•We compared four remote sensing technologies in forest inventories.•Sentinel-2 based features were compared to selection of 3D features.•Multi-date S-2 features only slightly improved results ...compared to single-date S-2.•S-2 features performed as well as TerraSAR-X stereo features.•Based on the results, multitemporal S-2 data are good for monitoring wide-area.
National Forest Inventories (NFI) are key data and tools to better understand the role of forests in the global carbon budget. Traditionally inventories have been carried out as field work, which makes them laborious and expensive. In recent years, the development of various remote sensing techniques to improve the cost-efficiency of the NFIs has accelerated. The goal of this study is to determine the usability of open and free multitemporal multispectral satellite images from the European Space Agency's Sentinel-2 satellite constellation and to compare their usability in forest inventories against airborne laserscanning (ALS) and three-dimensional data obtained with high-resolution optical satellite images from WorldView-2 and Synthetic Aperture Radar (SAR) stereo data from TerraSAR-X. Ground reference consisted of field data collected over 74 boreal forest plots in Southern Finland in 2014 and 2016. Features utilizing both single- and multiple-date information were designed and tested for Sentinel-2 data. Due to high cloud cover, only four Sentinel-2 images were available for the multitemporal feature analysis of all reference plots within the monitoring window. Random Forest technique was used to find the best descriptive feature sets to model five forest inventory parameters (mean height, mean diameter at breast height, basal area, volume, above-ground biomass) from all input remote sensing data. The results confirmed that the higher spatial resolution input data correlated with more accurate forest inventory parameter predictions, which is in line with other results presented in literature. The addition of temporal information to the Sentinel-2 results showed limited variation in prediction accuracy between the single and multidate cases ranging from 0.45 to 1.5 percentage points, whereof mean height, basal area and aboveground biomass are lower for single date with relative RMSEs of 14.07%, 20.66% and 24.71% respectively. Diameter at breast height and volume are lower for multi date feature combination with relative RMSEs of 18.38% and 27.21%. The results emphasize the importance of obtaining more evenly distributed data acquisitions over the growing season to fully exploit the potential of temporal features.
Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer ...oven‐dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees ≥ 5 cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter–height relationship depended linearly on a bioclimatic stress variable E, which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter–height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development.
•Long-term increase in forest growth in Finland mainly due to improved forest structure.•Environmental factors induced a significant share (20 – 31 %) of the growth increase.•The recent growth ...reduction mainly caused by environmental factors.
After a rising trend for 1971 – 2013, during which the annual volume growth of the forests of Finland increased by more than 70 %, a recent reduction has been observed. We analyzed the development of annual growth in the forest of Finland, focusing on the component not explainable by changes in growing stock. The data originate from nine consecutive Finnish National Forest Inventories. In the data, diameter increments were measured from increment cores and tree height increments from standing sample trees in the field. We developed models predicting periodic (5 years) annual volume increment per hectare with properties of the trees and stands as predictor variables. Deviations from model-predicted values were interpreted to be induced by environmental variation. The development was analyzed for all tree species combined and separately for three species groups: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and broadleaves. We observed a rising growth trend not solely explainable by increased growing stock. The species groups produced rather a similar pattern in different parts of Finland: from the 1960s to the mid-1990s, the observed volume increment was mainly below the model-predicted level, thereafter above it. During the current century, the difference between observed and predicted annual volume increment has shown a downward trend for Scots pine. For Norway spruce, the difference has continued to increase in southern Finland, but shows little change in the north. For broadleaved species, the difference between measured and predicted increment shows a recent increase as well, though not as large as for Norway spruce. The geographical pattern of the environment-induced increment component was described in more detail via maps using a 75 km × 75 km grid. The changing environment has enhanced forest productivity in Finland over a period of nearly six decades, but recent years have not been favorable for Scots pine, which represents 50 % of the growing stock volume of the forests of Finland.
•A random forest algorithm was applied for modelling basal area increment (BAI).•The R2 for independent data varied from 0.57 (silver fir) to 0.29 (Scots pine).•The most important predictor variables ...were the basal area of trees and competition.•The highest growth potential was modelled for Norway spruce and silver fir.•High competitive potential was shown by common beech.
