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Advances in Time-Series Analysis of Vegetation Dynamics under Changing Environments

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 25965

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Special Issue Editor

Dr. Hirohiko Nagano

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Guest Editor

Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Japan
Interests: forest; greenhouse gas flux; soil; vegetation dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to bring new remote sensing studies that improve our understanding of temporal variations in vegetation dynamics under changing environments. Over the past few decades, a number of remote sensing studies have been conducted in order to reveal spatially varied vegetation dynamics, which vary according not only to seasonal but also to interannual and decades-long environmental changes. These studies have significantly contributed to achieving reliable insights into future Earth environments. Despite this, substantial questions remain to be unanswered due to technical limitations in long-term observation using remote sensing. Recent progress in remote sensing techniques and data analysis (e.g. recently launched constellations of multi-satellites having a fine spatial resolution, successful application of sun-induced chlorophyll fluorescence, reduced cost for manufacturing of advanced sensors, well-developed machine learning in image analysis, improved data fusion approach, and so on) are showing potential to overcome those limitations.

For this Special Issue, we call for papers that make advances in remotely sensed time-series of vegetation dynamics under various environmental changes. Contributions may include, but are not limited to, the following:

Remotely sensed time-series of vegetation compositions and functions before and after episodic disturbances such as atmospheric hazards, wildfires, and land-use changes;
Those time-series under gradually changing environments from interannual to decadal scales;
Spatial variations in those time-series;
Expanding and improving time-series data by synthesizing multiple technologies of remote sensing.

We are welcome such contributions for agricultural ecosystems, not only for natural vegetation ecosystems.

Dr. Hirohiko Nagano
Guest Editor

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

Climate changes
Crop production
Data fusion
Ecosystem disturbances
Long–term vegetation dynamics
Time-series analysis

Published Papers (6 papers)

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Research

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19 pages, 4318 KiB

Open AccessArticle

Evaluation of Spatiotemporal Resilience and Resistance of Global Vegetation Responses to Climate Change

by Na Sun, Naijing Liu, Xiang Zhao, Jiacheng Zhao, Haoyu Wang and Donghai Wu

Remote Sens. 2022, 14(17), 4332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174332 - 01 Sep 2022

Cited by 3 | Viewed by 2401

Abstract

The quantitative assessment of vegetation resilience and resistance is worthwhile to deeply understand the responses of vegetation growth to climate anomalies. However, few studies comprehensively evaluate the spatiotemporal resilience and resistance of global vegetation responses to climate change (i.e., temperature, precipitation, and radiation). Furthermore, although ecosystem models are widely used to simulate global vegetation dynamics, it is still not clear whether ecosystem models can capture observation-based vegetation resilience and resistance. In this study, based on remotely sensed and model-simulated leaf area index (LAI) time series and climate datasets, we quantified spatial patterns and temporal changes in vegetation resilience and resistance from 1982–2015. The results reveal clear spatial patterns of observation-based vegetation resilience and resistance for the last three decades, which were closely related to the local environment. In general, most of the ecosystem models capture spatial patterns of vegetation resistance to climate to different extents at the grid scale (R = 0.43 ± 0.10 for temperature, R = 0.28 ± 0.12 for precipitation, and R = 0.22 ± 0.08 for radiation); however, they are unable to capture patterns of vegetation resilience (R = 0.05 ± 0.17). Furthermore, vegetation resilience and resistance to climate change have regionally changed over the last three decades. In particular, the results suggest that vegetation resilience has increased in tropical forests and that vegetation resistance to temperature has increased in northern Eurasia. In contrast, ecosystem models cannot capture changes in vegetation resilience and resistance over the past thirty years. Overall, this study establishes a benchmark of vegetation resilience and resistance to climate change at the global scale, which is useful for further understanding ecological mechanisms of vegetation dynamics and improving ecosystem models, especially for dynamic resilience and resistance. Full article

(This article belongs to the Special Issue Advances in Time-Series Analysis of Vegetation Dynamics under Changing Environments)

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25 pages, 37328 KiB

Open AccessArticle

A Global 250-m Downscaled NDVI Product from 1982 to 2018

by Zhimin Ma, Chunyu Dong, Kairong Lin, Yu Yan, Jianfeng Luo, Dingshen Jiang and Xiaohong Chen

Remote Sens. 2022, 14(15), 3639; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153639 - 29 Jul 2022

