Topic Editors

Prof. Dr. Francesco Pirotti
Department of Land, Environment, Agriculture and Forestry, University of Padova, Padova, Italy
Dr. L. Monika Moskal
School of Environmental and Forest Sciences, College of the Environment, University of Washington (UW), Director, UW Precision Forestry Cooperative and Remote Sensing and Geospatial Analysis Laboratory, Washington, Box 352100, Seattle, WA 98195-2100, USA
Dr. H. Jaime Hernández Palma
Facultad de Ciencias Forestales y de la Conservación de la Naturaleza, Universidad de Chile, Santiago 8330015, Chile
Dr. Gaia Vaglio Laurin
Department of Innovation in Biological, Agri-food and Forestry Systems, University of Tuscia, 01100 Viterbo, Italy
Mr. Erico Kutchartt
Department of Land, Environment, Agriculture and Forestry TESAF, University of Padova, 35122 Padova, Italy

Estimating Carbon Stocks in Forest Ecosystems: From Allometric Equations to Remote Sensing-Based Methods

Abstract submission deadline
closed (31 December 2021)
Manuscript submission deadline
31 May 2022
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16342

Topic Information

Dear Colleagues,

The determination of carbon stocks in forest ecosystems is a relevant input of global relevance. Many countries have signed the United Nations Framework Convention on Climate Change (UNFCCC) agreement, where initiatives related to reducing emissions from deforestation and forest degradation (REDD+) have been implemented. Considering such factors, rigorous methodologies regarding the quantification of carbon stocks are of paramount importance. Traditional approaches using allometric equations have been used for a long time in forest sciences. However, the accurate definition of allometric equations requires a destructive approach, which is also expensive, time-consuming, and in some cases inappropriate, considering that some species are protected by international organizations (e.g., CITES, IUCN).

Novel technologies that have become established and new ones that are being tested and validated can validly support, complement and in some cases substitute traditional methods. Remote sensing techniques and geomatics techniques in general i.e., measuring Earth data from satellite image analysis, photogrammetry, laser scanning etc.…, can be integrated by local and general allometric equations. However, even if the technologies are quite advanced, field data collection is always required, for training and/or validation.

The focus of this Special Issue is on documenting developing methodologies using existing site- and species-specific allometric equations to estimate carbon stocks and explore advanced and novel remote sensing technologies that allow measuring these data. Since allometric equations are specific to a species and to an environmental site’s condition, it will be relevant to include local equations as ground truth values. On the other hand, developing local and general allometric equations through photogrammetry, laser scanning and mobile applications can be a valid solution to the lack of allometric equations in the case of protected species which cannot undergo destructive analyses.

Suitable research papers for this Special Issue can be related to the following topics:

  • Active and passive earth-observation sensors;
  • Error propagation;
  • Photogrammetry;
  • Close-range sensing;
  • Airborne and terrestrial laser scanning;
  • SLAM-based hand-carried mobile laser scanning;
  • Forest inventory;
  • Forest biometry and modeling;
  • 3D modeling and augmented reality from remote sensing data;
  • Uncertainty metrics.

Prof. Dr. Francesco Pirotti
Dr. L. Monika Moskal
Dr. H. Jaime Hernández Palma
Dr. Gaia Vaglio Laurin
Mr. Erico Kutchartt
Topic Editors

Keywords

  • active/passive earth observation
  • photogrammetry
  • close-range sensing
  • error propagation
  • 3D modelling
  • augmented and virtual reality AR/VR
  • laser scanning / lidar

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Remote Sensing
remotesensing
4.848 6.6 2009 19.8 Days 2500 CHF Submit
Forests
forests
2.634 3.3 2010 19.9 Days 2000 CHF Submit
Sensors
sensors
3.576 5.8 2001 17.4 Days 2400 CHF Submit
Geomatics
geomatics
- - 2021 15.0 days * 1000 CHF Submit

* Median value for all MDPI journals in the second half of 2021.