Here, we present one of the first attempts to use a machine learning model for the prediction and interpretation of tree basal area increment (BAI) based on data from the National Forest Inventory (NFI). The developed model is based on the random forest (RF) algorithm, trained with 18 independent variables and 15,580 data points (trees from the Slovenian NFI). The RF model was trained for four individual species and two groups of species and evaluated using 10-fold blocked cross-validation. Squared correlation coefficients calculated for independent data ranged from 0.289 for Scots pine (Pinus Sylvestris) to 0.342 for maple and ash species (Acer sp. and Fraxinus sp.), 0.429 for oak species (Quercus sp.), 0.475 for Norway spruce (Picea abies), 0.486 for common beech (Fagus sylvatica), and 0.565 for silver fir (Abies alba). The most important predictor variables were the basal areas of individual trees and their competition status, expressed as the basal area in larger trees and tree social position. Simulations of selected key variables revealed different ecological traits of the studied species: silver fir and Norway spruce have the highest growth characteristics, while common beech has the strongest competition potential. For valuable broadleaves and silver fir, site specific conditions play an important role in tree growth, while oaks and Scots pine have less site-specific demands and wider ecological amplitudes. Finally, in comparison to BAI models from similar studies, the presented RF model showed similar accuracy and could potentially be used as a tool in forest management practices and for making professionally informed decisions.
Mangroves sequester large quantities of carbon (C) that become significant sources of greenhouse gases when disturbed through land-use change. Thus, they are of great value to incorporate into ...climate change adaptation and mitigation strategies. In response, a global network of mangrove plots was established to provide policy-relevant ecological data relating to interactions of mangrove C stocks with climatic, tidal, plant community, and geomorphic factors. Mangroves from 190 sites were sampled across five continents encompassing large biological, physical, and climatic gradients using consistent methodologies for the quantification of total ecosystem C stocks (TECS). Carbon stock data were collected along with vegetation, physical, and climatic data to explore potential predictive relationships. There was a 28-fold range in TECS (79–2,208 Mg C/ha) with a mean of 856 ± 32 Mg C/ha. Belowground C comprised an average 85% of the TECS. Mean soil depth was 216 cm, ranging from 22 to >300 cm, with 68 sites (35%) exceeding a depth of 300 cm. TECS were weakly correlated with metrics of forest structure, suggesting that aboveground forest structure alone cannot accurately predict TECS. Similarly, precipitation was not a strong predictor of TECS. Reasonable estimates of TECS were derived via multiple regression analysis using precipitation, soil depth, tree mass, and latitude (𝑅² = 0.54) as variables. Soil carbon to a 1 m depth averaged 44% of the TECS. Limiting analyses of soil C stocks to the top 1 m of soils result in large underestimates of TECS as well as in the greenhouse gas emissions that would arise from their conversion to other land uses. The current IPCC Tier 1 default TECS value for mangroves is 511 Mg C/ha, which is only 60% of our calculated global mean. This study improves current assessments of mangrove C stocks providing a foundation necessary for C valuation related to climate change mitigation. We estimate mangroves globally store about 11.7 Pg C: an aboveground carbon stock of 1.6 Pg C and a belowground carbon stock of 10.2 Pg C). The differences in the estimates of total ecosystem carbon stocks based on climate, salinity, forest structure, geomorphology, or geopolitical boundaries are not as much of an influence as the choice of soil depth included in the estimate. Choosing to limit soils to a 1 m depth resulted in estimates of <5 Pg whereas those that included the soil profile >1 m depth resulted in global carbon stock estimates that exceeded 11.2 Pg C.
Hurricanes can physically transform forest ecosystems, leading to immediate and potentially long-lasting impacts on carbon dynamics. Using Forest Inventory Analysis (FIA) data from the United States ...Forest Service, we compared the average carbon in trees (saplings, bole, stump, tops) and foliage pre- (2001–2003) and post- (2005–2007) Hurricane Ivan for different tree categories in the Perdido Bay Watershed located in the Florida Panhandle. The log-linear regression model indicates that variables stand type (softwood/mixed/hardwood), diameter, height, physiographic class (deep sands/flatwoods/rolling uplands/small drains/swamps/bottomlands), elevation, field age and plot affected status were statistically significant (p ≤ 0.05) determinants of carbon loss from the forest stands. Results showed that plots with medium-diameter stands lost more aboveground carbon than large and small-diameter stands, similarly, softwood-dominated stands lost more aboveground carbon than hardwood and mixed wood stands. Aboveground carbon decreased in stands with large (≥ 0.15 m) and medium (≥ 0.12 m) diameter-at-breast height (DBH) by 22.74 and 30.22 metric tons/ha, respectively. We ascertained a decrease of 74.51 and 17.82 metric tons/ha of aboveground carbon in hardwood and mixed plots after Hurricane Ivan, respectively. Aboveground carbon in young (< 25 y) taller trees (> 15 m) decreased by 121.55 metric tons/ha of carbon immediately after the hurricane. These findings underscore the critical necessity of comprehending the interplay between forest structure and hurricane activity to forecast the repercussions of such disturbances on carbon stocks. Our provided framework serves as a valuable tool for researchers and policymakers to assess the vulnerability of coastal forests and facilitate strategic planning to protect forests as carbon sinks.
