فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال چهاردهم شماره 3 (پاییز 1402)

Application of unmanned aerial vehicles (UAVs) is widespread in measurement of quantitative characteristics of single trees such as crown area. However, the efficiency of different types of data collected by UAVs are less compared. Therefore, the aim of this study was comparison of UAV RGB imagery and point clouds in crown area estimation of individual pine trees within a man-made forest in Pardisan Park, Northern Khorasan province, Iran. Both datatypes were analyzed by similar segmentation method (Multiresolution segmentation on the RGB images and Marker-Controlled Watershed algorithm in the point clouds) to estimate crown area of 324 sample pine trees. The results showed that the crown area measured on the point clouds had higher accuracy and precision compared to RGB imager (Spearman correlation of 0.95 and 0.81, coefficient of determination of 0.97 and 0.59, p-value of 0.97 and 0.001, respectively). Additionally, crown area of pine trees with large crown (> 18 m2) delineated on the point clouds showed the highest accuracy in comparison to trees with small and medium crowns. In general, it was concluded that segmentation of UAV point clouds was more efficient than RGB imagery segmentation in quantification of crown area of pine trees within the study area.

The distinction between barren and build-up areas is one of the most important issues in land use/land cover mapping ‎in arid and semi-arid climates. In this regard, many researchers have tried to increase the accuracy of classification ‎using different methods that, some of which are complex and time-consuming. Therefore, the present study conducted ‎aimed to apply micro-climate change through the implementation of Local Climate Zoning (LCZ) algorithm in land ‎use identification with emphasis on the separation of build-up areas in one of the arid cities of Iran, and the efficiency ‎of the method by investigation the classification accuracy was compared with various supervised methods including ‎maximum likelihood, minimum distance, Fisher, KNN, fuzzy, artificial neural network and support vector machine. ‎The study area is Zahedan city, which has a very significant growth of build-up areas in recent decades. For this ‎purpose, four periods of Landsat satellite images year 2020 were used. Training samples were extracted from Google ‎Earth and the validation of the classification results was performed using 218 random points. The accuracy results ‎showed that the use of LCZ algorithm with overall accuracy and kappa coefficient of 96.33% and 0.95, respectively is ‎the highest and then the support vector machine and Fisher methods with overall accuracy of 86.61 and 83.03 and ‎kappa coefficient of 0.82 and 0.75, respectively. Therefore, for land use / land cover studies, the LCZ method that ‎considers the micro-climate, is proposed.‎

In the present study, to investigate the occurrence of land subsidence in Isfahan plain, SNAP8.0 software and radar differential interferometry technique were used. For this purpose, after processing Sentinel-1A images in the period 2019-2015, rate and intensity subsidence maps were prepared in the desired period. Also, changes in groundwater water level in the period 2002-2018, as one of the geodetic factors affecting subsidence, were studied to match the points that have subsidence. Then the output of SNAP software was studied using ArcGIS10.8, with land use layers and average water level changes and Maps of mean subsidence changes, groundwater drop, standard deviation and time of maximum subsidence were prepared. The results show the occurrence of subsidence phenomenon in areas with groundwater decline with urban use, roads and agricultural lands with a maximum rate of 14.2- 20.8 cm in the years 2018-2019. The average change represents the maximum subsidence of 9.6 cm per year, in line with the satellite line of sight, in the urban area. Also, spatial analysis of subsidence hazards at strategic points showed that the study area needs to provide the necessary warnings for the occurrence of subsidence crisis on a larger scale in the future.

