Showing papers in "International Journal of Remote Sensing Applications in 2012"

Analysis of Forest Cover Change Detection

Abstract: While the concepts of change detection analysis is not new, the emergence of new imaging sensors and geospatial technologies has created a need for image processing techniques that can integrate observation from a variety of different sensors and datasets to map, detect and monitor forest resources. In addition to timber, forests provide such resources as grazing land for animals, wildlife habitat, water resources and recreation areas and these are threatened constantly by both human impacts like forest fires, air pollution, clearing for agricultural uses, and illegal cutting. Farming activities, continued sand winning operations and the allocation of plots of land to prospective developers in and around the catchments of the Owabi dam pose a serious threat to the forest covers and the lifespan of the dam. The overall objective of this study is to map out and analyze the structural changes of forest cover using Landsat and ASTER imageries of the study area. A supervised classification was performed on three multi-temporal satellite imageries and a total of eight major land use and land cover (LULC) classes were identified and mapped. By using post-classification techniques, from 1986 to 2002 and 2002 to 2007 the forest cover has decreased by an amount of 2136.6 ha and 1231.56 ha respectively representing 24.7% and 14.2%. Generally, the results indicate that from 1986 to 2007, forest cover reduced by 3368.16 ha, representing 38.9%. Decrease in vegetation has been as a result of anthropogenic activities in the study area. An NDVI analysis was performed on these images and it was noted that there was no significant difference between the NDVI classification and the supervised classification of the images. Overlay of the reserved forest of the 1974 and the classified map of 2007 shows vegetation changed during 1986-2007 remarkably.

Relationship between Topographic Variables and Land Cover in the Simen Mountains National Park, a World Heritage Site in Northern Ethiopia

Abstract: Altitude, aspect, and slope influence the distribution of land cover types. This paper shows the relationship of land cover distribution with these topographic variables. Geographic information system (GIS) and remote sensing (RS) technologies were used. Information on land cover was obtained by digital classification of a landsat satellite image of 2003. A digital elevation model (DEM) was used to generate elevation, slopes, and aspect information. The land cover map was overlaid with altitude, slope and aspect maps. The result gives quantitative data on the occurrence of the land cover classes of Erica- dominated forest, mixed forest, shrubland, agriculture and grassland as a function of altitude, slope and aspect. Forests were found mainly on north- and northwest-facing slopes of medium and high inclination. Agriculture prevailed at altitudes of 3,000- 3,500 m at gentle slopes of different aspects, while grassland dominated at 3,500-4,000 m on gentle, mainly south-facing slopes. Further study is suggested to investigate the significance of these findings for wildlife habitat distribution and, subsequently, for park management.

Spatial Variability of Ground Water Quality in Mathura District (Uttar Pradesh, India) with Geostatistical Method

Abstract: Groundwater is one of the major sources of water in arid and semi -arid regions. Groundwater quality data and its spatial distribution are important for the purpose of planning and management. Geo-statistical methods are one of the most advanced techniques for interpolation of groundwater quality. In this study, kriging methods were used for predicting spatial distribution of some groundwater quality parameters such as: Ca, Mg, Na, K, TDS, EC, Fˉ, HCO3ˉ, NO3ˉ, Clˉ, SO4and PO4. Data were collected from 13 wells in Mathura district (Uttar Pradesh, India). After normalization of data, semivariogram was drawn. For selecting suitable model for fitness on experimental semi-variogram, residual sum of squares (RSS) value was used. Use of geo-statistics (i.e., kriging) on our well sampling results provided valuable insight on the nature of the spatial and temporal variability of groundwater quality parameters. In analysis, found high values of NO3ˉ (=104.77 mg/l), K (=141.51 mg/l), PO4 (=2.99 mg/l) and high Fˉ value with a maximum of 4.6 mg/l (at Shahpur) are observed in ground water samples. Keywords-component; Geo-statistical methods, Spatial distribution, Interpolation and Ground water quality

Comparison of Large Aperture Scintillometer and Satellite-based Energy Balance Models in Sensible Heat Flux and Crop Evapotranspiration Determination

Abstract: The estimation of crop water use or evapotranspiration (ET) is an important aspect of water management especially in arid and semi-arid regions. Various methods have been used in the estimation of ET including remote sensing (RS) based models, and these have an added advantage of estimating ET over a large area (e.g., regionally). This study looked at two models of estimating ET; Mapping evapotranspiration at high Resolution with Internalized Calibration (METRIC) and the Surface Energy Balance Algorithm for Land (SEBAL). Satellite images from Landsat 5 for 2010 for two alfalfa fields in Rocky Ford, Colorado, were processed and analyzed to obtain sensible heat flux (H). Both RS models employ the energy balance (EB) method and estimate net radiation (Rn) and soil heat flux (G) similarly. However they differ in the approach to calculate H. Since ET is determined as a residual in the EB equation, the accurate estimation of H becomes critical. The objective of the study was to assess the RS estimates of H with H measured using a Large Aperture Scintillometer (LAS). Further comparison was done for ET. Results indicated that METRIC more accurately estimated H and ET than SEBAL. For hourly ET, SEBAL showed a relative error up to 38% while METRIC resulted in a relative error up to 11%. Both models reported larger errors for dry fields depicting smaller fractional vegetation cover values. The results of this study indicate that there is an opportunity to improve the RS methods discussed by incorporating surface heterogeneity and perhaps the correction of radiometric surface temperature for atmospheric effects.

Application of Genetic Algorithm and Support Vector Machine in Classification of Multisource Remote Sensing Data

Abstract: The use of multi-source remote sensing data for improved land cover classification has attracted the attention of many researchers. On the other hand, such an approach increases the data volume with more redundant information and increased levels of uncertainty within datasets, which may actually reduce the classification accuracy. It is therefore an essential, though challenging task to select appropriate features and combine datasets for classification. The combination of feature selection techniques using the Genetic Algorithm (GA) and Support Vector Machines (SVMs) classifiers has been used in various application fields in a number of studies on classification of hyperspectral data. However, the performance of this technique for classifying multi-source remote sensing data has not been well evaluated in the literature. In this study, the GA-SVM model was proposed and implemented to classify multiple combined datasets, consisting of Landsat 5 TM, multi- date dual polarization ALOS/PALSAR images and their multi- scale textural information. The performance of the proposed method was compared with that of the traditional stack-vector approach. A large number of different combined datasets were generated and classified. It is revealed that the proposed method is very efficient for handling multisource data. Results indicated that the GA-SVM approach clearly outperforms the stack-vector approach, with significantly higher classification accuracy and much fewer input features. The highest classification accuracy achieved was 96.47% with only 81 out of 189 features being selected. This study also demonstrated the advantages of using multi-source data over single source data.

Radar and Optical Parallel Modelling of Forest Remote Sensing Data

Abstract: This paper describes a parallel modeling of Remote Sensing radar and optic forest data which aims at retrieving forest parameters. It describes the dual model including a forest growth model fed with biophysical parameters (biomass, leaf moisture content…). The geometrical description is then the input of an optical model adapted to simulate hyperspectral information in the [0.4-2.5 μm] spectral domain, giving reflectance spectra, and a Synthetic Aperture Radar (SAR) model, giving the polarimetric and interferometric observables. As an illustration, the first results obtained by both models outputs are presented.

The UAV Video Image Stitching Based On Improved Moravec Corner Matching Method

Abstract: As a kind of new image sources, UAV (Unmanned Aerial Vehicle) Video is used more and more widely. In this paper, the image matching based on corner feature is used to achieve image stitching. Moravec operator is a kind of simple and efficient method for corner feature extraction. In this paper we do special treatment on it, which covers the distributed conjugate points over full frame with virtual grid to improve the precision of corner feature extraction. In the process of image mosaic, we use fade in/out method to achieve video image seamless stitching. KeywordsUAV Video; Moravec; Feature Extraction; Image Stitching

Iterative Sparse Maximum Likelihood-based Algorithms with Applications to SAR Imaging

Abstract: This paper presents a series of iterative sparse maximum likelihood-based approaches (SMLA) with applications to synthetic aperture radar (SAR) imaging. By using a particular form of Gaussian signal prior, iterative analytical expressions of the signal and noise power estimates are obtained by iteratively minimizing the stochastic maximum likelihood (SML) function with respect to only one scalar parameter at a time, resulting in power-based SMLA approaches. However, these power-based sparse approaches do not provide the phases of the unknown signals. To address this problem, a combined SMLA and Maximum A Posteriori (MAP) approach (referred to as the SMLA-MAP approach) for estimating the unknown complexvalued signals is proposed. The SMLA-MAP derivation is inspired by the sparse learning via iterative minimization (SLIM) approach, where a modified expression of the SLIM noise power estimation is proposed. We also show that SLIM can be viewed as a combination of the deterministic ML (DML) and iteratively reweighted least squares (IRLS) approaches. Finally, numerical examples of SAR imaging using Slicy data, Backhoe data and Gotcha data are generated to compare the performances of the proposed and existing approaches. KeywordsSynthetic Aperture Radar (SAR); SAR Imaging; Sparse Signal Recovery; Maximum Likelihood (ML); Maximum A Posteriori (MAP); Sparse Learning via Iterative Minimization (SLIM); Iterative Adaptive Approach (IAA).

