Book Review—Remote Sensing of Natural Resources

2015 ◽  
Vol 81 (7) ◽  
pp. 534-536
Author(s):  
Rudiger Gens
Keyword(s):  
Remote Sensing ◽  
Natural Resources

scholarly journals Operationalization of Remote Sensing Solutions for Sustainable Forest Management

2021 ◽  
Vol 13 (4) ◽  
pp. 572
Author(s):  
Gintautas Mozgeris ◽  
Ivan Balenović
Keyword(s):  
Remote Sensing ◽  
Forest Management ◽  
Natural Resources ◽  
Sustainable Management ◽  
Sustainable Forest Management ◽  
Cost Effective ◽  
Development Trends ◽  
Comprehensive Information ◽  
The Status

The pre-requisite for sustainable management of natural resources is the availability of timely, cost-effective, and comprehensive information on the status and development trends of the management object [...]

Measuring and Monitoring Urban Sprawl of Jaipur City using Remote Sensing and GIS

2015 ◽  
Vol 6 (2) ◽  
pp. 46-65
Author(s):  
Pushpendra Singh Sisodia ◽  
Vivekanand Tiwari ◽  
Anil Kumar Dahiya
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Sustainable Development ◽  
Developing Countries ◽  
Maximum Likelihood ◽  
Natural Resources ◽  
Urban Sprawl ◽  
Remote Sensing And Gis ◽  
Direct Impact ◽  
Maximum Likelihood Classification

The world's population increased drastically and forced people to migrate from rural area to major cities in search of basic amenities. The majority of the World's population are already living in the major cities and it is continuously increasing. The increase in population forced the major cities to expand. Expansion of cities acclaimed more unplanned settlement that leads unplanned growth. This is a global phenomenon that has a direct impact on natural resources. It is the biggest challenge for urban planners to achieve sustainable development. Developing countries like India, where the population is increasing at an alarming pace, require more attention towards this problem. In this study, an attempt has been made to measure and monitor urban sprawl in Jaipur (Capital, State of Rajasthan, India). Built-up area with corresponding population has been analysed over a period of 41 years (1972-2013). Remotely sensed images of 1972-2013 (MSS, TM and ETM+) have been classified using Supervised Maximum Likelihood Classification (MLC) for digital image processing. Shannon's entropy has been used to quantify the degree of urban sprawl, and eight landscape metrics have also been used to quantify urban sprawl and its pattern.

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