首页 | 本学科首页   官方微博 | 高级检索  
文章检索
  按 检索   检索词:      
出版年份:   被引次数:   他引次数: 提示:输入*表示无穷大
  收费全文   2篇
  免费   0篇
测绘学   2篇
  2018年   1篇
  2013年   1篇
排序方式: 共有2条查询结果,搜索用时 15 毫秒
1
1.
In recent years, the significant increase in research on spatial information is observed. Classification or clustering is one of the well-known methods in spatial data analysis. Traditionally, classifiers are generally based on per-pixel approaches and are not utilizing the spatial information within pixel, called mixels which is an important source of information to image classification. There are two foremost reasons behind the existence of mixels: (a) coarse or low spatial resolution of sensor and (b) topographic effects that recorded on optical satellite imagery due to differential terrain illuminations over rugged areas such as Himalayas. In the present study, different classification algorithms have been implemented to drive the impact of topography on them. Among various available, three algorithms for the mapping of snow cover region over north Indian Himalayas (India) are compared: (a) maximum likelihood classification (MLC) as supervised classifier; (b) k-mean clustering as unsupervised classifier; and (c) linear spectral mixing model (LSMM) as soft classifier. These algorithms have been implemented on AWiFS multispectral data, and analysis was carried out. The classification accuracy is estimated by the error matrices, and LSMM achieved higher accuracy (84.5–88.5%) as compared to MLC (81–84%) and k-mean (74–81%). The results highlight that topographically derived classifiers achieved better accuracy in mapping as compared to simple classifiers. The study has many applications in snow hydrology, glaciology and climatology of mountain topography.  相似文献   
2.
The present investigation has been designed to analyze the landform and soil relationship in a geologically complex terrain of Tirora tahsil of Gondia district, Maharashtra using remotely sensed data and GIS technique. The geomorphologic units of the study area were delineated through visual interpretation of IRS–ID LISS-III data based on the spatial variation of the image characteristics. Thirteen landform units have been identified in the tahsil. The slope varied from level to nearly level with an area of about 63.76% of the tahsil. Rest of the area ranged from very gentle to moderately steep slopes. During soil survey, soil profiles were studied for morphological features. Horizon-wise soil samples were collected from the representative soil profiles on each landform unit. The depth of soil varied from 25 to 160 cm and colour from dark brown to very dark grayish brown. The texture ranged from clay loam to clayey in accordance with higher and lower topographic positions respectively. Higher available water holding capacity (AWC 285 mm) is found in low-lying area and low to medium AWC (140 mm) is noticed in the soils developed at higher elevation. The soils reaction (pH) is strongly acidic in nature (pH 5.2) on dissected hills, linear ridge and moderately weathered pediments, whereas, the soils are moderately to slightly acidic in nature (pH 5.5 to 6.5) on hills, shallow weathered pediments, moderately weathered pediments, deeply weathered pediments, narrow valleys, and broad valley floors. Slightly alkaline condition (pH 7.6) was observed on foot slopes and aggraded valley fills. The electrical conductivity of the soils is found almost same in all landforms. The cation exchange capacity of the area varies from 10.5 to 51.5 cmol(p+)kg?1. The base saturation increases with decreasing elevation and slope. The four major soil orders viz, Entisols, Alfisols, Inceptisols and Vertisols are found in the study areas which are further classified into suborder and great group levels. The landform and soil relationship was analyzed to appraise the land resources in the tahsil. The study shows that the application of remotely sensed data and GIS are immensely helpful in land resources appraisal for their management on sustainable basis.  相似文献   
1
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号