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Remote Sensing Monitoring and Assessment of Forest, Grassland, Wetland and Urban Ecosystem

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 4631

Special Issue Editors

Dr. Ronghai Hu

E-Mail Website
Guest Editor
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Interests: vegetation remote sensing; leaf area index; foliage clumping; LiDAR; ecological remote sensing
Dr. Xiaoning Song

E-Mail Website
Guest Editor
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Interests: thermal remote sensing; land surface temperature; surface emissivity; soil moisture; leaf area index; ecological remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Fangcheng Zhou

E-Mail Website
Guest Editor
National Satellite Meteorological Center (National Center for Space Weather), Beijing 100081, China
Interests: retrieval of ecological parameters and assessment of ecological environmental quality based on remote sensing
Dr. Wenchao Han

E-Mail Website
Guest Editor
Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Interests: urban expansion; atmospheric environment; urban heat island; urban environment; urban climate

Special Issue Information

Dear Colleagues,

Ecosystems play a vital role in maintaining ecological balance and sustaining life on our planet. Forests, grasslands, wetlands, and cities are among the most important and vulnerable ecosystems, facing threats from climate change, land-use changes, and human activities. To effectively manage and protect these ecosystems, comprehensive and accurate monitoring and assessment are essential. Remote sensing technologies have emerged as powerful tools for this purpose, enabling researchers and environmental managers to gather large-scale and high-resolution data for ecosystem analysis.

This Special Issue aims to bring together cutting-edge research in the field of remote sensing and its applications in monitoring and assessing forest, grassland, wetland, and urban ecosystems. The primary focus is on the development and utilization of remote sensing techniques, data, and methodologies to understand ecosystem dynamics, assess environmental health, and provide valuable information for sustainable management. Topics of interest include, but are not limited to, the following:

  • The remote sensing retrieval of ecosystem parameters;
  • Advancements in remote sensing technologies for ecosystem monitoring;
  • Assessment of land cover and land-use changes in forest, grassland, wetland, and urban areas;
  • Ecohydrological dynamics and assessment;
  • Climate change impact assessments;
  • Vegetation health and biomass estimation;
  • Wetland mapping and characterization;
  • Ecosystem restoration and management strategies;
  • The integration of remote sensing with other data sources for comprehensive ecosystem analysis;
  • The effects of urbanization on the environment and climate change.

By promoting this Special Issue, we aim to foster collaboration among experts in the field, encourage knowledge exchange, and contribute to the better understanding and preservation of our valuable forest, grassland, wetland, and urban ecosystems.

We invite authors to submit their high-quality contributions to this Special Issue, facilitating the dissemination of innovative research findings and advancements in the realm of remote sensing applied to ecosystem monitoring and assessment. Join us in the endeavor to safeguard these vital ecosystems for current and future generations.

This abstract sets the background for a Special Issue call on the monitoring and assessment of forest, grassland, wetland, and urban ecosystems using remote sensing technologies. It highlights the importance of these ecosystems and the role of remote sensing in their understanding and protection.

Dr. Ronghai Hu
Dr. Xiaoning Song
Dr. Fangcheng Zhou
Dr. Wenchao Han
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 retrieval
  • ecological assessment
  • ecosystem monitoring
  • ecohydrological dynamics
  • ecosystem change
  • vegetation structure and physiology

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

Published Papers (4 papers)

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Research

18 pages, 9716 KiB  
Article
Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery
by Zihao Pan, Hengxing Xiang, Xinying Shi, Ming Wang, Kaishan Song, Dehua Mao and Chunlin Huang
Remote Sens. 2025, 17(2), 292; https://doi.org/10.3390/rs17020292 - 15 Jan 2025
Viewed by 440
Abstract
The extensive peatlands of the Tibetan Plateau (TP) play a vital role in sustaining the global ecological balance. However, the distribution of peatlands across this region and the related environmental factors remain poorly understood. To address this issue, we created a high-resolution (10 [...] Read more.
The extensive peatlands of the Tibetan Plateau (TP) play a vital role in sustaining the global ecological balance. However, the distribution of peatlands across this region and the related environmental factors remain poorly understood. To address this issue, we created a high-resolution (10 m) map for peatland distribution in the TP region using 6146 Sentinel-1 and 23,730 Sentinel-2 images obtained through the Google Earth Engine platform in 2023. We employed a random forest algorithm that integrated spatiotemporal features with field training samples. The overall accuracy of the peatland distribution map produced is high, at 86.33%. According to the classification results, the total area of peatlands on the TP is 57,671.55 km2, and they are predominantly located in the northeast and southwest, particularly in the Zoige Protected Area. The classification primarily relied on the NDVI, NDWI, and RVI, while the DVI and MNDWI were also used in peatland mapping. B11, B12, NDWI, RVI, NDVI, and slope are the most significant features for peatland mapping, while roughness, correlation, entropy, and ASM have relatively slight significance. The methodology and peatland map developed in this work will enhance the conservation and management of peatlands on the TP while informing policy decisions and supporting sustainable development assessments. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring and Assessment of Forest, Grassland, Wetland and Urban Ecosystem)
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28 pages, 16088 KiB  
Article
A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions
by Mirmajid Mousavi, James Kobina Mensah Biney, Barbara Kishchuk, Ali Youssef, Marcos R. C. Cordeiro, Glenn Friesen, Douglas Cattani, Mustapha Namous and Nasem Badreldin
Remote Sens. 2024, 16(24), 4730; https://doi.org/10.3390/rs16244730 - 18 Dec 2024
Viewed by 822
Abstract
Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed [...] Read more.
Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring and Assessment of Forest, Grassland, Wetland and Urban Ecosystem)
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26 pages, 24941 KiB  
Article
Assessment and Dynamic Prediction of Green Space Ecological Service Value in Guangzhou City, China
by Zhefan Li, Zhaokang Zhou, Zhenhua Liu, Jiahe Si and Jiaming Ou
Remote Sens. 2024, 16(22), 4180; https://doi.org/10.3390/rs16224180 - 8 Nov 2024
Viewed by 991
Abstract
As an important part of the urban ecosystem, urban green space provides a variety of ecosystem services, including climate regulation, soil conservation, carbon sink and oxygen release, and biodiversity protection. However, existing remote sensing evaluation methods for ecological service value lack the evaluation [...] Read more.
As an important part of the urban ecosystem, urban green space provides a variety of ecosystem services, including climate regulation, soil conservation, carbon sink and oxygen release, and biodiversity protection. However, existing remote sensing evaluation methods for ecological service value lack the evaluation indicators of ecosystem service value for Guangzhou, China, and the evaluation method depends on the land cover type. Based on remote sensing technology and random forest algorithm, this study addresses these gaps by integrating remote sensing technology with a random forest algorithm to enhance the accuracy and rationality of ESV assessments. Focusing on Guangzhou, China, we improved the ecological service value evaluation system and conducted dynamic predictions based on land-use change scenarios. Our results indicate that the total ESV of Guangzhou’s green space was USD 7.323 billion in 2020, with a projected decline to USD 6.496 billion by 2030, representing a 12.37% reduction due to urbanization-driven land-use changes. This research highlights the noticeable role of green spaces in urban sustainability and provides robust, data-driven insights for policymakers to design more effective green space protection and management strategies. The improved assessment framework offers a novel approach for accurately quantifying urban ecosystem services and predicting future trends. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring and Assessment of Forest, Grassland, Wetland and Urban Ecosystem)
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24 pages, 11964 KiB  
Article
Projecting Response of Ecological Vulnerability to Future Climate Change and Human Policies in the Yellow River Basin, China
by Xiaoyuan Zhang, Shudong Wang, Kai Liu, Xiankai Huang, Jinlian Shi and Xueke Li
Remote Sens. 2024, 16(18), 3410; https://doi.org/10.3390/rs16183410 - 13 Sep 2024
Viewed by 1304
Abstract
Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV [...] Read more.
Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV in the Yellow River Basin (YRB), a climate-sensitive and ecologically fragile area, by integrating climate change, land management, and ecological protection policies under various scenarios. To achieve this, we developed an EV assessment framework combining a scenario weight matrix, Markov chain, Patch-generating Land Use Simulation model, and exposure–sensitivity–adaptation. We further explored the spatiotemporal variations of EV and their potential socioeconomic impacts at the watershed scale. Our results show significant geospatial variations in future EV under the three scenarios, with the northern region of the upstream area being the most severely affected. Under the ecological conservation management scenario and historical trend scenario, the ecological environment of the basin improves, with a decrease in very high vulnerability areas by 4.45% and 3.08%, respectively, due to the protection and restoration of ecological land. Conversely, under the urban development and construction scenario, intensified climate change and increased land use artificialization exacerbate EV, with medium and high vulnerability areas increasing by 1.86% and 7.78%, respectively. The population in high and very high vulnerability areas is projected to constitute 32.75–33.68% and 34.59–39.21% of the YRB’s total population in 2040 and 2060, respectively, and may continue to grow. Overall, our scenario analysis effectively demonstrates the positive impact of ecological protection on reducing EV and the negative impact of urban expansion and economic development on increasing EV. Our work offers new insights into land resource allocation and the development of ecological restoration policies. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring and Assessment of Forest, Grassland, Wetland and Urban Ecosystem)
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