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Combining Drones and Deep Learning to Automate Coral Reef Assessment with RGB imagery

REMOTE SENSING 2023年第9期15卷 2238-2238页

作者： Giles, Anna Barbara Ren, Keven Davies, James Edward Abrego, David Kelaher, Brendan Southern Cross Univ Fac Sci & Engn Natl Marine Sci Ctr 2 Bay Dr Coffs Harbour NSW 2450 Australia Univ Melbourne Sch Phys Parkville Vic 3010 Australia

Coral reefs and their associated marine communities are increasingly threatened by anthropogenic climate change. A key step in the management of climate threats is an efficient and accurate end-to-end system of coral monitoring that can be generally applied to shallow water reefs. Here, we used RGB drone-based imagery and a deep learning algorithm to develop a system of classifying bleached and unbleached corals. imagery was collected five times across one year, between November 2018 and November 2019, to assess coral bleaching and potential recovery around Lord Howe Island, Australia, using object-based image analysis. This training mask was used to develop a large training dataset, and an mRES-uNet architecture was chosen for automated segmentation. Unbleached coral classifications achieved a precision of 0.96, a recall of 0.92, and a Jaccard index of 0.89, while bleached corals achieved 0.28 precision, 0.58 recall, and a 0.23 Jaccard index score. Subsequently, methods were further refined by creating bleached coral objects (>16 pixels total) using the neural network classifications of bleached coral pixels, to minimize pixel error and count bleached coral colonies. This method achieved a prediction precision of 0.76 in imagery regions with >2000 bleached corals present, and 0.58 when run on an entire orthomosaic image. Bleached corals accounted for the largest percentage of the study area in September 2019 (6.98%), and were also significantly present in March (2.21%). Unbleached corals were the least dominant in March (28.24%), but generally accounted for similar to 50% of imagery across other months. Overall, we demonstrate that drone-based RGB imagery, combined with artificial intelligence, is an effective method of coral reef monitoring, providing accurate and high-resolution information on shallow reef environments in a cost-effective manner.

关键词： UAV neural network Lord Howe Island coral bleaching climate change remote sensing object-based image analysis

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A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data

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REMOTE SENSING 2023年第17期15卷 4128-4128页

作者： Xu, Yi Wang, Tiejun Skidmore, Andrew K. Gara, Tawanda W. Univ Twente Fac Geoinformat Sci & Earth Observat ITC POB 217 NL-7500 AE Enschede Netherlands Macquarie Univ Sch Nat Sci 12 Wallys Walk Sydney NSW 2109 Australia Calif State Polytech Univ Humboldt Dept Environm Sci & Management Arcata CA 95521 USA

Integrating multimodal remote sensing data can optimize the mapping accuracy of individual trees. Yet, one issue that is not trivial but generally overlooked in previous studies is the spatial mismatch of individual trees between remote sensing datasets, especially in different imaging modalities. These offset errors between the same tree on different data that have been geometrically corrected can lead to substantial inaccuracies in applications. In this study, we propose a novel approach to match individual trees between aerial photographs and airborne LiDAR data. To achieve this, we first leveraged the maximum overlap of the tree crowns in a local area to determine the correct and the optimal offset vector, and then used the offset vector to rectify the mismatch on individual tree positions. Finally, we compared our proposed approach with a commonly used automatic image registration method. We used pairing rate (the percentage of correctly paired trees) and matching accuracy (the degree of overlap between the correctly paired trees) to measure the effectiveness of results. We evaluated the performance of our approach across six typical landscapes, including broadleaved forest, coniferous forest, mixed forest, roadside trees, garden trees, and parkland trees. Compared to the conventional method, the average pairing rate of individual trees for all six landscapes increased from 91.13% to 100.00% (p = 0.045, t-test), and the average matching accuracy increased from 0.692 & PLUSMN;0.175 (standard deviation) to 0.861 & PLUSMN;0.152 (p = 0.017, t-test). Our study demonstrates that the proposed tree-oriented matching approach significantly improves the registration accuracy of individual trees between aerial photographs and airborne LiDAR data.