Published Papers (19 papers)

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Article
Thinning Effects on Stand Structure and Carbon Content of Secondary Forests
Forests 2022, 13(4), 512; https://doi.org/10.3390/f13040512 - 25 Mar 2022
Abstract
In, this study, we analysed the effects of thinning on stand structure and carbon stocks for a mixed conifer and broadleaf natural secondary forests in the Small Khingan Mountains, China. Stand structure and carbon stocks were assessed in trees from unthinned control (CK), [...] Read more.
In, this study, we analysed the effects of thinning on stand structure and carbon stocks for a mixed conifer and broadleaf natural secondary forests in the Small Khingan Mountains, China. Stand structure and carbon stocks were assessed in trees from unthinned control (CK), lightly thinned (LT), moderately thinned (MT) and heavily thinned (HT) treatments. Results showed that the heavier the thinning, the larger the crown area became. Under the MT treatment, trees tended to be evenly distributed when compared to trees under the other treatments. All the trees of the LT and HT treatments were mixed strongly compared to those of the CK treatment. As the thinning intensitiy increased, the distributions of size differentiation and crowding degree gradually decreased. As a result, the competitive pressure diminished, and more dominant trees emerged. In addition, there was a significant positive correlation between individual tree carbon stock and canopy under all treatments. Carbon tends to accumulate in individuals with a random distribution, sparse spacing, strong mingling index and large competitive advantage. However, the results varied slightly under the HT treatment. Individuals in a dominant position occupied abundant resources and great niche space. Full article
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Article
Allometric Equations to Estimate Aboveground Biomass in Spotted Gum (Corymbia citriodora Subspecies variegata) Plantations in Queensland
Forests 2022, 13(3), 486; https://doi.org/10.3390/f13030486 - 21 Mar 2022
Abstract
Accurate equations are critical for estimating biomass and carbon accumulation for forest carbon projects, bioenergy, and other inventories. Allometric equations can provide a reliable and accurate method for estimating and predicting biomass and carbon sequestration. Cross-validatory assessments are also essential to evaluate the [...] Read more.
Accurate equations are critical for estimating biomass and carbon accumulation for forest carbon projects, bioenergy, and other inventories. Allometric equations can provide a reliable and accurate method for estimating and predicting biomass and carbon sequestration. Cross-validatory assessments are also essential to evaluate the prediction ability of the selected model with satisfactory accuracy. We destructively sampled and weighed 52 sample trees, ranging from 11.8 to 42.0 cm in diameter at breast height from three plantations in Queensland to determine biomass. Weighted nonlinear models were used to explore the influence of different variables using two datasets: the first dataset (52 trees) included diameter at breast height (D), height (H) and wood density (ρ); and the second dataset (40 trees) also included crown diameter (CD) and crown volume (CV). Cross validation of independent data showed that using D alone proved to be the best performing model, with the lowest values of AIC = 434.4, bias = −2.2% and MAPE = 7.2%. Adding H and ρ improved the adjusted. R2 (Δ adj. R2 from 0.099 to 0.135) but did not improve AIC, bias and MAPE. Using the single variable of CV to estimate aboveground biomass (AGB) was better than CD, with smaller AIC and MAPE less than 2.3%. We demonstrated that the allometric equations developed and validated during this study provide reasonable estimates of Corymbia citriodora subspecies variegata (spotted gum) biomass. This equation could be used to estimate AGB and carbon in similar spotted gum plantations. In the context of global forest AGB estimations and monitoring, the CV variable could allow prediction of aboveground biomass using remote sensing datasets. Full article
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Article
Modelling Aboveground Biomass of Miombo Woodlands in Niassa Special Reserve, Northern Mozambique
Forests 2022, 13(2), 311; https://doi.org/10.3390/f13020311 - 14 Feb 2022
Abstract
Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive and active remote-sensing technologies can provide spatially explicit information of AGB by using a limited number of [...] Read more.
Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive and active remote-sensing technologies can provide spatially explicit information of AGB by using a limited number of field samples, thus reducing the substantial budgetary cost of field inventories. The aim of the current study was to estimate AGB in the Niassa Special Reserve (NSR) using fusion of optical (Landsat 8/OLI and Sentinel 2A/MSI) and radar (Sentinel 1B and ALOS/PALSAR-2) data. The performance of multiple linear regression models to relate ground biomass with different combinations of sensor data was assessed using root-mean-square error (RMSE), and the Akaike and Bayesian information criteria (AIC and BIC). The mean AGB and carbon stock (CS) estimated from field data were estimated at 56 Mg ha−1 (ranging from 11 to 95 Mg ha−1) and 28 MgC ha−1, respectively. The best model estimated AGB at 63 ± 20.3 Mg ha−1 for NSR, ranging from 0.6 to 200 Mg ha−1 (r2 = 87.5%, AIC = 123, and BIC = 51.93). We obtained an RMSE % of 20.46 of the mean field estimate of 56 Mg ha−1. The estimation of AGB in this study was within the range that was reported in the existing literature for the miombo woodlands. The fusion of vegetation indices derived from Landsat/OLI and Sentinel 2A/MSI, and backscatter from ALOS/PALSAR-2 is a good predictor of AGB. Full article
