Forest Volume Research Articles

Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data

Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables.

The utility of terrestrial photogrammetry for assessment of tree volume and taper in boreal mixedwood forests

Key MessageThis study showed that digital terrestrial photogrammetry is able to produce accurate estimates of stem volume and diameter across a range of species and tree sizes that showed strong correspondence when compared with traditional inventory techniques. This paper demonstrates the utility of the technology for characterizing trees in complex habitats such as boreal mixedwood forests.ContextAccurate knowledge of tree stem taper and volume are key components of forest inventories to manage and study forest resources. Recent developments have seen the increasing use of ground-based point clouds, including from digital terrestrial photogrammetry (DTP), to provide accurate estimates of these key forest attributes.AimsIn this study, we evaluated the utility of DTP based on a small set of photos (12 per tree) for estimating stem volume and taper on a set of 15 trees from 6 different species (Populus tremuloides, Picea glauca, Pinus contorta latifolia, Betula papyrifera, Picea mariana, Abies balsamea) in a boreal mixedwood forest in Alberta, Canada.MethodsWe constructed accurate photogrammetric point clouds and derived taper and volume from three point cloud–based methods, which were then compared with estimates from conventional, field-based measurements. All methods were evaluated for their accuracy based on field-measured taper and volume of felled trees.ResultsOf the methods tested, we found that the point cloud–derived diameters in a taper curve matching approach performed the best at estimating diameters at the lowest parts of the stem (< 30% of total tree height), while using known DBH and height provided more accurate estimates for the upper parts of the stem (> 50% of total height). Using the field-measured DBH and height as inputs to calculate stem volume yielded the most accurate predictions; however, these were not significantly different from the best point cloud-based estimates.ConclusionThe methodology confirmed that using a small set of photographs provided accurate estimates of individual tree DBH, taper, and volume across a range of species and size gradients (10.8–40.4 cm DBH).

Estimation of Forest Biomass and Carbon Storage in China Based on Forest Resources Inventory Data

Forests are important in the global carbon cycle and it is necessary to quickly and accurately measure forest volume to estimate forest aboveground biomass (AGB) and aboveground carbon storage (AGC). In this paper, we used data from the eighth forest resources inventory of China to establish two stand volume models based on stand density and forest basal area for 37 arbor forest types (dominant species); and performed a comparative analysis to obtain the best model. Then the AGB, AGB density, AGC, and AGC density of the different forest types and regions were estimated by conversion function methods. The results showed that: (1) The volume model of tree height and forest basal area could better fit the natural growth process of forests, and 36 of the 37 forest types had R2 greater than 0.8; (2) The average AGB density of arbor forest in China was 95.03 Mg ha−1 and the average AGC density was 48.15 Mg ha−1 (3) Among forest types, Picea asperata Mast., Quercus spp., and Populus spp. had the highest AGB and AGC, while Cinnamomum camphora (L.) Presl, Pinus taiwanensis Hayata, and Pinus densiflora Sieb. et Zucc. had the lowest. The AGB density and AGC density of Phoebe zhennan S. Lee et F. N. Wei and Pinus densata Mast. were the highest, while those of Pinus densiflora Sieb. et Zucc., Pinus elliottii Engelmann, and Eucalyptus robusta Smith were the lowest. (4) Among regions, AGB and AGC ranging from high to low, were as follows: northwest, southwest, northeast, central south, east, and north. The northwest and southwest regions accounted for more than 70% of the country’s AGB and AGC. The average AGB density and AGC density among the regions were 91.34 Mg ha−1 and 46.4 Mg ha−1, respectively. Ranging from high to low as follows: southwest, northwest, northeast, east, central south, and north. The methods used in this paper provide a basis for fast and accurate estimation of stand volume, and the estimates of AGB and AGC have important reference value for explaining the role of ecosystems in coping with global climate change in China.

