CANOPY MODELING OF AQUATIC VEGETATION: CONSTRUCTION OF SUBMERGED VEGETATION INDEX

The unique spectral characteristics of submerged vegetation in wetlands determine that the conventional terrestrial vegetation index cannot be directly employed to species identification and parameter inversion of submerged vegetation. Based on the Aquatic Vegetation Radiative Transfer model (AVRT), this paper attempts to construct an index suitable for submerged vegetation, the model simulated data and a scene of Sentinel-2A image in Taihu Lake, China are utilized for assessing the performance of the newly constructed indices and the existent vegetation indices. The results show that the angle index composed by 525&thinsp;nm, 555&thinsp;nm and 670&thinsp;nm can resist the effects of water columns and is more sensitive to vegetation parameters such as LAI. Furthermore, it makes a well discrimination between submerged vegetation and water bodies in the satellite data. We hope that the new index will provide a theoretical basis for future research.

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Chlorophyll Changes Using Neural Network and Vegetation Indices in Tropical deciduous forest

10.21203/rs.3.rs-23278/v1 ◽

2020 ◽

Author(s):

Saurabh Kumar Gupta ◽

Arvind Chandra Pandey

Keyword(s):

Neural Network ◽

Vegetation Index ◽

Deciduous Forest ◽

Plant Stress ◽

Vegetation Indices ◽

Radiative Transfer Model ◽

Tropical Deciduous Forest ◽

Transfer Model ◽

Landsat 8 ◽

Spatio Temporal

Abstract Background: Ongoing climate and Earth’s atmosphere changes create profound effect on distribution and composition of forest, as well as on the fauna that depends on forest. The Sentinel-2A satellite data eases the mapping of Leaf Chlorophyll Content (LCC) at higher spatial and temporal resolution. In the present study, the temporal dimension of LCC was evaluated as an indicator of plant stress. LCC was retrieved using the inversion of the radiative transfer model based on an artificial neural network. The data used for Spatio-temporal modelling of LCC was Landsat data.Result: From the Sentinel imagery derived vegetation indices, it was found that the narrowband indices having high correlation with LCC were pigment specific simple ratio and normalized difference index (45) (R2 > 0.7; p < 0.001) centred at 665 nm, 705 nm, and 740 nm. Landsat 8 infrared percentage vegetation index had a strong relationship with LCC (R2 =0.8). The Spatio-temporal (1997 to 2017) plant stress were detected using changes in LCC through an equation of correlation. The negative changes and deterioration of LCC were seen in the forest during the year 1997 to 20I7(rate = -1.2 µgcm-2year-1) showing higher rate of forest health decline. Conclusion: The 33% of plant stress increased currently in the protected forest mainly because of anthropogenic influences. These vast decline in the chlorophyll gives rise to various photosynthetic vulnerabilities in forest ecosystem and indirectly affects human including wildlife.

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Analytical Relationship between Two-Band Spectral Vegetation Indices Measured at Multiple Sensors on a Parametric Representation of Soil Isoline Equations

Remote Sensing ◽

10.3390/rs11131620 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1620 ◽

Cited By ~ 1

Author(s):

Kenta Taniguchi ◽

Kenta Obata ◽

Hiroki Yoshioka

Keyword(s):

Vegetation Index ◽

Vegetation Indices ◽

Parametric Representation ◽

Radiative Transfer Model ◽

Analytical Relationship ◽

Transfer Model ◽

Multiple Sensors ◽

Spectral Vegetation Indices ◽

Relative Errors ◽

The Relationship

Differences between the wavelength band specifications of distinct sensors introduce systematic differences into the values of a spectral vegetation index (VI). Such relative errors must be minimized algorithmically after data acquisition, based on a relationship between the measurements. This study introduces a technique for deriving the analytical relationship between the VIs from two sensors. The derivation proceeds using a parametric form of the soil isoline equations, which relate the reflectances of two different wavelengths. First, the derivation steps are explained conceptually. Next, the conceptual steps are cast in a practical derivation by assuming a general form of the two-band VI. Finally, the derived expressions are demonstrated numerically using a coupled leaf and canopy radiative transfer model. The results confirm that the derived expression reduced the original differences between the VI values obtained from the two sensors, indicating the validity of the derived expressions. The derived expressions and numerical results suggested that the relationship between the VIs measured at different wavelengths varied with the soil reflectance spectrum beneath the vegetation canopy. These results indicate that caution is required when retrieving intersensor VI relationships over regions consisting of soil surfaces having distinctive spectra.

