Carbon stocks and flows in forest ecosystems based on forest inventory data

Forest health is an important aspect of sustainable forest management. The practical significance of health assessments of forest ecosystems is becoming more and more prominent because good knowledge about the health level of forests and the causes of unhealthy forests enables the identification of proper actions for enhancing sustainable development of forest ecosystems. This paper evaluated the health status of the forest ecosystem of Mount Taishan using the spatial analysis technique of GIS (Geographic Information System) and local forest inventory data. A comprehensive indicator system that reflects the health status of forestsin the study areawas established. Based on this indicator system, the health level of each sub-compartment of the forests in the study area was assessed. The results show that the high-quality grade forest (80.4 ha) and healthy grade forest (2671 ha) accounted for only 23.5% of the total forest area of Mount Taishan. About 60.5% of Mount Taishan forest was in a sub-health status. The area of unhealthy forests was 1865 ha (accounting for 16% of the total forest area), of which about 98 ha was inextremely unhealthy conditions.Asmore than two-thirds of the forests in Mount Taishan are in a sub-health or unhealthy state, effective measures for improving forest health are in urgent need in the study area.

Download Full-text

State forest inventory data as a new information base for estimating carbon stocks and carbon removals by forests: the feasibility and prospects for use in national reporting under international agreements

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/876/1/012028 ◽

2021 ◽

Vol 876 (1) ◽

pp. 012028

Author(s):

A N Filipchuk ◽

N V Malysheva ◽

A N Yugov ◽

T A Zolina ◽

R Yu Mironov

Keyword(s):

Forest Inventory ◽

International Agreements ◽

Carbon Stocks ◽

Inventory Data ◽

Information Base ◽

Forest Inventory Data ◽

New Information

Download Full-text

Carbon stocks in tropical forests decrease with liana density

Biology Letters ◽

10.1098/rsbl.2013.0301 ◽

2013 ◽

Vol 9 (4) ◽

pp. 20130301 ◽

Cited By ~ 53

Author(s):

Sandra M. Durán ◽

Ernesto Gianoli

Keyword(s):

Tropical Forests ◽

Forest Inventory ◽

Tree Mortality ◽

Structural Changes ◽

Carbon Stocks ◽

Global Changes ◽

Strong Impact ◽

Inventory Data ◽

Forest Inventory Data ◽

Large Trees

Tropical forests are experiencing structural changes that may reduce carbon storage potential. The recent increase in liana abundance and biomass is one such potential change. Lianas account for approximately 25 per cent of woody stems and may have a strong impact on tree dynamics because severe liana infestation reduces tree growth and increases tree mortality. Based on forest inventory data from 0.1 ha plots, we evaluated the association between above-ground carbon stocks and liana abundance in 145 tropical forests worldwide. Liana abundance was negatively associated with carbon stocks of large trees (greater than 10 cm diameter), while it was not related to small trees (10 cm diameter or less). Results suggest that liana abundance may have pervasive effects on carbon stocks in tropical forests, as large trees store about 90 per cent of total forest carbon. We stress the need to include liana abundance in carbon stocks estimates, as this can enhance the accuracy of predictions of global changes in tropical forests.

Download Full-text

Rapid Assessment of Tree Damage Resulting from a 2020 Windstorm in Iowa, USA

Forests ◽

10.3390/f12050555 ◽

2021 ◽

Vol 12 (5) ◽

pp. 555

Author(s):

Thomas C. Goff ◽

Mark D. Nelson ◽

Greg C. Liknes ◽

Tivon E. Feeley ◽

Scott A. Pugh ◽

...

Keyword(s):

Forest Inventory ◽

Damage Zone ◽

Satellite Image ◽

Rapid Assessment ◽

Aerial Survey ◽

Inventory Data ◽

Tree Damage ◽

Forest Inventory Data ◽

Damage Susceptibility ◽

The Impact

A need to quantify the impact of a particular wind disturbance on forest resources may require rapid yet reliable estimates of damage. We present an approach for combining pre-disturbance forest inventory data with post-disturbance aerial survey data to produce design-based estimates of affected forest area and number and volume of trees damaged or killed. The approach borrows strength from an indirect estimator to adjust estimates from a direct estimator when post-disturbance remeasurement data are unavailable. We demonstrate this approach with an example application from a recent windstorm, known as the 2020 Midwest Derecho, which struck Iowa, USA, and adjacent states on 10–11 August 2020, delivering catastrophic damage to structures, crops, and trees. We estimate that 2.67 million trees and 1.67 million m3 of sound bole volume were damaged or killed on 23 thousand ha of Iowa forest land affected by the 2020 derecho. Damage rates for volume were slightly higher than for number of trees, and damage on live trees due to stem breakage was more prevalent than branch breakage, both likely due to higher damage probability in the dominant canopy of larger trees. The absence of post-storm observations in the damage zone limited direct estimation of storm impacts. Further analysis of forest inventory data will improve understanding of tree damage susceptibility under varying levels of storm severity. We recommend approaches for improving estimates, including increasing spatial or temporal extents of reference data used for indirect estimation, and incorporating ancillary satellite image-based products.

Download Full-text

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.

Download Full-text