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 Eng Pub Co. [48 journals]
- Assessment of Forest and Juniperus Phoenicea Decline in Al Jabal Al Akhdar
Using NDVI-Remote Sensing and GIS Data (2006-2013)
Abstract: Assessment of Forest and Juniperus Phoenicea Decline in Al Jabal Al Akhdar Using NDVI-Remote Sensing and GIS Data (2006-2013)
Author Bashir M. SuleimanMusbah MohamedSalah HamadSami Elmehd
Al Jabal al Akhdar, Locally known as the Green Mountain, located in Cyrenaica on the north east coast of Libya. It is a limestone plateau with maximum altitudes of about 900 meters, holds the highest plant density and species diversity within the country. Among these plants is the Juniperus phoenicea (JP) which covers about 70% of this mountain. It has been sharply deteriorated by unidentified causes over large scales. In this work, we have used remote sensing and GIS data to monitor vegetation vigor variations and study the drought related issues to forest and JP decline in the this area. The analysis of the collected data involved Topological, Geological and Geographical (TGG) data modelling over the selected area and within a period of eight years from 2006 to 2013. We have included two main data sets among few sets of satellite images that were employed in this study. The first data set was from the updated version of Shuttle Radar Topography Mission (SRTM) DEM of ~90 m spatial resolution and the second data set from Landsat 7 and Landsat 8 Level 1T images of 30 m spatial resolution. The collected data were used to construct the images and figures, to monitor and map the forest and JP decline through the normalized difference vegetation index (NDVI) and, then to illustrate the expected reasons and attributes for this decline. The study attributes the decline due to several specific indicators related to TGG inter-related factors including; altitude, slope, aspect, drainage pattern, topographic curvature, seawater intrusion, wind and soil erosions. The results were consistent and in a good agreement with several published data on the effect of topography, geology and geography on vegetation covers of similar terrain regions.
- Using Remote Sensing to Detect Change in the Ndop Floodplain Wetlands of
Abstract: Using Remote Sensing to Detect Change in the Ndop Floodplain Wetlands of Cameroon
Author Stephen NdzeidzeRichard MbihGilbert Bamboy
Using remote sensing and GIS technology to determine change in the Ndop floodplain wetland area from 1973 to 2010, six Landsat MSS, TM and Landsat ETM+ images were classified. Twelve different observed land cover and land use patterns were classified and grouped into four major categories based on the Supervised Maximum Likelihood Classification algorithm. These included the humid floodplain wetland area, agro-pastoral zones, montane forest zone and settlements. Within the wetland area in the floodplain, the reservoir shows evidence of significant fluctuations in surface area since the construction of the Bamendjin reservoir in 1975. Within the reservoir area, acute siltation has been observed since 1988 (1.3%) and this has increased in area by 4.07% in 2002 and by 4.4% in 2010. These increases account for the observed drop in the level of water in the reservoir. A significant drop has been registered in the area occupied by permanent flooded prairies of 11.19% in 1978 and 2.01% in 2010 as well as in that of seasonally flooded prairies of 12.2% in 1978 and 5% in 2010. Areas under irrigated farmland also show decreasing trends from 1988 to 2010. The swamp forest equally exhibits significant corresponding drops in area cover, which directly correlates with the draining of the flood plain for swamp rice cultivation and irrigation. Concerning the agro-pastoral landscape, the upland grazing areas are generally decreasing in area, while the mixed farming area contrarily increased from 1978 to 2010. This study thus provides base data for monitoring human impacts on the Ndop floodplain wetlands in the Upper Noun drainage basin and natural habitats, especially within and around the wetland area.
- 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.
- 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.
- 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.
- 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.
- 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.
- Search and Rescue Operations with an Unmanned Helicopter
Abstract: Search and Rescue Operations with an Unmanned Helicopter
Author M.G. WingJ. BurnettJ. BrungardtD. DoblerV. CordellJ. Session
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.
- Modeling Soil Salinity and Mapping Using Spectral Remote Sensing Data in
the Arid and Semi-arid Region
Abstract: Modeling Soil Salinity and Mapping Using Spectral Remote Sensing Data in the Arid and Semi-arid Region
Author Majed Ibrahi
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.
- Sugarcane Water Productivity Assessments in the São Paulo state,
Abstract: Sugarcane Water Productivity Assessments in the São Paulo state, Brazil
Author Antônio Heriberto de Castro TeixeiraJanice LeivasCarlos RonquimDaniel Victori
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.
