution within the Google Earth Engine (GEE) cloud computing platform. In this regard, 86 Sentinel-1 and 41 Sentinel-2 data, acquired in 2019, were employed to generate seasonal optical and synthetic aperture radar (SAR) features. Afterward, seasonal features were inserted into a pixel-based random forest (RF) classifier, resulting in an accurate mangrove ecosystem map with average overall accuracy (OA) and Kappa coefficient (KC) of 93.23% and 0.92, respectively, wherein all classes (except aerial roots) achieved high producer and user accuracies of over 90%. Furthermore, comprehensive quantitative and qualitative assessments were performed to investigate the robustness of the proposed approach, and the accurate and stable results achieved through cross-validation and consistency checks confirmed its robustness and applicability. It was revealed that seasonal features and the integration of multi-source remote sensing data contributed towards obtaining a more reliable mangrove ecosystem map. The proposed approach relies on a straightforward yet effective workflow for mangrove ecosystem mapping, with a high rate of automation that can be easily implemented for frequent and precise mapping in other parts of the world. Overall, the proposed workflow can further improve the conservation and sustainable management of these valuable natural resources.
Arsalan Ghorbanian; Soheil Zaghian; Reza Asiyabi; Meisam Amani; Ali Mohammadzadeh; Sadegh Jamali. Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine. Remote Sensing 2021, 13, 2565 .
AMA StyleArsalan Ghorbanian, Soheil Zaghian, Reza Asiyabi, Meisam Amani, Ali Mohammadzadeh, Sadegh Jamali. Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine. Remote Sensing. 2021; 13 (13):2565.
Chicago/Turabian StyleArsalan Ghorbanian; Soheil Zaghian; Reza Asiyabi; Meisam Amani; Ali Mohammadzadeh; Sadegh Jamali. 2021. "Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine." Remote Sensing 13, no. 13: 2565.
Groundwater extraction at rates exceeding recharge is occurring throughout Iran for agricultural and industrial activities, resulting in land subsidence in many areas, particularly the Yazd-Ardakan Plain (YAP) in the dry and desert regions of central Iran. In this study, Interferometric Synthetic Aperture Radar (InSAR) time series analysis and statistical models are used to characterize the controls on land subsidence in the YAP, from 2003 to 2020. Our results reveal the existence of a northwest-southeast elongated area of 363 experiencing subsidence at rates up to 15 cm/yr. In the YAP, the international Airport, railway, transit road, and several industrial and historical sites are threatened by the differential subsidence. Well data confirm that groundwater levels have decreased by 18 meters between 1974 and 2018, driving the compaction of sediments within the underlying aquifer system. Our statistical analysis shows that the thickness of a shallow, clay-rich aquitard layer controls the extent of the observed subsidence and an Independent Component Analysis of the InSAR time series shows that inelastic compaction dominates. This work reveals that in central Iran, current groundwater extraction practices are not sustainable and result in permanent subsidence, ground fractures with impact on infrastructures, and a permanent decrease in water storage capacity.
Sayyed Mohammad Javad MirzadehiD; Shuanggen JiniD; Esmaeel PariziiD; Estelle ChaussardiD; Roland BurgmanniD; Jose Manuel Delgado BlascoiD; Meisam AmaniiD; Han BaoiD; Seyyed Hossein Mirzadeh. Characterization of Irreversible Land Subsidence in the Yazd-Ardakan Plain, Iran from 2003-2020 InSAR Time Series. 2021, 1 .
AMA StyleSayyed Mohammad Javad MirzadehiD, Shuanggen JiniD, Esmaeel PariziiD, Estelle ChaussardiD, Roland BurgmanniD, Jose Manuel Delgado BlascoiD, Meisam AmaniiD, Han BaoiD, Seyyed Hossein Mirzadeh. Characterization of Irreversible Land Subsidence in the Yazd-Ardakan Plain, Iran from 2003-2020 InSAR Time Series. . 2021; ():1.
Chicago/Turabian StyleSayyed Mohammad Javad MirzadehiD; Shuanggen JiniD; Esmaeel PariziiD; Estelle ChaussardiD; Roland BurgmanniD; Jose Manuel Delgado BlascoiD; Meisam AmaniiD; Han BaoiD; Seyyed Hossein Mirzadeh. 2021. "Characterization of Irreversible Land Subsidence in the Yazd-Ardakan Plain, Iran from 2003-2020 InSAR Time Series." , no. : 1.
In this article, we propose a novel framework to radiometrically correct unregistered multisensor image pairs based on the extracted feature points with the KAZE detector and the conditional probability (CP) process in the linear model fitting. In this method, the scale, rotation, and illumination invariant radiometric control set samples (SRII-RCSS) are first extracted by the blockwise KAZE strategy. They are then distributed uniformly over both textured and texture-less land use/land cover (LULC) using grid interpolation and a set of nearest-neighbors. Subsequently, SRII-RCSS are scored by a similarity measure, and the histogram of the scores is then used to refine SRII-RCSS. The normalized subject image is produced by adjusting the subject image to the reference image using the CP-based linear regression (CPLR) based on the optimal SRII-RCSS. The registered normalized image is finally generated by registration of the normalized subject image to the reference image through a two-pass registration method, namely affine-B-spline and, then, it is enhanced by updating the normalization coefficient of CPLR based on the SRII-RCSS. In this study, eight multitemporal data sets acquired by inter/intra satellite sensors were used in tests to comprehensively assess the efficiency of the proposed method. Experimental results show that the proposed method outperforms the existing state-of-the-art relative radiometric normalization (RRN) methods both qualitatively and quantitatively, indicating its capability for RRN of unregistered multisensor image pairs.
Armin Moghimi; Amin Sarmadian; Ali Mohammadzadeh; Turgay Celik; Meisam Amani; Huseyin Kusetogullari. Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -20.
AMA StyleArmin Moghimi, Amin Sarmadian, Ali Mohammadzadeh, Turgay Celik, Meisam Amani, Huseyin Kusetogullari. Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-20.
Chicago/Turabian StyleArmin Moghimi; Amin Sarmadian; Ali Mohammadzadeh; Turgay Celik; Meisam Amani; Huseyin Kusetogullari. 2021. "Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-20.
MODIS land surface temperature (LST) product (MOD11A1) has been widely used in analysing spatiotemporal trends of LST. However, its applicability is limited, partially due to its coarse spatial resolution (i.e., 1 km). In this study, an Adaptive random forest regression (ARFR) method was developed for LST downscaling at national scale. This study also provided a framework to shift from downscaling single-time image sets to extensive time-series of MOD11A1 LST images in an operational approach (i.e., a 19-years spatiotemporal LST trend analysis over Iran) using the Google Earth Engine (GEE) cloud computing platform. The performance of ARFR was assessed by comparing the results of the downscaled LSTs with the Landsat-8 LST data on different dates of six consecutive years (2014–2019) over ten different sub-areas in Iran. The results demonstrated the effectiveness of the proposed method with an average root mean square error and mean absolute error of 2.22 °C and 1.59 °C, respectively. The results of spatiotemporal LST trend analysis showed that 25.08%, 10.05%, 56.68%, 1.04%, and 32.84% of Iran experienced significant positive trends during a full year, spring, summer, fall, and winter, respectively. Significant negative trends were also observed over the 3.09%, 23.84%, 7.54%, 17.38%, and 18.77% of Iran during a full year, spring, summer, fall, and winter, respectively. In summary, the outcomes of this study not only exhibit the spatiotemporal trends of LST across Iran, but also reveal the substantial benefits of the ARFR method in downscaling LST using GEE.
Hamid Ebrahimy; Hossein Aghighi; Mohsen Azadbakht; Meisam Amani; Sahel Mahdavi; Ali Akbar Matkan. Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 2103 -2112.
AMA StyleHamid Ebrahimy, Hossein Aghighi, Mohsen Azadbakht, Meisam Amani, Sahel Mahdavi, Ali Akbar Matkan. Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 ():2103-2112.
Chicago/Turabian StyleHamid Ebrahimy; Hossein Aghighi; Mohsen Azadbakht; Meisam Amani; Sahel Mahdavi; Ali Akbar Matkan. 2021. "Downscaling MODIS Land Surface Temperature Product Using an Adaptive Random Forest Regression Method and Google Earth Engine for a 19-Years Spatiotemporal Trend Analysis Over Iran." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. : 2103-2112.
