Volume XL-8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 787-791, 2014
https://doi.org/10.5194/isprsarchives-XL-8-787-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 787-791, 2014
https://doi.org/10.5194/isprsarchives-XL-8-787-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  28 Nov 2014

28 Nov 2014

Climate Change for Agriculture, Forest Cover and 3d Urban Models

M. Kapoor1 and D. Bassir2,3 M. Kapoor and D. Bassir
  • 1Computer Engineering Department, Mukesh Patel School of Technology Management and Engineering, NMIMS deemed to be University, Mumbai, Maharashtra, India
  • 2Institute of Industry Technology, Guangzhou & Chinese Academy of Sciences (IIT, GZ&CAS) Room A1005, R&D Building, Haibin Rd, Nansha District, Guangzhou, China
  • 3Dept. GMC, Université de Technologie de Belfort-Montbéliard 90010 Belfort cedex, France

Keywords: Landsat TRIS/8/LDCM, Forest Change, Decision support, NDVI, Eclipse, 3D Urban Models

Abstract. This research demonstrates the important role of the remote sensing in finding out the different parameters behind the agricultural crop change, forest cover and urban 3D models. Standalone software is developed to view and analysis the different factors effecting the change in crop productions. Open-source libraries from the Open Source Geospatial Foundation have been used for the development of the shape-file viewer. Software can be used to get the attribute information, scale, zoom in/out and pan the shapefiles. Environmental changes due to pollution and population that are increasing the urbanisation and decreasing the forest cover on the earth. Satellite imagery such as Landsat 5(1984) to Landsat TRIS/8 (2014), Landsat Data Continuity Mission (LDCM) and NDVI are used to analyse the different parameters that are effecting the agricultural crop production change and forest change. It is advisable for the development of good quality of NDVI and forest cover maps to use data collected from the same processing methods for the complete region. Management practices have been developed from the analysed data for the betterment of the crop and saving the forest cover