Abstract
In the previous years and decades, large quantity of carbon has contributed to globcal climate change and ecosystem disturbance because of anthropogenic activities. To study the carbon cycle, forest biomass is an important element as a carbon sink, and it is also a chief factor in forest health and climate change. Satellite remote sensing is a useful approach for the monitoring of forest resources and biomass estimation. This study investigates the accuracies of spatially estimated and mapped Above Ground Biomass (AGB) of three main species i.e. “Dalbergia sissoo”, “Eucalyptus camaldulensis” and “Bombax ceiba” in Chichawatni Forest, Punjab, Pakistan. In this work, AGB modelling and mapping was performed through six different spectral indices, NDVI “Normalized Difference Vegetation Index”, TNDVI “Transformed Normalized Difference Vegetation Index”, SAVI “Soil Adjusted Vegetation Index”, SARVI “Soil and Atmospherically Resistant Vegetation Index”, Square root of (Infrared/Red) “SQRT(IR/R”, MSR “Modified Simple Ratio”) calculated from Pleiades satellite data with a spatial resolution of 2 m. Simple linear and multiple-linear regression models were developed across the field based AGB and spectral indices derived from satellite imagery.Firstly for “Dalbergia sissoo”, and “Bombax ceiba”, reasonable accuracy (R2 values of 0.65 and 0.88, respectively) for AGB biomass estimation was achieved by combining different indices from Pleaids optical sensor. Using the single indices, only moderate performance (R2 = 0.52 for “Dalbergia sissoo” and 0.87 for “Bombax ceiba”) was observed from the spectral reflectance of the Pleiades satellite data. Secondly for Eucalyptus reasonable accuracy (R2 = 0.73) was achieved either using single or combining different spectral indices. These developed models for three tree species future can be utilized for reliable estimation and assessment of the changes in biomass. The study will be helpful for “Sustainable Forest Management” and preserve biomass/carbon in the performance based payment mechanism in forest ecosystem.
Keyword(s)
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