Monitoring of Forest Cover Change in Pranahita Wildlife Sanctuary,Andhra Pradesh, India Using Remote Sensing and GIS

2008 ◽  
Vol 1 (2) ◽  
pp. 73-79 ◽  
Author(s):  
A. Giriraj ◽  
Shilpa Babar ◽  
C. Sudhakar R
Keyword(s):  
Remote Sensing ◽  
Andhra Pradesh ◽  
Forest Cover ◽  
Remote Sensing And Gis ◽  
Forest Cover Change ◽  
Wildlife Sanctuary

scholarly journals Application of Remote Sensing and GIS in Forest Cover Change in Tehsil Barawal, District Dir, Pakistan

2015 ◽  
Vol 06 (09) ◽  
pp. 1501-1508 ◽  
Author(s):  
Anwar Sajjad ◽  
Ahmad Hussain ◽  
Umar Wahab ◽  
Syed Adnan ◽  
Saqib Ali ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Remote Sensing And Gis ◽  
Forest Cover Change

scholarly journals Shedding New Light on Mountainous Forest Growth: A Cross-Scale Evaluation of the Effects of Topographic Illumination Correction on 25 Years of Forest Cover Change across Nepal

2021 ◽  
Vol 13 (11) ◽  
pp. 2131
Author(s):  
Jamon Van Den Hoek ◽  
Alexander C. Smith ◽  
Kaspar Hurni ◽  
Sumeet Saksena ◽  
Jefferson Fox
Keyword(s):  
Remote Sensing ◽  
Geographic Distribution ◽  
Classification Accuracy ◽  
Forest Cover ◽  
Forest Growth ◽  
Forest Cover Change ◽  
Scale Evaluation ◽  
Beneficial Effects ◽  
Affect Detection

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016) TIC. TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.

Quantifying forest cover changes in Koraput district, India using remote sensing and GIS

2021 ◽  
pp. 185-191
Author(s):  
Ch. Jyotiprava Dash ◽  
Partha Pratim Adhikary ◽  
M. Madhu ◽  
P.K. Mishra
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Remote Sensing And Gis ◽  
Forest Cover Changes
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