关键词： remote sensing multimodal images object-based image analysis spatial mismatch registration noise intersection over union

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Monitoring Spartina alterniflora Expansion Mode and Dieback Using Multisource High-Resolution imagery in Yancheng Coastal Wetland, China

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REMOTE SENSING 2023年第15期15卷 3853-3853页

作者： Yan, Dandan Luan, Zhaoqing Li, Jingtai Xie, Siying Wang, Yu Henan Univ Sci & Technol Coll Hort & Plant Protect Luoyang 471023 Peoples R China Nanjing Forestry Univ Coll Biol & Environm Coinnovat Ctr Sustainable Forestry Southern China Nanjing 210037 Peoples R China

Spartina alterniflora (smooth cordgrass), China's most common invasive species, has posed significant challenges to native plant communities and coastal environments. Monitoring the invasion and dieback process of S. alterniflora by multisource high-resolution imagery is necessary to manage the invasion of the species. Current spatial analyses, however, are insufficient. As a result, we first extracted S. alterniflora by integrating multisource high-resolution images through the multiscale object-oriented classification method, then identified the expansion patterns of S. alterniflora on the seaward side by the landscape expansion index, and conformed the main drivers of S. alterniflora dieback on the landward side in the Jiangsu Dafeng Milu National Nature Reserve. The findings revealed that the area of S. alterniflora decreased in size from 1511.26 ha in 2010 to 910.25 ha in 2020. S. alterniflora continues to grow to the sea and along the tidal creek on the seaward side, with a total increase of 159.13 ha. External isolation expansion patterns accounted for 65.16% of the total expansion patches, with marginal expansion patches accounting for 24.22% and tidal creek-leading expansion patches accounting for 10.62%. While the landward side showed a declining trend, the total area decreased by 852.36 ha, with an annual average change rate of 8.67%. S. alterniflora dieback was negatively related to the number of tidal creeks and positively related to the number of wild Elaphures davidianus and the length of artificial ditches. Our findings provide a scientific foundation for the ecological control of S. alterniflora. Its presence in coastal wetlands inspires evidence-based protection and management strategies to protect the coastal wetland ecosystem.

关键词： Spartina alterniflora high-resolution imagery object-based image analysis landscape expansion index Dafeng Milu National Nature Reserve

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Woody Plant Encroachment in a Seasonal Tropical Savanna: Lessons about Classifiers and Accuracy from UAV images

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REMOTE SENSING 2023年第9期15卷 2342页

作者： Costa, Lucas Silva Sano, Edson Eyji Ferreira, Manuel Eduardo Munhoz, Cassia Beatriz Rodrigues Costa, Joao Vitor Silva Alves Junior, Leomar Rufino de Mello, Thiago Roure Bandeira Bustamante, Mercedes Maria da Cunha Univ Brasilia Programa Posgrad Ecol Inst Ciencias Biol BR-70910900 Brasilia Brazil Empresa Brasileira Pesquisa Agr Embrapa Cerrados BR-020 BR-73301970 Planaltina Brazil Univ Fed Goias UFG Inst Estudos Socioambientais BR-74690900 Goiania Brazil

