scholarly journals Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

2018 ◽  
Vol 10 (8) ◽  
pp. 1293 ◽  
Author(s):  
Yunpeng Luo ◽  
Tarek S. El-Madany ◽  
Gianluca Filippa ◽  
Xuanlong Ma ◽  
Bernhard Ahrens ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Plant Phenology ◽  
Mediterranean Ecosystem ◽  
Near Infrared Reflectance ◽  
Seasonally Dry ◽  
Growing Season Length ◽  
Dry Down

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

Assessing Hemlock Decline Using Visible and Near-Infrared Spectroscopy: Indices Comparison and Algorithm Development

2005 ◽  
Vol 59 (6) ◽  
pp. 836-843 ◽  
Author(s):  
Jennifer Pontius ◽  
Richard Hallett ◽  
Mary Martin
Keyword(s):  
Linear Regression ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Stress Index ◽  
Regression Equations ◽  
Near Infrared Reflectance ◽  
Full Spectrum ◽  
Hemlock Decline

Near-infrared reflectance spectroscopy was evaluated for its effectiveness at predicting pre-visual decline in eastern hemlock trees. An ASD FieldSpec Pro FR field spectroradiometer measuring 2100 contiguous 1-nm-wide channels from 350 nm to 2500 nm was used to collect spectra from fresh hemlock foliage. Full spectrum partial least squares (PLS) regression equations and reduced stepwise linear regression equations were compared. The best decline predictive model was a 6-term linear regression equation ( R2 = 0.71, RMSE = 0.591) based on: Carter Miller Stress Index (R694/R760), Derivative Chlorophyll Index (FD705/FD723), Normalized Difference Vegetation Index ((R800 – R680)/(R800 + R680)), R950, R1922, and FD1388. Accuracy assessment showed that this equation predicted an 11-class decline rating with a 1-class tolerance accuracy of 96% and differentiated healthy trees from those in very early decline with 72% accuracy. These results indicate that narrow-band sensors could be developed to detect very early stages of hemlock decline, before visual symptoms are apparent. This capability would enable land managers to identify early hemlock woolly adelgid infestations and monitor forest health over large areas of the landscape.

scholarly journals Seasonal dynamics of lingonberry and blueberry spectra

Silva Fennica ◽  
2019 ◽  
Vol 53 (2) ◽  
Author(s):  
Petri Forsström ◽  
Jouni Peltoniemi ◽  
Miina Rautiainen
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Reference Data ◽  
Leaf Growth ◽  
Vegetation Indices ◽  
Growing Season ◽  
Moisture Stress ◽  
Stress Index ◽  
Infrared Spectral

Accurate mapping of the spatial distribution of understory species from spectral images requires ground reference data which represent the prevailing phenological stage at the time of image acquisition. We measured the spectral bidirectional reflectance factors (BRFs, 350–2500 nm) at varying view angles for lingonberry ( L.) and blueberry ( L.) throughout the growing season of 2017 using Finnish Geospatial Research Institute’s FIGIFIGO field goniometer. Additionally, we measured spectra of leaves and berries of both species, and flowers of lingonberry. Both lingonberry and blueberry showed seasonality in visible and near-infrared spectral regions which was linked to occurrences of leaf growth, flowering, berrying, and leaf senescence. The seasonality of spectra differed between species due to different phenologies (evergreen vs. deciduous). Vegetation indices, normalized difference vegetation index (NDVI), moisture stress index (MSI), plant senescence reflectance index (PSRI), and red-edge inflection point (REIP2), showed characteristic seasonal trends. NDVI and PSRI were sensitive to the presence of flowers and berries of lingonberry, while with blueberry the effects were less evident. Off-nadir observations supported differentiating the dwarf shrub species from each other but showed little improvement for detection of flowers and berries. Lingonberry and blueberry can be identified by their spectral signatures if ground reference data are available over the entire growing season. The spectral data measured in this study are reposited in the publicly open SPECCHIO Spectral Information System.Vaccinium vitis-idaeaVaccinium myrtillus

scholarly journals Growing Season Precipitation Rather than Growing Season Length Predominates Maximum Normalized Difference Vegetation Index in Alpine Grasslands on the Tibetan Plateau

