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Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2715

Special Issue Editors

Prof. Dr. Mingjun Ding

E-Mail Website
Guest Editor
Key Lab of Poyang Lake Wetland and Watershed Research of Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330028, China
Interests: land use and land cover; remote sensing; land resource management
Dr. Lanhui Li

E-Mail Website
Guest Editor
School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Interests: land cover change; human activity; climate change; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Linshan Liu

E-Mail Website
Guest Editor
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: land cover mapping; satellite image processing; spatial analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Haiyan Zhang

E-Mail Website
Guest Editor
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: land cover change; human activity; climate change; remote sensing ecology and evolution
Dr. Shoubao Geng

E-Mail Website
Guest Editor
Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Interests: land use; environmental management; remote sensing; ecosystem services

Special Issue Information

Dear Colleagues,

Land cover change is an ongoing process intertwined with changes in population dynamics, climate, and socio-economic factors. Nearly four-fifths of the global land surface has been altered by direct human activities, significantly impacting land–atmosphere interactions, biodiversity, hydrological processes, the carbon cycle, and therefore human well-being. With the dual influences of climate change and human activities, land cover changes are becoming increasingly complex, emphasizing the importance of studying land use and land cover changes from global to regional scales and their environmental impacts. Satellite remote sensing technology has emerged as a powerful tool for monitoring the dynamics of land cover changes at various scales and exploring the mechanisms behind them. However, current research based on remote sensing still exhibits some discrepancies, and the associated monitoring and analysis methods present challenges.

This Special Issue of Remote Sensing, “Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities,” aims to collect the latest advancements in remote sensing technologies and products for land cover change research and identify the impacts of human activities and climate change using various remote sensing techniques. The main areas include (but are not limited to) the following:

  • Land cover changes in forests, grasslands, and urban areas;
  • Vegetation degradation and biomass;
  • Integrating remote sensing with other data sources for land cover change analysis;
  • The impact of urbanization on environmental and climate change;
  • The impacts of human activities and climate change on land cover change.

Prof. Dr. Mingjun Ding
Dr. Lanhui Li
Dr. Linshan Liu
Dr. Haiyan Zhang
Dr. Shoubao Geng
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

  • land cover change
  • remote sensing
  • climate change
  • human activities
  • human well-being

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

Published Papers (3 papers)

