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Multi-Sensor UAV Tracking of Individual Seedlings and Seedling Communities at Millimetre Accuracy

DRONES 2019年第4期3卷 1-20页

作者： Buters, Todd M. Belton, David Cross, Adam T. Curtin Univ ARC Ctr Mine Site Restorat Sch Mol & Life Sci Kent St Bentley WA 6102 Australia Curtin Univ Spatial Sci Sch Earth & Planetary Sci Kent St Bentley WA 6102 Australia

The increasing spatial and temporal scales of ecological recovery projects demand more rapid and accurate methods of predicting restoration trajectory. Unmanned aerial vehicles (UAVs) offer greatly improved rapidity and efficiency compared to traditional biodiversity monitoring surveys and are increasingly employed in the monitoring of ecological restoration. However, the applicability of UAV-based remote sensing in the identification of small features of interest from captured imagery (e.g., small individual plants, <100 cm(2)) remains untested and the potential of UAVs to track the performance of individual plants or the development of seedlings remains unexplored. This study utilised low-altitude UAV imagery from multi-sensor flights (Red-Green-Blue and multispectral sensors) and an automated object-based image analysis software to detect target seedlings from among a matrix of non-target grasses in order to track the performance of individual target seedlings and the seedling community over a 14-week period. object-based image analysis (OBIA) classification effectively and accurately discriminated among target and non-target seedling objects and these groups exhibited distinct spectral signatures (six different visible-spectrum and multispectral indices) that responded differently over a 24-day drying period. OBIA classification from captured imagery also allowed for the accurate tracking of individual target seedling objects through time, clearly illustrating the capacity of UAV-based monitoring to undertake plant performance monitoring of individual plants at very fine spatial scales.

关键词： drones multispectral ecological restoration rehabilitation object-based image analysis

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Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated image Classification in the Monitoring of Ecological Recovery

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DRONES 2019年第3期3卷 1-16页

作者： Buters, Todd Belton, David Cross, Adam Curtin Univ Sch Mol & Life Sci ARC Ctr Mine Site Restorat Kent St Bentley WA 6102 Australia Curtin Univ Sch Earth & Planetary Sci Spatial Sci Bentley WA 6102 Australia

Monitoring is a crucial component of ecological recovery projects, yet it can be challenging to achieve at scale and during the formative stages of plant establishment. The monitoring of seeds and seedlings, which represent extremely vulnerable stages in the plant life cycle, is particularly challenging due to their diminutive size and lack of distinctive morphological characteristics. Counting and classifying seedlings to species level can be time-consuming and extremely difficult, and there is a need for technological approaches offering restoration practitioners with fine-resolution, rapid and scalable plant-based monitoring solutions. Unmanned aerial vehicles (UAVs) offer a novel approach to seed and seedling monitoring, as the combination of high-resolution sensors and low flight altitudes allow for the detection and monitoring of small objects, even in challenging terrain and in remote areas. This study utilized low-altitude UAV imagery and an automated object-based image analysis software to detect and count target seeds and seedlings from a matrix of non-target grasses across a variety of substrates reflective of local restoration substrates. Automated classification of target seeds and target seedlings was achieved at accuracies exceeding 90% and 80%, respectively, although the classification accuracy decreased with increasing flight altitude (i.e., decreasing image resolution) and increasing background surface complexity (increasing percentage cover of non-target grasses and substrate surface texture). Results represent the first empirical evidence that small objects such as seeds and seedlings can be classified from complex ecological backgrounds using automated processes from UAV-imagery with high levels of accuracy. We suggest that this novel application of UAV use in ecological monitoring offers restoration practitioners an excellent tool for rapid, reliable and non-destructive early restoration trajectory assessment.

关键词： ecological restoration object-based image analysis rehabilitation remote sensing monitoring

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Assessing the use of harmonized multisource backscatter data for thematic benthic habitat mapping

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SCIENCE OF REMOTE SENSING 2021年 3卷

作者： Misiuk, Benjamin Lacharite, Myriam Brown, Craig J. Dalhousie Univ Dept Oceanog 6299 South St Halifax NS B3H 4R2 Canada Univ Tasmania Inst Marine & Antarct Studies Private Bag 129 Hobart Tas 7001 Australia

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.

