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On-farm evaluation of UAV-based aerial imagery for season-long weed monitoring under contrasting management and pedoclimatic conditions in wheat

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年 204卷

作者： Anderegg, Jonas Tschurr, Flavian Kirchgessner, Norbert Treier, Simon Schmucki, Manuel Streit, Bernhard Walter, Achim Swiss Fed Inst Technol Inst Integrat Biol Plant Pathol Grp Zurich Switzerland Swiss Fed Inst Technol Inst Agr Sci Crop Sci Grp Zurich Switzerland Agroscope Varieties & Prod Tech Plants & Plant Prod Nyon Switzerland Bern Univ Appl Sci Sch Agr Forest & Food Sci HAFL Zollikofen Switzerland Univ Str 2 CH-8092 Zurich Switzerland

Timely availability of weed infestation maps is a key prerequisite for the implementation of site-specific weed management practices. Low-altitude aerial imagery obtained from unmanned aerial vehicles (UAVs) has shown significant potential for weed detection in crops. However, most studies focused on wide-spaced row crops such as maize and sunflower and evaluated proposed methods on single or few well-characterized experimental sites at specific points in time representing a limited range of application scenarios. This study evaluated the feasibility of weed detection in on-farm wheat fields characterized by a narrow row spacing, throughout the early and late developmental stages using UAV imagery and ground-based high-resolution imagery. image data was obtained for nine sites, representing a wide range of management and pedoclimatic conditions. These sites can be seen as a representative sample of scenarios that would be encountered in practice. A high within-and across-site as well as temporal variation was observed for weed infestation levels and weed population species composition, highlighting the need for spatially and temporally resolved weed mapping. image-based classification of vegetation objects as crop or weed plants was achieved with an accuracy of 0.88 and 0.72 in ground-based high resolution images and UAV-based aerial images captured from an altitude of 10 m, respectively. The accuracy of pixel-wise, vegetation-index-based weed infestation estimation during the late vegetative stages varied strongly across sites. Our results highlight the critical importance of a high ground resolution for weed detection using object-based image analysis during the critical growth stages of wheat and of robust methods that are applicable across a range of scenarios. This suggested that future research aiming at a rapid implementation of site-specific weed management in wheat should focus on the development of ground-based systems. Yet, aerial monitoring of wheat stan

关键词： Precision agriculture object-based image analysis OBIA Vegetation index Site-specific management Remote sensing

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Trying to Break New Ground in Aerial Archaeology

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REMOTE SENSING 2016年第11期8卷 918-918页

作者： Verhoeven, Geert Sevara, Christopher Ludwig Boltzmann Inst Archaeol Prospect & Virtual Franz Klein Gasse 1-3 A-1190 Vienna Austria Univ Vienna Dept Prehist & Hist Archaeol Franz Klein Gasse 1-3 A-1190 Vienna Austria

Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth's atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research

关键词： aerial archaeology aerial photography block survey camera pod landscape archaeology multispectral imaging object-based image analysis spatial data management

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Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive Phragmites australis in Treatment Areas Enrolled in an Adaptive Management Program

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REMOTE SENSING 2021年第10期13卷 1895-1895页

作者： Brooks, Colin Weinstein, Charlotte Poley, Andrew Grimm, Amanda Marion, Nicholas Bourgeau-Chavez, Laura Hansen, Dana Kowalski, Kurt Michigan Technol Univ Michigan Tech Res Inst 3600 Green CourtSuite 100 Ann Arbor MI 48105 USA Alaska Reg Off Natl Pk Serv 240 W 5th Ave Anchorage AK 99501 USA US Geol Survey Great Lakes Sci Ctr 1451 Green Rd Ann Arbor MI 48105 USA

Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user's accuracy of 90.3% and a mean producer's accuracy of 90.1%, and the Dead Phragmites class had a mean user's accuracy of 76.5% and a mean producer's accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites.

