Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing

Noumonvi, Koffi Dodji; Oblišar, Gal; Žust, Ana; Vilhar, Urša

doi:10.3390/rs13153015

Cited by 6 publications

(8 citation statements)

References 65 publications

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“…Several studies reported that canopy phenology (using the camera) was consistent with the phenology from GPP observations in the field, such as Bartlett Experimental Forest and Howland Forest in the United States (Richardson et al, 2009), the Lägeren Forest in northern Switzerland, and the Hainich Forest in central Germany (Ahrends et al, 2009). Therefore, GPP has been widely used as a reference for land surface phenology derived from satellite data analysis (Gonsamo et al, 2012;Jeong et al, 2017;Noumonvi et al, 2021).…”

Section: Introductionmentioning

confidence: 67%

Comparison of Phenology Estimated From Monthly Vegetation Indices and Solar-Induced Chlorophyll Fluorescence in China

Wang

Sun

et al. 2022

Front. Earth Sci.

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Phenology is an important biological indicator for monitoring terrestrial ecosystems and global change. Solar-induced chlorophyll fluorescence (SIF) emitted by chlorophyll has been proven to characterize vegetation photosynthesis and phenology. In this study, we used monthly normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and SIF products to qualitatively compare the effectiveness at detecting the phenological characteristics (SOS (start-of-season), EOS (end-of-season), and LOS (length-of-season)) over China during 2007–2013. The phenological characteristics determined by gross primary productivity (GPP) were applied as the reference to validate the phenological characteristics derived from NDVI, EVI, and SIF. The results demonstrated that the phenological characteristics derived from SIF were more consistent with that of GPP than VIs (NDVI and EVI) when considering all latitude grades, different elevation grades, and different land cover types in China. In the middle- and high-latitude regions, SOS derived from the vegetation indices (SOSVIs) did not deviate from those from GPP (SOSGPP) and SIF (SOSSIF), while in low latitudes, SOSVIs were about 20 d later than SOSSIF and SOSGPP. The VIs (EOSVIs) had a severe lag behind those of SIF (EOSSIF) in estimating the EOS at all latitudes. The EOSSIF had a deviation of fewer than 5 d compared with EOS estimated by GPP (EOSGPP), whereas the deviation of EOSVIs from EOSGPP was about 10–31 d across low to high latitude regions. The biases of SIF and VIs were due to the inconsistency between vegetation photosynthesis and leaf greenness. Also, VIs overestimated the LOS at all latitudes, the difference of LOS between estimated by NDVI and estimated by GPP was as long as 39 d in the high-latitude region. Our study suggests that SIF is suitable for estimating the phenological characteristics of vegetation regardless of different latitudes, elevation grades, and land cover types in China, providing a basis for SIF to study the vegetation phenological characteristics in a regional scope.

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Section: Introductionmentioning

confidence: 67%

Comparison of Phenology Estimated From Monthly Vegetation Indices and Solar-Induced Chlorophyll Fluorescence in China

Wang

Sun

et al. 2022

Front. Earth Sci.

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“…Most of the analyzed publications (over 70%) focused on natural or semi-natural areas, whereas almost 30% of publications presented studies on arable crops (Figure 12). Forests account for over 50% of the analyzed plant communities (Figure 12) [31,51,56,63,72,173,177,179,181,182,209,221,227,245,250,256,261,338,344,348,351,369,370]. Meadows, grasslands, and wetlands mainly represent the other cases [10,26,60,63,72,120,147,162,187,213,225,295,313,336,350].…”

Section: Vegetation Typesmentioning

confidence: 99%

“…Plant analyses are mainly focused on the chlorophyll content [25] or substances contained in plants [91] and, consequently, the condition of plants [24,27]. Research on the growth and development of vegetation can be carried out in both strictly controlled laboratory conditions [29,79,92,93] and using satellite techniques [42,44,46,[49][50][51]56,59,61,62,[64][65][66]68,71,[75][76][77][78]80,88, or other means of transporting remote sensing devices (UAV, airships, airplanes) [43,47,54,55,65,84,[108][109][110]118,[140][141][142][143][144][145][146][147][148]…”

Section: Introductionmentioning

confidence: 99%

“…For many years, measurements were based on traditional sources of information (i.e., direct phenological observations combined with meteorological data). The dynamic development of technology in recent decades has allowed for the use of remote sensing data, mainly properly processed satellite data with different temporal and spatial resolutions [10,14,23,26,28,[30][31][32][33][34][35][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72]94,95,[134][135][136][137]…”

Section: Introductionmentioning

confidence: 99%

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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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“…IMERG is a single algorithm that gives precipitation estimates by merging data from all GPM constellation passive microwave devices. GPM-M data were utilised in this work [34,35]. MODIS11A1 version 6 product data, which offers daily per-pixel land surface temperature and emissivity at 1 km spatial resolution, was used to obtain temperature data.…”

Section: Meteorological Datasetsmentioning

confidence: 99%

Impact of Environmental Gradients on Phenometrics of Major Forest Types of Kumaon Region of the Western Himalaya

Dugesar

Satish

Pandey

et al. 2022

Forests

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Understanding ecosystem functional behaviour and its response to climate change necessitates a detailed understanding of vegetation phenology. The present study investigates the effect of an elevational gradient, temperature, and precipitation on the start of the season (SOS) and end of the season (EOS), in major forest types of the Kumaon region of the western Himalaya. The analysis made use of the Normalised Difference Vegetation Index (NDVI) time series that was observed by the optical datasets between the years 2001 and 2019. The relationship between vegetation growth stages (phenophases) and climatic variables was investigated as an interannual variation, variation along the elevation, and variation with latitude. The SOS indicates a delayed trend along the elevational gradient (EG) till mid-latitude and shows an advancing pattern thereafter. The highest rate of change for the SOS and EOS is 3.3 and 2.9 days per year in grassland (GL). The lowest rate of temporal change for SOS is 0.9 days per year in mixed forests and for EOS it is 1.2 days per year in evergreen needle-leaf forests (ENF). Similarly, the highest rate of change in SOS along the elevation gradient is 2.4 days/100 m in evergreen broadleaf forest (EBF) and the lowest is −0.7 days/100 m in savanna, and for EOS, the highest rate of change is 2.2 days/100 m in EBF and lowest is −0.9 days/100 m in GL. Winter warming and low winter precipitation push EOS days further. In the present study area, due to winter warming and summer dryness, despite a warming trend in springseason or springtime, onset of the vegetation growth cycle shows a delayed trend across the vegetation types. As vegetation phenology responds differently over heterogeneous mountain landscapes to climate change, a detailed local-level observational insight could improve our understanding of climate change mitigation and adaptation policies.

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Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing

Cited by 6 publications

References 65 publications

Comparison of Phenology Estimated From Monthly Vegetation Indices and Solar-Induced Chlorophyll Fluorescence in China

Comparison of Phenology Estimated From Monthly Vegetation Indices and Solar-Induced Chlorophyll Fluorescence in China

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

Impact of Environmental Gradients on Phenometrics of Major Forest Types of Kumaon Region of the Western Himalaya

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