International Journal of Remote Sensing Applications
- OpenAccess
Science and Engineering Publishing Company,正常發行
選擇卷期
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- OpenAccess
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.
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- OpenAccess
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.
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- OpenAccess
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.
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- OpenAccess
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.
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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 agropastoral 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.
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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.