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Remote Sensing for Vegetation Phenology in a Changing Environment

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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: 26 May 2024 | Viewed by 7183

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Dr. Mei Yu

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Guest Editor

Department of Environmental Sciences, University of Puerto Rico, Rio Piedras, San Juan, PR 00926, USA
Interests: GIS; spatial statistics; spatiotemporal modeling in terrestrial ecosystems; species distribution modeling; biodiversity analysis

Dr. Yuyu Zhou

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Guest Editor

Department of Geological & Atmospheric Sciences, Iowa State University, 3019 Agronomy Hall, Ames, IA 50011, USA
Interests: urban remote sensing; urbanization; urban heat island; building energy use; vegetation phenology
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Dear Colleagues,

Climate changes, including warming and elevated variability, substantially influence the phenology of terrestrial vegetation, which in turn feeds back to the climate via altered carbon and water dynamics. Plants respond to the changes in climate from local to global scales and from natural to urban systems. Aside from changes in climate, especially temperature or precipitation regimes, elevated CO₂ and Nitrogen deposition also greatly affect vegetation phenology. Therefore, monitoring changes in phenology and exploring climate and other drivers of phenology changes can advance the mechanistic understanding of phenology changes, which will significantly contribute to the studies of climate and related global carbon dynamics.

The focus of this special issue is the applications of remote sensing science and technology to address the challenges in the vegetation phenology studies in a changing environment. Ground monitoring based on phenology images has been frequently used for various vegetation types in North America and other counties. Multisource satellite images at moderate spatial resolution and high temporal frequency have been widely applied in monitoring and understanding interannual changes and long-term trend of phenology in various ecosystems, such as forests and agricultural lands.

Remote sensing studies in vegetation phenology to monitor changes, explore causal drivers, and improve models are welcomed in this special issues.

Topics include, but are not limited to, the following:

Monitoring phenology changes based on ground phenology images such as those from the PhenoCam Network
Multisource remote sensing for vegetation phenology
SIF-derived changes in phenology
Phenology changes in high-latitude and high-altitude regions
Phenology changes due to local climate such as urban-heat island
Phenological models using remote sensing data.

Dr. Mei Yu
Dr. Yuyu Zhou
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
climate change
multisource remote sensing
SIF
ground phenology images
phenological model

Published Papers (4 papers)

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Research

25 pages, 6491 KiB

Open AccessArticle

Comparing Phenology of a Temperate Deciduous Forest Captured by Solar-Induced Fluorescence and Vegetation Indices

by Trina Merrick, Ralf Bennartz, Maria Luisa S. P. Jorge, Carli Merrick, Stephanie A. Bohlman, Carlos Alberto Silva and Stephanie Pau

Remote Sens. 2023, 15(21), 5101; https://doi.org/10.3390/rs15215101 - 25 Oct 2023

Viewed by 931

Abstract

A shifting phenology in deciduous broadleaf forests (DBFs) can indicate forest health, resilience, and changes in the face of a rapidly changing climate. The availability of satellite-based solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) promises to add to the understanding of the regional-level DBF phenology that has been developed, for instance, using proxies of gross primary productivity (GPP) from the Moderate Imaging Spectroradiometer (MODIS). It is unclear how OCO-2 and MODIS metrics compare in terms of capturing intra-annual variations and benchmarking DBF seasonality, thus necessitating a comparison. In this study, spatiotemporally matched OCO-2 SIF metrics (at footprint level) and corresponding MODIS GPP, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) products within a temperate DBF were used to compare the phenology captured by the productivity metrics. Additionally, an estimate of the SIF yield (SIF_y), derived from OCO-2 SIF measurements, and a MODIS fraction of photosynthetically active radiation (fPAR) were tested. An examination of the trends and correlations showed relatively few qualitative differences among productivity metrics and environmental variables, but it highlighted a lack of seasonal signal in the calculation of SIF_y. However, a seasonality analysis quantitatively showed similar seasonal timings and levels of seasonal production in and out of the growing season between SIF and GPP. In contrast, NDVI seasonality was least comparable to that of SIF and GPP, with senescence occurring approximately one month apart. Taken together, we conclude that satellite-based SIF and GPP (and EVI to a smaller degree) provide the most similar measurements of forest function, while NDVI is not sensitive to the same changes. In this regard, phenological metrics calculated with satellite-based SIF, along with those calculated with GPP and EVI from MODIS, can enhance our current understanding of deciduous forest structures and functions and provide additional information over NDVI. We recommend that future studies consider metrics other than NDVI for phenology analyses. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Phenology in a Changing Environment)

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30 pages, 100141 KiB

Open AccessArticle

Monitoring Individual Tree Phenology in a Multi-Species Forest Using High Resolution UAV Images

by Jasper Kleinsmann, Jan Verbesselt and Lammert Kooistra

Remote Sens. 2023, 15(14), 3599; https://doi.org/10.3390/rs15143599 - 19 Jul 2023

