Aquatic vegetation indices assessment through radiative transfer modeling and linear mixture simulation

Villa, Paolo; Mousivand, Alijafar; Bresciani, Mariano

doi:10.1016/j.jag.2014.01.017

Cited by 68 publications

(63 citation statements)

References 59 publications

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“…In fact, except for case (c), the NST of LAI to NDAVI is higher than to the other indices, which indicates that for emergent vegetation, NDAVI is more suitable for LAI inversion than the other indices except the deep-sparse case. This result is consistent with the conclusion of Villa et al that for aquatic vegetation LAI is more sensitive to NDAVI than to the conventional terrestrial vegetation index [38]. In case (c), LAI scores higher sensitivity to NDVI, indicating that NDVI is a better option in deep-sparse case.…”

Section: Ndvisupporting

confidence: 91%

“…more sensitive to NDAVI than to the conventional terrestrial vegetation index [38]. In case (c), LAI scores higher sensitivity to NDVI, indicating that NDVI is a better option in deep-sparse case.…”

Section: Emergent Vegetationmentioning

confidence: 94%

“…Among them, the results of Modtran-5 showed that aerosol optical parameters such as AOT and Angstrom Coefficient had very significant effects in the range 400-2500 nm [37]. Aquatic vegetation indices and their related sensitivity analysis in reflectance spectra have recently been reported [38]. However, the model used in this work for aquatic vegetation is a terrestrial vegetation model applied in an aquatic context.…”

Section: Introductionmentioning

confidence: 99%

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Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

Zhou

Sathyendranath

et al. 2018

Remote Sensing

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Optical remote sensing of aquatic vegetation in shallow water is an essential aid to ecosystem protection, but it is difficult because the spectral characteristics of the vegetation are sensitive to external features such as water background effects, atmospheric effects, and the structural properties of the canopy. A global sensitivity analysis of an aquatic vegetation radiative transfer model provides invaluable background for algorithm development for use in optical remote sensing. Here, we use the extended Fourier Amplitude Sensitivity Test (EFAST) method for the modelling. Four different cases were identified by subdividing the ranges of water depth and leaf area index (LAI) involved. The results indicate that the reflectance of emergent vegetation is affected mainly by the concentrations of chlorophyll a + b in leaves (Cab), leaf inclination distribution function parameter (LIDFa) and LAI. The parameter LAI is influential in sparse vegetation cases whereas Cab and LIDFa are influential in dense vegetation cases. Canopy reflectance for submerged vegetation is dominated by water parameters. Relatively, LAI and Cab are highly sensitive vegetation parameters. The analysis is extended to vegetation index as well, which takes the Sentinel-2A as the reference sensor. It shows that NDAVI (Normalized Difference Aquatic Vegetation Index) is suitable for retrieving LAI in all cases except deep-sparse for emergent vegetation, whereas NDVI (Normalized Difference Vegetation Index) would be better in the deep-sparse case. NDVI, NDAVI and WAVI (Water Adjusted Vegetation Index), respectively, are suitable for retrieving Cab, Car and LIDFa in dense cases. For submerged vegetation, the sensitivity of LAI to NDAVI is relatively high only in the shallow-sparse case. The adjustment factor L in SAVI and WAVI fails to suppress the sensitivity to water constituent parameters. The sensitivity of LAI and Cab to NDVI in deep cases is relatively higher than that to the other indices, which may provide clues for the construction of inversion algorithms in macrophyte remote sensing in the aquatic environment using spectral signatures in the visible and near infrared regions.

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Section: Ndvisupporting

confidence: 91%

Section: Emergent Vegetationmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

Zhou

Sathyendranath

et al. 2018

Remote Sensing

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“…In order to assess the SAV classification accuracy for the other images (i.e., 2013, 2015, and 2016), we identified total 17 SAV stations used for validation (i.e., Station 1,5,6,9,15,18,27,34,36,38,41,42,43,45,48, 49 and 52) out of 52 observation points (Figure 1) based on the available data in Table 1 and the in situ survey (2016). We also used the SAV biomass maps available in the literature for the south basin of Lake Biwa, primarily for the SAV growth period (i.e., mainly September) in 2002, 2007, 2012, and 2014 [63,65].…”

