Salman Goodarzdashti; Mohamad Seifi; Mahshid Kohandel; Davoud Ashourloo; Hossein Aghighi
Abstract
Potatoes are the fourth most cultivated crop worldwide. Regarding the strategic role of this crop in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation /prediction at any scale. Although remote sensing (RS) approaches based on ...
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Potatoes are the fourth most cultivated crop worldwide. Regarding the strategic role of this crop in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation /prediction at any scale. Although remote sensing (RS) approaches based on optical and/or microwave sensors have been widely employed to monitor cultivated land (including crop area, type, condition, and yield forecasting), the identification of potato planting areas using RS data has not been much addressed. Hence this study addresses the literature gap by suggesting an effective potato mapping approach that uses the time series of the Sentinel-2 (S2) images, Google Earth Engine (GEE) platform and machine learning methods. Since most crops have specific spectral and temporal characteristics during the growing season, this research has presented a method to discriminate potato fields from other crops using time series images without explicit thresholding. We employed 1648 ground truth data to optimize, train, and evaluate the model at the study site, which includes potatoes and other fields. A handheld GPS receiver was used to collect these data. The performance of this approach is evaluated by conducting a set of experiments in Hamedan and Bahar cities, as the regions grow more potatoes than any other places in Iran. Accurate identification of potato fields was completed by extracting the required features, namely the potato phenology feature and NDVI medians, from the time series of the S2 satellite bands. After that, these features were utilized as the input parameters to Support Vector Machine (SVM) technique. In order to train the most optimal SVM model using RBF kernel, Gamma and C values were optimized with the help of the 5-fold cross-validation method. These values were then employed during the algorithm's implementation on GEE platform. The estimated overall accuracy and Kappa coefficient are 90.9% and 0.82 for Hamedan and 93.3% and 0.87 for Bahar, respectively. The results of this research indicate the efficiency of SVM technique in potato acreage mapping. Moreover, the selected features such as potato phenology feature can be considered as discriminating features for improved identifying of crop farms.
Farzaneh Aghighi; Omid Mahdi Ebadati E.; Hossein Aghighi
Abstract
Lidar point cloud dataset and 3-D models are widely used in urban feature extraction, forest, urban and tourism management, robotics, computer game production etcetera. On the other hand, The existence of outliers in the lidar point cloud is inevitable. Therefore, outlier detection and removing them ...
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Lidar point cloud dataset and 3-D models are widely used in urban feature extraction, forest, urban and tourism management, robotics, computer game production etcetera. On the other hand, The existence of outliers in the lidar point cloud is inevitable. Therefore, outlier detection and removing them from lidar point cloud data have been known as necessary steps in lidar point cloud processing. Over the past decade, several outlier detection techniques have been introduced in the literature; however, most of them are time-consuming, expensive, and computationally complicated. For overcoming these limitations, this article introduces a new automatic approach for outlier detection using a support vector machine-based conditional random field (SVM-CRF) technique and box plots methods. In this approach, a box plot analyzes the output energyvector of SVM-CRF to recognize outliers. The methods were evaluated using ISPRS benchmark datasets of Vaihingen provided in order to urban classification and 3D building reconstruction. To evaluate this method, first of all, outliers, that are almost closed to objects, were added to the data set manually. Then the research steps were done to evaluate the proposed method's ability for detecting outliers. The evaluation of this research showed an overall accuracy of 62% as the performance of the proposed model. Although the RANSAC algorithm has better performanc, it is a more costly and time-consuming technique than the proposed outlier detection technique.
Mohammad Reza Gili; Davoud Ashourloo; Hossein Aghighi; Ali Akbar Matkan; Alireza Shakiba
Abstract
Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy ...
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Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy and greenness during the growing season is one of the most prominent characteristics of vegetation, including agricultural products, which can be monitored by using time series of vegetation indices that have useful information about the sequence of phenological features of crops. The use of deep learning methods with the ability of learning sequential information obtained from these time series can be useful in crop mapping and reducing dependence on ground data. The LSTM network is one of the types of RNNs in sequential data analysis that has the ability to learn long-term sequences of time-series information. Therefore, in this study, after extracting the NDVI time-series of 9 different dates from Sentinel-2 satellite images for a region located in Moghan plain, with ground labeled data related to the type of crops cultivated, we trained a convolutional LSTM network. Then we used this trained network to classify agricultural products in another region of the plain as a test site, and achieved an overall accuracy of 82% and a kappa coefficient of 0.8. Increasing the number of ground samples and selecting the exact boundary of crops, can increase the efficiency of the method used.
Nahid Haghshenas; Ali shamsoddini; Hossein Aghighi
Abstract
It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted ...