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Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices

Remote Sensing ◽

10.3390/rs13163175 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3175

Author(s):

Naichen Xing ◽

Wenjiang Huang ◽

Huichun Ye ◽

Yu Ren ◽

Qiaoyun Xie

Keyword(s):

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Vegetation Indices ◽

Radiative Transfer Model ◽

Transfer Model ◽

Growth Stages ◽

Growth Monitoring ◽

Area Index

Leaf area index (LAI) and canopy chlorophyll density (CCD) are key biophysical and biochemical parameters utilized in winter wheat growth monitoring. In this study, we would like to exploit the advantages of three canonical types of spectral vegetation indices: indices sensitive to LAI, indices sensitive to chlorophyll content, and indices suitable for both parameters. In addition, two methods for joint retrieval were proposed. The first method is to develop integration-based indices incorporating LAI-sensitive and CCD-sensitive indices. The second method is to create a transformed triangular vegetation index (TTVI2) based on the spectral and physiological characteristics of the parameters. PROSAIL, as a typical radiative transfer model embedded with physical laws, was used to build estimation models between the indices and the relevant parameters. Validation was conducted against a field-measured hyperspectral dataset for four distinct growth stages and pooled data. The results indicate that: (1) the performance of the integrated indices from the first method are various because of the component indices; (2) TTVI2 is an excellent predictor for joint retrieval, with the highest R2 values of 0.76 and 0.59, the RMSE of 0.93 m2/m2 and 104.66 μg/cm2, and the RRMSE (Relative RMSE) of 12.76% and 16.96% for LAI and CCD, respectively.

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Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Remote Sensing ◽

10.3390/rs13112060 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2060

Author(s):

Trylee Nyasha Matongera ◽

Onisimo Mutanga ◽

Mbulisi Sibanda ◽

John Odindi

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Critical Role ◽

Spectral Characteristics ◽

Vegetation Indices ◽

Machine Learning Algorithms ◽

Special Focus ◽

Land Surface Phenology ◽

Spectral Vegetation Indices ◽

Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

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Error simulations of uncorrected NDVI and DCVI during remote sensing measurements from UAS

Miscellanea Geographica ◽

10.2478/mgrsd-2014-0017 ◽

2014 ◽

Vol 18 (2) ◽

pp. 35-45 ◽

Cited By ~ 2

Author(s):

Michał T. Chiliński ◽

Marek Ostrowski

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Vegetation Index ◽

Vegetation Mapping ◽

Radiative Transfer Model ◽

Unmanned Aerial Systems ◽

Transfer Model ◽

Digital Cameras ◽

Aerial Systems

Abstract Remote sensing from unmanned aerial systems (UAS) has been gaining popularity in the last few years. In the field of vegetation mapping, digital cameras converted to calculate vegetation index (DCVI) are one of the most popular sensors. This paper presents simulations using a radiative transfer model (libRadtran) of DCVI and NDVI results in an environment of possible UAS flight scenarios. The analysis of the results is focused on the comparison of atmosphere influence on both indices. The results revealed uncertainties in uncorrected DCVI measurements up to 25% at the altitude of 5 km, 5% at 1 km and around 1% at 0.15 km, which suggests that DCVI can be widely used on small UAS operating below 0.2 km.

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Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

Sensors ◽

10.3390/s20092460 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2460 ◽

Cited By ~ 1

Author(s):

Yangyang Zhang ◽

Jian Yang ◽

Xiuguo Liu ◽

Lin Du ◽

Shuo Shi ◽

...

Keyword(s):

Gaussian Process ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Gaussian Process Regression ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index ◽

Look Up Table ◽

Band Combinations

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

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VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

Remote Sensing ◽

10.3390/rs12142254 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2254 ◽

Cited By ~ 1

Author(s):

Hafiz Ali Imran ◽

Damiano Gianelle ◽

Duccio Rocchini ◽

Michele Dalponte ◽

M. Pilar Martín ◽

...

Keyword(s):

Near Infrared ◽

Spatial Scales ◽

Vegetation Indices ◽

Radiative Transfer Model ◽

Transfer Model ◽

Sharp Change ◽

Full Potential ◽

Area Index ◽

Red Edge ◽

Temporal And Spatial

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

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Influence of Leaf Specular Reflection on Canopy Radiative Regime Using an Improved Version of the Stochastic Radiative Transfer Model

Remote Sensing ◽

10.3390/rs10101632 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1632 ◽

Cited By ~ 1

Author(s):

Bin Yang ◽

Yuri Knyazikhin ◽

Donghui Xie ◽

Haimeng Zhao ◽

Junqiang Zhang ◽

...