2020 ◽  
Vol 12 (3) ◽  
pp. 968
Author(s):  
Jiang Wei Wang ◽  
Meng Li ◽  
Guang Yu Zhang ◽  
Hao Rui Zhang ◽  
Cheng Qun Yu
Keyword(s):  
Tibetan Plateau ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
The Tibetan Plateau ◽  
Season Length ◽  
Alpine Grasslands ◽  
Vegetation Productivity ◽  
Growing Season Length ◽  
Growing Season Precipitation

Precipitation and growing season length (GSL) are vital abiotic and biotic variables in controlling vegetation productivity in alpine regions. However, their relative effects on vegetation productivity have not been fully understood. In this study, we examined the responses of the maximum normalized difference vegetation index (NDVImax) to growing season precipitation (GSP) and GSL from 2000 to 2013 in 36 alpine grassland sites on the Tibetan Plateau. Our results indicated that NDVImax showed a positive relationship with prolonged GSL (R2 = 0.12) and GSP (R2 = 0.39). The linear slope of NDVImax increased with that of GSP rather than GSL. Therefore, GSP had a stronger effect on NDVImax than did GSL in alpine grasslands on the Tibetan Plateau.

scholarly journals Phenology Response to Climatic Dynamic across China’s Grasslands from 1985 to 2010

2018 ◽  
Vol 7 (8) ◽  
pp. 290 ◽  
Author(s):  
Jun Wang ◽  
Tiancai Zhou ◽  
Peihao Peng
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Northern China ◽  
Plant Phenology ◽  
Meadow Steppe ◽  
Typical Steppe ◽  
Alpine Steppe

Because the dynamics of phenology in response to climate change may be diverse in different grasslands, quantifying how climate change influences plant growth in different grasslands across northern China should be particularly informative. In this study, we explored the spatiotemporal variation of the phenology (start of the growing season [SOS], peak of the growing season [POS], end of the growing season [EOS], and length of the growing season [LOS]) across China’s grasslands using a dataset of the GIMMS3g normalized difference vegetation index (NDVI, 1985–2010), and determined the effects of the annual mean temperature (AMT) and annual mean precipitation (AMP) on the significantly changed phenology. We found that the SOS, POS, and EOS advanced at the rates of 0.54 days/year, 0.64 days/year, and 0.65 days/year, respectively; the LOS was shortened at a rate of 0.62 days/year across China’s grasslands. Additionally, the AMT combined with the AMP explained the different rates (ER) for the significantly dynamic SOS in the meadow steppe (R2 = 0.26, p = 0.007, ER = 12.65%) and typical steppe (R2 = 0.28, p = 0.005, ER = 32.52%); the EOS in the alpine steppe (R2 = 0.16, p < 0.05, ER = 6.22%); and the LOS in the alpine (R2 = 0.20, p < 0.05, ER = 6.06%), meadow (R2 = 0.18, p < 0.05, ER = 16.69%) and typical (R2 = 0.18, p < 0.05, ER = 19.58%) steppes. Our findings demonstrated that the plant phenology in different grasslands presented discrepant dynamic patterns, highlighting the fact that climate change has played an important role in the variation of the plant phenology across China’s grasslands, and suggested that the variation and relationships between the climatic factors and phenology in different grasslands should be explored further in the future.

scholarly journals Unsynchronized Driving Mechanisms of Spring and Autumn Phenology Over Northern Hemisphere Grasslands

2021 ◽  
Vol 3 ◽  
Author(s):  
Nan Cong ◽  
Ke Huang ◽  
Yangjian Zhang
Keyword(s):  
Global Warming ◽  
Northern Hemisphere ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Dominant Role ◽  
Growing Season ◽  
Growing Season Length ◽  
Driving Mechanisms ◽  
Temperature And Precipitation

