Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges

Topic Information

Dear Colleagues,

Forests play a vital role in sustainable development, ensuring human well-being, a healthy environment, and economic development, while their ecosystems play an important role in supporting a green economy; mitigating climate change; protecting biodiversity; improving water quality; combating desertification; supporting global carbon, nutrient, and water cycles; and temperature regulation in human living environments. As the main body of terrestrial ecosystems, forests function as massive carbon sinks and play an important role in adaptation to global climate change. Remote-sensing technology has evolved rapidly over the past few decades, with new sensors and methods contributing to the generation of updated and highly detailed information for supporting forest management and planning. Remote-sensing technology can be used to estimate the carbon storage of forests’ aboveground biomass and soil and coupled with ecosystem models to simulate the carbon cycle of forest ecosystems. Forests are important contributors to terrestrial biomass productivity and carbon storage, and provide many ecosystem services that benefit humans, including climate regulation, the production of forest products, the provision of important raw materials, and the conservation of biodiversity. Given the scale and size of plantations, forest systems are threatened by traditional monitoring methods, which highlights the need to identify other methods to aid the management of forest systems.

This Topic encourages empirical and theoretical papers from the fields of environmental, geographical, and remote-sensing science, aiming to provide new technologies and methods to support the sustainable use of forest ecosystem services. Papers with interdisciplinary approaches are especially welcome, including, but not limited to, the following:

• Advances in ecosystem modeling for estimating forest variables and addressing forest-mapping issues based on the integration of remotely sensed and in situ data;
• Recent advances in optical remote sensing for the assessment of carbon storage and sequestration, and trends in biodiversity in forest ecosystems;
• The environmental impacts of forestry and related industries in relation to supply chains;
• Integrated assessment tools, such as environmental impact assessments and sustainability impact assessments, with which to quantify the impact of alternative forest management practices;
• Papers on current and relevant issues in monitoring forest inventories and management using remote sensing or remote-sensing-based products.

Prof. Dr. Huaqiang Du
Prof. Dr. Dengsheng Lu
Prof. Dr. Huaguo Huang
Prof. Dr. Mingshi Li
Dr. Yanjun Su
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Forests forests	2.4	4.4	2010	16.9 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit

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Published Papers (16 papers)

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18 pages, 21434 KiB  

Open AccessArticle

Improved Branch Volume Prediction of Multi-Stemmed Shrubs: Implications in Shrub Volume Inventory and Fuel Characterization

by Chuan Yuan, Jiayu Zhou, Wenhua Xiang, Nan Lu, Yanting Hu, Li Guo, Yi Wang, Weiliang Chen, Guangyao Gao, Qiang Tang, Sheng Wang, Xian Cheng, Jie Gao and Xiaohua Wei

Forests 2024, 15(8), 1437; https://doi.org/10.3390/f15081437 - 15 Aug 2024

Viewed by 650

Abstract

Accurately estimating the volume of woody vegetation is critical for assessing fuel characteristics and associated wildfire risks in shrublands. However, few studies have investigated the branch volume of multi-stemmed shrubs, a dominant life form in wildfire-prone drylands. This study predicts branch volume using [...] Read more.

Accurately estimating the volume of woody vegetation is critical for assessing fuel characteristics and associated wildfire risks in shrublands. However, few studies have investigated the branch volume of multi-stemmed shrubs, a dominant life form in wildfire-prone drylands. This study predicts branch volume using the inflection point of branch diameter. This inflection point, identified using the “Segmented” package in R, marks the transition from a gradual decrease to a significant reduction in diameter along the stem. The volume of branch segment above this point is calculated as a cone, and below it, a cylinder. We validated this method on various species such as Caragana korshinskii, Salix psammophila, and Vitex negundo. Good estimations were achieved with an average 19.2% bias relative to reference branch volumes, outperforming conventional methods that subjectively treated the whole branch as either a cylinder (96.9% bias) or a cone (−34.4% bias). We tallied branches by basal diameter and provided inventories for easily locating the inflection point, as well as using two-way branch volume tables for rapid volume predictions in shrubland. In general, we developed an effective method for estimating branch volumes of multi-stemmed shrubs, enabling its application to larger-scale shrubland volumetric prediction. This advancement supports wildfire hazard assessment and informs decision-making in fuel treatments. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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22 pages, 5214 KiB  

Open AccessArticle

The Impact of Climate Change and Human Activities on the Spatial and Temporal Variations of Vegetation NPP in the Hilly-Plain Region of Shandong Province, China

by Yangyang Wu, Jinli Yang, Siliang Li, Honggang Yu, Guangjie Luo, Xiaodong Yang, Fujun Yue, Chunzi Guo, Ying Zhang, Lei Gu, Haobiao Wu and Panli Yuan

Forests 2024, 15(6), 898; https://doi.org/10.3390/f15060898 - 22 May 2024

Cited by 1 | Viewed by 2081

Abstract

Studying the spatio-temporal changes and driving mechanisms of vegetation’s net primary productivity (NPP) is critical for achieving green and low-carbon development, as well as the carbon peaking and carbon neutrality goals. This article employs various analytical approaches, including the Carnegie–Ames–Stanford approach (CASA) model, [...] Read more.

Studying the spatio-temporal changes and driving mechanisms of vegetation’s net primary productivity (NPP) is critical for achieving green and low-carbon development, as well as the carbon peaking and carbon neutrality goals. This article employs various analytical approaches, including the Carnegie–Ames–Stanford approach (CASA) model, Theil–Sen median estimator, coefficient of variation, Hurst index, and land-use and land-cover change (LUCC) transition matrix, to conduct a thorough study of NPP variations in the Shandong Hilly Plain (SDHP) region. Furthermore, the geographic detector method was used to investigate the synergistic effects of meteorological changes and human activities on NPP in this region. Between 2000 and 2020, the vegetation NPP in the SDHP exhibited an average increase rate of 0.537 g C·m⁻²·a⁻¹. However, the fluctuation in mean annual NPP, ranging from 203 to 230 g C·m⁻²·a⁻¹, underscores an uneven growth pattern. Significant regional disparities are evident in vegetation NPP, gradually ascending from the southeast to the northwest and from the coastal areas to inland regions. The average Hurst index for the entire study area stands at 0.556, indicating an overall sustained growth trend in the time series of SDHP vegetation NPP. The vegetation NPP changes in SDHP can be well explained by climate variables (mean annual temperature, mean annual precipitation) and human activities (LUCC, night light index); of these, LUCC (q = 0.684) has the highest explanatory power on the impact of NPP and is a major influencing factor. This study deepens the understanding of the driving factors and patterns of vegetation’s dynamic response to climate change and human activities in the SDHP region. At the same time, it provides valuable scientific insights for improving ecosystem quality and promoting the carbon peaking and carbon neutrality goals. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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40 pages, 18945 KiB  

Open AccessArticle

Sensitivity of Sentinel-1 Backscatter to Management-Related Disturbances in Temperate Forests

by Sietse van der Woude, Johannes Reiche, Frank Sterck, Gert-Jan Nabuurs, Marleen Vos and Martin Herold

Remote Sens. 2024, 16(9), 1553; https://doi.org/10.3390/rs16091553 - 27 Apr 2024

Viewed by 1174

Abstract

The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed [...] Read more.

The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016–2022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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25 pages, 9535 KiB  

Open AccessArticle

Satellite Image Fusion Airborne LiDAR Point-Clouds-Driven Machine Learning Modeling to Predict the Carbon Stock of Typical Subtropical Plantation in China

by Guangpeng Fan, Binghong Zhang, Jialing Zhou, Ruoyoulan Wang, Qingtao Xu, Xiangquan Zeng, Feng Lu, Weisheng Luo, Huide Cai, Yongguo Wang, Zhihai Dong and Chao Gao

Forests 2024, 15(5), 751; https://doi.org/10.3390/f15050751 - 25 Apr 2024

Viewed by 1304

Abstract

In the current context of carbon neutrality, afforestation is an effective means of absorbing carbon dioxide. Stock can be used not only as an economic value index of forest wood resources but also as an important index of biomass and carbon storage estimation [...] Read more.

In the current context of carbon neutrality, afforestation is an effective means of absorbing carbon dioxide. Stock can be used not only as an economic value index of forest wood resources but also as an important index of biomass and carbon storage estimation in forest emission reduction project evaluation. In this paper, we propose a data-driven machine learning framework and method for predicting plantation stock based on airborne LiDAR + satellite remote sensing, and carried out experimental verification at the site of the National Forest emission reduction project in Southern China. We used step-up regression and random forest (RF) to screen LiDAR and Landsat 8 OLI multispectral indicators suitable for the prediction of plantation stock, and constructed a plantation stock model based on machine learning (support vector machine regression, RF regression). Our method is compared with traditional statistical methods (stepwise regression and partial least squares regression). Through the verification of 57 plantation field survey data, the accuracy of the stand estimation model constructed using the RF method is generally better (ΔR² = 0.01~0.27, ΔRMSE = 1.88~13.77 m³·hm⁻², ΔMAE = 1.17~13.57 m³·hm⁻²). The model evaluation accuracy based on machine learning is higher than that of the traditional statistical method, and the fitting R² is greater than 0.91, while the fitting R² of the traditional statistical method is 0.85. The best fitting models were all support vector regression models. The combination of UAV point clouds and satellite multi-spectral images has the best modeling effect, followed by LiDAR point clouds and Landsat 8. At present, this method is only applicable to artificial forests; further verification is needed for natural forests. In the future, the density and quality of higher clouds could be increased. The validity and accuracy of the method were further verified. This paper provides a method for predicting the accumulation of typical Chinese plantations at the forest farm scale based on the “airborne LiDAR + satellite remote sensing” data-driven machine learning modeling, which has potential application value for the current carbon neutrality goal of the southern plantation forest emission reduction project. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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24 pages, 6140 KiB  