On the Sensitivity of TanDEM-X-Observations to Boreal Forest Structure

The structure of forests is important to observe for understanding coupling to global dynamics of ecosystems, biodiversity, and management aspects. In this paper, the sensitivity of X-band to boreal forest stem volume and to vertical and horizontal structure in the form of forest height and horizontal vegetation density is studied using TanDEM-X satellite observations from two study sites in Sweden: Remningstorp and Krycklan. The forest was analyzed with the Interferometric Water Cloud Model (IWCM), without the use of local data for model training, and compared with measurements by Airborne Lidar Scanning (ALS). On one hand, a large number of stands were studied, and in addition, plots with different types of changes between 2010 and 2014 were also studied. It is shown that the TanDEM-X phase height is, under certain conditions, equal to the product of the ALS quantities for height and density. Therefore, the sensitivity of phase height to relative changes in height and density is the same. For stands with a phase height &gt;5 m we obtained an root-mean-square error, RMSE, of 8% and 10% for tree height in Remningstorp and Krycklan, respectively, and for vegetation density an RMSE of 13% for both. Furthermore, we obtained an RMSE of 17% for estimation of above ground biomass at stand level in Remningstorp and in Krycklan. The forest changes estimated with TanDEM-X/IWCM and ALS are small for all plots except clear cuts but show similar trends. Plots without forest management changes show a mean estimated height growth of 2.7% with TanDEM-X/IWCM versus 2.1% with ALS and a biomass growth of 4.3% versus 4.2% per year. The agreement between the estimates from TanDEM-X/IWCM and ALS is in general good, except for stands with low phase height.

A combinatorial identity for rooted labeled forests

In this brief note a straightforward combinatorial proof for an identity directly connecting rooted forests and unordered set partitions is provided. Furthermore, references that put this type of identity in the context of forest volumes and multinomial identities are given.

The current state and efficiency use of in situ and ex situ conservation units for seed harvesting in the central part of Ukraine

Abstract Significant increase of biological stability, timber volume, and economic value of forests is impossible without refinement and improvement of the organization, and implementation of forest seed production on a genetic and breeding basis. It is possible to solve this task by creating and efficiently functioning in situ and ex situ facilities. In Ukraine, the basic principles of plus selection are envisaged, which include the selection and testing progeny of plus trees. The total number of progeny tests in Ukraine is 106 plots, in which 1,079 genotypes of plus trees are presented. Taking into account the total number of plus trees in Ukraine (4,560), only 23.7% was subjected to progeny tests. The largest number of progeny tests are represented by Scots pine (Pinus sylvestris L.) – 76 plots and a significantly smaller number of plots for English oak (Quercus robur L.) – 21. The corresponding representation of genotypes is for pine – 520, for oak – 365. The area of tree breeding units in Ukraine is very small (0.4–0.6%) of the total forest area in the region. Despite the available areas, the use of in situ and ex situ objects for harvesting seed in Ukraine is low, around 30%. In the central region of Ukraine, the share of in situ and ex situ objects from the total forest area is 0.1–0.5%. On these sites, from 0 to 54% is harvested, which is insufficient. According to the results of the conducted research, it was established that the areas of permanent forest-seed base (in situ and ex situ units) are insignificant and their use is insufficient. Testing of more than 70% of the selected plus trees is required. An important trend is to study the genotype-environment interaction in connection with trends of global climate change.

Multiple drivers and pathways to China's forest transition

Forest transition theory proposes pathways that countries might follow to experience forest recovery, but countries currently undergoing forest transition do so in a global context fundamentally different from what it was just 50 years ago. Our study analyzes China's ongoing forest transition to extend forest transition theory in three ways. First, we analyze province-level outcomes to disentangle the multiple pathways through which forest transition occurs at the national level. Analysis of diverse drivers and outcomes at the provincial level is an important step toward revealing micro-level causal explanations of forest transition. Second, we investigate specific drivers of the forest transition operating across space and time. Specifically, mechanisms that operated across provinces within China were examined in addition to international factors using appropriate econometric methods. Third, we analyze changes in area, density, and volume of forests to represent different dimensions of forest recovery. The results show that environmental concerns and public investments in forest improvement were important determinants of increased forest area. Increase in forest volume was associated with a complex chain of factors involving China's shift to an export-oriented economy, labor out-migration, and the related reduction in forest dependence for livelihoods.