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It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted on the images, digitally. There are several parameters which must be optimized prior to use of object oriented classification. One of these parameters is Scale affecting the segmentation results, significantly. Scale is usually set by trial and error which is an experimental approach. One of the aims of this study is to optimize Scale parameter, automatically. In addition, after segmentation process based on a proper Scale, it is required to classify the identified segments based on the attributes which are extracted from these segments. In this stage, the selection of suitable classification method fed by the proper attributes is critical. In this research, LiDAR data and aerial image acquired on Vaihingen, Germany, were utilized for segmenting the urban area. In order to identify suitable attributes, random forest feature selection was applied on the attributes derived from the identified segments. Machine learning methods including support vector machine, random forest, and decision tree were compared for classifying the segments based on their suitable attributes into two classes including tree canopy cover and others. The results indicated that Scale of 25 is the best one to segment this area. Also, the tree canopy cover map derived from support vector machine with quality index of 79.90 showed the best performance among different classifiers used in this study.
Soheil Radiom; hossein Aghighi; Hamid Salehi Shahrabi
Abstract
Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of ...
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Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of reference evapotranspiration and thus measuring air temperature, humidity and wind speed. Calculation of evapotranspiration is usually based on obtained information from the nearest weather stations to the study area, which can be error-prone. Therefore, in this study, IoT sensors were used to accurately measure air temperature at 2 m above the ground, as well as air humidity and wind speed in the study area. The study area is the farms of Moghan Agricultural Company in Ardabil province. In this study, 23 nodes were installed in a number of farms. The ground-based energy balance algorithm (SEBAL) was used to calculate the evapotranspiration using Landsat 8 images in 2015.
Farzaneh Hadadi; Hossain Aghighi; Ayoub Moradi
Volume 10, Issue 4 , February 2019, , Pages 99-120
Abstract
The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate ...
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The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that index with and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses. The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that index with and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses.
F Aghighi; O.M Ebadati; H Aghighi
Volume 9, Issue 2 , December 2017, , Pages 41-60
Abstract
Light Detection and Ranging (LiDAR) point cloud dataset and 3 dimensional (3-D) models have been extensively used for urban feature extraction, urban management, forestry management, managing urban green space, tourism management, robotics, and video and computer games' production. One of the main steps ...
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Light Detection and Ranging (LiDAR) point cloud dataset and 3 dimensional (3-D) models have been extensively used for urban feature extraction, urban management, forestry management, managing urban green space, tourism management, robotics, and video and computer games' production. One of the main steps toward reaching accurate 3-D models is clustering and classification of LiDAR point clouds data. The main purpose of this research is to find out, particular machine learning techniques, which are promising for best learning and classification of LiDAR point cloud data in an urban area. Therefore, the performances of K-nearest neighbor (KNN), Decision Trees (D3), Artificial Neural Networks (ANN), Naive Bayes (NB), Support Vector Machine (SVM), and Markov Random Field (MRF) classifiers were evaluated on the LiDAR and aerial image dataset of Vaihingen, Germany, in the context of the "ISPRS Test Project on Urban Classification and 3D Building Reconstruction." In regard to the literature review, MRF model has not been used to classify LiDAR point cloud data in Iran. In this research, we utilized all the geometrical features, intensity values of LiDAR and aerial images as well as extracted eigenvalues based features to distinguish five urban object classes, including impervious surfaces, buildings, low vegetation, trees and cars. In order to compute eigenvalues using local point distribution, this paper introduces a new cubic structure, which has been not found in previous studies. The final results of 3D classification techniques in this research were 2D maps that evaluated by the benchmark ISPRS tests maps. The evaluation shows that the performance of MRF model with an overall accuracy of 88.08% and the kappa value of 0.83 is higher than other techniques to classify the employed LiDAR point clouds.
D Ashourloo; H Aghighi; A.A Matkan; H Nematollahi
Volume 9, Issue 4 , May 2017, , Pages 111-128
Abstract
Wheat rust is one of the important diseases of cereal crops in Iran and other countries in the world which imposes irreparable damages to the agricultural economy. In this study, the effects of the leaf and yellow rust disease on wheat leaves reflectance were studied. For this purpose, various vegetation ...
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Wheat rust is one of the important diseases of cereal crops in Iran and other countries in the world which imposes irreparable damages to the agricultural economy. In this study, the effects of the leaf and yellow rust disease on wheat leaves reflectance were studied. For this purpose, various vegetation indices derived from leaf spectra were measured. To do this, diseases ratio and varying degrees of disease were extracted by using digital camera and multi-step algorithm including color Transformation, mask preparation, texture and maximum likelihood classification. Results show variation in the values of the parameters with changing in proportion of disease whereas the data scattering of indexes Increase quickly. The highest correlation was for the NDVI (0.9) and the minimum was for the red slope (0.2). With the similarity criteria, range and inter-class scattering relations of spectra and disease were studied and with Increasing of the disease ratio. These criteria are altered by developing of disease ratio .Further investigation showed, spectrum mixing in different fraction of yellow, orange, brown and dead is a cause for data scattering with disease development.
