Mapping aboveground carbon density (ACD) in tropical forests can enhance large-scale ecological studies and support CO2 emissions monitoring. Light Detection and Ranging (LiDAR) has proven useful for ...estimating carbon density patterns outside of field plot inventory networks. However, the accuracy and generality of calibrations between LiDAR-assisted ACD predictions (EACDLiDAR) and estimated ACD based on field inventory techniques (EACDfield) must be increased in order to make tropical forest carbon mapping more widely available. Using a network of 804 field inventory plots distributed across a wide range of tropical vegetation types, climates and successional states, we present a general conceptual and technical approach for linking tropical forest EACDfield to LiDAR top-of-canopy height (TCH) using regional-scale inputs of basal area and wood density. With this approach, we show that EACDLiDAR and EACDfield reach nearly 90% agreement at 1-ha resolution for a wide array of tropical vegetation types. We also show that Lorey's Height – a common metric used to calibrate LiDAR measurements to biomass – is severely flawed in open canopy forests that are common to the tropics. Our proposed approach can advance the use of airborne and space-based LiDAR measurements for estimation of tropical forest carbon stocks.
•The accuracy and generality of LiDAR-assisted carbon mapping remains uncertain in tropical forests.•An approach is developed to calibrate LiDAR top-of-canopy height to field-estimated aboveground carbon density.•At 1-ha resolution, LiDAR estimates of aboveground carbon density approach 90% agreement with field-estimated carbon density.•This approach will reduce cost and effort to calibrate LiDAR metrics to field estimates of tropical forest carbon stocks.
Abstract Key message Authors have analyzed the possible correlation between measurements/indicators of forest structure and species richness of many taxonomic or functional groups over three regions ...of Germany. Results show the potential to use structural attributes as a surrogate for species richness of most of the analyzed taxonomic and functional groups. This information can be transferred to large-scale forest inventories to support biodiversity monitoring. Context We are currently facing a dramatic loss in biodiversity worldwide and this initiated many monitoring programs aiming at documenting further trends. However, monitoring species diversity directly is very resource demanding, in particular in highly diverse forest ecosystems. Aims We investigated whether variables applied in an index of stand structural diversity, which was developed based on forest attributes assessed in the German National Forest Inventory, can be calibrated against richness of forest-dwelling species within a wide range of taxonomic and functional groups. Methods We used information on forest structure and species richness that has been comprehensively assessed on 150 forest plots of the German biodiversity exploratories project, comprising a large range of management intensities in three regions. We tested, whether the forest structure index calculated for these forest plots well correlate with the number of species across 29 taxonomic and functional groups, assuming that the structural attributes applied in the index represent their habitat requirements. Results The strength of correlations between the structural variables applied in the index and number of species within taxonomic or functional groups was highly variable. For some groups such as Aves, Formicidae or vascular plants, structural variables had a high explanatory power for species richness across forest types. Species richness in other taxonomic and functional groups (e.g., soil and root-associated fungi) was not explained by individual structural attributes of the index. Results indicate that some taxonomic and functional groups depend on a high structural diversity, whereas others seem to be insensitive to it or even prefer structurally poor stands. Conclusion Therefore, combinations of forest stands with different degrees of structural diversity most likely optimize taxonomic diversity at the landscape level. Our results can support biodiversity monitoring through quantification of forest structure in large-scale forest inventories. Changes in structural variables over inventory periods can indicate changes in habitat quality for individual taxonomic groups and thus points towards national forest inventories being an effective tool to detect unintended effects of changes in forest management on biodiversity.
•Combining Sentinel-1/-2 and a DTM improved classification in complex terrain.•Sentinel-2 predictors were more contributive than Sentinel-1.•Higher accuracies were achieved compared to Copernicus HRL ...2018 DLT.•UNET outperformed random forest by single usage of Sentinel-1 predictors.•Map accuracy assessment using independent NFI plot data.
