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5 results for “object-based image analysis”
Object-based image analysis for monitoring plant invasions, can we use an open-source solution?
<p><strong>Introduction</strong></p> <p>This is a practical exercise testing possibilities of open-source solutions (FOSS) for object-based image analysis (OBIA) to monitor plant invasion using unoccupied aerial system (UAS, drone).</p> <p>The material is accompanying a chapter <strong><em>Müllerová, J. et al. (2023). Vegetation mapping and monitoring by unoccupied aerial systems – current state and perspectives. In: Manfreda, S. et Eyal B.D. (eds). Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments. Elsevier.</em></strong></p> <p>The material is meant for readers to run the workflow and detect invasion of giant hogweed on the UAS data themselves testing different FOSS solutions.</p> <p> </p> <p><strong>Data</strong></p> <p>• a subset of UAS-borne data (consumer camera) collected in Czech Republic during the flowering of a noxious invasive plant species giant hogweed (<em>Heracleum mantegazzianum</em>)</p> <p>• training dataset</p> <p>• eCognition rulebase (proprietary OBIA software)</p> <p>• a script for SegOptim package implemented in R</p> <p> </p> <p><strong>Description</strong></p> <p>The use case represents a simple application of OBIA approach based on the SegOptim package implemented in R.</p> <p>Four bands (RGBN) UAS image subset are available, capturing the central area of a heavily invaded location (CZ) by giant hogweed (Heracleum mantegazzianum). Thanks to the proper image timing, the invasive species is clearly observable as white objects (in RGB) representing the various stage of the blossom. Considering the complex shape of the flower heads, detection based on image segmentation outperforms pixel-based classification (Müllerová et al., 2017). Simple segmentation of input imagery is performed (for simplicity only the spectral bands are considered both for segmentation and feature space definition, however additional features such as vegetation indices or textural measures may be included), followed by supervised classification using training data. Finally, a visual comparison of result detection both from proprietary (eCognition) and open-source (SegOptim) implementation is provided, confirming comparable results.</p> <p>Based on #github("joaofgoncalves/SegOptim")</p> <p> </p> <p><strong>References</strong></p> <p>Gonçalves, J., Pôças, I., Marcos, B., Mücher, C. A., & Honrado, J. P. (2019). SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data. <em>International Journal of Applied Earth Observation and Geoinformation</em>, <em>76</em>, 218-230.</p> <p>Müllerová, J., Brůna, J., Bartaloš, T., Dvořák, P., Vítková, M. & Pyšek, P. (2017b). Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring. Frontiers in Plant Science 8:1–13.</p> <p>Accompanying material for</p> <p>Müllerová, J. et al. (2023). Vegetation mapping and monitoring by unoccupied aerial systems – current state and perspectives. In: Manfreda, S. et Eyal B.D. (eds). Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments. Elsevier</p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
<p>Very shallow coral reefs (< 5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products to ecologically relevant habitat maps are still relatively underdeveloped. Here we demonstrate that incorporating geomorphometric variables (the DEM and its derivatives) in addition to spectral information (the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM, and classified through Object-Based Image Analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV's DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.</p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
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Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
<p><strong>Abstract: </strong>Invasive alien species reduce biodiversity. In southern Brazil, the genus <em>Pinus</em> is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as it was the case for the restingas on the island of Santa Catarina. Invasion control requires persistent efforts to identify and treat each new invasion case as a priority. In this study, areas invaded by <em>Pinus</em> sp. in restingas were mapped using images taken by a remotely piloted aircraft system (RPAS, or drone) to identify the invasion areas in great detail, enabling management to be planned for the most recently invaded areas, where management is simpler, more effective, and less costly. Geographic object-based image analysis (GEOBIA) was applied on images taken from a conventional RGB camera embedded in a RPAS, which resulted in a global accuracy of 89.56%, a mean kappa index of 0.86, and an F-score of 0.90 for the <em>Pinus</em> sp. Processing was conducted with open source software to reduce operational costs.</p>
Data from: Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas
<p>Spatially-explicit information featuring a wide range of detailed vegetation structural types are necessary to support conservation and ecological analyses. A systematic approach that accurately maps such detailed categories at regional scales is currently lacking for neotropical savannas. We developed a systematic Geographic Object-Based Image Analysis (GEOBIA) framework that accounts for spectral and spatial properties to map Cerrado vegetation structural types at 5-m resolution. This framework counts with a two-step mapping approach: (1) image segmentation and a Random Forest land cover classification based on spectral information (Level 1 classification), followed by (2) a GEOBIA knowledge-based classification that follows contextual and topological spatial rules developed in a systematic manner for mapping Cerrado ecological classes (Level 2 classification).</p><p>The framework was tested for two large study sites covering most major Cerrado physiognomic types: a control site in central Brazil (Taquara watershed site, located in Brasilia, Federal District, and embraces the IBGE Ecological Reserve) and another larger site that is located in an agricultural landscape in the western portion of Bahia State (encompasses parts of the municipalities of: Sao Desiderio, Luis Eduardo Magalhaes, Barreiras, and Riachao das Neves), which brings additional mapping challenges related to intra-class spectral similarity. </p><p>Results demonstrate that our GEOBIA approach is effective for mapping 13 land cover classes with 87.6% overall accuracy, of which all 11 major vegetation classes were identified. For additional details, please check the associated publication with these datasets (Ribeiro et al. 2020).</p><p>The datasets developed in this study are available in shapefile format with attributed metadata following ISO 19115 standards: </p><ul><li><strong>wba_lcmap_geobiaL2_2011lucmask:</strong> final land cover map (Level 2 -- physiognomic types) for a section of the western bahia region in the Cerrado. This product uses a land use mask of 2011 derived from ancillary data</li><li><strong>wba_lcmap_geobiaL2_2013lucmask:</strong> final land cover map (Level 2 -- physiognomic types) for a section of the western bahia region in the Cerrado. This product accounts for the land use mask of 2011 as well as an updated land use mask of 2013 derived from ancillary data</li><li><strong>ibge_taquara_luc_L1_L2_2013_original:</strong> land cover maps (Levels 1 and 2) developed for the Taquara/IBGE site</li><li><strong>ibge_taquara_detailed_luc_2013:</strong> final land cover map (Level 2 -- physiognomic types) developed for the Taquara/IBGE site with additional detailed land use categories</li></ul><p> </p><p><strong>Data</strong></p><p>Mapped land cover classes: 1) cerrado woodland, 2) savanna, 3) open savanna, 4) shrubby grassland, 5) grassland (only present in the Taquara site), 6) cerrado scrub (only present in the western bahia site), 7) marsh, 8) shrub swamp, 9) palm swamp, 10) riparian forest, 11) semi-deciduous forest, 12) seasonally dry tropical forest (only present in the western bahia site), 13) invasive forbes (only present in the Taquara site), 14) non-natural/barren, 15) water, 16) shade, 17) clouds</p><p>The land-use mask incorporated into our maps feature the following classes: main roads, farming/crops, silviculture, and urban areas. </p><p> </p><p><strong>Coordinate Reference System</strong></p><p>Datasets were projected to <strong>South America Albers Equal Area</strong> <strong>Conic</strong>, with all areal calculations based on this projection.</p><p> </p><p><strong>Data Usage</strong> </p><p>The datasets are publicly available and should be cited appropriately (main publication: Ribeiro et al. 2020).</p><p> </p><p><strong>Additional information</strong></p><p>For any other data requests or questions, please contact Fernanda Ribeiro (fernanda.ffr@gmail.com).</p>
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