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Article
Deforestation for Agriculture Temporarily Improved Soil Quality and Soil Organic Carbon Stocks
Forests 2022, 13(2), 228; https://doi.org/10.3390/f13020228 - 02 Feb 2022
Abstract
Deforestation for agricultural development or extension is a common land-use problem that may cause a series of changes in the ecological environment and soil carbon stock in planting systems. However, the response of soil physical, chemical properties and carbon stocks in agricultural systems [...] Read more.
Deforestation for agricultural development or extension is a common land-use problem that may cause a series of changes in the ecological environment and soil carbon stock in planting systems. However, the response of soil physical, chemical properties and carbon stocks in agricultural systems in the initial period after deforestation have not been thoroughly examined, especially in the subsoil. We investigated the variations in the soil physicochemical properties and organic carbon stocks to a depth of 100 cm in a poplar (Populus deltoides cv. 35) plantation, a summer maize (Zea mays L.) followed by winter wheat (Triticum aestivum L.) field after 1 year of deforestation of a poplar plantation, and a wheat–maize rotation field used for decades. The soil bulk density and pH decreased, and the soil total nitrogen (TN), total phosphorus, and total potassium contents increased considerably. The soil organic carbon (SOC) content and stocks (to 100 cm) increased by 32.8% and 20.1%, respectively. The soil TN content was significantly (p < 0.001) positively correlated with the SOC content, and the C:N ratio increased for the field following deforestation. Furthermore, the nitrogen in the poplar plantation and the field following deforestation was limited. We recommend increasing the amount of nitrogen fertilizer following deforestation to improve fertility and this will be beneficial to SOC storage. Full article
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Article
Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery
Remote Sens. 2022, 14(2), 271; https://doi.org/10.3390/rs14020271 - 07 Jan 2022
Cited by 1
Abstract
Individual-tree aboveground biomass (AGB) estimation can highlight the spatial distribution of AGB and is vital for precision forestry. Accurately estimating individual tree AGB is a requisite for accurate forest carbon stock assessment of natural secondary forests (NSFs). In this study, we investigated the [...] Read more.
Individual-tree aboveground biomass (AGB) estimation can highlight the spatial distribution of AGB and is vital for precision forestry. Accurately estimating individual tree AGB is a requisite for accurate forest carbon stock assessment of natural secondary forests (NSFs). In this study, we investigated the performance of three machine learning and three ensemble learning algorithms in tree species classification based on airborne laser scanning (ALS) and WorldView-3 imagery, inversed the diameter at breast height (DBH) using an optimal tree height curve model, and mapped individual tree AGB for a site in northeast China using additive biomass equations, tree species, and inversed DBH. The results showed that the combination of ALS and WorldView-3 performed better than either single data source in tree species classification, and ensemble learning algorithms outperformed machine learning algorithms (except CNN). Seven tree species had satisfactory accuracy of individual tree AGB estimation, with R2 values ranging from 0.68 to 0.85 and RMSE ranging from 7.47 kg to 36.83 kg. The average individual tree AGB was 125.32 kg and the forest AGB was 113.58 Mg/ha in the Maoershan study site in Heilongjiang Province, China. This study provides a way to classify tree species and estimate individual tree AGB of NSFs based on ALS data and WorldView-3 imagery. Full article
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Article
Estimating Forest Aboveground Biomass Using Gaofen-1 Images, Sentinel-1 Images, and Machine Learning Algorithms: A Case Study of the Dabie Mountain Region, China
Remote Sens. 2022, 14(1), 176; https://doi.org/10.3390/rs14010176 - 31 Dec 2021
Abstract
Quantitatively mapping forest aboveground biomass (AGB) is of great significance for the study of terrestrial carbon storage and global carbon cycles, and remote sensing-based data are a valuable source of estimating forest AGB. In this study, we evaluated the potential of machine learning [...] Read more.