Countrywide winter and summer Sentinel-1 (S1) backscatter data, cloud-free summer Sentinel-2 (S2) images, an Airborne Laser Scanning (ALS)-based Digital Terrain Model (DTM) and a forest mask were used to model and subsequently map Dominant Leaf Type (DLT) with the thematic classes broadleaved and coniferous trees for the whole of Switzerland. A novel workflow was developed that is robust, cost-efficient and highly automated using reference data from aerial image interpretation. Two machine learning approaches based on Random Forest (RF) and deep learning (UNET) for the whole country with three sets of predictor variables were applied. 24 subareas based on aspect and slope categories were applied to explore effects of the complex mountainous topography on model performances. The reference data split into training, validation and test data sets was spatially stratified using a 25 km regular grid. Model accuracies of both RF and UNET were generally highest with Kappa (K) around 0.95 when predictors were included from both S1/S2 and the topographic variables aspect, elevation and slope from the DTM. While only slightly lower accuracies were obtained when using S2 and DTM data, lowest accuracies were obtained when only predictors from S1 and DTM were included, with RF performing worse than UNET. While on countrywide level RF and UNET performed overall similarly, substantial differences in model performances, i.e. higher variances and lower accuracies, were found in subareas with northwest to northeast orientations. The combined use of S1/S2 and DTM predictors mitigated these problems related to topography and shadows and was therefore superior to the single use of S1 and DTM or S2 and DTM data. The comparison with independent National Forest Inventory (NFI) plot data demonstrated precisions of K around 0.6 in the predictions of DLT and indicated a trend of increasing deviations in mixed forests. A comparison with the Copernicus High Resolution Layer (HRL) DLT 2018 revealed overall higher map accuracies with the exception of pure broadleaved forest. Although, spatial patterns of DTL were overall similar, UNET performed better than RF in areas with a distinct DLT on forest stand level, with the largest differences occurring when only S1 and DTM data was used. In contrast, predictions obtained from RF were more accurate in mixed stands. This study goes beyond the case study level and meets the requirements of countrywide data sets, in particular regarding repeatability, updating, costs and characteristics of training data sets. The 10 m countrywide DLT maps add complementary and spatially explicit information to the existing NFI estimates and are thus highly relevant for forestry practice and other related fields.
•Trends of basal area growth and mean height were studied in the period 1983–2020.•The study was made for 20 to 60 year old pines and spruces in Sweden.•On average, mean height at a given age of both ...species has increased by 2 m.•The basal area growth level was stable in the period.•Current trees are becoming taller and slenderer in Swedish forests.
Changes over time in annual basal area growth and mean height for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) over the period, 1983–2020 were studied using sample tree data from temporary plots recorded in the Swedish National Forest Inventory. The annual basal area growth was derived from the last measured full ring on increment cores. Using 20 to 60-year-old dominant trees, the mean height and annual basal area growth were examined as functions of tree, stand and site conditions, and trends were assessed mainly using residual analyses over time. A significant increase in mean height at a given age was found for both species, but the annual basal area growth level remained stable over the 38-year period. Currently, at a given age of 50 annual rings at breast height, the mean heights of pines and spruces increased on average by 10.1% (i.e. ∼2 m), compared to 50 year-old pines and spruces in the 1980s, and the increase was similar in the different regions. The results suggest that trees have become taller and slenderer in Swedish forests. Increasing tree height over time at a given age in Northern Europe has been documented in several reports and many causes have been suggested, such as changed forest management, increasing temperatures and nitrogen deposition. We suggest that elevated CO2 in the air and improved water-use efficiency for the trees might also be strong drivers.
•Evaluating methods to derive stand descriptions from large-scale sampling data.•Multi-scale approach to improve initialisation of dynamic forest models.•Simultaneous parameter prediction method best ...to predict tree diameter distributions.•Random Forest approach best to predict tree species composition.
Most strategic and operational forest management decisions are taken based on stand-level information, and quantitative models of forest dynamics are key for developing sustainable management strategies. However, data on forest stands for the initialisation of such models that are representative at large spatial scales, e.g., countries or ecoregions, are often lacking. National Forest Inventories (NFIs) provide forest data from small sample plots at large spatial scales, yet deriving full stand information based on such data is challenging. Here, we evaluate seven methods of varying complexity for deriving quantitative stand descriptions based on sample data as provided by the Swiss NFI. We selected 271 extensively measured Swiss forests stands with unimodal diameter distributions, classified them as beech- vs. spruce-dominated in five development stages and randomly placed a small sized sample plot in each stand using the Swiss NFI sampling design (i.e., a circular plot of 500 m2). Seven modelling approaches were used to derive diameter distributions and species-specific stem numbers (i.e., tree species composition) from the sample data that are representative for a particular stand (local scale) and for stand types in general (generalised scale). The prediction performance of the modelling approaches was evaluated using 100 random samples per stand to calculate prediction errors. Generalised even-aged diameter distributions were best predicted by the simultaneous parameter prediction method (PPM), i.e. a combined three-step regression approach, with on average 1.3 to 2.5 times lower prediction errors compared to the simple pooling of diameter samples. However, uneven-aged diameter distributions were best predicted by pooling. At the local scale, the simultaneous PPM performed best for data from sample plots with fewer than 17 to 19 trees across all development stages. Prediction performance of the PPMs increased for structurally and spatially diverse local stands with positively skewed diameter distributions. A Random Forest approach was most suitable for predicting species composition at both the generalised and the local scale. Our study evaluates the strengths and weaknesses of methods to model stands based on data from small sample plots. We emphasise terminological pitfalls by consequently distinguishing local accuracy and generalised representativity of the stand descriptions. We demonstrate the feasibility of deriving locally accurate stands using data from small forest sample plots and evaluate the derivation of generalised stands representative at large regions. At both scales, our developments contribute to an improved initialisation of forest models and thus to a more realistic modelling of forest development under future boundary conditions.
•Forest biomass and productivity are correlated to tree density and NDVI.•Species models differ on the effect of soil factors on biomass and productivity.•Aridity negatively affects forest biomass ...and productivity.•Projections of increasing aridity show a decrease on forest biomass and productivity.
One of the main challenges under global warming is understanding and predicting the effects of increased aridity on the carbon sink role of forests, particularly in Mediterranean regions. Forest inventories monitor the real state of the forest at a high temporal and financial cost. Cloud computing tools and high spatio-temporal resolution datasets generate fast and low-cost remote sensing data. Our objective is to understand the underlying variables explaining carbon storage (aboveground biomass) and forest productivity of Mediterranean forests using remote and in field-based variables and predict expected future trends. Then, we quantify the potential effects of a hypothetical increase in aridity under climate change on aboveground biomass and forest productivity. We included remote sensing indices (NDVI), abiotic factors (climate, soil and topography) and biotic factors (forest structure) as key variables of forest biomass and productivity in a large and heterogeneous Mediterranean region (Andalusia, southern Spain). We used around 7000 forest plots from the second and the third Spanish National Forest Inventory (1995 and 2006) considering the eight most abundant species (Olea europaea, Pinus pinea, P. pinaster, P. halepensis, P. nigra, P. sylvestris, Quercus ilex subsp ballota, and Q. suber). The variance explained by the models ranged from 25% in Q. ilex forests to 65% in P. sylvestris forests. Aridity affected all-species and Quercus biomass and most productivity models. NDVI and tree density had a strong positive effect on forest biomass and productivity with a significant interaction effect in all-species models, whereas aridity had a negative effect on both. The predicted increase in aridity under future climate change scenarios could seriously reduce forest biomass by 18% and productivity by 16%. Our study suggests that aridity is a key factor determining forest biomass and productivity in Mediterranean forests, that could potentially lead to reductions of their carbon sink role.
Display omitted
Abstract Key message This paper proposes a methodology that could be considered as a base for a harmonized protocol for stem-quality reporting in Europe while conducting National Forest Inventories, ...in order to cost-efficiently obtain a visual wood quality proxy. The importance of the variables selected, the limitations identified, and some improvements to the methodology are suggested. Forest areas with better wood quality, which in turn it would be useful for breeding programs, can be easily detected. Context The establishment of harmonized standards or indicators that allow us to determine the quality of the wood present in a forest prior to its exploitation has long been demanded by the European forestry sector, although agreed methodologies for the evaluation of wood quality in standing trees, which is one of the sector’s most urgent requirements, have not, as yet, been implemented. Aims To develop a protocol that visually characterizes wood quality on standing trees in a cost-effective way for the National Forest Inventory (NFI). After some improvements, it can be considered as a base for a European harmonized protocol. Methods In this article, we analyze the implementation, in the NFI, of a visual wood-quality assessment methodology in forests of Central Spain based on the different European standards as well as on research papers addressing this issue. Results The silvicultural practices employed are of the utmost importance to obtain the best wood quality, regardless of the species. Several areas with higher wood quality were identified as well as areas most affected by specific pests in the studied region. The impact of the variables measured (e.g., branchiness, crookedness, maximum branch diameter) is discussed. Conclusion It is feasible to estimate a proxy for wood quality on standing trees in the NFI. Furthermore, after studying the inventory data provided, several enhancements are proposed, not only to improve wood-quality estimates but also to optimize fieldwork costs. Harmonizing NFIs to assess and map European standing wood quality can be achieved.
•Modern aspen forest management guides are based on data from nearly a century ago.•Current atmospheric CO2, climate, and management objectives changed over a century.•Mismatched data for guides and ...forest conditions reduces forest health and management.•Aspen stocking tables recalculated with modern data show that guides are outdated.•New management guides should be dynamic to empower adaptive management.
Since the development of contemporary stocking techniques a century ago, the combination of climatic, atmospheric, financial, and social factors that determine forest management strategies have changed, altering aspen stand dynamics in the western Great Lakes, USA. Despite this, aspen management is still informed by 1970s management guides that are based on 1920s inventories; hence, a century exists between the data that underlie current management guidelines and current stand conditions. We hypothesized that current aspen stands may support higher stocking and height growth than nearly a century ago at relatively similar age and site indices, due to increased atmospheric CO2 concentrations and fertilization, intensive coppice harvests, and other factors. To explore this question, we compared historic aspen observations with comparable contemporary data from the USDA Forest Service’s Forest Inventory and Analysis program. The results show increased stand stocking levels as well as increased height growth of aspen throughout the region over the historic inventory data. Although other controlled experimental studies support the hypothesis of increased carbon fertilization altering aspen size-density relationships, our study is the first to examine an empirical application to forest management guides. Our results suggest a comprehensive reevaluation of aspen growth dynamics under contemporary environmental conditions is warranted. We highlight the need to assess the value of current stocking standards in an era of increasingly variable environmental conditions and to reimagine a more dynamic, responsive, and predictive approach to guide forest management for future application as global change may accelerate.
