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Integrating information about the spatial distribution of carbon stocks and species diversity in tropical forests over large areas is fundamental for climate change mitigation and biodiversity conservation. In this study, spatial models showing the distribution of carbon stocks and the number of species were produced in order to identify areas that maximize carbon storage and biodiversity in the tropical forests of the Yucatan Peninsula, Mexico. We mapped carbon density and species richness of trees using L-band radar backscatter data as well as radar texture metrics, climatic and field data with the random forest regression algorithm. We reduced sources of errors in plot data of the national forest inventory by using correction factors to account for carbon stocks of small trees (2) of 0.28 and 0.31 and a relative mean square error of 38.5% and 33.0% for aboveground biomass and species richness, respectively, at pixel level. Estimates of carbon density were influenced mostly by radar backscatter and climatic data, while those of species richness were influenced mostly by radar texture and climatic variables. Correlation between carbon density and species richness was positive in 79.3% of the peninsula, while bivariate maps showed that 39.6% of the area in the peninsula had high carbon stocks and species richness. Our results highlight the importance of combining carbon and diversity maps to identify areas that are critical—both for maintaining carbon stocks and for conserving biodiversity.

Individual tree-based growth models precisely describe the growth of individual trees irrespective of stand complexity. These models are more useful than the stand-based growth models for effective management of forests. We developed an individual tree diameter growth model for Norway spruce (Picea abies /Linnaeus/ H. Karsten) using permanent research plot data collected from Krkonoše National Park in the Czech Republic. The model was tested against a part of the Czech National Forest Inventory (NFI) data that originated from the western region of the country. Among various models derived by a generalized algebraic difference approach (GADA), the GADA model derived from the Chapman-Richards function best suited to our data. Tree-specific parameters unique to each growth series, which describe tree-specific growth conditions, were estimated simultaneously with global parameters common to all growth series using the iterative nested regressions. The model described most of the variations in diameter growth for model calibration data (R2adj = 0.9901, RMSE = 0.5962), leaving no significant trends in the residuals. A test against NFI data also confirms that the model is precise enough for predictions of diameter growth for ranges of site quality, tree size, age, and growth condition. The model also possesses biologically desirable properties because it produces the curves with growth rates and asymptotes that increase with increasing site quality. The GADA model is path-invariant and therefore applicable for both forward and backward predictions, meaning that the model can precisely predict diameter growth at any past ages of the trees.

Carbon sequestration has become an important issue in forest management in the light of concerns about global warming. The two methods proposed by IPCC to quantify the net carbon sequestered by trees are based either on the estimation of annual gains and losses of carbon due to the growth of trees and their removals (default method), or on the difference in carbon stocks between two consecutive inventories (stock-change method). The objectives of this study are to compare the net carbon sequestered by the tree layer of forest ecosystems in Andalusia estimated by the two methods, and to identify what measured variables contribute the most to the differences in the estimations. Multivariate analysis was performed in order to identify the causes of these variations. The results revealed differences of up to 200% in the estimated net carbon sequestration depending on the methodology employed, and changes in the direction of the capture/emission in some areas. The net carbon sequestration ranged between 0.08 ± 0.01 and 1.93 ± 0.52 Mg C ha−1 year−1 depending on the method used and on the tree species analyzed. In global terms, net carbon sequestration at the regional level reaches 2,101,715 ± 560,906 Mg C according to the default method and 1,693,412 ± 272,529 Mg C using the stock-change method.

The Land and Property Register (LPR) of Poland contains information on land use for the entire country. Additionally, a sample-based National Forest Inventory (NFI) provides statistical data for forest areas and detailed information on numerous forest parameters. However, until 2014 NFI plots were established only on areas classified as forest in the LPR. In this article, we present results of an estimation of forest area by extending the NFI measurements on all theoretical points in a grid, and compare the results with LPR information for one province (the Podlaskie voivodeship). At each point, we assess land use with recent aerial photographs and verify the ambiguous points in the field. Forest area in Poland is increasing due to afforestation and natural expansion of forest. Delays in the updating process of the LPR, and unwillingness of the owners to agree to reclassification of their land, have led to an underestimation of overall forest area. Our results demonstrate that forest area estimates made by the improved NFI are higher than those based upon the LPR. The modified NFI may be an appropriate tool for monitoring forest area changes in Poland.

