International Journal of Remote Sensing Applications
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Open Access journal
ISSN (Print) 2226-4361 - ISSN (Online) 2226-4353
Published by Science and Engineering Publishing Company [49 journals]
- A Change Detection Method of Multi-temporal SAR Images Based on Contourlet
Abstract: A Change Detection Method of Multi-temporal SAR Images Based on Contourlet Transform
Author Shiqi HuangWenzhun HuangTing Zhan
Synthetic aperture radar (SAR) imaging is very sensitive to direction, so the information that SAR images contain is often not completely same from different directions. The information obtained from multi-directions must be more abundant and more accurate than that of from a single direction in a SAR image. The Contourlet transform is a multi-scale geometric analysis theory, holding many advantages for signal processing, such as multi-resolution, multi-direction and anisotropy; therefore, it is in favor of extracting different direction information for SAR images. According to the directional sensitivity of SAR imaging and the characteristics of multi-scale and multi direction to the Contourlet transform, this paper proposed a new SAR image change detection method based on Contourlet transform, called CTCD algorithm. Using the multi-direction characteristic of Contourlet transform, the CTCD method can get more accurate changed information for multi-temporal SAR images. The practical SAR image data is employed to test the CTCD algorithm and results show that the CTCD algorithm is a feasible change detection algorithm for multi-temporal SAR images, and it can obtain more abundant and more accurate information than the direct difference change detection (DDCD) algorithm.
- Estimation and Mapping of Carbon Stocks in Bosomkese Forest Reserve
Abstract: Estimation and Mapping of Carbon Stocks in Bosomkese Forest Reserve
Author Emmanuel DonkorEdward Matthew Osei jnrBenjamin Eric Kwesi PrahAdwoa Sarpong AmoahMohammed Yakub
Biomass estimation has become a critical element in global environmental studies, because the change in biomass is deemed as an important component of climate change. The aim of this research is to estimate and map carbon stocks in Bosomkese forest reserve using remote sensing, GIS applications and field measurement method. Out of the six carbon pools of terrestrial ecosystem involving biomass (aboveground biomass, belowground biomass, deadwood, non tree, litter and soil organic matter), carbon sequestration of three (aboveground, belowground and deadwood) were assessed. Advanced Land Observing Satellite (ALOS) image acquired in 2010 was classified using Erdas Imagine. Total of five land use/cover classes were identified; Closed canopy natural forest, open canopy natural forest, plantation, farmland and fallow land. Diameter at breast height and total height of standing trees as well as the end diameters and the length of downed deadwood were measured in fifty sample plots in the five land use classes. These measurements were converted into aboveground carbon (AGC), belowground carbon (BGC) and deadwood carbon (DWC) using allometric equations developed in 2012 by Forest Research Institute of Ghana (FORIG). Total carbon for each plot was the summation of AGC, BGC and DWC. This research showed that closed canopy natural forest (1748.37 ton/Ha) contained more carbon than the rest of the land use/cover classes. This was followed by open canopy natural forest (1164.12 ton/Ha), plantation (775 ton/Ha), fallow land (110.69 ton/Ha) and farmland (45.13 ton/Ha) in descending order of total carbon stocks. The carbon/carbon dioxide equivalent values together with the plots coordinates were used to generate carbon stock and carbon dioxide equivalent map using Geostatistics tool of ArcGIS 10.0. The total carbon stock for the whole Bosomkese forest is in the range of 2,236,938.90 – 2,865,148.33 tons and carbon dioxide equivalent in the range of 8,534,225.45 – 10,507,952.05 tons.
- The Relationship between Vegetation and Rainfall in Central Sudan
Abstract: The Relationship between Vegetation and Rainfall in Central Sudan
Author Nadir MohamedAbderrazak Bannar
Daily dynamic vegetation cover mapping at the global scale is the most important parameter to retrieve from coarse-spatial resolution global land surface optical satellite remote sensing to understand the climate change impact on the rainfall cycle and its variability in time. The objective of this research was the investigation of the change in vegetation cover dynamic in time and its relationship with rainfall in Central regions of Sudan for a decade (2000- 2010). To achieve our objective, the Normalized Difference Vegetation Index (NDVI) time series obtained from SPOT-VGT sensor, precipitation data measured over the study area by different weather stations, GIS and statistical analysis were used. The obtained results show significant level of agreement between NDVI and rainfall values during the study period (0.6 ≤ R2 ≤ 0.8). Certainly, such derived results could be useful as imputing in the carbon cycle models and/or climate impact modeling, as well the development of new policy for climate change adaptation.