PM2.5 is an important atmospheric constituent associated to human health. Therefore, the capability of estimating PM2.5 concentrations at high spatiotemporal resolutions, particularly in places with no ground stations, would be invaluable. Although several studies have involved the estimation of PM2.5, few have estimated PM2.5 concentrations at high spatial resolutions. In this study, we leverage the aerosol optical depth (AOD) and random forest (RF) algorithm to estimate daily 1-km PM2.5 concentrations over Texas from 2014 to 2018. For this purpose, we use collection 6 Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD products from the Moderate Resolution Imaging Spectroradiometer (MODIS). To address the different sources and speciation of PM2.5 over Texas, we use several important control parameters. As a result, the accuracy of RF remains consistent throughout the study area. After estimating ground-level PM2.5 levels, we apply a ten-fold cross-validation approach to obtain a correlation coefficient (R) of 0.83–0.90 and a mean absolute bias (MAB) of 1.47–1.77 μg/m3. Our results show that RF is highly capable of estimating ground-level PM2.5 concentrations. In addition to the RF model, we also compare the capability of commonly used models, including multiple linear regression (MLR) and mixed effects model (MEM), for estimating the PM2.5 concentrations of global regions. Results indicate that RF, compared to the other models, has the highest accuracy, MEM the second-highest, and MLR the third. We also leverage the USEPA Environmental Benefits Mapping and Analysis Program Community Edition (BenMAP-CE) to estimate the impact of changes in PM2.5 levels on the number of respiratory-related premature mortalities in Texas in 2014–2018. Considering 2014 as the baseline year, the BenMAP analyses reveal that PM2.5 reductions could have prevented a large number of premature mortalities, particularly among adults aged 25–99, from 2014 to 2018 in Texas.
Masoud Ghahremanloo; Yunsoo Choi; Alqamah Sayeed; Ahmed Khan Salman; Shuai Pan; Meisam Amani. Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach. Atmospheric Environment 2021, 247, 118209 .
AMA StyleMasoud Ghahremanloo, Yunsoo Choi, Alqamah Sayeed, Ahmed Khan Salman, Shuai Pan, Meisam Amani. Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach. Atmospheric Environment. 2021; 247 ():118209.
Chicago/Turabian StyleMasoud Ghahremanloo; Yunsoo Choi; Alqamah Sayeed; Ahmed Khan Salman; Shuai Pan; Meisam Amani. 2021. "Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach." Atmospheric Environment 247, no. : 118209.
Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.
Seyd Seydi; Mehdi Akhoondzadeh; Meisam Amani; Sahel Mahdavi. Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform. Remote Sensing 2021, 13, 220 .
AMA StyleSeyd Seydi, Mehdi Akhoondzadeh, Meisam Amani, Sahel Mahdavi. Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform. Remote Sensing. 2021; 13 (2):220.
Chicago/Turabian StyleSeyd Seydi; Mehdi Akhoondzadeh; Meisam Amani; Sahel Mahdavi. 2021. "Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform." Remote Sensing 13, no. 2: 220.
Various models have been proposed to estimate the degree of backscatter in Synthetic Aperture Radar (SAR) images. However, it is still necessary to calibrate these models based on the characteristics of different study areas and to propose new models to achieve the highest possible accuracy in estimating the backscattering coefficient ( σ 0 ) SAR. In this study, three empirical models, including Champion, Sahebi and Zribi/Dechambre, were initially calibrated for two SAR datasets (i.e. The Airborne Synthetic Aperture Radar (AIRSAR) and Canadian Space Agency radar satellite (RADARSAT-1)) acquired over two bare soil study areas with various soil characteristics. The Zribi/Dechambre model was then modified by revising the roughness parameter to obtain higher accuracy in estimating σ 0 over a larger range of incidence angles (θ). A new empirical model was also proposed by combining the four parameters of Soil Moisture (SM), standard deviation of surface height -root mean square- (rms), correlation length (l), and θ. To this end, the most appropriate form of the regression model was investigated and used for each of these parameters to obtain the highest correlation between the in-situ data and σ 0 values. A comparison of the empirical models showed that the modified Zribi/Dechambre had the highest accuracy in predicting σ 0 values with the Root Mean Square Errors (RMSE) of 1.20 dB and 1.59 dB over Oklahoma and Quebec, respectively. Furthermore, coefficients values of the new proposed model remained stable in the two datasets unlike the other investigated models. In this study, the effects of l on the accuracy of the new proposed model were also assessed. It was concluded that l had a considerable impact on the accuracy of the proposed model and including this parameter can improve the accuracy by up to 1 dB.