关键词： Phragmites australis uncrewed drone monitoring invasive adaptive management mapping object-based image analysis

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Identification of Abandoned Logging Roads in Point Reyes National Seashore

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REMOTE SENSING 2023年第13期15卷 3369页

作者： Wiskes, William Blesius, Leonhard Hines, Ellen San Francisco State Univ Dept Geog & Environm 1600 Holloway Ave San Francisco CA 94132 USA San Francisco State Univ Estuary & Ocean Sci Ctr Tiburon CA 94920 USA

Temporary roads are often placed in mountainous regions for logging purposes but then never decommissioned and removed. These abandoned forest roads often have unwanted environmental consequences. They can lead to altered hydrological regimes, excess erosion, and mass wasting events. These events can affect sediment budgets in streams, with negative consequences for anadromous fish populations. Maps of these roads are frequently non-existent;therefore, methods need to be created to identify and locate these roads for decommissioning. Abandoned logging roads in the Point Reyes National Seashore in California, an area partially under heavy forest canopy, were mapped using object-based image processing in concert with machine learning. High-resolution Q1 LiDAR point clouds from 2019 were used to create a bare earth model of the region, from which a slope model was derived. This slope model was then subjected to segmentation algorithms to identify and isolate regions of differing slopes. Regions of differing slopes were then used in a convolutional neural network (CNN), and a maximum likelihood classifier was used to delineate the historic road network. The accuracy assessment was conducted using historic aerial photos of the state of the region post-logging, along with ground surveys to verify the presence of logging roads in areas of question. This method was successfully able to identify road networks with a precision of 0.991 and an accuracy of 0.992. It was also found that the CNN was able to identify areas of highest disturbance to the slope gradient. This methodology is a valuable tool for decision makers who need to identify areas of high disturbance in order to mitigate adverse effects.

关键词： LiDAR object-based image analysis image segmentation CNN machine learning maximum likelihood classifier feature extraction aerial photos forest roads logging roads

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Monitoring post-fire neighborhood competition effects on pine saplings under different environmental conditions by means of UAV multispectral data and structure-from-motion photogrammetry

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年 305卷 114373-114373页

作者： Manuel Fernandez-Guisuraga, Jose Calvo, Leonor Suarez-Seoane, Susana Univ Leon Fac Biol & Environm Sci Area Ecol Leon 24071 Spain Univ Oviedo Dept Organisms & Syst Biol Ecol Unit Oviedo Mieres Spain Univ Oviedo Res Unit Biodivers UO CSIC PA Oviedo Mieres Spain

In burned landscapes, the recruitment success of the tree dominant species mainly depends on plant competition mechanisms operating at fine spatial scale, that may hinder resource availability during the former years after the disturbance. Data acquisition at very high spatial resolution from unmanned aerial vehicles (UAV) have promoted new opportunities for understanding context-dependent competition processes in post-fire environments. Here, we explored the potentiality of UAV-borne data for assessing inter-specific competition effects of understory woody vegetation on pine saplings, as well as intra-specific interactions of neighboring saplings, across three burned landscapes located along a climatic/productivity gradient in the Iberian Peninsula. Geographic object-based image analysis (GEOBIA), including multiresolution segmentation and support vector machine (SVM) classification, was used to map pine saplings and understory shrubs at species level. Input data were, on the one hand, multispectral (11.31 ***(-1)) and Structure-from-Motion (SfM) canopy height model (CHM) data fusion, hereafter MS-CHM, and, on the other, RGB (3.29 ***(-1)) and CHM data fusion, hereafter RGB-CHM. A Random Forest (RF) regression algorithm was used to evaluate the effects of neighborhood competition on the relative growth in height of 50 pine saplings randomly sampled across the MS-CHM classified map. Circular plots of 3 m radius were set from the centroid of each target pine sapling to measure percentage cover, mean height of all individuals in the plot and mean height of individuals contacting the target sapling. Competing shrub species were differentiated according to their fire-adaptive traits (i.e. seeders vs resprouters). object-based image classification applied on MS-CHM yielded higher overall accuracy for the three sites (83.67% +/- 3.06%) than RGB-CHM (74.33% +/- 3.21%). Intra-specific competitive effects were not detected, whereas increasing cover and height of sh

关键词： Competition Fire object-based image analysis Pine Shrub Unmanned aerial vehicle

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Comparing Pixel- and object-based Approaches for Classifying Multispectral Drone imagery of a Salt Marsh Restoration and Reference Site

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REMOTE SENSING 2024年第6期16卷 1049页

作者： Norris, Gregory S. LaRocque, Armand Leblon, Brigitte Barbeau, Myriam A. Hanson, Alan R. Univ New Brunswick Fac Forestry & Environm Management Fredericton NB E3B 5A3 Canada Lakehead Univ Fac Nat Resource Management Thunder Bay ON P7B 5E1 Canada Univ New Brunswick Dept Biol Fredericton NB E3B 5A3 Canada Canadian Wildlife Serv Environm Canada POB 6227 Sackville NB E4L 4N1 Canada

Monitoring salt marshes with remote sensing is necessary to evaluate their state and restoration. Determining appropriate techniques for this can be overwhelming. Our study provides insight into whether a pixel- or object-based Random Forest classification approach is best for mapping vegetation in north temperate salt marshes. We used input variables from drone images (raw reflectances, vegetation indices, and textural features) acquired in June, July, and August 2021 of a salt marsh restoration and reference site in Aulac, New Brunswick, Canada. We also investigated the importance of input variables and whether using landcover classes representing areas of change was a practical way to evaluate variation in the monthly images. Our results indicated that (1) the classifiers achieved overall validation accuracies of 91.1-95.2%;(2) pixel-based classifiers outperformed object-based classifiers by 1.3-2.0%;(3) input variables extracted from the August images were more important than those extracted from the June and July images;(4) certain raw reflectances, vegetation indices, and textural features were among the most important variables;and (5) classes that changed temporally were mapped with user's and producer's validation accuracies of 86.7-100.0%. Knowledge gained during this study will inform assessments of salt marsh restoration trajectories spanning multiple years.

关键词： image classification ecological restoration wetland pixel-based image analysis object-based image analysis Random Forest Bay of Fundy

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Estimation of crop plant density at early mixed growth stages using UAV imagery

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PLANT METHODS 2019年第1 p64-64期15卷 64-64页

作者： Koh, Joshua C. O. Hayden, Matthew Daetwyler, Hans Kant, Surya Agr Victoria Grains Innovat Pk110 Natimuk Rd Horsham Vic 3400 Australia Agr Victoria AgriBio Ctr AgriBiosci 5 Ring Rd Bundoora Vic 3083 Australia La Trobe Univ Sch Appl Syst Biol Bundoora Vic 3086 Australia

BackgroundUnmanned aerial vehicles (UAVs) equipped with lightweight sensors are making a significant impact in field-based crop phenotyping. UAV platforms have been successfully deployed to acquire phenotypic data in a precise and efficient manner that would otherwise be time-consuming and costly to acquire when undertaken through manual assessment. One example is the estimation of plant density (or counts) in field experiments. Challenges posed to digital plant counting models are heterogenous germination and mixed growth stages that are present in field experiments with diverse genotypes. Here we describe, using safflower as an example, a method based on template matching for seedling count estimation at early mixed growth stages using UAV *** object-based image analysis algorithm based on template matching was developed for safflower seedling detection at early mixed growth stages in field experiments conducted in 2017 and 2018. Seedling detection was successful when tested using a grouped template type with 10 subgroups representing safflower at 2-4 leaves growth stage in 100 selected plots from the 2017 field experiment. The algorithm was validated for 300 plots each from the 2017 and 2018 field experiments, where estimated seedling counts correlated closely with manual counting;R-2=0.87, MAE=8.18, RSME=9.38 for 2017 field experiment and R-2=0.86, MAE=9.16, RSME=10.51 for *** method for safflower seedling count at early mixed growth stages using UAV imagery was developed and validated. The model performed well across heterogenous growth stages and has the potential to be used for plant density estimation across various crop species.