The main objective of this study was to estimate past and future dynamics of forest carbon pools in South Korea, as classified by the Intergovernmental Panel on Climate Change (IPCC) Good Practice Guidance (GPG) and to test the Carbon Budget Model of the Canadian Forest Sector 3 (CBM-CFS3). Inventory data required in CBM-CFS3 were extracted from Korea’s 3rd Forest Type Map. The site index in the inventory data was estimated through regression models developed using 5th National Forest Inventory (NFI) data with 4000 plots. Necessary yield tables to a stand age of over 100 years, stratified by species, were prepared through the estimation of volume per ha with tree species, site index and stand age. We considered thinning and cutting regulations for sustainable forest management announced by the Korean Forest Service and that the forest area was constant over from 1992 to 2092. We estimated the carbon stocks and their changes of aboveground (AGB) and belowground (BGB) biomass, litter (L), dead wood (DW), and soil organic matter (SOM). Validation was conducted based on the 5th NFI and statistical data. Our results showed that the carbon content of the total forest area increased from 509.7 Tg C to 1007.3 Tg C at a rate of 11.8 Tg C yr-1 during 1992-2034. Results also showed that AGB, BGB, DW, L and SOM changed from 54.6, 30.2, 15.9, 43.6, and 373.9 Tg C in 1992 to 455.6, 100.77, 32.89, 65.46, and 369.2 Tg C in 2034, respectively. However, the amount of forest carbon was projected to decrease due to large increases in harvest rates as most of the forest reaches the legislated cutting age during 2035-2045. Our simulation estimated that by 2045 AGB, BGB, and SOM decreased to 347.8, 78.2, and 368.9 Tg C, respectively, while DW and L increased to 46.8 and 89.1 Tg C, respectively. The carbon content of the total forested area starts to stabilize after 2045 as the annual stand growth gradually decreases. Finally in 2092, the carbon content of AGB, BGB, DW, L, and SOM was estimated as 422.3, 93.9, 31.2, 63.0, and 365.1 Tg C, respectively. Through harvesting, over the 100 year period more than 200 Tg C were transferred from South Korea’s forests to meet demands for timber, fiber and energy. Good agreement between model results, NFI data and independent studies, demonstrates the applicability of CBM-CFS3 for estimating past and future forest carbon budgets in South Korean forests and for exploring forest management activity impacts in managed forests.

Land use and land use change calculation matrix is one of the most important parts of the national greenhouse gas (GHG) inventory in land use, land use change and forestry (LULUCF) sector providing information of an overall summary and changes in land use at a national level over a specified period of time. Information on land use and land use changes are further used to calculate other parameters important for determination of GHG emissions and carbon stock changes in living and dead biomass, soil and litter, as well as basic information on the impact of applied climate change mitigation measures. Calculations of land use change can be carried out in a partly automated process using GIS tools, which makes calculations easier to perform, reduces time consumption for this task and occasional mistakes due to manual operations. The aim of this study is to improve the methodology for development of land use and land use change matrix in the national GHG inventory system using geospatial data of National forest inventory (NFI) and auxiliary data sources. The developed system uses geospatial NFI data and auxiliary information provided by the land parcel information system (LPIS) and stand-wise forest inventory, and it improves accuracy and consistency of the land use and land use change matrix, providing the ability to apply the same land use accounting method for the whole reporting period since 1990 without a significant increase of uncertainty. The developed method determines land use changes in a 5-year period by comparing three successive NFI cycles. To determine the actual land use category in a particular year, we adjusted weights for different land use categories. Interpolation is used to determine year-by-year transitions. [ABSTRACT FROM AUTHOR]

Semisystematic sampling designs—in which a population area frame is tessellated into cells, and a randomly located sample is taken from each cell—affords random tessellated stratified (RTS) Horvitz–Thompson-type estimators. Forest inventory applications with RTS estimators are rare, possibly because of computational complexities with the estimation of variance. To reduce this challenge, we propose a jackknife estimator of variance for RTS designs. We demonstrate an application with a model-assisted ratio of totals estimator and data from the Danish National Forest Inventory. RTS estimators of standard error were, as a rule, smaller than comparable estimates obtained under the assumption of simple random sampling. The proposed jackknife estimator performed well. [ABSTRACT FROM AUTHOR]