关键词： Benthic habitat mapping Multibeam echosounder Backscatter Seabed mapping Benthoscape object-based image analysis

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Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification

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GISCIENCE & REMOTE SENSING 2019年第4期56卷 536-553页

作者： Antunes, R. R. Blaschke, T. Tiede, D. Bias, E. S. Costa, G. A. O. P. Happ, P. N. Fed Univ Brasilia Dept Geosci Brasilia DF Brazil Univ Salzburg Dept Geoinformat Z GIS Salzburg Austria Univ Estado Rio De Janeiro Dept Informat & Comp Sci Rio De Janeiro Brazil Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro Brazil

Advances in the development of Earth observation data acquisition systems have led to the continuously growing production of remote sensing datasets, for which timely analysis has become a major challenge. In this context, distributed computing technology can provide support for efficiently handling large amounts of data. Moreover, the use of distributed computing techniques, once restricted by the availability of physical computer clusters, is currently widespread due to the increasing offer of cloud computing infrastructure services. In this work, we introduce a cloud computing approach for object-based image analysis and classification of arbitrarily large remote sensing datasets. The approach is an original combination of different distributed methods which enables exploiting machine learning methods in the creation of classification models, through the use of a web-based notebook system. A prototype of the proposed approach was implemented with the methods available in the InterCloud system integrated with the Apache Zeppelin notebook system, for collaborative data analysis and visualization. In this implementation, the Apache Zeppelin system provided the means for using the scikit-learn Python machine learning library in the design of a classification model. In this work we also evaluated the approach with an object-based image land-cover classification of a GeoEye-1 scene, using resources from a commercial cloud computing infrastructure service provided. The obtained results showed the effectiveness of the approach in efficiently handling a large data volume in a scalable way, in terms of the number of allocated computing resources.

关键词： object-based image analysis Intercloud machine learning scikit-learn Apache Zeppelin

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A customized framework for regional classification of conifers using automated feature extraction

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METHODSX 2021年 8卷 101379页

作者： Roth, Cali L. Coates, Peter S. Gustafson, K. Benjamin Chenaille, Michael P. Ricca, Mark A. Sanchez-Chopitea, Erika Casazza, Michael L. US Geol Survey Western Ecol Res Ctr Dixon CA USA

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.

关键词： Automated feature extraction Classification image processing object-based image analysis Pinyon-juniper

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Fate of pioneering vegetation patches in a dynamic meandering river

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EARTH SURFACE PROCESSES AND LANDFORMS 2019年第8期44卷 1618-1632页

作者： Kleinhans, Maarten Douma, Harke Addink, Elisabeth A. Univ Utrecht Fac Geosci POB 80115 NL-3508 TC Utrecht Netherlands

Fluvial biomorphodynamics in actively meandering rivers entail interactions between hydromorphodynamics and pioneering tree species that have eco-engineering effects. Here we study spatiotemporal patterns of vegetation patches smaller than 150 m(2) in a 4 km reach of the river Allier in France in order to unravel causes for tree persistence and mortality and identify spatial trends across the river valley. To this end we analysed aerial photographs by object-based image analysis over a period of 56 years and tracked individual patches through time. Furthermore the cover and surface age of the study reach were classified. The large-scale shifts of channels, bars and vegetation are consistent with the meandering process and chute cutoffs. However, the spatiotemporal patterns of the vegetation patches are surprising in that they are ubiquitous and have ages up to decades on the highly dynamic meander belt, but hardly expand into larger vegetation patches. Patches disappear exponentially as a function of their age, and faster so in the last decades. Causes are amalgamation into the riparian forest flanking the meander belt and mortality likely due to desiccation or erosion. Patches have a higher probability of survival when further away from the active channel and closer to high vegetation patches and valley boundary. The window of opportunity of vegetation settlement widens towards the valley boundaries and in floodplain lows of former channels and chutes. These results imply a gradual cross-valley gradient of riparian vegetation settling, survival and succession. (c) 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.

