fahime arabi aliabad

Investigating the area under cultivation and yield estimation of agricultural products like rice; can greatly ensure food security, analyze the status of agricultural products and, finaly, sustainable development of developing countries. This study used Sentinel-2 satellite images, to estimate the area under cultivation and the yield of rice paddies in Kunduz province, Afghanistan in the 2020 crop year. Using the time series of NDVI index, the phenology stages of rice plants were obtained and the phenology parameters (SoS and EoS) were extracted using the maximum resolution method. Then, object-oriented classification method based on phenology was used to identify and determine the under-cultivated area of rice fields. In this method, three types of data of reflectivity of reflective bands, NDVI vegetation index and phenology parameters were used as auxiliary data. Yield estimation was done using the experimental method of regression analysis between remote sensing plant indices and the data obtained from ground harvesting. Also, the experimental method based on the regression analysis of ground data and distance measurement with the coefficient of determination of 0.86 and the Pearson correlation coefficient of 0.92 showed its high accuracy in estimating the yield of rice fields. The accuracy of the estimated performance in this research was evaluated by comparing the actual performance (field harvest data) in 27 control points. For this purpose, Pearson's correlation test was used. This test showed that there is a positive and very strong relationship between actual performance and estimated performance (P=0.000, N=27 and R2=0.929).

The preparation of geological maps based on field data and the application of aerial photographs have always been an error because of the structural diversity of the earth and the difficulty of accessing certain regions. But in recent decades, the use of satellite imagery has but in recent decades, the use of satellite imagery has gone a long way to increasing the accuracy and timeliness of geological mapping. The purpose of the current study is to investigate the applicability of Landsat 8 satellite images and object-oriented pixel classification methods in mapping the geological formations of some part of the Shirkuh Mountain range in Yazd province. This area is part of the scattered mountain range of Central Iran with a dry climate and minimal vegetation. Initially, enhanced operations were performed to identify geological information’s using MNF, PCA and FCC processing. Then, the images were classified using object-oriented algorithms (BAYES, SVM, KNN, Decision Tree and Random Forest), neural network (ARTMAP, RBF, MLP and SOM), and base pixels (Maximum Likelihood, Minimum Distance, Mahalanobis and SAM). Next, the error rate of each method was calculated using Boolean logic and kappa coefficient. The results showed that the maximum probability classification with kappa coefficient of 75% in the base pixel category, Fuzzy ARTMAP classification in neural network method with kappa coefficient of 72% and Bayesian classification in object-oriented method with kappa coefficient of 82% have the best results among other methods. These results show that the methods mentioned in the identification and separation of geological formations are effective. The SAM of pixel-based methods, SOM of neural network methods and RF of object-oriented methods with 49%, 64% and 61%, respectively, showed the lowest accuracy in each category.

The growth of urbanization and the expansion of cities have caused significant changes in the surrounding land cover. Changes in land surface characteristics as a result of rapid urbanization can affect climatic conditions in seasonal and long-term periods on a local, regional and global scales. Land surface change (LST) change is directly related to land cover changes, so that land cover change causes changes in the land surface temperature. Yazd plain as the study area, has witnessed many changes in land cover during the past two decades due to immigrants, so studying its temperature changes is very important. The aim of this study was to investigate the changes in land surface temperature of the Yazd plain over an 18-year period and compare it with land cover changes in order to determine the effect of these land cover changes on land surface temperature. The distinction between the current study and previous studies is the effect of land cover change on land surface temperature in different months of the year.

In this study, Landsat 5 and 8 satellite images were used to investigate the effect of land cover changes on land surface temperature in 2001 and 2019. Land surface temperature map were estimated for Landsat 5 satellite images using single band method, and for Landsat 8 images using split-window algorithm. The land cover maps for the studied years were prepared using the supervised classification method of maximum likelihood. Changes in land cover made from 2001 to 2019 and its effect on land surface temperature were examined.

