Wetland Assessment Using Unmanned Aerial Vehicle (Uav) Photogrammetry

Boon, M. A.; Greenfield, Richard; Tesfamichael, S.

doi:10.5194/isprsarchives-xli-b1-781-2016

Cited by 29 publications

(21 citation statements)

References 9 publications

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“…3 (ZY-3) satellite. In future research, it is suggested to use UAV Low Altitude Surveying to acquire data of higher spatial resolution for the sake of greater accuracy [80,81].…”

Section: Methodological Limits and Future Researchmentioning

confidence: 99%

The Effect of the Gully Land Consolidation Project on Soil Erosion and Crop Production on a Typical Watershed in the Loess Plateau

Han

Zhao

et al. 2018

Land

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The Gully Land Consolidation Project (GLCP) was launched to create more arable land by excavating soil from the slopes on both sides of gullies, combined with simultaneous comprehensive gully prevention and control measures. The purpose of the GLCP is to increase crop production and reduce soil erosion to achieve ecological and agricultural sustainability. In this study, we assess the effects of the GLCP on soil erosion and crop production by studying the BaoChengGou Watershed in the Loess Plateau, primarily by means of high spatial-resolution satellite images (taken by the GF-1 and ZY-3 satellites) combined with the InVEST model and field investigations. Sloping cropland, sparse forestland, and natural grassland are the main land use types in the study area. After implementing the GLCP, consolidated land in the cropland increased by 7.35%, an increase that has come largely at the expense of grassland and forestland. The GLCP has markedly reduced soil erosion in the BaoChengGou Watershed, especially in the sense that soil erosion intensity was also reduced significantly in the project region on the whole, despite intensifying in certain places, such as excavated slopes; furthermore, it has improved crop yields in the study area by 10.9%. Comprehensive measurement shows the GLCP to be scientific, reasonable, and clearly efficacious. This study presents findings regarding the positive significance of the GLCP in promoting ecological and agricultural sustainability in the Loess Plateau.

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“…3 (ZY-3) satellite. In future research, it is suggested to use UAV Low Altitude Surveying to acquire data of higher spatial resolution for the sake of greater accuracy [80,81].…”

Section: Methodological Limits and Future Researchmentioning

confidence: 99%

The Effect of the Gully Land Consolidation Project on Soil Erosion and Crop Production on a Typical Watershed in the Loess Plateau

Han

Zhao

et al. 2018

Land

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“…Remote sensing (RS) is the major source of spatial information related to the earth's surface, offering a wide range of sensors and platforms to monitor land cover and its spatial distribution. Recently, Unmanned Aircraft Systems (UASs) are widely employed in numerous RS applications including natural resource management [1][2][3]. In comparison with traditional RS, UAS technology stands out for its low-cost operation and ability to acquire image data with high spatial and temporal resolution in a flexible fashion at local scales.…”

Section: Introductionmentioning

confidence: 99%

Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

et al. 2020

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Deep learning has already been proved as a powerful state-of-the-art technique for many image understanding tasks in computer vision and other applications including remote sensing (RS) image analysis. Unmanned aircraft systems (UASs) offer a viable and economical alternative to a conventional sensor and platform for acquiring high spatial and high temporal resolution data with high operational flexibility. Coastal wetlands are among some of the most challenging and complex ecosystems for land cover prediction and mapping tasks because land cover targets often show high intra-class and low inter-class variances. In recent years, several deep convolutional neural network (CNN) architectures have been proposed for pixel-wise image labeling, commonly called semantic image segmentation. In this paper, some of the more recent deep CNN architectures proposed for semantic image segmentation are reviewed, and each model’s training efficiency and classification performance are evaluated by training it on a limited labeled image set. Training samples are provided using the hyper-spatial resolution UAS imagery over a wetland area and the required ground truth images are prepared by manual image labeling. Experimental results demonstrate that deep CNNs have a great potential for accurate land cover prediction task using UAS hyper-spatial resolution images. Some simple deep learning architectures perform comparable or even better than complex and very deep architectures with remarkably fewer training epochs. This performance is especially valuable when limited training samples are available, which is a common case in most RS applications.

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“…Nevertheless, wetland characteristics are mapped with remote sensing based mainly on multispectral data from a variety of platforms, such as unmanned aerial vehicles (UAVs) (e.g., [76][77][78][79]), or satellite passive sensors, such as the Landsat Thematic Mapper (TM) and Indian Remote Sensing Satellite (IRS) [80], Satellite pour l'Observation de la Terre (SPOT) [81], WorldView-2 [82] and Sentinel-2 [83]). Figure 7 indicates that there are only a few broadband spectral regions correlating with the parameters under study, and specifically the reflectance of the shade leaves with α at the shorter wavelengths.…”

Section: Discussionmentioning

confidence: 99%

Photophysiology and Spectroscopy of Sun and Shade Leaves of Phragmites australis and the Effect on Patches of Different Densities

Stratoulias

Tóth

2020

Remote Sensing

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Remote sensing of vegetation has largely been revolving around the measurement of passive or active electromagnetic radiation of the top of the canopy. Nevertheless, plants hold a vertical structure and different processes and intensities take place within a plant organism depending on the environmental conditions. One of the main inputs for photosynthesis is photosynthetic active radiation (PAR) and a few studies have taken into account the effect of the qualitative and quantitative changes of the available PAR within the plants canopies. Mostly large plants (trees, shrubs) are affected by this phenomena, while signs of it could be observed in dense monocultures, too. Lake Balaton is a large lake with 12 km2 dense reed stands, some of which have been suffering from reed die-back; consequently, the reed density and stress condition exhibit a vertical PAR variability within the canopy due to the structure and condition of the plants but also a horizontal variability attributed to the reedbed’s heterogeneous density. In this study we investigate the expression of photosynthetic and spectroscopic parameters in different PAR conditions. We concentrate on chlorophyll fluorescence as this is an early-stage indicator of stress manifestation in plants. We first investigate how these parameters differ across leaf samples which are exposed to a higher degree of PAR variability due to their vertical position in the reed culm (sun and shade leaves). In the second part, we concentrate on how the same parameters exhibit in reed patches of different densities. We then look into hyperspectral regions through graphs of coefficient of determination and associate the former with the physiological parameters. We report on the large variability found from measurements taken at different parts of the canopy and the association with spectral regions in the visible and near-infrared domain. We find that at low irradiance plants increase their acclimation to low light conditions. Plant density at Phragmites stands affects the vertical light attenuation and consequently the photophysiological response of basal leaves. Moreover, the hyperspectral response from the sun and shade leaves has been found to differ; charts of the coefficient of determination indicate that the spectral region around the red-edge inflection point for each case of sun and shade leaves correlate strongly with ETRmax and α. When analysing the data cumulatively, independent of their vertical position within the stand, we found correlations of R2 = 0.65 (band combination 696 and 651) and R2 = 0.61 (band combination 636 and 642) for the ETRmax and α, respectively.

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Wetland Assessment Using Unmanned Aerial Vehicle (Uav) Photogrammetry

Cited by 29 publications

References 9 publications

The Effect of the Gully Land Consolidation Project on Soil Erosion and Crop Production on a Typical Watershed in the Loess Plateau

The Effect of the Gully Land Consolidation Project on Soil Erosion and Crop Production on a Typical Watershed in the Loess Plateau

Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

Photophysiology and Spectroscopy of Sun and Shade Leaves of Phragmites australis and the Effect on Patches of Different Densities

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