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.
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.
•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.
In recent years, Climate-Smart Forestry (CSF) has emerged as an innovative approach to sustainable forest management, aiming to enhance forest resilience and to balance the provision of ecosystem ...services facing climate-related threats. This study introduces for the first time a new composite climate-smart index (ICSF) to assess CSF. The methodological approach comprises the following steps: (i) the selection and evaluation of CSF indicators; (ii) the weighting of these indicators; and (iii) the assessment of CSF for Mediterranean forests in two distinct periods, specifically 2005 and 2015. Eight indicators were selected from a systematic literature review. The Analytic Hierarchy Process was applied to translate the preferences obtained through an online questionnaire from a network of CSF-expert stakeholders into weights, at both indicators and criteria levels (i.e., adaptation, mitigation, and the social dimension). Results reveals that indicators “tree species composition”, “forest damage”, and “regeneration” are of crucial importance for CSF assessment. The comparison of the CSF value between the years 2005 and 2015, shows a slight increase in CSF ratings. The ICSF serves as a comprehensive index of CSF covering all aspects of that concept, i.e. adaptation, mitigation, and the social dimension (including production). The national-scale analysis provides an overview of the dynamics that involve forest management of Mediterranean forests against climate change. The study offers a practicable method for CSF evaluation with its allover set of indicators, representing a suitable tool for supporting forest managers to mitigate the negative impacts of climate change.
•Climate-smart-forestry is an innovative approach of sustainable forest management•A composite climate-smart index was developed and tested in Mediterranean forests•Climate-smart forestry in Italy showed a positive trend in the last two decades•Tree species composition was the most influencing climate-smart forestry indicator•Composite index aids forest managers in adapting to and mitigating climate change
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.
•Unmanned Aerial Vehicles (UAV) were used to estimate mangrove vegetation properties.•Lightweight UAV and Structure from Motion are useful to support forest monitoring.•Accurate estimates from a ...productive managed forest were obtained.•Guidelines were presented for the local management of a mangrove reserve.
Retrieval of biophysical properties of mangrove vegetation (e.g. height and above ground biomass) has typically relied upon traditional forest inventory data collection methods. Recently, the availability of Unmanned Aerial Vehicles (UAV) with different types of sensors and capabilities has proliferated, opening the possibility to expand the methods to retrieve biophysical properties of vegetation. Focusing on the Matang Mangrove Forest Reserve (MMFR) in Perak Province, Malaysia, this study aimed to investigate the use of UAV imagery for retrieving structural information on mangroves. We focused on a structurally complex 90-year-old protective forest zone and a simpler 15-year-old productive forest zone that had been silviculturally managed for charcoal production. The UAV data were acquired in June 2016. In the productive zone, the median tree stand heights retrieved from the UAV and field data were, respectively, 13.7 m and 14 m (no significant difference, p-value = .375). In the protective zone, the median tree stand heights retrieved from the UAV and field data were, respectively, 25.8 and 16.5 m (significant difference, p-value = .0001) taking into account only the upper canopy. The above ground biomass (AGB) in the productive zone was estimated at 217 Mg ha−1 using UAV data and 238 Mg ha−1 using ground inventory data. In the protective zone, the AGB was estimated at 210 Mg ha−1 using UAV data and 143 Mg ha−1 using ground inventory data, taking into account only upper canopy trees in both estimations. These observations suggested that UAV data were most useful for retrieving canopy height and biomass from forests that were relatively homogeneous and with a single dominant layer. A set of guidelines for enabling the use of UAV data for local management is presented, including suggestions as to how to use these data in combination with field observations to support management activities. This approach would be applicable in other regions where mangroves occur, particularly as these are environments that are often remote, inaccessible or difficult to work in.
The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods for the mapping ...of forest tree species do not exploit the respective, rich spatial information. Here, we assessed the potential of convolutional neural networks (CNNs) and very high-resolution RGB imagery from UAVs for the mapping of tree species in temperate forests. We used multicopter UAVs to obtain very high-resolution (<2 cm) RGB imagery over 51 ha of temperate forests in the Southern Black Forest region, and the Hainich National Park in Germany. To fully harness the end-to-end learning capabilities of CNNs, we used a semantic segmentation approach (U-net) that concurrently segments and classifies tree species from imagery. With a diverse dataset in terms of study areas, site conditions, illumination properties, and phenology, we accurately mapped nine tree species, three genus-level classes, deadwood, and forest floor (mean F1-score 0.73). A larger tile size during CNN training negatively affected the model accuracies for underrepresented classes. Additional height information from normalized digital surface models slightly increased the model accuracy but increased computational complexity and data requirements. A coarser spatial resolution substantially reduced the model accuracy (mean F1-score of 0.26 at 32 cm resolution). Our results highlight the key role that UAVs can play in the mapping of forest tree species, given that air- and spaceborne remote sensing currently does not provide comparable spatial resolutions. The end-to-end learning capability of CNNs makes extensive preprocessing partly obsolete. The use of large and diverse datasets facilitate a high degree of generalization of the CNN, thus fostering transferability. The synergy of high-resolution UAV imagery and CNN provide a fast and flexible yet accurate means of mapping forest tree species.
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