فصلنامه سنجش از دور و GIS ایران
سال چهاردهم شماره 2 (پیاپی 54، تابستان 1401)

Organic carbon plays a activate role in environmental sustainability, soil quality and health index, so identifying the spatial distribution of carbon sequestration is a requirement of environmental planning and soil management. The purpose of this study is to investigate the amount of carbon sequestration in sugarcane and traditional uses of sugarcane, traditional agriculture and barren. In each land use, 60 soil samples were taken and organic carbon, salinity, lime, soil reaction and solution sodium were measured. Using Landsat 8 satellite OLI and TIRS spectral data, the variable ​​of soil and vegetation indices including: NDVI, SAVI, TSAVI, OSAVI, MSAVI, SOCI, WDVI, PVI, RVI and BI in the sample points was obtained and the relationship between them and the amount of soil organic matter was calculated. The results show that in agro-industrial use, SOCI index with 50.30% and band 3 with 53.82% have the highest correlation, in traditional agriculture, PVI index with a correlation of 60.35% and band 7 with 60.63% and in Barren lands ,RVI index with a correlation of 34.27% and band 2 with 36.67% have the highest correlation with the amount of soil organic matter. The results of statistical analysis by partial least squares fitting method showed that the average of calibration and validation results are 43.48 and 39.08%, respectively. The results of estimating soil organic matter by kriging method and M5 tree model show that the correlation between measured and predicted organic matter was 66.20% and 82.00%, respectively. The results show that there is a significant correlation between soil organic matter and Landsat 8 satellite indices and bands, and it is possible to estimate the soil organic matter levels of the study area and other areas with similar conditions with acceptable probability.

By improvement of the spatial resolution of remote sensing images, more accurate information are provided from the image scene such as texture structures. However, extraction of land cover information from these datas has become a challenging process due to the high spectral diversity and the heterogeneity of surface materials. Visual interpretation is costly and time consuming and automatic interpretation of images does not necessarily lead to high accuracy. Achieving optimal interpretation accuracy requires the design of automatic algorithms that are capable of dealing with the complexity of the image scene. To overcome this problem, object-based image analysis (OBIA) that is sensitive to the image scene morphology, can be particularly effective in an urban area where the density of man-made structures is high. In object-based classification, pixels of a segment are analyzed in combination with each other. So the dimensions of the problem space are reduced, in compared to the pixel-based method, which leads to increasing the computational speed. Meanwhile, due to the different sizes of image segments, supervised object-based classification faces challenges in creating an optimal training set. In this research, AdaBoost algorithm was selected for the object-based classification, to overcome the problem of feature space imbalance, due to the small number of training samples in comparison with the high dimensions of the feature space (including spectral, spatial and geometric features), two strategies were proposed. In the first approach an active learning mechanism was integrated with AdaBoost to produce optimal training data set (OTD) and in another approach based on the feature-to-feature correlation (redundancy) and the feature-to-class correlation (relevance), the candidate feature subset (CFS) was generated to reduce the size of the feature space. To evaluate the proposed method, the developed algorithm was performed on the standard dataset of Vaihingen in Germany and the results were compared with the pixel-based classification. In order to evaluate the signification of the results, the McNemar statistical test was used. The experimental results showed that the proposed object-based approach improved the overall accuracy by 6% and the kappa coefficient by 7% compared to the pixel-based approach. Also, the computational speed of proposed object-based AdaBoost was significantly increased compared to the pixel-based approach. These results indicate the superiority of the proposed approach both in terms of accuracy and processing speed.

Determination of the Crop Water Requirement (CWR) of different crops and the value of crop water consumption is one of the problems at a large scale and in real-time to the soil and water expert. The first step to compute this variable is to determine the reference evapotranspiration (ET0). The standard method to compute this parameter is to utilize the climate data and experimental equations. The problem with classic methods is that the meteorological station isn’t available in the agricultural lands and usually, we have data limitations. The optimized solution is to utilize remote sensing data. So with the combination of different datasets then the reference evapotranspiration and actual evapotranspiration will be estimated. The goal of the study is to an evaluation of open-source WaPOR and ERA5 to compute daily reference evapotranspiration based on the FAO-Penman Monthis equation at the meteorological stations of Sistan and Baluchestan province. The result has shown that the open-source dataset estimated the reference evapotranspiration as more than 80 percent accurate at the place of the meteorological station and in all of the stations RMSE was less than 2 mm per day. The accuracy assessment of results shown at different crop seasons that ET0 in the autumn season is better than in the spring season. So that the ERA5 combined with the GLDAS Wind data has a better correlation with in situ measurement of ET0 than to the WaPOR. All of the results shown that this dataset can be used in each place in the province to estimate ET0. Therefore, the present study is to investigate the possibility of using the products of WaPOR and ERA5 systems to calculate the amount of daily reference evapotranspiration based on the experimental method of Penman-Monteith and to evaluate and validate its outputs in Sistan and Baluchestan Province of Iran.The results showed that remote sensing systems with an accuracy of over 80% at meteorological stations estimated the amount of reference evapotranspiration and an error of less than 2 mm was reported in all stations. Also, studies during the growing season (June 15 to November 6) compared to the growing season (1 November to 15 May) showed that the reference evapotranspiration obtained from satellite data in the first growing season has a higher (R2). Also, the results of NRMSE index evaluation indicate that the reference evapotranspiration obtained from ERA-GLDAS2.1 data is appropriate.Therefore, since the estimated and validated values had acceptable accuracy, in the next step, these systems can be used anywhere in the province.