Cited by 14 | Viewed by 4391

Abstract

Satellite-based normalized difference vegetation index (NDVI) time series data are useful for monitoring the changes in vegetation ecosystems in the context of global climate change. However, most of the current NDVI products cannot effectively reconcile high spatial resolution and continuous observations in time. Here, to produce a global-scale, long-term, and high-resolution NDVI database, we developed a simple and new data downscaling approach. The downscaling algorithm considers the pixel-wise ratios of the coefficient of variation (CV) between the coarse- and fine-resolution NDVI data and relative changes in the NDVI against a baseline period. The algorithm successfully created a worldwide monthly NDVI database with 250 m resolution from 1982 to 2018 by translating the fine spatial information from MODIS (Moderate-resolution Imaging Spectroradiometer) data and the long-term temporal information from AVHRR (Advanced Very High Resolution Radiometer) data. We employed the evaluation indices of root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (Pearson’s R) to assess the accuracy of the downscaled data against the MODIS NDVI. Both the RMSE and MAE values at the regional and global scales are typically between 0 and 0.2, whereas the Pearson’s R values are mostly above 0.7, which implies that the downscaled NDVI product is similar to the MODIS NDVI product. We then used the downscaled data to monitor the NDVI changes in different plant types and places with significant vegetation heterogeneity, as well as to investigate global vegetation trends over the last four decades. The Google Earth Engine platform was used for all the data downscaling processes, and here we provide a code for users to easily acquire data corresponding to any part of the world. The downscaled global-scale NDVI time series has high potential for the monitoring of the long-term temporal and spatial dynamics of terrestrial ecosystems under changing environments. Full article

(This article belongs to the Special Issue Advances in Time-Series Analysis of Vegetation Dynamics under Changing Environments)

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Graphical abstract

16 pages, 2059 KiB

Open AccessArticle

Diversity Effects on Canopy Structure Change throughout a Growing Season in Experimental Grassland Communities

by Claudia Guimarães-Steinicke, Alexandra Weigelt, Anne Ebeling, Nico Eisenhauer and Christian Wirth

Remote Sens. 2022, 14(7), 1557; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071557 - 23 Mar 2022

Cited by 2 | Viewed by 6177

Abstract

Increasing plant diversity commonly enhances standing biomass and other ecosystem functions (i.e., carbon fluxes, water use efficiency, herbivory). The standing biomass is correlated with vegetation volume, which describes plant biomass allocation within a complex canopy structure. As the canopy structure of plant communities is not static throughout time, it is expected that its changes also control diversity effects on ecosystem functioning. Yet, most studies are based on one or two measures of ecosystem function per year. Here, we examine the temporal effects of diversity of grassland communities on canopy structural components in high temporal (bi-weekly throughout the growing season) and spatial resolutions as a proxy for ecosystem functioning. Using terrestrial laser scanning, we estimate metrics of vertical structure, such as biomass distribution (evenness) and highest biomass allocation (center of gravity) along height strata. For horizontal metrics, we calculated community stand gaps and canopy surface variation. Our findings show that species-rich communities start filling the vertical space (evenness) earlier in the growing season, suggesting a more extended period of resource use (i.e., light-harvesting). Moreover, more diverse communities raised their center of gravity only at the peak of biomass in spring, likely triggered by higher interspecific competition inducing higher biomass allocation at upper layers of the canopy. Furthermore, richer communities were clumpier only after mowing, revealing species-specific differences in regrowth. Lastly, species richness strongly affected canopy variation when the phenology status and height differences were maximal, suggesting differences in plant functional strategies (space to grow, resource use, and flowering phenology). Therefore, the effects of diversity on ecosystem functions depending on those structural components such as biomass production, decomposition, and herbivory, may also change throughout the season due to various mechanisms, such as niche differences, increased complementarity, and temporal and spatial variation in biological activity. Full article

(This article belongs to the Special Issue Advances in Time-Series Analysis of Vegetation Dynamics under Changing Environments)

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17 pages, 3366 KiB

Open AccessArticle

Temperature Variation and Climate Resilience Action within a Changing Landscape

by Leah Marajh and Yuhong He

Remote Sens. 2022, 14(3), 701; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030701 - 02 Feb 2022