- Prediction of Land Surface Temperature (LST) Changes within Ikom City in
Nigeria Using Artificial Neural Network (ANN)
Abstract: Prediction of Land Surface Temperature (LST) Changes within Ikom City in Nigeria Using Artificial Neural Network (ANN)
Author Ikechukwu MaduakoElijah EbinneYun ZhangPatrick Basse
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.
- Land Features Extraction from Landsat TM Image Using Decision Tree Method
Abstract: Land Features Extraction from Landsat TM Image Using Decision Tree Method
Author Jason YangFeihong Wan
In this paper we presented a method based on the decision tree to extract land feature information for an urban area of Taiyuan City in China. One Landsat TM image obtained on September 23, 2010 covering the entire city of Taiyuan was obtained and processed to extract information. Digital elevation model (DEM) and some derived index images about water, vegetation, and crop land were used to develop and construct the decision tree. Six general land categories including water body, developed land, bare land, grass land, forest land, and crop land of the study area were classified using the established decision tree. The results were evaluated using high resolution satellite imagery and reported in a confusion matrix table. An overall accuracy of 89.52% with a kappa statistic of 0.87 were obtained using our method, which is higher than those from other traditional methods.
- Superresolution Method Approach for Vietnam Remote Sensing Imagery
Abstract: Superresolution Method Approach for Vietnam Remote Sensing Imagery
Author Le Quoc HungDang Truong GiangNguyen Ngoc Quan
Spatial resolution enhancement of satellite imagery is one of the most important aspects in the field of remote sensing science. Resolution enhancement by up-grading satellite imaging or developing advanced optical instrument it is very costly to obtain the high resolution. On the other hand, the increase in spatial resolution has to be balanced with the state capacity in transmission rates, archiving and processing capabilities. Thus, the other parameters of satellite system must be reduced such as swath width, spectral and radiometric resolution, observation and data transmission duration. These reasons promote researchers in order to propose the approach of using multiple images for enhancing spatial resolution from low to high. VNREDSAT-1 is the first Vietnamese remote sensing satellite which was launched and has operated since 2013. This paper will present some very first result of enhancing its spatial resolution based on the super-resolution method. With this method, the 1.25m resolution was created from the 2.5m original resolution of VNREDSAT-1 image. The result shows how the improved resolution can help to explore more information of objects on the earth for serving the mission of natural resources and environment monitoring.
- Remote Sensing Application in Forest Monitoring: An Object Based Approach
Abstract: Remote Sensing Application in Forest Monitoring: An Object Based Approach
Author Bao Tran QuangHoa Nguyen Th
The objective of this study was to establish forest map in 2015 using object-based classification technique in SPOT 6 image and analyze land use/land cover changes in landscape of Yen Nhan commune, ThanhHoa province in Vietnam over a period of 15 yeas (2000-2015).Object-based methods allow integration of different object features, such as spectral values, shape, and texture. One of its strength is the ability to combine spectral information and spatial information for extracting target objects. Few studies have explored the application of object-based approaches to classify forest. This paper introduced an object based method to SPOT6 image to map the land cover in Yen Nhan commune in 2015. This approach applied multi-resolution segmentation algorithm of eCognition Developer and an object based classification framework. In addition, existed forest maps from 2000 to 2015 were used to analyze the change in forest cover in each 5 years period.The object based method clearly discriminated the different land cover classes in Yen Nhan. The overall kappa value 0.73 was achieved. The estimation of forest area was 89.05 % of total area in 2015. By overlaying achieve forest maps of 2000, 2005, 2010, the classified map of 2015 showed vegetation changed remarkably during 2000-2015.
- Detecting Flooding Trends in the Mekong Delta through Flood Ranking Based
on a MODIS-derived Time-series Water Index
Abstract: Detecting Flooding Trends in the Mekong Delta through Flood Ranking Based on a MODIS-derived Time-series Water Index
Author Keisuke HoshikawaYoichi FujiharaHideto FujiiShigeki Yokoyam
The upper part of the Mekong Delta typically suffers heavy floods from September to November each year due to runoff from the Mekong River. On the Vietnamese side of the delta, the area of paddy fields surrounded by high dykes in order to allow continued rice cultivation throughout the flooding period has been rapidly increasing since the mid-2000s. In this study, we show how the effects of high dyke construction on the flooding characteristics of surrounding areas can be detected using the normalised difference water index (NDWI) of MODIS images. Clusters generated by a k-means analysis for each year from 2000 to 2015 were ranked based on their median NDWI value. Trends of flood rankings for different pixels during the 16-year period suggest that the potential for flooding increased in the regions upstream of high dykes that are situated at the top of the delta.