Resumen


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The present research evaluated spatio-temporal change in the sub-tropical forest of district Malakand through Remote Sensing and GIS techniques. The main objective was to identify different land cover classes and to determine temporal changes in forest cover in the study area. This study was based on the two different classification techniques for the land cover classification. In this study, four Landsat images were acquired with the interval of 10 years from 1988 to 2018. The maximum likelihood classification and Minimum distance classification was applied on all the four images step by step. The total area of district Malakand was recorded as 975.32 km2. Regarding percent wise area coverage Forest covered 22.1 % of the total area while other classes’ settlements, agriculture, barren land and water have covered 17.7, 23.3, 33 and 3.9% respectively in 1988. Whereas in 2018, percent wise area coverage forest covered 9.3 % of the total area although other classes settlements, agriculture, barren land and water have covered 33.8, 22.3, 31.8 and 3.7 respectively. The change in forest area over the time was 60.4 km2 which is equal to 6.2 % during 1998 to 2008 and ultimately overall deforestation was 124.4 km2 which is equal to 12.7 % loss of forest area lost from 1998 to 2018. Thus, the forest area was changed to settlements and barren lands from 1988 to 2018. Normalized Difference Vegetation Index (NDVI) was calculated for 1988, 1998, 2008 and 2018.  Based on NDVI analysis, total deforestation over the time was 166.29 km2 in last 30 years (1988-2018) with percent deforestation of 43.66 % area lost. The Landsat images have 30 meters resolution and small forest area changes can not detected by Landsat images, thus high-resolution products can detect more deforestation comparatively.


Autores/as

Syed Hassan Raza

Department of Forestry and Range Management, PMAS Arid Agriculture University, Rawalpindi
Pakistán

Muhammad Irfan Ashraf

Department of Forestry and Range Management, PMAS Arid Agriculture University, Rawalpindi
Pakistán

Areeba Binte Imran

Department of Forestry and Range Management, PMAS Arid Agriculture University, Rawalpindi
Pakistán

Ishfaq Ahmad Khan

Department of Forestry, University of Putra Malaysia, Malaysia
Malasia

Syed Ghayoor Ali Shah

Department of Forestry and Range Management, PMAS Arid Agriculture University, Rawalpindi
Pakistán

Palabras clave

Deforestation; Spatio-temporal analysis; Landsat, Supervised Classification; NDVI.

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Licencia de Creative Commons
Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial 4.0 Internacional. Copyright (c) Syed Hassan Raza, Muhammad Irfan Ashraf, Areeba Binte Imran, Ishfaq Ahmad Khan, Syed Ghayoor Ali Shah


UPR

©2017 Universidad de Pinar del Río "Hermanos Saíz Montes de Oca"