The aim of this study was to estimate the aboveground net primary production (ANPP) of life forms and total ANPP using Landsat 8 images in Siahpoosh and Ganjgah rangelands of Ardabil province. Field sampling was conducted in June 2019 and a satellite image was received at the same time. Seven sampling sites was selected, and three 100-meter transects parallel and perpendicular to the slope direction was established in each sites, and along each transects from 10 plots (1m2), the ANPP of life forms with random-systematic method were collected (210 plots in total). Then, 22 plant indices were selected according to previous references and calculated for the region. Next, the correlation between the ANPP of life forms and total ANPP with plant indices was calculated and the plant index with the highest correlation was selected for modeling. The first-order linear equation was used for modeling and the equations were estimated in ArcMap software as a map. The results showed that among the studied indices, NDVI was the most appropriate index for modeling; however, the highest correlation of this index was with total ANPP (0.88), and grasses ANPP (0.78); while it had less correlation with the forbs ANPP (0.41), and shrubs ANPP (0.31). The range of changes were 0 to 1857 kg/ha for grasses, 9 to 766 kg/ha for forbs, 0 to 458 kg/ha for shrubs, and 9 to 3081 kg/ha for the total ANPP. The accuracy of the models was evaluated by RMSE, MDE and MAE criteria and the accuracy was acceptable. Also, there was essentially no difference between the mean of real and modeled data. The results of this study can be used to balance the supply and demand of rangeland production for sustainable development of rangeland ecosystems.

While the Greenhouse gases impacts of Powerhouse، cars، and other man-made particulate matter on air quality and public health، well known، their impact on climate is not fully understood. Scientists have shown that aerosols can lower surface temperatures either directly، by reflecting sunlight skyward، or indirectly، by increasing the reflectivity of clouds، but until now have not figured out the role airborne particles play in shaping the distribution of rain and snowfall around the world. Suspended dust particles, especially in urban and industrial areas, act as a reducing agent for rainfall. Large fine dust (larger than 1 micron) can increase rainfall. But very fine dust particles in the upper atmosphere can suppress heavy rainfall. The current study aimed at investigating atmospheric precipitable water capacity and its relationship with periods of dust occurrences data in South west of Iran during (1986 –2016). In this paper، MODIS surface classification data was used to consider this influence. In this paper، the effect of dust occurances on rainfall studied by using classified data of MODIS/Terra Calibrated Radiances (MOD06). The results of this study by examining remote sensing data such as the amount of atmospheric precipitable water content and the occurrences of dust in some selected cases، showed that over time when dust rises، the amount of atmospheric precipitable water content which indicates the potential for rainfall، significantly reduced.The results of this study showed that one of the effects of dust events in southwestern Iran، there was a decrease in rainfall during a period of thirty years(1986-2016) and dust can significantly to act as reducing agent or rain suppressor in study region. On the other hand، in this study، in proof of dust reducing effect on atmospheric precipitable water content، the high performance of the MOD06 product appeared in the southwestern region of Iran.

In this study, the goal is; Find suitable statistical and experimental models for estimating ground carbon storage by combining spectral and radar data from Sentinel 1, 2. There are 150 random circular samples with an area of 10 acres and a total of 150 samples. With global coverage, all height classes were selected. Species of species type, the total height of trees, and diameter equal to the chest of trees with more than 7.5 cm were recorded in each sample plot. After that, the amount of biomass at the surface of the sample parts was calculated based on the FAO global model and the amount of carbon storage on the ground by applying a coefficient. Radar and spectral images were subjected to various preprocessing operations and necessary processing. Then, the numerical values corresponding to the ground sample plots were extracted from the spectral bands and considered as independent variables. Modeling was performed by non-parametric methods of RF, SVM, kNN, and parametric methods of multiple linear regressions. The results showed that the average ground biomass was 469.07 tons per hectare and carbon storage was 234.53 tons per hectare. Also, the highest correlation was obtained between the main and artificial bands with the two characteristics related to the near-infrared band. The results of modeling validation showed the combination of optical and radar data of Sentinel 1, 2 satellites with biomass and surface carbon storage; Random forest method with the RMSE%, and percentage of bias. The studied characteristics (32.79, -2.24) and (30.79 and 0.01), respectively, have had a better performance in modeling. In general, the results obtained from the validation showed that in estimating the two characteristics the RF method showed better results if the Sentinel 1, 2 data were combined, and in contrast to the SVM.