ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY
Year:2016 | Volume:4 | Issue:3|Papers Count:8

Cultivation of agricultural land in the basin Zarineh has changed over the years and as one of the main reasons for reducing the inflow to Lake Urmia has been raised. Due to the possible development, Estimation of prospects of changes in the basin can play in key role in taking effective decisions and provide guidelines for dealing with the environmental crisis Lake Urmia is facing with. In this study has been tried to extract and assess the historical changes in land use in the Zarineh basin using satellite images and a projection of the future land use changes has been provided. In this regard, a combined of CA-MARKOV method was used. Results show that between 2000 and 2013, on average, most of changes lean toward increase in land use, such as irrigated agriculture (40%) and gardens (57%) and also to reduce pasture land (5%) and dryland farming (10%). This trend has also been observing for horizon of year 2020.

Integration of global positioning system (GPS) and inertial navigation system (INS) is used in airborne gravimetry to gravity field recovery. Since GPS computed position is noisy therefore the GPS acceleration which is the result of twice differentiation of GPS position will be too noisy as well. In this paper IIR low-pass filter and Kalman filter are used to smoothing the GPS acceleration and their result compared to B-spline smoother result. B-spline smoothing accuracy is reported about 1mGal in this paper data, therefore B-spline smoothing considered as a reference smoothing method. The correlation of IIR low-pass filter and Kalman filter results with B-spline smoothing result is about 97.55 and 99.83 percent, respectively. It shows that the Kalman filter result is closer to B-spline smoother. On the other hand, along with ease of design of IIR lowpass filter some other advantages such as fast computing algorithm in signal processing unlimited response hit and less memory requirement are worth mentioning. Therefore, in project with huge among of data the IIR low-pass filter could be efficient and causes the time and cost saving. Mentioned smoothing methods can also be used in INS instrumental noise reduction. Therefore, less accurate INS can be used in integration with GPS, which causes the INS cost saving and project productivity promotion.

Biomass estimation plays an important role in the investigation of climate changes and global warming on terrestrial ecosystems. In recent years, related researches show PolInSAR techniques can significantly improve biomass estimation. Tree height can be estimated using PolInSAR techniques which by using that, the tree’s biomass can also be estimated. It is known that coherence optimization has an effective role on improvement of tree height estimation using PolInSAR. In this paper, various tree height estimation methods, such as coherence amplitude inversion algorithms, DEM differentiating, and combined methods are validated and compared using simulated data. Coherence optimization methods which are applied in these algorithms are numerical radius and phase diversity coherence optimization algorithms were estimated respectively. According to the fact that phase diversity algorithm was a phase based method, it didn’t have significant effect on improvement of tree height using coherence amplitude algorithm. However, improvement of tree height estimation by DEM differentiating method is obvious. In comparison to the previous method, although numerical radius method is time consuming and has a complicated process but it improves tree height estimation in great deals.

Satellite image providers usually present Rational Polynomial Coefficients (RPCs) as a user friendly solution for georeferencing of images. As RPCs are determined independently for each image scene, there are both absolute and relative georeferencing biases will in stereo scenes. Relative orientation of a stereo scene needs some conjugate image points. Speeded up robust features (SURF) operator is a powerful computer vision algorithm for image feature extraction and matching. Usually some of the features are not actually matched and are outliers. In this paper RANSAC+RPC algorithm is employed to simultaneously detect these outliers and modify the relative bias of RPCs. Our experiments on GeoEye-1 over Qom city and IRS-P5 over Rudehen district, both in central Iran, demonstrated the capability of our proposed algorithm. Though the RPC modification was done robustly for relative orientation of stereo scenes, yet improvement in the reconstructed 3D coordinates are in the range of sub-pixel accuracy. Our experiments demonstrate that the relative RPC shift and drift error will not cause any accuracy improvement in 3D reconstruction problem.

There is the possibility of complete 3D modeling using terrestrial laser scanner. To have a complete coverage of the target region for the scan, the device must be located in various locations and carried out measurement operations. Nevertheless, the movement to increase the deployment requires more field measurements which respectively will increase the cost and time. In this paper, the goal is to provide a tool to assess the selected locations for the deployment of terrestrial laser scanners. In this regard, to achieve this goal, the genetic optimization algorithm is being used. In the proposed method, the total registered point cloud implementation of all stations in the range of scanning devices is used as the search space for the genetic algorithm. The cost function with two goals, one reduction in occlusion areas and the other is to take fewest possible points for placements. By selecting a random set of candidate locations for placement as an initial response, the algorithm will start to obtain optimal layout placement and in the search space, during successive iterations by applying the selection operators, crossover and mutation will be provided. In this process, the optimal choice of device placement is automatic and repetitive and ensure correct alignment will achieve with the minimum number of points required for full measurement. The results show the genetic optimization algorithm to optimize the laser scanner device placements across a large number of the selected candidate. Thereby, it can be created a tool to assess the efficiency of the selected placements. The chance of 99% scanning of the area with absolute certainty using the proposed method with the least possible number of stations was established.