Open AccessArticle

UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests

by Lingjun Zhang, Yinyin Zhao, Chao Chen, Xuejian Li, Fangjie Mao, Lujin Lv, Jiacong Yu, Meixuan Song, Lei Huang, Jinjin Chen, Zhaodong Zheng and Huaqiang Du

Remote Sens. 2024, 16(4), 705; https://doi.org/10.3390/rs16040705 - 17 Feb 2024

Cited by 2 | Viewed by 1565

Abstract

Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within [...] Read more.

Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within forest ecosystems at a regional scale. In this study, we focused on the Moso bamboo forest located in Shanchuan Township, Zhejiang Province, China. The primary objective was to utilize various data sources, namely UAV-LiDAR (UL), Sentinel-2 (ST), and a combination of UAV-LiDAR with Sentinel-2 (UL + ST). Employing the Boruta algorithm, we carefully selected characterization variables for analysis. Our investigation delved into establishing correlations between UAV-LiDAR characterization parameters, Sentinel-2 feature parameters, and the aboveground biomass (AGB) of the Moso bamboo forest. Ground survey data on Moso bamboo forest biomass served as the basis for our analysis. To enhance the accuracy of AGB estimation in the Moso bamboo forest, we employed three distinct modeling techniques: multivariate linear regression (MLR), support vector regression (SVR), and random forest (RF). Through this approach, we aimed to compare the impact of different data sources and modeling methods on the precision of AGB estimation in the studied bamboo forest. This study revealed that (1) the point cloud intensity of UL, the variables of canopy cover (CC), gap fraction (GF), and leaf area index (LAI) reflect the structure of Moso bamboo forests, and the variables indicating the height of the forest stand (AIH1, AIHiq, and Hiq) had a significant effect on the AGB of Moso bamboo forests, significantly impact Moso bamboo forest AGB. Vegetation indices such as DVI and SAVI in ST also exert a considerable effect on Moso bamboo forest AGB. (2) AGB estimation models constructed based on UL consistently demonstrated higher accuracy compared with ST, achieving R² values exceeding 0.7. Regardless of the model used, UL consistently delivered superior accuracy in Moso bamboo forest AGB estimation, with RF achieving the highest precision at R² = 0.88. (3) Integration of ST with UL substantially improved the accuracy of AGB estimation for Moso bamboo forests across all three models. Specifically, using RF, the accuracy of AGB estimation increased by 97.7%, with R² reaching 0.89 and RMSE reduced by 124.4%. As a result, the incorporation of LiDAR data, which reflects the stand structure, has proven to enhance the accuracy of aboveground biomass (AGB) estimation in Moso bamboo forests when combined with multispectral remote sensing data. This integration serves as an effective solution to address the limitations of single optical remote sensing methods, which often suffer from signal saturation, leading to lower accuracy in estimating Moso bamboo forest biomass. This approach offers a novel perspective and opens up new possibilities for improving the precision of Moso bamboo forest biomass estimation through the utilization of multiple remote sensing sources. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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26 pages, 4144 KiB  

Open AccessArticle

The Dynamics and Potential of Carbon Stocks as an Indicator of Sustainable Development for Forest Bioeconomy in Ghana

by Isaac Nyarko, Chukwudi Nwaogu, Bridget E. Diagi and Miroslav Hájek

Forests 2024, 15(2), 256; https://doi.org/10.3390/f15020256 - 29 Jan 2024

Cited by 2 | Viewed by 1851

Abstract

Sustainable forest bioeconomy (SFB), as a multidimensional approach for establishing mutual benefits between forest ecosystems, the environment, the economy, and humans, is a nature-based solution for a promising future. The study aims to evaluate the potential of carbon stocks (Cstocks) and variability for [...] Read more.