Quantitatively mapping forest aboveground biomass (AGB) is of great significance for the study of terrestrial carbon storage and global carbon cycles, and remote sensing-based data are a valuable source of estimating forest AGB. In this study, we evaluated the potential of machine learning algorithms (MLAs) by integrating Gaofen-1 (GF1) images, Sentinel-1 (S1) images, and topographic data for AGB estimation in the Dabie Mountain region, China. Variables extracted from GF1 and S1 images and digital elevation model data from sample plots were used to explain the field AGB value variations. The prediction capability of stepwise multiple regression and three MLAs, i.e., support vector machine (SVM), random forest (RF), and backpropagation neural network were compared. The results showed that the RF model achieved the highest prediction accuracy (R2 = 0.70, RMSE = 16.26 t/ha), followed by the SVM model (R2 = 0.66, RMSE = 18.03 t/ha) for the testing datasets. Some variables extracted from the GF1 images (e.g., normalized differential vegetation index, band 1-blue, the mean texture feature of band 3-red with windows of 3 × 3), S1 images (e.g., vertical transmit-horizontal receive and vertical transmit-vertical receive backscatter coefficient), and altitude had strong correlations with field AGB values (p < 0.01). Among the explanatory variables in MLAs, variables extracted from GF1 made a greater contribution to estimating forest AGB than those derived from S1 images. These results indicate the potential of the RF model for evaluating forest AGB by combining GF1 and S1, and that it could provide a reference for biomass estimation using multi-source images. Full article
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Article
Root Growth Was Enhanced in China Fir (Cunninghamia lanceolata) after Mechanical Disturbance by Ice Storm
Forests 2021, 12(12), 1800; https://doi.org/10.3390/f12121800 - 18 Dec 2021
Abstract
Accurate estimation of forest biomass and its growth potential could be important in assessing the mitigation potential of forest for climate change. However, severe mechanical disturbance such as stem breakage imposed significant changes to tree individuals in biomass structure, which could bring new [...] Read more.
Accurate estimation of forest biomass and its growth potential could be important in assessing the mitigation potential of forest for climate change. However, severe mechanical disturbance such as stem breakage imposed significant changes to tree individuals in biomass structure, which could bring new inaccuracy to biomass estimation. In order to investigate the influence of severe mechanical disturbance on tree biomass accumulation and to construct accurate models for biomass and carbon storage estimation, this paper analyzed the relationship between tree size and biomass for China fir (Cunninghamia lanceolata (Lamb.) Hook) which suffered stem breakage from, and survived, an ice storm. The performance of independent variables diameter (D) and height (H) of China fir, were also compared in biomass estimation. The results showed that D as an independent variable was adequate in biomass estimation for China fir, and tree height was not necessary in this case. Root growth was faster in China fir which had suffered breakage in the main stem by the ice storm, than China fir which were undamaged for at least 7 years after the mechanical disturbance, which, in addition to biomass loss in stem, caused changes in the allocation pattern of the damaged trees. This suggests biomass models constructed before severe mechanical disturbance would be less suitable in application for a subsequent period, and accurate estimations of biomass and forest carbon storage would take more effort. Full article
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Article
Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China
Remote Sens. 2021, 13(24), 5081; https://doi.org/10.3390/rs13245081 - 14 Dec 2021
Cited by 1
Abstract
Understanding the driving mechanism of vegetation changes is essential for vegetation restoration and management. Vegetation coverage in the Poyang Lake basin (PYLB) has changed dramatically under the context of climate change and human activities in recent decades. It remains challenging to quantify the [...] Read more.
Understanding the driving mechanism of vegetation changes is essential for vegetation restoration and management. Vegetation coverage in the Poyang Lake basin (PYLB) has changed dramatically under the context of climate change and human activities in recent decades. It remains challenging to quantify the relative contribution of natural and anthropogenic factors to vegetation change due to their complicated interaction effects. In this study, we selected the Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation growth and used trend analysis and the Mann-Kendall test to analyze its spatiotemporal change in the PYLB from 2000 to 2020. Then we applied the Geodetector model, a novel spatial analysis method, to quantify the effects of natural and anthropogenic factors on vegetation change. The results showed that most regions of the basin were experiencing vegetation restoration and the overall average NDVI value in the basin increased from 0.756 to 0.809 with an upward yearly trend of +0.0026. Land-use type exerted the greatest influence on vegetation change, followed by slope, elevation, and soil types. Except for conversions to construction land, most types of land use conversion induced an increase in NDVI in the basin. The influence of one factor on vegetation NDVI was always enhanced when interacting with another. The interaction effect of land use types and population density was the largest, which could explain 45.6% of the vegetation change, indicating that human activities dominated vegetation change in the PYLB. Moreover, we determined the ranges or types of factors most suitable for vegetation growth, which can be helpful for decision-makers to optimize the implementation of ecological projects in the PYLB in the future. The results of this study could improve the understanding of the driving mechanisms of vegetation change and provide a valuable reference for ecological restoration in subtropical humid regions. Full article
