Knowledge of tree species is required to inform management, planning, and monitoring of forests as well as to characterize habitat and ecosystem function. Remotely sensed data and spatial modeling ...enable mapping of tree species presence and distribution. Following an assessment of tree species identified in the sample-based National Forest Inventory (NFI), we mapped 37 tree species over the 650-Mha, forest-dominated ecosystems of Canada representing 2019 conditions. Landsat imagery and related spectral indices, geographic and climate data, elevation derivatives, and remote sensing-derived phenology are used as predictor variables trained with calibration samples from Canada's NFI using the Random Forests machine learning algorithm. Based upon prior knowledge of tree species distributions, classification models were implemented on a regional basis, meaning only the tree species that are expected in a given mapping region were modeled using local calibration samples. Modeling resulted in class membership probabilities values for each regionally eligible tree species for all treed pixels as well as an indicator of attribution confidence derived from the distance in feature space between the two leading classes. Accuracy assessment was conducted using independent validation data also drawn from the NFI following the same selection rules and indicated an overall accuracy of 93.1% ± 0.1% (95%-confidence interval). Predictor variables informing on geographic, climatic and topographic conditions had the largest importance on the classification models. Nationally, the most common leading tree species were black spruce (Picea mariana; 203 Mha or 57.3% of the treed area), trembling aspen (Populus tremuloides; 34.7 Mha, 9.8%), and lodgepole pine (Pinus contorta; 21.1 Mha, 5.9%). Regionally, there was ecozone-level dominance of other tree species, including subalpine fir (Abies lasiocarpa; Montane Cordillera), western hemlock (Tsuga heterophylla; Pacific Maritime), and balsam fir (Abies balsamea; Atlantic Maritime). Based upon the per-pixel class membership probabilities, species assemblages akin to those in forest inventories can also be produced. Further, given the calibrated reflectance of Landsat imagery, the methods presented herein can be implemented over a time series of images. The approach uses open data as predictor variables, making the method portable to other areas given availability of tree species training data.
•Methodological approach to map presence and distribution of tree species.•37 tree species mapped over the 650 Mha forest-dominated ecosystems of Canada.•Calibration data derived by refining Canada’s national forest inventory.•Predictor variables derived from Landsat imagery, climate, terrain, and phenology data.•Approach portable internationally due to common calibration data and use of open data.
Forest ecosystems contribute substantially to carbon (C) storage. The dynamics of litter decomposition, translocation and stabilization into soil layers are essential processes in the functioning of ...forest ecosystems, as these processes control the cycling of soil organic matter and the accumulation and release of C to the atmosphere. Therefore, the spatial distribution of litter and soil C stocks are important in greenhouse gas estimation and reporting and inform land management decisions, policy, and climate change mitigation strategies. Here we explored the effects of spatial aggregation of climatic, biotic, topographic and soil variables on national estimates of litter and soil C stocks and characterized the spatial distribution of litter and soil C stocks in the conterminous United States (CONUS). Litter and soil variables were measured on permanent sample plots (n = 3303) from the National Forest Inventory (NFI) within the United States from 2000 to 2011. These data were used with vegetation phenology data estimated from LANDSAT imagery (30 m) and raster data describing environmental variables for the entire CONUS to predict litter and soil C stocks. The total estimated litter C stock was 2.07 ± 0.97 Pg with an average density of 10.45 ± 2.38 Mg ha−1, and the soil C stock at 0–20 cm depth was 14.68 ± 3.50 Pg with an average density of 62.68 ± 8.98 Mg ha−1. This study extends NFI data from points to pixels providing spatially explicit and continuous predictions of litter and soil C stocks on forest land in the CONUS. The approaches described illustrate the utility of harmonizing field measurements with remotely sensed data to facilitate modeling and prediction across spatial scales in support of inventory, monitoring, and reporting activities, particularly in countries with ready access to remotely sensed data but with limited observations of litter and soil variables.
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•Spatial patterns found in the estimated litter and soil carbon stocks in forests•Including Normalized Difference Vegetation Index facilitated the model predictions.•Forest disturbances caused statistically significant differences in litter carbon.•Estimates of litter and soil carbon stocks were 2.07 Pg and 14.68 Pg, respectively.
•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.
Hurricanes can physically transform forest ecosystems, leading to immediate and potentially long-lasting impacts on carbon dynamics. Using Forest Inventory Analysis (FIA) data from the United States ...Forest Service, we compared the average carbon in trees (saplings, bole, stump, tops) and foliage pre- (2001–2003) and post- (2005–2007) Hurricane Ivan for different tree categories in the Perdido Bay Watershed located in the Florida Panhandle. The log-linear regression model indicates that variables stand type (softwood/mixed/hardwood), diameter, height, physiographic class (deep sands/flatwoods/rolling uplands/small drains/swamps/bottomlands), elevation, field age and plot affected status were statistically significant (p ≤ 0.05) determinants of carbon loss from the forest stands. Results showed that plots with medium-diameter stands lost more aboveground carbon than large and small-diameter stands, similarly, softwood-dominated stands lost more aboveground carbon than hardwood and mixed wood stands. Aboveground carbon decreased in stands with large (≥ 0.15 m) and medium (≥ 0.12 m) diameter-at-breast height (DBH) by 22.74 and 30.22 metric tons/ha, respectively. We ascertained a decrease of 74.51 and 17.82 metric tons/ha of aboveground carbon in hardwood and mixed plots after Hurricane Ivan, respectively. Aboveground carbon in young (< 25 y) taller trees (> 15 m) decreased by 121.55 metric tons/ha of carbon immediately after the hurricane. These findings underscore the critical necessity of comprehending the interplay between forest structure and hurricane activity to forecast the repercussions of such disturbances on carbon stocks. Our provided framework serves as a valuable tool for researchers and policymakers to assess the vulnerability of coastal forests and facilitate strategic planning to protect forests as carbon sinks.