Sustainable forest bioeconomy (SFB), as a multidimensional approach for establishing mutual benefits between forest ecosystems, the environment, the economy, and humans, is a nature-based solution for a promising future. The study aims to evaluate the potential of carbon stocks (Cstocks) and variability for SFB. It is hypothesized that the decrease in Cstocks is related to an increase in population and agriculture, which caused a decrease in forest area and growing stock and consequently affected SFB. Primary and secondary data were collected from the field, national, and international databases, and analyzed using some statistical and geospatial software packages including IBM SPSS 29.0, CANOCO 5.0, and ArcGIS 10.5. The results revealed that large forest areas were converted to arable lands between 2000 and 2020. Across the forest zones, the aboveground and belowground Cstocks varied significantly, with the aboveground biomass being higher than the belowground biomass. The main drivers of Cstocks were politics and governance (57%), population growth (50%), soil degradation practices (50%), and socio-cultural beliefs (45%). Cstocks had significant negative correlation with population growth, carbon emissions, forest growing stock, forest loss, and the use of forest for biofuel. Evergreen forest zones (rainforest and moist) had more Cstocks than the moist deciduous and swamp/mangrove forests. The study demonstrated that the variability in Cstocks over the last three decades is attributed to an increase in population and agriculture, but Cstocks variability between the forest-vegetation belts could be better explained by differences in trees abundance than population. The study also revealed that the increase in Cstocks contributed to the realization of many SDGs, especially SDG 1, 2, 3, 6, 7, 11, 12, 13, and 15, which in turn support a sustainable forest bioeconomy. Future study is necessary to evaluate Cstocks in individual tree species, biodiversity, and other forest ecosystem services to promote SFB in the country. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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19 pages, 8464 KiB  

Open AccessArticle

Regional Contribution and Attribution of the Interannual Variation of Net Primary Production in the Yellow River Basin, China

by Yue Cao, Huiwen Li, Yali Liu, Yifan Zhang, Yingkun Jiang, Wenting Dai, Minxia Shen, Xiao Guo, Weining Qi, Lu Li and Jianjun Li

Remote Sens. 2023, 15(21), 5212; https://doi.org/10.3390/rs15215212 - 2 Nov 2023

Cited by 11 | Viewed by 1581

Abstract

Net primary production (NPP) serves as a crucial indicator of the ecosystem’s capacity to capture atmospheric CO₂. Gaining insights into the dynamics of NPP and its driving mechanisms is pivotal for optimizing ecosystem carbon sink resource management. Since the implementation of [...] Read more.

Net primary production (NPP) serves as a crucial indicator of the ecosystem’s capacity to capture atmospheric CO₂. Gaining insights into the dynamics of NPP and its driving mechanisms is pivotal for optimizing ecosystem carbon sink resource management. Since the implementation of the Grain-for-Green Program (GFGP) in 1999, the Yellow River Basin (YRB) has been one of the most significant areas for ecological restoration in China. However, our knowledge regarding the interannual variability (IAV) of NPP and the underlying driving forces in this region remains incomplete. In this study, we utilized a light use efficiency model to assess the spatiotemporal dynamics, IAV, and driving factors of NPP in the YRB during the period from 1999 to 2018. Our findings revealed that the average annual NPP in the YRB approximated 189.81 Tg C. Over the study duration, NPP significantly increased in 79.63% of the basin with an overall increasing rate of 6.76 g C m⁻² yr⁻¹. The most prominent increase was observed in the key GFGP implementation area, predominantly in the semi-humid region. Notably, the middle altitude region (1–1.5 km), semi-humid region, and grassland emerged as the primary contributors to the basin’s total vegetation carbon sequestration. However, it is worth emphasizing that there was substantial IAV in the temporal trends of NPP, with the semi-humid region being the most influential contributor (62.66%) to the overall NPP IAV in the YRB. Further analysis of the driving mechanisms unveiled precipitation as the primary driver of NPP IAV in the YRB with a contribution of 62.9%, followed by temperature (23.07%) and radiation (14.03%). Overall, this study deepened our understanding of the IAV and driving mechanisms of NPP in the YRB under ecological restoration, and provided scientific support for optimizing the management of regional carbon sequestration resources. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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17 pages, 7234 KiB  

Open AccessArticle

Evolution and Multi-Scenario Prediction of Land Use and Carbon Storage in Jiangxi Province

by Yue Huang, Fangting Xie, Zhenjiang Song and Shubin Zhu

Forests 2023, 14(10), 1933; https://doi.org/10.3390/f14101933 - 22 Sep 2023

Cited by 6 | Viewed by 1544

Abstract

In recent years, escalating global warming and frequent extreme weather events have caused carbon emission reduction to become a pressing issue on a global scale. Land use change significantly impacts ecosystem carbon storage and is a crucial factor to consider. This study aimed [...] Read more.