Section: Sav Distribution Mapmentioning

confidence: 99%

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

et al. 2017

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Assessing the abundance of submerged aquatic vegetation (SAV), particularly in shallow lakes, is essential for effective lake management activities. In the present study we applied satellite remote sensing (a Landsat-8 image) in order to evaluate the SAV coverage area and its biomass for the peak growth period, which is mainly in September or October (2013October ( to 2016, in the eutrophic and shallow south basin of Lake Biwa. We developed and validated a satellite-based water transparency retrieval algorithm based on the linear regression approach (R 2 = 0.77) to determine the water clarity (2013)(2014)(2015)(2016), which was later used for SAV classification and biomass estimation. For SAV classification, we used Spectral Mixture Analysis (SMA), a Spectral Angle Mapper (SAM), and a binary decision tree, giving an overall classification accuracy of 86.5% and SAV classification accuracy of 76.5% (SAV kappa coefficient 0.74), based on in situ measurements. For biomass estimation, a new Spectral Decomposition Algorithm was developed. The satellite-derived biomass (R 2 = 0.79) for the SAV classified area gives an overall root-mean-square error (RMSE) of 0.26 kg dry weight (DW) m −2 . The mapped SAV coverage area was 20% and 40% in 2013 and 2016, respectively. Estimated SAV biomass for the mapped area shows an increase in recent years, with values of 3390 t (tons, dry weight) in 2013 as compared to 4550 t in 2016. The maximum biomass density (4.89 kg DW m −2 ) was obtained for a year with high water transparency (September 2014). With the change in water clarity, a slow change in SAV growth was noted from 2013 to 2016. The study shows that water clarity is important for the SAV detection and biomass estimation using satellite remote sensing in shallow eutrophic lakes. The present study also demonstrates the successful application of the developed satellite-based approach for SAV biomass estimation in the shallow eutrophic lake, which can be tested in other lakes.

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“…These important indices are used to describe various aspects of the whole plant community (Magurran 1988). It should be noted that the VI was not the vegetation indices which was widely used in remote sensing (Elvidge and Chen 1995;Villa et al 2014). The VI in this study was a composite index which was established according to the principle in founding SQI.…”

Section: Sqi and VImentioning

confidence: 99%

Potential effects of large linear pipeline construction on soil and vegetation in ecologically fragile regions

Xiao

Wang

Shi

et al. 2014

Environ Monit Assess

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Long-distance pipeline construction results in marked human disturbance of the regional ecosystem and brings into question the safety of pipeline construction with respect to the environment. Thus, the direct environmental impact and proper handling of such large projects have received much attention. The potential environmental effects, however, have not been fully addressed, particularly for large linear pipeline projects, and the threshold of such effects is unclear. In this study, two typical eco-fragile areas in western China, where large linear construction projects have been conducted, were chosen as the case study areas. Soil quality indices (SQI) and vegetation indices (VI), representing the most important potential effects, were used to analyze the scope of the effect of large pipeline construction on the surrounding environment. These two indices in different buffer zones along the pipeline were compared against the background values. The analysis resulted in three main findings. First, pipeline construction continues to influence the nearby eco-environment even after a 4-year recovery period. During this period, the effect on vegetation due to pipeline construction reaches 300 m beyond the working area, and is much larger in distance than the effect on soil, which is mainly confined to within 30 m either side of the pipeline, indicating that vegetation is more sensitive than soil to this type of human disturbance. However, the effect may not reach beyond 500 m from the pipeline. Second, the scope of the effect in terms of distance on vegetation may also be determined by the frequency of disturbance and the intensity of the pipeline construction. The greater the number of pipelines in an area, the higher the construction intensity and the more frequent the disturbance. Frequent disturbance may expand the effect on vegetation on both sides of the pipeline, but not on soil quality. Third, the construction may eliminate the stable, resident plant community. During the recovery period, the plant community in the work area of the pipeline is replaced by some species that are rare or uncommon in the resident plant community because of human disturbance, thereby increasing the plant diversity in the work area. In terms of plant succession, the duration of the recovery period has a direct effect on the composition and structure of the plant community. The findings provide a theoretical basis and scientific foundation for improving the environmental impact assessment (EIA) of oil and gas pipeline construction as it pertains to the desert steppe ecosystem, and provide a reference point for recovery and management of the eco-environment during the pipeline construction period.

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Aquatic vegetation indices assessment through radiative transfer modeling and linear mixture simulation

Cited by 68 publications

References 59 publications

Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

Potential effects of large linear pipeline construction on soil and vegetation in ecologically fragile regions

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