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Integration of Remote Sensing and GIS to Forest and Grassland...
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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: closed (26 April 2024) | Viewed by 1204

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. Vega Xu

E-Mail Website
Guest Editor
New Zealand School of Forestry, University of Canterbury, Christchurch 8140, New Zealand
Interests: forestry; GIS; remote sensing; LiDAR; UAV
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

Published Papers (2 papers)

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Research

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
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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)
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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
Viewed by 411
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)
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