Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

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A study on improving DEM-based forest biomass estimation using PALSAR and forest inventory data

2014 IEEE Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2014.6946531 ◽

2014 ◽

Cited By ~ 1

Author(s):

Min-Gee Hong ◽

Hyo-Suk Lim ◽

Choen Kim

Keyword(s):

Forest Inventory ◽

Forest Biomass ◽

Biomass Estimation ◽

Inventory Data ◽

Forest Inventory Data

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Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

Remote Sensing ◽

10.3390/rs13081592 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1592

Author(s):

Nikolai Knapp ◽

Andreas Huth ◽

Rico Fischer

Keyword(s):

Remote Sensing ◽

Forest Inventory ◽

Canopy Height ◽

Biomass Estimation ◽

Inventory Data ◽

Border Effects ◽

Tree Crowns ◽

Forest Inventory Data ◽

Estimation Uncertainty ◽

Voxel Space

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

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