Mapping of riparian zone attributes using discrete return LiDAR, QuickBird and SPOT-5 imagery: Assessing accuracy and costs

Johansen, Kasper; Witte, Christian

doi:10.1016/j.rse.2010.06.004

Cited by 82 publications

(72 citation statements)

References 33 publications

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“…Mapping riparian vegetation attributes is an established field of research, in which multiple types of RS data have been used to identify and characterise vegetation. Whilst much research has been conducted using aerial imagery and multispectral data, Johansen et al (2010b) found that discrete return LiDAR is more costeffective than QuickBird and SPOT-5 data for mapping riparian zone attributes over long river networks (26,000 km of stream length in this study). Moreover they found that SPOT-5 data were not useful for mapping most of the riparian attributes because of its coarse spatial resolution (pixel size = 10 m).…”

Section: Woody Debris (Smikrud and Prakash 2006)mentioning

confidence: 91%

The use of remote sensing to characterise hydromorphological properties of European rivers

et al. 2015

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Remote sensing (RS) technology offers unparalleled opportunities to explore river systems using RADAR, multispectral, hyper spectral, and LiDAR data. The accuracy reached by these technologies recently has started to satisfy the spatial and spectral resolutions required to properly analyse the hydromorphological character of river systems at multiple scales. Using the River Hierarchical Framework (RHF) as a reference we describe the state-of-the-art RS technologies that can be implemented to quantify hydromorphological characteristics at each of the spatial scales incorporated in the RHF (i. e. catchment, landscape unit, river segment, river reach, sub-reach-geomorphic and hydraulic units). We also report the results of a survey on RS data availability in EU member states that shows the current potential to derive RHF hydromorphological indicators from high-resolution multispectral images and topographic LiDAR at the national scale across Europe. This paper shows that many of the assessment indicators proposed by the RHF can be derived by different RS sources and existing methodologies, and that EU countries have sufficient RS data at present to already begin their incorporation into hydromorphological assessment and monitoring, as mandated by WFD. With cooperation and planning, RS data can form a fundamental component of hydromorphological assessment and monitoring in the future to help support the effective and sustainable management of rivers, and this would be done most effectively through the establishment of multi-purpose RS acquisition campaigns and the development of shared and standardized hydromorphological RS databases updated regularly through planned resurveyed campaigns.

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Section: Woody Debris (Smikrud and Prakash 2006)mentioning

confidence: 91%

The use of remote sensing to characterise hydromorphological properties of European rivers

et al. 2015

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“…Furthermore, the ease of application (e.g., only two model parameters, see Section 2.3.1) and the ability to run efficiently over large datasets makes random forest an ideal choice for large area attribution [32]. A number of studies have utilized random forest for mapping forest attributes with remotely sensed data, including biomass [28,33], species extent [34], forest extent [21,35,36], canopy cover [7,37,38] and canopy height [24,26,[38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Mapping Forest Canopy Height Across Large Areas by Upscaling ALS Estimates with Freely Available Satellite Data

et al. 2015

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Operational assessment of forest structure is an on-going challenge for land managers, particularly over large, remote or inaccessible areas. Here, we present an easily adopted method for generating a continuous map of canopy height at a 30 m resolution, demonstrated over 2.9 million hectares of highly heterogeneous forest (canopy height 0-70 m) in Victoria, Australia. A two-stage approach was utilized where Airborne Laser Scanning (ALS) derived canopy height, captured over ~18% of the study area, was used to train a regression tree ensemble method; random forest. Predictor variables, which have a global coverage and are freely available, included Landsat Thematic Mapper (Tasselled Cap OPEN ACCESSRemote Sens. 2015, 7 2 transformed), Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index time series, Shuttle Radar Topography Mission elevation data and other ancillary datasets. Reflectance variables were further processed to extract additional spatial and temporal contextual and textural variables. Modeled canopy height was validated following two approaches; (i) random sample cross validation, and (ii) with 108 inventory plots from outside the ALS capture extent. Both the cross validation and comparison with inventory data indicate canopy height can be estimated with a Root Mean Square Error (RMSE) of ≤ 31% (~5.6 m) at the 95th percentile confidence interval. Subtraction of the systematic component of model error, estimated from training data error residuals, rescaled canopy height values to more accurately represent the response variable distribution tails e.g., tall and short forest. Two further experiments were carried out to test the applicability and scalability of the presented method. Results suggest that (a) no improvement in canopy height estimation is achieved when models were constructed and validated for smaller geographic areas, suggesting there is no upper limit to model scalability; and (b) training data can be captured over a small percentage of the study area (~6%) if response and predictor variable variance is captured within the training cohort, however RMSE is higher than when compared to a stratified random sample.

