فصلنامه سنجش از دور و GIS ایران
سال سیزدهم شماره 3 (پیاپی 51، پاییز 1400)

Twin Sentinel-2 MSI sensors are spatially like the Landsat-8 OLI super spectral instrument, aiming to additional data continuity for land surface monitoring were launched by European Space Agency. In this paper, the potential of these data was evaluated for discrimination of lithological units and alterations in the Esfordi phosphate deposit area and was compared with OLI and fused OLI data. Decorrelation stretch method was used for enhancing the lithological units of the study area, and all of the 3 datasets acceptably discriminated the rock units. Among these, MSI data could produce the lithological map with high resolution and highest level of reality owing to its high spatial resolution. For statistical comparison, Support Vector Machine and Random Forest methods were applied on datasets for classification of the lithological units and their accuracy was assessed using confusion matrices. Furthermore, the corresponding band ratios to which were defined for Landsat-5 TM, were applied on datasets for detecting the altered areas. Then the areas of each highlighted alteration zones were estimated for comparison. Furthermore, the scatterplots of band ratio images were prepared. MSI dataset revealed the highest overall accuracy and Kappa coefficient in Support Vector Machine and Random Forest classification. Also, the results of band ratioing showed that MSI and fused OLI data have the most correlation and similarities. This study demonstrated that MSI data are more optimal than OLI data for lithological and alteration mapping. Also, using fused OLI data in dates which there is no MSI data acquisition or for producing seamless geological maps in continental scale besides to MSI data, is efficient.

Despite the low area coverage, riparian vegetation presents several ecosystem services. But there is no precise spatial information on these ecosystems in Iran. Considering the lack of such information, mapping and providing a spatial database is essential. Due to the mixture of these vegetation types and other land covers, the detection of these types of vegetation is challenging, and choosing an appropriate classification method is of great significance. This study examines the performance of pixel-based and object-based classification approaches for the detection of these vegetation types using freely available Sentinel-2 imagery. Five different riparian areas in Chaharmahal-va-Bkhtiari province were selected and used for training the classification models. We used random forest algorithm and multi-temporal Sentinel-2 data to perform the classification models. The validation of classification models was based on independent validation points spread across the province. Our results showed that multi-temporal Sentinel-2 imagery has a high capability to map riparian vegetation in the Zagros Mountains. Moreover, the pixel-based classification approach (overall accuracy of 83.9%) represents more accurate results compared to the object-based classification approach (overall accuracy of 76.7%). Overall, this study recommends a pixel-based classification approach to map these vegetation types using freely available multi-temporal Sentinel-2 imagery. Note that it is important to use pure pixels for training the classification models.

Today, building information modeling (BIM) technology has become an important and practical platform for achieving the goals of smart cities. However, this technology considers buildings alone and without regard to their exterior space and other buildings in the city and also lacks the necessary tools to perform various spatial analysis. In this regard, the intrinsic sharing between BIM and GIS shows that integrating them can provide proper knowledge in various urban applications in both indoor and outdoor. Although BIM and GIS are designed to achieve separate goals, they both manage 3D modeling information. BIM providing and displaying micro-level information about building and interior space, as a rich source of building information. GIS often provides and displays macro-level information in the exterior of the building as environmental information and has suitable spatial tools. In general, the integration of BIM and GIS enables better management of spatial information and better decision-making to solve issues related to different urban applications that have been studied extensively in recent years. This paper, while reviewing BIM technology and information exchange standards in BIM and GIS, reviews some research on the integration of BIM and GIS from 2008 to 2020. This paper attempts to provide researchers with a general classification of previous studies in this specific field by classifying the methods and extracting the application areas of BIM and GIS integration.

Spectral vegetation indices have been used as a useful tool in remote sensing to estimate the yield of agricultural crops. However, one factor, which reduces the capability of indices for crop yield estimation, is the limited number of available satellite images. Furthermore, in cases when there are not enough Landsat images, the capabilities of spectral indices in yield estimation using a fusion of MODIS and Landsat data, have been less investigated. The aim of this paper is, first, to introduce the most efficient index/indices for estimating the canola yield and, second, to try to use data fusion techniques in order to increase the efficiency of the selected index/indices. Due to flowering in the growth period, canola has special spectral features. In this research, to estimate the yield of canola, a yield database along with the time series of the Landsat and MODIS data of Moghan Agro-Industry Company fields were provided. Then, 10 spectral indices were evaluated for estimating the canola yield. The relations between the canola yield and the candidate indices were investigated and it was revealed that, during the flowering period, the NDYI index obtained a higher accuracy compared with other indices (r = 0.73). The fusion of the Landsat and MODIS time series data based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), resulted in a 7%-increase and an 11%-decrease in correlation and RMSE (kg/ha), respectively. This research indicated that data fusion techniques are able to improve the performance of spectral indices and hence increase the accuracy of crop yield estimation.