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Article
Assessing Soil Organic Carbon Stock Dynamics under Future Climate Change Scenarios in the Middle Qilian Mountains
Forests 2021, 12(12), 1698; https://doi.org/10.3390/f12121698 - 04 Dec 2021
Abstract
Soil organic carbon (SOC) simply cannot be managed if its amounts, changes and locations are not well known. Thus, evaluations of the spatio-temporal dynamics of SOC stock under future climate change are crucial for the adaptive management of regional carbon sequestration. Here, we [...] Read more.
Soil organic carbon (SOC) simply cannot be managed if its amounts, changes and locations are not well known. Thus, evaluations of the spatio-temporal dynamics of SOC stock under future climate change are crucial for the adaptive management of regional carbon sequestration. Here, we evaluated the dynamics of SOC stock to a 60 cm depth in the middle Qilian Mountains (1755–5051 m a.s.l.) by combining systematic measurements from 138 sampling sites with a machine learning model. Our results reveal that the combination of systematic measurements with the machine learning model allowed spatially explicit estimates of SOC change to be made. The average SOC stock in the middle Qilian Mountains was expected to decrease under future climate change, while the size and direction of SOC stock changes seemed to be elevation-dependent. Specifically, in comparison with the 2000s, the mean annual precipitation was projected to increase by 18.37, 19.80 and 30.80 mm, and the mean annual temperature was projected to increase by 1.9, 2.4 and 2.9 °C under the Representative Concentration Pathway (RCP) 2.6 (low-emissions pathway), RCP4.5 (low-to-moderate-emissions pathway), and RCP8.5 (high-emissions pathway) scenarios by the 2050s, respectively. Accordingly, the area-weighted SOC stock and total storage for the whole study area were estimated to decrease by 0.43, 0.63 and 1.01 kg m2 and 4.55, 6.66 and 10.62 Tg under the RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. In addition, the mid-elevation zones (3100–3900 m), especially the subalpine shrub-meadow Mollic Leptosols, were projected to experience the most intense carbon loss. However, the higher elevation zones (>3900 m), especially the alpine desert zone, were characterized by significant carbon accumulation. As for the low-elevation zones (<2900 m), SOC was projected to be less varied under future climate change scenarios. Thus, the mid-elevation zones, especially the subalpine shrub-meadows and Mollic Leptosols, should be given priority in terms of reducing CO2 emissions in the Qilian Mountains. Full article
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Article
Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery
Remote Sens. 2021, 13(23), 4859; https://doi.org/10.3390/rs13234859 - 30 Nov 2021
Abstract
Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation [...] Read more.
Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation types and background types. In this study, a novel woody AGB estimation methodology (VI-AGB model stratified based on herbaceous vegetation coverage) using a combination of Landsat-8, GaoFen-2, and unmanned aerial vehicle (UAV) images was developed. The results show the following: (1) the woody and herbaceous canopy can be accurately identified using the object-based support vector machine (SVM) classification method based on UAV red-green-blue (RGB) images, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively; (2) compared with the estimation uncertainties of the woody coverage-AGB models without considering the woody vegetation types (RMSE = 14.98 t∙ha−1 and rRMSE = 96.31%), the woody coverage-AGB models stratified based on five woody species (RMSE = 5.82 t∙ha−1 and rRMSE = 37.46%) were 61.1% lower; (3) of the six VIs used in this study, the near-infrared reflectance of pure vegetation (NIRv)-AGB model performed best (RMSE = 7.91 t∙ha−1 and rRMSE = 50.89%), but its performance was still seriously affected by the heterogeneity of the green herbaceous coverage. The normalized difference moisture index (NDMI)-AGB model was the least sensitive to the background. The stratification-based VI-AGB models considering the herbaceous vegetation coverage derived from GaoFen-2 and UAV images can significantly improve the accuracy of the woody AGB estimated using only Landsat VIs, with the RMSE and rRMSE of 6.6 t∙ha−1 and 42.43% for the stratification-based NIRv-AGB models. High spatial resolution information derived from UAV and satellite images has a great potential for improving the woody AGB estimated using only Landsat images in sparsely vegetated areas. This study presents a practical method of estimating woody AGB in sparse mixed forest in dryland areas. Full article
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Article
Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics
Forests 2021, 12(12), 1663; https://doi.org/10.3390/f12121663 - 30 Nov 2021
Abstract
Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to [...] Read more.
Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps. Full article
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Article
Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees
Forests 2021, 12(11), 1521; https://doi.org/10.3390/f12111521 - 04 Nov 2021
Cited by 3
Abstract
