Tracking the temporal dynamics of insect defoliation by high‐resolution radar satellite data

Bae, Soyeon; Müller, Jörg; Förster, