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Biomass Estimation and Carbon Stocks in Forest Ecosystems

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Ecology and Management".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 38971

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Dr. Andrzej Węgiel

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Guest Editor

Department of Forest Management, Faculty of Forestry and Wood Technology, University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
Interests: forest management; biomass estimation; forest inventory; remote sensing; forest ecology
Special Issues, Collections and Topics in MDPI journals

Dr. Adrian Łukowski

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Faculty of Forestry and Wood Technology, University of Life Sciences, 60-637 Poznań, Poland
Interests: silviculture ecophysiology; game management; plant–insect interactions; shrub species; particulate matter pollution; climate change and forestry; invasive species; social dimensions of forestry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest biomass and carbon are key elements in the development of climate change mitigation strategies. Due to the growing demand for renewable energy, there is also a growing interest in the wider use of forest biomass for energy as a possible substitute for fossil fuels. The use of woody biomass as an energy source can make a significant contribution to mitigating climate change. Forest biomass estimation is also important in the evaluation of carbon sequestration and the carbon balance capacity of forest ecosystems. Forests, being the most important carbon sink, are a good tool to reduce the carbon content of the atmosphere. Estimating the amount of carbon stored by forests is essential to support climate change mitigation and promote the transition to a low-carbon-emission economy.

This Special Issue aims to present updated knowledge relating to biomass estimation and carbon storage in forest ecosystems. The topics will include:

Advanced methods for forest biomass modelling, mapping, and estimation;
Linking field and remote sensing measurements;
Biomass components of forest ecosystem: tree compartments, vegetation, fungi, bacteria, soil fauna, etc.;
Modelling growth and biomass production;
Effects of forest management practices on biomass allocation;
Forest biomass utilization;
Factors influencing carbon and nutrient storage.

Dr. Andrzej Węgiel
Dr. Adrian Łukowski
Guest Editors

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Keywords

biomass allocation
allometric equations
growth models
biomass expansion factors
remote sensing
bioenergy
carbon and nutrient storage
ecosystem biodiversity
soil productivity
forest management

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Published Papers (10 papers)

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16 pages, 8805 KiB

Open AccessArticle

Spatial Distribution of Secondary Forests by Age Group and Biomass Accumulation in the Brazilian Amazon

by Gabriel M. da Silva, Marcos Adami, David Galbraith, Rodrigo G. M. Nascimento, Yunxia Wang, Yosio E. Shimabukuro and Fabiano Emmert

Forests 2023, 14(5), 924; https://doi.org/10.3390/f14050924 - 29 Apr 2023

Viewed by 2167

Abstract

Secondary forests provide essential ecosystem services, especially in helping to mitigate climate change with the storage of carbon in the aboveground biomass of tree species. In this context, the present research aimed to analyze the spatial distribution of secondary forests and estimate the aboveground biomass accumulation of land cover of different ages in the state of Pará. The spatial patterns of the secondary forests in Pará state were evaluated with hot spot analysis algorithms using data from the TerraClass project for the 2004–2014 time period. The results showed that the spatial distribution of the secondary forests did not occur randomly in space, but suggested local geopolitical influences. The younger secondary forests had the most deforested areas during the study period. Approximately 5% of Pará had its secondary forests deforested in 2014. In general, the balance of the secondary forests was positive. The aboveground biomass accumulation differed according to the secondary forest ages during the study period as evaluated in two pilot areas. It was observed that the secondary forests > 10 years old in pilot area A had an average of 23% of old-growth forest aboveground biomass in the same area, while in pilot area B, the secondary forests > 10 years old had an average of 32.7% of old-growth forest aboveground biomass. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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20 pages, 3403 KiB

Open AccessArticle

Contribution of Tree Size and Species on Aboveground Biomass across Land Cover Types in the Taita Hills, Southern Kenya

by Edward Amara, Hari Adhikari, James M. Mwamodenyi, Petri K. E. Pellikka and Janne Heiskanen

