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Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method

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OPEN GEOSCIENCES 2024年第1期16卷

作者： Zhao, Longhui Qin, Yigen Yang, Genlan Zhang, Wuzhou Lu, Kunpeng Guizhou Water Resources & Hydropower Survey Design Guiyang 550004 Peoples R China Sun Yat Sen Univ Sch Earth Sci & Engn Guangdong Prov Key Lab Geodynam & Geohazards Guangzhou Peoples R China Guizhou Univ Coll Resource & Environm Engn Guiyang 550025 Peoples R China Guizhou Zhiheng Engn Survey & Design Consulting Co Guiyang 550004 Peoples R China

This study aims to develop a comprehensive method for automated rockfall mapping and susceptibility assessment using unmanned aerial vehicle (UAV) tilt photography and the support vector machine (SVM) algorithm. By employing Jinzhong Town in the mountainous forest landscapes of Southwest China as a case study, we leverage photogrammetry principles and computer vision algorithms to generate high-precision, high-resolution digital surface models (DSMs), and digital orthophoto maps through a UAV remote sensing system. The rockfall inventory is accurately and automatically mapped using the object-based classification method and SVM algorithm. The automated rockfall identification method achieves a 93% accuracy with a Kappa coefficient of 0.7967. Statistical analyses of spatial distribution characteristics reveal a significant correlation between rockfall volume and area with a correlation coefficient (R-2) of 0.92316 under logarithmic coordinates. In addition, a power function relationship describes the link between rockfall volume and slope height (R-2 = 0.87), while the relationship with sliding distance is characterized by a weaker linear correlation (R-2 = 0.65). Rockfall runout distance also shows a significant linear correlation with slope height (R-2 = 0.79) but exhibits a less-pronounced association with rockfall volume (R-2 = 0.58). The SVM model employed to assess rockfall susceptibility indicates high accuracy (area under the curve = 0.896), affirming its efficacy in rockfall susceptibility assessment. Our findings underscore the utility of UAV remote sensing for rockfall information extraction and susceptibility evaluation, particularly in challenging mountainous forest environments characterized by intricate topography and geological complexities.

关键词： UAV object-based image analysis SVM algorithm rockfall susceptibility evaluation mountainous forest

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A novel NDSM fusion approach to improve UAV-based LCC accuracy in sloping urban areas

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EUROPEAN JOURNAL OF REMOTE SENSING 2025年第1期58卷

作者： Sefercik, Umut Gunes Aydin, Ilyas Nazar, Mertcan Atalay, Can Kavzoglu, Taskin Gebze Tech Univ Dept Geomat Engn Kocaeli Turkiye Bulent Ecevit Univ Dept Geomat Engn Zonguldak Turkiye

Offering rapidly and periodically achievable and very high resolution (VHR) data at low cost, unmanned aerial vehicles (UAVs) have recently become one of the most popular remote sensing technologies. In this study, a novel Normalized Digital Surface Model (NDSM) fusion approach is proposed to improve the object-based land cover classification (LCC) performance of the RGB camera equipped UAVs. The UAV flights were completed in a multi-class sloping urban area with oblique viewing geometry and a VHR orthomosaic was generated. The object-based LCC was performed applying bottom-up (BU) multiresolution segmentation and Nearest Neighbor and Rule-based classification algorithms. In order to improve the limited segmentation performance of the RGB orthomosaic, high-quality NDSM was produced and fused as an additional imaging band. The highest LCC accuracy utilizing four imaging bands were achieved with a weight of 0.3 for NDSM and 1 for RGB bands. To preserve the information provided by the NDSM data for each image object, a single-stage segmentation process has been performed in contrast to BU segmentation. The results demonstrated that the proposed approach increased the LCC precision up to 15%, recall up to 4.71%, F1 score up to 8.58%, Kappa as 7% and overall accuracy as 4.57%.

关键词： UAV orthomosaic object-based image analysis land cover classification accuracy NDSM

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Urban expansion based on remote sensing extraction of impervious surface information

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INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2024年第1期18卷 106-123页

作者： Kang, Weifeng Chen, Dayong Quanzhou Normal Univ Sch Resources & Environm Sci Quanzhou 362000 Fujian Peoples R China

Impervious surface is an important evaluation factor for the study of urban regional expansion and its social and environmental effects using remote sensing technology and the city has expanded rapidly, which is obviously reflected in the great changes in the impervious surface. 3S technology was relied on to study the classification method of urban impervious surfaces in this area and to explore the dynamic change law of urban impervious surfaces, and its driving force has the dual value of scientific research and social economy. This paper explores the computer automatic extraction method of impervious surface in my country in the past 20 years by using the time series Landsat TM images and object-based image analysis and processing technology and laws to explore the changes and internal causes of urban expansion. Moreover, this method can be utilised for hydrological modelling, flood mapping and monitoring, water quality monitoring, and urban heat island analysis.

关键词： impervious surface improved normalised difference impervious surface index object-based image analysis OBIA image segmentation

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Greenery change and its impact on human-elephant conflict in Sri Lanka: a model-based assessment using Sentinel-2 imagery

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第16期44卷 5121-5146页

作者： Gunawansa, Thakshila D. Perera, Kithsiri Apan, Armando Hettiarachchi, Nandita K. Bandara, Dananjana Y. Univ Southern Queensland Fac Hlth Engn & Sci Sch Surveying & Build Environm Toowoomba Qld Australia Uva Wellassa Univ Fac Technol Studies Dept Engn Technol Badulla Sri Lanka Univ Ruhuna Fac Engn Dept Mech & Mfg Engn Galle Sri Lanka Minist Water Supply & Estate Infrastruct Dev Water Supply & Sanitat Improvement Project Battaramulla Sri Lanka Univ Southern Queensland Fac Hlth Engn & Sci Sch Surveying & Built Environm West St Toowoomba Qld 4350 Australia

Human-elephant conflict (HEC) is a significant conservation issue for Asian elephants (Elephas maximus) and an environmental and socioeconomic crisis in elephant range countries, including Sri Lanka. Approximately 14,897 HEC incidents were recorded in Sri Lanka between 2015 and 2021. In this study, we present a Sri Lanka-wide analysis to explore the impact of greenery change on HEC. Our sources were official government data, and land use and land cover maps developed using Sentinel-2 satellite imagery. We applied the support vector machine (SVM), random forest (RF), and object-based image analysis classifications to classify land cover into six categories. This classification scheme also considered the differences observed in Sri Lanka's woody vegetation, consisting of forest, open forest, paddy fields, homestead gardens, and other crops. analysis of the accuracies of the three types of classifiers confirmed that the supervised classification with two machine learning algorithms, RF and SVM, delivered a higher level of precision in land cover classification. RF was the best option, with a 97.34% overall accuracy and a 0.94 kappa coefficient, while SVM recorded a 94.68% overall accuracy and a 0.89 kappa coefficient. According to the findings, most HEC incidences were recorded in open forests (54%), while 62% were recorded within 2 km of the forest edge. Results indicated that HEC coincides with the human-occupied changed landscape adjacent to forest reservations and patches. The findings could be valuable for HEC management by identifying areas where elephants are most likely to conflict with humans, and the government may declare these as protected areas. Also, we propose an early warning system as an effective approach that helps detect and monitor elephant herds' movement. Therefore, implementing long-term land use planning is crucial for protecting the forest and natural habitats, restoring elephant habitats, and mitigating HEC by minimizing human encroachment an

关键词： Human elephant conflict remote sensing random forest support vector machine object-based image analysis greenery changes

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Evaluation of advanced time series similarity measures for object-based cropland mapping

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第12期44卷 3777-3800页

作者： He, Weiqiang Ma, Lei Yan, Ziyun Lu, Heng Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Nanjing Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu Peoples R China Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Nanjing 210023 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China

With more and more high-resolution remote-sensing data becoming available, satellite image time series (SITS) analysis is often used to map crops at the pixel level. Although the object-based image analysis paradigm has proven superior to pixel-based image analysis for overcoming salt-and-pepper effects, object-based time series crop classification is still rare. Except for time-weighted dynamic time warping (TWDTW), other time series similarity measuring algorithms popular in the signal processing domain, such as shape-based distance (SBD) and global alignment kernel (GAK), remain poorly explored at the object level. Accordingly, this study aimed to explore better object-based time series classification frameworks by investigating similarity measures, including SBD, GAK and TWDTW, and thus experiments were designed to analyse the response of these methods to various parameters, including crop types, time series density, processing efficiency and so on. The results show that GAK is superior to SBD and TWDTW in general. Specifically, GAK performs better than TWDTW with the limited available images, e.g. in cloudy and rainy climate regions. Also, GAK outperforms SBD in identifying crops with a complex phenological pattern, such as alfalfa. Furthermore, TWDTW has the lowest processing efficiency. This study addresses the scarcity of similarity measurement methods in the object-based time series paradigm, and it is expected that this study could provide some guidance for the selection of cropland mapping methods.

关键词： Crop classification satellite image time series object-based image analysis time series similarity measures

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Spaciotemporal distribution characteristics of glacial lakes and the factors influencing the Southeast Tibetan Plateau from 1993 to 2023

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SCIENTIFIC REPORTS 2025年第1期15卷 1-14页

作者： Yu, Mingwei Guo, Yonggang Zhang, Jian Li, Feng Su, Libin Qin, Deshun Tibet Agr & Anim Husb Univ Coll Water Conservancy & Civil Engn Nyingchi 860000 Peoples R China Res Ctr Civil Hydraul & Power Engn Tibet Nyingchi 860000 Peoples R China

The southeastern region of Tibet, which serves as the primary concentration area for marine-type glaciers, has fostered a multitude of glacial lakes that are highly sensitive to global climate change. Glacial lakes play a crucial role in regulating the freshwater ecosystems of the region, but they also pose a significant threat to local infrastructure and populations due to flooding caused by glacial lake outbursts. Currently, a limited amount of research has focused on the monitoring and analysis of glacial lakes in southeastern Tibet. Using Google Earth Engine (GEE) data in conjunction with meteorological data, this study examined the variation characteristics of glacial lakes in southeastern Tibet from 1993 to 2023. The region is subjected to segmentation and extraction of glacial lake boundaries via the FCN model. According to research findings, by 2023, southeastern Tibet is projected to contain 3,877 glacial lakes, encompassing an area of approximately 395.74 +/- 22.72 km(2). The distribution of glacial lakes in this region follows a pattern characterized by a relatively high concentration in the southern region and a relatively low concentration in the northern region. The glacial lakes with areas less than 0.1 km(2) accounted for 94.24%, whereas those exceeding 3.0 km(2) experienced the least amount of change. The analysis of regional elevation using the natural segmentation method reveals that glacial lakes are predominantly distributed within the altitude range of 4442 similar to 4909 m. The fluctuations in the annual mean precipitation and average annual temperature in the study area have decreased since 2008, while the growth trajectories of both the area and number of glacial lakes have gradually stabilized. However, given the backdrop of a slight increase in precipitation and a gradual increase in temperature, the proliferation of glacial lakes is projected to accelerate predominantly in Nyingchi City's eastern and western regions.

关键词： Southeastern Tibetan Plateau Glacial lake GEE Influencing factors object-based image analysis Spatiotemporal evolution

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Giving gully detection a HAND - Testing the scalability and transferability of a semi-automated object-orientated approach to map permanent gullies

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CATENA 2024年 236卷

作者： Olivier, George Van de Wiel, Marco J. Castillo, Carlos Orti, Miguel Vallejo de Clercq, Willem P. Coventry Univ Ctr Agroecol Water & Resilience Priory St Coventry CV1 5LW England Stellenbosch Univ Water Inst Private Bag X1 ZA-7602 Stellenbosch South Africa Stellenbosch Univ Dept Earth Sci Private Bag X1 ZA-7602 Stellenbosch South Africa Coll Agr & Environm Sci UNISA ZA-1709 Florida South Africa Univ Cordoba Dept Rural Engn Civil Construct & Project Engn Campus RabanalesLeonardo da Vinci Bldg Cordoba 14071 Spain Namibia Univ Sci & Technol Fac Engn & Built Environm 13 Jackson Kaujeua St Windhoek 13388 Namibia Heidelberg Univ Inst Geog Neuenheimer Feld 348 D-69120 Heidelberg Germany

Gully erosion can incur on-and off-site impacts with severe environmental and socio-economic consequences. Semi-automated mapping provides a means to map gullies systematically and without bias, providing information on their location and extent. If used temporally, semi-automated mapping can be used to quantify soil loss and identify soil loss source areas. The information can be used to identify mitigation strategies and test the efficacy thereof. We develop, describe, and test a novel semi-automated mapping workflow, gHAND, based on the distinct topographic landform features of a gully to enhance transferability to different climatic regions. Firstly, topographic heights of a Digital Elevation Model are normalised with reference to the gully channel thalweg to extract gully floor elements, and secondly, slope are calculated along the direction of flow to determine gully wall elements. As the gHAND workflow eliminates the need to define kernel thresholds that are sensitive towards gully size, it is more scalable than kernel-based methods. The workflow is rigorously tested at different gully geomorphic scales, in contrasting geo-environments, and compared to benchmark methods explicitly developed for region-specific gullies. Performance is similar to benchmark methods (variance between 1.4 % and 14.8 %). Regarding scalability, gHAND produced under-and over-estimation errors below 30.6 % and 16.1 % for gullies with planimetric areas varying between 1421.6 m2 and 355403.7 m2, without editing the workflow. Although the gHAND workflow has limitations, most markedly the requirement of manually digitising gully headcuts, it shows potential to be further developed to reliably map gullies of small-to large-scales in different geo-environments.

关键词： Gully erosion Automated mapping Gully morphology DEM GIS object-based image analysis Height above nearest drainage

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Evaluating Coastal Wetland Restoration Using Drones and High-Resolution imagery

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ESTUARIES AND COASTS 2024年第6期47卷 1359-1375页

作者： Harris, J. Mason Broussard, Whitney P. Nelson, James A. Univ Louisiana Lafayette Dept Biol 410 E St Mary Blvd Lafayette LA 70504 USA Freese & Nichols Inc 314 Jefferson St Lafayette LA 70501 USA Univ Georgia Dept Marine Sci 325 Sanford Dr Athens GA 30602 USA

Coastal marsh ecosystems are changing and being lost at a rapid rate around the world. One of the fastest rates of land loss, specifically coastal marsh habitat, occurs in Louisiana on the Northern Gulf Coast of the USA. To address this issue, state and federal agencies have undertaken massive wetland restoration efforts to preserve and restore coastal marsh habitats in Louisiana. For these efforts to be successful in the long-term, it is critical to understand what methodologies and techniques result in resilient restoration projects. However, traditional methods to monitor restoration success rely on labor intensive field measurements that are often limited in scope, difficult to maintain, and underfunded. Recent technological developments with uncrewed aircraft systems (UASs) and image processing have substantially improved the ability of restoration practitioners to use off-the-shelf UASs and cameras to map projects. We present a streamlined method using a commercially available drone with a high-resolution red, green, blue (RGB) camera to assess the effects of wetland restoration and integrate more modern tools into evaluation approaches. We conducted drone flights at restored brackish marshes of various ages using a space for time substitution with the goal of understanding the long-term success of marsh restoration. We observed that created marshes had higher land to water ratios than natural marshes. This finding suggests that these restored areas were gaining and maintaining elevation after approximately 10 years. Our method shows that drone surveys offer low-cost, minimally invasive methods for evaluating restored wetlands and ultimately tell us more about ecosystem function through realistic site-level habitat configurations.

关键词： Spartina Louisiana Saltmarsh UAS Drone object-based image analysis Habitat Mapping

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Mapping individual tree crowns to extract morphological attributes in urban areas using unmanned aerial vehicle-based LiDAR and RGB data

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ECOLOGICAL INFORMATICS 2025年 88卷

作者： Park, Geonung Song, Bonggeun Park, Kyunghun Changwon Natl Univ Inst Ind Technol 20 Changwondaehak Ro Chang Won 51140 Gyeongsangnam D South Korea Changwon Natl Univ ICT Res Ctr Satellite Imagery Anal 20 Changwondaehak Ro Chang Won 51140 Gyeongsangnam D South Korea ENVILABS Co Ltd 3 Yulha Ro Gimhae 51019 Gyeongsangnam D South Korea Changwon Natl Univ Sch Smart Green Engn 20 Changwondaehak Ro Chang Won 51140 Gyeongsangnam D South Korea

Mapping individual tree crowns (ITCs) along with their morphological attributes provides foundational variables for estimating functions, such as thermal stress and carbon emissions, within the urban environment. However, to calculate morphological attributes, it is necessary to delineate ITCs, and applying the watershed segmentation (WS) algorithm, commonly used in forests, to urban environments presents challenges due to the reliance on single-band data and complexity of heterogeneous urban elements. Additionally, deep learning (DL) models, which excel in image analysis, are constrained by the labor-intensive label generation process. This study introduces a novel framework integrating machine learning (ML)- and DL-based approaches to map ITCs and extract the morphological attributes in urban areas using unmanned aerial vehicles (UAVs). Using ML-based approaches, we conducted object-based image analysis to optimize the use of the WS algorithm because applying WS directly to urban environments, as in forests, overestimated the ITC size by 151.37 %. This approach also improved the effectiveness of the DL model, Mask R-CNN, by resolving challenges in label generation. Mask R-CNN delineated ITCs with an accuracy of 0.942, suggesting its robustness in handling the heterogeneity of tree arrangements. The results demonstrate that the proposed framework is applicable for use in urban areas globally that have similar ecological conditions. ITCs with morphological attributes provide fundamental variables for evaluating ecological functions, which are scalable for improving urban environmental planning. However, UAV data may face time and cost limitations as the monitoring coverage expands, which should be considered when applying this framework.

关键词： UAVs Individual tree crown delineation Tree morphological attributes Watershed segmentation object-based image analysis Instance segmentation

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Open-source automatic extraction of Urban Green Space: Application to assessing improvement in green space access

Open-source automatic extraction of Urban Green Space: Appli...

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ISPRS TC II Mid-term Symposium on Role of Photogrammetry for a Sustainable World

作者： Estacio, Ian Roman-Palacios, Cristian Hoover, Joseph Li, Xiaojiang Lim, Chris Univ Arizona Arizona Inst Resilience Tucson AZ 85721 USA Univ Arizona Sch Informat Tucson AZ 85721 USA Univ Arizona Coll Agr Life & Environm Sci Tucson AZ 85721 USA Univ Penn Dept City & Reg Planning Philadelphia PA 19104 USA Univ Arizona Mel & Enid Zuckerman Coll Publ Hlth Tucson AZ 85721 USA

Urban Green Space (UGS) is vital for improving the public health and sustainability of cities. Vector data on UGS such as open data from governments and OpenStreetMap are available for retrieval by interested users, but the availability of UGS data is still limited on global and temporal scales. This study develops the UGS Extractor, a web-based application for the automatic extraction of UGS given user inputs of Area of Interest and Date of Interest. To accommodate various types of green spaces, such as parks or lawns, the application additionally allows users to set parameters for the minimum size of each UGS and the Minimum Urban Neighbor Density, enabling customization of what qualifies as UGS. The UGS Extractor implements a methodological framework that applies object-based image processing, edge detection and extraction, and image neighborhood analysis on the near real-time 10m Dynamic World collection of Land Use/Land Cover images. The application's utility was demonstrated through two case studies. In the first, the UGS Extractor accurately mapped major parks when compared to open data sources in New Orleans, USA. In the second, the UGS Extractor demonstrated significant increases in the total area of UGS from 2015 to 2023 in Songdo, South Korea, which consequently improved green space accessibility. These results underscore the UGS Extractor's utility in extracting specific types of UGS and analyzing their temporal trends. This user-friendly application overall offers higher spatial resolution compared to publicly available satellite-based methods while facilitating temporal studies not possible with vector datasets.

关键词： Google Earth Engine Dynamic World object-based image analysis Edge detection Neighborhood analysis Urban parks

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