Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan
文献类型：期刊论文
| 作者 | Rehman, Arif Ur; Ullah, Sami; Shafique, Muhammad; Khan, Muhammad Sadiq; Badshah, Muhammad Tariq; Liu Qi-jing |
| 刊名 | JOURNAL OF MOUNTAIN SCIENCE
 |
| 出版日期 | 2021-09 |
| 卷号 | 18期号:9页码:2388-2401 |
| 关键词 | Forest types Landuse Landcover Landsat-8 Random forest Ancillary variables Mountain environment |
| ISSN号 | 1672-6316 |
| 英文摘要 | 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 r(2)<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 Difference Snow 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. |
| WOS研究方向 | Environmental Sciences |
| 源URL | [http://ir.rcees.ac.cn/handle/311016/45436]  |
| 专题 | 生态环境研究中心_城市与区域生态国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China 2.Univ Peshawar, Natl Ctr GIS & Space Applicat, GIS & Space Applicat Geosci G SAG Lab NCE Geol, Peshawar 25120, Pakistan 3.Shaheed Benazir Bhutto Univ Sheringal, Dept Forestry, Dir 18050, Pakistan 4.Beijing Forestry Univ, Coll Forestry, Beijing 100083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Rehman, Arif Ur,Ullah, Sami,Shafique, Muhammad,et al. Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan[J]. JOURNAL OF MOUNTAIN SCIENCE,2021,18(9):2388-2401. |
| APA | Rehman, Arif Ur,Ullah, Sami,Shafique, Muhammad,Khan, Muhammad Sadiq,Badshah, Muhammad Tariq,&Liu Qi-jing.(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),2388-2401. |
| MLA | Rehman, Arif Ur,et al."Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan".JOURNAL OF MOUNTAIN SCIENCE 18.9(2021):2388-2401. |
入库方式： OAI收割
来源：生态环境研究中心
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