Assessment of Bidirectional Effects over Aquatic Macrophyte Vegetation in CIR Aerial Photographs

Valta-Hulkkonen, Kirsi; Peltoniemi, Jouni

doi:10.14358/pers.70.5.581

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…Some of the most common computer-aided analyses applied in SAV research are described below. Readers are directed to [80,81,[283][284][285] for examples of the quantitative analysis of photographs not covered here.…”

Section: Analysis Of Passive Optical Rs Imagerymentioning

confidence: 99%

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Rowan

Kalácska

2021

Remote Sensing

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Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

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Section: Analysis Of Passive Optical Rs Imagerymentioning

confidence: 99%

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Rowan

Kalácska

2021

Remote Sensing

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“…It has been indicated that aquatic vegetation yields spectrally distinct signals governed by the density of the vegetation, the openness of the canopy and the amounts, forms and orientations of the leaves [ 2 - 5 ]. Since traditional quantitative ground investigations on the scale of a whole lake are laborious, remote sensing methods are increasingly being used for mapping aquatic vegetation and estimating their distribution and biomass [ 6 - 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Malthus and George [ 19 ] demonstrated that a combination of band 3 (520-600 nm), band 7 (760-900 nm), and band 8 (910-1050 nm) data from the Daedalus Airborne Thematic Mapper could discriminate between different macrophyte growth forms. The DN (Digital Number) value, a sum of contributions due to atmosphere, water column and bottom, has been most commonly used to estimate vegetation biomass with an empirical linear or non-linear fitting model [ 9 ]. If the influence of the water column could be removed from the remotely sensed images, the potential of optical remote sensing in littoral applications would be extended and the accuracy of classification and biomass retrieval would be improved to a certain extent [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Detecting Aquatic Vegetation Changes in Taihu Lake, China Using Multi-temporal Satellite Imagery

Duan

et al. 2008

Sensors

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We have measured the water quality and bio-optical parameters of 94 samples from Taihu Lake in situ and/or in the lab between June 10-18, 2007. A transparency-assisted decision tree was developed to more accurately divide the aquatic vegetation zone into a floating vegetation-dominated zone and a submerged vegetation-dominated zone, whose respective present biomass retrieval models were easily developed with an empirical approach because of the quasi-concurrence of ground field investigations with the satellite sensor flight over the lake. The significant quantitative relationships between the vegetation index NDVI (Normalized Difference Vegetation Index) of different images at different times were used to help develop the past biomass retrieval model on the basis of the present developed model. In Taihu Lake, the total covering area of aquatic vegetations decreased from 454.6 km2 in 2001 to 364.1 km2 in 2007. Correspondingly, the total biomass decreased from 489,000 tons in 2001 to 406,000 tons in 2007, suggesting that a great change in the ecological environment has been taking place in Taihu Lake over this period.

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“…A combination of a 520 nm cut-off filter, a filter cutting out part of the reflectance in the near-infrared wavelength (NIR) area, and an anti-vignette coating to remove most of the light falloff effect (Lillesand and Kiefer 1994) were used when taking the photographs in 1996 and 2001, but as the 1953 photographs were acquired without such filters, brightness variations due to light falloff are expected to be present in these. The aerial photographs were acquired according to the criteria and plan described elsewhere by Valta-Hulkkonen et al (2004). Those for 1953 and Table 1.…”

Section: Aerial Photograph Datamentioning

confidence: 99%

“…On the other hand, methods for correcting these effects in frame format data have been developed further than those for airborne scanner data, for instance. The causes of brightness variations, their effects and the available correction methods are presented in detail in Pellikka (1998), Mikkola and Pellikka (2002) and Valta-Hulkkonen et al (2004).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing and GIS for detecting changes in the aquatic vegetation of a rehabilitated lake

Valta-Hulkkonen

Kanninen²

2004

International Journal of Remote Sensing

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Remote sensing and geographical information system (GIS) methods combined with ground estimations were used to assess the effects of rehabilitation on the aquatic vegetation of a shallow, eutrophic lake in Finland. Aerial photograph interpretation was used to study the distribution of aquatic vegetation before (1953, 1996) and after (2001) rehabilitation in 1997. A digital elevation model was derived to relate the change in the aquatic vegetation to water depth. In addition, changes in the biomass of the most abundant species of the lake, Common Club-rush (Schoenoplectus lacustris), were studied by means of a regression analysis relating the ground estimations to the reflectance values (R 2~0 .889, pv0.001). The results indicated that the objective of the rehabilitation-to stop the overgrowth process-has at least temporarily been achieved. After rehabilitation the most noticeable change had taken place in the area covered by floating-leaved vegetation. Greater proportional changes in the aquatic vegetation areas had occurred in the deep rather than in the shallow areas. A decrease in biomass of Common Club-rush was estimated to be 30% due to rehabilitation. The use of remote sensing and GIS provided valuable information on temporal and spatial changes in the aquatic vegetation, and the methods could be applied more extensively for lake monitoring purposes.

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Assessment of Bidirectional Effects over Aquatic Macrophyte Vegetation in CIR Aerial Photographs

Cited by 4 publications

References 20 publications

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Detecting Aquatic Vegetation Changes in Taihu Lake, China Using Multi-temporal Satellite Imagery

Remote sensing and GIS for detecting changes in the aquatic vegetation of a rehabilitated lake

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