In this work we develop and compare three different supervised approaches for semi-automatic mapping of landslides, including the separation of landslide source and transport areas, using a single GeoEye-1 image acqui...
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ISBN:
(纸本)9781479979301
In this work we develop and compare three different supervised approaches for semi-automatic mapping of landslides, including the separation of landslide source and transport areas, using a single GeoEye-1 image acquired after a rainfall-induced landslide event in Madeira Island. The methodologies cover object-based classification using support vector machine (SVM) algorithms;pixel-based classification using textons;and object-based classification with a rule-set framework. The assessment was made by comparison of the results obtained in the validation areas with the ground-truth landslide mapping. In what concerns landslide recognition, the results of the object-based and pixel-based machine-learning approaches have higher accuracy when compared with the rule-set method. The object-based SVM approach achieves false positive rate FPR=20% and false negative rate FNR=18% for landslide area detection, while the pixel-based texton method displays even higher accuracy (FPR=19% and FNR=9%) although at higher computational cost and slower execution. In what concerns internal mapping of landslide source areas, the three methods show lower but still reasonably good performance, in particular in the sunnier east-facing slopes.
Legacy seabed mapping datasets are increasingly common as the need for detailed seabed information is recognized. Acoustic backscatter data from multibeam echosounders can be a useful surrogate for seabed properties a...
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Legacy seabed mapping datasets are increasingly common as the need for detailed seabed information is recognized. Acoustic backscatter data from multibeam echosounders can be a useful surrogate for seabed properties and are commonly used for benthic habitat mapping. Legacy backscatter data, however, are often uncalibrated, rendering measurements relative to a given survey and complicating the use of multisource acoustic datasets for habitat mapping. Recently, `bulk shift' methods have been proposed to harmonize multisource backscatter layers that overlap spatially, but their application to benthic habitat mapping has not been evaluated. Here, four relative backscatter datasets at the St. Anns Bank Marine Protected Area were harmonized to produce a single continuous surface spanning the extent of available bathymetric data. The harmonized surface was used as a predictor in a benthic habitat (`benthoscape') classification, which was compared to previous results using individual backscatter coverages. Results were similar to those obtained previously, but the harmonized surface provided increased class discrimination, fewer unclassified areas, and predictions that cross dataset boundaries eliminating the need for manual reclassification by the user. While this generally increases the efficiency and repeatability of the analysis and the useability of the data, we caution that an inappropriate harmonization model is a potential source of error for the classification.
During the conflict and in the years afterward, the Kurdistan Region of Iraq (KRI) saw substantial changes in land use. The mapping and monitoring of land use/land cover (LULC) is critical for its long-term developmen...
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During the conflict and in the years afterward, the Kurdistan Region of Iraq (KRI) saw substantial changes in land use. The mapping and monitoring of land use/land cover (LULC) is critical for its long-term development and natural resource management. Therefore in this study, we developed a semi-automated object-based land use/land cover classification to identify and quantify LULC changes and change detection analysis in the Kurdistan Region of Iraq for the period 1990 to 2020 using Landsat satellite data (TM, ETM+, and OLI). To determine the optimum segmentation scales for each phase, we first applied and evaluated different scales of a multi-resolution segmentation technique. After that, spatial (digital elevation information) and spectral information were combined in an object-based image analysis (OBIA) technique. For each LULC class, object features were found. We then used the standard nearest neighbor (SNN) approach to derive their individual properties. Field data and validation units collected from high resolution Google earth pro and historical maps in SAS planet open source were used to conduct accuracy evaluations based on the error matrix and kappa coefficient for each reference year. With overall accuracies ranging from 86.072% to 88.9% and Kappa coefficients of 0.845-0.878, ten LULCs were effectively recorded. After that, a post classification comparison was used to perform a change analysis. The findings demonstrated that LULC change trends were notably different, as all categories were altered at different times throughout the study. Strikingly, 52.1% of the land use/land cover in the Kurdistan Region has changed between 1990 and 2020. All changes corresponded to the KRI's own challenges throughout the last three decades. The OBIA-based approaches and features, we conclude, have a lot of potential for LULC mapping and monitoring. The results shall support state institutions and experts to manage the land in a more controlled way.
Pinyon and juniper expansion into sagebrush ecosystems is one of the major challenges facing land managers in the Great Basin. Effective pinyon and juniper treatment requires maps that accurately and precisely depict ...
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Pinyon and juniper expansion into sagebrush ecosystems is one of the major challenges facing land managers in the Great Basin. Effective pinyon and juniper treatment requires maps that accurately and precisely depict tree location and degree of woodland development so managers can target restoration efforts for early stages of pinyon and juniper expansion. However, available remotely sensed layers that cover a regional spatial extent lack the spatial resolution or accuracy to meet this need. Accuracy can be improved using object-based image analysis methods such as automated feature extraction, which has proven successful in accurately classifying land cover at the site-level but to date has yet to be applied to regional extents due to time and computational limitations. Using Feature Analyst (TM), we implement our framework with 1-m(2) reference imagery provided by National Agricultural imagery Program to classify conifers across Nevada and northeastern California. Our resulting binary conifer map has an overall accuracy of 86%. We discuss the advantages to accuracy and precision our framework provides compared to other classification methods. This framework allows automated feature extraction for large quantities of data and very high spatial resolution imagery It leverages supervised learning It results in high accuracy maps for regional spatial extents (C) 2021 Published by Elsevier B.V.
This study utilised very high-resolution (VHR) imagery to monitor and evaluate the impact of humanitarian demining activities in a peri-urban environment in Afghanistan. Identifying buildings and mapping the spatial d...
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This study utilised very high-resolution (VHR) imagery to monitor and evaluate the impact of humanitarian demining activities in a peri-urban environment in Afghanistan. Identifying buildings and mapping the spatial distribution of different types of land cover is of great practical significance for demining organisations, such as the HALO Trust, to assess the impact of their mine-clearance activities, by quantifying change and the growth of a population in a specific area over time. This study had two main objectives: (i) to map the post-clearance land cover, and (ii) to detect and quantify the change in the number and area of buildings. Two independent workflows were implemented and evaluated. To map land cover, this study investigated the implementation of various machine learning algorithms in object-basedimage classification (OBIA) of VHR satellite imagery (Worldview-1,2,3). image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, before classification was performed based on a machine learning Random Forests (RF) approach. Different parameters and spatial distribution of training samples were tested to analyse the model's performance. Further analysis determined that by using only the segments' mean value per spectral band (Red, Green, Blue), data redundancy in the training stage was eliminated. The final classified map had an overall accuracy of 90.67% and a total builtarea of 643,660.28 m2 was detected in the 4.11 km2 study area. To detect and quantify buildings present in the study area, an alternative, automatic, unsupervised approach based on the morphological building index (MBI) was implemented using MATLAB. Two VHR (0.5 m) panchromatic images acquired by WorldView-1 in 2008 and 2018 were processed using a series of multi-scale and multi-directional morphological operators, before a series of post-processing thresholds were applied to refine the output. Parameters were systematically optimised for the datasets and their sens
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