Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Integration of Remote Sensing and GIS to Forest and Grassland...
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 8328

Special Issue Editors

Prof. Dr. Xiuchun Yang

E-Mail Website
Guest Editor
School of Grassland Science, Beijing Forestry University, Beijing 100083, China
Interests: remote sensing monitoring grassland vegetation structure and function changes; monitoring grassland resources quality; assessment of grassland ecosystem degradation and health
Dr. Francois Girard

E-Mail Website
Guest Editor
Department of Geography, Université de Montréal, Montréal, QC, Canada
Interests: plant ecology; forest biogeography; geographic information systems and their applications; modelling and statistics; dendro-ecology and dendro-climatology
Special Issues, Collections and Topics in MDPI journals
Dr. Cong (Vega) Xu

E-Mail Website
Guest Editor
New Zealand School of Forestry, University of Canterbury, Christchurch 8140, New Zealand
Interests: forestry; remote sensing; LiDAR; optimal imaging; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yungang Cao

E-Mail Website
Guest Editor
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: remote sensing of ecosystem and environment; spatial-temporal-spectral information fusion

Special Issue Information

Dear Colleagues,

Forests and grasslands are two of our planet's most vital ecosystems, offering a multitude of critical ecosystem services that underpin environmental health and human well-being. These services include erosion control, climate regulation, nutrient cycling, raw material provision, forage production, habitat for diverse species, and recreational opportunities. Under the combined effects of natural factors and human disturbance, forest and grassland ecosystems are constantly changing. With the development of remote sensing and GIS technology, the efficiency, level, and scientific decision-making processes of forest and grassland ecosystem monitoring have been greatly improved. Effectively monitoring and understanding these ecosystems is essential for informed decision making and conservation efforts. This Special Issue focuses on the "Integration of Remote Sensing and Geographic Information Systems (GIS) for Monitoring Forest and Grassland Ecosystems." It aims to explore the latest advancements in these technologies and their applications in managing and preserving these invaluable ecosystems.

Our goal is to collect state-of-the-art research, showcasing the innovative use of remote sensing and GIS for monitoring forest and grassland ecosystems. We welcome contributions that investigate various aspects, from monitoring forest and grassland vegetation structures and functions changes, assessing land cover changes, tracking biodiversity, and quantifying carbon sequestration to monitoring wildfire events and improving the sustainability of forest and grassland management practices.

We invite researchers, scientists, and professionals to submit original research papers and review articles that explore the integration of remote sensing and GIS technologies in the monitoring and management of forest and grassland ecosystems. Topics of interest include, but are not limited to, the following:

  • Advanced remote sensing techniques: use of cutting-edge remote sensing technologies, such as hyperspectral, LiDAR, and synthetic aperture radar (SAR), for precise ecosystem monitoring.
  • Vegetation dynamic monitoring: monitoring of forest and grassland ecosystem structure and function dynamic changes.
  • Biodiversity assessment: application of remote sensing and GIS in biodiversity assessment, habitat modelling, and conservation efforts.
  • Land cover and land use change: investigations into land cover and land use changes in forest and grassland ecosystems and their environmental consequences.
  • Carbon sequestration: studies on carbon sequestration estimation and its relation to climate change mitigation in these ecosystems.
  • Ecosystem degradation/health and resilience: papers focusing on assessing ecosystem degradation, health and resilience using remote sensing indicators, and its driving mechanism.
  •  Wildfire and disturbance monitoring: research on monitoring wildfires, disturbances, and post-fire recovery in these ecosystems

Prof. Dr. Xiuchun Yang
Dr. Francois Girard
Dr. Vega Xu
Prof. Dr. Yungang Cao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • geographic information systems (GIS)
  • forest ecosystem
  • grassland ecosystem
  • vegetation change
  • biodiversity assessment
  • land cover change
  • carbon sequestration
  • ecosystem degradation
  • ecosystem resilience
  • wildfire monitoring
  • environmental conservation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (8 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

