Image fusion for enhanced forest structural assessment

Roberts, JW; Aardt, Jan A. N. van; Ahmed, Fethi

doi:10.1080/01431160903463684

Cited by 19 publications

(7 citation statements)

References 46 publications

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“…Concerning this, the results here are in line with other reports on forestry applications of spatially enhanced remote sensing data on a medium scale, in which no significant gain (except for an improved visual interpretability) has been achieved through different spatial enhancement techniques (e.g. Roberts et al, 2011). This also supports a recent statement of Witharana et al (2014) on the necessity of further research to understand the synergies of data fusion and image segmentation.…”

Section: Discussionsupporting

confidence: 92%

Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery

Latifi

Fassnacht

Schumann

et al. 2014

Progress in Physical Geography: Earth and Environment

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As major agents of biological disturbances, bark beetle infestations have been reported to account for a large portion of damage that occur in European forest stands. As a result, accurate spatiotemporal characterization of the vulnerable areas is crucial for subsequent post-infestation management. Remote sensing-assisted mapping of bark beetle-induced forest mortality has been an important research focus during the last decade. Due to the occurrence of mostly small- to medium-scale infestation patches in European stands, high-resolution optical data is commonly applied for mapping mortality. Despite this, we hypothesize the widely available satellite products to be potentially advantageous due to their multitemporal availability and reasonable costs. Here, we combined multi-date LANDSAT and SPOT scenes across an 11-year time span in which various epidemic and non-epidemic infestations occurred within the Bavarian Forest National Park in Germany. The aim was to map temporally adjacent mortality classes. The spectral, geometric and textural metrics extracted from the segmented imagery were applied to perform a full object-based classification, for which a digital terrain model was additionally employed. A number of potentially influential factors were also explored, including the spatial aggregation of image segments and the spatial enhancement of the multispectral imagery. The analysis resulted in a nearly perfect separation of non-infested and dead trees, while different levels of confusion were observed when classifying the transitional mortality classes. While the pan-sharpening of selected image scenes contributed to the stability of mapping results for non-infested and dead trees, no explicit trend was observed when aggregating small image segments prior to classification. Furthermore, combining the metrics from image objects and the digital terrain model suggested an obviously improved classification compared to the previously achieved pixel-based results across the same study site. In this paper, we thoroughly discuss the practical aspects of applying object-based image processing for monitoring bark beetle-induced forest mortality.

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Section: Discussionsupporting

confidence: 92%

Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery

Latifi

Fassnacht

Schumann

et al. 2014

Progress in Physical Geography: Earth and Environment

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show abstract

“…Therefore, DE algorithm was employed to iterate the size of the block region to obtain the optimized block size and corresponding fusion result adaptively [18]. A weighted evaluation function

ℒ

is proposed to evaluate the maximum amplitude projection (MAP) image of fused 3D data, which combines three quantification parameters, namely information entropy (EN) [19], average gradient (AVG) [20], and structural similarity index (SSIM) [21] as shown in formula (1)

ℒ = λ_{AVG} {normalℒ}_{AVG} + λ_{EN} {normalℒ}_{EN} + λ_{SSIM} {normalℒ}_{SSIM}

where

{normalℒ}_{AVG}

{normalℒ}_{EN}

, and

{normalℒ}_{SSIM}

are the quantitative evaluations for the MAP image of the fused 3D data.

λ_{AVG}

λ_{EN}

, and

λ_{SSIM}

are the pre‐defined weights for the three quantification parameters.…”

Section: Methodsmentioning

confidence: 99%

High‐resolution volumetric information fusion for depth of field enhancement in photoacoustic microscopy

Song

Wang

et al. 2022

Journal of Biophotonics

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As an important branch of photoacoustic microscopy, optical-resolution photoacoustic microscopy suffers from limited depth of field due to the strongly focused laser beam. In this work, a 3D information fusion algorithm based on 3D stationary wavelet transform and joint weighted evaluation optimization is proposed to fuse multi-focus photoacoustic data to achieve large-volumetric and high-resolution 3D imaging. First, a three-dimensional stationary wavelet transform was performed on the multi-focus data to obtain eight wavelet coefficients. Differential evolution algorithm based on joint weighted evaluation was then employed to optimize the block size of division for each wavelet coefficient. Corresponding sub-coefficients of multi-focus 3D data were fused with the proposed fusion rule utilizing standard deviation for focus detection. Finally, photoacoustic microscopy with large depth of field can be achieved by applying the inverse stationary wavelet transform on the 8 fused sub-coefficients. The fusion result of multi-focus vertically tilted fiber shows that the depth of field of optical-resolution photoacoustic microscopy is doubled without sacrificing lateral resolution via the proposed method. Furthermore, the effectiveness of the proposed method was verified through the fusion results of multi-focus vessel data. Our work provides a feasible solution for achieving large-volumetric, high-resolution photoacoustic microscopy for further data analysis, processing and applications.

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“…Over the years, the fusing of data from multiple sensors has been a common technique and this trend is bound to continue as the geospatial technologies improve (Roberts et al 2011). According to Chavez et al (1991), one of the reasons for this increase in fusing multiple data-sets is due to the complimentary information of the different data-sets.…”

Section: Research Objectivesmentioning

confidence: 99%

Optical and radar data comparison and integration: Kenya example

Sheoran¹,

Haack

2013

Geocarto International

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The purpose of this study was to evaluate the relative classification accuracies of four land covers/uses in Kenya using spaceborne quad polarization radar from the Japanese ALOS PALSAR system and optical Landsat Thematic Mapper data. Supervised signature extraction and classification (maximum likelihood) was used to classify the different land covers/uses followed by an accuracy assessment. The original four band radar had an overall accuracy of 77%. Variance texture was the most useful of four measures examined and did improve overall accuracy to 80% and improved the producer's accuracy for urban by almost 25% over the original radar. Landsat provided a higher overall classification accuracy (86%) as compared to radar. The merger of Landsat with the radar texture did not increase overall accuracy but did improve the producer's accuracy for urban indicated some advantages for sensor integration. IntroductionAssessing many problems within environmental studies, economic planning, resource management, restoration projects and disaster preparedness requires a scientific analysis of data using processes available within the geospatial industry. With the launch of recent spaceborne radar systems, including RADARSAT-2, ALOS PALSAR and TerraSAR-X, the application and use of now operationally available quad polarization radar data for spatial information may prove beneficial for multiple situations. However, the potential of quad polarization radar data for land cover/use classification and other applications remains in its early stages as a result of previously limited data availability and analysis. Only with more examination of these relatively new spaceborne quad polarization data can the scientific and application community maximize the potential benefits of these technological innovations.Traditional means of providing reliable land cover/use information has been primarily undertaken by multispectral systems such as Landsat Thematic Mapper (TM). These systems, because of their limited functionality in cloud covered areas, are not able to fully sustain the demands for providing land cover/use information for many areas around the world. However, with the success of RADARSAT-1 and other spaceborne radar systems, this limitation has been addressed to a certain extent.

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Image fusion for enhanced forest structural assessment

Cited by 19 publications

References 46 publications

Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery

Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery

High‐resolution volumetric information fusion for depth of field enhancement in photoacoustic microscopy

Optical and radar data comparison and integration: Kenya example

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