Forest cover change and fragmentation using Landsat data in Maçka State Forest Enterprise in Turkey

2007 ◽  
Vol 137 (1-3) ◽  
pp. 51-66 ◽  
Author(s):  
Günay Çakir ◽  
Fatih Sivrikaya ◽  
Sedat Keleş
Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 23 ◽  
Author(s):  
Fernando Aguilar ◽  
Abderrahim Nemmaoui ◽  
Manuel Aguilar ◽  
Mimoun Chourak ◽  
Yassine Zarhloule ◽  
...  

2007 ◽  
Vol 140 (1-3) ◽  
pp. 1-14 ◽  
Author(s):  
Sedat Keleş ◽  
Fatih Sivrikaya ◽  
Günay Çakir ◽  
Selahattin Köse

2013 ◽  
Vol 21 (2) ◽  
pp. 40-44 ◽  
Author(s):  
R. R. Aryal ◽  
H. L. Shrestha ◽  
S. Khanal

The study, carried out at Laljhadi corridor in Kanchanpur district of Nepal, aimed at assessing forest cover change and fragmentation using multi-temporal Landsat data. Post classifi cation change detection was applied on temporal forest cover class datasets obtained by supervised classifi cation technique with maximum likelihood algorithm. The overall change analysis indicated a decreasing trend in forest cover. Statistics on selected landscape metrics were generated to quantify the change in spatial structure resulting from fragmentation. The analysis of the landscape metrics depicted increase in fragmentation over the analysis time period along with progression of deforestation.DOI: http://dx.doi.org/10.3126/banko.v21i2.9142Banko Janakari Vol. 21, No. 2, 2011 Page: 40-44 Uploaded date: November 11, 2013 


2021 ◽  
Vol 13 (11) ◽  
pp. 2131
Author(s):  
Jamon Van Den Hoek ◽  
Alexander C. Smith ◽  
Kaspar Hurni ◽  
Sumeet Saksena ◽  
Jefferson Fox

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.


2003 ◽  
Vol 79 (1) ◽  
pp. 132-146 ◽  
Author(s):  
Dennis Yemshanov ◽  
Ajith H Perera

We reviewed the published knowledge on forest succession in the North American boreal biome for its applicability in modelling forest cover change over large extents. At broader scales, forest succession can be viewed as forest cover change over time. Quantitative case studies of forest succession in peer-reviewed literature are reliable sources of information about changes in forest canopy composition. We reviewed the following aspects of forest succession in literature: disturbances; pathways of post-disturbance forest cover change; timing of successional steps; probabilities of post-disturbance forest cover change, and effects of geographic location and ecological site conditions on forest cover change. The results from studies in the literature, which were mostly based on sample plot observations, appeared to be sufficient to describe boreal forest cover change as a generalized discrete-state transition process, with the discrete states denoted by tree species dominance. In this paper, we outline an approach for incorporating published knowledge on forest succession into stochastic simulation models of boreal forest cover change in a standardized manner. We found that the lack of details in the literature on long-term forest succession, particularly on the influence of pre-disturbance forest cover composition, may be limiting factors in parameterizing simulation models. We suggest that the simulation models based on published information can provide a good foundation as null models, which can be further calibrated as detailed quantitative information on forest cover change becomes available. Key words: probabilistic model, transition matrix, boreal biome, landscape ecology


2021 ◽  
Vol 62 ◽  
pp. 101279
Author(s):  
L. Bragagnolo ◽  
R.V. da Silva ◽  
J.M.V. Grzybowski

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