•More than 90 % of FIA plots inside the study area were affected by Hurricane Ivan.•About 28.64 % and 62.15 % decrease in carbon in trees (sapling, stump, bole, top) and foliage, respectively.•About 1.22 % and 13.25 % carbon decreased in mixed and softwood stands; softwoods losing 51.30 % more carbon than hardwoods.•Taller trees (>10 m) lost 23.22 % more carbon than smaller trees (<10 m).•Aboveground carbon in trees and foliage decreased 18.15% and 1.36% respectively, in trees with a medium diameter (≥0.12 m).
Forest age is an important variable for assessments of biodiversity and habitat, sustainable forest and land management, as well as forest carbon science and modeling. Tree and stand age are ...typically measured directly on site, or estimated through visual photo interpretation, with spatially explicit maps of forest age not often produced over large areas. Remote sensing enables the generation of wall-to wall, spatially explicit maps of disturbance events within the satellite record; however, as disturbance is relatively rare on the landscape in a given year, additional means of determining forest age are required. As reviewed herein, the estimation of forest age using optical Earth observation data is challenging due to the limited spectral link to the attribute of interest, especially as forests get older. The temporally dictated multi-method approach to forest age estimation outlined herein acknowledges these limitations, by applying the approach that is best suited to the quality of the information available, depending on the epoch of interest. In this research, we combine three approaches to estimate forest age at a 30-m spatial resolution using Landsat data. The first approach uses change detection protocols to detect disturbance from 1985 to 2019, with time since disturbance used as a proxy for forest age. The second approach uses Landsat surface reflectance composites to identify pixels exhibiting evidence of recovery from a disturbance that occurred within the twenty years prior to 1985, allowing for the extension of forest age estimates to 1965. Finally, given an understanding of the linkage between forest age and canopy height, inverted allometric equations are coupled with maps of forest structure and productivity metrics to model forest age for those pixels that show no evidence of disturbance or recovery to a maximum of 150 years, acknowledging that uncertainty in age estimate increases with increasing age. Combining these three approaches, forest age estimates are made for every treed pixel found within the 650 Mha forested ecosystems of Canada. Nationwide, mean estimated forest age for forests ≤150 years old (representing 94.1% of treed area) was 70 years (standard deviation = 32.1 years). For confidence building, forest age estimates were compared to reported forest age in the National Forest Inventory (NFI) both spatially and aspatially. Nationally, 5.9% of the forested area was estimated to be older than 150 years, while 9.5% of area within in the NFI sample was recorded as older than 150 years. The median estimated forest age for forested pixels ≤150 years old was 68 years while median forest age reported in the NFI was 73 years, with regional variability matching expectations related to disturbance regimes and productivity. Spatially explicit maps of forest age provide important information for understanding forest ecosystems and can be used to inform a wide range of policy, science, and management needs.
•Forest age was estimated at 30-m scale across all of Canada’s forested ecosystems.•Three approaches were used to estimate age depending on temporal era.•Agreement with forest inventory recorded age decreased as estimated age increased.•Nationally, mean estimated age for forests <150 years old was 70 years.•5.9% of Canada’s forested area has an age of >150 years.
Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the ...status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.
There is a rising interest in the role of species diversity in ecosystem functioning and services, including productivity. Yet, how the diversity–productivity relationship depends on species identity ...and abiotic conditions remains a challenging issue. We analysed mixture effects on species productivity along site productivity gradients, calculated from a set of abiotic factors, in two biogeographic contexts (highlands and lowlands). We compared the productivity of 5 two‐species mixtures (i.e. 10 cases of mixed species) with that of monocultures of the same species. Five main European tree species were considered: sessile oak (Quercus petraea Liebl.), Scots pine (Pinus sylvestris L.), European beech (Fagus sylvatica L.), silver fir (Abies alba Mill.) and Norway spruce (Picea abies (L.) H. Karst). Our data set was compiled from the 2006 to 2010 French National Forest Inventory data base and covers 2361 plots including pure and mixed stands. Overall productivity of mixtures in highlands, that is European beech–Norway spruce, European beech–silver fir and to a lesser extent, silver fir–Norway spruce, was found to be higher than expected from the productivity of corresponding monospecific stands. Overyielding was mainly due to European beech for the first two mixtures and to silver fir for the third one. No effect of mixture was found for sessile oak–Scots pine and sessile oak–European beech stands in lowlands. Overyielding of sessile oak mixed with Scots pine was not strong enough to significantly increase overall stand productivity. Overyielding of European beech was balanced by an underyielding of sessile oak. The mixture effect changed along site productivity gradients for six cases out of the 10 studied, with a stronger and positive effect on sites with low productivity. The magnitude of this change along site productivity gradients varied up to 89% depending on the tree species. Synthesis. The nature of species interaction in mixtures with regard to productivity changes with species assemblage and abiotic conditions. Overyielding is strongest when species grow in highlands on less productive sites. A negative link between mixture effect and site productivity was found, in line with the stress‐gradient hypothesis.
