Algorithms Based on Spectral Decomposition Algorithm for Retrieval of Constituents in Taihu Lake

Lu, Chaoping; Lv, Hongbo; Li, Yunmei

doi:10.3724/sp.j.1047.2011.00687

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…First, we extract the river network to identify the plain areas (Figure 3e). We calculate the flow direction using the priority‐flood algorithm, which preserves the influence of the depression's internal terrain on the water flow direction (Lu et al, 2017). Second, we determine the flow accumulation based on the flow direction and set an appropriate threshold to extract the river network.…”

Section: Methodsmentioning

confidence: 99%

Karst landform classification considering surface flow characteristics derived from digital elevation models

Cao,

Xiong,

et al. 2023

Earth Surf Processes Landf

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Karst landforms are one of the most typical geographical units with a specific physical process on the earth's surface. The classification of karst landforms is an important aspect for understanding their landform processes and mechanisms. However, influenced by various interior and external forces, karst landforms have an extremely complex surface morphology, increasing the difficulty of their automatic classification. In this study, we considered hydrological features as an important factor in characterizing karst landforms and proposed a method that considers surface flow for karst landform classification. In this method, terrain was reversed for hydrological analysis to achieve the landform units. Then, the watershed boundary of the reversed terrain is extracted by hydrological analysis. The boundary of the karst landform unit is determined by erasing the plain area from the watershed boundary. Thereafter, the graph theory segmentation method is employed to merge the landform units belonging to the same karst landform entity. The proposed approach is validated and applied in two sample karst areas, Fenglin and Fengcong, located in Guilin, China, using digital elevation model data with 30 m spatial resolution. In addition, a comparative analysis is conducted to evaluate the accuracy of the proposed method. The results demonstrated that the typical karst landform units of Fenglin and Fengcong can be effectively classified. The overall classification accuracy is 94.44%. The proposed method produced more reasonable and accurate boundaries compared with the contour tree and terrain feature point methods. Furthermore, the classification results indicate various landform development stages of the karst landform process in the study area. The proposed method considering surface flow characteristics can be further extended to other landform types with highly complex landforms.

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Section: Methodsmentioning

confidence: 99%

Karst landform classification considering surface flow characteristics derived from digital elevation models

Cao,

Xiong,

et al. 2023

Earth Surf Processes Landf

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“… Inherent optical property (IOP) specification model: CASE2;  Pure water IOP [39];  IOP specifications for Chla, NPSS and CDOM including a concentration profile, specific absorption and specific scattering spectra [26];  Internal Source and Inelastic Scatter Selection linked to Chlorophyll Fluorescence, CDOM Fluorescence and Raman Scattering;  Wind speed of 3.5 m/s (average of wind speed in Lake Taihu) and solar zenith angle of 30°;  Parameters of air-water surface boundary conditions, sky conditions and bottom boundary condition were set to their default values.…”

Section: Simulated Datasetmentioning

confidence: 99%

“…However, in the former case, the reflection from interior walls of the mesocosm (or tank) was found to influence the accuracy of the end-members' spectra. In the latter case, differences in the signal to noise and atmospheric correction processes for different images were found to reduce the accuracy of end-members' spectra [26]. Pilorz and Davis (1990) suggested the use of libraries of absorption and scattering coefficients to model upwelling reflectance [27].…”

Section: Introductionmentioning

confidence: 99%

A Spectral Decomposition Algorithm for Estimating Chlorophyll-a Concentrations in Lake Taihu, China

Zhang

Duan

et al. 2014

Remote Sensing

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Abstract:The complex interactions among optically active substances in Case II waters make it difficult to associate the variability in spectral radiance (or reflectance) to any single component. In the present study, we developed a four end-member spectral decomposition model to estimate chlorophyll-a concentrations in a eutrophic shallow lake-Lake Taihu. The new model was constructed by simulated spectral data from Hydrolight and was successfully validated using both of simulated reflectance and in situ reflectance data. Using MEdium Resolution Imaging Spectrometer (MERIS) images, the accuracy of the new model was estimated and compared with other published models. According to the MERIS retrieved results, the spatial distribution of chlorophyll-a concentrations and its relationship with environment factors were analyzed. The application of the new model and its limits to estimate water surface chlorophyll-a concentrations in turbid lakes is also discussed.

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Algorithms Based on Spectral Decomposition Algorithm for Retrieval of Constituents in Taihu Lake

Cited by 2 publications

References 7 publications

Karst landform classification considering surface flow characteristics derived from digital elevation models

Karst landform classification considering surface flow characteristics derived from digital elevation models

A Spectral Decomposition Algorithm for Estimating Chlorophyll-a Concentrations in Lake Taihu, China

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