Research Highlights: This study advances the effort to accurately estimate the biomass of trees in peatlands, which cover 13% of Canada’s land surface. Background and Objectives: Trees remove carbon from the atmosphere and store it as biomass. Terrestrial laser scanning (TLS) has [...] Read more.
Research Highlights: This study advances the effort to accurately estimate the biomass of trees in peatlands, which cover 13% of Canada’s land surface. Background and Objectives: Trees remove carbon from the atmosphere and store it as biomass. Terrestrial laser scanning (TLS) has become a useful tool for modelling forest structure and estimating the above ground biomass (AGB) of trees. Allometric equations are often used to estimate individual tree AGB as a function of height and diameter at breast height (DBH), but these variables can often be laborious to measure using traditional methods. The main objective of this study was to develop allometric equations using TLS-measured variables and compare their accuracy with that of other widely used equations that rely on DBH. Materials and Methods: The study focusses on small black spruce trees (<5 m) located in peatland ecosystems of the Taiga Plains Ecozone in the Northwest Territories, Canada. Black spruce growing in peatlands are often stunted when compared to upland black spruce and having models specific to them would allow for more precise biomass estimates. One hundred small trees were destructively sampled from 10 plots and the dry weight of each tree was measured in the lab. With this reference data, we fitted biomass models specific to peatland black spruce using DBH, crown diameter, crown area, height, tree volume, and bounding box volume as predictors. Results: Our best models had crown size and height as predictors and outperformed established AGB equations that rely on DBH. Conclusions: Our equations are based on predictors that can be measured from above, and therefore they may enable the plotless creation of accurate biomass reference data for a prominent tree species in a common ecosystem (treed peatlands) in North America’s boreal. Full article
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Article
Application of Models to Predict Stand Volume, Aboveground Biomass Accumulation, and Carbon Storage Capacity for a Konishii Fir (Cunninghamia konishii Hayata) Plantation in Central Taiwan
Forests 2021, 12(10), 1406; https://doi.org/10.3390/f12101406 - 15 Oct 2021
Abstract
Konishii fir (Cunninghamia konishii Hayata) is an important conifer in Taiwan. The purpose of this study was to predict stand volume (V), aboveground biomass accumulation (AGB), and aboveground carbon storage (AGCST) for a Konishii fir plantation. This study was located at the [...] Read more.
Konishii fir (Cunninghamia konishii Hayata) is an important conifer in Taiwan. The purpose of this study was to predict stand volume (V), aboveground biomass accumulation (AGB), and aboveground carbon storage (AGCST) for a Konishii fir plantation. This study was located at the Huisun Experimental Forest Station of Nantou County located in central Taiwan. Four sample plots, each with an area of 0.05 ha, were installed and surveyed from 29 June to 2 July 2020. Two models, the diameter distribution model (DDM) and allometric model (AM), were used to predict V, AGB, and AGCST. Each item predicted by these two models was compared by the paired sample t-test. We employed the Weibull function to quantify stand diameter distribution and this function can effectively quantify diameter distribution, because all plots passed the examination by the Kolmogorov–Smirnov test (non-significant). Therefore, the Weibull function was suitable for developing the DDM. The predicted V, AGB, and AGCST were 538.43 ± 140.52 m3 ha−1, 203.25 ± 52.79 Mg ha−1, and 100.85 ± 26.30 Mg ha−1 by DDM; and 555.90 ± 145.42 m3 ha−1, 209.10 ± 51.25 Mg ha−1, and 103.78 ± 25.51 Mg ha−1 by AM, respectively. Each item was insignificantly different between DDM and AM, indicating similarity in results for both predictions. Meanwhile, using DDM is advantageous, as it can provide more yield information in diameter classes; therefore, this approach was recommended for yield prediction of the Konishii fir plantation. Full article
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Article
Ground Cover—Biomass Functions for Early-Seral Vegetation
Forests 2021, 12(9), 1272; https://doi.org/10.3390/f12091272 - 17 Sep 2021
Cited by 1
Abstract
Vegetation biomass is commonly measured through destructive sampling, but this method is time-consuming and is not applicable for certain studies. Therefore, it is necessary to find reliable methods to estimate vegetation biomass indirectly. Quantification of early-seral vegetation biomass in reforested stands in the [...] Read more.