关键词： meandering river pioneer vegetation object-based image analysis

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Comparing ultra-high spatial resolution remote-sensing methods in mapping peatland vegetation

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JOURNAL OF VEGETATION SCIENCE 2019年第5期30卷 1016-1026页

作者： Rasanen, Aleksi Juutinen, Sari Tuittila, Eeva-Stiina Aurela, Mika Virtanen, Tarmo Univ Helsinki Fac Biol & Environm Sci Ecosyst & Environm Res Programme Helsinki Finland Univ Helsinki Helsinki Inst Sustainabil Sci HELSUS Helsinki Finland Norwegian Univ Sci & Technol Dept Geog Trondheim Norway Univ Eastern Finland Sch Forest Sci Joensuu Finland Finnish Meteorol Inst Helsinki Finland

Questions How to map floristic variation in a patterned fen in an ecologically meaningfully way? Can plant communities be delineated with species data generalized into plant functional types? What are the benefits and drawbacks of the two selected remote-sensing approaches in mapping vegetation patterns, namely: (a) regression models of floristically defined fuzzy plant community clusters and (b) classification of predefined habitat types that combine vegetation and land cover information? Location Treeless 0.4 km(2) mesotrophic string-flark fen in Kaamanen, northern Finland. Methods We delineated plant community clusters with fuzzy c-means clustering based on two different inventories of plant species and functional type distribution. We used multiple optical remote-sensing data sets, digital elevation models and vegetation height models derived from drone, aerial and satellite platforms from ultra-high to very high spatial resolution (0.05-3 m) in an object-based approach. We mapped spatial patterns for fuzzy and crisp plant community clusters using boosted regression trees, and fuzzy and crisp habitat types using supervised random forest classification. Results Clusters delineated with species-specific data or plant functional type data produced comparable results. However, species-specific data for graminoids and mosses improved the accuracy of clustering in the case of flarks and string margins. Mapping accuracy was higher for habitat types (overall accuracy 0.72) than for fuzzy plant community clusters (R-2 values between 0.27 and 0.67). Conclusions For ecologically meaningful mapping of a patterned fen vegetation, plant functional types provide enough information. However, if the aim is to capture floristic variation in vegetation as realistically as possible, species-specific data should be used. Maps of plant community clusters and habitat types complement each other. While fuzzy plant communities appear to be floristically most accurate, crisp habitat type

关键词： digital elevation model drone floristic analysis fuzzy northern boreal object-based image analysis plant community plant functional types unmanned aerial system (UAS) unmanned aerial vehicle (UAV) very-high spatial resolution satellite imagery

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Comparing random forests and convoluted neural networks for mapping ghost crab burrows using imagery from an unmanned aerial vehicle

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ESTUARINE COASTAL AND SHELF SCIENCE 2019年 224卷 84-93页

作者： Bycroft, Rachel Leon, Javier X. Schoeman, David Univ Sunshine Coast Sch Sci & Engn Global Change Ecol Res Grp Sippy Downs Qld 4556 Australia Nelson Mandela Univ Dept Zool Ctr African Conservat Ecol Port Elizabeth South Africa

Sandy beaches are important ecosystems that line a third of the world's ice-free coastlines. Unfortunately, these environments and the life they support are often threatened by various anthropogenic and natural factors. Monitoring sandy beach health is important to aid in appropriate management decisions. One such method of quantifying environmental health is using bioindicators. Ghost crabs are a commonly used sandy beach bioindicator. Current techniques for assessing ghost crab abundance and distribution data involve manually counting each individual burrow opening, which can intrusive and timely for a large area. The aim of this study was to assesses the use of imagery from an unmanned aerial vehicle and machine-learning algorithms as an alternative approach to monitoring ghost crab burrows. The accuracy and transferability of random forest (RF) and convolutional neural networks (CNN) classifiers within an object-based image analysis (OBIA) framework were tested using hyper-resolution (0.04 m) orthomosaics from four different dates. CNN was a more robust classifier with higher accuracies (max F-score 0.84). Transferability of rule sets and models was limited for both classifiers, particularly when applied to sub-optimal imagery. Overall, we present a feasible workflow that provides ecologist and environmental managers with a cost-effective and less invasive alternative to mapping ghost crab burrows.

