Search results for "growing stock volume"
showing 3 items of 3 documents
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
2021
Funding Information: We are thankful to the GlobBiomass project team and Frank Martin Seifert (ESA) for valuable suggestions and stimulating scientific discussions. We are thankful to Takeo Tadono (JAXA EORC), Masato Hayashi, (JAXA EORC), Kazufumi Kobayashi (RESTEC), Åke Rosenqvist (soloEO), and Josef Kellndorfer (EBD) for support with the use and interpretation of the ALOS PALSAR mosaics. Support by the CCI Land Cover project team, in particular Sophie Bontemps (UCL), is greatly acknowledged. The help from Martin Jung (MPI-BGC) in feature selection and Ulrich Weber (MPI-BGC) for data processing for the GSV-to-AGB conversions is greatly acknowledged. Forest inventory data for the validation…
Growing stock volume from multi-temporal landsat imagery through google earth engine
2019
Growing stock volume (GSV) is one of the most important variables for.forest management and is traditionally- estimated from ground measurements. These measurements are expensive and therefore sparse and hard to maintain in time on a regular basis. Remote sensing data combined with national forest inventories constitute a helpful tool to estimate and map forest attributes. However, most studies on GSV estimation from remote sensing data focus on small forest areas with a single or only a few species. The current study aims to map GSV in peninsular Spain, a rather large and very heterogeneous area. Around 50 000 wooded land plots from the Third Spanish National Forest Inventory (NFI3) were u…
Optimizing the Sampling Area across an Old-Growth Forest via UAV-Borne Laser Scanning, GNSS, and Radial Surveying
2022
Aboveground biomass, volume, and basal area are among the most important structural attributes in forestry. Direct measurements are cost-intensive and time-consuming, especially for old-growth forests exhibiting a complex structure over a rugged topography. We defined a methodology to optimize the plot size and the (total) sampling area, allowing for structural attributes with a tolerable error to be estimated. The plot size was assessed by analyzing the semivariogram of a CHM model derived via UAV laser scanning, while the sampling area was based on the calculation of the absolute relative error as a function of allometric relationships. The allometric relationships allowed the structural …