Phenology affects differentiation of crop and weed species using hyperspectral remote sensing

2020 ◽  
Vol 34 (6) ◽  
pp. 897-908
Author(s):  
Nicholas T. Basinger ◽  
Katherine M. Jennings ◽  
Erin L. Hestir ◽  
David W. Monks ◽  
David L. Jordan ◽  
...  
Keyword(s):  
Near Infrared ◽  
Canopy Structure ◽  
Growing Season ◽  
Plant Phenology ◽  
Species Differentiation ◽  
In Situ Spectroscopy ◽  
Weed Species ◽  
Leaf Level ◽  
Shortwave Infrared

AbstractThe effect of plant phenology and canopy structure of four crops and four weed species on reflectance spectra were evaluated in 2016 and 2017 using in situ spectroscopy. Leaf-level and canopy-level reflectance were collected at multiple phenologic time points in each growing season. Reflectance values at 2 wk after planting (WAP) in both years indicated strong spectral differences between species across the visible (VIS; 350–700 nm), near-infrared (NIR; 701–1,300 nm), shortwave-infrared I (SWIR1; 1,301–1,900 nm), and shortwave-infrared II (SWIR2; 1,901–2,500 nm) regions. Results from this study indicate that plant spectral reflectance changes with plant phenology and is influenced by plant biophysical characteristics. Canopy-level differences were detected in both years across all dates except for 1 WAP in 2017. Species with similar canopy types (e.g., broadleaf prostrate, broadleaf erect, or grass/sedge) were more readily discriminated from species with different canopy types. Asynchronous phenology between species also resulted in spectral differences between species. SWIR1 and SWIR2 wavelengths are often not included in multispectral sensors but should be considered for species differentiation. Results from this research indicate that wavelengths in SWIR1 and SWIR2 in conjunction with VIS and NIR reflectance can provide differentiation across plant phenologies and, therefore should be considered for use in future sensor technologies for species differentiation.

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 Deriving seasonal dynamics in ecosystem properties of semi-arid savanna grasslands from in situ-based hyperspectral reflectance

2015 ◽  
Vol 12 (15) ◽  
pp. 4621-4635 ◽  
Author(s):  
T. Tagesson ◽  
R. Fensholt ◽  
S. Huber ◽  
S. Horion ◽  
I. Guiro ◽  
...  
Keyword(s):  
Seasonal Dynamics ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Arid Ecosystems ◽  
Hyperspectral Reflectance ◽  
Data Set ◽  
Ecosystem Properties ◽  
Shortwave Infrared ◽  
Semi Arid

Abstract. This paper investigates how hyperspectral reflectance (between 350 and 1800 nm) can be used to infer ecosystem properties for a semi-arid savanna grassland in West Africa using a unique in situ-based multi-angular data set of hemispherical conical reflectance factor (HCRF) measurements. Relationships between seasonal dynamics in hyperspectral HCRF and ecosystem properties (biomass, gross primary productivity (GPP), light use efficiency (LUE), and fraction of photosynthetically active radiation absorbed by vegetation (FAPAR)) were analysed. HCRF data (ρ) were used to study the relationship between normalised difference spectral indices (NDSIs) and the measured ecosystem properties. Finally, the effects of variable sun sensor viewing geometry on different NDSI wavelength combinations were analysed. The wavelengths with the strongest correlation to seasonal dynamics in ecosystem properties were shortwave infrared (biomass), the peak absorption band for chlorophyll a and b (at 682 nm) (GPP), the oxygen A band at 761 nm used for estimating chlorophyll fluorescence (GPP and LUE), and blue wavelengths (ρ412) (FAPAR). The NDSI with the strongest correlation to (i) biomass combined red-edge HCRF (ρ705) with green HCRF (ρ587), (ii) GPP combined wavelengths at the peak of green reflection (ρ518, ρ556), (iii) LUE combined red (ρ688) with blue HCRF (ρ436), and (iv) FAPAR combined blue (ρ399) and near-infrared (ρ1295) wavelengths. NDSIs combining near infrared and shortwave infrared were strongly affected by solar zenith angles and sensor viewing geometry, as were many combinations of visible wavelengths. This study provides analyses based upon novel multi-angular hyperspectral data for validation of Earth-observation-based properties of semi-arid ecosystems, as well as insights for designing spectral characteristics of future sensors for ecosystem monitoring.

scholarly journals Hyperspectral Monitoring of Non-Native Tropical Grasses over Phenological Seasons

2021 ◽  
Vol 13 (4) ◽  
pp. 738
Author(s):  
Kirrilly Pfitzner ◽  
Renee Bartolo ◽  
Tim Whiteside ◽  
David Loewensteiner ◽  
Andrew Esparon
Keyword(s):  
Near Infrared ◽  
Spectral Response ◽  
Grass Species ◽  
Wet Season ◽  
Native Grass ◽  
Data Collections ◽  
Spectral Patterns ◽  
Shortwave Infrared ◽  
Over Time