关键词： object-based image analysis Plant phenotyping Safflower Seedling count Unmanned aerial vehicle

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Mapping Tea Plantations from VHR images Using OBIA and Convolutional Neural Networks

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REMOTE SENSING 2020年第18期12卷 2935-2935页

作者： Tang, Zixia Li, Mengmeng Wang, Xiaoqin Fuzhou Univ Key Lab Spatial Data Min & Informat Sharing Minist EducAcad Digital China Fujian Natl & Local Joint Engn Res Ctr Satellite Geospat Fuzhou 350108 Peoples R China

Tea is an important economic plant, which is widely cultivated in many countries, particularly in China. Accurately mapping tea plantations is crucial in the operations, management, and supervision of the growth and development of the tea industry. We propose an object-based convolutional neural network (CNN) to extract tea plantations from very high resolution remote sensing images. image segmentation was performed to obtain image objects, while a fine-tuned CNN model was used to extract deep image features. We conducted feature selection based on the Gini index to reduce the dimensionality of deep features, and the selected features were then used for classifying tea objects via a random forest. The proposed method was first applied to Google Earth images and then transferred to GF-2 satellite images. We compared the proposed classification with existing methods: object-based classification using random forest, Mask R-CNN, and object-based CNN without fine-tuning. The results show the proposed method achieved a higher classification accuracy than other methods and produced smaller over- and under-classification geometric errors than Mask R-CNN in terms of shape integrity and boundary consistency. The proposed approach, trained using Google Earth images, achieved comparable results when transferring to the classification of tea objects from GF-2 images. We conclude that the proposed method is effective for mapping tea plantations using very high-resolution remote sensing images even with limited training samples and has huge potential for mapping tea plantations in large areas.

关键词： tea plantation mapping object-based image analysis convolutional neural network very high resolution remote sensing

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Smallholder oil palm plantation sustainability assessment using multi-criteria analysis and unmanned aerial vehicles

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2023年第5期195卷 577-577页

作者： Wong, Yong Bin Gibbins, Chris Azhar, Badrul Phan, Su Shen Scholefield, Paul Azmi, Reza Lechner, Alex M. Univ Nottingham Malaysia Sch Environm & Geog Sci Semenyih 43500 Selangor Malaysia Univ Putra Malaysia Fac Forestry & Environm Serdang 43400 Selangor Malaysia Wild Asia 2 Jalan Raja Abdullah Kuala Lumpur 56000 Malaysia Lancaster Environm Ctr Ctr Ecol & Hydrol Bailrigg England Monash Univ Indonesia S Tangerang 15345 Indonesia

Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (similar to 5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).

关键词： UAV Oil palm smallholder Sustainability assessment Land cover and land use object-based image analysis Multi-criteria analysis

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A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California's Coastal Shrubs

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DRONES 2023年第7期7卷

作者： Detka, Jon Coyle, Hayley Gomez, Marcella Gilbert, Gregory S. Univ Calif Santa Cruz Dept Environm Studies 1156 High St Santa Cruz CA 95064 USA

Wildland conservation efforts require accurate maps of plant species distribution across large spatial scales. High-resolution species mapping is difficult in diverse, dense plant communities, where extensive ground-based surveys are labor-intensive and risk damaging sensitive flora. High-resolution satellite imagery is available at scales needed for plant community conservation across large areas, but can be cost prohibitive and lack resolution to identify species. Deep learning analysis of drone-based imagery can aid in accurate classification of plant species in these communities across large regions. This study assessed whether drone-based imagery and deep learning modeling approaches could be used to map species in complex chaparral, coastal sage scrub, and oak woodland communities. We tested the effectiveness of random forest, support vector machine, and convolutional neural network (CNN) coupled with object-based image analysis (OBIA) for mapping in diverse shrublands. Our CNN + OBIA approach outperformed random forest and support vector machine methods to accurately identify tree and shrub species, vegetation gaps, and communities, even distinguishing two congeneric shrub species with similar morphological characteristics. Similar accuracies were attained when applied to neighboring sites. This work is key to the accurate species identification and large scale mapping needed for conservation research and monitoring in chaparral and other wildland plant communities. Uncertainty in model application is associated with less common species and intermixed canopies.

关键词： chaparral deep learning drone machine learning neural network object-based image analysis photogrammetry remote sensing shrubland uncrewed aerial vehicle (UAV)

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