Aboveground Forest Biomass Trends for the Conterminous U.s. Inferred from Landsat Time-series and Field Inventory Data

Potential of Landsat-8 spectral indices to estimate forest biomass

Forest ecosystems are among the largest terrestrial carbon reservoirs on our planet earth thus playing a vital role in global carbon cycle. Presently, remote sensing techniques provide proper estimates of forest biomass and quantify carbon stocks. The present study has explored Landsat-8 sensor product and evaluated its application in biomass mapping and estimation. The specific objectives were...

Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches

Integrating Landsat-Derived Disturbance Maps with FIA Inventory Data: Applications for State-Level Forest Resource Assessments

Landsat images have been widely used for assessing forest characteristics and dynamics. Recently, signifi cant progress has been made towards indepth exploration of the rich Landsat archive kept by the U.S. Geological Survey to improve our under standing of forest disturbance and recovery processes. In this study, we used Landsat images to map forest disturbances at biennial intervals from 1984...

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Accurate estimates of aboveground biomass (AGB) are crucial to assess terrestrial C-stocks and C-emissions as well as to develop sustainable forest management strategies. In this study we used Synthetic Aperture Radar (SAR) data acquired at L-band and the Landsat tree cover product together with Moderate Resolution Image Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) ti...

Estimating Live Forest Carbon Dynamics with a Landsat-based Curve-fitting Approach

Direct estimation of aboveground biomass with spectral reflectance data has proven challenging for high biomass forests of the Pacific Northwestern United States. We present an alternative modeling strategy which uses Landsat’s spatial, spectral and temporal characteristics to predict live forest carbon through integration of stand age and site index maps and locally calibrated Chapman-Richards...