S. Mohammad MirMazloumi; Mahmod Reza Sahebi; Meisam Amani. New empirical backscattering models for estimating bare soil surface parameters. International Journal of Remote Sensing 2020, 42, 1928 -1947.
AMA StyleS. Mohammad MirMazloumi, Mahmod Reza Sahebi, Meisam Amani. New empirical backscattering models for estimating bare soil surface parameters. International Journal of Remote Sensing. 2020; 42 (5):1928-1947.
Chicago/Turabian StyleS. Mohammad MirMazloumi; Mahmod Reza Sahebi; Meisam Amani. 2020. "New empirical backscattering models for estimating bare soil surface parameters." International Journal of Remote Sensing 42, no. 5: 1928-1947.
The first Canadian Wetland Inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost-, time-, and computationally efficient approach. Although the initial effort to produce an CWI map was valuable with a 71% Overall Accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental non-profit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in-situ data, photo-interpreted reference samples, Land Cover/Land Use (LCLU) maps, and high-resolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in-situ data was 60%. Moreover, including reliable in-situ data, using an object-based classification method, and adding more optical and Synthetic Aperture RADAR (SAR) datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.
Meisam Amani; Brian Brisco; Sahel Mahdavi; Arsalan Ghorbanian; Armin Moghimi; Evan R. DeLancey; Michael Allan Merchant; Raymond Jahncke; Lee Fedorchuk; Amy Mui; Thierry Fisette; Mohammad Kakooei; Seyed Ali Ahmadi; Brigitte Leblon; Armand LaRocque. Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources: Challenges of Large-Scale Wetland Classification Using Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 32 -52.
AMA StyleMeisam Amani, Brian Brisco, Sahel Mahdavi, Arsalan Ghorbanian, Armin Moghimi, Evan R. DeLancey, Michael Allan Merchant, Raymond Jahncke, Lee Fedorchuk, Amy Mui, Thierry Fisette, Mohammad Kakooei, Seyed Ali Ahmadi, Brigitte Leblon, Armand LaRocque. Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources: Challenges of Large-Scale Wetland Classification Using Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):32-52.
Chicago/Turabian StyleMeisam Amani; Brian Brisco; Sahel Mahdavi; Arsalan Ghorbanian; Armin Moghimi; Evan R. DeLancey; Michael Allan Merchant; Raymond Jahncke; Lee Fedorchuk; Amy Mui; Thierry Fisette; Mohammad Kakooei; Seyed Ali Ahmadi; Brigitte Leblon; Armand LaRocque. 2020. "Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources: Challenges of Large-Scale Wetland Classification Using Remote Sensing." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 32-52.
Fog is a hazardous weather event that can endanger navigation, aviation, and transportation. While human has several limitations in detecting and forecasting offshore fog, satellite Remote Sensing (RS) offers cost-effective images. In this study, a probability-based daytime sea fog detection algorithm, applied to GOES-16 satellite data over the Grand Banks offshore Eastern Canada, is presented and compared with the National Oceanographic and Atmospheric Administration (NOAA)'s Low Instrument Flight Rules (LIFR) probability map. Initially, clear-sky and ice cloud classes were delineated in the GOES-16 image and then the remaining pixels were assigned a fog probability by conducting small droplet proxy, spatial homogeneity, and temperature difference tests. Moreover, a green band was linearly interpolated using the first three bands of GOES-16 images to generate pseudo true color composites. The resulting maps were evaluated both during an extended sea fog event and using several statistical measures. The Average Detection Probability (ADP) for the observed advection fog events was 66% for the proposed method, while that for NOAA's LIFR map was 38%. Furthermore, by thresholding the generated maps at the probability of 60%, the False Alarm Rate (FAR), Probability of Detection (POD), Hit Rate (HR), and Hanssen-Kuiper Skill Score (KSS) were 0.09, 0.77, 0.83, and 0.68, respectively. The proposed method is operationally being used in this region to detect and monitor sea fog, facilitating safe navigation and aviation. This is the first study which uses GOES-16 for daytime fog detection and discusses a satellite-based solution for fog modelling in Grand Banks, NL.