Woody plant encroachment in grassy ecosystems is a widely reported phenomenon associated with negative impacts on ecosystem functions. Most studies of this phenomenon have been carried out in arid and semi-arid grasslands. Therefore, studies in tropical regions, particularly savannas, which are composed of grassland and woodland mosaics, are needed. Our objective was to evaluate the accuracy of woody encroachment classification in the Brazilian Cerrado, a tropical savanna. We acquired dry and wet season unmanned aerial vehicle (UAV) images using RGB and multispectral cameras that were processed by the support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers. We also compared two validation methods: the orthomosaic and in situ methods. We targeted two native woody species: Baccharis retusa and Trembleya parviflora. Identification of these two species was statistically (p < 0.05) most accurate in the wet season RGB images classified by the RF algorithm, with an overall accuracy (OA) of 92.7%. Relating to validation assessments, the in situ method was more susceptible to underfitting scenarios, especially using an RF classifier. The OA was higher in grassland than in woodland formations. Our results show that woody encroachment classification in a tropical savanna is possible using UAV images and field surveys and is suggested to be conducted during the wet season. It is challenging to classify UAV images in highly diverse ecosystems such as the Cerrado;therefore, whenever possible, researchers should use multiple accuracy assessment methods. In the case of using in situ accuracy assessment, we suggest a minimum of 40 training samples per class and to use multiple classifiers (e.g., RF and DT). Our findings contribute to the generation of tools that optimize time and cost for the monitoring and management of woody encroachment in tropical savannas.

关键词： Cerrado object-based image analysis mesic biome plant invasion drone multispectral machine learning grasslands woodlands in situ ground truth

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UAV-based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring

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DRONES 2023年第11期7卷

作者： Thomasberger, Aris Nielsen, Mette Moller Tech Univ Denmark Natl Inst Aquat Resources Sect Coastal Ecol DK-2800 Kgs. Lyngby Denmark

Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, as well as for the identification of ecologically relevant information such as the vegetation depth limit. To address these limitations, a payload system was developed to collect subsurface data in the form of videos and depth measurements. In a 7 ha large study area, 136 point observations were collected and subsequently used to (1) train and validate the object-based classification of aerial imagery, (2) create a class distribution map based on the interpolation of point observations, (3) identify additional ecological relevant information and (4) create a bathymetry map of the study area. The classification based on ground-truthing samples achieved an overall accuracy of 98% and agreed to 84% with the class distribution map based on point interpolation. Additional ecologically relevant information, such as the vegetation depth limit, was recorded, and a bathymetry map of the study site was created. The findings of this study show that UAV-based shallow-water monitoring can be improved by applying the proposed tool.

关键词： unoccupied aerial vehicles UAV drones ground-truthing object-based image analysis photogrammetry seagrasses coastal zone mapping shallow bathymetry benthic ecology Limfjorden

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Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey)

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COMPUTERS & GEOSCIENCES 2012年第1期38卷 87-98页

作者： Aksoy, Beliz Ercanoglu, Murat Hacettepe Univ Geol Engn Dept TR-06800 Ankara Turkey

This study presents a data-driven and semiautomatic classification system carried out by object-based image analysis and fuzzy logic in a selected landslide-prone area in the Western Black Sea region of Turkey. In the first stage, a multiresolution segmentation process was performed using Landsat ETM+ satellite images of the study area. The model was established on 5235 image objects obtained by the segmentation process. A total of 70 landslide locations and 10 input parameters including normalized difference vegetation index, slope angle, curvature, brightness, mean band blue, asymmetry, shape index, length/width ratio, gray level co-occurrence matrix, and mean difference to infrared band were considered in the analyses. Membership functions were used to classify the study area by five fuzzy operators such as "and", "or", "mean arithmetic", "mean geometric", and "algebraic product". In order to assess the performances of the so-produced maps, 700 image objects, which were not used in the model, were taken into consideration. based on the results, the map produced by "fuzzy and" operator performed better than those classified by the other fuzzy operators. The proposed methodology applied in this study may be useful for decision makers, local administrations, and scientists interested in landslides. It may also be useful in landslide-prone areas for planning, management, and regional development purposes. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Fuzzy logic Landslide object-based image analysis Segmentation

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Automatic tuning of segmentation parameters for tree crown delineation with VHR imagery

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GEOCARTO INTERNATIONAL 2021年第19期36卷 2241-2259页