Aim: To test whether intraspecific trait responses to climate among populations across species distribution ranges can be untangled using field observations, under the rationale that, in natural forest tree populations, long‐term climate shapes population responses while recent climate change drives phenotypic plasticity. Location: Europe. Time period: 1901–2014. Taxa: Silver fir (Abies alba Mill.) and sessile oak [Quercus petraea (Matt.) Liebl.]. Methods: We estimated the variation of individual tree height as a function of long‐term and short‐term climates to tease apart provenance effects (variation among populations of different geographical origin), plasticity and their interaction, using mixed‐effect models calibrated with national forest inventory data (in‐situ models). To validate our approach, we tested the ability of in‐situ models to predict independently tree height observations in common gardens experiments where provenance and plastic effects can be measured and separated. In‐situ model predictions of tree height variation among provenances and among planting sites were compared to observations in common gardens and to predictions from a similar model calibrated using common garden data (ex‐situ model). Results: In Q. petraea, we found high correlations between in‐situ and ex‐situ model predictions of provenance and plasticity effects and their interaction for tree height (r > .80). We showed that the in‐situ models significantly predicted tree height variation among provenances and sites for A. alba and Q. petraea. Spatial predictions of phenotypic plasticity across species distribution ranges indicate decreasing tree height in populations of warmer climates in response to recent anthropogenic climate warming. Main conclusions: Our modelling approach using national forest inventory observations provides a new perspective for understanding patterns of intraspecific trait variation across species ranges. Its application is particularly interesting for species for which common garden experiments do not exist or do not cover the entire climatic range of the species. [ABSTRACT FROM AUTHOR]

Highlights • Mediterranean Pinus species showed a dissimilar increase in greening during summer. • P. nigra has heterogeneous behaviour that suggests the existence of different ecotypes. • Phenology and physical variables allowed a robust discrimination between species. • Start and end of the growing season, amplitude, and physical variables were important in discriminating species. Abstract Land surface phenology (LSP) can improve the monitoring of forest areas and their change processes. The aim of this study was to characterize the temporal dynamics in Mediterranean pines and evaluate the potential of LSP for species discrimination. We used 661 mono-specific plots for five different Pinus species (Pinus halepensis , P. pinea, P. pinaster ; P. sylvestris , P. nigra) and the MOD13Q1-NDVI time series (2000–2016) to perform the analyses. The time series were smoothed to extract the phenological parameters and calculate multi-temporal metrics, to synthesize the inter-annual variability. The potential of LSP for discriminating between Pinus species was evaluated by the application of the Random Forest (RF) classifier from different subsets of explanatory variables: i) the smooth time series; ii) the multi-temporal metrics; and iii) the multi-temporal metrics plus the auxiliary physical variables. This latter subset was also used as input to a Classification and Regression Tree (CART) algorithm to better explain the differences between Pinus species regarding LSP parameters and other environmental drivers. The analysis showed two different patterns: an important NDVI decrease during the summer for P. halepensis , P. pinea , and P. pinaster ; and lower NDVI variation along the year for P. sylvestris. P. nigra showed a heterogeneous intra-specific behavior, having locations with different patterns. We distinguished Pinus species plots with a global accuracy of 0.82, when we used multi-temporal metrics of LSP and auxiliary physical variables. More generally, the Mediterranean Pinus species could be differentiated considering the 23rd of July as the start of season and 179 km and 1100 m as distance to the coastline and elevation, respectively. [ABSTRACT FROM AUTHOR]

Highlights • In a span of seven years Texas experienced three major disturbance events. • National forest inventory data are useful for assessing disturbance effects. • Masking of trends was observed in dead tree estimates using the full set of data. • Estimates using specific subsets of data were helpful in assessing effects. • Adding new estimation capabilities to online analytical tools is recommended. Abstract Understanding the impacts of large-scale disturbances on forest conditions is necessary to support forest management, planning, and policy decision making. National forest inventories (NFIs) are an important information source that provide consistent data encompassing large areas, covering all ownerships, and extending through time. Here we compare how temporal aggregation approaches with NFI data affects estimates of standing dead trees as these respond to extreme disturbance events. East Texas was selected for this study owing to the occurrence of three significant disturbance events in a short span: Hurricane Rita in 2005, Hurricane Ike in 2008, and a historic drought in 2011. Wide-spread tree damage and mortality were reported after each disturbance and estimates of standing dead trees were used as the inventory variable for assessment. In the NFI of the US, the plot network is systematically divided into panels and one panel is measured each year. A measurement cycle is completed when all panels have been measured, which varies between 5 and 10 years depending on the region. Using the standard estimation approach of the US NFI, we computed population estimates using data from the full set of panels (FSP), multiple sets of panels (MSP), and single set of panels (SSP). For estimation, a single plot observation is computed from the most recent measurement of the plot that does not exceed the estimate year. Because one panel is measured per year, FSP and MSP estimates will necessarily consist of plot observations whose measurements were collected over a number of years. The SSP estimate is computed from one panel and thus all the plot observations are based on measurements collected over one year. We found that interpretations of disturbance event impacts varied depending on which sets of estimates were considered. All sets of estimates suggested a large and significant drought impact. However, differences existed among the estimates in the timing and magnitude of the impacts. The FSP estimates showed clear lag bias and smoothing of trends relative to the SSP estimates. MSP estimates were intermediate between FSP and SSP estimates. Differences in Hurricane Rita impacts were also observed between sets of estimates. Evidence of a net impact on standing dead trees following Hurricane Ike was weak among all sets of estimates. Given the potential for lag bias and smoothing, we recommend that SSP and MSP estimates be considered along with FSP estimates in assessments of large-scale disturbance impacts on forest conditions. [ABSTRACT FROM AUTHOR]