Sahel Mahdavi; Meisam Amani; Terry Bullock; Steven Beale. A Probability-Based Daytime Algorithm for Sea Fog Detection Using GOES-16 Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 1363 -1373.
AMA StyleSahel Mahdavi, Meisam Amani, Terry Bullock, Steven Beale. A Probability-Based Daytime Algorithm for Sea Fog Detection Using GOES-16 Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):1363-1373.
Chicago/Turabian StyleSahel Mahdavi; Meisam Amani; Terry Bullock; Steven Beale. 2020. "A Probability-Based Daytime Algorithm for Sea Fog Detection Using GOES-16 Imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 1363-1373.
The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Developing and implementing novel methods for improving these products are an ongoing priority of AAFC. Consequently, it is beneficial to implement advanced machine learning and big data processing methods along with open-access satellite imagery to effectively produce accurate ACI maps. In this study, for the first time, the Google Earth Engine (GEE) cloud computing platform was used along with an Artificial Neural Networks (ANN) algorithm and Sentinel-1, -2 images to produce an object-based ACI map for 2018. Furthermore, different limitations of the proposed method were discussed, and several suggestions were provided for future studies. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final 2018 ACI map using the proposed GEE cloud method were 77% and 0.74, respectively. Moreover, the average Producer Accuracy (PA) and User Accuracy (UA) for the 17 cropland classes were 79% and 77%, respectively. Although these levels of accuracies were slightly lower than those of the AAFC’s ACI map, this study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation.
Meisam Amani; Mohammad Kakooei; Armin Moghimi; Arsalan Ghorbanian; Babak Ranjgar; Sahel Mahdavi; Andrew Davidson; Thierry Fisette; Patrick Rollin; Brian Brisco; Ali Mohammadzadeh. Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada. Remote Sensing 2020, 12, 3561 .
AMA StyleMeisam Amani, Mohammad Kakooei, Armin Moghimi, Arsalan Ghorbanian, Babak Ranjgar, Sahel Mahdavi, Andrew Davidson, Thierry Fisette, Patrick Rollin, Brian Brisco, Ali Mohammadzadeh. Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada. Remote Sensing. 2020; 12 (21):3561.
Chicago/Turabian StyleMeisam Amani; Mohammad Kakooei; Armin Moghimi; Arsalan Ghorbanian; Babak Ranjgar; Sahel Mahdavi; Andrew Davidson; Thierry Fisette; Patrick Rollin; Brian Brisco; Ali Mohammadzadeh. 2020. "Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada." Remote Sensing 12, no. 21: 3561.
Timely and accurate Land Cover (LC) information is required for various applications, such as climate change analysis and sustainable development. Although machine learning algorithms are most likely successful in LC mapping tasks, the class imbalance problem is known as a common challenge in this regard. This problem occurs during the training phase and reduces classification accuracy for infrequent and rare LC classes. To address this issue, this study proposes a new method by integrating random under-sampling of majority classes and an ensemble of Support Vector Machines, namely Random Under-sampling Ensemble of Support Vector Machines (RUESVMs). The performance of RUESVMs for LC classification was evaluated in Google Earth Engine (GEE) over two different case studies using Sentinel-2 time-series data and five well-known spectral indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The performance of RUESVMs was also compared with the traditional SVM and combination of SVM with three benchmark data balancing techniques namely the Random Over-Sampling (ROS), Random Under-Sampling (RUS), and Synthetic Minority Over-sampling Technique (SMOTE). It was observed that the proposed method considerably improved the accuracy of LC classification, especially for the minority classes. After adopting RUESVMs, the overall accuracy of the generated LC map increased by approximately 4.95 percentage points, and this amount for the geometric mean of producer’s accuracies was almost 3.75 percentage points, in comparison to the most accurate data balancing method (i.e., SVM-SMOTE). Regarding the geometric mean of users’ accuracies, RUESVMs also outperformed the SVM-SMOTE method with an average increase of 6.45 percentage points.
Amin Naboureh; Hamid Ebrahimy; Mohsen Azadbakht; Jinhu Bian; Meisam Amani. RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine. Remote Sensing 2020, 12, 3484 .
AMA StyleAmin Naboureh, Hamid Ebrahimy, Mohsen Azadbakht, Jinhu Bian, Meisam Amani. RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine. Remote Sensing. 2020; 12 (21):3484.