Cited by 2 | Viewed by 2242

Abstract

Monitoring tree phenology is important for understanding ecosystem functioning and for assessing ecosystem responses to climate change. Satellite imagery offers open-access global coverage but is restricted to forest-level analyses, due to its coarse spatial resolution. Unmanned aerial vehicle (UAV) imagery can monitor phenology at the individual tree level by utilizing a centimeter-scale resolution. Two research objectives were identified for this study: (1) to derive phenological metrics at the individual tree level, using various vegetation indices (VIs); and (2) to assess the accuracy of automatic crown delineation in a diverse ecosystem. To achieve this, fourteen multi-spectral UAV flights were performed, and the ability of the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), optimized soil-adjusted vegetation index (OSAVI), and chlorophyll index red-edge (CI_re) to model seasonal phenology was assessed. A double logistic model was fitted on the VI observations for each individual tree, to derive the start of season (SOS) and end of season (EOS). Individual tree crowns were delineated automatically using marker-controlled watershed segmentation (MCWS), and the treetops were identified using a local maximum filter (LMF). Overall, the automatic segmentation performed well (F-score: 0.79, IoU: 0.58), with higher accuracies in single-species areas, while it underperformed in complex mixed forest structures. All VIs captured a strong seasonal signal for the deciduous trees and derived SOS and EOS estimates consistent with literature and ground observations. General phenological patterns included an early silver birch SOS, a quick beech budburst, and large within-species phenology variations for oak trees. Seasonal VI variation for coniferous evergreen trees was limited, and the resulting phenology estimates proved unreliable. In conclusion, these findings emphasize the capabilities of UAV imagery for individual tree crown phenology monitoring. However, they also show the difficulty of monitoring evergreen phenology with the commonly-used VIs and stress the need for further investigations. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Phenology in a Changing Environment)

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21 pages, 8434 KiB

Open AccessArticle

Characterizing Spatiotemporal Patterns of Winter Wheat Phenology from 1981 to 2016 in North China by Improving Phenology Estimation

by Shuai Wang, Jin Chen, Miaogen Shen, Tingting Shi, Licong Liu, Luyun Zhang, Qi Dong and Cong Wang

Remote Sens. 2022, 14(19), 4930; https://doi.org/10.3390/rs14194930 - 02 Oct 2022

Cited by 2 | Viewed by 1352

Abstract

Phenology provides important information for wheat growth management and the estimation of wheat yield and quality. The relative threshold method has been widely used to retrieve phenological metrics from remotely sensed data owing to its simplicity. However, the thresholds vary substantially among phenological metrics and locations, hampering us from effectively detecting spatial and temporal variations in winter wheat phenology. In this study, we developed a calibrated relative threshold method based on ground phenological observations. Compared with the traditional relative threshold method, our method can minimize the bias and uncertainty caused by unreasonable thresholds in determining phenological dates. On this basis, seven key phenological dates and three growth periods of winter wheat were estimated from long-term series (1981–2016) of the remotely sensed Normalized Difference Vegetation Index for North China (106°18′–122°41′E, 28°59′–39°57′N). Results show that the pre-wintering phenological dates of winter wheat (i.e., emergence and tillering) occurred in December in the south and in mid- to late- October in the north, while the post-wintering phenological dates (i.e., green-up onset, jointing, heading, milky stage, and maturity) exhibited the opposite pattern, that is, January to May in the south and February to June in the north. Consequently, the vegetative growth period increased from 49 days in the south to 77 in the north, and the reproductive growth period decreased from 51 days to 29 days. At the regional scale, all winter wheat phenological dates predominantly advanced, with the most pronounced advancement being for green-up onset (–0.10 days/year, p > 0.1), emergence (–0.09 days/year, p > 0.1), and jointing (–0.08 days/year, p > 0.1). The vegetative growth period and reproductive growth period at the regional scale predominantly extended by 0.03 (p > 0.1) and 0.09 (p < 0.001) days/year, respectively. In general, the later phenological events (i.e., heading, milky stage, and maturity) tended to advance with higher temperature, while the earlier phenological events (i.e., emergence, tillering, green-up onset, and jointing) showed a weak correlation with temperature, suggesting that the earlier events might be mainly affected by management while later ones were more responsive to warming. These findings provide a critical reference for improving winter wheat management under the ongoing climate warming. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Phenology in a Changing Environment)

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19 pages, 7708 KiB

Open AccessArticle

Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion

by Jianping Wu, Zhongbing Chang, Yongxian Su, Chaoqun Zhang, Xiong Wu, Chongyuan Bi, Liyang Liu, Xueqin Yang and Xueyan Li

Remote Sens. 2022, 14(17), 4349; https://doi.org/10.3390/rs14174349 - 01 Sep 2022

Viewed by 1600

Abstract

Multiple methods have been developed to identify the transition threshold from the reconstructed satellite-derived normalized difference vegetation indices (NDVI) time series and to determine the inflection point corresponding to a certain phenology phase (e.g., the spring green-up date (GUD)). We address an issue that large uncertainties might occur in the inflection point identification of spring GUD using the traditional satellite-based methods since different vegetation types exhibit asynchronous phenological phases over a heterogeneous ecoregion. We tentatively developed a Maximum-derivative-based (MDB) method and provided inter-comparisons with two traditional methods to detect the turning points by the reconstructed time-series data of NDVI for identifying the GUD against long-term observations from the sites covered by a mixture of deciduous forest and herbages in the Pan European Phenology network. Results showed that higher annual mean temperature would advance the spring GUD, but the sensitive magnitudes differed depending on the vegetation type. Therefore, the asynchronization of phenological phases among different vegetation types would be more pronounced in the context of global warming. We found that the MDB method outperforms two other traditional methods (the 0.5-threshold-based method and the maximum-ratio-based method) in predicting the GUD of the subsequent-green-up vegetation type when compared with ground observation, especially at sites with observed GUD of herbages earlier than deciduous forest, while the Maximum-ratio-based method showed better performance for identifying GUDs of the foremost-green-up vegetation type. Although the new method improved in our study is not universally applicable on a global scale, our results, however, highlight the limitation of current inflection point identify algorithms in predicting the GUD derived from satellite-based vegetation indices datasets in an ecoregion with heterogeneous vegetation types and asynchronous phenological phases, which makes it helpful for us to better predict plant phenology on an ecoregion-scale under future ongoing climate warming. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Phenology in a Changing Environment)

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