Carbon Stock Assessment using Forest Inventory and Remote Sensing Data: A Case Study of Mansehra District

Abstract

Forests are important store of carbon within the global carbon cycle and also play a significant role in climate change adaptation and mitigation. In the recent past, the increase in greenhouse gases emissions including carbon dioxide (CO2) is contributing to the rise in natural disasters and global warming. Limiting these increase emissions, an effective mitigation strategy for the protection and conservation of forests is extremely essential. This study aims estimating above & below ground biomass for the assessment and mapping of carbon stock using forest field inventory and remotely sensed data. Landsat imagery was used for the mapping of forest cover in the study area. Total forest area in the study area is about 108861 hectares. For AGB and carbon stock assessment, forest inventory data, including DBH, height, and soil samples were collected through field survey. These data sets were processed using allometric equations for different tree types. The results show the mean AGB and below ground biomass with in the forest cover area of about 160.242 t/ha and 41.663 t/ha, respectively, and mean total biomass of 201.90 t/ha. Similarly, the estimated above ground and below ground carbon stock were 75.314 t/ha and 19.582 t/ha respectively and a total of 94.895 t/ha. The total carbon stock once converted into CO2 equivalent gave a mean value of about 347.316 t/ha. Total carbon (AGC + BGC + Soil Carbon) in study area was 112.63 t/ha. For the estimation of soil carbon, soil samples were tested and analyzed in the lab. The lab test results yield a mean soil carbon value of 17. 7 4 t/ha. Linear regression model was developed to assess the relationship between AGB and Vegetation Indices (VIs) derived from Landsat 8 imagery for the estimation and prediction of above ground biomass. The analysis revealed a better estimation of AGB and NDVI, with R2 of 0.60 and P-value less than 0.01. The mean predicted AGB was 166.03 t/ha, very close value to that estimated from filed data. The results show that use of remote sensing data can help for the quick estimation of AGB over a very broad forested area. Additionally, the land cover classification results show that about 55.06 % of the area is bare or uncultivated, which can be used for afforestation.