In recent years, escalating global warming and frequent extreme weather events have caused carbon emission reduction to become a pressing issue on a global scale. Land use change significantly impacts ecosystem carbon storage and is a crucial factor to consider. This study aimed to examine the evolutions in land use and their impact on carbon storage in Jiangxi Province, China. Using the coupled PLUS-InVEST model, we analyzed the spatial patterns alterations of both land use and carbon storage from 2000 to 2020 and set four scenarios for 2040. Our findings indicated the following: (1) From 2000 to 2020, the area of cropland, forest, grassland, and unused land declined, whereas the area of water and built-up land increased, with changes mainly occurring in 2010–2020. (2) From 2000 to 2020, due to the land use change, carbon storage in Jiangxi Province demonstrated a decreasing trend, with a total reduction of 2882.99 × 10⁴ t. (3) By 2040, under the dual protection scenario for cropland and ecology, the expansion of built-up land will be most restricted among the four scenarios, and the largest projected carbon storage was foreseen. This suggests that carbon loss can be minimized by focusing on cropland and ecological conservation, especially forests. Our research findings can facilitate policy decisions to balance economic development and environmental protection in Jiangxi Province in the future. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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18 pages, 12491 KiB  

Open AccessArticle

Relationship between CO₂ Fertilization Effects, and Stand Age, Stand Type, and Site Conditions

by Shaojie Bian, Bin Wang, Mingze Li, Xiangqi Kong, Jinning Shi, Yanxi Chen and Wenyi Fan

Remote Sens. 2023, 15(17), 4197; https://doi.org/10.3390/rs15174197 - 26 Aug 2023

Viewed by 1209

Abstract

The CO₂ fertilization effect (CFE) plays a crucial role in the amelioration of climate change. Many physiological and environmental factors, such as stand age, stand type, and site conditions, may affect the extent of the CFE. However, the relationship between the CFE [...] Read more.

The CO₂ fertilization effect (CFE) plays a crucial role in the amelioration of climate change. Many physiological and environmental factors, such as stand age, stand type, and site conditions, may affect the extent of the CFE. However, the relationship between the CFE and these factors remains elusive. In this study, we used the emerging gross primary production (GPP) remote sensing products, with GPP predicted using eddy covariance–light use efficiency models (EC-LUE GPP) based on satellite near-infrared reflectance of vegetation (NIRv GPP) and assessed with a random forest model to explore the CFE trends with stand age in a coniferous forest and a broad-leaved forest in Heilongjiang Province, China. We additionally compared the differences among the CFEs under different site conditions. The CFEs in coniferous forests and broad-leaved forests both showed a rapid increase in stands of 10 to 20 years of age, followed by a decline after reaching a maximum, with the rate of decline reducing with age. Eventually, CFE remained stable in stands near 100 years of age. However, the CFE in coniferous forests exhibited more extended periods of rapid increase and a higher maximum than in broad-leaved forests. Moreover, in this study, we used the site class index (SCI) to grade site conditions. The results demonstrate that the CFE differed significantly under different levels of site conditions, and these differences gradually decreased with age. The site with the highest SCI had fewer environmental restrictions on the CFE, and consequently, the CFE rate of decline was faster. Our results are of significance in understanding the CFE and adapting to future changes in atmospheric CO₂ concentration. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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19 pages, 11651 KiB  

Open AccessArticle

Optimizing the Spatial Structure of Metasequoia Plantation Forest Based on UAV-LiDAR and Backpack-LiDAR

by Chao Chen, Lv Zhou, Xuejian Li, Yinyin Zhao, Jiacong Yu, Lujin Lv and Huaqiang Du

Remote Sens. 2023, 15(16), 4090; https://doi.org/10.3390/rs15164090 - 20 Aug 2023

Cited by 1 | Viewed by 1585

Abstract

Optimizing the spatial structure of forests is important for improving the quality of forest ecosystems. Light detection and ranging (LiDAR) could accurately extract forest spatial structural parameters, which has significant advantages in spatial optimization and resource monitoring. In this study, we used unmanned [...] Read more.

Optimizing the spatial structure of forests is important for improving the quality of forest ecosystems. Light detection and ranging (LiDAR) could accurately extract forest spatial structural parameters, which has significant advantages in spatial optimization and resource monitoring. In this study, we used unmanned aerial vehicle LiDAR (UAV-LiDAR) and backpack-LiDAR to acquire point cloud data of Metasequoia plantation forests from different perspectives. Then the parameters, such as diameter at breast height and tree height, were extracted based on the point cloud data, while the accuracy was verified using ground-truth data. Finally, a single-tree-level thinning tool was developed to optimize the spatial structure of the stand based on multi-objective planning and the Monte Carlo algorithm. The results of the study showed that the accuracy of LiDAR-based extraction was (R² = 0.96, RMSE = 3.09 cm) for diameter at breast height, and the accuracy of R² and RMSE for tree height extraction were 0.85 and 0.92 m, respectively. Thinning improved stand objective function value Q by 25.40%, with the most significant improvement in competition index CI and openness K of 17.65% and 22.22%, respectively, compared to the pre-optimization period. The direct effects of each spatial structure parameter on the objective function values were ranked as follows: openness K (1.18) > aggregation index R (0.67) > competition index CI (0.42) > diameter at breast height size ratio U (0.06). Additionally, the indirect effects were ranked as follows: aggregation index R (0.86) > diameter at breast height size ratio U (0.48) > competition index CI (0.33). The study realized the optimization of stand spatial structure based on double LiDAR data, providing a new reference for forest management and structure optimization. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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27 pages, 11709 KiB  