Cited by 3 | Viewed by 2624

Abstract

Temperature change can have profound impacts on livelihood activities and human well-being. Specific factors such as land transitions and climate knowledge can influence temperature variation and actions for adaptation. In addition to meteorological data, this study integrates land surface temperature (LST) derived from satellite imagery and local temperature perceptions obtained through interviews to advance a deeper understanding of spatial temperature and its impacts, which is not often seen within climate studies. This study examines local temperature across three different land types (rural mountains, rural agricultural lowlands, urban areas) in the Greater Angkor Region of Cambodia to highlight important insights about temperature and climate resilience action. The results revealed that changes in temperature were most pronounced in Phnom Kulen National Park (rural mountain) and in the rural agricultural lowlands, where residents discussed direct impacts and disruptions to their lives. Temperature, in both the LST results and through local perceptions, demonstrated a strong correlation to ground features, where areas with low vegetation exhibited high temperatures and areas with high vegetation observed low temperatures. While climate action in the form of tree planting and forest conservation are major climate mitigation strategies being undertaken in this region, social awareness and the ability to adapt to changes in temperature was revealed to be uneven across the landscape, suggesting that local entities should mobilize around gaining more education and training for all residents. Full article

(This article belongs to the Special Issue Advances in Time-Series Analysis of Vegetation Dynamics under Changing Environments)

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Graphical abstract

16 pages, 9185 KiB

Open AccessArticle

Spatio-Temporal Variation Characteristics of Aboveground Biomass in the Headwater of the Yellow River Based on Machine Learning

by Rong Tang, Yuting Zhao and Huilong Lin

Remote Sens. 2021, 13(17), 3404; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173404 - 27 Aug 2021

Cited by 15 | Viewed by 1900

Abstract

Accurate estimation of the aboveground biomass (AGB) of grassland is a key link in understanding the regional carbon cycle. We used 501 aboveground measurements, 29 environmental variables, and machine learning algorithms to construct and verify a custom model of grassland biomass in the Headwater of the Yellow River (HYR) and selected the random forest model to analyze the temporal and spatial distribution characteristics and dynamic trends of the biomass in the HYR from 2001 to 2020. The research results show that: (1) the random forest model is superior to the other three models (R²_val = 0.56, RMSE_val = 51.3 g/m²); (2) the aboveground biomass in the HYR decreases spatially from southeast to northwest, and the annual average value and total values are 176.8 g/m² and 20.73 Tg, respectively; (3) 69.51% of the area has shown an increasing trend and 30.14% of the area showed a downward trend, mainly concentrated in the southeast of Hongyuan County, the northeast of Aba County, and the north of Qumalai County. The research results can provide accurate spatial data and scientific basis for the protection of grassland resources in the HYR. Full article

(This article belongs to the Special Issue Advances in Time-Series Analysis of Vegetation Dynamics under Changing Environments)

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Review

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26 pages, 2897 KiB

Open AccessReview

Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review

by Abhasha Joshi, Biswajeet Pradhan, Shilpa Gite and Subrata Chakraborty

Remote Sens. 2023, 15(8), 2014; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15082014 - 11 Apr 2023

Cited by 23 | Viewed by 6672

Abstract

Reliable and timely crop-yield prediction and crop mapping are crucial for food security and decision making in the food industry and in agro-environmental management. The global coverage, rich spectral and spatial information and repetitive nature of remote sensing (RS) data have made them effective tools for mapping crop extent and predicting yield before harvesting. Advanced machine-learning methods, particularly deep learning (DL), can accurately represent the complex features essential for crop mapping and yield predictions by accounting for the nonlinear relationships between variables. The DL algorithm has attained remarkable success in different fields of RS and its use in crop monitoring is also increasing. Although a few reviews cover the use of DL techniques in broader RS and agricultural applications, only a small number of references are made to RS-based crop-mapping and yield-prediction studies. A few recently conducted reviews attempted to provide overviews of the applications of DL in crop-yield prediction. However, they did not cover crop mapping and did not consider some of the critical attributes that reveal the essential issues in the field. This study is one of the first in the literature to provide a thorough systematic review of the important scientific works related to state-of-the-art DL techniques and RS in crop mapping and yield estimation. This review systematically identified 90 papers from databases of peer-reviewed scientific publications and comprehensively reviewed the aspects related to the employed platforms, sensors, input features, architectures, frameworks, training data, spatial distributions of study sites, output scales, evaluation metrics and performances. The review suggests that multiple DL-based solutions using different RS data and DL architectures have been developed in recent years, thereby providing reliable solutions for crop mapping and yield prediction. However, challenges related to scarce training data, the development of effective, efficient and generalisable models and the transparency of predictions should be addressed to implement these solutions at scale for diverse locations and crops. Full article

(This article belongs to the Special Issue Advances in Time-Series Analysis of Vegetation Dynamics under Changing Environments)

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