Measuring Vegetation Phenology with Near-Surface Remote Sensing in a Temperate Deciduous Forest: Effects of Sensor Type and Deployment

Long, Fan; Wang, Xingchang; Wang, Chuankuan

doi:10.3390/rs11091063

Cited by 10 publications

(5 citation statements)

References 69 publications

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“…Noteworthy, the analysis of the set of publications in terms of the length of the research period revealed that individual data resources (data based on climatic indicators, data from direct observations of plants, remote sensing data) were not temporally consistent. The climate data used originate from the middle of the last century [36,330]; therefore, they represent the longest continuous data series available. Phenological data derived from direct observations have also been conducted for several decades at permanent research sites [343].…”

Section: Discussionmentioning

confidence: 99%

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

2022

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Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

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Section: Discussionmentioning

confidence: 99%

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

2022

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“…It is interesting to note that the first fully digital camera came into existence after a decade of the first operational earth observation satellite. In recent years, satellite-based monitoring techniques have been complemented by near-surface remote sensing methods, such as time-lapse digital cameras (PhenoCam) [28], in situ measurements taken with radiometric instruments [29], monitoring based on eddy covariance (EC) data [30], and images captured by unmanned aerial vehicles (UAV-temporal resolution depends on the frequency of the flight) [31]. These near-surface remote sensing techniques offer high temporal resolution and better spatial coverage compared to direct ground observations.…”

Section: Methods For Monitoring Phenologymentioning

confidence: 99%

Plugging the Gaps in the Global PhenoCam Monitoring of Forests—The Need for a PhenoCam Network across Indian Forests

Jose,

Chaturvedi,

Jeganathan

et al. 2023

Remote Sensing

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Our understanding of the impact of climate change on forests is constrained by a lack of long-term phenological monitoring. It is generally carried out via (1) ground observations, (2) satellite-based remote sensing, and (3) near-surface remote sensing (e.g., PhenoCams, unmanned aerial vehicles, etc.). Ground-based observations are limited by space, time, funds, and human observer bias. Satellite-based phenological monitoring does not carry these limitations; however, it is generally associated with larger uncertainties due to atmospheric noise, land cover mixing, and the modifiable area unit problem. In this context, near-surface remote sensing technologies, e.g., PhenoCam, emerge as a promising alternative complementing ground and satellite-based observations. Ground-based phenological observations generally record the following key parameters: leaves (bud stage, mature, abscission), flowers (bud stage, anthesis, abscission), and fruit (bud stage, maturation, and abscission). This review suggests that most of these nine parameters can be recorded using PhenoCam with >90% accuracy. Currently, Phenocameras are situated in the US, Europe, and East Asia, with a stark paucity over Africa, South America, Central, South-East, and South Asia. There is a need to expand PhenoCam monitoring in underrepresented regions, especially in the tropics, to better understand global forest dynamics as well as the impact of global change on forest ecosystems. Here, we spotlight India and discuss the need for a new PhenoCam network covering the diversity of Indian forests and its possible applications in forest management at a local level.

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“…Per‐crown data were extracted based on the digitized crown extents and using the 20% brightest pixel method described in the previous section. As this analysis only covered vegetation green‐up, NDVI progression was modelled by a single logistic function (Liu et al, 2019; Zhang et al, 2003). We used the following generalized logistic function:

y (t) = \frac{c}{1 + {normale}^{‐ b (t ‐ a)}} + d

…”

Section: Methodsmentioning

confidence: 99%

Monitoring spring phenology of individual tree crowns using drone‐acquired NDVI data

Fawcett

Bennie

Anderson

2020

Remote Sens Ecol Conserv

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Quantifying the timing of vegetation phenology is critical for monitoring and modelling ecosystem responses to environmental change. Phenological processes have been studied from landscape to global scales using Earth observing satellite data, and at local scale by in situ surveys of individual plants. Now, data acquired from multi-spectral sensors on drone platforms provide flexible opportunities for monitoring phenology from individual plants to small ecosystem scales efficiently, allowing community and species level information to be derived. We captured a time-series of drone-acquired normalized difference vegetation index (NDVI) data with a multi-spectral sensor (Parrot Sequoia, (Parrot, France)) over a highly heterogeneous ecosystem in Cornwall, UK, during a period of spring green-up. We monitored NDVI trajectories at the individual crown and species' level. For deciduous crowns, we derived metrics representative of spring phenological stages: Start-of-spring (SOS), middle-ofspring green-up (MOG) and start-of-peak greenness (SOP) using a logistic function. While the exact timing of SOS, MOG and SOP appeared susceptible to understorey effects and saturation of the NDVI, relative timing of green-up for a subset of species was plausible in relation to phenological observations from an extended geographic region and in situ plant area index (PAI) measurements. In evergreen vegetation (Pinus spp.) subtle changes were also detected through the growing season. The impact of illumination differences was analysed for image pairs during leaf-off and leaf-on conditions. While significant, these effects were small (mean absolute NDVI deviation of up to 0.034 for leaf-off, 0.013 for leaf-on conditions), meaning that data captured under both constant direct and diffuse irradiance conditions can be used together and that cloudy conditions should not lead to data gaps. We conclude that the capability of drone-mounted multi-spectral instruments for spatio-temporal characterization of crown-level phenology shows great promise for improving the understanding of intra-and inter-species differences in strategy, and offers an efficient means of doing so over areas of a few hectares.

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Measuring Vegetation Phenology with Near-Surface Remote Sensing in a Temperate Deciduous Forest: Effects of Sensor Type and Deployment

Cited by 10 publications

References 69 publications

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Plugging the Gaps in the Global PhenoCam Monitoring of Forests—The Need for a PhenoCam Network across Indian Forests

Monitoring spring phenology of individual tree crowns using drone‐acquired NDVI data

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