Forests 2023, 14(3), 642; https://doi.org/10.3390/f14030642 - 21 Mar 2023

Cited by 2 | Viewed by 2662

Abstract

Tropical landscapes comprise a variety of land cover (LC) types with characteristic canopy structure and tree species. Depending on the LC type, large-diameter trees and certain tree species can contribute disproportionately to aboveground biomass (AGB), and these patterns are not described at landscape-level in LC type specific studies. Therefore, we investigated the impact of large trees and tree species on AGB across a range of LC types in Taita Hills, Kenya. Data included 239 field plots from seven LC types: Montane forest, Plantation forest, Mixed forest, Riverine forest, Bushland, Grassland, and Cropland and homestead. Our results show that the contribution of large trees (DBH > 60 cm) on AGB was greatest in Riverine forest, Montane forest and Mixed forest (34–87%). Large trees were also common in Plantation forests and Cropland and homestead. Small trees (DBH < 20 cm) covered less than 10% of the total AGB in all forest types. In Grassland, and Cropland and homestead, smaller DBH classes made a greater contribution. Bushland differed from other classes as large trees were rare. Furthermore, the results show that each LC type had characteristic species with high AGB. In the Montane and Mixed forest, Albizia gummifera contributed 21.1% and 18.3% to AGB, respectively. Eucalyptus spp., exotic species planted in the area, were important in Mixed and Plantation forests. Newtonia hildebrandtii was the most important species in Riverine forests. In Bushland, Acacia mearnsii, species with invasive character, was abundant among trees with DBH < 30 cm. Vachellia tortillis, a common species in savannahs of East Africa, made the largest contribution in Grassland. Finally, in Cropland and homestead, Grevillea robusta was the most important species (>25% of AGB). Our results highlight the importance of conserving large trees and certain species to retain AGB stocks in the landscape. Furthermore, the results demonstrate that exotic tree species, even though invasive, can have large contribution to AGB. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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28 pages, 5209 KiB

Open AccessArticle

Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass

by Naveed Ahmad, Saleem Ullah, Na Zhao, Faisal Mumtaz, Asad Ali, Anwar Ali, Aqil Tariq, Mariam Kareem, Areeba Binte Imran, Ishfaq Ahmad Khan and Muhammad Shakir

Forests 2023, 14(2), 379; https://doi.org/10.3390/f14020379 - 13 Feb 2023

Cited by 13 | Viewed by 3942

Abstract

Accurately characterizing carbon stock is vital for reporting carbon emissions from forest ecosystems. We studied the estimation of biomass using Sentinel-2 remote sensing data in moist temperate forests in the Galies region of Abbottabad Pakistan. Above-ground biomass (AGB), estimated from 60 field plots, was correlated with vegetation indices obtained from Sentinel-2 image-to-map AGB using regression models. Furthermore, additional explanatory variables were also associated with AGB in the geo-statistical technique, and kriging interpolation was used to predict AGB. The results illustrate that the atmospherically resistant vegetation index (ARVI) is the best index (R² = 0.67) for estimating AGB. In spectral reflectance, Band 1(Coastal Aerosol 443 nm) performs better than other bands. Multiple linear regression models calibrated with ARVI, NNIR and NDVI yielded better results (R² = 0.46) with the lowest RMSE (48.53) and MAE (38.42) and were therefore considered better for biomass estimation. On the other hand, in the geo-statistical technique, distance to settlements, ARVI and annual precipitation were significantly correlated with biomass compared to others. In the stepwise regression method, the forward selection resulted in a very significant value (less than 0.000) for ARVI. Therefore, it can be considered best for prediction and used to interpolate AGB through kriging. Compared to the geo-statistical technique, the remote sensing-based models performed relatively well. Regarding potential sites for REDD+ implementation, temporal analysis of Landsat images showed a decrease in forest area from 8896.23 ha in 1988 to 7692.03 ha in 2018. Therefore, this study concludes that the state-of-the-art open-source sensor, the Sentinel-2 data, has significant potential for forest biomass and carbon stock estimation and can be used for robust regional AGB estimation with acceptable accuracy and frequent availability. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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18 pages, 6831 KiB

