scholarly journals A land use and land cover classification system for use with remote sensor data

10.3133/pp964 ◽  
1976 ◽  
Author(s):  
James R. Anderson ◽  
Ernest E. Hardy ◽  
John T. Roach ◽  
Richard E. Witmer
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Classification System ◽  
Land Cover Classification ◽  
Sensor Data ◽  
Remote Sensor

scholarly journals A land-use classification system for use with remote sensor data

Circular ◽  
1972 ◽  
Author(s):  
James Richard Anderson ◽  
Ernest E. Hardy ◽  
John T. Roach
Keyword(s):  
Land Use ◽  
Classification System ◽  
Sensor Data ◽  
Land Use Classification ◽  
Remote Sensor

scholarly journals Land Use and Land Cover Detection by Different Classification Systems using Remotely Sensed Data of Kuala Tiga, Tanah Merah Kelantan, Malaysia

2017 ◽  
Vol 5 (3) ◽  
pp. 145-151
Author(s):  
Wani Sofia Udin ◽  
Zuhaira Nadhila Zahuri
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Classification System ◽  
Supervised Classification ◽  
Satellite Images ◽  
Agricultural Land ◽  
Unsupervised Classification ◽  
Land Cover Classification ◽  
Classification Systems

Land use and land cover classification system has been used widely in many applications such as for baseline mapping for Geographic Information System (GIS) input and also target identification for identification of roads, clearings and also land and water interface. The research was conducted in Kuala Tiga, Tanah Merah, Kelantan and the study area covering about 25 km2. The main purpose of this research is to access the possibilities of using remote sensing for the detection of regional land-use change by developing a land cover classification system. Another goal is to compare the accuracy of supervised and unsupervised classification systems by using remote sensing. In this research, both supervised and unsupervised classifications were tested on satellite images of Landsat 7 and 8 in the years 2001 and 2016. As for supervised classification, the satellite images are combined and classified. Information and data from the field and land cover classification are utilized to identify training areas that represent land cover classes. Then, for unsupervised classification, the satellite images are combined and classified by means of unsupervised classification by using an Iterative Self- Organizing Data Analysis Techniques (ISODATA) algorithm. Information and data from the field and land cover classification are utilized to assign the resulting spectral classes to the land cover classes. This research was then comparing the accuracy of two classification systems at dividing the landscape into five classes; built-up land, agricultural land, bare soil, forest land, water bodies. Overall accuracies for unsupervised classification are 36.34 % for 2016 and 51.76% for 2001 while for supervised classification, accuracy assessments are 95.59 % for 2016 and 96.29 % for 2001.

scholarly journals Land Use and Land Cover Classification from ETM Sensor Data : A Case Study from Tamakoshi River Basin of Nepal

2014 ◽  
Vol XL-8 ◽  
pp. 943-948
Author(s):  
U. S. Shrestha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Land Cover Classification ◽  
Sensor Data ◽  
Classification Methods ◽  
Barren Land ◽  
Grass Land ◽  
Mountain Watershed

The mountain watershed of Nepal is highly rugged, inaccessible and difficult for acquiring field data. The application of ETM sensor Data Sat satellite image of 30 meter pixel resolutions has been used for land use and land cover classification of Tamakoshi River Basin (TRB) of Nepal. The paper tries to examine the strength of image classification methods in derivation of land use and land classification. Supervised digital image classification techniques was used for examination the thematic classification. Field verification, Google earth image, aerial photographs, topographical sheet and GPS locations were used for land use and land cover type classification, selecting training samples and assessing accuracy of classification results. Six major land use and land cover types: forest land, water bodies, bush/grass land, barren land, snow land and agricultural land was extracted using the method. Moreover, there is spatial variation of statistics of classified land uses and land cover types depending upon the classification methods. <br><br> The image data revealed that the major portion of the surface area is covered by unclassified bush and grass land covering 34.62 per cent followed by barren land (28 per cent). The knowledge derived from supervised classification was applied for the study. The result based on the field survey of the area during July 2014 also verifies the same result. So image classification is found more reliable in land use and land cover classification of mountain watershed of Nepal.

Integration of Landsat ETM+ and Radarsat SAR Data for Land Cover Classification

2005 ◽  
Vol 277-279 ◽  
pp. 838-844
Author(s):  
Sun Hwa Kim ◽  
Kyu Sung Lee
Keyword(s):  
Land Cover ◽  
Image Interpretation ◽  
Coastal Region ◽  
Land Cover Classification ◽  
Sensor Data ◽  
Synergic Effect ◽  
Land Cover Types ◽  
Remote Sensor ◽  
Single Dataset ◽  
Sar Data

This study aims to analyze the synergic effect of integrating two distinctive satellite remote sensor data (optical and microwave imagery) for land cover classification. The study area covers diverse land cover types in the Western coastal region of the Korean peninsula. Eleven land cover types were classified using several datasets of combined Landsat ETM+ and Radarsat synthetic aperture radar (SAR) data as well as only a single dataset of either ETM+ or SAR data alone. Furthermore, we introduced a texture image that was derived from the SAR data. The synergic effect of these combined datasets was evident by both image interpretation and computerassisted classification. The overall classification accuracy of the combined datasets was improved to 74.60% as compared to the 69.35% accuracy of using ETM+ data alone.

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