Open AccessArticle

Analysis of the Income Enhancement Potential of the Terrestrial Carbon Sink in China Based on Remotely Sensed Data

by Jiaying Ying, Jiafei Jiang, Huayi Wang, Yilin Liu, Wei Gong, Boming Liu and Ge Han

Remote Sens. 2023, 15(15), 3849; https://doi.org/10.3390/rs15153849 - 2 Aug 2023

Cited by 2 | Viewed by 1752

Abstract

A key focus of international climate action is achieving a terrestrial carbon sink within the framework of carbon neutrality. For certain regions with vital ecological functions and high poverty rates, the generation of surplus ecological carbon income is crucial for mitigating global inequality. [...] Read more.

A key focus of international climate action is achieving a terrestrial carbon sink within the framework of carbon neutrality. For certain regions with vital ecological functions and high poverty rates, the generation of surplus ecological carbon income is crucial for mitigating global inequality. While the evaluation of the economic benefits of carbon income still faces limitations in terms of inadequacy and inaccuracy, enhancing green carbon poverty alleviation schemes is urgently needed. This project introduces an analysis framework for assessing the land-based ecological carbon sink and poverty alleviation potential based on a per capita ideal carbon sink income evaluation, which compares the regional economic benefits of a carbon sink under different carbon price benchmarks and explores tailored green poverty alleviation strategies. It indicates that the per capita ideal carbon sink income in China exhibits a seasonal variation, ranging from approximately USD 16.50 to USD 261.41 per person per month on average. Its spatial distribution shows a pattern of lower values in the central region and higher values in the north and south, following a “high differentiation, low clustering” distribution pattern. The per capita carbon sink income can reach 30% to 70% of the per capita GDP, with a peak value of USD 19,138.10 per year, meeting the minimum livelihood guarantee for the needs in economically underdeveloped areas. Simultaneously, the per capita carbon sequestration income within the Chinese carbon market is expected to demonstrate a yearly ascending trajectory, with an approximate growth rate of USD 23.6 per individual annually. The southwest, northeast, and north China regions can be prioritized as key areas for carbon market development, facilitating more comprehensive inter-regional and sustainable carbon trading. This study plays a significant role in disclosing the regional ecological function and economic benefits, promoting the use of “carbon neutrality” as a driving force to alleviate global inequality and contributing to global climate action and poverty eradication strategies. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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Supplementary material:
Supplementary File 1 (ZIP, 128 KiB)

19 pages, 5745 KiB  

Open AccessArticle

Canopy-Height and Stand-Age Estimation in Northeast China at Sub-Compartment Level Using Multi-Resource Remote Sensing Data

by Xuebing Guan, Xiguang Yang, Ying Yu, Yan Pan, Hanyuan Dong and Tao Yang

Remote Sens. 2023, 15(15), 3738; https://doi.org/10.3390/rs15153738 - 27 Jul 2023

Cited by 1 | Viewed by 1581

Abstract

Stand age is a significant factor when investigating forest resource management. How to obtain age data at a sub-compartment level on a large regional scale conveniently and in real time has become an urgent scientific challenge in forestry research. In this study, we [...] Read more.

Stand age is a significant factor when investigating forest resource management. How to obtain age data at a sub-compartment level on a large regional scale conveniently and in real time has become an urgent scientific challenge in forestry research. In this study, we established two strategies for stand-age estimation at sub-compartment and pixel levels, specifically object-based and pixel-based approaches. First, the relationship between canopy height and stand age was established based on field measurement data, which was achieved at the Mao’er Mountain Experimental Forest Farm in 2020 and 2021. The stand age was estimated using the relationship between the canopy height, the stand age, and the canopy-height map, which was generated from multi-resource remote sensing data. The results showed that the validation accuracy of the object-based estimation results of the stand age and the canopy height was better than that of the pixel-based estimation results, with a root mean squared error (RMSE) increase of 40.17% and 33.47%, respectively. Then, the estimated stand age was divided into different age classes and compared with the forest inventory data (FID). As a comparison, the object-based estimation results had better consistency with the FID in the region of the broad-leaved forests and the coniferous forests. In addition, the pixel-based estimation results had better accuracy in the mixed forest regions. This study provided a reference for estimating stand age and met the requirements for stand-age data at the pixel and sub-compartment levels for studies involving different forestry applications. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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17 pages, 4352 KiB  

Open AccessArticle

Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery

by Yulong Lv, Ning Han and Huaqiang Du

Remote Sens. 2023, 15(10), 2566; https://doi.org/10.3390/rs15102566 - 14 May 2023

Cited by 12 | Viewed by 2279

Abstract

Remote sensing is an important tool for the quantitative estimation of forest carbon stock. This study presents a multiscale, object-based method for the estimation of aboveground carbon stock in Moso bamboo forests. The method differs from conventional pixel-based approaches and is more suitable [...] Read more.

