scholarly journals Land use/land cover mapping using multi-scale texture processing of high resolution data

2014 ◽  
Vol 18 ◽  
pp. 012185
Author(s):  
S N Wong ◽  
M L R Sarker
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
High Resolution Data ◽  
Multi Scale ◽  
Resolution Data ◽  
Texture Processing

scholarly journals Hyperspectral Data for Land use/Land cover classification

2014 ◽  
Vol XL-8 ◽  
pp. 991-995 ◽  
Author(s):  
D. Vijayan ◽  
G. Ravi Shankar ◽  
T. Ravi Shankar
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Classification Systems ◽  
Land Use Land Cover ◽  
High Resolution Data ◽  
Liss Iii ◽  
Hyperion Image ◽  
Resolution Data

An attempt has been made to compare the multispectral Resourcesat-2 LISS III and Hyperion image for the selected area at sub class level classes of major land use/ land cover. On-screen interpretation of LISS III (resolution 23.5 m) was compared with Spectral Angle Mapping (SAM) classification of Hyperion (resolution 30m). Results of the preliminary interpretation of both images showed that features like fallow, built up and wasteland classes in Hyperion image are clearer than LISS-III and Hyperion is comparable with any high resolution data. Even canopy types of vegetation classes, aquatic vegetation and aquatic systems are distinct in Hyperion data. Accuracy assessment of SAM classification of Hyperion compared with the common classification systems followed for LISS III there was no much significant difference between the two. However, more number of vegetation classes could be classified in SAM. There is a misinterpretation of built up and fallow classes in SAM. The advantages of Hyperion over visual interpretation are the differentiation of the type of crop canopy and also crop stage could be confirmed with the spectral signature. The Red edge phenomenon was found for different canopy type of the study area and it clearly differentiated the stage of vegetation, which was verified with high resolution image. Hyperion image for a specific area is on par with high resolution data along with LISS III data.

Exploring the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based approach

2015 ◽  
Vol 36 (13) ◽  
pp. 3544-3562 ◽  
Author(s):  
Ning Han ◽  
Huaqiang Du ◽  
Guomo Zhou ◽  
Xiaojun Xu ◽  
Hongli Ge ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Multi Scale ◽  
Object Based ◽  
Image Object

scholarly journals Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

2017 ◽  
Vol 50 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Mirco Sturari ◽  
Emanuele Frontoni ◽  
Roberto Pierdicca ◽  
Adriano Mancini ◽  
Eva Savina Malinverni ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Elevation Data ◽  
High Resolution Images

Optical and Radar Data Analysis for Land Use Land Cover Mapping in Peru

2019 ◽  
Vol 3 (1) ◽  
pp. 14-27
Author(s):  
Barry Haack ◽  
Ron Mahabir
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Window Size ◽  
Radar Data ◽  
Optical Data ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Texture Image ◽  
Site Class ◽  
Spectral Signatures

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.

scholarly journals Land Use/Land Cover Mapping of Sambar Watershed by Using Remote Sensing and GIS

2021 ◽  
Vol 10 (9) ◽  
pp. 578-585
Author(s):  
B. Varpe Shriniwas D. Payal Sandip
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Remote Sensing And Gis ◽  
Rapid Expansion ◽  
Water Bodies ◽  
Land Cover Mapping ◽  
Fallow Land ◽  
Land Use Land Cover ◽  
Barren Land

In the present study, an effort has been made to study in detail of Land Use/Land Cover Mapping for Sambar watershed by using Remote Sensing and GIS technique was carried out during the year of 2020-2021 in Parbhani district. In this research the Remote Sensing and Geographical Information system technique was used for identifying the land use/land cover classes with the help of ArcGIS 10.8 software. The Sambar watershed is located in 19º35ʹ78.78˝ N and 76º87ʹ88.44˝ E in the Parbhani district of Marathwada region in Maharashtra. It is covered a total area 97.01 km2. The land use/land cover map and its classes were identified by the Supervised Classification Method in ArcGIS 10.8 software by using the Landsat 8 satellite image. Total six classes are identified namely as Agricultural area, Forest area, Urban area, Barren land, Water bodies and Fallow land. The Agricultural lands are well distributed throughout the watershed area and it covers 4135 ha. (43 per cent). Forest occupies 502 ha area and sharing about 5 per cent of the total land use land cover of the study area. The Urban land occupies 390 ha. area (4 per cent) and there was a rapid expansion of settlement area. Barren land occupies 3392 ha. area (35 per cent). A water bodies occupy 630 ha. area (6 per cent) and the Fallow land occupies 650 ha (7 per cent) but well-developed dendritic drainage pattern and good water availability is in the Sambar watershed.

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