24 pages, 42565 KiB  
Article
Reconstructing a Fine Resolution Landscape of Annual Gross Primary Product (1895–2013) with Tree-Ring Indices
by Hang Li, James H. Speer, Collins C. Malubeni and Emma Wilson
Remote Sens. 2024, 16(19), 3744; https://doi.org/10.3390/rs16193744 - 9 Oct 2024
Viewed by 428
Abstract
Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP [...] Read more.
Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP data source, were no earlier than 1980. We identified a close relationship between the tree-ring index (TRI) and vegetation carbon dioxide uptake (as measured by GPP) and then developed a nested TRI-GPP model to reconstruct spatially explicit GPP values since 1895 from seven tree-ring chronologies. The model performance in both phases was acceptable: We chose general regression neural network regression and random forest regression in Phase 1 (1895–1937) and Phase 2 (1938–1985). With the simulated and real GPP maps, we observed that the GPP for grassland and overall GPP were increasing. The GPP landscape patterns were stable, but in recent years, the GPP’s increasing rate surpassed any other period in the past 130 years. The main local climate driver was the Palmer Drought Severity Index (PDSI), and GPP had a significant positive correlation with PDSI in the growing season (June, July, and August). With the GPP maps derived from the nested TRI-GPP model, we can create fine-scale GPP maps to understand vegetation change and carbon uptake over the past century. Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
►▼ Show Figures

Figure 1

20 pages, 31892 KiB  
Article
Remote Sensing Classification and Mapping of Forest Dominant Tree Species in the Three Gorges Reservoir Area of China Based on Sample Migration and Machine Learning
by Wenbo Zhang, Xiaohuang Liu, Bin Xu, Jiufen Liu, Hongyu Li, Xiaofeng Zhao, Xinping Luo, Ran Wang, Liyuan Xing, Chao Wang and Honghui Zhao
Remote Sens. 2024, 16(14), 2547; https://doi.org/10.3390/rs16142547 - 11 Jul 2024
Viewed by 720
Abstract
The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in [...] Read more.
The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018–2022, achieving high migration accuracy (R2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine. Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
►▼ Show Figures

Figure 1

25 pages, 9883 KiB  
Article
Distinguishing the Multifactorial Impacts on Ecosystem Services under the Long-Term Ecological Restoration in the Gonghe Basin of China
by Hong Jia, Siqi Yang, Lianyou Liu, Rui Wang, Zeshi Li, Hang Li and Jifu Liu
Remote Sens. 2024, 16(13), 2460; https://doi.org/10.3390/rs16132460 - 4 Jul 2024
Viewed by 845
Abstract
The ongoing shifts in climate, coupled with human activities, are leading to significant land desertification; thus, understanding the long-term variations in ecosystem services as well as the driving factors has a significant value for ensuring ecological security in ecologically fragile arid regions. In [...] Read more.
The ongoing shifts in climate, coupled with human activities, are leading to significant land desertification; thus, understanding the long-term variations in ecosystem services as well as the driving factors has a significant value for ensuring ecological security in ecologically fragile arid regions. In this study, we used the RUSLE, RWEQ, CASA, and InVEST models to evaluate five typical ecosystem services (ESs) from 1990 to 2020 in the Gonghe Basin, including soil conservation, sand fixation, carbon sequestration, water yield, and habitat quality. Then, we analyzed the trade-offs between ESs and proposed scientific indications. Finally, we identified the driving mechanisms of ES spatiotemporal variations. The results showed that (1) the ecosystem services in the Gonghe Basin have, overall, improved over the past 30 years. Soil conservation, sand fixation, carbon sequestration, and water yield showed upward trends, while habitat quality showed a downward trend. (2) The relationships between ESs in the Gonghe Basin were characterized by strong synergies and weak trade-offs, with significant spatial heterogeneity in terms of the trade-off intensity. In addition, the implementation of ecological engineering may strengthen the intensity of the trade-offs. (3) Among all the factors (temperature, precipitation, wind speed, NDVI, land use type, slope, DEM and soil type) that affected ESs, NDVI had the greatest impact, and the explanatory power was 49%, followed by soil type. The explanatory power of the interactions between each factor was higher than that of a single factor, and the interaction between NDVI and soil type had the greatest impact. ESs increased by 12% mainly due to the implementation of ecological engineering projects and natural factors. The most suitable area for ESs was the southeastern edge of the Gonghe Basin. Our study will enrich the understanding of the mechanisms of ecosystem services in drylands and provide a scientific basis for the future implementation of ecological engineering on the Qinghai Tibet Plateau. Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
►▼ Show Figures

