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Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems II

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 1133

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Special Issue Editors

Dr. Chung-Te Chang

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Guest Editor

Taiwan International Graduate Program (TIGP), Ph.D. Program on Biodiversity, Tunghai University, Taichung, Taiwan
Interests: geoinformatics; land surface phenology; long-term ecological study; biogeochemistry
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Junhu Dai

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Guest Editor

Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: plant and vegetation phenology; vegetation geography; global change and phenology; global change and plant geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vegetation phenology plays an important role in regulating the water cycle, carbon cycle, productivity, etc., which are largely related to region-specific climatic and non-climatic factors. In the context of climate change, the dynamics of local regular climate and large-scale climatic variations, such as the El Niño-Southern Oscillation (ENSO), are expected to become more striking which may have substantial effects on vegetation phenology. In addition, climatic extremes such as storms, flash drought, tropical cyclones and sporadic events, as well as anthropogenic activities, have abruptly altered the development of vegetation across all scales, from regional to global. With the assistance of long-term in situ observations, PhenoCam monitoring networks and multisource remotely-sensed datasets, the variations in vegetation phenology and its associations with regular climate, climatic fluctuations or extremes can be potentially captured and disentangled. Additionally, attempts to understand the impacts of phenological shifts on vegetation structure and ecosystems are also of significance.

For this Special Issue, we invite papers that apply remote sensing and spatial technology to explore the variations in vegetation phenology in relation to climate. For example, the combination of field observations with remote sensing techniques across different scales, relationships between satellite-derived phenology (land surface phenology; LSP), and climate, including regional climate conditions and large-scale atmospheric anomalies, are potential issues. Studies on the effects of phenological variations in landscape on hydrological processes, water resources and biogeochemical cycles and on alterations in LSP along the land-cover gradient and projections of phenology across all scales are also welcome.

Related topics may include, but are not limited to, the following:

The combination and data fusion of in situ plant phenological observation and remotely-sensed data across scales;
Near-surface remote sensing, PhenoCam and data analysis in relation to climate and disturbances;
LSP across various climate regions, vegetation types, landscapes and their controls;
LSP along rural-to-urban gradient;
Variations in LSP on evapotranspiration, storage, runoff, sediments or nutrients in watershed or large scales;
LSP projections.

Dr. Chung-Te Chang
Prof. Dr. Junhu Dai
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

vegetation phenology
regular climate
climatic fluctuation
disturbance
phenocam
multisource remotely sensed data
time-series
water resources
productivity
biogeochemical cycles

Published Papers (2 papers)

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Research

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20 pages, 1059 KiB

Open AccessArticle

Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features

by Li Wen, Tanya Mason, Megan Powell, Joanne Ling, Shawn Ryan, Adam Bernich and Guyo Gufu

Remote Sens. 2024, 16(10), 1786; https://doi.org/10.3390/rs16101786 - 17 May 2024

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Abstract

Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia’s largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems II)

Other

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16 pages, 2971 KiB

Open AccessTechnical Note

Arctic Greening Trends: Change Points in Satellite-Derived Normalized Difference Vegetation Indexes and Their Correlation with Climate Variables over the Last Two Decades

by Minji Seo and Hyun-Cheol Kim

Remote Sens. 2024, 16(7), 1160; https://doi.org/10.3390/rs16071160 - 27 Mar 2024

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Abstract

In this study, we utilized NDVI data from the moderate resolution imaging spectroradiometer (MODIS) alongside climatic variables obtained from a reanalyzed dataset to analyze Arctic greening during the summer months (June–September) of the last two decades. This investigation entailed a detailed analysis of these changes across various temporal scales. The data indicated a continuous trend of Arctic greening, evidenced by a 1.8% per decade increment in the NDVI. Notably, significant change points were identified in June 2012 and September 2013. A comparative assessment of NDVI pre- and post-these inflection points revealed an elongation of the Arctic greening trend. Furthermore, an anomalous increase in NDVI of 2% per decade was observed, suggesting an acceleration in greening. A comprehensive analysis was conducted to decipher the correlation between NDVI, temperature, and energy budget parameters to elucidate the underlying causes of these change points. Although the correlation between these variables was relatively low throughout the summer months, a distinct pattern emerged when these periods were dissected and examined in the context of the identified change points. Preceding the change point, a strong correlation (approximately 0.6) was observed between all variables; however, this correlation significantly diminished after the change point, dropping to less than half. This shift implies an introduction of additional external factors influencing the Arctic greening trend after the change point. Our findings provide foundational data for estimating the tipping point in Arctic terrestrial ecosystems. This is achieved by integrating the observed NDVI change points with their relationship with climatic variables, which are essential in comprehensively understanding the dynamics of Arctic climate change, particularly with alterations in tundra vegetation. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems II)

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