Global warming has impacted Northern Hemisphere (NH) grassland ecosystems to a great extent. Vegetation growing season length (GSL) has been extended by concurrent advances in spring green-up and postponements in autumn dormancy. However, the driving mechanisms of phenology are unclear as limited factors have been considered so far. Therefore, it is still elusive to what extent phenological changes shaped GSL. In this study, we used remote sensing normalized difference vegetation index (NDVI) to extract spring and autumn phenology of NH grasslands, and further explored the contribution of each phenophase to GSL through the coefficient of variation (CV) and contribution coefficient (CntC). We found that 65% of NH grasslands exhibited advanced start-of-season (SOS) and circa 58% showed delayed end-of-season (EOS) in the three decades. Changes in GSL was regulated more by EOS changes than by SOS changes, as evidenced by their respective 52 vs. 48% CntC. As for the relationship between phenology and environmental elements, the causing factor analysis revealed that climatic factors (temperature, precipitation, and their interactions) played a dominant role in SOS variations, while environmental and internal factors exerted dominant effects on EOS. Also, interactions of temperature and precipitation contributed a higher variation of SOS than either of them individually. The differentiated factors controlling the two bounding ends of the growing season suggested that it is impossible for GSL to continue to extend without limits under global warming.

scholarly journals A double peak in the seasonality of California's photosynthesis as observed from space

2020 ◽  
Vol 17 (2) ◽  
pp. 405-422 ◽  
Author(s):  
Alexander J. Turner ◽  
Philipp Köhler ◽  
Troy S. Magney ◽  
Christian Frankenberg ◽  
Inez Fung ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Spatiotemporal Patterns ◽  
Eof Analysis ◽  
Near Infrared Reflectance ◽  
Double Peak

Abstract. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIRv) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.

scholarly journals Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqin Tong ◽  
Yuhai Bao ◽  
Rigele Te ◽  
Qiyun Ma ◽  
Si Ha ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pearson Correlation ◽  
Growing Season ◽  
Vegetation Coverage ◽  
Semiarid Region ◽  
Linear Regression Method ◽  
Upward Trend ◽  
Drought Effect ◽  
Positive Correlation

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

scholarly journals Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3676 ◽  
Author(s):  
Hao Chen ◽  
Xiangnan Liu ◽  
Chao Ding ◽  
Fang Huang
Keyword(s):  
Trend Analysis ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Practical Method ◽  
Large Area ◽  
Modis Ndvi ◽  
Daily Precipitation Data ◽  
Improved Model

Land degradation is a widespread environmental issue and an important factor in limiting sustainability. In this study, we aimed to improve the accuracy of monitoring human-induced land degradation by using phenological signal detection and residual trend analysis (RESTREND). We proposed an improved model for assessing land degradation named phenology-based RESTREND (P-RESTREND). This method quantifies the influence of precipitation on normalized difference vegetation index (NDVI) variation by using the bivariate linear regression between NDVI and precipitation in pre-growing season and growing season. The performances of RESTREND and P-RESTREND for discriminating land degradation caused by climate and human activities were compared based on vegetation-precipitation relationship. The test area is in Western Songnen Plain, Northeast China. It is a typical region with a large area of degraded drylands. The MODIS 8-day composite reflectance product and daily precipitation data during 2000–2015 were used. Our results showed that P-RESTREND was more effective in distinguishing different drivers of land degradation than the RESTREND. Degraded areas in the Songnen grasslands can be effectively detected by P-RESTREND. Therefore, this modified model can be regarded as a practical method for assessing human-induced land degradation.

scholarly journals Using remote sensing for identification of late-season grass weed patches in wheat

Weed Science ◽  
2006 ◽  
Vol 54 (02) ◽  
pp. 346-353 ◽  
Author(s):  
Francisca López-Granados ◽  
Montse Jurado-Expósito ◽  
Jose M. Peña-Barragán ◽  
Luis García-Torres
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Research ◽  
Aerial Images ◽  
Wild Oat ◽  
Late Season ◽  
High Resolution Satellite Imagery ◽  
Grass Weed

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

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