Scholars of international law and international relations largely agree that global governance today, and global environmental governance in particular, is marked by institutional complexity. Environmental policy fields are, to varying degrees, governed by a plurality of institutions with different levels of legalization, membership, and jurisdictional scope, and with different degrees of coherence among them. The international governance architecture on Reducing Emissions from Deforestation and Forest Degradation (REDD+) is a case in point. Located at the intersection of the governance systems on climate change, biodiversity, forestry, and development, REDD+ governance provides a stage where a large variety of intergovernmental and transnational institutions come together, collaborate, or compete on questions of standard-setting, financing, implementation, and evaluation. This complexity poses challenges to the effectiveness of REDD+ governance in general, but also to specific actor groups and organizations that lack the resources to understand and navigate such a fragmented governance landscape. Against this backdrop, we introduce an analytical framework to read and structure a complex governance architecture. The framework breaks new ground by adopting argumentative discourse analysis and the concept of storylines to the study of institutional complexity. We argue that beyond the messy surface of institutional complexity there may be a surprising degree of convergence, in the sense of discursive hierarchies that run across institutions, practices, and scales. We illustrate such a cross-cutting hierarchy for the complex REDD+ governance system, focusing on the sensitive issue of forest carbon monitoring. In our analysis of respective guidance documents and country reports, we find, underneath the institutional complexity across governance scales, a considerable dominance of techno-managerial perspectives and a preference for carbon commodification. This discursive hegemony and convergence resonates with the dominance of certain REDD+ funding institutions and the prioritization of the monitoring practice of remote sensing. [ABSTRACT FROM AUTHOR]

Abstract 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. Graphical abstract Unlabelled Image Highlights • 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. [ABSTRACT FROM AUTHOR]

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Key message: The proposed height-diameter model applicable to many tree species in the multi-layered and mixed stands across Czech Republic shows a high accuracy in the height prediction. This model can be useful for estimating forest yield and biomass, and simulation of the vertical stand structures.Abstract: We developed a generalized nonlinear mixed-effects height-diameter (H-D) model applicable to Norway spruce (Picea abies (L.) Karst.), European beech (Fagus sylvatica L.) and other conifer and broadleaved tree species using the modelling method that includes dummy variables accounting for species-specific height differences and random component accounting for within- and between-sample plot height differences and randomness in the data. We used two large datasets: the first set (model fitting dataset) originated from permanent research sample plots and second set (model-testing dataset) originated from the Czech national forest inventory (NFI) sample plots. The former dataset comprises 224 sample plots with 29,390 trees and the latter dataset comprises 14,903 sample plots with 382,540 trees, each representing wide variabilities of tree size, ecological zone, growth condition, stand structure and development stage, and management regime across the country. Among the four versatile growth functions evaluated as base functions with diameter at breast height (DBH) included as a single predictor, the Chapman-Richards function showed the most attractive fit statistics. This function was then extended through the integration of other predictor variables, which better describe the stand density (stand basal area), stand development and site quality (dominant height), competition (ratio of DBH to quadratic mean DBH), that would act as modifiers of the original parameters of the function. The mixed-effects H-D model described a large part of the variations in the H-D relationships (Radj2 = 0.9182; RMSE = 2.7786) without substantial trends in the residuals. Testing this model against model-testing dataset confirmed the model's high accuracy. The model may be used for estimating forest yield and biomass, and therefore will serve as an important tool for decision making in forestry. [ABSTRACT FROM AUTHOR]

