Increasing demand for groundwater for agricultural and industrial needs puts pressure on the water table, especially in arid and semiarid climates. In the Kabudar Ahang plain, in Hamedan province, groundwater overexploitation for agricultural and industrial application has increased significantly over recent decades. The InSAR technique is used to monitor the subsidence induced by groundwater overexploitation in the Kabudar Ahang plain from 2003 to 2010. For this purpose, three different Envisat tracks are used. Interferogram stacking and time-series analysis are performed to study the short-term and long-term behavior of the subsidence. Interferogram time-series analysis from 2003 to 2006 estimates an average subsidence of 19 cm per year with a maximum of 25 cm. The maximum rate of 27 and 28 cm per year is estimated from interferogram stacking in 2004 to 2005 and 2007 to 2010, respectively. The results are further compared with the geological and hydrological information to investigate the relation between the subsidence and groundwater level variations. A high correlation is found in areas affected by subsidence and the rate of water level drop in 10 piezometric wells. The results show that a large part of the plain subsides, mostly in the south and southeast where the aquifer is thicker and in the areas with the highest water table dropdown of about 100 m. With the continued use of groundwater resources, subsidence is another hazard in this plain in addition to the sinkholes.
Remote sensing is a useful tool for detecting change over time. We introduce a hybrid change-detection method for forest and protected-area vegetation and demonstrate its use with two satellite images of Golestan National Park in northern Iran (1998 and 2010). We report on the advantages and disadvantages of the hybrid method relative to the standard change-detection method. In the proposed hybrid algorithm, the change vector analysis technique was used to determine changes in vegetation. Following this, we used postclassification comparison to determine the nature of the changes observed and their accuracy and to evaluate the effects of different parameters on the performance of the proposed method. We determined 85% accuracy for the proposed hybrid change-detection method, thus demonstrating a method for discovering and assessing environmental threats to natural treasures.
This paper proposes an automatic approach for building extraction in suburban and rural areas by integrating the light detection and ranging (LIDAR) data and WorldView imagery. This approach consists of two major steps: building detection and 2D building reconstruction. In the first step, a normalized digital surface model (nDSM) is produced from LIDAR data. Then a building mask is obtained from nDSM based on the elevation and roughness analyses. In the second step, the building mask, along with orthoimage are used to determine preliminary position of the building boundaries. Then a least square line fitting process is applied to produce line segments. Finally the boundaries of buildings are reconstructed by grouping line segments, and intersection of the favorable groups. The proposed approach has shown a 92.5% completeness and 94.75% correctness.
The development of the polarimetric synthetic aperture radar (PolSAR) applications has been accelerated by coming of
new generation of SAR polarimetric satellites (TerraSAR-X, COSMO-SkyMed, RADARSAT-2, ALOS, etc.). The aim
of this article is to extract the information content of the polarimetric SAR data. Cross products of four channels "HH,
HV, VH, and VV" could be at least nine features in vector space and by applying the different class separability
criterion, the impacts of each feature, for extracting different patterns, could be tested. We have chosen the large distance
between classes and small distance within-class variances as our criterion to rank the features. Due to high mutual
correlation between some of the features, it is preferable to combine the features which result in the lower number of
features. Also the computational complexity will be decreased when we have lower number of features. Due to these
advantages, our goal would be to decrease the number of features in vector space. To achieve that, a subset of ranked
features consists of two to nine ranked features will be classified and the classification accuracy of different subsets will
be evaluated. It is possible that some of the new features that have been added to the old subsets change the classification
accuracy. Finally different feature subsets which were selected based on the various class-separability approaches will be
compared. The subset that gives the highest overall accuracy would be the best representative of the nine originally