Vegetation biomass is commonly measured through destructive sampling, but this method is time-consuming and is not applicable for certain studies. Therefore, it is necessary to find reliable methods to estimate vegetation biomass indirectly. Quantification of early-seral vegetation biomass in reforested stands in the United States Pacific Northwest (PNW) is important as competition between the vegetation community and planted conifer seedlings can have important consequences on seedling performance. The goal of this study was to develop models to indirectly estimate early-seral vegetation biomass using vegetation cover, height, or a combination of the two for different growth habits (ferns, forbs, graminoids, brambles, and shrubs) and environments (wet and dry) in reforested timber stands in Western Oregon, USA. Six different linear and non-linear regression models were tested using cover or the product of cover and height as the only predicting variable, and two additional models tested the use of cover and height as independent variables. The models were developed for six different growth habits and two different environments. Generalized models tested the combination of all growth habits (total) and sites (pooled data set). Power models were used to estimate early-seral vegetation biomass for most of the growth habits, at both sites, and for the pooled data set. Furthermore, when power models were preferred, most of the growth habits used vegetation cover and height separately as predicting variables. Selecting generalized models for predicting early-seral vegetation biomass across different growth habits and environments is a good option and does not involve an important trade-off by losing accuracy and/or precision. The presented models offer an efficient and non-destructive method for foresters and scientists to estimate vegetation biomass from simple field or aerial measurement of cover and height. Depending on the objectives and availability of input data, users may select which model to apply. Full article
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Article
Species-Specific Allometric Equations for Predicting Belowground Root Biomass in Plantations: Case Study of Spotted Gums (Corymbia citriodora subspecies variegata) in Queensland
Forests 2021, 12(9), 1210; https://doi.org/10.3390/f12091210 - 06 Sep 2021
Cited by 1
Abstract
Spotted gum (Corymbia citriodora spp. variegata; CCV) has been widely planted, has a wide natural distribution, and is the most important commercially harvested hardwood species in Queensland, Australia. It has a great capacity to sequester carbon, thus reducing the impact of [...] Read more.
Spotted gum (Corymbia citriodora spp. variegata; CCV) has been widely planted, has a wide natural distribution, and is the most important commercially harvested hardwood species in Queensland, Australia. It has a great capacity to sequester carbon, thus reducing the impact of CO2 emissions on climate. Belowground root biomass (BGB) plays an important role as a carbon sink in terrestrial ecosystems. To explore the potential of biomass and carbon accumulation belowground, we developed and validated models for CCV plantations in Queensland. The roots of twenty-three individual trees (size range 11.8–42.0 cm diameter at breast height) from three sites were excavated to a 1-m depth and were weighed to obtain BGB. Weighted nonlinear regression models were most reliable for estimating BGB. To evaluate the candidate models, the data set was cross-validated with 70% of the data used for training and 30% of the data used for testing. The cross-validation process was repeated 23 times and the validation of the models were averaged over 23 iterations. The best model for predicting spotted gum BGB was based on a single parameter, with the diameter at breast height (D) as an independent variable. The best equation BGB = 0.02933 × D2.5805 had an adjusted R2 of 0.854 and a mean absolute percentage error of 0.090%. This equation was tested against published BGB equations; the findings from this are discussed. Our equation is recommended to allow improved estimates of BGB for this species. Full article
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Article
Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter
Forests 2021, 12(7), 944; https://doi.org/10.3390/f12070944 - 17 Jul 2021
Abstract
While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. [...] Read more.
While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3 ha−1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors. Full article
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Article
Estimation of Biomass Increase and CUE at a Young Temperate Scots Pine Stand Concerning Drought Occurrence by Combining Eddy Covariance and Biometric Methods
Forests 2021, 12(7), 867; https://doi.org/10.3390/f12070867 - 30 Jun 2021
Cited by 1
Abstract
The accurate estimation of an increase in forest stand biomass has remained a challenge. Traditionally, in situ measurements are done by inventorying a number of trees and their biometric parameters such as diameter at the breast height (DBH) and height; sometimes these are [...] Read more.
The accurate estimation of an increase in forest stand biomass has remained a challenge. Traditionally, in situ measurements are done by inventorying a number of trees and their biometric parameters such as diameter at the breast height (DBH) and height; sometimes these are complemented by carbon (C) content studies. Here we present the estimation of net primary productivity (NPP) over a two years period (2019–2020) at a 25-year-old Scots pine stand. Research was based on allometric equations made by direct biomass analysis (tree extraction) and carbon content estimations in individual components of sampled trees, combined with a series of stem diameter increments recorded by a network of band dendrometers. Site-specific allometric equations were obtained using two different approaches: using the whole tree biomass vs DBH (M1), and total dry biomass-derived as a sum of the results from individual tree components’ biomass vs DBH (M2). Moreover, equations for similar forest stands from the literature were used for comparison. Gross primary productivity (GPP) estimated from the eddy-covariance measurements allowed the calculation of carbon use efficiency (CUE = NPP/GPP). The two investigated years differed in terms of the sum and patterns of precipitation distribution, with a moderately dry year of 2019 that followed the extremely dry 2018, and the relatively average year of 2020. As expected, a higher increase in biomass was recorded in 2020 compared to 2019, as determined by both allometric equations based on in situ and literature data. For the former approach, annual NPP estimates reached ca. 2.0–2.1 t C ha−1 in 2019 and 2.6–2.7 t C ha−1 in 2020 depending on the “in situ equations” (M1-M2) used, while literature-derived equations for the same site resulted in NPP values ca. 20–30% lower. CUE was higher in 2020, which resulted from a higher NPP total than in 2019, with lower summer and spring GPP in 2020. However, the CUE values were lower than those reported in the literature for comparable temperate forest stands. A thorough analysis of the low CUE value would require a full interpretation of interrelated physiological responses to extreme conditions. Full article
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Article
A Method for Quantifying the Impacts of Human Activities on Net Primary Production of Grasslands in Northwest China
Remote Sens. 2021, 13(13), 2479; https://doi.org/10.3390/rs13132479 - 25 Jun 2021
Cited by 1
Abstract
Accurately assessing the impact of human activities on net primary productivity (NPP) of vegetation is of great significance to the achievement of sustainable development. However, it is difficult to disentangle the effects of climate conditions and human activities on NPP, and bridging this [...] Read more.
Accurately assessing the impact of human activities on net primary productivity (NPP) of vegetation is of great significance to the achievement of sustainable development. However, it is difficult to disentangle the effects of climate conditions and human activities on NPP, and bridging this knowledge gap largely depends on the calculation of the NPP under natural conditions. Here, we propose a method for calculating natural vegetation NPP (NNPP) based on non-human influence grids, which are obtained according to the consistent rate of climate and actual NPP (ANPP) temporal changes. We selected Northwest China as study area, and we used a light use efficiency (LUE) model to estimate ANPP and used the random forest algorithm (RF) to estimate the NNPP. The results show that NNPP is very close to ANPP, and the human activities on NPP (HNPP) based on NNPP is close to the actual situation of human activities on NPP. From 2001 to 2017, the positive HNPP accounts for 40.28% of the total grassland area, with an average value of 28.65 gC·m−2·yr−1, while the negative HNPP accounts for 59.72% of the total area, with an average value of −31.19 gC·m−2·yr−1. The grassland NPP shows an increasing trend, which is dominated by climate factors. Human activity is the dominant factor for the grassland degradation, accounting for 42.78% of the degraded area, but promoting grassland growth in 11.4% of the restored area. This study provides a new method to estimate the impacts of human activities on vegetation, and the results can be used to evaluate the effectiveness of ecological environmental governance, providing a quantitative basis for scientifically building the harmonious relationship between human and nature. Full article
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Article
Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil
Forests 2021, 12(4), 473; https://doi.org/10.3390/f12040473 - 13 Apr 2021
Cited by 1
Abstract
In recent years, with the growing environmental concern regarding climate change, there has been a search for efficient alternatives in indirect methods for the quantification of biomass and forest carbon stock. In this article, we seek to obtain pioneering results of biomass and [...] Read more.
In recent years, with the growing environmental concern regarding climate change, there has been a search for efficient alternatives in indirect methods for the quantification of biomass and forest carbon stock. In this article, we seek to obtain pioneering results of biomass and carbon estimates from forest inventory data and LiDAR technology in a dry tropical forest in Brazil. We use forest inventory data in two areas together with data from the LiDAR flyby, generating estimates of local biomass and carbon levels obtained from local species. We approach three types of models for data analysis: Multiple linear regression with principal components (PCA), conventional multiple linear regression and stepwise multiple linear regression. The best fit total above ground biomass (TAGB) and total above ground carbon (TAGC) model was the stepwise multiple linear regression, concluding, then, that LiDAR data can be used to estimate biomass and total carbon in dry tropical forest, proven by an adjustment considered in the models employed, with a significant correlation between the LiDAR metrics. Our finding provides important information about the spatial distribution of TAGB and TAGC in the study area, which can be used to manage the reserve for optimal carbon sequestration. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Mike Salazar, PhD student at Institute of Photogrammetry and Remote Sensing, Faculty of Environmental Sciences. Technische Universität Dresden, Germany, “Integration of sentinel SAR and MSI data using machine learning for enhancing AGB estimation in Tropical Secondary Dry Forest, Colombia
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