AIM: Biodiversity loss could reduce primary productivity and the carbon storage provided by forests; however, the mechanisms underpinning the effects of biodiversity on multiple ecosystem functions ...are not completely understood. Spanish forests are of particular interest because of the broad variation in environmental conditions and management history. We tested for the existence of a relationship between diversity effects and both carbon storage and tree productivity, and examined the relative importance of complementarity and selection mechanisms in a wide variety of forests, from cold deciduous Atlantic to xeric Mediterranean evergreen forests. LOCATION: Continental Spain. METHODS: We used c. 54,000 plots of the Spanish Forest Inventory and maximum likelihood techniques to quantify how climate, stand structure and diversity shape carbon storage and tree productivity. Diversity effects included both complementarity and selection mechanisms, measured respectively through functional diversity and functional identity measures. RESULTS: Diversity had a significant effect on both carbon storage and tree productivity, even when controlling for confounding factors of climate and stand structure. A consistent positive effect of functional diversity on carbon storage and tree productivity was observed in all seven forest types studied. This relationship was not linear, and the largest changes in carbon storage and tree productivity were observed at low levels of functional diversity. However, the importance of complementarity effects was not consistent with the productivity of different forest types. Selection effects were particularly important in deciduous and Mediterranean pine forests, but had very little effect on mountain pines. MAIN CONCLUSIONS: We found a generally positive effect of diversity on carbon storage and tree productivity, supported by both complementarity and selection mechanisms. Thus, both functionally diverse forests and functionally important species should be maintained to adequately preserve and promote key ecosystem functions such as carbon storage and tree productivity.
The Bitterlich method was studied with multiple basal area factors (BAF) in 3 Pinus taeda L. plantations located in the Midwest of Santa Catarina, Brazil using different management regimes: without ...thinning (1344 trees.ha-1), with one thinning (789 trees.ha-1), and with two thinnings (475 trees.ha-1),. Using the parameters obtained from censuses, we sought to verify the data from different sample treatments for the variables V.ha-1, G.ha-1, N.ha-1 and “d” through a completely randomized design for each management regime and observing the precision obtained by the sampling error and the accuracy detected by the real error. It was observed that the variable “d” was less impacted by the change in the basal area factor (BAF), but the estimates of V.ha-1, G.ha-1 and N.ha-1 were more consistent (precision and accuracy) using factors equal to or less than 5, mainly in thinned areas. Even with no significant differences in any BAF in the 3 experiments, the sampling performed with BAF greater than 5 showed contrasting results in the managed areas depending on the BAF used. Sampling precision is influenced by BAFs, decreasing with the reduction in the number of trees counted within the Bitterlich sampling unit, a direct consequence of the BAF increase. Sampling errors were always overestimated, regardless of the dendrometric variable or area management. Even the largest differences between estimates and parameters being identified in BAF greater than or equal to 5, the experiment indicated the use of any BAF, with the largest requiring greater sampling intensity.
Changes in forest growth have been found in European forests and worldwide. However most observations have been derived from samples of restricted size, whose representativeness at a regional forest ...scale is questionable. National forest inventories provide an interesting perspective for both regional scale assessment of these trends and the investigation of their variations over environmental gradients, but have been little used.
The aim of our work was to carry out an exploratory modelling analysis of productivity changes, based on the French national forest inventory data. The objectives were: (i) to assess recent trends in forest productivity and to investigate a possible recent decline as found in previous studies; and (ii) to investigate trend variations relative to site fertility factors. We focused on pure and even-aged stands of common beech (
Fagus sylvatica L.) in north-eastern France, already documented from previous studies based on retrospective data.
The dataset consisted of 925 temporary plots inventoried between 1979 and 2007. We developed two regression models of stand basal area increment (
BAI) against stand developmental stage (dominant height), site fertility (site index in the
SI model, environmental indicators in the
EI model) and stand density (relative density index). The effect of calendar date was tested in order to investigate possible historical trends. Site fertility–date interactions were also tested to investigate the site-dependence of trends.
The fitted models showed a high goodness of fit (adj.
R
2 over 0.69). We showed an increase in stand
BAI of 27.8% between 1977 and 1987, (10.4% between 1979 and 1987). Stand
BAI then decreased by approximately 5% between 1987 and 2004. We thus confirmed the hypothesis of a recent decline in common beech vitality in its temperate range. The chronologies clearly depicted the effect of severe drought events (1976 and 2003), pointing out the predominant role of water availability in the changes observed. No significant site-dependence of the trend was identified.
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