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20 pages, 8335 KiB  
Article
Evaluating the Multidimensional Stability of Regional Ecosystems Using the LandTrendr Algorithm
by Lijuan Li, Jiaqiang Du, Jin Wu, Zhilu Sheng, Xiaoqian Zhu, Zebang Song, Guangqing Zhai and Fangfang Chong
Remote Sens. 2024, 16(20), 3762; https://doi.org/10.3390/rs16203762 - 10 Oct 2024
Viewed by 299
Abstract
Stability is a key characteristic for understanding ecosystem processes and evolution. However, research on the stability of complex ecosystems often faces limitations, such as reliance on single parameters and insufficient representation of continuous changes. This study developed a multidimensional stability assessment system for [...] Read more.
Stability is a key characteristic for understanding ecosystem processes and evolution. However, research on the stability of complex ecosystems often faces limitations, such as reliance on single parameters and insufficient representation of continuous changes. This study developed a multidimensional stability assessment system for regional ecosystems based on disturbances. Focusing on the lower reaches of the Yellow River Basin (LR-YRB), we integrated the remote sensing ecological index (RSEI) with texture structural parameters, and applied the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to analyze the continuous changes in disturbances and recovery from 1986 to 2021, facilitating the quantification and evaluation of resistance, resilience, and temporal stability. The results showed that 72.27% of the pixels experienced 1–9 disturbances, indicating the region’s sensitivity to external factors. The maximum disturbances primarily lasted 2–3 years, with resistance and resilience displaying inverse spatial patterns. Over the 35-year period, 61.01% of the pixels exhibited moderate temporal stability. Approximately 59.83% of the pixels recovered or improved upon returning to pre-disturbance conditions after maximum disturbances, suggesting a strong recovery capability. The correlation among stability dimensions was low and influenced by disturbance intensity, underscoring the necessity for a multidimensional assessment of regional ecosystem stability based on satellite remote sensing. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)
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23 pages, 39394 KiB  
Article
Fine-Scale Mangrove Species Classification Based on UAV Multispectral and Hyperspectral Remote Sensing Using Machine Learning
by Yuanzheng Yang, Zhouju Meng, Jiaxing Zu, Wenhua Cai, Jiali Wang, Hongxin Su and Jian Yang
Remote Sens. 2024, 16(16), 3093; https://doi.org/10.3390/rs16163093 - 22 Aug 2024
Viewed by 1142
Abstract
Mangrove ecosystems play an irreplaceable role in coastal environments by providing essential ecosystem services. Diverse mangrove species have different functions due to their morphological and physiological characteristics. A precise spatial distribution map of mangrove species is therefore crucial for biodiversity maintenance and environmental [...] Read more.
Mangrove ecosystems play an irreplaceable role in coastal environments by providing essential ecosystem services. Diverse mangrove species have different functions due to their morphological and physiological characteristics. A precise spatial distribution map of mangrove species is therefore crucial for biodiversity maintenance and environmental conservation of coastal ecosystems. Traditional satellite data are limited in fine-scale mangrove species classification due to low spatial resolution and less spectral information. This study employed unmanned aerial vehicle (UAV) technology to acquire high-resolution multispectral and hyperspectral mangrove forest imagery in Guangxi, China. We leveraged advanced algorithms, including RFE-RF for feature selection and machine learning models (Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), to achieve mangrove species mapping with high classification accuracy. The study assessed the classification performance of these four machine learning models for two types of image data (UAV multispectral and hyperspectral imagery), respectively. The results demonstrated that hyperspectral imagery had superiority over multispectral data by offering enhanced noise reduction and classification performance. Hyperspectral imagery produced mangrove species classification with overall accuracy (OA) higher than 91% across the four machine learning models. LightGBM achieved the highest OA of 97.15% and kappa coefficient (Kappa) of 0.97 based on hyperspectral imagery. Dimensionality reduction and feature extraction techniques were effectively applied to the UAV data, with vegetation indices proving to be particularly valuable for species classification. The present research underscored the effectiveness of UAV hyperspectral images using machine learning models for fine-scale mangrove species classification. This approach has the potential to significantly improve ecological management and conservation strategies, providing a robust framework for monitoring and safeguarding these essential coastal habitats. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)
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Other

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13 pages, 3497 KiB  
Technical Note
Analysis of Changes in Forest Vegetation Peak Growth Metrics and Driving Factors in a Typical Climatic Transition Zone: A Case Study of the Funiu Mountain, China
by Jiao Tang, Huimin Wang, Nan Cong, Jiaxing Zu and Yuanzheng Yang
Remote Sens. 2024, 16(16), 2921; https://doi.org/10.3390/rs16162921 - 9 Aug 2024
Viewed by 747
Abstract
Phenology and photosynthetic capacity both regulate carbon uptake by vegetation. Previous research investigating the impact of phenology on vegetation productivity has focused predominantly on the start and end of growing seasons (SOS and EOS), leaving the influence of peak phenology metrics—particularly in typical [...] Read more.
Phenology and photosynthetic capacity both regulate carbon uptake by vegetation. Previous research investigating the impact of phenology on vegetation productivity has focused predominantly on the start and end of growing seasons (SOS and EOS), leaving the influence of peak phenology metrics—particularly in typical climatic transition zones—relatively unexplored. Using a 24-year (2000–2023) enhanced vegetation index (EVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS), we extracted and examined the spatiotemporal variation for peak of season (POS) and peak growth (defined as EVImax) of forest vegetation in the Funiu Mountain region, China. In addition to quantifying the factors influencing the peak phenology metrics, the relationship between vegetation productivity and peak phenological metrics (POS and EVImax) was investigated. Our findings reveal that POS and EVImax showed advancement and increase, respectively, negatively and positively correlated with vegetation productivity. This suggested that variations in EVImax and peak phenology both increase vegetation productivity. Our analysis also showed that EVImax was heavily impacted by precipitation, whereas SOS had the greatest effect on POS variation. Our findings highlighted the significance of considering climate variables as well as biological rhythms when examining the global carbon cycle and phenological shifts in response to climate change. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)
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