Change detection of landscape connectivity arisen by forest transformation in Hazaribagh wildlife sanctuary, Jharkhand (India)

2019 ◽  
Vol 28 (4) ◽  
pp. 391-404
Author(s):  
Saurabh Kumar Gupta ◽  
Arvind Chandra Pandey
Author(s):  
H. Bilyaminu ◽  
P. Radhakrishnan ◽  
K. Vidyasagaran ◽  
K. Srinivasan

Understanding forest degradation due to human and natural phenomena is crucial to conserving and managing remnant forest resources. However, forest ecosystem assessment over a large and remote area is usually complex and arduous. The present study on land use and land cover change detection of the Shendurney Wildlife Sanctuary forest ecosystems was carried out to utilize the potential application of remote sensing (RS) and geographic information system (GIS). Moreover, to understand the trend in the forest ecosystem changes. The supervised classification with Maximum Likelihood Algorithm and change detection comparison approach was employed to study the land use and land cover changes, using the Landsat Enhanced Thematic Mapper (ETM±) and Landsat 8 OLI-TIRS using data captured on July 01, 2001, and January 14, 2018. The study indicated the rigorous land cover changes. It showed a significant increase in the proportion of degraded forest with negligible gain in the proportion of evergreen forest from 21.31% in 2001 to 22.97% in 2018.  A substantial loss was also observed in moist deciduous from 27.11 % in 2001 to 17.23 % in 2018. The result of the current study indicated the degree of impacts on forests from the various activities of their surroundings. This study provides baseline information for planning and sustainable management decisions.


2006 ◽  
Vol 27 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Paul Rodway ◽  
Karen Gillies ◽  
Astrid Schepman

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17  s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.


Author(s):  
Mitchell R. P. LaPointe ◽  
Rachael Cullen ◽  
Bianca Baltaretu ◽  
Melissa Campos ◽  
Natalie Michalski ◽  
...  

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