OBIA4RTM – towards an operational open-source solution for coupling object-based image analysis with radiative transfer modelling

With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the automatic detection of landslides is promising to significantly speed up the compilation of inventories. Previous studies on automatic landslide detection often used a combination of optical imagery and geomorphometric data, and were implemented in commercial software. The objective of this study was to investigate the potential of open source software for automated landslide detection solely based on HRDTM-derived data in a study area in Burgenland, Austria. We implemented a geographic object-based image analysis (GEOBIA) consisting of (1) the calculation of land-surface variables, textural features and shape metrics, (2) the automated optimization of segmentation scale parameters, (3) region-growing segmentation of the landscape, (4) the supervised classification of landslide parts (scarp and body) using support vector machines (SVM), and (5) an assessment of the overall classification performance using a landslide inventory. We used the free and open source data-analysis environment R and its coupled geographic information system (GIS) software for the analysis; our code is included in the Supplementary Materials. The developed approach achieved a good performance (κ = 0.42) in the identification of landslides.

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Quantitative analysis of subcellular distributions with an open-source, object-based tool

Biology Open ◽

10.1242/bio.055228 ◽

2020 ◽

Vol 9 (10) ◽

pp. bio055228 ◽

Cited By ~ 2

Author(s):

Pearl V. Ryder ◽

Dorothy A. Lerit

Keyword(s):

Image Analysis ◽

Open Source ◽

Three Dimensional ◽

Analysis Data ◽

Form And Function ◽

Object Based Image Analysis ◽

Object Based ◽

And Function ◽

Microscopy Images ◽

Complete Process

ABSTRACTThe subcellular localization of objects, such as organelles, proteins, or other molecules, instructs cellular form and function. Understanding the underlying spatial relationships between objects through colocalization analysis of microscopy images is a fundamental approach used to inform biological mechanisms. We generated an automated and customizable computational tool, the SubcellularDistribution pipeline, to facilitate object-based image analysis from three-dimensional (3D) fluorescence microcopy images. To test the utility of the SubcellularDistribution pipeline, we examined the subcellular distribution of mRNA relative to centrosomes within syncytial Drosophila embryos. Centrosomes are microtubule-organizing centers, and RNA enrichments at centrosomes are of emerging importance. Our open-source and freely available software detected RNA distributions comparably to commercially available image analysis software. The SubcellularDistribution pipeline is designed to guide the user through the complete process of preparing image analysis data for publication, from image segmentation and data processing to visualization.This article has an associated First Person interview with the first author of the paper.

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A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

Remote Sensing ◽

10.3390/rs6076111 ◽

2014 ◽

Vol 6 (7) ◽

pp. 6111-6135 ◽

Cited By ~ 41

Author(s):

Daniel Clewley ◽

Peter Bunting ◽

James Shepherd ◽

Sam Gillingham ◽

Neil Flood ◽

...

Keyword(s):

Image Analysis ◽

Open Source ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object

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An open-source approach to characterizing Chihuahuan Desert vegetation communities using object-based image analysis

Journal of Arid Environments ◽

10.1016/j.jaridenv.2020.104383 ◽

2020 ◽

pp. 104383

Author(s):

Andrew R. Bankert ◽

Erin H. Strasser ◽

Cristy G. Burch ◽

Maureen D. Correll

Keyword(s):

Image Analysis ◽

Open Source ◽

Chihuahuan Desert ◽

Desert Vegetation ◽

Vegetation Communities ◽

Object Based Image Analysis ◽

Object Based

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Metodologia de análise de imagens baseada em objetos geográficos (GEOBIA) utilizando RPAS (drone) com sensor RGB

Estrabão ◽

10.53455/re.v2i.5 ◽

2021 ◽

Vol 2 ◽

pp. 41-85

Author(s):

Vinicius Gonçalves

Keyword(s):

Image Analysis ◽

High Resolution ◽

Open Source ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object ◽

Very High

O presente trabalho apresenta um método para o mapeamento de vegetação, por um processo de classificação por regiões geográficas, denominado GEOBIA (Geographic Object-Based Image Analysis) considerado adequado para classificar imagens de muito alta resolução (very high resolution – VHR). É possível executar o procedimento com qualquer equipamento que disponha de um sensor RGB de boa qualidade e permita execução de aplicativos para plano de voo. O método foi desenvolvido com base em softwares de código aberto (open source) para evitar custos com licenças, em todas as etapas, desde a captação das imagens, elaboração de produtos cartográficos, processamento da classificação por regiões e conclusão mediante cálculos de áreas. O estudo foi aplicado em quatro áreas de interesse, todas na região da Grande Florianópolis-SC, contendo porções do ecossistema de Formações Pioneiras - Vegetação com Influência Marinha, também denominadas áreas de restinga, cujo principal alvo da classificação foi o mapeamento das áreas invadidas por Pinus sp. O método demonstrou útil para classificação de imagens em geral, podendo ser utilizado no manejo de outras espécies vegetais exóticas, ou até em outras aplicações ambientais.

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Landslide mapping using object-based image analysis and open source tools

Engineering Geology ◽

10.1016/j.enggeo.2021.106000 ◽

2021 ◽

Vol 282 ◽

pp. 106000

Author(s):

Pukar Amatya ◽

Dalia Kirschbaum ◽

Thomas Stanley ◽

Hakan Tanyas

Keyword(s):

Image Analysis ◽

Open Source ◽

Object Based Image Analysis ◽

Object Based ◽

Landslide Mapping

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New tools for old problems — comparing drone- and field-based assessments of a problematic plant species

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-08852-2 ◽

2021 ◽

Vol 193 (2) ◽

Author(s):

Jens Oldeland ◽

Rasmus Revermann ◽

Jona Luther-Mosebach ◽

Tillmann Buttschardt ◽

Jan R. K. Lehmann

Keyword(s):

Image Analysis ◽

Plant Species ◽

Just In Time ◽

Post Processing ◽

Tall Grass ◽

Field Surveys ◽

Distribution Maps ◽

Object Based Image Analysis ◽

Object Based ◽

Before And After

AbstractPlant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

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Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

Remote Sensing ◽

10.3390/rs13040830 ◽

2021 ◽

Vol 13 (4) ◽

pp. 830

Author(s):

Adam R. Benjamin ◽

Amr Abd-Elrahman ◽

Lyn A. Gettys ◽

Hartwig H. Hochmair ◽

Kyle Thayer

Keyword(s):

Image Analysis ◽

Aquatic Vegetation ◽

Accuracy Assessment ◽

Unmanned Aerial Systems ◽

Temporal Area ◽

Object Based Image Analysis ◽

Object Based ◽

Significant Difference ◽

Additional Processing ◽

Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

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