Assessing Forest Ecosystems across the Vertical Edge of the Mid-Latitude Ecotone Using the BioGeoChemistry Management Model (BGC-MAN)

The mid-latitude ecotone (MLE)—a transition zone between boreal and temperate forests, which includes the regions of Northeast Asia around 30°–60° N latitudes—delivers different ecosystem functions depending on different management activities. In this study, we assessed forest volume and net primary productivity changes in the MLE of Northeast Asia under different ecological characteristics, as well as various current management activities, using the BioGeoChemistry Management Model (BGC-MAN). We selected five pilot sites for pine (Scots pine and Korean red pine; Pinus sylvestris and P. densiflora), oak (Quercus spp.), and larch forests (Dahurian larch and Siberian larch; Larix gmelinii and L. sibirica), respectively, which covered the transition zone across the MLE from Lake Baikal, Russia to Kyushu, Japan, including Mongolia, Northeast China, and the Korean Peninsula. With site-specific information, soil characteristics, and management descriptions by forest species, we established their management characteristics as natural preserved forests, degraded forests, sandy and cold forest stands, and forests exposed to fires. We simulated forest volume (m3) and net primary productivity (Mg C ha−1) during 1960–2005 and compared the results with published literature. They were in the range of those specified in previous studies, with some site-levels under or over estimation, but unbiased estimates in their mean values for pine, oak, and larch forests. Annual rates of change in volume and net primary productivity differed by latitude, site conditions, and climatic characteristics. For larch forests, we identified a high mountain ecotype which warrants a separate model parameterization. We detected changes in forest ecosystems, explaining ecological transition in the Northeast Asian MLE. Under the transition, we need to resolve expected problems through appropriate forest management and social efforts.

Exploring coarse- to fine-scale approaches for mapping and estimating forest volume from Brazilian National Forest Inventory data

Abstract The aim of this study was to explore methods to: (1) enhance coarse-scale estimates of wood volume from National Forest Inventories (NFIs) data and (2) map them at finer scales. The ‘standard’ coarse-scale estimation extrapolates wood volume from clusters to the grid cell they represent, using the cluster’s represented forested area (RFA) to predict the cell’s forested area. Data from a subset of Brazil’s NFI clusters were combined with Landsat-8 imagery to explore a new coarse-scale method, where forested area derived from image classification (FADIC) is used instead of RFA. The RFA- and FADIC-derived estimates of total volume were, respectively, 197.4 million m3 and 116.3 million m3. For fine-scale methods, volume was estimated and mapped at pixel level using: (i) surface reflectance-based models (SRMs), and (ii) regression-kriging (RK) and a RK model (RKM) whose inputs were latitude and longitude of pixels. The SRM-based method captured the mean and the general spatial trend of the volume well. The RK-based method also estimated the mean well, but it failed to predict higher and lower volumes. The SRM- and RK-based estimates of total volume were 211.7 million m3 and 203.3 million m3, an overestimate of 7 per cent and 3 per cent, respectively, of the ‘standard’ NFI estimate (197.4 million m3), though both estimates were within the 95 per cent confidence interval, meaning that both fine-scale methods yield total volume statistically similar to the ‘standard’ coarse-scale method.

Large Area Forest Yield Estimation with Pushbroom Digital Aerial Photogrammetry

Low-cost methods to measure forest structure are needed to consistently and repeatedly inventory forest conditions over large areas. In this study we investigate low-cost pushbroom Digital Aerial Photography (DAP) to aid in the estimation of forest volume over large areas in Washington State (USA). We also examine the effects of plot location precision (low versus high) and Digital Terrain Model (DTM) resolution (1 m versus 10 m) on estimation performance. Estimation with DAP and post-stratification with high-precision plot locations and a 1 m DTM was 4 times as efficient (precision per number of plots) as estimation without remote sensing and 3 times as efficient when using low-precision plot locations and a 10 m DTM. These findings can contribute significantly to efforts to consistently estimate and map forest yield across entire states (or equivalent) or even nations. The broad-scale, high-resolution, and high-precision information provided by pushbroom DAP facilitates used by a wide variety of user types such a towns and cities, small private timber owners, fire prevention groups, Non-Governmental Organizations (NGOs), counties, and state and federal organizations.

Estimating Mangrove Forest Volume Using Terrestrial Laser Scanning and UAV-Derived Structure-from-Motion

Mangroves provide a variety of ecosystem services, which can be related to their structuralcomplexity and ability to store carbon in the above ground biomass (AGB). Quantifying AGB inmangroves has traditionally been conducted using destructive, time-consuming, and costlymethods, however, Structure-from-Motion Multi-View Stereo (SfM-MVS) combined withunmanned aerial vehicle (UAV) imagery may provide an alternative. Here, we compared the abilityof SfM-MVS with terrestrial laser scanning (TLS) to capture forest structure and volume in threemangrove sites of differing stand age and species composition. We describe forest structure in termsof point density, while forest volume is estimated as a proxy for AGB using the surface differencingmethod. In general, SfM-MVS poorly captured mangrove forest structure, but was efficient incapturing the canopy height for volume estimations. The differences in volume estimations betweenTLS and SfM-MVS were higher in the juvenile age site (42.95%) than the mixed (28.23%) or mature(12.72%) age sites, with a higher stem density affecting point capture in both methods. These resultscan be used to inform non-destructive, cost-effective, and timely assessments of forest structure orAGB in mangroves in the future.