作者： Sothe, Camile de Almeida, Claudia Maria Schimalski, Marcos Benedito Liesenberg, Veraldo Diaz, Pedro Achanccaray Natl Inst Space Res INPE Remote Sensing Div Sao Jose Dos Campos SP Brazil Santa Catarina State Univ UDESC Dept Forest Engn Lages SC Brazil Pontifical Catholic Univ Rio de Janeiro PUC Dept Elect Engn Rio De Janeiro RJ Brazil

In the case of tree species delineation with very high spatial resolution (VHR) images, is desirable that each segment corresponds to one individual tree crown (ITC). However, in order to have a segmentation algorithm that generates segments matching to ITCs, its parameters ought to be properly tuned. Aiming to avoid time-consuming trial-and-error procedures associated with this task, some initiatives for the automatic search of segmentation parameters have been developed, such as metaheuristic methods. The objective of this work was to test the automatic tuning of segmentation parameters of three segmentation algorithms for the delineation of ITCs belonging to a native endangered species in a subtropical forest area, comparing this method with the traditional trial-and-error approach. Two datasets (WorldView-2 and an orthoimage) and three segmentation algorithms (multiresolution, mean-shift and graph-based) were tested. For the automatic approach, a hybrid metaheuristic method was applied to accomplish the automatic search of parameters for the segmentation algorithms, while for the trial-and-error, a visual assessment was conducted for each set of parameters tested. Four supervised metrics were used to assess the quality of the segmentation results for the optimization approach and for the final set of parameters chosen in the trial-and-error approach. Results showed that none of the algorithms, datasets or approaches differ too much. The evaluation metrics values were lower, indicating that the reference ITCs polygons matched with the segmentation results. Despite the similar results, the automatic tuning of segmentation parameters proved to be a feasible alternative to reduce the subjectivity and the human effort in the choice of segmentation parameters as compared to the trial-and error approach.

关键词： Individual tree crown image segmentation object-based image analysis optimization function metaheuristic methods

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Comparative Assessment of Five Machine Learning Algorithms for Supervised object-based Classification of Submerged Seagrass Beds Using High-Resolution UAS imagery

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REMOTE SENSING 2023年第14期15卷 3600-3600页

作者： Thomasberger, Aris Nielsen, Mette Moller Flindt, Mogens Rene Pawar, Satish Svane, Niels Tech Univ Denmark Natl Inst Aquat Resources Sect Coastal Ecol DK-2800 Lyngby Denmark Univ Southern Denmark Dept Biol DK-5230 Odense Denmark

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%;however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings.

关键词： object-based image analysis OBIA unoccupied aerial systems UAS drones photogrammetry machine learning hyperparameter seagrasses coastal zone mapping Limfjorden

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Comparison and Assessment of Data Sources with Different Spatial and Temporal Resolution for Efficiency Orchard Mapping: Case Studies in Five Grape-Growing Regions

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REMOTE SENSING 2023年第3期15卷 655-655页

作者： Yao, Zhiying Zhao, Yuanyuan Wang, Hengbin Li, Hongdong Yuan, Xinqun Ren, Tianwei Yu, Le Liu, Zhe Zhang, Xiaodong Li, Shaoming China Agr Univ Coll Land Sci & Technol Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Remote Sensing Agrihazards Beijing 100083 Peoples R China Minist Nat Resources Peoples Republ China Key Lab Agr Land Qual Beijing 100083 Peoples R China Tsinghua Univ Inst Global Change Studies Dept Earth Syst Sci Minist EducKey Lab Earth Syst Modelling Beijing 100084 Peoples R China Tsinghua Univ Dept Earth Syst Sci Minist Educ Ecol Field Stn East Asian Migratory Bi Beijing 100084 Peoples R China