Remote sensing is an important tool for the quantitative estimation of forest carbon stock. This study presents a multiscale, object-based method for the estimation of aboveground carbon stock in Moso bamboo forests. The method differs from conventional pixel-based approaches and is more suitable for Chinese forest management inventory. This research indicates that the construction of a SPOT-6 multiscale hierarchy with the 30 scale as the optimal segmentation scale achieves accurate information extraction for Moso bamboo forests. The producer’s and user’s accuracy are 88.89% and 86.96%, respectively. A random generalized linear model (RGLM), constructed using the multiscale hierarchy, can accurately estimate carbon storage of the bamboo forest in the study area, with a fitting and test accuracy (R²) of 0.74 and 0.64, respectively. In contrast, pixel-based methods using the RGLM model have a fitting and prediction accuracy of 0.24 and 0.01, respectively; thus, the object-based RGLM is a major improvement. The multiscale object hierarchy correctly analyzed the multiscale correlation and responses of bamboo forest elements to carbon storage. Objects at the 30 scale responded to the microstructure of the bamboo forest and had the strongest correlation between estimated carbon storage and measured values. Objects at the 60 scale did not directly inherit the forest information, so the response to the measured carbon storage of the bamboo forest was the smallest. Objects at the 90 scale serve as super-objects containing the forest feature information and have a significant correlation with the measured carbon storage. Therefore, in this study, a carbon storage estimation model was constructed based on the multiscale characteristics of the bamboo forest so as to analyze correlations and greatly improve the fitting and prediction accuracy of carbon storage. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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24 pages, 5643 KiB  

Open AccessArticle

Wavelet Vegetation Index to Improve the Inversion Accuracy of Leaf V²⁵_cmax of Bamboo Forests

by Keruo Guo, Xuejian Li, Huaqiang Du, Fangjie Mao, Chi Ni, Qi Chen, Yanxin Xu and Zihao Huang

Remote Sens. 2023, 15(9), 2362; https://doi.org/10.3390/rs15092362 - 29 Apr 2023

Cited by 6 | Viewed by 2027

Abstract

Maximum carboxylation rate (V_cmax) is a key parameter to characterize the forest carbon cycle process. Hence, monitoring the V_cmax of different forest types is a hot research topic at home and abroad, and hyperspectral remote sensing is an important technique [...] Read more.

Maximum carboxylation rate (V_cmax) is a key parameter to characterize the forest carbon cycle process. Hence, monitoring the V_cmax of different forest types is a hot research topic at home and abroad, and hyperspectral remote sensing is an important technique for V_cmax inversion. Moso bamboo is a unique forest type with a high carbon sequestration capacity in subtropical regions, but the lack of V_cmax data is a major limitation to accurately modeling carbon cycling processes in moso bamboo forests. Our study area was selected in the moso bamboo forest carbon sink research base in Shanchuan Township, Anji County, Zhejiang Province, China, which has a typical subtropical climate and is widely distributed with moso bamboo forests. In this study, the hyperspectral reflectance and V²⁵_cmax (V_cmax converted to 25 °C) of leaves of newborn moso bamboo (I du bamboo) and 2- to 3-year-old moso bamboo (II du bamboo) were measured at different canopy positions, i.e., the top, middle and bottom, in the moso bamboo forest. Then, we applied a discrete wavelet transform (DWT) to the obtained leaf hyperspectral reflectance to construct the wavelet vegetation index (WVI), analyzed the relationship between the WVI and moso bamboo leaf V²⁵_cmax, and compared the WVI to the existing hyperspectral vegetation index (HVI). The ability of the WVI to characterize the moso bamboo V²⁵_cmax was interpreted. Finally, the partial least squares regression (PLSR) method was used to construct a model to invert the V²⁵_cmax of moso bamboo leaves. We showed the following: (1) There are significant leaf V²⁵_cmax differences between I du and II du bamboo, and there are also significant leaf V²⁵_cmax differences between the top, middle and bottom canopy positions of I du bamboo. (2) Compared to that with the HVI, the detection wavelength of the V²⁵_cmax of the WVI expanded to the shortwave infrared region, and thus the WVI had a higher correlation with the V²⁵_cmax. The absolute value of the correlation coefficient between the V²⁵_cmax of I du bamboo and SR_2148,2188 constructed by cD₁ was 0.75, and the absolute value of the correlation coefficient between the V²⁵_cmax of II du bamboo and DVI_2069,407 constructed by cD₂ was 0.67. The highest absolute value of the correlation coefficient between V²⁵_cmax and WVI at the three different canopy positions was also 13–21% higher than that with the HVI. The longest wavelength used by the WVI was 2441 nm. (3) The validation accuracies of the V²⁵_cmax inversion models constructed with the WVI as a variable were all higher than those of the models constructed with the HVI as a variable for all ages and positions, with the highest R² value of 0.97 and a reduction of 20–60% in the root mean square error (RMSE) value. After the wavelet decomposition of the hyperspectral reflectance of moso bamboo leaves, the low-frequency components contained no noise, and the high-frequency components highlighted the original spectral detail features. The WVI constructed by these components increases the wavelength range of V²⁵_cmax interpretation. Therefore, the V²⁵_cmax retrieval model based on the WVI encompasses different resolutions and levels of spectral characteristics, which can better reflect the changes in bamboo leaves and can provide a new method for the inversion of the V²⁵_cmax of moso bamboo forests based on hyperspectral remote sensing. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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15 pages, 2370 KiB  