Monitoring the spatial and temporal variations of evapotranspiration is crucial for irrigation management and the crop water requirement, especially in arid and semi-arid areas. The purpose of the present study is to estimate the actual evapotranspiration using the SEBAL algorithm and compare it with the FAO 56 standard evapotranspiration to determine pistachio orchards under drought stress in Yazd province. To do so, Landsat 8 satellite images time series with 15 images in 2015 were used. At first, actual evapotranspiration was calculated in 15 days of pistachio phenology and then by summation of evapotranspiration in 15 days, total evapotranspiration was determined in four main stages of pistachio phenology covering the whole period of annual growth. The FAO 56 standard evapotranspiration was also obtained by using the KC-NDVI relationship as the standard for comparing with actual evapotranspiration. Based on the results, SEBAL algorithm has an acceptable capability to determine the evapotranspiration rate in the study area. However, due to lack of valid Lysimeter data in the study area, It was not possible to validate the results of the SEBAL algorithm. But comparing the results with the FAO 56 standard method showed that the two methods are in good agreement with each other. In average, the coefficient determination, RMSE and MAE between the results of SEBAL algorithm and FAO KC-NDVI approach were 0.8, 16.7 mm and 14.5 mm, respectively, for the 15-day of pistachio phenology stages. The average of actual and standard evapotranspiration rates during a pistachio growing season at the study area were 950 and 1086 mm, respectively. Comparison of actual and standard evapotranspiration shows that in most of the study area actual evapotranspiration is lower than standard conditions.

Temporal and spatial distribution of soil moisture is a key variable for hydrological planning. In the research area, increasing soil moisture accelerates the formation of runoff. The purpose of this study is to investigate the L-band potential of the Pulsar 2 sensor from the ALOS satellite in estimating soil surface moisture in order to manage water resources and reduce flood hazards. Samples were taken using random sampling. Simultaneously with obtaining SAR data, weight moisture, surface roughness and plant water content, average radar backscattering coefficients and indices angle were measured on the image. The pre-processing, processing and post-processing stages of SAR data were calculated using SNAP software and extraction of plant and moisture indices in ArcGIS 5.10 environment. Surface roughness was obtained using two angled cameras with 20 images with 10 control points (GCP) for each cluster, then using Agisoft Photo Scan software, dense cloud meshes and meshing were performed to produce DTM. Longitudinal profiles of surface roughness for each cluster were extracted from the Analysts 3D tab in ArcGIS. In order to select the appropriate model in the region, three models were considered in estimating soil surface moisture including Dubois-MLR-WCM models. The results of three models in estimating soil surface moisture in HH polarization in Dubois model with R2 = 0.82 and RMSE = 0.027, MLR model with R2 = 0.71 and RMSE = 0.03 and WCM model with R2 = 0.67 and RMSE, respectively = 0.033 was estimated. The results showed that Dubois model is more suitable for research areas and similar conditions in lands with sparse to medium cover.

Fars province has a shortage of surface water resources due to its location in arid and semi-arid regions in Iran. Therefore, in order to exploit groundwater resources, determining water quality is very important. Due to the importance of the subject in this study, the aim is to determine the quality of the southern regions of Shiraz in Fars province. To prepare zoning maps of the sampled points, the Inverse distance weighting (IDW) method was used. Then, to homogenize each of the prepared layers, the fuzzy method was used. In this method, by defining the membership function for each of the zoning maps, the data were placed between zero and one based on the degree of importance they have for water quality. Finally, the analytic network process (ANP) method was used to weigh the layers and prepare the final water quality map. The results showed that about 80% of the study area has good water quality. While about 4% of the region (parts of the north and east) has poor quality in terms of drinking.