In the land cover map of the study area, six land cover types of mountainous rangelands, plain rangelands, agriculture, sand dunes, barren lands and residential areas were identified. For better separation of land cover, images related to the peak of vegetation period were used in the classification processes. After determining the degree of class segregation, the supervised classification was performed by the maximum likelihood method, and then land cover changes were detected in the area. In order to investigate the changes made in the study area, the area and changes of each land cover were also extracted. Urban areas with an increase of 12.1% area had the highest growth, and agricultural lands with 28% reduction had the highest reduction in area. At the same time, the study period with increasing migration from villages to urban areas, the phenomenon of urbanization and industrialization of the study area can be the reason for this issue. Also, the 24.5% increase in the area of sand dunes is due to recent droughts and the placing of the study area in the sedimentation site of the wind erosion process. Although little vegetation in sand dunes has stabilized them, in recent years with the loss of this cover, the area of wind erosion process has increased. Land cover changes map was extracted from the area in 18-year period. The Land surface temperature maps for four months of the year were prepared using the single channel method in 2001, and the split window algorithm in 2019. For better comparison, the relevant Land surface temperature indexes per month was classified into 10 categories at 2 °C intervals and the areas of the classes were compared with each other. In order to investigate the effect of land cover change on land surface temperature, the mean, minimum and maximum land surface temperatures in each land cover on the mentioned dates were examined. The results show that the land surface temperature in each area of the earth is influenced by surface factors and its characteristics and the temperature obtained in different land cover is different. Land surface temperatures in bare lands and sand dunes that have been converted into other land covers have dropped significantly, so that, in July and September it dropped about 2°C. Most of the temperature increase is related to the conversion of agricultural lands to bare lands, sand dunes and mountainous rangelands. Contrary to the existing hypotheses that the greatest increase in temperature is related to changing agricultural to residential land cover, in this area located in arid regions of central Iran, the conversion of agricultural lands to bare lands and sand dunes has led to higher temperatures. The change in land surface temperatures in the changed coverage in February was higher than in other months and less in September.

The results of this study showed that during 2001-2019, the area of residential lands and sand dunes increased and the area of agricultural lands, bare lands, and rangelands decreased. The results of land surface temperature assessment in changed land cover showed that the land surface temperature in bare lands and sand dunes that have been converted to other coatings has decreased significantly. Conversion of agricultural lands to bare lands and sand dunes has led to higher temperatures than other land cover type and physical development has created numerous environmental problems.

Temperature is one of the most important physical parameters that control the transfer and exchange of energy between different layers of the earth and the atmosphere. LST estimation methods based on satellite images require surface and atmospheric parameters such as surface emissivity, average air temperature, atmospheric transfer coefficient, and water vapor as input. Uncertainty in these parameters causes errors in the retrieval of land surface temperature. This study aimed to compare the accuracy of different methods for estimating atmospheric water vapor in estimating land surface temperature using Landsat 8 images. In this study, atmospheric water vapor was estimated using FLAASH atmospheric correction methods, MODIS sensor images, and SWCVR method. Then, the impact of atmospheric water vapor on land surface temperature accuracy was investigated using the split window and single-channel methods. Validation of Land surface temperature images was performed using cross-validation and ground measurement methods. Therefore, 20 Landsat 8 images related to 2018 and 2019 were used to estimate atmospheric water vapor by the FLAASH atmospheric correction and SWCVR methods, and land surface temperature estimation. MODIS radiance images were used to estimate atmospheric water vapor and the land surface temperature product of this sensor was used for cross-validation. The surface temperature was measured using a thermometer in places with homogeneous cover, for ground-based validation. Results showed that among water vapor estimation methods, the SWCVR method is more suitable for estimating land surface temperature and the split-window method based on the SWCVR method shows the lowest RMSE and MADE at 3.47 and 3.18. Results of RMSE image classification of split-window algorithm based on the SWCVR showed that 1.67% of the area has an error of more than 4 °C and 98% of the study area has less than 4 °C error.

Specifics regarding land cover and land use is an essential element of the planning process, as it can undoubtedly lead towards the debate around the present plans and patterns and the necessity to modify land use included in a regional plan. In this research, land use maps were prepared using Landsat TM (2000), (2008) and OLI (2016) satellite imaged. Land cover mapping was conducted after pre-processing and processing satellite images, creation of training samples and assessing maps accurate was done by coefficient kappa and overall accuracy. Supervised classification technique with maximum likelihood method were used to show the land use map. In this research, we use the 2000 and 2008 land cover maps to predict the 2016 land cover map and then use the 2008 and 2016 land cover maps to predict the 2024 land cover map.According to the results, with passing time the area of built-up area and mountainous increased with the passage of time while the dense poor rangeland, rich rangeland and agriculture area decreased during the period 2000-2016. The results of predicting changes in the time interval 2016-2024, showed that 55/0 of agriculture, 82% of rich rangeland, 80% of poor rangeland, 51% of built-up, and 0.97 of mountainous will remain unchanged

Albedo is a very important physical component in meteorology and climate, radiation transfer studies and calculation of Earth's radiation reserve and plays a very important role in climate change research and land surface models. In this study, Landsat 8 images during the summer of 2013 to 2016 were used to investigate the land surface albedo in different land uses in Yazd-Ardakan plain. Then, land use map of the study area was prepared using a variety of supervised classification methods for satellite imagery. The results showed that the map prepared with the maximum likelihood method had the highest accuracy and that map was used as the basis. The results showed that the highest albedo belonged to the Barren lands with an average of 0.31 and the lowest to the agricultural land with an average of 0.20 and respectively, from the highest to the lowest albedo coefficient for Barren lands, residential, sand dune, pasture, mountain, and agricultural land uses and Landsat image can be used with great confidence to estimate the surface albedo.