As one of the most important agricultural production types in the world, orchards have high economic, ecological, and cultural value, so the accurate and timely mapping of orchards is highly demanded for many applications. Selecting a remote-sensing (RS) data source is a critical step in efficient orchard mapping, and it is hard to have a RS image with both rich temporal and spatial information. A trade-off between spatial and temporal resolution must be made. Taking grape-growing regions as an example, we tested imagery at different spatial and temporal resolutions as classification inputs (including from Worldview-2, Landsat-8, and Sentinel-2) and compared and assessed their orchard-mapping performance using the same classifier of random forest. Our results showed that the overall accuracies improved from 0.6 to 0.8 as the spatial resolution of the input images increased from 58.86 m to 0.46 m (simulated from Worldview-2 imagery). The overall accuracy improved from 0.7 to 0.86 when the number of images used for classification was increased from 2 to 20 (Landsat-8) or approximately 60 (Sentinel-2) in one year. The marginal benefit of increasing the level of details (LoD) of temporal features on accuracy is higher than that of spatial features, indicating that the classification ability of temporal information is higher than that of spatial information. The highest accuracy of using a very high-resolution (VHR) image can be exceeded only by using four to five medium-resolution multi-temporal images, or even two to three growing season images with the same classifier. Combining the spatial and temporal features from multi-source data can improve the overall accuracies by 5% to 7% compared to using only temporal features. It can also compensate for the accuracy loss caused by missing data or low-quality images in single-source input. Although selecting multi-source data can obtain the best accuracy, selecting single-source data can improve computational efficiency and

关键词： orchard mapping spatial features temporal features data selection object-based image analysis Google Earth Engine

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Exploring agricultural landscape change from the second half of the twentieth century onwards: combining aerial imagery with farmer perspectives

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LANDSCAPE ECOLOGY 2024年第7期39卷 120-120页

作者： Mohr, Franziska Pazur, Robert Debonne, Niels Dossche, Rebekka Helfenstein, Julian Hepner, Samuel Levers, Christian Verburg, Peter H. Buergi, Matthias Swiss Fed Res Inst WSL Zurcherstr 111 CH-8903 Birmensdorf Switzerland Univ Bern Inst Geog Bern Switzerland Slovak Acad Sci Inst Geog Bratislava Slovakia Vrije Univ Amsterdam Inst Environm Studies IVM Environm Geog Grp Amsterdam Netherlands Univ Genoa Dept Antiqu Philosophy Hist DAFIST Genoa Italy Wageningen Univ Soil Geog & Landscape Grp Wageningen Netherlands Agroscope Agroecol & Environm Zurich Switzerland Fed Res Inst Rural Areas Forestry & Fisheries Thunen Inst Biodivers Johann Heinrich Thunen Inst Braunschweig Germany

Context Anthropogenic landscape change is an important driver shaping our environment. Historical landscape analysis contributes to the monitoring and understanding of these change processes. Such analyses are often focused on specific spatial scales and single research methods, thus covering only limited aspects of landscape *** Here, we aim to assess the potential of combining the analysis of historical aerial imagery and local stakeholder interviews for landscape change studies using a standardized mapping and interviewing *** We compared six agricultural landscapes across Europe and mapped land-cover using historical aerial imagery (starting between 1930 and 1980, depending on data availability, until recent years) with an object-based image analysis and random forest classification. For local perspectives of landscape change, we conducted oral history interviews (OHIs) with (almost) retired farmers. Comparing recorded landscape changes from both approaches provided insight into advantages of combining these two *** object-based analysis enabled the identification of high-resolution land-cover dynamics, with scale enlargement and cropland/grassland expansion being the most commonly recurring trends across European landscapes. Perceived landscape changes identified in the OHIs included changes in farm management, landscape structure, and infrastructure. Farmers also reported drivers and personal values associated with landscape change. Combining the two historical landscape analysis tools resulted in a qualitative and quantitative understanding of changes in land-cover, land use, and land *** Comparing physical land-cover change with local farmer perspectives is key to a comprehensive understanding of landscape change. There are different ways the two methods can be combined, leading to different venues for science and policy making.

关键词： Agricultural landscape Landscape history Land-use change Mixed-method approach object-based image analysis Oral history

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