Abstract
Aquatic vegetation plays an important role in maintaining the balance of lake ecosystems. Thus, classifying and mapping aquatic vegetation is a priority for lake management. Classification tree (CT) approaches have been used successfully to map aquatic vegetation from spectral indices obtained from remotely sensed images. However, due to the effects of extrinsic and intrinsic factors, applying a CT model developed for imagery from one date to imagery from another date or a different dataset likely would reduce the classification accuracy. In this study, three spectral features (SFs) were selected to develop a CT model for identifying aquatic vegetation in Taihu Lake. Three traditional CT models with three SFs were developed using CT analysis based on satellite images acquired on 11 July, 16 August and 26 September 2013, and corresponding ground-truth samples, from the Huangjing-1A/B Charge-Coupled Device (HJ-CCD) images, environment and disaster reduction small satellites that were launched by China Center for Resources Satellite Data and Application (CRESDA). The overall accuracies of traditional CT models were 82%, 80% and 84%. We then tested two methods to modify CT model thresholds to adjust the traditional CT models based on image date to determine if the results would enable us to map and classify aquatic vegetation for periods when no ground-based data were available. We assessed the results with ground-truth samples and area agreement with traditional CT models. Results showed that CT models modified from a linear adjustment based on the relationship between ranked values of SFs between two image dates produced map accuracies comparable with those obtained from the traditional CT models and suggest that the method we proposed is feasible for mapping aquatic vegetation types in lakes when ground data are not available.
Highlights
Aquatic macrophytes play an important role in maintaining the balance of lake ecosystems and supporting socioeconomic functions [1]
Classification trees constructed for the three image dates all based the first split on SF1 to identify emergent vegetation, the second split on SF2 to identify floating-leaved vegetation, and the third split on SF3 to identify submerged vegetation and water (Figure 3)
Three traditional classification tree models of 11 July, 16 August and 26 September were developed based on three spectral features and corresponding group-truth samples for mapping aquatic vegetation types in Taihu Lake
Summary
Aquatic macrophytes play an important role in maintaining the balance of lake ecosystems and supporting socioeconomic functions [1]. In the eutrophic Taihu Lake of China (30°55′40′′–31°32′58′′N, 119°52′32′′–120°36′10′′E), it is important to accurately map the distribution of and detect changes in aquatic vegetation for lake management. Developing efficient, large-scale monitoring strategies is helpful for effectively tracking and mitigating harmful changes in the lake. Remote sensing has proven to be an effective tool for mapping the distribution of aquatic vegetation over such a large area. With the rapid development of satellite sensors, multispectral and hyperspectral remote sensing have become more feasible for mapping and monitoring aquatic vegetation. Researchers have used high spatial resolution satellite images (e.g., IKONOS and QuickBird images) to illustrate fine-level habitat discrimination [8,9] and to identify submerged vegetation [10]. Considering availability and accessibility, moderate spatial resolution multispectral satellite data, such as from the Landsat
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