Chicago/Turabian StyleAmin Naboureh; Hamid Ebrahimy; Mohsen Azadbakht; Jinhu Bian; Meisam Amani. 2020. "RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine." Remote Sensing 12, no. 21: 3484.
Distribution of Land Cover (LC) classes is mostly imbalanced with some majority LC classes dominating against minority classes in mountainous areas. Although standard Machine Learning (ML) classifiers can achieve high accuracies for majority classes, they largely fail to provide reasonable accuracies for minority classes. This is mainly due to the class imbalance problem. In this study, a hybrid data balancing method, called the Partial Random Over-Sampling and Random Under-Sampling (PROSRUS), was proposed to resolve the class imbalance issue. Unlike most data balancing techniques which seek to fully balance datasets, PROSRUS uses a partial balancing approach with hundreds of fractions for majority and minority classes to balance datasets. For this, time-series of Landsat-8 and SRTM topographic data along with various spectral indices and topographic data were used over three mountainous sites within the Google Earth Engine (GEE) cloud platform. It was observed that PROSRUS had better performance than several other balancing methods and increased the accuracy of minority classes without a reduction in overall classification accuracy. Furthermore, adopting complementary information, particularly topographic data, considerably increased the accuracy of minority classes in mountainous areas. Finally, the obtained results from PROSRUS indicated that every imbalanced dataset requires a specific fraction(s) for addressing the class imbalance problem, because different datasets contain various characteristics.
Amin Naboureh; Ainong Li; Jinhu Bian; Guangbin Lei; Meisam Amani. A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions. Remote Sensing 2020, 12, 3301 .
AMA StyleAmin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Meisam Amani. A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions. Remote Sensing. 2020; 12 (20):3301.
Chicago/Turabian StyleAmin Naboureh; Ainong Li; Jinhu Bian; Guangbin Lei; Meisam Amani. 2020. "A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions." Remote Sensing 12, no. 20: 3301.
Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.
Meisam Amani; Arsalan Ghorbanian; Seyed Ali Ahmadi; Mohammad Kakooei; Armin Moghimi; S. Mohammad Mirmazloumi; Sayyed Hamed Alizadeh Moghaddam; Sahel Mahdavi; Masoud Ghahremanloo; Saeid Parsian; Qiusheng Wu; Brian Brisco. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5326 -5350.
AMA StyleMeisam Amani, Arsalan Ghorbanian, Seyed Ali Ahmadi, Mohammad Kakooei, Armin Moghimi, S. Mohammad Mirmazloumi, Sayyed Hamed Alizadeh Moghaddam, Sahel Mahdavi, Masoud Ghahremanloo, Saeid Parsian, Qiusheng Wu, Brian Brisco. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):5326-5350.
Chicago/Turabian StyleMeisam Amani; Arsalan Ghorbanian; Seyed Ali Ahmadi; Mohammad Kakooei; Armin Moghimi; S. Mohammad Mirmazloumi; Sayyed Hamed Alizadeh Moghaddam; Sahel Mahdavi; Masoud Ghahremanloo; Saeid Parsian; Qiusheng Wu; Brian Brisco. 2020. "Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 5326-5350.
Accurate information about the location, extent, and type of Land Cover (LC) is essential for various applications. The only recent available country-wide LC map of Iran was generated in 2016 by the Iranian Space Agency (ISA) using Moderate Resolution Imaging Spectroradiometer (MODIS) images with a considerably low accuracy. Therefore, the production of an up-to-date and accurate Iran-wide LC map using the most recent remote sensing, machine learning, and big data processing algorithms is required. Moreover, it is important to develop an efficient method for automatic LC generation for various time periods without the need to collect additional ground truth data from this immense country. Therefore, this study was conducted to fulfill two objectives. First, an improved Iranian LC map with 13 LC classes and a spatial resolution of 10 m was produced using multi-temporal synergy of Sentinel-1 and Sentinel-2 satellite datasets applied to an object-based Random forest (RF) algorithm. For this purpose, 2,869 Sentinel-1 and 11,994 Sentinel-2 scenes acquired in 2017 were processed and classified within the Google Earth Engine (GEE) cloud computing platform allowing big geospatial data analysis. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final Iran-wide LC map for 2017 was 95.6% and 0.95, respectively, indicating the considerable potential of the proposed big data processing method. Second, an efficient automatic method was developed based on Sentinel-2 images to migrate ground truth samples from a reference year to automatically generate an LC map for any target year. The OA and KC for the LC map produced for the target year 2019 were 91.35% and 0.91, respectively, demonstrating the efficiency of the proposed method for automatic LC mapping. Based on the obtained accuracies, this method can potentially be applied to other regions of interest for LC mapping without the need for ground truth data from the target year.
