2020
DOI: 10.1038/s41598-020-69743-z
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Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and Google Earth Engine

Abstract: Spatial and temporal changes in land cover have direct impacts on the hydrological cycle and stream quality. Techniques for accurately and efficiently mapping these changes are evolving quickly, and it is important to evaluate how useful these techniques are to address the environmental impact of land cover on riparian buffer areas. The objectives of this study were to: (1) determine the classes and distribution of land cover in the riparian areas of streams; (2) examine the discrepancies within the existing l… Show more

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Cited by 31 publications
(15 citation statements)
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References 50 publications
(74 reference statements)
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“…The GEE cloud platform effectively promoted this research. GEE has been proved to be very suitable for high-speed data analysis with large spatial processing functions [34,35]. In this study, we used GEE to select Sentinel-1 images, used refined Lee filter to reduce speckles, composited the processed Sentinel-1 images according to different time intervals, used the SNIC algorithm for image segmentation and finally used random forest for crop classification.…”
Section: Advantages Of Using Geementioning
confidence: 99%
See 1 more Smart Citation
“…The GEE cloud platform effectively promoted this research. GEE has been proved to be very suitable for high-speed data analysis with large spatial processing functions [34,35]. In this study, we used GEE to select Sentinel-1 images, used refined Lee filter to reduce speckles, composited the processed Sentinel-1 images according to different time intervals, used the SNIC algorithm for image segmentation and finally used random forest for crop classification.…”
Section: Advantages Of Using Geementioning
confidence: 99%
“…The GEE platform stores Pb-level processingready data and researchers can process several images quickly in parallel tasks, which greatly improves the efficiency of image processing [31]. GEE has been applied to various scales of geospatial mapping, such as rice distribution mapping, fallow land mapping, tidal flats mapping, land cover mapping and so on [32][33][34][35]. C-band Sentinel-1 is considered to be the most promising radar data for crop classification because it has medium temporal and spatial resolutions and is provided free of charge to the public [36].…”
Section: Introductionmentioning
confidence: 99%
“…In addition, low runoff implied from a low water yield potential can mean a reduced possibility of flooding. Overall, this improves the water quality of nearby water bodies and enhances soil quality, which ultimately can reduce the farm input costs and improve contribution to flood control [61,83,84]. Therefore, the implementation of cover crops as a sustainable farming practice can improve the ES across the landscape.…”
Section: Discussionmentioning
confidence: 99%
“…With the rapid development of hyperspectral imaging technologies, it is feasible to collect hundreds of contiguous narrow spectral bands for each pixel in a scene [1,2]. This abundant spectral and spatial information in hyperspectral remote sensing data has been widely used in a broad range of applications with unprecedented accuracy [3]. Among these applications, hyperspectral image (HSI) classification (or semantic segmentation), which aims at assigning a unique label to each pixel of HSI, is a critical enabling step for land-cover monitoring, ecological science, environmental science, and precision agriculture [4,5].…”
Section: Introductionmentioning
confidence: 99%