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&amp;2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

Water Yield Responses to Gradual Changes in Forest Structure and Species Composition in a Subboreal Watershed in Northeastern China

Relationships between forest cover and streamflow have been studied worldwide, but only a few studies have examined how gradual changes in forest structure and species composition due to logging and climate change affect watershed water yield (Q) and flow regimes. In this study, we analyzed long-term (45 years) hydrologic, climate and forest dynamics data from the subboreal Tahe watershed in northeastern China. Our purpose was to evaluate the effects of forest logging and regeneration on changes in forest biomass and species and to quantify the subsequent impact on mean annual streamflow and flow regime under a changing climate. The study watershed was dominated by old-growth larch (Larix gmelinii Rupr.) during the 1970s, but gradually transformed into young deciduous larch mixed with deciduous broad-leaved birch (Betula platyphylla Sukaczev) during the 2010s. During the same period, the watershed experienced climate change with a significant increase in air temperature of 0.028 ℃/year. We applied eight sensitivity-based techniques to separate the effects of climate change on water yield from those due to forest changes. We used flow duration curves (FDCs) to characterize flow regimes by dividing the study into four key periods based on the proportional change of larch and birch trees. We found that the mean annual streamflow decreased by 10 mm (−16 mm attributed to forest change and +6 mm to climate change) between the 1984–1994 period and the 2006–2016 period when the proportion of birch increased by 20% with a similar total forest volume in the later period. The mean annual streamflow increased from 216 mm to 270 mm (+35.5 mm due to forest change vs +17.7 mm due to climate change) when forest volume decreased by 18.7% (17 m3/ha) between the 1970s and 1984–1994. Water yield changed only slightly (3.5 mm) when forest volume increased by 8.7% (6 m3/ha) from 2000 to 2011. In addition, the magnitude of high flow and low flow increased following deforestation and a shift in species composition from a period (1984–1994) with 70% larch with 30% birch to a later period (2006–2016) with 50% larch with 50% birch. Both high flow and low flow decreased coinciding with a reforestation period (2006–2016). Our results highlight complex interactions among climate, forest structure, total biomass, and plant diversity (trees species composition) in influencing watershed hydrology. Further study is needed to examine the effects of ecohydrological processes such as evapotranspiration in larch and birch forests on hydrologic changes across multiple scales.

Tree height explains stand volume of closed-canopy stands: Evidence from forest inventory data of China

The use of tree height to estimate forest volume allows the calculation of forest biomass at large scales, and this calculation is of great importance in forest management and carbon accounting for temperate forests. Additionally, LiDAR (Light Detecting and Ranging) shows an increasing dependence on tree height in large-scale forest volume or biomass estimation and carbon accounting. However, the extent to which tree height determines forest volume remains controversial. Additionally, how the relationship between stand volume and tree height is modulated by canopy density has seldom been quantified at large scales across different forest types. This relationship may be important for improving large-scale forest volume or biomass estimations based on LiDAR technology. In this study, based on national forestry inventory data from China containing 34,130 forest plots, we examined the effect of canopy density on the relationship between tree height and stand volume across different forest types. The results showed forest height alone explained 72.9% of variation in stand volume across China and was far more powerful than canopy density (R2 = 49.7%). However, the relationship between stand volume and forest height was significantly affected by canopy density. When the effect of canopy density was included in the model, stand volume could be well predicted from forest height, with an R2 of 0.863. In closed-canopy stands, tree height could explain 91.5% of stand volume at a canopy density of 1.0. In conclusion, we provided the first national-scale model for estimating stand volume from forest height and canopy density across China with a large number of field-measured plots. The results were a robust predicted combination for forest volume, regardless of biotic and abiotic factors, in national scales across different forest types in China. Therefore, they can be utilized by LiDAR or other remote-sensing studies in the future.

Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data

Accurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliable estimates of the distributions of structure attributes in forests. In this study, we predicted individual tree volume distributions over a subtropical forest of southeast China using airborne LiDAR data and field measurements. We first estimated the plot-level total volume by LiDAR-derived standard and canopy metrics. Then the performances of three Weibull parameter prediction methods, i.e., parameter prediction method (PPM), percentile-based parameter recover method (PPRM), and moment-based parameter recover method (MPRM) were assessed to estimate the Weibull scale and shape parameters. Stem density for each plot was calculated by dividing the estimated plot total volume using mean tree volume (i.e., mean value of distributions) derived from the LiDAR-estimated Weibull parameters. Finally, the individual tree volume distributions were generated by the predicted scale and shape parameters, and then scaled by the predicted stem density. The results demonstrated that, compared with the general models, the forest type-specific (i.e., coniferous forests, broadleaved forests, and mixed forests) models had relatively higher accuracies for estimating total volume and stem density, as well as predicting Weibull parameters, percentiles, and raw moments. The relationship between the predicted and reference volume distributions showed a relatively high agreement when the predicted frequencies were scaled to the LiDAR-predicted stem density (mean Reynolds error index eR = 31.47–54.07, mean Packalén error index eP = 0.14–0.21). In addition, the predicted individual tree volume distributions predicted by PPRM of (average mean eR = 37.75) performed the best, followed by MPRM (average mean eR = 40.43) and PPM (average mean eR = 41.22). This study demonstrated that the LiDAR can potentially offer improved estimates of the distributions of tree volume in subtropical forests.

Association between Urban Greenness and Depressive Symptoms: Evaluation of Greenness Using Various Indicators.

An increasing number of studies have suggested benefits of greenness exposure on mental health. We examined the association between urban greenness and depressive symptoms in adults in the general population living in the seven major cities in Korea (N = 65,128). Using data from the Korean Community Health Survey 2009, depressive symptoms were measured on the Center for Epidemiological Studies Depression Scale (CES-D). Greenness was assessed using Normalized Difference Vegetation Index (NDVI) and land-use data (forest area and forest volume). Logistic regression models were fitted to adjust for potential confounders. Individuals in regions with the highest NDVI (quartile 4) had the lowest odds for depressive symptoms compared to quartile 1, after adjusting for potential confounders (OR = 0.813; 95% CI: 0.747, 0.884). For all greenness indicators except for forest area per district area (%), the highest rate of depressive symptoms was found for the individuals in the lowest quartile of greenness (quartile 1) and the lowest rate of depressive symptoms for those in the highest quartile of greenness (quartile 4). We found an inverse association between urban greenness and depressive symptoms, which was consistent across a variety of greenness indicators. Our study suggests health benefits of greenness and could provide a scientific basis for policy making and urban planning.

Evaluation of single-date and multi-seasonal spatial and spectral information of Sentinel-2 imagery to assess growing stock volume of a Mediterranean forest

Accurate growing stock volume (GSV) estimation is essential for forest inventory updating, terrestrial carbon stocks reporting, and ecosystem services assessment. This study investigates the potential of spectral and spatial features derived from single-date and multi-seasonal Sentinel-2 Multi Spectral Instrument (Sentinel-2 MSI) images, for GSV estimation in a Mediterranean region of Northeastern Greece. Original spectral bands, spectral indices, first-order statistics, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, and Local Indicators of Spatial Association (LISA), based on the multi-seasonal and single-date Sentinel-2 MSI imagery, were used for GSV model development using the bagging LASSO algorithm. For both single and multi-date approaches, the spectral indices models were more accurate compared to the respective ones developed with the original Sentinel-2 MSI bands. Also, models based on texture were more efficient than the spectral models. The GLCM measures derived from July image, provided the most accurate single-date estimate of GSV (R2 = 0.89, RMSE = 35.21), while their multi-seasonal counterparts improved slightly the accuracy (R2 = 0.91, RMSE = 32.77). Fusion of spatial and textural information resulted in marginal or no-improvement on the texture model accuracy, however the fused models yielded higher predictive results than the spectral models alone.