- ML-based Approaches for Joint SAR Imaging and Phase Error Correction
Abstract: ML-based Approaches for Joint SAR Imaging and Phase Error Correction
Author Habti Abeid
This paper addresses a series of iterative sparse recovery approaches with application to the synthetic aperture radar (SAR) imaging which suffers from motion-induced model errors. These types of errors result in phase errors in SAR data, which cause defocusing of the reconstructed images. The proposed phase-error correction approaches combine the maximum a posterior (MAP) algorithm and the iterative sparse maximum likelihood-based (SMLA) approaches (referred to as the PE-MAP-SMLA approaches) to solve a joint optimization problem to achieve phase errors estimation and SAR image formation simultaneously. A new PE-SLIM approach is also proposed that extends the idea of the classical sparse and learning via iterative minimization (SLIM) approach. A closed-form expression for the recursive estimate of the phase errors parameters is derived. A general form of each of these iterative approaches consists of three steps, the first of which is for image formation, the second is for phase errors estimation and the last is for nuisance parameters estimation. The proposed approaches can correct the phase errors accurately, and the reconstruction quality of the SAR images can be improved significantly. Finally, simulation results of 1-D spectral estimation and 2-D SAR imaging examples are generated to show the effectiveness of the proposed approaches.
- Using of Laser Scanning and Dense Stereo Matching for 3D Documentation and
Virtual Reconstruction of the Ancient Sama Monastery/ Jordan
Abstract: Using of Laser Scanning and Dense Stereo Matching for 3D Documentation and Virtual Reconstruction of the Ancient Sama Monastery/ Jordan
Author A'kif Al-FugaraRida Al-AdamatMwfeg Al HaddadYahya Al-ShawabkehMohammed El-KhaliliDaifallah Obaida
Sama Monastery is one of the best preserved architecture built in the third century AD at Jordan’s eastern desert. Before the twentieth century, the roofing materials of Monastery, even most of the arch stones, were carried off for the construction of bridges and ferries above the valleys and water channels in the region. Documenting the Monastery which remains in scientific and precise ways is an important goal in order to protect and preserve this important part of the cultural heritage of Jordan. In this study, laser scanner and digital photogrammetry have been used for this purpose. In spite of the potential of each single approach, the integration aims at supporting the visual quality as well as the geometric accuracy of the collected textured 3D models. The purpose of such visual information will serve as a tool for the identification of the nature, extent and severity of the deterioration.
- GIS Analysis of Crime Incidence and Spatial Variation in
Abstract: GIS Analysis of Crime Incidence and Spatial Variation in Thiruvananthapuram City
Author ACHU A LR S Suja Ros
The rate of crime incidents is increasing in developing countries mainly due to the unequal distribution of wealth and societal status. The present study attempts to identify and explore the rate and spatial variation of crime in Thiruvananthapuram city for a period from 2010 to 2014. The improved computer based technologies like GIS and availability of Geographic data make it possible for law and enforcement agencies to create analytical maps and various analysis to identify the crime hotspot area .The hotspot analysis in Geographic Information System is helpful for the identification of crime hotspot through spatial auto correlation, spatial analysis and interpolation. The Moran's I test statistic of spatial auto correlation has been done prior to Getis-Ord Gi* hotspot analysis to find out the clustering pattern as well as the outliers in the data. The crime hotspot analysis uses vectors to identify the locations of statistically significant crime hotspots and cold spots and IDW interpolation method is used for better visualization. These methods are applied on the crime data of Thiruvananthapuram city of Kerala state to find the hotspots for crime incidents like Murder, Robbery, Snatching and Theft.