•More than 90 % of FIA plots inside the study area were affected by Hurricane Ivan.•About 28.64 % and 62.15 % decrease in carbon in trees (sapling, stump, bole, top) and foliage, respectively.•About 1.22 % and 13.25 % carbon decreased in mixed and softwood stands; softwoods losing 51.30 % more carbon than hardwoods.•Taller trees (>10 m) lost 23.22 % more carbon than smaller trees (<10 m).•Aboveground carbon in trees and foliage decreased 18.15% and 1.36% respectively, in trees with a medium diameter (≥0.12 m).
► We quantify oak abundance change during the last two decades in a regional-wide context. ► Eastern U.S. experienced a decline in oak abundance but the trend is not universal. ► Areas with a high ...oak abundance have a higher likelihood of oak reduction. ► Some oak species increased in oak abundance.
Although oaks (
Quercus spp.) have historically dominated much of the forest land in eastern North America, a great deal of fragmentary and sometimes anecdotal evidence suggests that they have been yielding dominance in recent decades to other, typically more shade-tolerant species. Using FIA data, our work formally quantifies the change in oak abundance in the eastern U.S. during the period of 1980–2008. The results indicate that most areas in the eastern U.S. experienced some decline in oak abundance, but the decrease was not universal either geographically or among species. Declines were especially marked in the Central Hardwood Region, which lost oak abundance on 81% its forested area as measured by importance value (IV). Areas with a high oak abundance were more likely to see a reduction in abundance. Among all 25 species analyzed, eight species decreased significantly in IV while two increased. Both the top two most prevalent white oak species (white oak (
Quercus alba) and post oak (
Quercus stellata)) and red oak species (northern red oak (
Quercus rubra) and black oak (
Quercus velutina)) had significant decreases in density and IV. Water oak (
Quercus nigra) is one of the red oak species that had a near universal increase of its abundance throughout its native range (83% of area). This study provided a comprehensive quantification of the dynamic of oak species in a regional-wide geographic context, which will provoke forest researchers and managers to revisit the oak decline problem by using knowledge from other regions and other species.
Models of individual trees are composed of sub-models that generally estimate competition, mortality, and growth in height and diameter of each tree. They are usually adopted when we want more ...detailed information to estimate forest multiproduct. In these models, estimates of growth in diameter at 1.30m above the ground (DBH) and total height (H) are obtained by regression analysis. Recently, artificial intelligence techniques (AIT) have been used with satisfactory performance in forest measurement. Therefore, the objective of this study was to evaluate the performance of two AIT, artificial neural networks and adaptive neuro-fuzzy inference system, to estimate the growth in DBH and H of eucalyptus trees. We used data of continuous forest inventories of eucalyptus, with annual measurements of DBH, H, and the dominant height of trees of 398 plots, plus two qualitative variables: genetic material and site index. It was observed that the two AIT showed accuracy in growth estimation of DBH and H. Therefore, the two techniques discussed can be used for the prognosis of DBH and H in even-aged eucalyptus stands. The techniques used could also be adapted to other areas and forest species.
Display omitted
•We use artificial neural networks to estimate the growth in DBH and height of eucalyptus trees.•Using new techniques in forestry measurement.•The techniques of artificial intelligence showed accuracy in growth estimation in DBH and total height.•The techniques of artificial intelligence are appropriate in estimating the growth of eucalyptus trees.•The techniques used can be adapted to other areas and forest crops.
The demand for large-scale and long-term information on tree growth is increasing rapidly as environmental change research strives to quantify and forecast the impacts of continued warming on forest ...ecosystems. This demand, combined with the now quasi-global availability of tree-ring observations, has inspired researchers to compile large tree-ring networks to address continental or even global-scale research questions. However, these emergent spatial objectives contrast with paleo-oriented research ideas that have guided the development of many existing records. A series of challenges related to how, where, and when samples have been collected is complicating the transition of tree rings from a local to a global resource on the question of tree growth. Herein, we review possibilities to scale tree-ring data (A) from the sample to the whole tree, (B) from the tree to the site, and (C) from the site to larger spatial domains. Representative tree-ring sampling supported by creative statistical approaches is thereby key to robustly capture the heterogeneity of climate-growth responses across forested landscapes. We highlight the benefits of combining the temporal information embedded in tree rings with the spatial information offered by forest inventories and earth observations to quantify tree growth and its drivers. In addition, we show how the continued development of mechanistic tree-ring models can help address some of the non-linearities and feedbacks that complicate making inference from tree-ring data. By embracing scaling issues, the discipline of dendrochronology will greatly increase its contributions to assessing climate impacts on forests and support the development of adaptation strategies.
•Scaling issues complicate the growing use of tree rings in large-spatial analyses.•New tree-ring networks should represent and quantify forest growth across scales.•Forest plot and remote sensing data help account for non-climatic drivers of growth.•Hind- and forecasting tree growth requires broadly applicable mechanistic models.•Dendrochronology needs new quality criteria for the collection and scaling of data.
PDF