Open AccessArticle

Evaluation of Plant Growth and Potential of Carbon Storage in the Restored Mangrove of an Abandoned Pond in Lubuk Kertang, North Sumatra, Indonesia

by Rizka Amelia, Mohammad Basyuni, Alfinsyahri Alfinsyahri, Nurdin Sulistiyono, Bejo Slamet, Yuntha Bimantara, Salma Safrina Hashilah Harahap, Mikrajni Harahap, Insar Maulid Harahap, Shofiyah Sabilah Al Mustaniroh, Sigit D. Sasmito and Virni Budi Arifanti

Forests 2023, 14(1), 158; https://doi.org/10.3390/f14010158 - 15 Jan 2023

Cited by 10 | Viewed by 3596

Abstract

Mangrove forest in Lubuk Kertang Village, West Brandan sub-district has been converted around 20 ha annually (1996–2016) into various non-forest land use. Rehabilitation can be a solution to restore the condition of the ecosystem so that it can resume its ecological and economic functions. This paper discusses the evaluation of mangrove rehabilitation carried out by planting 6000 propagules in December 2015 and 5000 seedlings in May 2016 with Rhizophora apiculata species in abandoned ponds. Monitoring was carried out every 6 months from 2016 to 2022. In the restored area, 11 true mangrove species and 3 associated mangrove species were found. The percentage of plants that survived after seven years was 69.42% for planting using propagules and 86.38% for planting with seedlings. The total biomass carbon stocks stored by 7-year-old plants using propagules was 51.18 Mg ha⁻¹, while the carbon stored by planting using seedlings was 56.79 Mg ha⁻¹. Soil carbon stocks at the planted site with propagules were 506.89 ± 250.74 MgC ha⁻¹, and at the planted site with seedlings were 461.85 ± 102.23 MgC ha⁻¹. The total ecosystem carbon stocks (including aboveground carbon) in the planted site using propagules were 558.07 MgC ha⁻¹, while planting using seedlings were 518.64 MgC ha⁻¹. The dataset and findings on the carbon storage evaluation of mangrove rehabilitation will be useful for blue carbon research community and policymakers in the context of the climate change mitigation strategy for Indonesia. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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20 pages, 6037 KiB

Open AccessArticle

Mapping Mangrove Above-Ground Carbon Using Multi-Source Remote Sensing Data and Machine Learning Approach in Loh Buaya, Komodo National Park, Indonesia

by Seftiawan Samsu Rijal, Tien Dat Pham, Salma Noer’Aulia, Muhammad Ikbal Putera and Neil Saintilan

Forests 2023, 14(1), 94; https://doi.org/10.3390/f14010094 - 4 Jan 2023

Cited by 12 | Viewed by 5138

Abstract

Mangrove forests provide numerous valuable ecosystem services and can sequester a large volume of carbon that can help mitigate climate change impacts. Modeling mangrove carbon with robust and valid approaches is crucial to better understanding existing conditions. The study aims to estimate mangrove Above-Ground Carbon (AGC) at Loh Buaya located in the Komodo National Park (Indonesia) using novel Extreme Gradient Boosting (XGB) and Genetic Algorithm (GA) analyses integrating multiple sources of remote sensing (optical, Synthetic Aperture Radar (SAR), and Digital Elevation Model (DEM)) data. Several steps were conducted to assess the model’s accuracy, starting with a field survey of 50 sampling plots, processing the images, selecting the variables, and examining the appropriate machine learning (ML) models. The effectiveness of the proposed XGB-GA was assessed via comparison with other well-known ML techniques, i.e., the Random Forest (RF) and the Support Vector Machine (SVM) models. Our results show that the hybrid XGB-GA model yielded the best results (R² = 0.857 in the training and R² = 0.758 in the testing phase). The proposed hybrid model optimized by the GA consisted of six spectral bands and five vegetation indices generated from Sentinel 2B together with a national DEM that had an RMSE = 15.40 Mg C ha⁻¹ and outperformed other ML models for quantifying mangrove AGC. The XGB-GA model estimated mangrove AGC ranging from 2.52 to 123.89 Mg C ha⁻¹ (with an average of 57.16 Mg C ha⁻¹). Our findings contribute an innovative method, which is fast and reliable using open-source data and software. Multisource remotely sensed data combined with advanced machine learning techniques can potentially be used to estimate AGC in tropical mangrove ecosystems worldwide. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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11 pages, 4125 KiB