Figure 1

23 pages, 16364 KiB  
Article
Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network
by Fei Xing, Ru An, Xulin Guo and Xiaoji Shen
Remote Sens. 2024, 16(9), 1648; https://doi.org/10.3390/rs16091648 - 6 May 2024
Cited by 1 | Viewed by 985
Abstract
Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a [...] Read more.
Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a large scale is required for their control and management. However, the cooccurrence of INWS and native grass species results in highly heterogeneous grass communities and generates mixed pixels detected by remote sensors, which causes uncertainty in classification. The continuous coverage of INWS at the pixel level has not yet been achieved. In this study, objective 1 was to test the capability of Senginel-2 imagery at estimating continuous INWS cover across complex alpine grasslands over a large scale and objective 2 was to assess the performance of the state-of-the-art convolutional neural network-based regression (CNNR) model in estimating continuous INWS cover. Therefore, a novel CNNR model and a random forest regression (RFR) model were evaluated for estimating INWS continuous cover using Sentinel-2 imagery. INWS continuous cover was estimated directly from Sentinel-2 imagery with an R2 ranging from 0.88 to 0.93 using the CNNR model. The RFR model combined with multiple features had a comparable accuracy, which was slightly lower than that of the CNNR model, with an R2 of approximately 0.85. Twelve green band-, red-edge band-, and near-infrared band-related features had important contributions to the RFR model. Our results demonstrate that the CNNR model performs well when estimating INWS continuous cover directly from Sentinel-2 imagery, and the RFR model combined with multiple features derived from the Sentinel-2 imager can also be used for INWS continuous cover mapping. Sentinel-2 imagery is suitable for mapping continuous INWS cover across complex alpine grasslands over a large scale. Our research provides information for the advanced mapping of the continuous cover of invasive species across complex grassland ecosystems or, more widely, terrestrial ecosystems over large spatial areas using remote sensors such as Sentinel-2. Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
►▼ Show Figures

Figure 1

20 pages, 5296 KiB  
Article
A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices
by Jiahui Fan, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Jia Xu, Ruiyang Yu, Lu Liu, Zijing Xie, Jing Ning and Luna Zhang
Remote Sens. 2024, 16(9), 1539; https://doi.org/10.3390/rs16091539 - 26 Apr 2024
Cited by 1 | Viewed by 1112
Abstract
The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover [...] Read more.
The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features. Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
►▼ Show Figures

Graphical abstract

18 pages, 4482 KiB  
Article
Nature-Based Solutions vs. Human-Induced Approaches for Alpine Grassland Ecosystem: “Climate-Help” Overwhelms “Human Act” to Promote Ecological Restoration in the Three-River-Source Region of Qinghai–Tibet Plateau
by Zhouyuan Li, Qiyu Shen, Wendi Fan, Shikui Dong, Ziying Wang, Yudan Xu, Tianxiao Ma and Yue Cao
Remote Sens. 2024, 16(7), 1156; https://doi.org/10.3390/rs16071156 - 26 Mar 2024
Cited by 2 | Viewed by 916
Abstract
How climate change and human activities drive the evolution of the regional environment and where the quality of ecosystems improve or decline over time have become widespread concerns. In this study, we took the Three-River-Source (TRS) region of the Qinghai–Tibet Plateau as a [...] Read more.
How climate change and human activities drive the evolution of the regional environment and where the quality of ecosystems improve or decline over time have become widespread concerns. In this study, we took the Three-River-Source (TRS) region of the Qinghai–Tibet Plateau as a case, aiming to identify and quantify the contribution of the natural and anthropogenic factors to the ecosystem changes over the past years from 1980 to 2018 using the methods of remote sensing and spatial statistical analysis. Based on the land cover map interpreted by reference to satellite remote sensing imagery data, we defined the Ecological Restoration Area Proportion (ERAP) as the bare land patch decrement to indicate the ecologically restored quantity in space. Assembling the restoration project information, we digitalized and vectorized the ecological Restoration Intensity (RI) including the spatial range and temporal duration. Combining the ERAP and the net primary productivity (NPP), which indicates the quantity and quality of ecosystems, respectively, the ecological asset Index (EAI) was developed and calculated. Having integrated the datasets of the vegetation monitoring, climatic factors, geographical factors, and human activities, we performed multi-variable analysis of the attribution of how the change in the EAI, the NPP, and the EAI have been affected by these factors together. The NPP of the middle and eastern parts of the TRS region has improved the most, as the average growth rate of NPP reached approximately 2.5 kg C/m2/10a. Due to such dynamic pattern, we found that human-induced re-vegetation has made limited contributions in our multi-regression model as the variance explained by the RI merely amounts to 4.4% to 8.8%, while the changes were mostly dependent on the regional temperature and the precipitation which contributed over 45% to the ecological restoration on average. It was summarized that “climate-help” overwhelms “human act” in such alpine grassland ecosystem. The regression results for the different aspects of the ERAP and NPP demonstrated that the ecological restoration project helped most in regard to ecosystem quality improvement rather than the restored ecosystem quantity. Our study has developed a comprehensive assessment methodology that can be reused to account for more ecological asset. The case is an example of an alpine ecosystem in which the success of ecological restoration needs favorable climatic conditions as supporting evidence for the nature-based solution. Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research, Other

17 pages, 781 KiB  
Review
Assessment of Carbon Sink and Carbon Flux in Forest Ecosystems: Instrumentation and the Influence of Seasonal Changes
by Dangui Lu, Yuan Chen, Zhongke Feng and Zhichao Wang
Remote Sens. 2024, 16(13), 2293; https://doi.org/10.3390/rs16132293 - 23 Jun 2024
Viewed by 1214
Abstract
Accurate measurement and estimation of forest carbon sinks and fluxes are essential for developing effective national and global climate strategies aimed at reducing atmospheric carbon concentrations and mitigating climate change. Various errors arise during forest monitoring, especially measurement instability due to seasonal variations, [...] Read more.
Accurate measurement and estimation of forest carbon sinks and fluxes are essential for developing effective national and global climate strategies aimed at reducing atmospheric carbon concentrations and mitigating climate change. Various errors arise during forest monitoring, especially measurement instability due to seasonal variations, which require to be adequately addressed in forest ecosystem research and applications. Seasonal fluctuations in temperature, precipitation, aerosols, and solar radiation can significantly impact the physical observations of mapping equipment or platforms, thereby reducing the data’s accuracy. Here, we review the technologies and equipment used for monitoring forest carbon sinks and carbon fluxes across different remote sensing platforms, including ground-based, airborne, and spaceborne remote sensing. We further investigate the uncertainties introduced by seasonal variations to the observing equipment, compare the strengths and weaknesses of various monitoring technologies, and propose the corresponding solutions and recommendations. We aim to gain a comprehensive understanding of the impact of seasonal variations on the accuracy of forest map data, thereby improving the accuracy of forest carbon sinks and fluxes. Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

14 pages, 7614 KiB  
Technical Note
Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China
by Xiaoxue Li, Juan Wu, Shunfa Lu, Dengqiu Li and Dengsheng Lu
Remote Sens. 2024, 16(12), 2088; https://doi.org/10.3390/rs16122088 - 9 Jun 2024
Viewed by 843
Abstract
Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian [...] Read more.
Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian Province, China, where soil erosion has been a severe problem for a long time, as a case study to explore the method to estimate biomass, including total and aboveground biomass, through the integration of field measurements, handheld laser scanning (HLS), and airborne laser scanning (ALS) data. A stepwise regression model and an allometric equation form model were used to develop biomass estimation models based on Lidar-derived variables at typical areas and at a regional scale. The results indicate that at typical areas, both total and aboveground biomass were best estimated using an allometric equation form model when HLS-derived height and density variables were extracted from a window size of 6 m × 6 m, with the coefficients of determination (R2) of 0.64 and 0.58 and relative root mean square error (rRMSE) of 28.2% and 35.8%, respectively. When connecting HLS-estimated biomass with ALS-derived variables at a regional scale, total and aboveground biomass were effectively predicted with rRMSE values of 17.68% and 17.91%, respectively. The HLS data played an important role in linking field measurements and ALS data. This research provides a valuable method to map Dicranopteris biomass distribution using ALS data when other remotely sensed data cannot effectively estimate the understory vegetation biomass. The estimated biomass spatial pattern will be helpful to understand the role of Dicranopteris in reducing soil erosion and improving the degraded ecosystem. Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-8
Back to TopTop