Satellite monitoring of land-use and land-cover changes in northern Togo protected areas

Remote-sensing data for protected areas in northern Togo, obtained in three different years (2007, 2000, and 1987), were used to assess and map changes in land cover and land use for this drought prone zone. The normalized difference vegetation index (NDVI) was applied to the images to map changes in vegetation. An unsupervised classification, followed by classes recoding, filtering, identifications, area computing and post-classification process were applied to the composite of the three years of NDVI images. Maximum likelihood classification was applied to the 2007 image (ETM+2007) using a supervised classification process. Seven vegetation classes were defined from training data sets. The seven classes included the following biomes: riparian forest, dry forest, flooded vegetation, wooded savanna, fallows, parkland, and water. For these classes, the overall accuracy and the overall kappa statistic for the classified map were 72.5% and 0.67, respectively. Data analyses indicated a great change in land resources; especially between 1987 and 2000 probably due to the impact of democratization process social, economic, and political disorder from 1990. Wide-scale loss of vegetation occurred during this period. However, areas of vegetation clearing and regrowth were more visible between 2000 and 2007. The main source of confusion in the contingency matrix was due to heterogeneity within certain classes. It could also be due to spectral homogeneity among the classes. This research provides a baseline for future ecological landscape research and for the next management program in the area.

[1]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[2]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[3]  P. S. Chavez,et al.  Automatic detection of vegetation changes in the southwestern United States using remotely sensed images , 1994 .

[4]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[5]  Scott N. Miller,et al.  Assessing land cover change in Kenya's Mau Forest region using remotely sensed data , 2008 .

[6]  T. Bayes LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S , 1763, Philosophical Transactions of the Royal Society of London.

[7]  Steven A. Sader,et al.  Analyzing a forest conversion history database to explore the spatial and temporal characteristics of land cover change in Guatemala's Maya Biosphere Reserve , 2002, Landscape Ecology.

[8]  P. Chavez Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[9]  Ecological and numerical analyses of plant communities of the most conserved protected area in North-Togo , 2010 .

[10]  Thomas Gaiser,et al.  Land Use/Cover Map and its Accuracy in the Oueme Basin of Benin (West Africa) , 2006 .

[11]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[12]  Ola Ahlqvist,et al.  Extending post-classification change detection using semantic similarity metrics to overcome class heterogeneity: A study of 1992 and 2001 U.S. National Land Cover Database changes , 2008 .

[13]  K. Akpagana,et al.  Analyse spatiale des différentes formes de pressions anthropiques dans la réserve de faune de l’Oti-Mandouri (Togo) , 2012 .

[14]  P. Sellers Canopy reflectance, photosynthesis and transpiration , 1985 .

[15]  R. Whittaker Classification of Plant Communities , 1978, Classification of Plant Communities.

[16]  Emily Hoffhine Wilson,et al.  Satellite Change Detection of Forest Harvest Patterns on an Industrial Forest Landscape , 2003, Forest Science.

[17]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[18]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[19]  Yan-xin Wang,et al.  Using Landsat data to determine land use changes in Datong basin, China , 2009 .

[20]  E. Maarel,et al.  The Braun-Blanquet Approach , 1978 .

[21]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[22]  A. Viera,et al.  Understanding interobserver agreement: the kappa statistic. , 2005, Family medicine.

[23]  K. Kokou,et al.  Les forêts sacrées du couloir du Dahomey , 2006 .

[24]  S. Sader,et al.  Detection of forest harvest type using multiple dates of Landsat TM imagery , 2002 .

[25]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[26]  Nils Chr. Stenseth,et al.  Sub‐saharan desertification and productivity are linked to hemispheric climate variability , 2001 .

[27]  Steven A. Sader,et al.  RGB-NDVI colour composites for visualizing forest change dynamics , 1992 .

[28]  Chunyu Zhang,et al.  Woody vegetation of protected areas in northern Togo. Cases of Barkoissi, Galangashi and Oti-Keran: ecological and structure analyses of plant communities , 2011 .

[29]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .