Vol18 No.9: 2388-2401
【Title】Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan
【Author】Arif UR REHMAN1; Sami ULLAH2,3; Muhammad SHAFIQUE3; Muhammad Sadiq KHAN4; Muhammad Tariq BADSHAH1; LIU Qi-jing1*
【Addresses】1 College of Forestry, Beijing Forestry University, Beijing 100083, China; 2 Department of Forestry, Shaheed Benazir Bhutto University Sheringal, Dir 18050, Pakistan; 3 GIS & Space Applications in Geosciences (G-SAG) laboratory at the NCE in Geology, University of Peshawar, National Center of GIS and Space Applications, Peshawar 25120, Pakistan; 4 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
【Corresponding author】LIU Qi-jing
【Citation】Rehma A, Ullah S, Shafique M, et al. (2021) Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan. Journal of Mountain Science 18(9). https://doi.org/10.1007/s11629-020-6548-7
【Abstract】Landsat-8 spectral values have been used to map the earth's surface information for decades. However, forest types and other land-use/land-cover (LULC) in the mountain terrains exist on different altitudes and climatic conditions. Hence, spectral information alone cannot be sufficient to accurately classify the forest types and other LULC, especially in high mountain complex. In this study, the suitability of Landsat-8 spectral bands and ancillary variables to discriminate forest types, and other LULC, using random forest (RF) classification algorithm for the Hindu Kush mountain ranges of northern Pakistan, was discussed. After prior-examination (multicollinearity) of spectral bands and ancillary variables, three out of six spectral bands and five out of eight ancillary variables were selected with threshold correlation coefficients r2<0.7. The selected datasets were stepwise stacked together and six Input Datasets (ID) were created. The first ID-1 includes only the Surface Reflectance (SR) of spectral bands, and then in each ID, the extra one ancillary variable including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized DifferenceSnow Index (NDSI), Land Surface Temperature (LST), and Digital Elevation Model (DEM) was added. We found an overall accuracy (OA) = 72.8% and kappa coefficient (KC) =61.9% for the classification of forest types, and other LULC classes by using the only SR bands of Landsat-8. The OA = 81.5% and KC=73.7% was improved by the addition of NDVI, NDWI, and NDSI to the spectral bands of Landsat-8. However, the addition of LST and DEM further increased the OA, and Kappa coefficient (KC) by 87.5% and 82.6%, respectively. This indicates that ancillary variables play an important role in the classification, especially in the mountain terrain, and should be adopted in addition to spectral bands. The output of the study will be useful for the protection and conservation, analysis, climate change research, and other mountains forest-related management information.
【Keywords】Forest types; Landuse Landcover; Landsat-8; Random forest; Ancillary variables; Mountain environment