Journal of Radar and Optical Remote Sensing
Volume:2 Issue: 1, Winter 2019

Estimating and determining the area under cultivation of agricultural products are among theimportant aspects in planning and decisions making. Remote sensing data can provide usefulinformation in this regard to agricultural experts by identifying the type and determining the croparea. In this research, agricultural land and garden areas as well as the land use changes wereevaluated by using Quickbird and Landsat images for 1984, 2003 and 2015 in Rudasht basin ofEsfahan. The required preprocessing was done on the images and then, the educational samples weretaken using GPS for classification by the maximum likelihood method and verifying theclassification. The geometric correction results for 1984 and 2003 images with acceptable RMSEwere 0.48 and 0.42 respectively. The image classification results of Landsat showed that theagricultural land and gardens areas are reduced by 1036.236 and 27.3146 hectares from 1984 to2015, and Quickbird images showed the reduction of 1036.236 and 119.8833 hectares from 2003 to2015. In estimating the Quickbird classification error for 2003 and 2015, the Kappa coefficient ofmaximum likelihood was 0.8576 and 0.8643 and for the years 1984 and 2015 were 0.7967 and0.8641 respectively.

In optical remote sensing, hyper-spectral (HS) image which contains color information is produced byhundreds of spectral bands. Because of the trade-off imposed by the physical constraint between spatialand spectral resolutions, the HS image has poor spatial resolution. On the contrary, the panchromatic(PAN) images have high spatial resolution but no color information. Image fusion can combine thegeometric detail of PAN image and the color information of the HS image to produce a high-resolutionHS image. The aim of this study is the fusion of Hyperion and OrbView-3 PAN images based on DiscreteWavelet Transform (DWT). Firstly, the preprocessing methods were applied on Hyperion and OrbView-3images and registration method based on nearest neighbor method were applied on two dataset. In order tofit images pixel size, the resampling operation was applied. PAN image was decomposed by DWT andthen fused by hyper spectral image with GST algorithm. DIV, CC, Q and RMSE accuracy assessmentmethods were used on final fused image to evaluate the results accuracy. The results showed that usingDWT based decomposition PAN image, preserve the spatial information during fusion rule. Also thistechnique gains high accuracy in term of spectral information of hyper spectral image.

Remote sensing provides data types and useful resources for forest mapping. Today, one of the mostcommonly used application in forestry is the identification of single tree and tree species compassionusing object-based analysis and classification of satellite or aerial images. Forest data, which is derivedfrom remote sensing methods, mainly focuses on the mass i.e. parts of the forest that are largelyhomogeneous, in particular, interconnected) and plot-level data. Haft-Barm Lake is the case study whichis located in Fars province, representing closed forest in which oak is the valuable species. HighResolution Satellite Imagery of WV-2 has been used in this study. In this study, A UAV equipped with acompact digital camera has been used calibrated and modified to record not only the visual but also thenear infrared reflection (NIR) of possibly infested oaks. The present study evaluated the estimation offorest parameters by focusing on single tree extraction using Object-Based method of classification with acomplex matrix evaluation and AUC method with the help of the 4th UAV phantom bird image in twodistinct regions. The object-based classification has the highest and best accuracy in estimating single-treeparameters. Object-Based classification method is a useful method to identify Oak tree Zagros Mountainsforest. This study confirms that using WV-2 data one can extract the parameters of single trees in theforest. An overall Kappa Index of Agreement (KIA) of 0.97 and 0.96 for each study site has beenachieved. It is also concluded that while UAV has the potential to provide flexible and feasible solutionsfor forest mapping, some issues related to image quality still need to be addressed in order to improve theclassification performance.

Almost one third of the earth is covered by soil, which has several essential parameters. The soil moisturecontent is one of the essential parameters. The current research calculates the soil moisture content. Thereare known methods to calculate soil moisture; however, a new method has been chosen for this research.Microwave imagery is a novel appropriate way to detect and calculate the amount of moisture in soil. TheSENTINEL-1 with SAR sensor has been a good satellite for research purpose. The microwaves sent bythe satellite to the earth receives the backscatters which has been directly related to the amount ofmoisture. Thus four images were obtained at different time intervals of the year; 21st November 2015, 1stMay 2016, 5th June 2016 and 29th September 2016. The study area of Miyankale is covered by fourimages. Furthermore, to calculate the moisture in Miyankale which was done by another method theresults were finally compared with the percentage measured by satellite imagery. The accuracy of satellitedata is confirmed by measuring the soil moisture by two different methods. The coefficient ofdetermination R2 has been chosen to compare the data and check the microwave imagery. TheR2coefficient is able to compare two independent data. The R2 coefficient is 0.82, 0.82, 0.78 and 0.81 fordifferent time periods. The R2 ranges from 0 to 1, as the R2 values are closer to 1 the moisture obtainedfrom SAR images is confirmed

Massive material movements are natural geomorphic processes. This process refers to separation anddownward transportation of soil and rock materials under the influence of gravity and causes the transfer of alarge amount of material, such as pebbles. In Iran, the given climate, geology and topography, massivemovements, debris, conditions results in low altitude areas, significant casualties, financial andenvironmental damages. Modeling physical processes of rockfall calls for examining the fracture of rockyelements, dimensional fall or jump, crushing, rotation, or slipping and the final subsidence, regardless of thevolume constraints of rockfall which are defined by their high energy and mobility. Dynamic processes ofrockfalls are overshadowed by spatial and temporal distribution properties, including the disruptionconditions, geometric and mechanical properties of the rock blocks and rocky slopes. One of the mostsuitable methods for identification of rockfall phenomenon is using radar interferometry (D-INSAR)technique. The study examined Haraz road with twelve Sentinel 1 sensor images from March to May 2016.Then, using an interferometry technique of radar with artificial aperture, the rockfall rate of SAR data relatedto Sentinel 1 sensor was measured, obtained in high and low pass modes. In addition, three rockfallsregistered on March 20, 2015, March 31, 2015, and May 10, 2015 were examined in this study. The resultsshowed that the rockfall times in all three pilot maps of displacement have significant changes compared tothe unchanged times in the images. Using radar satellites and differential interferometry techniques, one candetect the amount of rockfall and its location.

Subsidence is the earth’s surface movement towards down relative to a datum such as sea level. The main reason of subsidence in Iran is groundwater overuse which if not managed correctly, it causes irreparable damages. Therefore, the first step in solving this problem is identification of subsidence areas and estimating the rate which will have a significant role in controlling this phenomenon. One of the most suitable methods of identification of subsidence is using Interferometric Synthetic Aperture Radar (InSAR)technique. This method is superior to other detection T in terms of cost, precision, extent of the study area and time and it can provide an accurate estimate of the area. In this research, zone of the Rafsanjan plain has been investigated between 2006 and 2010. In order to calculate subsidence rate, SAR data related to the ASAR sensor in C-band and ALOS PALSAR in L-band were used. Generalized linear models in C-band and L-band with values of 0.91 and 0.89 and RMSE coefficient of 0.37 and 0.61 represented a strong linear relationship. Also the relationship between subsidence and the changes inpiezometric levels (groundwater extraction) in the study area showed that for each 4.7 centimeters groundwater level decrease, there has been 1 centimeter subsidence.