• 期刊
• OpenAccess

GIS Analysis of Crime Incidence and Spatial Variation in Thiruvananthapuram City

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.

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• OpenAccess

ML-based Approaches for Joint SAR Imaging and Phase Error Correction

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 PESLIM 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.

• 期刊
• OpenAccess

Using of Laser Scanning and Dense Stereo Matching for 3D Documentation and Virtual Reconstruction of the Ancient Sama Monastery/ Jordan

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.

• 期刊
• OpenAccess

The Relationship between Vegetation and Rainfall in Central Sudan

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.

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• OpenAccess

Estimation and Mapping of Carbon Stocks in Bosomkese Forest Reserve

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.

• 期刊
• OpenAccess

A Change Detection Method of Multi-temporal SAR Images Based on Contourlet Transform

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.

• 期刊
• OpenAccess

Search and Rescue Operations with an Unmanned Helicopter

We tested the performance of both electro-optical (EO) and long-wave infrared (LWIR) video sensors on an unmanned helicopter in a simulated Search and Rescue (SAR) setting. Our objectives were to examine whether humans and other objects associated with (SAR) operations could be reliably observed in the video sensor imagery in direct light and shade. Our unmanned helicopter flight occurred in a forest in the U.S. Pacific Northwest under a Certificate of Authorization (COA) issued by the Federal Aviation Association (FAA). Analysis of the EO and LWIR video imagery from several hover positions determined that many of the SAR objects could be reliably detected. Detection success was heavily influenced by factors such as distance, object size, material and color. In direct sun light, the EO sensor performed more reliably than the LWIR sensor in detecting most objects. The LWIR sensor, however, appeared to be preferable to the EO sensor when tracking human subjects in shady environments. We believe unmanned aircraft can offer SAR operations flexibility in reaching areas that might endanger manned flight crews. In addition, unmanned aircraft also have the capability to fly closer to the ground, and at slower speeds than some manned aircraft, which potentially leads to greater image resolution and ultimately increases detection success.

• 期刊
• OpenAccess

Modeling Soil Salinity and Mapping Using Spectral Remote Sensing Data in the Arid and Semi-arid Region

Arid and semi-arid region is one of the most areas exposed to the risk of salinization, where the soil salinity is the one of the severe global problems and environmental issues which faces the world; in addition, it has effects on land cover and productivity of the agriculture land leading to a decrease fertility and quality of the soil. This degradation occurs in agriculture practices (Human activities) or naturally, therefore it is important to detect the soil salinity at an early stage through mapping and monitor the salinization to support soil reclamation and increase effective and productivity of the soil that decreases the effect of salinization in soil quality and helps prevent increasing the impact of salinity in the future. This paper aims to study possibility of using an image of the spectral remote sensing data and field survey to sampling soil and measure salinity using significant parameter e.g, Sodium Absorption Ratio (SAR) to develop experimentally model allowing the mapping of soil salinity in the arid and semi-arid region.

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• OpenAccess

Sugarcane Water Productivity Assessments in the São Paulo state, Brazil

São Paulo state, Brazil, has been highlighted by the sugarcane crop expansion. The actual scenario of climate and land use changes, bring attention for the large-scale water productivity (WP) analyses. MODIS images were used together with gridded weather data for these analyses. A generalized sugarcane growing cycle inside a crop land mask, from September 2011 to October 2012, was considered in the main growing regions of the state. Actual evapotranspiration (ET) is quantified by the SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm, the biomass production (BIO) by the RUE (Radiation Use Efficiency) Monteith's model and WP is considered as the ratio of BIO to ET. During the four generalized sugarcane crop phases, the mean ET values ranged from 0.6 to 4.0 mm day^(-1); BIO rates were between 20 and 200 kg ha^(-1) day^(-1), resulting in WP ranging from 2.8 to 6.0 kg m^(-3). Soil moisture indicators are applied, indicating benefits from supplementary irrigation during the grand growth phase, wherever there is water availability for this practice. The quantification of the large-scale water variables may subsidize the rational water resources management under the sugarcane expansion and water scarcity scenarios.

• 期刊
• OpenAccess

Prediction of Land Surface Temperature (LST) Changes within Ikom City in Nigeria Using Artificial Neural Network (ANN)

Land Surface Temperature (LST) is one of the factors associated to urban heat rise and micro climatic warming within a city. Researches relating to the development new technologies or the improvement on the existing ones are very important in urban climate studies. This paper expounds our study on the simulation and prediction of specific future time LST quantitative trend in Ikom city of Nigeria using Feed Forward Back Propagation Artificial Neural Network technology. This study was based on time series ANN model that takes a sequence of past LST values, understand the pattern of change within the dataset and further predict or future time values. Similar studies have been carried out in this manner from our literature review but none used earth observation time series satellite data of a coarse resolution epoch interval for LST time series prediction using ANN. The novelty of this study centers on the attempt to predict some specific future time LST values city-wide using ANN from past LST values derived from earth observation remote sensing imagery (Landsat 7 ETM). The results derived from this study reaffirm the efficiency of ANN (part of deep learning technologies) in learning, understanding and making accurate predictions from a non-linear chaotic real world complex dataset.