Arsalan Ghorbanian; Mohammad Kakooei; Meisam Amani; Sahel Mahdavi; Ali Mohammadzadeh; Mahdi Hasanlou. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 167, 276 -288.
AMA StyleArsalan Ghorbanian, Mohammad Kakooei, Meisam Amani, Sahel Mahdavi, Ali Mohammadzadeh, Mahdi Hasanlou. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 167 ():276-288.
Chicago/Turabian StyleArsalan Ghorbanian; Mohammad Kakooei; Meisam Amani; Sahel Mahdavi; Ali Mohammadzadeh; Mahdi Hasanlou. 2020. "Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples." ISPRS Journal of Photogrammetry and Remote Sensing 167, no. : 276-288.
This article presents a new relative radiometric normalization (RRN) method for multitemporal satellite images based on the automatic selection and multistep optimization of the radiometric control set samples (RCSS). A novel image-fusion strategy based on the fast local Laplacian filter is employed to generate a difference index using the complementary information extracted from the change vector analysis and absolute gradient difference of the bitemporal satellite images. The difference index is then segmented into changed and unchanged pixels using a fast level-set method. A novel local outlier method is then applied to the unchanged pixels of the bitemporal images to identify the initial RCSS, which are then scored by a novel unchanged purity index, and the histogram of the scores is used to produce the final RCSS. The RRN between the bitemporal images is achieved by adjusting the subject image to the reference image using orthogonal linear regression on the final RCSS. The proposed method is applied to seven different data sets comprised of bitemporal images acquired by various satellites, including Landsat TM/ETM+, Sentinel 2B, Worldview 2/3, and Aster. The experimental results show that the method outperforms the state-of-the-art RRN methods. It reduces the average root-mean-square error (RMSE) of the best baseline method (IR-MAD) by up to 32% considering all data sets.
Armin Moghimi; Ali Mohammadzadeh; Turgay Celik; Meisam Amani. A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 2503 -2519.
AMA StyleArmin Moghimi, Ali Mohammadzadeh, Turgay Celik, Meisam Amani. A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (3):2503-2519.
Chicago/Turabian StyleArmin Moghimi; Ali Mohammadzadeh; Turgay Celik; Meisam Amani. 2020. "A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images." IEEE Transactions on Geoscience and Remote Sensing 59, no. 3: 2503-2519.
The diversity of change detection (CD) methods and the limitations in generalizing these techniques using different types of remote sensing datasets over various study areas have been a challenge for CD applications. Additionally, most CD methods have been implemented in two intensive and time-consuming steps: (a) predicting change areas, and (b) decision on predicted areas. In this study, a novel CD framework based on the convolutional neural network (CNN) is proposed to not only address the aforementioned problems but also to considerably improve the level of accuracy. The proposed CNN-based CD network contains three parallel channels: the first and second channels, respectively, extract deep features on the original first- and second-time imagery and the third channel focuses on the extraction of change deep features based on differencing and staking deep features. Additionally, each channel includes three types of convolution kernels: 1D-, 2D-, and 3D-dilated-convolution. The effectiveness and reliability of the proposed CD method are evaluated using three different types of remote sensing benchmark datasets (i.e., multispectral, hyperspectral, and Polarimetric Synthetic Aperture RADAR (PolSAR)). The results of the CD maps are also evaluated both visually and statistically by calculating nine different accuracy indices. Moreover, the results of the CD using the proposed method are compared to those of several state-of-the-art CD algorithms. All the results prove that the proposed method outperforms the other remote sensing CD techniques. For instance, considering different scenarios, the Overall Accuracies (OAs) and Kappa Coefficients (KCs) of the proposed CD method are better than 95.89% and 0.805, respectively, and the Miss Detection (MD) and the False Alarm (FA) rates are lower than 12% and 3%, respectively.
Seyd Seydi; Mahdi Hasanlou; Meisam Amani. A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets. Remote Sensing 2020, 12, 2010 .
