Forest ecosystems contribute substantially to carbon (C) storage. The dynamics of litter decomposition, translocation and stabilization into soil layers are essential processes in the functioning of ...forest ecosystems, as these processes control the cycling of soil organic matter and the accumulation and release of C to the atmosphere. Therefore, the spatial distribution of litter and soil C stocks are important in greenhouse gas estimation and reporting and inform land management decisions, policy, and climate change mitigation strategies. Here we explored the effects of spatial aggregation of climatic, biotic, topographic and soil variables on national estimates of litter and soil C stocks and characterized the spatial distribution of litter and soil C stocks in the conterminous United States (CONUS). Litter and soil variables were measured on permanent sample plots (n = 3303) from the National Forest Inventory (NFI) within the United States from 2000 to 2011. These data were used with vegetation phenology data estimated from LANDSAT imagery (30 m) and raster data describing environmental variables for the entire CONUS to predict litter and soil C stocks. The total estimated litter C stock was 2.07 ± 0.97 Pg with an average density of 10.45 ± 2.38 Mg ha−1, and the soil C stock at 0–20 cm depth was 14.68 ± 3.50 Pg with an average density of 62.68 ± 8.98 Mg ha−1. This study extends NFI data from points to pixels providing spatially explicit and continuous predictions of litter and soil C stocks on forest land in the CONUS. The approaches described illustrate the utility of harmonizing field measurements with remotely sensed data to facilitate modeling and prediction across spatial scales in support of inventory, monitoring, and reporting activities, particularly in countries with ready access to remotely sensed data but with limited observations of litter and soil variables.
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•Spatial patterns found in the estimated litter and soil carbon stocks in forests•Including Normalized Difference Vegetation Index facilitated the model predictions.•Forest disturbances caused statistically significant differences in litter carbon.•Estimates of litter and soil carbon stocks were 2.07 Pg and 14.68 Pg, respectively.
•We explored recent changes of ecosystem services (ES) in Mediterranean forests.•Timber volume increment, water provision, and carbon sequestration showed a decline since 1990.•Recent changes in ES ...were largely driven by the structure of the forest stands.•Temporal analyses revealed more potential trade-offs between ES than purely static assessments.
The Mediterranean Region constitutes a biodiversity hotspot and its forests have provided multiple ecosystem services (ES) to human societies for millennia. In the last decades, many Mediterranean forests have undergone a decreasing level of direct human pressure and a growing exposure to environmental stress factors (e.g. wildfires and droughts). However, the degree to which these processes have affected the provision of ES remains largely unexplored. We used an extensive database of 3417 permanent plots (period 1990–2015, 25 years) from the Spanish National Forest Inventory in Catalonia (North-Eastern Spain) and a range of four ecological models to measure and estimate changes in five different ES: wild mushrooms production, timber volume increment, water provision, carbon sequestration and erosion mitigation. We then assessed general trends in ES, their spatial–temporal patterns and searched for potential trade-offs in their delivery. Using mixed-effects models, we explored the differences among three biogeographical regions, as well as the effect of different environmental and site level drivers, including descriptors of stand structure and development, the legacies of management practices and disturbances, as well as the influence of historical climate conditions and their recent anomalies. Our results show a general decline of timber volume increment, water provision and carbon sequestration, along with an increase in erosion mitigation across inland and montane regions. Fitted model parameters suggest a predominant role of stand structure in driving changes in forest ES supply in the study area. In particular, stands with high basal areas were associated with steeper declines in most ES, whereas high mean tree diameter generally contributed to ES increases. Finally, our results showed a series of potential trade-offs among temporal changes in ES that were not reflected in exclusively static analyses, highlighting the relevance of including the temporal dimension in regional assessments of ES. Future forest management and planning could better account for overall ES value as well as expected changes in their future provision, paving the way to landscape planning that balances these two essential components of forest ES.
•The study aims at constructing wall-to-wall estimates of forest growing stock (GSV).•We combine NFI plot data, remotely sensed and auxiliary variables.•We applied the methodology in Mediterranean ...Forest.•We create a wall-to-wall GSV forest map in a large test area.•The GSV map was used to produce model-based estimates of GSV at small scale.
Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.
For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.
Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.
•High Nature Value (HNV) forest are characterised by high levels of naturalness.•A framework for identifying HNV forest is proposed; six indicators were selected for constructing a Nature Value (NV) ...index.•The NV index was determined for 1676 NFI plots in the ROI and c. 11% of the forest plots were identified as HNV forest.•The framework presented can serve as a guide for HNV forest identification in other biogeographical regions.
High Nature Value (HNV) forests contribute to maintaining European biodiversity and public good supply. This study aimed to a) develop an objective and quantitative Nature Value (NV) index for the identification of HNV forests in the Republic of Ireland; and b) apply and validate the index using available data from the Irish National Forest Inventory (NFI).
Following recent European definitions of HNV forest, a six-step framework was adapted from literature and used for assessing forest naturalness. The reference forest (in an Irish context) and its naturalness traits were first described. Six indicators were selected to construct a NV index and three categories (low, medium and high NV) were defined based on the range of NV scores. Using data from the Irish NFI, the approach was implemented by calculating the indicators’ values and the NV score for 1,676 forest plots. The selected indicators were tested for redundancy and the NV index was validated with available floristic variables and with forest sub-type classes. A sensitivity analysis was conducted on the weighting values of the indicators.
Approximately 11% of the NFI plots were categorised as HNV. There was no redundancy between the selected indicators. The NV index was significantly positively correlated with data from the floristic variables collated by the NFI. The averages of the floristic variables per NV category were significantly different. NFI plots classified as HNV had a higher percentage of natural/semi-natural forest types than medium NV and low NV plots. The sensitivity analyses showed little effect of changes to the indicators' weighting values on a) the correlation coefficients between the floristic variables’ data and the new NV scores obtained and b) on the proportion of natural/semi-natural forests in HNV plots.
This work provides an approach for the development of a NV index to identify HNV forests in a European country following the naturalness concept exclusively. The selected indicators and their weighting should be tailored to each country’s particular conditions, especially due to potential differences in the reference level of naturalness of forests and differences within NFIs.
•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.
The National Mapping Agency in Sweden has conducted an airborne laser scanning (ALS) campaign covering almost the entire country for the purpose of creating a new national Digital Elevation Model ...(DEM). The ALS data were collected between 2009 and 2015 using Leica, Optech, Riegl, and Trimble scanners and have a point density of 0.5–1.0pulses/m2. A high resolution national raster database (12.5m×12.5m cell size) with forest variables was produced by combining the ALS data with field data from the Swedish National Forest Inventory (NFI). Approximately 11500 NFI plots (10meter radius) located on productive forest land, inventoried between 2009 and 2013, were used to create linear regression models relating selected forest variables, or transformations of the variables, to metrics derived from the ALS data. The resulting stand level relative RMSEs for predictions of stem volume, basal area, basal-area weighted mean tree height, and basal-area weighted mean stem diameter were in the ranges of 17.2–22.0%, 13.9–18.2%, 5.4–9.5%, and 8.7–13.1%, respectively. It was concluded that the predictions had an accuracy that were at least as good as data typically used in forest management planning. Above ground tree biomass was also included in the national raster database but not validated on a stand-level.
An important part of the project was to make the raster database available to private forest owners, forest associations, forest companies, authorities, researchers, and the general public. Thus, all predicted forest variables can be viewed and downloaded free of charge at the Swedish Forest Agency's homepage (http://www.skogsstyrelsen.se/skogligagrunddata).
•A wall-to-wall forest attribute map was produced for all of Sweden.•ALS data were combined with field data from the Swedish NFI.•Regression models were used to predict forest attributes.•The attribute maps are provided free of charge via internet.
•Some 1.1 billion ha are covered by all of the SFM tools investigated in FRA 2015.•Policies, laws and regulations supporting SFM cover 98% of permanent forest land.•Forest inventories have recently ...been conducted in 112 countries.•Some 52% of the total forest area was under Forest Management Plan (FMP) in 2010.•International forest certification was most extensive in high income countries.•There are positive increases in most SFM indicators globally.
Sustainable forest management (SFM) is many things to many people – yet a common thread is the production of forest goods and services for the present and future generations. The promise of sustainability is rooted in the two premises; first that ecosystems have the potential to renew themselves and second that economic activities and social perceptions or values that define human interaction with the environment are choices that can be modified to ensure the long term productivity and health of the ecosystem. SFM addresses a great challenge in matching the increasing demands of a growing human population while maintaining ecological functions of healthy forest ecosystems. This paper does not seek to define SFM, but rather provides analyses of key indicators for the national-scale enabling environment to gain a global insight into progress in implementing enabling and implementing SFM at the national and operational levels. Analyses of the Global Forest Resources Assessment 2015 (FRA) country report data are used to provide insights into the current state of progress in implementing the enabling conditions for SFM. Over 2.17 billion ha of the world’s forest area are predicted by governments to remain in permanent forest land use, of which some 1.1 billion ha are covered by all of the SFM tools investigated in FRA 2015. At the global scale, SFM-related policies and regulations are reported to be in place on 97% of global forest area. While the number of countries with national forest inventories has increased over that past ten years from 48 to 112, only 37% of forests in low income countries are covered by forest inventories. Forest management planning and monitoring of plans has increased substantially as has forest management certification, which exceeded a total of over 430 million ha in 2014. However, 90% of internationally verified certification is in the boreal and temperate climatic domains – only 6% of permanent forests in the tropical domain have been certified as of 2014. Results show that more work is needed to expand the extent and depth of work on establishing the enabling conditions that support SFM over the long term and suggests where those needs are greatest.
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