Open AccessArticle

Maintaining Carbon Storage Does Not Reduce Fish Production from Mangrove-Fish Pond System: A Case Study in Coastal Area of Subang District, West Java, Indonesia

by Elham Sumarga, Tati Suryati Syamsudin, Sheila Pertiwi Rahman, Azzahra Ramadhanti Kurnia Putri, Velia, Alfiazka Anargha Aldi and Mohammad Basyuni

Forests 2022, 13(8), 1308; https://doi.org/10.3390/f13081308 - 16 Aug 2022

Cited by 5 | Viewed by 2323

Abstract

Deforestation and degradation of mangrove forests can be categorized as key environmental problems in Indonesia. These problems are majorly driven by overexploitation and the conversion of mangroves into brackish water aquaculture areas. One of the most common aquaculture systems traditionally developed in the coastal areas is the mangrove-fish pond system that combines fish production with existing trees. This study aims to analyze the environmental and economic aspects of mangrove-fish pond aquaculture in different levels of mangrove cover in the coastal area of Subang District, West Java, Indonesia. The spatial analysis method was used to analyze mangrove distribution and identify the current coverage in the aquaculture area. The economic aspect was analyzed, based on the costs and revenue from fish production, while the environmental aspect was represented by carbon storage, which is among the crucial mangrove ecosystem services. This study estimated carbon storage in the four-carbon pools: above- and below-ground biomass, deadwood, and litterfall. Based on the combination of visual interpretation of Sentinel 2A satellite images and field observations, approximately 667 ha of mangrove-fish pond was identified. This study found that there were no significant differences in fish production and net income from mangrove-fish pond aquaculture at various levels of mangrove cover. Meanwhile, the ponds with high mangrove cover stored higher carbon than those with medium and low mangrove covers. This indicates that maintaining carbon storage does not reduce fish production from mangrove-fish pond aquaculture. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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11 pages, 2618 KiB

Open AccessArticle

Conservation of Soil Organic Carbon in the National Park Santuario de Fauna y Flora Iguaque, Boyacá-Colombia

by Hernán J. Andrade, Milena A. Segura and Diana S. Canal-Daza

Forests 2022, 13(8), 1275; https://doi.org/10.3390/f13081275 - 12 Aug 2022

Cited by 2 | Viewed by 1814

Abstract

Protected areas are important zones for the conservation of strategic ecosystems that provide environmental services to human populations. The Santuario de Fauna y Flora Iguaque (SFFI) (Boyacá, Colombia) preserves an important area of páramos and andean high-land forests that offer water and other services. Soil organic carbon (SOC) was estimated at a depth of 0–30 cm in the four dominant land uses: (1) natural grasslands prevailingly without trees and shrubs (NSWT), (2) broad-leaved forests with continuous canopy, not on mire (BFCC), (3) open heathlands and moorlands (OMH), and (4) dense heathlands and moorlands (DMH). This classification is based on Corine Land Cover, adapted for Colombia. Land uses did not differ significantly (p > 0.05) in the SOC stock, with values of 139.0, 131.1; 101.1; and 83.0 Mg C/ha in OMH, BFCC, NSWT, and DMH, respectively. In total, SFFI retains 593 Gg C in that soil layer. Projections of effects caused by potential land use changes show that up to 461.0 Gg CO₂ could be transferred to the atmosphere if this conservation area is not preserved. SFFI, due to its conservation strategies, allows storing significant amounts of atmospheric carbon and becomes an effective strategy of climate change mitigation. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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15 pages, 1935 KiB