Efficient forest operations are required for the provision of biodiversity and numerous ecosystem services, such as wood production, carbon sequestration, protection against natural hazards and ...recreation. In numerous countries, under difficult terrain conditions, the costs of forest management and harvesting are not covered by timber revenue. One possible option to increase the cost-effectiveness of the forestry sector is the application of state-of-the-art harvesting and extraction techniques, so-called best suitable harvesting methods. We present a case study from Switzerland, where a lack of competitiveness in the forestry sector is of particular interest, with the aim of quantifying the efficiency gains if estimated best suitable harvesting methods were to be rigorously applied instead of the currently applied harvesting methods. For this purpose, we developed a spatial decision support system to allocate estimated best suitable harvesting methods to plots, while concurrently considering hauling route limitations, extraction route properties and stand characteristics. Our approach was based on productivity models and supported with expert-defined decision trees. The evaluation of the estimated best suitable harvesting methods and the comparison with the currently applied harvesting methods were completed for all 6500 National Forest Inventory (NFI) plots in Switzerland. We draw the following three major conclusions from our study: First, our modeling approach is an effective method to allocate estimated best suitable harvesting methods to NFI plots. Second, applying estimated best suitable harvesting methods would lead to cost reductions, in particular in the regions that include steep terrain and where harvesting mainly relies on cable- and air based extraction methods. Third, assuming an average timber price of 75 CHF m −3, 64 % instead of 52 % of the forest area could be harvested economically over the whole country if estimated best suitable methods were applied. This advantage would mainly be caused by a shift towards more mechanized harvesting methods. Improving the cost-effectiveness of the forestry sector is of high global relevance, as the increased use of domestic timber resources is a cost-efficient way to reduce atmospheric carbon emissions. The methodological framework described here was developed for Switzerland in particular, but it could be applied to Central Europe and other parts of Europe with a large amount of mountain forests.
•An effective method to allocate the best suitable harvesting methods to NFI plots.•Integration of productivity models and expert-defined decision trees.•A larger proportion of the forest can be managed to cover costs or generate profits.•In Switzerland, 64 instead of 52 % of all forest area could be accessed economically.•This gain is mainly caused by a shift towards more mechanized harvesting methods.
•MSDR and SDImax estimations are significantly influenced by climate.•All selected climate-dependent MSDRs improved SDImax estimations over the basic MSDRs.•Seasonal climatic variables better explain ...SDImax variations than general climatic indexes.•Spring and summer climate changes are key drivers affecting the MSDR and SDImax.•Lower values of SDImax are linked to warmer and drier conditions.
Climate change projections for the Mediterranean basin predict a continuous increase in extreme drought and heat episodes, which will affect forest dynamics, structure and composition. Understanding how climate influences the maximum size-density relationship (MSDR) is therefore critical to designing adaptive silvicultural guidelines based on the potential stand carrying capacity of tree species. With this aim, data from the Third Spanish National Forest Inventory (3NFI) and WorldClim databases were used to analyze climate-related variations of the maximum stand carrying capacity for 15 species from the Pinus, Fagus and Quercus genera. First, basic MSDR were fitted using linear quantile regression and observed size-density data from monospecific 3NFI plots. Reference values for maximum stocking, expressed in terms of the Maximum Stand Density Index (SDImax), were estimated by species. Then, climate-dependent MSDR models including 35 annual and seasonal climatic variables were fitted. The best climate-dependent models, based on the Akaike Information Criteria (AIC) index, were used to determine the climatic drivers affecting MSDR, to analyze general and species-specific patterns and to quantify the impact of climate on maximum stand carrying capacity. The results showed that all the selected climate-dependent models improved the goodness of fit over the basic models. Among the climatic variables, spring and summer maximum temperatures were found to be key drivers affecting MSDR for the species studied. A common trend was also found across species, linking warmer and drier conditions to smaller SDImax values. Based on projected climate scenarios, this suggests potential reductions in maximum stocking for these species. In this study, a new index was proposed, the Q index, for evaluating the impact of climate on maximum stand carrying capacity. Our findings highlight the importance of using specific climatic variables to better characterize how they affect MSDR. The models presented in this study will allow us to better explain interactions between climate and MSDR while also providing more precise estimates concerning maximum stocking for different Mediterranean coniferous and broadleaf tree species.
PDF
