Geographic Object-Based Image Analysis – Towards a new paradigm

A forest stand with sudden oak death: three common image spatial resolutions: 30 m, 4 m and 1 m.Blaschke, T., G.J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R. Feitosa, F. Van Der Meer, H. Van Der Werff, F. Van Coillie, D. Tiede. 2014. Geographic Object-based Image Analysis: a new paradigm in Remote Sensing and Geographic Information Science. ISPRS International Journal of Photogrammetry and Remote Sensing 87(1), 180-191.

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience).

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Mapping weeds with UAV and obia

On-ground photographs and UAV images of the 1x1-m frames used in the ground-truth samplingPeña JM, Torres-Sánchez J, de Castro AI, Kelly M, López-Granados F. 2013. Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images. PLoS ONE 8(10): e77151. doi:10.1371/journal.pone.0077151

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results.

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A proposed early-warning fire detection system

our graphic of the FUEGO conceptPennypacker, CR, MK Jakubowski, M Kelly, M Lampton, C Schmidt, S Stephens, and R Tripp.  2013. FUEGO—Fire Urgency Estimator in Geosynchronous Orbit—A proposed early-warning fire detection system. Remote Sensing 5(10): 5173-5192

Current and planned wildfire detection systems are impressive but lack both sensitivity and rapid response times. A small telescope with modern detectors and significant computing capacity in geosynchronous orbit can detect small (12 m²) fires on the surface of the earth, cover most of the western United States (under conditions of moderately clear skies) every few minutes or so, and attain very good signal-to-noise ratio against Poisson fluctuations in a second. Hence, these favorable statistical significances have initiated a study of how such a satellite could operate and reject the large number of expected systematic false alarms from a number of sources. Here we present both studies of the backgrounds in Geostationary Operational Environmental Satellites (GOES) 15 data and studies that probe the sensitivity of a fire detection satellite in geosynchronous orbit.

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Delineating individual trees from lidar data

One SNAMP forest plot viewed with lidar dataJakubowski, MK, L Wenkai, Q Guo, and M Kelly. 2013. Delineating individual trees from lidar data: a comparison of vector- and raster-based segmentation approaches. Remote Sensing 5, 4163-4186; doi:10.3390/rs5094163

This work concentrates on delineating individual trees from discrete lidar data in topographically-complex, mixed conifer forest across the California’s Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA) of a canopy height model (CHM). The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m²), discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types.

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Where will SF Bay wetlands be in 100 years?

Stralberg, D., M. Brennan, J. C. Callaway, J. K. Wood, L. M. Schile, D. Jongsomjit, M. Kelly, V. T. Parker, and S. Crooks. 2011. Evaluating tidal marsh sustainability in the face of sea-level rise: a hybrid modeling approach applied to San Francisco Bay. PLoS ONE 6(11): e27388.

Tidal marshes will be threatened by increasing rates of sea-level rise (SLR) over the next century. Managers seek guidance on whether existing and restored marshes will be resilient under a range of potential future conditions, and on prioritizing marsh restoration and conservation activities. Building upon established models, we developed a hybrid approach that involves a mechanistic treatment of marsh accretion dynamics and incorporates spatial variation at a scale relevant for conservation and restoration decision-making. We applied this model to San Francisco Bay, using best-available elevation data and estimates of sediment supply and organic matter accumulation developed for 15 Bay subregions.

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Mapping downed logs with lidar + obia

downed logs in redBlanchard, S., M. Jakubowski, and M. Kelly. 2011. Object-based image analysis of downed logs in a disturbed forest landscape using lidar. Remote Sensing 3(11): 2420-2439.

Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA) and classification algorithms.

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Use of obia in public health - a review, and call for more

Kelly M., S. Blanchard. E. Kersten and K. Koy. 2011. Object-based analysis of imagery in support of public health: new avenues of research. Remote Sensing 3:2321-2345

The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or “OBIA”) can provide advantages for public health research. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work.

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