Topic Editors

College of Environment and Resources, Zhejiang A&F University, Hangzhou 311300, China
School of Geographic Sciences, Fujian Normal University, No. 8 Shangsan Road, Cangshan District, Fuzhou 350007, China
Forestry College, Beijing Forestry University, No. 35 Qinghua East Road, Beijing 100083, China
Department of Forest Resoruces Management, College of Forestry, Nanjing Forestry University, Nanjing 210037, China
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Science, Beijing 100093, China

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

Abstract submission deadline
31 October 2024
Manuscript submission deadline
31 December 2024
Viewed by
16322

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

  • remote sensing
  • forest productivity
  • carbon dynamics and eco-environmental response
  • forest management

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Forests
forests
2.9 4.5 2010 16.9 Days CHF 2600 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit

Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

  1. Immediately share your ideas ahead of publication and establish your research priority;
  2. Protect your idea from being stolen with this time-stamped preprint article;
  3. Enhance the exposure and impact of your research;
  4. Receive feedback from your peers in advance;
  5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (12 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
24 pages, 6140 KiB  
Article
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
Viewed by 646
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
Show Figures

Figure 1

26 pages, 4144 KiB  
Article
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
Viewed by 879
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
Show Figures

Figure 1

19 pages, 8464 KiB  
Article
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 - 02 Nov 2023
Cited by 2 | Viewed by 972
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
Show Figures

Graphical abstract

17 pages, 7234 KiB  
Article
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 4 | Viewed by 929
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
Show Figures

Figure 1

18 pages, 12491 KiB  
Article
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 723
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
Show Figures

Figure 1

19 pages, 11651 KiB  
Article
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 1013
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
Show Figures

Figure 1

27 pages, 11709 KiB  
Article
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 - 02 Aug 2023
Cited by 2 | Viewed by 1201
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
Show Figures

Graphical abstract

19 pages, 5745 KiB  
Article
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 1036
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
Show Figures

Graphical abstract

17 pages, 4352 KiB  
Article
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 6 | Viewed by 1742
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
Show Figures

Figure 1

24 pages, 5643 KiB  
Article
Wavelet Vegetation Index to Improve the Inversion Accuracy of Leaf V25cmax 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 2 | Viewed by 1472
Abstract
Maximum carboxylation rate (Vcmax) is a key parameter to characterize the forest carbon cycle process. Hence, monitoring the Vcmax 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 (Vcmax) is a key parameter to characterize the forest carbon cycle process. Hence, monitoring the Vcmax of different forest types is a hot research topic at home and abroad, and hyperspectral remote sensing is an important technique for Vcmax inversion. Moso bamboo is a unique forest type with a high carbon sequestration capacity in subtropical regions, but the lack of Vcmax 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 V25cmax (Vcmax 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 V25cmax, and compared the WVI to the existing hyperspectral vegetation index (HVI). The ability of the WVI to characterize the moso bamboo V25cmax was interpreted. Finally, the partial least squares regression (PLSR) method was used to construct a model to invert the V25cmax of moso bamboo leaves. We showed the following: (1) There are significant leaf V25cmax differences between I du and II du bamboo, and there are also significant leaf V25cmax 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 V25cmax of the WVI expanded to the shortwave infrared region, and thus the WVI had a higher correlation with the V25cmax. The absolute value of the correlation coefficient between the V25cmax of I du bamboo and SR2148,2188 constructed by cD1 was 0.75, and the absolute value of the correlation coefficient between the V25cmax of II du bamboo and DVI2069,407 constructed by cD2 was 0.67. The highest absolute value of the correlation coefficient between V25cmax 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 V25cmax 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 V25cmax interpretation. Therefore, the V25cmax 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 V25cmax of moso bamboo forests based on hyperspectral remote sensing. Full article
Show Figures

Figure 1

15 pages, 2370 KiB  
Article
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 1 | Viewed by 1533
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
Show Figures

Figure 1

19 pages, 5134 KiB  
Article
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 - 03 Jan 2023
Cited by 8 | Viewed by 2654
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
Show Figures

Figure 1

Back to TopTop