Natural disasters such as floods, earthquakes, hurricanes and tsunamis have always been the greatest human problems. Among them, the earthquakes, because of its unpredictability, are more important than the others. After an earthquake, damage assessment plays an important role in leading rescue teams in order to minimize the damages. Meanwhile, damage map, a map that demonstrates collapsed buildings with their degree of damage count as one of the most important information sources for crisis management. In this paper, we propose an algorithm for automatic generation of damage map after an earthquake using post-event LiDAR data and pre-event vector map. In the proposed method, in order to find the location of all buildings on LiDAR data, in the first step, LiDAR data and vector map are registered by using a few numbers of ground control points. Then, the buildings, in vector map, are overlaid on the LiDAR data to extract all the pixels inside buildings area. After that, Using SVM classification algorithm all the extracted pixels are classified into two classes of “debris”, “intact”. Next, damage degree for every building is estimated based on the relation between the numbers of pixels labeled as “debris” class to the whole building area. To evaluate the ability of the proposed method in generating damage map, a dataset from Port-au-Prince, Haiti’s capital after the 2010 Haiti earthquake was used. In this case, after calculating all buildings in the tested area using the proposed method, the results were compared to the damage degree which estimated through visual interpretation of post-event satellite image. Obtained results proved the reliability of the proposed method in damage map generation using LiDAR data.

The megapolis areas are new types of urban settlements created in recent decades along with rapid urbanization. These areas constructed by clusters of small and large urban patches with various growth patterns. Spatial characteristics of urban patches are affected by some driving forces such as closeness to cities Central Business Center CBD and transportation network. The Cellular automata as a most common model for simulating urban growth, is unable in modeling spatial configuration of urban patches because of bottom up procedure and despite of high simulation power at cell level, CA has weaker performance in patch level. So in this study a method is presented for simulation of urban patches growth that is integrated with Logistic CA to modeling urban growth. In this method, on the one hand the growth potential map derived using logistic regression and on the other hand size and growth type of patch in each location is derived using integration of driving forces of growth patterns of urban patches. Finally according to proposed framework, a patch is constructed around selected cell and urban growth map will be prepared. The proposed model is implemented in the Tehran’s megalopolis area in 1379-1385-1391-1397 periods. The overall accuracy and FOM of results is equal to 91.01 and 37.96, respectively that are better than logistic CA model. Also the results of validation of produces urban growth map by using spatial metrics reviled high precision of methodology in simulation of spatial configuration of urban pattern.

The gravity field modeling can be performed in global or local scales utilizing satellite, airborne, terrestrial gravity data or a combination of these observations. One of the common methods in gravity field approximation is to use Spherical Harmonic expansion. Due to the global characteristics of the Spherical Harmonic base functions, a small local signal variation can change all the coefficients in the expansion, and therefore, they are not a suitable choice for regional applications. In order to overcome this problem, there are several groups of base functions, including Radial Basis Functions (RBFs). In gravity field modeling using RBFs, the disturbing potential is represented by a linear combination of an infinite set of RBFs. Hence, any linear functional of the disturbing potential, such as gravity anomaly or gravity disturbance, can be also expressed based on the RBF expansion. Thus, measurable quantities of the Earth's gravity field can be utilized in order to determine the RBF parameters in gravimetric modeling. In this study, system of observation equations is set based on the Radial Multi-Poles of order 2 and free-air gravity anomalies and unknown parameters, including RBF centers, RBF bandwidths (or depths) and scaling coefficients, are determined using a least-squares method. In fact, the Levenberg-Marquardt algorithm is applied as a non-linear regularization method to simultaneously optimize all the RBF parameters. In order to enhance the numerical efficiency of this algorithm, a novel scheme is proposed to initialize and update the regularization parameter. Finally, numerical results obtained from the modified Levenberg-Marquardt algorithm are compared with the ones obtained from the simple form of this algorithm. Applying the proposed modifications to this algorithm, the unknown parameters are determined in a fast procedure and with a significant reduction in the number of iterations. Moreover, these modifications can increase the probability of convergence of the solution to the global minimum.