The miniaturisation of hyperspectral sensors for use on drones has provided an opportunity to obtain hyper temporal data that may be used to identify and monitor non-native grass species. However, a good understanding of variation in spectra for species over time is required to target such data collections. Five taxological and morphologically similar non-native grass species were hyper spectrally characterised from multitemporal spectra (17 samples over 14 months) over phenological seasons to determine their temporal spectral response. The grasses were sampled from maintained plots of homogenous non-native grass cover. A robust in situ standardised sampling method using a non-imaging field spectrometer measuring reflectance across the 350–2500 nm wavelength range was used to obtain reliable spectral replicates both within and between plots. The visible-near infrared (VNIR) to shortwave infrared (SWIR) and continuum removed spectra were utilised. The spectra were then resampled to the VNIR only range to simulate the spectral response from more affordable VNIR only hyperspectral scanners suitable to be mounted on drones. We found that species were separable compared to similar but different species. The spectral patterns were similar over time, but the spectral shape and absorption features differed between species, indicating these subtle characteristics could be used to distinguish between species. It was the late dry season and the end of the wet season that provided maximum separability of the non-native grass species sampled. Overall the VNIR-SWIR results highlighted more dissimilarity for unlike species when compared to the VNIR results alone. The SWIR is useful for discriminating species, particularly around water absorption.

scholarly journals Deriving seasonal dynamics in ecosystem properties of semi-arid savannas using in situ based hyperspectral reflectance

2015 ◽  
Vol 12 (4) ◽  
pp. 3315-3347
Author(s):  
T. Tagesson ◽  
R. Fensholt ◽  
S. Huber ◽  
S. Horion ◽  
I. Guiro ◽  
...  
Keyword(s):  
Seasonal Dynamics ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Arid Ecosystems ◽  
Hyperspectral Reflectance ◽  
Ecosystem Properties ◽  
Shortwave Infrared ◽  
Reflectance Data ◽  
Semi Arid

Abstract. This paper investigates how seasonal hyperspectral reflectance data (between 350 and 1800 nm) can be used to infer ecosystem properties for a semi-arid savanna ecosystem in West Africa using a unique in situ based dataset. Relationships between seasonal dynamics in hyperspectral reflectance, and ecosystem properties (biomass, gross primary productivity (GPP), light use efficiency (LUE), and fraction of photosynthetically active radiation absorbed by vegetation (FAPAR)) were analysed. Reflectance data (ρ) were used to study the relationship between normalised difference spectral indices (NDSI) and the measured ecosystem properties. Finally, also the effects of variable sun sensor viewing geometry on different NDSI wavelength combinations were analysed. The wavelengths with the strongest correlation to seasonal dynamics in ecosystem properties were shortwave infrared (biomass), the peak absorption band for chlorophyll a and b (at 682 nm) (GPP), the oxygen A-band at 761 nm used for estimating chlorophyll fluorescence (GPP, and LUE), and blue wavelengths (FAPAR). The NDSI with the strongest correlation to: (i) biomass combined red edge reflectance (ρ705) with green reflectance (ρ587), (ii) GPP combined wavelengths at the peak of green reflection (ρ518, ρ556), (iii) the LUE combined red (ρ688) with blue reflectance (ρ436), and (iv) FAPAR combined blue (ρ399) and near infrared (ρ1295) wavelengths. NDSI combining near infrared and shortwave infrared were strongly affected by solar zenith angles and sensor viewing geometry, as were many combinations of visible wavelengths. This study provides analyses based upon novel multi-angular hyperspectral data for validation of Earth Observation based properties of semi-arid ecosystems, as well as insights for designing spectral characteristics of future sensors for ecosystem monitoring.

Two-Dimensional Near-Infrared and Shortwave Infrared Molecular Aggregates: Taking Kasha’s Model to the Next Dimension

2019 ◽  
Author(s):  
Arundhati Deshmukh ◽  
Danielle Koppel ◽  
Chern Chuang ◽  
Danielle Cadena ◽  
Jianshu Cao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Photophysical Properties ◽  
Satellite Telemetry ◽  
Transition Dipole Moment ◽  
Short Wave ◽  
Tissue Imaging ◽  
Transition Dipole ◽  
Deep Tissue Imaging ◽  
Excitonic States ◽  
Shortwave Infrared

Technologies which utilize near-infrared (700 – 1000 nm) and short-wave infrared (1000 – 2000 nm) electromagnetic radiation have applications in deep-tissue imaging, telecommunications and satellite telemetry due to low scattering and decreased background signal in this spectral region. However, there are few molecular species, which absorb efficiently beyond 1000 nm. Transition dipole moment coupling (e.g. J-aggregation) allows for redshifted excitonic states and provides a pathway to highly absorptive electronic states in the infrared. We present aggregates of two cyanine dyes whose absorption peaks redshift dramatically upon aggregation in water from ~ 800 nm to 1000 nm and 1050 nm with sheet-like morphologies and high molar absorptivities (e ~ 10<sup>5 </sup>M<sup>-1</sup>cm<sup>-1</sup>). To describe this phenomenology, we extend Kasha’s model for J- and H-aggregation to describe the excitonic states of <i> 2-dimensional aggregates</i> whose slip is controlled by steric hindrance in the assembled structure. A consequence of the increased dimensionality is the phenomenon of an <i>intermediate </i>“I-aggregate”, one which redshifts yet displays spectral signatures of band-edge dark states akin to an H-aggregate. We distinguish between H-, I- and J-aggregates by showing the relative position of the bright (absorptive) state within the density of states using temperature dependent spectroscopy. Our results can be used to better design chromophores with predictable and tunable aggregation with new photophysical properties.

