فصلنامه سنجش از دور و GIS ایران
سال دهم شماره 3 (پیاپی 39، پاییز 1397)

Agriculture is one of the most important economic parts in each country, which each product requires specific climatic and environmental conditions. So climatologists pay special attention to landuse planning and managing ecological resources with appropriate methods. The purpose of this study is to identify the effective climatic factors and elements in fig planting in Fars province and zoning the areas susceptible to planting this product climatically and environmentally, using the ability of GIS to combine different layers and in the form of different models. In this study, six climatic elements (average temperature, maximum and minimum absolute temperature, average and maximum humidity and amounts of precipitation) from 21 stations of synoptic, climatology and Rain gauge stations in Fars province and 5 environmental parameters (elevation, slope, soil type, erosion and landuse) has been used. First, the climatic elements have been reconstructed using Differences and Ratios methods due to their incompleteness. Then maps of these parameters and elements are plotted in GIS and these maps are standardized and weighted using Fuzzy logic and the criteria for fig tree planting, and combined with Fuzzy logic, and zoning map of susceptible land obtained in Fars province. The results showed that 32 percent of the lands are very suitable for planting Figs, 40 percent has a moderate ability, and 22 percent are also inappropriate for fig tree planting. In addition, 6% of the lands is not worthy of Fig tree planting (lake lands, salty lands, etc.), which is excluded from the analysis.

K-Means is one of the most frequently used unsupervised classification approaches for remotely sensed image analysis. In standard K-Means version, the Euclidean distance (ED) has used to estimate the dissimilarity between an unknown vector data and the cluster center. Since, this measure is very sensitive to topographic and environmental effects on spectral observations, we have proposed to replace it with a new one for goal of hyperspectral image clustering. The Spectral Information Divergence (SID) is a stochastic measure that is a more reliable dissimilarity measure when compared to ED as a deterministic measure. Where the ED measure the spectral distance between vector data and the clusters, SID models the probability distributions for vector data and clusters by normalizing their spectral signatures and measures the distances between them. This idea has applied to develop an enhanced clustering framework. The experimental results on three real hyperspectral images collected by HyMap, HYDICE and Hyperion sensors show that the proposed method improves classification results. In the manner that the Kappa coefficient of the classification results of three hyperspectral imagery datasets increased by about 7%, 56% and 10%, respectively.

Subsidence is a downward motion of ground surface with small horizontal displacement vector. It may happen due to natural factors or human activities. In Iran,subsidence may occurbecause of the human activities and excessive extractionof groundwater resources. In this study, we applied Synthetic aperture Radar Interferometry (InSAR) to investigate the rate of subsidence. We estimatedthe rate of subsidence in Khoramdareh plain using Permanent Scattering (PS) for the duration time 2003-2005. The mean velocity map indicated that the subsidence is occurring with the rate of 35 mm/yr in direction of Satellite Line of Sight. Afterward, we used Geospatial Information System (GIS) to evaluate the risks of subsidence. The results show some parts of the railways, main roads and highways are affected by subsidence.

Over the past several decades, many vegetation indices have been developed for crop yield estimation, each being sensitive to different levels of crop density and leaf area index, based on the bands and the algebraic formulas used in its design. However, the study of some perennial crops such as alfalfa, which are harvested several times annually, is very complicated and has received less attention. Therefore, in this paper, the most important vegetation indices developed to estimate alfalfa yield are using Sentinel-2 time series images. In this research, 144 alfalfa samples were collected periodically in a destructive way from alfalfa farms of Magsal Agricultural and Production Company (Qazvin) near the time of satellite pass, and then the efficiency of 10 of the most famous vegetation indices to estimate alfalfa yield was evaluated based on Sentinel-2 images. The results of this research showed that the estimated alfalfa yield using the index had the highest correlation ( ) and the lowest root-mean-square-error (RMSE = 0.316 ) compared to the field data collected in the middle of August. In addition, the results showed that the red edge indices did not solve the saturation problem of vegetation indices and that the green vegetation indices were more capable of estimating alfalfa yield than the red edge indices.