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“…Since Landsat-1 was launched into the orbit in 1972 by the United States National Aeronautics and Space Agency (NASA), optical remote sensing data have been widely used to collect data over a wide coverage providing accurate and high spatial 2D information about the earth's surface for various applications (Chen et al, 2009;Yin et al, 2012). The advantages of optical imagery include rich spectral and textural information and clear feature boundaries delineation (Chen et al, 2009;Johansen et al, 2010). However, light and weather dependency of optical sensors, Science Publications AJAS complexity of spectral and textural information of urban environment (Yu et al, 2009) and the absence of height components limit the amount of information that can be derived from them.…”

Section: Introductionmentioning

confidence: 99%

“…LiDAR provides good 3D geometry of urban feature and can discriminate distinct patches of the same material covered objects at different height (Chen et al, 2009;Johansen et al, 2010). For example buildings of different heights, road, bare ground and different vegetation cover (Edson and Wing, 2011) can easily be distinguishable in LiDAR image.…”

Section: Introductionmentioning

confidence: 99%

Imaging Spectroscopy and Light Detection and Ranging Data Fusion for Urban Features Extraction

Idrees

Shafri

Saeidi

2013

American Journal of Applied Sciences

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This study presents our findings on the fusion of Imaging Spectroscopy (IS) and LiDAR data for urban feature extraction. We carried out necessary preprocessing of the hyperspectral image. Minimum Noise Fraction (MNF) transforms was used for ordering hyperspectral bands according to their noise. Thereafter, we employed Optimum Index Factor (OIF) to statistically select the three most appropriate bands combination from MNF result. The composite image was classified using unsupervised classification (k-mean algorithm) and the accuracy of the classification assessed. Digital Surface Model (DSM) and LiDAR intensity were generated from the LiDAR point cloud. The LiDAR intensity was filtered to remove the noise. Hue Saturation Intensity (HSI) fusion algorithm was used to fuse the imaging spectroscopy and DSM as well as imaging spectroscopy and filtered intensity. The fusion of imaging spectroscopy and DSM was found to be better than that of imaging spectroscopy and LiDAR intensity quantitatively. The three datasets (imaging spectrocopy, DSM and Lidar intensity fused data) were classified into four classes: building, pavement, trees and grass using unsupervised classification and the accuracy of the classification assessed. The result of the study shows that fusion of imaging spectroscopy and LiDAR data improved the visual identification of surface features. Also, the classification accuracy improved from an overall accuracy of 84.6% for the imaging spectroscopy data to 90.2% for the DSM fused data. Similarly, the Kappa Coefficient increased from 0.71 to 0.82. on the other hand, classification of the fused LiDAR intensity and imaging spectroscopy data perform poorly quantitatively with overall accuracy of 27.8% and kappa coefficient of 0.0988.

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Mapping of riparian zone attributes using discrete return LiDAR, QuickBird and SPOT-5 imagery: Assessing accuracy and costs

Cited by 82 publications

References 33 publications

The use of remote sensing to characterise hydromorphological properties of European rivers

The use of remote sensing to characterise hydromorphological properties of European rivers

Mapping Forest Canopy Height Across Large Areas by Upscaling ALS Estimates with Freely Available Satellite Data

Imaging Spectroscopy and Light Detection and Ranging Data Fusion for Urban Features Extraction

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