American Journal of Environmental Protection
ISSN (Print): 2328-7241 ISSN (Online): 2328-7233 Website: http://www.sciepub.com/journal/env Editor-in-chief: Mohsen Saeedi, Hyo Choi
Open Access
Journal Browser
Go
American Journal of Environmental Protection. 2017, 5(2), 44-51
DOI: 10.12691/env-5-2-3
Open AccessArticle

Analysis of Vegetation Change and Mapping Tree Species in Mountainous Area Using Multi-Source Satellite Data: A Case Study of Djebel El Ouahch, Algeria

Mohamed Gana1, , Azzedine Mohamed Toufik Arfa1, Mohamed El Habib Benderradji1 and Djamel Alatou1

1Laboratory of Development and Valorisation of Phyto-Genetics Ressources, Department of Biology and Ecology, University of Frères Mentouri Constantine, Algeria

Pub. Date: July 31, 2017

Cite this paper:
Mohamed Gana, Azzedine Mohamed Toufik Arfa, Mohamed El Habib Benderradji and Djamel Alatou. Analysis of Vegetation Change and Mapping Tree Species in Mountainous Area Using Multi-Source Satellite Data: A Case Study of Djebel El Ouahch, Algeria. American Journal of Environmental Protection. 2017; 5(2):44-51. doi: 10.12691/env-5-2-3

Abstract

Monitoring vegetation cover change and mapping the distribution of forest tree species has been considered as a key issue in sustainable development policies, the purpose of this research is to detect vegetation cover changes over 30 years (1987- 2016), and perform forest type classification by using Multi-Source Satellite Data and field survey investigations in the massive of Djebel El Ouahch. Algeria. The vegetation cover changes were quantified using Normalized Difference Vegetation Index (NDVI) combined with supervised classification methods, based on time series Landsat images. Furthermore, the dominant tree species in the wooded area was estimated using Google Earth imagery, the fieldwork was done simultaneously with a visual interpretation of Google Earth images, the thematic maps were prepared and accuracy assessment results are considered satisfactory. The results revealed that the cropland and non-vegetated areas have increased by 22%, and 6.22% respectively. In contrast, significant spatial reduction in natural vegetation was observed. Both of Moderate and dense vegetation were decreased by 26.23% and 1.98%, respectively, the current results play an important role in any planning process and indicating the necessity to create a new strategy based on protecting the natural vegetation and plants diversity.

Keywords:
vegetation NDVI Djebel El Ouahch tree species mountainous

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Figures

Figure of 5

References:

[1]  Wentz, E., D. Nelson, A., Rahman, W. Stefanov, W., and Roy, S, “Expert system classification of urban land use/cover for Delhi, India,” International Journal of Remote Sensing, 29 (15). 4405-4427. 2008.
 
[2]  Olaleye J.B, “Land-use and land-cover analysis of Ilorin Emirate between 1986 and 2006 using landsat imageries,” African Journal of Environmental Science and Technology, 6 (4). 189-198. 2012.
 
[3]  FAO, Global Forest Resources Assessment 2005: Progress Towards Sustainable Forest Management. Forest Resources Assessment (FRA) Report. FAO Forestry Paper 147 (Rome: Food and Agriculture Organization). 2006,
 
[4]  FAO, State of the World’s Forest 2007 (Rome: Food and Agriculture Organization).2007.
 
[5]  Fischer, J., and Lindenmayer, D.B, “Landscape modification and habitat fragmentation: a synthesis,” Global Ecology and Biogeography, 16 (3). 265-280. 2007.
 
[6]  Rahman, M.M., Ainun, N., and Vacik, H, “Anthropogenic disturbances and plant diversity of the Madhupur Sal forests (Shorea robusta C.F. Gaertn.) of Bangladesh,” International Journal of Biodiversity Science, Ecosystem Services & Management, 5 (3). 162-173. 2009.
 
[7]  Tonye, E., and Akono, A, le traitement des images de télédétection par l’exemple. Paris: Edit. Gordon and Breach, 2000.
 
[8]  Nath, B, “Quantitative Assessment of Forest Cover Change of a Part of Bandarban Hill Tracts Using NDVI Techniques,” Journal of Geosciences and Geomatics, 2 (1). 21-27. 2014.
 
[9]  Sellers, P.J, “Canopy reflectance, photosynthesis and transpiration,” International Journal of Remote Sensing, 6 (8). 1335-1372. 1985
 
[10]  Campbell, J.B, Introduction to remote sensing, CRC Press, 2002.
 
[11]  Mishra, S., Shrivastava, P., and Dhurvey, P, “Change Detection Techniques in Remote Sensing,” Journal Of Advanced Information Technology And Convergence, 6 (2). 51. 2016
 
[12]  Sheeren, D., Fauvel, M., Josipović, V., Lopes, M., Planque, C., Willm, J., and Dejoux, J, “Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series,” Remote Sensing, 8 (9). 734. 2016.
 
[13]  Joseph, G, Data Analysis. In: Joseph G (ed) Fundamentals of remote sensing, 2nd ed, Universities Press, India, 2005, 319–348.
 
[14]  Mering, C., Baro, J., and Upegui, E, “Retrieving urban areas on Google Earth images: application to towns of West Africa,” International Journal of Remote Sensing, 31 (22). 5867-5877. 2010.
 
[15]  Hirche, A., Salamani, M., Abdellaoui, A., Benhouhou, S., and Valderrama, J, “Landscape changes of desertification in arid areas: the case of south-west Algeria,” Environmental Monitoring and Assessment, 179 (1-4). 403-420. 2010.
 
[16]  Saädi, S., and Gintzburger, G, “A spatial desertification indicator for Mediterranean arid rangelands: a case study in Algeria,” The Rangeland Journal, 35 (1). 47. 2013.
 
[17]  Yacouba, D., Guangdao, H., and Xingping, W, “Assessment of Land Use Cover Changes Using NDVI and DEM in Puer and Simao Counties, Yunnan Province, China.” World Rural Observations, 1 (2). 1-11. 2009.
 
[18]  Panda, S.S., Ames, D.P., Panigrahi, S, “Application of vegetation indices for agricultural crop yield prediction using neural network techniques,” Remote Sensing, 2 (3). 673-696. 2010.
 
[19]  Sabins, F.F, Remote sensing, principles and interpretation. Third edition, H. Freeman and Company, New York, 1997.
 
[20]  Jensen, J.R, Remote sensing of environment: an earth resource prospective. Prentice Hall, upper saddle River.N.J, UAS, 2000.
 
[21]  Pu, R., Gong, P., Tian, Y., Miao, X., Carruthers, R.I., and Anderson, G.L, “Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA,” International Journal of Remote Sensing, 29 (14). 3987-4011. 2008
 
[22]  Tso, B., and Mather, P, Classification methods for remotely sensed data. 1st ed. Boca Raton, Fla.: CRC/Taylor & Francis, 2009.
 
[23]  Zhou, Q., Li, B., Kurban, A, “Trajectory analysis of land cover change in arid environment of China,” International Journal of Remote Sensing, 29 (4). 1093-1107. 2008.
 
[24]  Shah-Hosseini, R., Homayouni, S., and Safari. A, “A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space,” Remote Sensing, 7 (10). 12829-12858. 2015
 
[25]  Molina, I., Martinez, E., Morillo, C., Velasco, J., and Jara, A, “Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes.” Sensors, 16 (10).1621. 2016