Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modeling

Drgu, Lucian; Schauppenlehner, Thomas; Muhar, Andreas; Strobl, Josef; Blaschke, Thomas

doi:10.1016/j.cageo.2008.10.008

Cited by 68 publications

(19 citation statements)

References 36 publications

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“…Near-infrared, red, and green bands were used for grouping spectrally similar pixels, in order for the objects to be representative of the vegetation ecology on the ground. Two image object sizes that were physically meaningful [52,53] were chosen, with the smaller objects for both locations ranging between approximately 50 and 6000 m 2 , and the larger objects ranging from 50 to 35,000 m 2 in size.…”

Section: Object-based Image Analysis (Obia)mentioning

confidence: 99%

Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

et al. 2014

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Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r 2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r 2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r 2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r 2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling OPEN ACCESSRemote Sens. 2014, 6 2135 above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

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Section: Object-based Image Analysis (Obia)mentioning

confidence: 99%

Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

et al. 2014

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“…At first, each LSV was re-scaled to successively broader representations of topography with focal mean statistics in increasing squared windows, starting from 3 × 3 cells (Dragut et al 2009), using the RSAGA package (Brenning 2008).…”

Section: Scaling Of Lsvsmentioning

confidence: 99%

Scaling land-surface variables for landslide detection

Sîrbu

Drăguţ

Oguchi

et al. 2019

Prog Earth Planet Sci

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As geomorphological processes operate at various spatial scales, their morphological expressions, i.e., land-surface variables (LSVs) should be scaled accordingly. Most approaches on landslide susceptibility modeling and landslide detection have been performed based on arbitrarily scaled LSVs. We propose a methodology to improve automated landslide detection by fitting each LSV to its optimal scale. We test our approach on two landslide inventories, with different landslide morphology. First, we derive seven LSVs from a DEM in a standard 3 × 3 moving window. Then, we rescale each LSV using focal mean statistics in increasingly larger moving windows until the optimal scale is found, i.e., scale at which logistic regression shows the best fit between the existence of landslide scarps and individual LSVs. The LSVs at the optimal scale are used as input data in a random forest (RF) model. In order to calculate the effect of scaling predictors on the accuracy of the model, we compare the results, using the area under the curve (AUC), against the results from an RF model with unscaled LSVs as input data. The results show (i) that different LSVs have different optimal scales, and (ii) the multi-scale approach improved the models significantly, from AUC = 0.73 to 0.80 for the first study area and from AUC = 0.59 to 0.73 for the second study area. Based on these results, we conclude that a multiscale approach should be considered when automated models are used in order to detect landslides, in complex terrain settings.

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“…In eCognition, a post-evaluation-based approach is often applied in multi-resolution segmentation, which is difficult to quantitatively achieve the most suitable parameters in image segmentation even with repetitious trial processing [46,55]. To reduce the subjectiveness and to quantify the parameters during segmentation, researches have adopted statistics-based or spatial statistics-based procedures (a focal mean, local variance, estimation of scale parameter tool, or semi-variagram related methods) to select the optimized parameters [55][56][57][58][59]. However, most of this research is based on the post-evaluation-based approach [55].…”

Section: Uncertainty Analysis For Rubber Plantation Mappingmentioning

confidence: 99%

Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes

Zhai

Jie

Cadisch

et al. 2016

Preprint

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Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

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Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modeling

Cited by 68 publications

References 36 publications

Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

Scaling land-surface variables for landslide detection

Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes

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