关键词： Machine-learning Sandy beach object-based image analysis Drones

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object-based delineation of urban tree canopy: assessing change in Oklahoma City, 2006-2013

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2019年 73卷 85-94页

作者： Ellis, Emily A. Mathews, Adam J. Oklahoma State Univ Dept Geog Ctr Applicat Remote Sensing Stillwater OK 74078 USA Western Michigan Univ Dept Geog Kalamazoo MI 49008 USA

With a burgeoning global population, the pressures of urbanization are increasingly prevalent. The need to quantify urban greenness remains significant due to environmental impact and its relationship with human wellbeing. Utilizing 1 m discrete-return airborne lidar-derived digital terrain models (DTMs) and digital surface models (DSMs), aerial imagery, and lidar-imagery fusion, this study assesses vegetation, specifically tree canopy, change within Oklahoma City between 2006 and 2013. Specifically, we (1) identify an accurate object-based image analysis (OBIA) method for the detection of urban vegetation outlines, and (2) apply that method to locate and quantify vegetation change and assess spatial patterns in Oklahoma City between 2006 and 2013. The proposed OBIA approach extracts urban vegetation coverage from aerial imagery and lidar-based models with around 89% accuracy. Regarding vegetation change, Oklahoma City lost 9.69 km(2) (3.74 mi(2)) of tree canopy coverage, which accounted for a 2% loss in total greenness.

关键词： object-based image analysis Remote sensing Lidar Data fusion Tree canopy delineation Oklahoma City

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Self-adaptive segmentation of satellite images based on a weighted aggregation approach

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GISCIENCE & REMOTE SENSING 2019年第2期56卷 233-255页

作者： Xiong, Ziqian Zhang, Xiuyuan Wang, Xiaonan Yuan, Jing Wuhan Digital Engn Res Inst Wuhan Hubei Peoples R China Peking Univ Inst Remote Sensing & GIS Beijing Peoples R China Capital Univ Econ & Business Beijing Key Lab Megareg Sustainable Dev Modelling Beijing Peoples R China CETC Res Inst 54 Shijiazhuang Hebei Peoples R China

image segmentation is a decisive process in object-based image analysis, while the uncertainty of segmentation scales can significantly influence the results. To resolve this issue, this study proposes a Self-Adaptive Segmentation (SAS) method which bridges the gap between the inherent scale and segmentation scale of each object. Firstly, SAS is defined as a variable-scale segmentation approach, aiming at generating local optimum results. It is then implemented based on Segmentation by Weighted Aggregation (SWA) method and optimized by selfhood scale estimation technique. Secondly, two images derived from WorldView-2 (an urban area) and Landsat-8 (a farmland area) are employed to verify the effectiveness and adaptability of this method. Thirdly, it is further compared with SWA and mean shift method by reference to two evaluations, including an unsupervised evaluation, i.e., Global Score (GS), and a supervised one named Geometry-based comparison. The experimental results indicate the SAS method presented in this study is effective in solving the scale issues in multiscale segmentations. It shows higher accuracy than SWA through assigning an appropriate segmentation scale for each object. Moreover, the segmentation results achieved by SAS method in both study areas are more accurate than those by mean shift method, which demonstrates the stable performance of SAS method across diverse scenes and data sets. This advantage is more obvious in farmland area than in urban scene, according to the accuracy assessment results.

关键词： Self-adaptive segmentation Segmentation by Weighted Aggregation (SWA) selfhood scale multiscale segmentation object-based image analysis

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