scholarly journals Multi‐temporal Forest Cover Change and Forest Density Trend Detection in a Mediterranean Environment

2015 ◽  
Vol 28 (4) ◽  
pp. 1188-1198 ◽  
Author(s):  
Elias Symeonakis ◽  
Peter A. Caccetta ◽  
Jeremy F. Wallace ◽  
Eva Arnau‐Rosalen ◽  
Adolfo Calvo‐Cases ◽  
...  
Keyword(s):  
Forest Cover ◽  
Trend Detection ◽  
Mediterranean Environment ◽  
Forest Cover Change ◽  
Forest Density ◽  
Multi Temporal

scholarly journals Multi-temporal forest cover change detection in the Metchie-Ngoum Protection Forest Reserve, West Region of Cameroon

2020 ◽  
Vol 23 (1) ◽  
pp. 113-124 ◽  
Author(s):  
Reeves Meli Fokeng ◽  
Walter Gadinga Forje ◽  
Vivien Meli Meli ◽  
Bernard Nyuyki Bodzemo
Keyword(s):  
Change Detection ◽  
Forest Cover ◽  
Forest Cover Change ◽  
Forest Reserve ◽  
Protection Forest ◽  
West Region ◽  
Multi Temporal

scholarly journals Change assessment of forest cover in Ghodaghodi lake area in Kailali district of Nepal

1970 ◽  
Vol 19 (2) ◽  
pp. 15-19 ◽  
Author(s):  
S Khanal
Keyword(s):  
Forest Cover ◽  
Landsat Imagery ◽  
Forest Degradation ◽  
Lake Area ◽  
Forest Cover Change ◽  
Comparison Approach ◽  
Multi Temporal ◽  
One Year

Ghodaghodi Lake in Far-West Nepal has been listed as a Ramsar Site due to its significance as a habitat for several endangered species of flora and fauna. The wetland and its surrounding area is facing deforestation, forest degradation and encroachment. In this case study, unsupervised and finally supervised classification of multi-temporal Landsat imagery covering the wetland area was applied. A post-classification comparison approach was used to derive forest cover change maps. The results depicted the loss of forest cover over a thirty- one year period, in three time slices. The highest rate of loss was observed in the 1990 to1999 time slice. Keywords: Change detection; forest cover; Ghodaghodi lake; Landsat DOI: 10.3126/banko.v19i2.2980 Banko Janakari, Vol. 19, No.2 2009 pp.15-19

scholarly journals A New Multi-Temporal Forest Cover Classification for the Xingu River Basin, Brazil

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 114 ◽  
Author(s):  
Margaret Kalacska ◽  
Oliver Lucanus ◽  
Leandro Sousa ◽  
J. Pablo Arroyo-Mora
Keyword(s):  
River Basin ◽  
Forest Cover ◽  
Regional Scale ◽  
Google Earth ◽  
Classification And Regression Tree ◽  
Forest Cover Change ◽  
Anthropogenic Pressures ◽  
Mapping Unit ◽  
Xingu River ◽  
Multi Temporal

We describe a new multi-temporal classification for forest/non-forest classes for a 1.3 million square kilometer area encompassing the Xingu River basin, Brazil. This region is well known for its exceptionally high biodiversity, especially in terms of the ichthyofauna, with approximately 600 known species, 10% of which are endemic to the river basin. Global and regional scale datasets do not adequately capture the rapidly changing land cover in this region. Accurate forest cover and forest cover change data are important for understanding the anthropogenic pressures on the aquatic ecosystems. We developed the new classifications with a minimum mapping unit of 0.8 ha from cloud free mosaics of Landsat TM5 and OLI 8 imagery in Google Earth Engine using a classification and regression tree (CART) aided by field photographs for the selection of training and validation points.

Using Landsat data for assessing forest cover change and fragmentation in Laljhadi corridor of Kanchanpur district, Nepal

2013 ◽  
Vol 21 (2) ◽  
pp. 40-44 ◽  
Author(s):  
R. R. Aryal ◽  
H. L. Shrestha ◽  
S. Khanal
Keyword(s):  
Maximum Likelihood ◽  
Landscape Metrics ◽  
Forest Cover ◽  
Landsat Data ◽  
Maximum Likelihood Algorithm ◽  
Forest Cover Change ◽  
Change Analysis ◽  
Time Period ◽  
Multi Temporal ◽  
Cover Class

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 

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