Combining Decision Trees with Hierarchical Object-oriented Image Analysis for Mapping Arid Rangelands

Laliberte, Andrea S.; Fredrickson, Ed L.; Rango, A.

doi:10.14358/pers.73.2.197

Cited by 141 publications

(101 citation statements)

References 30 publications

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“…The segmentation technique in eCognition 9.0 is a bottom-up region merging technique where smaller image objects are merged into larger ones with the scale parameter controlling the growth in heterogeneity between adjacent image objects. The merging is stopped when image object growth exceeds the threshold defined by the scale parameter-the maximum allowable heterogeneity of image object [52]. Adjusting the scale parameter influences the average object size.…”

Section: Image Segmentationmentioning

confidence: 99%

“…The color parameter, ranging from 0 to 1, determines the weight of spectral (color) heterogeneity against shape heterogeneity in the total image object heterogeneity. Previous studies suggest that more meaningful objects are extracted with a higher weight for the color criterion [52,53]. The shape heterogeneity is further defined as a weighted sum of smoothness (the ratio of the border length and the shortest possible border length of the bounding box of an image object) and compactness (the ratio of the border length and the square root of the number of object pixels).…”

Section: Image Segmentationmentioning

confidence: 99%

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Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Zhu

Liu

et al. 2015

Remote Sensing

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Abstract:Previous research studies have demonstrated that the relationship between remote sensing-derived parameters and aboveground biomass (AGB) could vary across different species types. However, there are few studies that calibrate reliable statistical models for mangrove AGB. This study quantifies the differences of accuracy in AGB estimation between the results obtained with and without the consideration of species types using Worldview-2 images and field surveys. A Back Propagation Artificial Neural Network (BP ANN) based model is developed for the accurate estimation of uneven-aged and dense mangrove forest biomass. The contributions of the input variables are further quantified using a "Weights" method based on BP ANN model. Two types of mangrove species, Sonneratia apetala (S. apetala) and Kandelia candel (K. candel), are examined in this study. Results show that the species type information is the most important variable for AGB estimation, and the red edge band and the associated vegetation indices from WorldView-2 images are more sensitive to mangrove AGB than other bands and vegetation indices. The RMSE of biomass estimation at the incorporation of species as a dummy variable is 19.17% OPEN ACCESSRemote Sens. 2015, 7 12193 lower than that of the mixed species level. The results demonstrate that species type information obtained from the WorldView-2 images can significantly improve of the accuracy of the biomass estimation.

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Section: Image Segmentationmentioning

confidence: 99%

Section: Image Segmentationmentioning

confidence: 99%

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Zhu

Liu

et al. 2015

Remote Sensing

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“…Furthermore, this approach was less effective in heterogeneous and denser hardwood stands than conifer landscapes [51,56]. For vegetation stands, decomposing landscapes into smaller objects (vegetation stands, a group of trees) and labeling species by the majority classified pixels within segmentations [15], vegetation gradient model adding spectral mixture analysis [30] or non-parametric classifiers (e.g., [18,23]) were usually applied (e.g., [18,[57][58][59][60]). Nevertheless, segmentations in sparse tree stands may not be partitioned well, due to effects of non-vegetation areas or shadows.…”

Section: Motivationmentioning

confidence: 99%

“…Detailed vegetation objects were further partitioned following the image segmentation optimized for delineation of tree density using multiresolution in eCognition 8, which has been extensively used in studies of object-base image classification for vegetation inventories (e.g., [18,58,59]). The algorithm of multiresolution segmentation was demonstrated by optimization procedures to minimize heterogeneity for each individual merging [24].…”

Section: Species Segmentationmentioning

confidence: 99%

Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California

Liau

2014

Remote Sensing

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Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing at high costs are required. In this study, I applied a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects on a digital orthophoto quadrangles with 1 m spatial resolution.Step one and step two were for image segmentation optimized for delineation of tree density, which involved global Otsu's method followed by the random walker algorithm.Step three was for detailed species delineations, which were derived from multiresolution segmentation, in two test areas.Step one and step two were able to delineating tree density segments and label species association robustly, compared to previous hierarchical frameworks. However, step three was limited by less image information to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework has potential to develop baseline data for evaluating climate change impacts on vegetation at lower cost using widely available data and a personal laptop.Keywords: object-based image analysis; image segmentation optimized for delineation of tree density; very high resolution imagery; species association labeling; the Z values of Moran's I

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“…Eighty-seven percent of all shrubs greater than 2 m2 were detected with QuickBird, and 29% of shrubs smaller than 2 m 2 were detected. Various categories of grass and bare soils could also be delineated after the shrubs had been masked out of the QuickBird data (Laliberte, Fredrickson, and Rango, 2006). Because the 61 cm resolution from space is very similar to a variety of existing aerial photographs, QuickBird is an important resource for supplementing an aerial photography database.…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

Research Article: Using Unmanned Aerial Vehicles for Rangelands: Current Applications and Future Potentials

Rango

Laliberte

Steele

et al. 2006

Environmental Practice

Self Cite

163

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H igh resolution aerial photographs have important rangeland applications, such as monitoring vegetation change, developing grazing management practices, determining rangeland health and condition, and assessing remediation treatment effectiveness (Rango and Havstad, 2003). Unmanned Aerial Vehicles (UAVs) have several advantages over piloted aircraft for acquiring high resolution images. These advantages include a less expensive remote sensing platform, improved safety for operators, and a more rapid deployment capability than piloted aircraft.Most applications of UAVs in rangeland areas, which make up 50% to 70% of the world's land surface (Holechek, Pieper, and Herbel, 1995), only require simple high resolution photography and thermal infrared imagery that can be provided with existing sensors over selected sites. Unmanned Aerial Vehicle flight requirements over rangeland are also simple, with slow flight speeds, low altitudes, and flight durations of two to six hours usually being adequate. Such capabilities should be sufficient to satisfy the need for high resolution photography of remote rangeland areas. Satellite and piloted aircraft missions (both high and low altitudes) have provided excellent data for rangeland applications, but a major gap exists between these large area coverages and boom-mounted vertical ground photography of small areas; UAVs can fill this gap. Because the large area coverage can provide landscape level views, high resolution satellite data can be used to identify rangeland areas (in the case of rangeland health assessments) that need more detailed observations; UAVs then can be uti-

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Combining Decision Trees with Hierarchical Object-oriented Image Analysis for Mapping Arid Rangelands

Cited by 141 publications

References 30 publications

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California

Research Article: Using Unmanned Aerial Vehicles for Rangelands: Current Applications and Future Potentials

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