The aim of this study was to evaluate the accuracy of MODIS satellite data in monitoring aerosol (PM10 particles) to compare with ground pollution station data It was done in Sanandaj. In this case, the performance of satellite data in measuring dust particles at Sanandaj ground station is identified. At first, the aerosol optical depth data provided by MODIS sensor was prepared based on the corresponding of the PM10 measured by pollution monitoring station located in Sanandaj.Then, the correlation coefficient between two series of data was calculated. In order to obtain the accurate prediction of PM10 the ARIMA and artificial neural network were used.The AOD of MODIS sensor was combined using maximum likelihood and root mean square error for input of prediction models. At last, a single comparison method for each model as well as models comparison was evaluated to identify the accurate model in predicting of PM10. In the ANN model R2 was acquired in training phase as 0.52, and testing phase as 0.53 with RMSE=1.62 and MAE=2.62. The analysis showed that the ARIMA model 1-0-3 with R2=0.46, MAE=0.06 and RMSE=0.69 is the only acceptable model.It states that ARIMA model, is a suitable model for prediction of PM10. However, the ANN model was more accurately estimated for the correlation between the data.The results of presented study showed that there is direct relationship between the MODIS sensor AOD data and ground station PM10 data.The results conclude that this algorithm is capable for detecting of dust and can be good alternative to the PM10 provided by the ground stations measurement.

Vegetation is a key component of the earth planet, which controls the energy and water exchanges between atmosphere and the Earth surface and plays an important role in the global energy cycles, such as oxygen, carbon dioxide, and water. Monitoring and management of vegetation are done using its biophysical and biochemical parameters such as LAI. Leaf area index (LAI) is one of the most important vegetation parameters that used in most of the applications such as water and carbon cycles modeling. Remote sensing in terms of their continuous and extensive cover is a unique tool for generating vegetation variables. Different retrieval approaches have been developed to extract biophysical parameters information from remote sensing data, which is divided into two broad classes, the statistical/experimental approaches and the physical approach. In the present study, the PROSAIL RT model (Radiation Transfer Model) based on the LUT table have been used to retrieve the LAI variable. Ground reference data collected during the SPARC 2003 campaign were also used to evaluate the accuracy of the retrieved variable. To drawback, the ill-posed problem, four categories of cost functions have been used: Information Measurement (IM), Minimum contrast (MC), Angle Measurement (SAM) and Least Square Error (LSE) and used the multiple Best solution instead of Single best solution. The results showed improvement in the LAI estimation of up to 12% for the multi-species canopy.

Due to the fact that droughts can affect both water quality and quantity, the purpose of this study is to determine the effect of droughts on water quality and quantity in Northern Fars province, Iran, based on drought indicators. The drought indices PCI, TVDI, and NDVI are used to study drought from 2000 to 2020. Also, the kriging method is used to generate zoning maps of elements in water (Ca, Cl, EC, K, Na, Mg). Then, using the neural network (MLP) method, the amount of elements in the water is predicted based on drought indices. Based on the values of the drought indicators, the trend of drought changes in the region is increasing from 2000 to 2020, with the southern areas of the region experiencing a more acute drought than the rest of the region. In addition, the zoning map of the elements in water indicated that salt concentrations are higher in the southern parts than in the northern parts. Correlation between drought indices and the amounts of elements in water showed that Ca has a high correlation (R2= 0.820) with TVDI index, and also Cl, EC, K, Na, and Mg have significant correlations (R > 0.8) with the index. Using drought indicators, MLP results for predicting water quality status show that southern regions have more solutes and lower water quality. Furthermore, the R2 values of the model for predicting the elements Cl, EC, K, Na, Mg, TDS, TH using PCI index equal to 0.85 and for Ca using TVDI index equal to 0.71, which indicates high accuracy.

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.