Snow is a major source of water flow in each region. Therefore, knowledge of the spatial and temporal distribution of snow is essential for proper management of water resources in the region. Due to the severe physical conditions of mountainous environments, there is no permanent ground measurement for estimating snowfall resources and the establishment of a database. So, using remote sensing data to monitor snow level changes is very effective. Therefore, the aim of this study was to investigate the temporal and spatial variations of snow cover in Kurdistan province using MODIS (MOD10A1, MOD10A2) snowstorm products in the 17-year period (2000-2017). Also, to evaluate the accuracy of the images and to analyze the relationship between snow changes with rainfall and temperature data, the synoptic station data of the study area was used. The results of the evaluation of the images with the weather station data show that these images have the appropriate accuracy in extracting snow surfaces. Also, the results of snow cover variations in Kurdistan province indicate that the highest snow cover area was in 2000, 2001, 2004, 2006, 2007, 2008, 2010, 2011, 2012, 2013, and 2015, respectively, and the lowest in the years 2005, 2009, 2016 and 2017, with the largest snow cover area in December 2007 with a 2.8914 square km area. The study of snowfall variations in the province shows that the highest snowfall in the province from November to March was in the city of Diwandareh (November 2004, 59.57%) in Bijar (Feb. 2000, 25.93%) and Qorveh city (January 2017, 25.38%). Also, the analysis of the relationship between snow melting and climatic data shows that in the months of April and May rainfall increased and in June, with decreasing rainfall, the increasing trend of temperature caused the snow depths to melt in the province.

One of important objectives in sustainable agriculture is preservation of healthy ecosystems with focus on natural aquatic and terrestrial resources management in order to accomplish food security at local and global scales. Time-series remotely sensed datasets are precious and valuable resource of temporal and spectral information that could support researchers to access field management goals. Farm management have been always encountered some challenges such as lack of access to quantitative and qualitative information of agricultural crops. This research aims to develop crop and field condition indices using time-series of NDVI (Sentinel-2) and crop type maps of Moghan Agro- Industry (MAI) in 2016-2017 and also Shahid Rajaei Agro-Industry (SRAI) in 2017-2018. Then we tried to identify parts of the fields that are affected by Environmental factors such as disease, pest, weed, soil-related deficiencies and uneven distribution of water due to Inefficient irrigation system. To this end, Time-series of NDVI for four crops (wheat, maize, alfalfa and sugar beet) in various fields was provided. Finaly, field and crop condition indices were developed to show the variations of crop in each field and also the fields in comparison with each other. Finally, the proposed indices showed high accuracy with ground observations. The results were 88.88% for Alfalfa fields in MAI, and 94.11% for wheat fields in SRAI. After evaluation of the results of indices with ground observations, it was revealed that where field (homogeneity) index is low, growth limiting factors are activated.

Satellites acquire data in low, medium, and high spatial resolutions. Freely-available high temporal resolution images are often acquired in medium (or low) spatial resolution and high spatial resolution images usually suffer from a low temporal resolution or from high costs. Moreover, high spatial resolution images are prevented to use in modeling of processes such as evapotranspiration due to the lack of thermal bands. Evapotranspiration mapping with a high spatial and temporal resolutions have been always one of the main subjects in the field of remote sensing. Daily evapotranspiration mapping with a 30 meter spatial resolution is the aim of current study. The case study of the research is Amir- Kabir agro-industrial farms. For this purpose, among 36 bands of MODIS image, those being more spectrally similar to Landsat bands were selected. Then, SADFAT and STARFM algorithms were applied on Landsat 8 and MODIS images to simulate visible and infrared bands with daily temporal resolution and 30-m spatial resolution. Afterward, the simulated bands were used as input for SEBAL algorithm to calculate actual evapotranspiration. Comparing the results with the actual evapotranspiration derived from FAO-Penman-Monteith equation indicated a RMSE of 2.53 mm/day and R2 of 0.69. Also, an RMSE of 0.68 mm/day and R2 of 0.94 were derived when the actual evapotranspiration derived from the downscaled bands were compared with that derived from the Landsat-8 bands. Accordingly, these results showed the efficient performance of the downscaling framework proposed in this study.