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 c...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 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.展开更多
文摘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 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.