Keyword(s):

Radiative Transfer ◽

Flux Density ◽

Vegetation Index ◽

Radiation Flux ◽

Specular Reflection ◽

Radiative Transfer Model ◽

Transfer Model ◽

Remotely Sensed Data ◽

Wide Range ◽

Radiative Regime

Interpreting remotely-sensed data requires realistic, but simple, models of radiative transfer that occurs within a vegetation canopy. In this paper, an improved version of the stochastic radiative transfer model (SRTM) is proposed by assuming that all photons that have not been specularly reflected enter the leaf interior. The contribution of leaf specular reflection is considered by modifying leaf scattering phase function using Fresnel reflectance. The canopy bidirectional reflectance factor (BRF) estimated from this model is evaluated through comparisons with field-measured maize BRF. The result shows that accounting for leaf specular reflection can provide better performance than that when leaf specular reflection is neglected over a wide range of view zenith angles. The improved version of the SRTM is further adopted to investigate the influence of leaf specular reflection on the canopy radiative regime, with emphases on vertical profiles of mean radiation flux density, canopy absorptance, BRF, and normalized difference vegetation index (NDVI). It is demonstrated that accounting for leaf specular reflection can increase leaf albedo, which consequently increases canopy mean upward/downward mean radiation flux density and canopy nadir BRF and decreases canopy absorptance and canopy nadir NDVI when leaf angles are spherically distributed. The influence is greater for downward/upward radiation flux densities and canopy nadir BRF than that for canopy absorptance and NDVI. The results provide knowledge of leaf specular reflection and canopy radiative regime, and are helpful for forward reflectance simulations and backward inversions. Moreover, polarization measurements are suggested for studies of leaf specular reflection, as leaf specular reflection is closely related to the canopy polarization.

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Evaluation and Normalization of Topographic Effects on Vegetation Indices

Remote Sensing ◽

10.3390/rs12142290 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2290

Author(s):

Rui Chen ◽

Gaofei Yin ◽

Guoxiang Liu ◽

Jing Li ◽

Aleixandre Verger

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Vegetation Indices ◽

Topographic Effects ◽

Terrestrial Vegetation ◽

Mountainous Areas ◽

Near Infrared Reflectance ◽

Illumination Condition ◽

Vegetation Monitoring ◽

Local Illumination

The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. The evaluation was based on two criteria: the correlation with local illumination condition and the dependence on aspect. Results show that topographic effects can be neglected for the NDVI, while they heavily influence the SAVI, EVI, and NIRv: the local illumination condition explains 19.85%, 25.37%, and 26.69% of the variation of the SAVI, EVI, and NIRv, respectively, and the coefficients of variation across different aspects are, respectively, 8.13%, 10.46%, and 14.07%. We demonstrated the applicability of existing correction methods, including statistical-empirical (SE), sun-canopy-sensor with C-correction (SCS + C), and path length correction (PLC), dedicatedly designed for reflectance, to normalize topographic effects on VIs. Our study will benefit vegetation monitoring with VIs over mountainous areas.

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Modelling understory light availability in a heterogeneous landscape using drone-derived structural parameters and a 3D radiative transfer model

10.5194/egusphere-egu2020-19003 ◽

2020 ◽

Author(s):

Dominic Fawcett ◽

Jonathan Bennie ◽

Karen Anderson

Keyword(s):

Radiative Transfer ◽

Vegetation Index ◽

Structural Parameters ◽

Point Clouds ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index ◽

Vegetation Height ◽

Plant Area Index ◽

The Impact

The light environment within vegetated landscapes is a key driver of microclimate, creating varied habitats over small spatial extents and controls the distribution of understory plant species. Modelling spatial variations of light at these scales requires finely resolved (< 1 m) information on topography and canopy properties. We demonstrate an approach to modelling spatial distributions and temporal progression of understory photosynthetically active radiation (PAR) utilising a three dimensional radiative transfer model (discrete anisotropic radiative transfer model: DART) where the scene is parameterised by drone-based data.The study site, located in west Cornwall, UK, includes a small mixed woodland as well as isolated free-standing trees. Data were acquired from March to August 2019. Vegetation height and distribution were derived from point clouds generated from drone image data using structure-from-motion (SfM) photogrammetry. These data were supplemented by multi-temporal multispectral imagery (Parrot Sequoia camera) which were used to generate an empirical model by relating a vegetation index to plant area index derived from hemispherical photography taken over the same time period. Simulations of the 3D radiative budget were performed for the PAR wavelength interval (400 &#8211; 700 nm) using DART.Besides maps of instantaneous above and below canopy irradiance, we provide models of daily light integrals (DLI) which are assessed against field validation measurements with PAR quantum sensors. We find relatively good agreement for simulated PAR in the woodland. The impact of simplifying assumptions regarding leaf angular distributions and optical properties are discussed. Finally, further opportunities which fine-grained drone data can provide in a radiative transfer context are highlighted.

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