Open AccessArticle

Allometric Equations for the Biomass Estimation of Calophyllum inophyllum L. in Java, Indonesia

by Tyas Mutiara Basuki, Budi Leksono, Himlal Baral, Sarah Andini, Novi Sari Wahyuni, Yustina Artati, Eunho Choi, Seongmin Shin, Raehyun Kim, A-Ram Yang, Yusuf B. Samsudin and Eritrina Windyarini

Forests 2022, 13(7), 1057; https://doi.org/10.3390/f13071057 - 5 Jul 2022

Cited by 6 | Viewed by 3388

Abstract

Reliable data on CO₂ quantification is increasingly important to quantify the climate benefits of forest landscape restoration and international commitments, such as the Warsaw REDD+ Framework and Nationally Determined Contributions under the Paris Agreement. Calophyllum inophyllum L. (nyamplung as a local name or tamanu tree for the commercial name) is an increasingly popular tree species in forest landscape restoration and bioenergy production for a variety of reasons. In this paper, we present allometric equations for aboveground biomass (AGB), belowground biomass (BGB), and total above- and belowground biomass (TABGB) predictions of C. inophyllum L. Data collection was carried out twice (2017 and 2021) from 40 trees in Java, Indonesia. Allometric equations using the natural logarithm of diameter at breast height (lnDBH) and ln height (lnH) for biomass prediction qualified the model’s fit with statistical significance at 95% of the confidence interval for AGB, BGB, and TABGB predictions. The results showed that the linear models using both lnDBH and lnH were well fit and accurate. However, the model with lnDBH is more precise than the model using lnH. Using lnDBH as a predictor, the R² values were 0.923, 0.945, and 0.932, and MAPE were 24.7, 37.0, and 25.8 for AGB, BGB, and TABGB, respectively. Using lnH as a predictor, the R² values were 0.887, 0.918, and 0.898 and MAPE were 37.4, 49.0, and 39.8 for AGB, BGB, and TABGB, respectively. Consequently, the driven allometric equations can help accurate biomass quantification for carbon-trading schemes of C. inophyllum L. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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20 pages, 77111 KiB

Open AccessArticle

Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models

by Xiaoyong Zhang, Yuman Sun, Weiwei Jia, Fan Wang, Haotian Guo and Ziqi Ao

Forests 2022, 13(2), 346; https://doi.org/10.3390/f13020346 - 18 Feb 2022

Cited by 9 | Viewed by 2413

Abstract

Background and Objectives: It is important to understand the temporal and spatial distributions of standing wood carbon storage in forests to maintain ecological balance and forest dynamics. Such information can provide technical and data support for promoting ecological construction, formulating different afforestation policies, and implementing forest management strategies. Long-term series of Landsat 5 (Thematic Mapper, TM) and Landsat 8 (Operational Land Imager, OLI) remote sensing images and digital elevation models (DEM), as well as multiphase survey data, provide new opportunities for research on the temporal and spatial distributions of standing wood carbon storage in forests. Methods: The extracted remote sensing factors, terrain factors, and forest stand factors were analyzed with stepwise regression in relation to standing wood carbon storage to identify significant influential factors, build a global ordinary least squares (OLS) model and a linear mixed model (LMM), and construct a local geographically weighted regression (GWR), multiscale geographically weighted regression model (MGWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR). Model evaluation indicators were used to calculate residual Moran’s I values, and the optimal model was selected to explore the spatiotemporal dynamics of standing wood carbon storage in the Liangshui Nature Reserve. Results: Remote sensing factors, topographic factors (Slope), and stand factors (Age and DBH) were significantly correlated with standing wood carbon storage, and the constructed global models exhibited fitting effects inferior to those of the established local models. LMM is also used as a global model to add random effects on the basis of OLS, and R² is increased to 0.52 compared with OLS. The local models based on geographically weighted regression, namely, GWR, MGWR, TWR, and GTWR, all have good performance. Compared with OLS, the R² is increased to 0.572, 0.589, 0.643, and 0.734, and the fitting effect of GTWR is the best. GTWR can overcome spatial autocorrelation and temporal autocorrelation problems, with a higher R² (0.734) and a more ideal model residual than other models. This study develops a model for carbon storage (CS) considering various influential factors in the Liangshui area and provides a possible solution for the estimation of long-term carbon storage distribution. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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15 pages, 2053 KiB

