On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: A case study of the Honghu Lake, PR China

Zhang, X.

doi:10.1080/014311698216396

Cited by 53 publications

(20 citation statements)

References 10 publications

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“…It is important to note that with increases in biomass, the relationship between the spectral signal and the actual biomass approaches an asymptote (Peñuelas et al 1993). Zhang (1998), using the first and second principal components of a PCA transformed TM image, estimated the biomass of submerged stands in the Honghu Lake (China), obtaining a coefficient of determination of R 2 = 0.85. Also, submerged vegetation biomass has been estimated by Armstrong (1993), using depth normalized TM images and obtaining an overall R 2 = 0.79.…”

Section: Spectral Behavior Of Emergent Speciesmentioning

confidence: 99%

“…Although the spatial resolution of these systems is, in most cases, incapable of discriminating aquatic vegetation at the species level (Jensen et al 1993), satellite imagery is useful for mapping macrophytes communities. Landsat MSS and TM images have been employed for mapping submerged (Zhang 1998;Armstrong 1993;Ackleson and Klemas 1987) and emergent vegetation. Images with spatial resolutions higher than Landsat have also been applied to both vegetation types, e.g., SPOT data (Pasqualini et al 2005;Jensen et al 1995Jensen et al , 1993Jensen et al , 1986 and IKONOS images, with 1m resolution (Sawaya et al 2003).…”

Section: Spectral Behavior Of Emergent Speciesmentioning

confidence: 99%

See 1 more Smart Citation

Remote sensing of aquatic vegetation: theory and applications

Silva

Costa

Mélack

et al. 2007

Environ Monit Assess

270

205

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Aquatic vegetation is an important component of wetland and coastal ecosystems, playing a key role in the ecological functions of these environments. Surveys of macrophyte communities are commonly hindered by logistic problems, and remote sensing represents a powerful alternative, allowing comprehensive assessment and monitoring. Also, many vegetation characteristics can be estimated from reflectance measurements, such as species composition, vegetation structure, biomass, and plant physiological parameters. However, proper use of these methods requires an understanding of the physical processes behind the interaction between electromagnetic radiation and vegetation, and remote sensing of aquatic plants have some particular difficulties that have to be properly addressed in order to obtain successful results. The present paper reviews the theoretical background and possible applications of remote sensing techniques to the study of aquatic vegetation.

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Section: Spectral Behavior Of Emergent Speciesmentioning

confidence: 99%

Section: Spectral Behavior Of Emergent Speciesmentioning

confidence: 99%

Remote sensing of aquatic vegetation: theory and applications

Silva

Costa

Mélack

et al. 2007

Environ Monit Assess

270

205

View full text Add to dashboard Cite

show abstract

“…Another major application of wetland remote sensing is parameter inversion, which includes mainly biomass [9][10][11][12] and leaf area index (LAI) [13,14]. The inversion is usually based upon statistical methods that contain no fundamental mechanistic information about the optical properties of the vegetation itself.…”

Section: Introductionmentioning

confidence: 99%

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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“…A number of researchers have investigated the potential of using current operational airborne systems for the mapping and monitoring of SAV. A Landsat 5 TM image was used to assess the total biomass of SAV in Honghu Lake, by deducing from the relationship between SAV biomass and its spectral data measured at a series of sampling sites (Zhang, 1998). In that study, the weighed SAV biomass was set into a tank filled with the lake water and the spectral reflectance was measured, in a manner similar to our laboratory experiments.…”

Section: Comparison Between the Laboratory And Field Experimentsmentioning

confidence: 99%

The spectral responses of a submerged plant Vallisneria spiralis with varying biomass using spectroradiometer

Lin

Zhang

2007

Hydrobiologia

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The relationship between land features and their spectral characteristics is a key for the interpretation of remote sensing images. This study was designed to investigate the spectral responses of Vallisneria spiralis, a common submerged aquatic plant in Shanghai, with varying biomass both in the laboratory and in the Middle Lake section of a field-scale constructed wetland, using a FieldSpec TM Pro JR Field Portable Spectroradiometer. The results showed that the reflectance rate of V. spiralis increased with its increasing biomass, and this was exhibited both at the visible band (500-650 nm) and the near infrared band (700-900 nm). The water environment influenced the reflectance rate and the primary differences between the laboratory and field results mainly occurred at the near-infrared band (700-900 nm). A regression analysis was carried out between the biomass of V. spiralis and the reflectance rate at the wavelengths of QuickBird TM bands where the biomass responded most strongly. The results of this analysis showed a clear linear relationship by which the biomass of V. spiralis could be quantitatively deduced from the reflectance rate measured in situ. The implications of this observation, in terms of the ability of hyperspectral remote sensing to estimate and monitor the distribution and dynamics of submerged aquatic vegetation on a large scale, are discussed.

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On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: A case study of the Honghu Lake, PR China

Cited by 53 publications

References 10 publications

Remote sensing of aquatic vegetation: theory and applications

Remote sensing of aquatic vegetation: theory and applications

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

The spectral responses of a submerged plant Vallisneria spiralis with varying biomass using spectroradiometer

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