Identification of Hotspots and Safe Zones of Crime in Uttar Pradesh, India: Geo-spatial Analysis Approach

Abstract: The crime is an act that an offence against the public and the perpetrator of that act are liable to legal punishment It is closely associated with geographical and demographic variables Present study aims to identify the area suffering major crimes named hotspot and the area with fewer crimes named safe zone with respect to different heads of crime against body The data collected by State Crime Record Bureau, Uttar Pradesh, India are taken for study, and using the cluster analysis, the hotspots and safe zones of crime are identified Key wordsSpatial Analysis; IPC; Crime Against Body; Cluster Analysis

Preparation of spherical monodispersed titanium dioxide by Microwave assisted

Abstract: Spherical Titania (TiO2) with narrow size distribution and nice dispersibility has been obtained by hydrolysis in the mixed solvent of 1-propanol to de-ionized water under microwave assistance. The results show that the main effects on preparation of spherical TiO2 with small particle size, narrow size distribute and nice dispersibility include precursor concentration, microwave heating method and pH value. And microwave power has no obvious effect on particle morphology and size distribution. Keywords-Microwave Assisted; Spherical Titanium Dioxide; Mono-dispersed

Ground-Based Scatterometer Measurements and Inversion of Surface Parameters Using Neural Networks

Abstract: In this study, a multi-band FM-CW ground-based scatterometer is used to measure the backscattering coefficient of bare soil surface. Combined with the neural network (NN) and the advanced integrated equation model (AIEM), it inverses all the soil parameters, including dielectric constant, root mean square (RMS) height and correlation length. According to different data sources, the NN can be constituted as different input-output mapping mode. The NN is trained by using theoretical data that are simulated by the AIEM. The measured data are an input to the NN to inverse the soil parameters. The inversion results have a better consistency with the sampling parameters of soil. More redundancy scattering data can improve accuracy and stability of the inversion. In addition, the scattering measurement experiment is an effective mean of studying scattering model and the inversion algorithm of microwave remote sensing. KeywordsScatterometer; Neural Network; AIEM; Roughness; Dielectric Constant; Soil Moisture

Multi-Temporal Remote Sensing Image-Based Extraction on the Crops Planting Information

Abstract: The crop planting information extraction is crucial to the estimation of crop output, the key of which is to speedily and accurately extract planting information through the remote sensing image. The multi-temporal remote sensing data, together with the supervised classification and decision tree classification method, are used in this study to speedily and accurately extract crop planting information from TM/ETM+ remote sensing images and sixteen MODIS time series remote sensing images, to interpret major crops in the Heilonggang area. Overall, classification accuracy is up to 91.3%, compared with one simple supervised classification of TM images. The relative errors of cotton, maize, wheat and vegetables are reduced by 1.3%, 20.5%, 2.0% and 13.8%, respectively. It has been proved that this method has high accuracy and it can serve as an effective index for reflecting the crop planting distribution. The data can provide important scientific basis for the adjustment of the major crop planting structure in the Heilonggang area, and could also provide reference for information extraction in other areas.

Application of Geospatial Technologies to Determine Imperviousness in Peri-Urban Areas

Abstract: In homogeneous (either urban or rural) areas it is generally possible to get a better land use classification due to the distinct spectral reflectance values between different types of land use. But in peri-urban areas due to dual influence of rural and urban land covers, it is difficult to find out the land uses as identical reflectance values can correspond to different type of land uses serving different types of functions. Hence the quality of classification is poorer both in terms of the number of individual classes that can be identified and the accuracy with which these classes can be determined. To minimize this type of misclassification in land use classification, additional tools like GIS derived data has been used in the current study and hence a comparison is presented between the imperviousness obtained by two methodologies. The study can be useful for accessing the accuracy of runoff prediction by employing the imperviousness obtained above in the hydrologic model.

Design and Implementation on Water Environmental Data Sharing Platform

Abstract: Water environment problem of large river basins is increasingly serious in China. However, most of the data provided by the entire water environment monitored department is relatively independent and its format has various types, which make it difficult to solve the problem in a comprehensive way as to how to deal with the worsening water environment. To solve the problem, design and implementation on the sharing platform are expatiated based on the author's more than 2 years' practice on Water Project. As achieved certain effect, this study not only has built a new efficient and accurate geographical information support platform and services which is cross-industry, cross-sector and cross-platform, but also has implemented information exchange on multiple fields. Thus it provides high quality and efficient services of water environmental data to the departments of urban planning, construction and management.