• Initial estimate of United States National Forest System (NFS) old-growth forest area. • Applied existing NFS old growth definitions to Forest Inventory and Analysis data. • We estimate there are ∼ 10 million hectares of National Forest System (NFS) old growth. • Estimates and methods were designed respond to Executive Order #14072 in co-production with NFS officials. Old-growth forests are globally valued for their ecological attributes, cultural significance, and in many cases their rarity. Yet, defining and quantifying these forests has been a difficult task. This study developed an approach to consistently estimate extent of old-growth forest on United States Department of Agriculture (USDA) Forest Service National Forest System (NFS) lands, using NFS regional old-growth definitions applied to the US national forest inventory (conducted by the USDA Forest Service Forest Inventory and Analysis [FIA] program). This method was developed in response to a presidential order (EO#14072, April 22, 2022) and federal laws (e.g., Infrastructure Investment and Jobs Act, 2021; Inflation Reduction Act, 2022). We worked with NFS experts to obtain regionally approved criteria for establishing old growth status based on NFS definitions, assessments, and related documents. NFS regional old growth definitions focus on structural characteristics of forests with criteria for old growth status commonly including minimum abundance of large live trees (in eight of nine regions), tree or stand age (in eight of nine regions), and dead large tree density (in three of nine regions). Determining the regional criteria to use was straightforward for some NFS regions where old-growth forest definitions were specific, and in some cases, had already been applied to FIA data to quantify old-growth forest area. In other NFS regions, such as where definitions have never been applied in an operational manner, or where there were merely assessments of remnant old-growth forest conditions, determining exact criteria was more difficult. We estimate that there are approximately 10 million ha of old growth across NFS forests, as defined by NFS criteria, with the preponderance in the western US states. This study produces the first old-growth forest assessment at the national scale based on NFS definitions and FIA's statistically-rigorous national forest inventory of the US. These methods can be repeated with future inventories or modified when definitions change to produce updated estimates of old-growth forest attributes, and such work is already underway. [ABSTRACT FROM AUTHOR]

• We explored the potential of bi-temporal ALS data for site index estimation. • Species-specific site index models were developed using NFI and ALS data. • ALS estimated height and height increment were key predictors of site index. • Validation checks indicate that the functions seem suitable for their purpose. • Nationwide site index maps are provided at a resolution of 12.5 × 12.5 m. Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even-aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper-parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 – 2.11 m, and the relative RMSE was less than 10 % (7.68 – 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden. [ABSTRACT FROM AUTHOR]

Unsustainable exploitation of tropical forest resources is raising worldwide concern. In Papua New Guinea (PNG) timber harvesting has been identified as a major contributor to deforestation and forest degradation but its impact on biodiversity is still poorly understood. In this study we investigated the effect of selective logging on tree taxonomic composition, structure and diversity of PNG forests. We used data from 101 one-hectare permanent sample plots (PSPs) belonging to two vegetation types: low altitude forests on plains and fans (type P) and low altitude forests on uplands (type H). We used multivariate techniques to test for significant differences in species composition between plots of different vegetation types and disturbance regimes, identifying the tree taxa to which these differences could be ascribed. ANOVA was used to test for differences between logged-over and unlogged forest PSPs with respect to biodiversity (richness, Shannon’s diversity, Pielou’s evenness) and stand structure (stem density, basal area - BA). Temporal trends of forest features were analyzed using linear regression. Significant differences in taxonomic composition were found between logged-over and unlogged plots of the H type (p = 0.04). No differences were found in richness, diversity and evenness between logged-over and unlogged forest plots, while stem density was higher in the latter (421 ± 153 stems ha-1). Greater BA was found in unlogged forests (30.28 ± 4.45 m2 ha-1) of the H type when compared to the logged-over stands (15.52 ± 4.04 m2 ha-1). We detected positive trends in richness (0.55 ± 0.19 taxa ha-1 yr-1) and diversity after logging. Furthermore, H type forest exhibited positive trends in stem density (9 ± 1 stems ha-1 yr-1) and BA (0.42 ± 0.06 m2 ha-1 yr-1) with elapsed time since harvesting. Our analysis highlights some significant effects of logging activities on biodiversity and structure of PNG forests. Additionally, forests exhibited a significant recovery with respect to richness, diversity and stand structure. These preliminary results will be compared with data collected by the forthcoming National Forest Inventory in order to assess and monitor the effects of human activities and ecological factors on PNG forest biodiversity and develop appropriate conservation measures and sustainable management strategies.

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons.