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Emerging Sensor Platforms Allow for Seagrass Extent Mapping in a Turbid Estuary and from the Meadow to Ecosystem Scale

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REMOTE SENSING 2021年第18期13卷 3681-3681页

作者： Krause, Johannes R. Hinojosa-Corona, Alejandro Gray, Andrew B. Burke Watson, Elizabeth Drexel Univ Dept Biodivers Earth & Environm Sci Philadelphia PA 19104 USA Ctr Invest Cient & Educ Super Ensenada Dept Geol Ensenada 22860 BC Mexico Univ Calif Riverside Dept Environm Sci Riverside CA 92521 USA

Seagrass meadows are globally important habitats, protecting shorelines, providing nursery areas for fish, and sequestering carbon. However, both anthropogenic and natural environmental stressors have led to a worldwide reduction seagrass habitats. For purposes of management and restoration, it is essential to produce accurate maps of seagrass meadows over a variety of spatial scales, resolutions, and at temporal frequencies ranging from months to years. Satellite remote sensing has been successfully employed to produce maps of seagrass in the past, but turbid waters and difficulty in obtaining low-tide scenes pose persistent challenges. This study builds on an increased availability of affordable high temporal frequency imaging platforms, using seasonal unmanned aerial vehicle (UAV) surveys of seagrass extent at the meadow scale, to inform machine learning classifications of satellite imagery of a 40 km(2) bay. We find that object-based image analysis is suitable to detect seasonal trends in seagrass extent from UAV imagery and find that trends vary between individual meadows at our study site Bahia de San Quintin, Baja California, Mexico, during our study period in 2019. We further suggest that compositing multiple satellite imagery classifications into a seagrass probability map allows for an estimation of seagrass extent in turbid waters and report that in 2019, seagrass covered 2324 ha of Bahia de San Quintin, indicating a recovery from losses reported for previous decades.

关键词： seagrass unmanned aerial vehicle (UAV) object-based image analysis planet machine learning turbid estuary

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Beach wrack mapping using unmanned aerial vehicles for coastal environmental management

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OCEAN & COASTAL MANAGEMENT 2021年 213卷 105843-105843页

作者： Pan, Yaoru Flindt, Mogens Schneider-Kamp, Peter Holmer, Marianne Univ Southern Denmark Dept Biol DK-5230 Odense M Denmark Univ Southern Denmark Dept Math & Comp Sci DK-5230 Odense M Denmark

As a common phenomenon along the global coastline, beach wrack, which is part of the blue carbon ecosystems (BCEs), has significant ecological values. However, the excessive accumulation of beach wrack can be a nuisance for local residents and tourism. Meanwhile, beach wrack can become a source of greenhouse gas due to the decomposition. Hence, effective monitoring of beach wrack has become a priority for coastal environmental management. As a cost- and labor-saving approach, unmanned aerial vehicles (UAVs) can perform customized flight tasks and achieve aerial images with sub-decimeter spatial resolution. This study investigated the feasibility of using UAVs to map wrack on three different types of beaches. The method of object-based image analysis (OBIA) was applied to classify the aerial images. Three typical machine learning methods, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forests (RF), were examined with different feature spaces at several segmentation levels. The results showed that the three machine learning methods performed well with the overall classification accuracy >75%. The tested algorithm, SVM with only RGB as feature space at the segmentation scale 50, was geographically transferable to beaches with different characteristics. This study demonstrated that UAVs can be developed as an applicable tool for beach wrack mapping and monitoring, which will help to better explore the role of beach wrack in BCEs and assist the local municipalities in environmental management of the coastal zone.

关键词： Beach wrack Unmanned aerial vehicles object-based image analysis Blue carbon ecosystems Environmental management

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Application of the tilt derivative transform to bathymetric data for structural lineament mapping

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JOURNAL OF STRUCTURAL GEOLOGY 2021年 146卷 104301-104301页

作者： Yeomans, Christopher M. Head, Matthew Lindsay, Jordan J. Univ Exeter Coll Engn Math & Phys Sci Camborne Sch Mines Penryn Campus Penryn TR10 9FE Cornwall England

High-resolution bathymetry surveys provide an opportunity to analyse local geological structure where onshore areas afford limited exposure. Semi-automated lineament detection methods are necessary for areas of large coverage where a manual analysis would be subjective and time-consuming. However, semi-automated approaches are dependent on effective feature extraction methods to identify genuine lineaments. This study offers solutions to common problems that can impede processing methods where sharp steps in the seafloor (e.g. palaeocoastlines) are present. Directional gradient, Sobel and Laplacian filters are explored as well as the hillshade and tilt derivative transform for feature extraction prior to applying an object-based image analysis lineament detection approach. The filtered datasets generally perform poorly with a marked improvement when using the hillshade transform. However, we find the azimuth-invariant tilt derivative, which incorporates a convolved vertical derivative, to be the most successful, identifying lineaments in a range of orientations and across a sharp step in the seafloor.