Volume estimation in a Eucalyptus plantation using multi-source remote sensing and digital terrain data: a case study in Minas Gerais State, Brazil

ABSTRACTIn this study, we tested the effectiveness of stand age, multispectral optical imagery obtained from the Landsat 8 Operational Land Imager (OLI), synthetic aperture radar (SAR) data acquired by the Sentinel-1B satellite, and digital terrain attributes extracted from a digital elevation model (DEM), in estimating forest volume in 351 plots in a 1,498 ha Eucalyptus plantation in northern Minas Gerais state, Brazil. A Random Forest (RF) machine learning algorithm was used following the Principal Component Analysis (PCA) of various data combinations, including multispectr al and SAR texture variables and DEM-based geomorphometric derivatives. Using multispectral, SAR or DEM variables alone (i.e. Experiments (ii)–(iv)) did not provide accurate estimates of volume (RMSE (Root Mean Square Error) > 32.00 m3 ha−1) compared to predictions based on age since planting of Eucalyptus stands (Experiment (i)). However, when these datasets were individually combined with stand age (i.e. Experiments (v)–(vii)), the RF models resulted in better volume estimates than those obtained when using the individual multispectral, SAR and DEM datasets (RMSE < 28.00 m3 ha−1). Furthermore, a model that integrated the selected variables of these data with stand age (Experiment (viii)) improved volume estimation significantly (RMSE = 22.33 m3 ha−1). The large and increasing area of Eucalyptus forest plantations in Brazil and elsewhere suggests that this new approach to volume estimation has the potential to support Eucalyptus plantation monitoring and forest management practices.

Вплив природних факторів на динаміку букового (fagus sylvatica L.) пралісу Українських карпат

Міжнародні дослідження 15-річної динаміки букового пралісу проведено на 10-гектарному стаціонарному дослідному об'єкті в Угольському масиві Карпатського біосферного заповідника. Проаналізовано вплив вітровалу 2007 р. та потепління клімату на структурні елементи екосистеми пралісу. Виявлено достовірні зміни окремих показників пралісу внаслідок сильного впливу вітру: найбільші коливання характерні для густоти дерев – після втрати 10 % (у 2007 р.) вже в 2010 р. густота збільшилася на 6, а в 2015 р. – на 59 %; істотно зріс і запас мертвої деревини (відповідно на 41 та 6 %); відносне зменшення найбільшим було для кількості підросту (на 19 та 14 %); зменшення запасу деревини було вже меншим (на 7 і 4 %); зменшення суми площ поперечного перетину було неістотним (на 4 і 2 %). Аналіз реакції букового пралісу на сильний вплив вітру підтверджує гіпотезу про максимальну стійкість таких лісових екосистем до стихійних явищ, а ідентифіковані кількісні зміни дали змогу розробити принципову схему сильного впливу вітру на праліс. Дискусійним залишається питання збільшення приросту за діаметром під впливом потепління клімату – у 2010 і в 2015 рр. воно було достовірним для більшості ступенів товщини, але на його динаміку вплинув також і вітровал через зрідження намету.

Assessing the availability of forest biomass for bioenergy by publicly available satellite imagery

Abstract: Forest biomass is a renewable energy source, more climate-friendly than fossil fuels and widely available in Europe. The wood energy chain has been suggested as a means to re-activate forest management and improve the value of forest stands in marginalized rural areas. However, wall-to-wall estimates of forest biomass, needed to design the location and size of power and heat biomass plants in any given territory, are notoriously difficult to obtain. This paper tests an algorithm to predict forest biomass using publicly available Landsat satellite imagery in the Liguria region, northern Italy. We used regional forest inventory data to train and validate an artificial neural network (ANN) classifier that uses remotely-sensed information such as three principal components of Landsat-5 TM spectral bands, the Enhanced Vegetation Index (EVI), and topography, to retrieve aboveground live tree volume. Percent root mean square error was -9% and -23% for conifers and broadleaves respectively in the calibration dataset, and -27% and -24% in the validation dataset. The reconstructed volume map was updated to present day using current volume increment rates reported by the Italian National Forest Inventory. A wall-to-wall map of forest biomass from harvest residues was finally produced based on species-specific wood density, biomass expansion factors, volume logged for timber assortments, forest accessibility, and topography. Predicted aboveground forest volume ranged from 81 to 391 m3 ha-1. In forests available for wood supply (70% of the total), planned volume removals averaged 25.4 m3 ha-1, or 18.7% of the average standing stock across. Biomass available for bioenergy supply was 1.295.921 million Mg dry matter or 8.95 Mg ha-1. This analysis workflow can be replicated in all mountain regions with a predominant broadleaved coppice component.