Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends

2008 ◽  
Vol 32 (5) ◽  
pp. 503-528 ◽  
Author(s):  
Steve N. Gillanders ◽  
Nicholas C. Coops ◽  
Michael A. Wulder ◽  
Sarah E. Gergel ◽  
Trisalyn Nelson

Science and reporting information needs for monitoring dynamics in land cover over time have prompted research, and made operational, a wide variety of change detection methods utilizing multiple dates of remotely sensed data. Change detection procedures based upon spectral values are common; however, landscape pattern analysis approaches which utilize spatial information inherent within imagery present opportunities for the generation of unique and ecologically important information. While the use of two images may provide the means to identify change, the use of more than two images for long-term monitoring affords the ability to identify a greater range of processes of landscape change, including rates and dynamics. The main objective of this review is to investigate and summarize the methods and applications of land cover spatial pattern analysis using three or more image dates. The potential and the limitations of landscape pattern indices are identified and discussed to inform application recommendations. The second objective of this review is to make recommendations, including appropriate landscape pattern indices, for the application of landscape pattern analysis of a long time series of remotely sensed data to a case study involving the mountain pine beetle in British Columbia, Canada. The review concludes with recommendations for future research.

Author(s):  
Yi Fang ◽  
Auroop Ganguly ◽  
Nagendra Singh ◽  
Veeraraghavan Vijayaraj ◽  
Neal Feierabend ◽  
...  

2017 ◽  
Vol 9 (1) ◽  
pp. 191-199 ◽  
Author(s):  
Martin Wegmann ◽  
Benjamin F. Leutner ◽  
Markus Metz ◽  
Markus Neteler ◽  
Stefan Dech ◽  
...  

2011 ◽  
Vol 41 (10) ◽  
pp. 2090-2096 ◽  
Author(s):  
Guillermo Castilla ◽  
Julia Linke ◽  
Adam J. McLane ◽  
Gregory J. McDermid

Modern ecological models often account for the influence of the surrounding environment by using landscape pattern indices (LPIs) as measures of landscape structure. Ideally, the landscape samples from which these LPIs are extracted should be centered on the locations where the response variable was measured. However, in situations where this is not possible due to a lack of adequate full-coverage landcover data, the question arises as to what degree this circumstance creates a bias in the value of the LPIs, thereby obscuring their relation with the response variable. To address this question, we extracted four representative LPIs from 30 rectangular (3 × 6 km) landscape samples evenly distributed across a 10 000 km2 boreal forest study area. These rectangles were subjected to systematic displacements across a range of distances (0.5 to 2.5 km) and directions, after which we recomputed the LPIs. We found that a 1 km spatial offset led to an average of 15% deviation of original LPI values. Unfortunately, as the offset increased, the range of resulting deviations also widened, making it difficult to predict this effect. Our findings fill a gap in the literature on landscape pattern analysis and suggest that researchers should avoid LPIs extracted from spatially offset landscape samples.


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