关键词： Tilt derivative transform Bathymetry Lineament detection object-based image analysis Southwest England

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Classifying Individual Shrub Species in UAV images-A Case Study of the Gobi Region of Northwest China

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REMOTE SENSING 2021年第24期13卷 4995-4995页

作者： Li, Zhipeng Ding, Jie Zhang, Heyu Feng, Yiming Chinese Acad Forestry Inst Desertificat Study Beijing 100091 Peoples R China

Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification.

关键词： shrub species classification unmanned aerial vehicle RGB image object-based image analysis spectral indices texture indices

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Mapping Floods in Lowland Forest Using Sentinel-1 and Sentinel-2 Data and an object-based Approach

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FORESTS 2021年第5期12卷 553页

作者： Gasparovic, Mateo Klobucar, Damir Univ Zagreb Fac Geodesy Chair Photogrammetry & Remote Sensing Kaciceva 26 Zagreb 10000 Croatia Croatian Forests Ltd Prod & Dev Dept Ivana Mestrovica 28 Koprivnica 48000 Croatia

The impact of floods on forests is immediate, so it is necessary to quickly define the boundaries of flooded areas. Determining the extent of flooding in situ has shortcomings due to the possible limited spatial and temporal resolutions of data and the cost of data collection. Therefore, this research focused on flood mapping using geospatial data and remote sensing. The research area is located in the central part of the Republic of Croatia, an environmentally diverse area of lowland forests of the Sava River and its tributaries. Flood mapping was performed by merging Sentinel-1 (S1) and Sentinel-2 (S2) mission data and applying object-based image analysis (OBIA). For this purpose, synthetic aperture radar (SAR) data (GRD processing level) were primarily used during the flood period due to the possibility of all-day imaging in all weather conditions and flood detection under the density of canopy. The pre-flood S2 imagery, a summer acquisition, was used as a source of additional spectral data. Geographical information system (GIS) layers-a multisource forest inventory, habitat map, and flood hazard map-were used as additional sources of information in assessing the accuracy of and interpreting the obtained results. The spectral signature, geometric and textural features, and vegetation indices were applied in the OBIA process. The result of the work was a developed methodological framework with a high accuracy and speed of production. The overall accuracy of the classification is 94.94%. based on the conducted research, the usefulness of the C band of the S1 in flood mapping in lowland forests in the leaf-off season was determined. The paper presents previous research and describes the SAR parameters and characteristics of floodplain forest with a significant impact on the accuracy of classification.

关键词： rapid mapping floods forests machine learning Sentinel-1 Sentinel-2 remote sensing image classification object-based image analysis

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Quantifying the influence of individual trees on slope stability at landscape scale

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 286卷 112194-112194页

作者： Spiekermann, Raphael I. McColl, Sam Fuller, Ian Dymond, John Burkitt, Lucy Smith, Hugh G. Manaaki Whenua Landcare Res Palmerston North New Zealand Massey Univ Sch Agr & Environm Palmerston North New Zealand

Silvopastoralism in New Zealand's highly erodible hill country is an important form of erosion and sediment control. Yet, there has been little quantitative work to establish the effectiveness of space-planted trees in reducing shallow landslide erosion. We propose a method to provide high-resolution spatially explicit individual tree influence models at landscape scale for the dominant species in pastoral hill country. The combined hydrological and mechanical influence of trees on slopes is inferred through the spatial relationship between trees and landslide erosion. First, we delineate individual tree crowns and classify these into four dominant species classes found in New Zealand's pastoral hill country. This is the first species classification of individual trees at landscape scale in New Zealand using freely accessible data, achieving an overall accuracy of 92.6%. Second, we develop tree influence models for each species class by means of inductive inference. The inferred empirical tree influence models largely agree with the shape and distribution of existing physical root reinforcement models. Of exotic species that were planted for erosion and sediment control, poplars (Populus spp.) and willows (Salix spp.) make up 51% (109,000 trees) in pastoral hill country at a mean density of 3.2 trees/ha. In line with previous studies, poplars and willows have the greatest contribution to slope stability with an average maximum effective distance of 20 m. Yet, native kanuka (Kunzea spp.) is the most abundant woody vegetation species in pastoral hill country within the study area, with an average of 24.1 stems per ha (sph), providing an important soil conservation function. A large proportion (56% or 212.5 km(2)) of pastoral hill-country in the study area remains untreated. The tree influence models presented in this study can be integrated into landslide susceptibility modelling in silvopastoral landscapes to both quantify the reduction in landslide susceptibility ac