AMA StyleSeyd Seydi, Mahdi Hasanlou, Meisam Amani. A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets. Remote Sensing. 2020; 12 (12):2010.
Chicago/Turabian StyleSeyd Seydi; Mahdi Hasanlou; Meisam Amani. 2020. "A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets." Remote Sensing 12, no. 12: 2010.
Erosion is the continuous degradation of the soil surface mainly caused by water and/or wind. In the erosion process, the nutrients of the soil, including potassium, are eroded. Therefore, developing new models to estimate soil potassium contents prone to erosion is important for soil degradation monitoring. In this study, three different soil types including loam, sandy loam, and silty loam were studied, and different amounts of potassium sulfate (K2SO4) fertilizer were added to the samples. Subsequently, lab spectrometry measurements were performed on all samples, and the most informative spectral bands were determined and used for developing models for soil potassium estimation. In this procedure, reflectance curves and their derivatives were used. Finally, several models to assess the potassium content of each soil types separately as well as irrespective of soil type were developed. These models showed a high potential for soil potassium prediction with the correlation coefficients (r) and Root Mean Square Errors varying between 0.95–0.98 and 1.317–1.973 g/kg, respectively.
Mohammad Reza Mobasheri; Meisam Amani; Roghayeh Fathi-Almas; Sahel Mahdavi; Hamid Reza Zabihi. Developing a model for soil potassium estimation using spectrometry data. Communications in Soil Science and Plant Analysis 2020, 51, 794 -803.
AMA StyleMohammad Reza Mobasheri, Meisam Amani, Roghayeh Fathi-Almas, Sahel Mahdavi, Hamid Reza Zabihi. Developing a model for soil potassium estimation using spectrometry data. Communications in Soil Science and Plant Analysis. 2020; 51 (6):794-803.
Chicago/Turabian StyleMohammad Reza Mobasheri; Meisam Amani; Roghayeh Fathi-Almas; Sahel Mahdavi; Hamid Reza Zabihi. 2020. "Developing a model for soil potassium estimation using spectrometry data." Communications in Soil Science and Plant Analysis 51, no. 6: 794-803.
Nitrogen is an important nutrient in the process of crop growth. The aim of this research was to develop several models to estimate the Soil Total Nitrogen Content (STNC) using soil reflectance values. For this, 138 samples for three different types of soil: loam, sandy loam, and silty loam with different nitrogen contents were prepared. After pre-processing of data, the most effective spectral bands for the STNC assessment were determined using two different approaches. These bands were 1415, 1465, 1475, 1895, 1925, 1955, 1985, 2025, 2215, and 2305 nm. Subsequently, several statistical models were developed using the selected bands and multivariate linear regression analysis. The root mean square error (RMSE) of the most accurate models for loam, sandy loam, and silty loam was 0.35, 0.38, and 0.57g/kg, respectively. Additionally, three models were developed regardless of soil types. These models were independent of the soil type and can be applied to any agricultural soil. The most accurate independent model had an RMSE of 0.54 g/kg and a correlation coefficient (R) of 0.97. Although these models are valid for laboratory measurements, but the extension of these models to satellite application is the objective of future studies in the Khavaran Remote Sensing Laboratory (KRSLab).
Mohammadreza Mobasheri; Meisam Amani; Maryam Ranjbaran; Sahel Mahdavi; Hamid Reza Zabihi. Introducing an Index in Determination of Soil Total Nitrogen Content in an Agricultural Soil Using Laboratory Spectrometry. Communications in Soil Science and Plant Analysis 2019, 51, 288 -296.
AMA StyleMohammadreza Mobasheri, Meisam Amani, Maryam Ranjbaran, Sahel Mahdavi, Hamid Reza Zabihi. Introducing an Index in Determination of Soil Total Nitrogen Content in an Agricultural Soil Using Laboratory Spectrometry. Communications in Soil Science and Plant Analysis. 2019; 51 (2):288-296.
Chicago/Turabian StyleMohammadreza Mobasheri; Meisam Amani; Maryam Ranjbaran; Sahel Mahdavi; Hamid Reza Zabihi. 2019. "Introducing an Index in Determination of Soil Total Nitrogen Content in an Agricultural Soil Using Laboratory Spectrometry." Communications in Soil Science and Plant Analysis 51, no. 2: 288-296.