Open AccessReview

Biomass Production and Carbon Sequestration Potential of Different Agroforestry Systems in India: A Critical Review

by Pankaj Panwar, Devagiri G. Mahalingappa, Rajesh Kaushal, Daulat Ram Bhardwaj, Sumit Chakravarty, Gopal Shukla, Narender Singh Thakur, Sangram Bhanudas Chavan, Sharmistha Pal, Baliram G. Nayak, Hareesh T. Srinivasaiah, Ravikumar Dharmaraj, Naveen Veerabhadraswamy, Khulakpam Apshahana, Chellackan Perinba Suresh, Dhirender Kumar, Prashant Sharma, Vijaysinha Kakade, Mavinakoppa S. Nagaraja, Manendra Singh, Subrata Das, Mendup Tamang, Kanchan, Abhilash Dutta Roy and Trishala Gurung Show full author list Hide full author list

Forests 2022, 13(8), 1274; https://doi.org/10.3390/f13081274 - 12 Aug 2022

Cited by 30 | Viewed by 8549

Abstract

Agroforestry systems (AFS) and practices followed in India are highly diverse due to varied climatic conditions ranging from temperate to humid tropics. The estimated area under AFS in India is 13.75 million ha with the highest concentration being in the states of Uttar Pradesh (1.86 million ha), followed by Maharashtra (1.61 million ha), Rajasthan (1.55 million ha) and Andhra Pradesh (1.17 million ha). There are many forms of agroforestry practice in India ranging from intensified simple systems of monoculture, such as block plantations and boundary planting, to far more diverse and complex systems, such as home gardens. As a result, the biomass production and carbon sequestration potential of AFS are highly variable across different agro-climatic zones of India. Studies pertaining to the assessment of biomass and carbon storage in different agroforestry systems in the Indian sub-continent are scanty and most of these studies have reported region and system specific carbon stocks. However, while biomass and carbon stock data from different AFS at national scale has been scanty hitherto, such information is essential for national accounting, reporting of C sinks and sources, as well as for realizing the benefits of carbon credit to farmers engaged in tree-based production activities. Therefore, the objective of this study was to collate and synthesize the existing information on biomass carbon and SOC stocks associated with agroforestry practices across agro-climatic zones of India. The results revealed considerable variation in biomass and carbon stocks among AFS, as well as between different agro-climatic zones. Higher total biomass (>200 Mg ha⁻¹) was observed in the humid tropics of India which are prevalent in southern and northeastern regions, while lower total biomass (<50 Mg ha⁻¹) was reported from Indo-Gangetic, western and central India. Total biomass carbon varied in the range of 1.84 to 131 Mg ha⁻¹ in the agrihorticulture systems of western and central India and the coffee agroforests of southern peninsular India. Similarly, soil organic carbon (SOC) ranged between 12.26–170.43 Mg ha⁻¹, with the highest SOC in the coffee agroforests of southern India and the lowest in the agrisilviculture systems of western India. The AFS which recorded relatively higher SOC included plantation crop-based practices of southern, eastern and northeastern India, followed by the agrihorticulture and agrisilviculture systems of the northern Himalayas. The meta-analysis indicated that the growth and nature of different agroforestry tree species is the key factor affecting the carbon storage capacity of an agroforestry system. The baseline data obtained across various regions could be useful for devising policies on carbon trading or financing for agroforestry. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems)

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