关键词： Individual tree crowns Tree species classification Slope stability Landslide susceptibility object-based image analysis Silvopastoralism

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VINEYARD CLASSIFICATION USING MACHINE LEARNING TECHNIQUES APPLIED TO RGB-UAV imageRY

VINEYARD CLASSIFICATION USING MACHINE LEARNING TECHNIQUES AP...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Padua, Luis Adao, Telmo Hruska, Jonas Guimaraes, Nathalie Marques, Pedro Peres, Emanuel Sousa, Joaquim J. Univ Tras Os Montes & Alto Douro Vila Real Portugal INESC TEC Ctr Robot Ind & Intelligent Syst Porto Portugal Ctr Res & Technol Agroenvironm & Biol Sci Vila Real Portugal

ISBN: (纸本)9781728163741

In this study machine learning methods were applied to RGB data obtained by an unmanned aerial vehicle (UAV) to assess this effectiveness in vineyard classification. The very high-resolution UAV-based imagery was subjected to a photogrammetric processing allowing the generation of different outcomes: orthophoto mosaic, crop surface model and five vegetation indices. The orthophoto mosaic was used in an object-based image analysis approach to group pixels with similar values into objects. Three machine learning techniques-support vector machine (SVM), random forest (RF) and artificial neural network (ANN)-were applied to classify the data into four classes: grapevine, shadow, soil and other vegetation. The data were divided with 22% (n=240, 60 per class) for training purposes and 78% (n = 850) for testing purposes. The mean value of the objects from each feature were used to create a dataset for prediction. The results demonstrated that both RF and ANN models showed a good performance, yet the RF classifier achieved better results.

关键词： Precision viticulture object-based image analysis artificial neural networks random forests support vector machines

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UAV-based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)

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REMOTE SENSING 2021年第16期13卷 3272页

作者： Ivosevic, Bojana Lugonja, Predrag Brdar, Sanja Radulovic, Mirjana Vujic, Ante Valente, Joao Univ Novi Sad BioSense Inst Res Inst Informat Technol Biosyst Novi Sad 21101 Serbia Univ Novi Sad Dept Biol & Ecol Fac Sci Novi Sad 21102 Serbia Wageningen Univ & Res Informat Technol Grp NL-6706 KN Wageningen Netherlands

Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms-support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)-were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.

关键词： unmanned aerial vehicle object-based image analysis Orfeo ToolBox QGIS random forest hoverfly Map of the Natural Vegetation of Europe

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Change of land use/land cover in kurdistan region of Iraq: A semi-automated object-based approach

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2022年 26卷

作者： Nasir, Salam Mahmood Kamran, Khalil Valizadeh Blaschke, Thomas Karimzadeh, Sadra Univ Tabriz Dept Remote Sensing & GIS Tabriz Iran Univ Salzburg Dept Geoinformat Z GIS Salzburg Austria Univ Raparin Coll Humanities Dept Geog Rania Sulaimani Iraq

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.

关键词： Land use land covers mapping and monitoring object-based image analysis Digital elevation information Confusion matrix Kurdistan region of Iraq

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Impact assessment of humanitarian demining using object-based peri-urban land cover classification and morphological building detection from VHR Worldview imagery

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2022年 27卷

作者： Killeen, Jennifer Jaupi, Luan Barrett, Brian Univ Glasgow Sch Geog & Earth Sci Glasgow G128QQ Scotland HALO Trust Carronfoot Dumfries DG35BF Scotland Sywell Aerodrome Tiger House 2Excel geo Northampton NN60BN Northants England

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-based image 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

关键词： Building detection Morphological building index Landmine clearance Afghanistan object-based image analysis

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