Open AccessArticle

Modeling the Effect of Stand Characteristics on Oak Volume Increment in Poland Using Generalized Additive Models

by Hoang Duong Xo Viet, Luiza Tymińska-Czabańska and Jarosław Socha

Forests 2023, 14(1), 123; https://doi.org/10.3390/f14010123 - 10 Jan 2023

Cited by 3 | Viewed by 2024

Abstract

Volume increment is one of the main concerns in forestry practice. The aim of our study was to examine the impact of factors influencing the periodic annual increment of oak. To meet our objective, we used measurement data from the national forest inventory [...] Read more.

Volume increment is one of the main concerns in forestry practice. The aim of our study was to examine the impact of factors influencing the periodic annual increment of oak. To meet our objective, we used measurement data from the national forest inventory in Poland from 2005 to 2019 for oak-dominated stands. Our study used data of 1464 sample plots with dominant oak species (Quercus sessilis Ehrh. ex Schur and Quercus robur L.) measured within the national forest inventory in Poland. We developed models explaining the dependence of the periodic annual volume increment on stand characteristics using the generalized additive model. The generalized additive model allows us to analyze each variable’s effect on the dependent variable, with all other variables fixed. We documented the effect of age, height, basal area, and relative spacing index (RSI) on the periodic annual volume increment (PAIv) of oaks in Poland. The PAIv of oaks decreased gradually as the tree aged. The dependence of the PAIv on stand density was shown through its relationship with the basal area and RSI. The developed model explained about 64.6% of the periodic annual volume increment variance. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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19 pages, 5134 KiB  

Open AccessArticle

Prediction of Urban Forest Aboveground Carbon Using Machine Learning Based on Landsat 8 and Sentinel-2: A Case Study of Shanghai, China

by Huimian Li, Guilian Zhang, Qicheng Zhong, Luqi Xing and Huaqiang Du

Remote Sens. 2023, 15(1), 284; https://doi.org/10.3390/rs15010284 - 3 Jan 2023

Cited by 17 | Viewed by 3575

Abstract

The aboveground carbon storage (AGC) of urban forests is an important indicator reflecting the ecological function of urban forests. It is essential to monitor the AGC of urban forests and analyze their spatiotemporal distributions. Remote sensing is a technical tool that can be [...] Read more.

The aboveground carbon storage (AGC) of urban forests is an important indicator reflecting the ecological function of urban forests. It is essential to monitor the AGC of urban forests and analyze their spatiotemporal distributions. Remote sensing is a technical tool that can be leveraged to accurately monitor forest AGC, whereas machine learning is an important algorithm for the accurate prediction of AGC. Therefore, in this study, single Landsat 8 (L) remote sensing data, single Sentinel-2 (S) remote sensing data, and combined Landsat 8 and Sentinel-2 (L + S) data are used as data sources. Four machine learning methods, support vector regression (SVR), random forest (RF), XGBoost (extreme gradient boosting), and CatBoost (categorical boosting), are used to predict forest AGC based on two phases of forest sample plots in Shanghai. We chose the optimal model to predict the AGC and simulate the spatiotemporal distribution. The study shows that both machine learning models based on separate Landsat 8 OLI and Sentinel-2 satellite remote sensing data can accurately predict the AGC and spatiotemporal distribution of the Shanghai urban forest. Nevertheless, the accuracy of the combined data (L + S) and CatBoost-integrated AGC models is higher than the others, with fitting and validation accuracy R2 values of 0.99 and 0.70, respectively. The RMSE was also smaller at 0.67 and 6.29 Mg/ha, respectively. The uncertainty of the AGC spatial distribution in the Shanghai urban forest derived from the CatBoost model prediction from the 2016–2019 data was small and consistent with the actual situation. Furthermore, the statistics showed that the AGC of the Shanghai forest increased from 24.90 Mg/ha in 2016 to 25.61 Mg/ha in 2019. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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