Scaling leaf photosynthesis to canopy in a mixed deciduous forest. I. model description

1997 ◽  
Vol 62 ◽  
Author(s):  
R. Samson ◽  
S. Follens ◽  
R. Lemeur
Keyword(s):  
Temperature Dependence ◽  
Quercus Robur ◽  
Deciduous Forest ◽  
Growing Season ◽  
Model Description ◽  
Canopy Photosynthesis ◽  
Leaf Photosynthesis ◽  
Layer Model ◽  
Mixed Deciduous Forest ◽  
Leaf Level

A  multi-layer model (FORUG) was developed, to simulate the canopy  photosynthesis of a mixed deciduous forest during the growing season.  Measured photosynthesis parameters, for beech (Fagus  sylvatica), oak (Quercus  robur) and ash (Fraxinus  excelsior), were used as input to the model. This  information at the leaf level is then scaled up to the level of the canopy,  taking into account the radiation profiles (diffuse and direct PAR) in the  canopy, the vertical LAI distribution, the evolution of the LAI and the  photosynthesis parameters during the growing season, and the temperature  dependence of the latter parameters.

The electron-transfer intermediates of the oxygen evolution reaction (OER) as polarons by in situ spectroscopy

2021 ◽  
Author(s):  
Hanna Lyle ◽  
Suryansh Singh ◽  
Michael Paolino ◽  
Ilya Vinogradov ◽  
Tanja Cuk
Keyword(s):  
Electron Transfer ◽  
Oxygen Evolution ◽  
Oxygen Evolution Reaction ◽  
Catalytic Reactions ◽  
In Situ Spectroscopy ◽  
Chemical Bonds

The conversion of diffusive forms of energy (electrical and light) into short, compact chemical bonds by catalytic reactions regularly involves moving a carrier from an environment that favors delocalization to one that favors localization.

Near infrared spectroscopy of Eucalyptus pellita for foliar nutrients and the potential for real-time monitoring of trees in fertiliser trial plots

2021 ◽  
pp. 096703352110079
Author(s):  
Agustan Alwi ◽  
Roger Meder ◽  
Yani Japarudin ◽  
Hazandy A Hamid ◽  
Ruzana Sanusi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Acacia Mangium ◽  
Nir Spectroscopy ◽  
Nutrient Status ◽  
Significant Loss ◽  
Eucalyptus Pellita ◽  
Operational Decision Making ◽  
Fertiliser Application ◽  
Planting Distance

Eucalyptus pellita F. Muell. has become an important tree species in the forest plantations of SE Asia, and in Malaysian Borneo in particular, to replace thousands of hectares of Acacia mangium Willd. which has suffered significant loss caused by Ceratocystis manginecans infection in Sabah, Malaysia. Since its first introduction at a commercial scale in 2012, E. pellita has been planted in many areas in the region. The species replacement requires new silvicultural practices to induce the adaptability of E. pellita to grow in the region and this includes relevant research to optimise such regimes as planting distance, pruning, weeding practices and nutrition regimes. In this present study, the nutritional status of the foliage was investigated with the aim to develop near infrared spectroscopic calibrations that can be used to monitor and quantify nutrient status, particularly total foliar nitrogen (N) and phosphorus (P) in the field. Spectra acquired on fresh foliage in situ on the tree could be used to predict N and P with accuracy suitable for operational decision-making regards fertiliser application. If greater accuracy is required, spectra acquired on dry, milled foliage could be used to predict N and P within a relative error of 10% (R2c, r2CV, RMSEP, RPD = 0.77, 0.71, 0.02 g 100/g, 1.9 for foliar P and = 0.90, 0.88, 0.21 g 100/g, 3.0 for foliar N on dry, milled foliage). The ultimate application of this is in situ nutrient monitoring, particularly to aid longitudinal studies in fertiliser trial plots and forest operations, as the non-destructive nature of NIR spectroscopy would enable regular monitoring of individual leaves over time without the need to destructively sample them. This would aid the temporal and spatial analysis of field data.

scholarly journals Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

2020 ◽  
Author(s):  
L. Granlund ◽  
M. Keinänen ◽  
T. Tahvanainen
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
High Resolution Imaging ◽  
Factors Affecting ◽  
Vector Machines ◽  
Resolution Imaging ◽  
Shortwave Infrared ◽  
Type Classification ◽  
Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

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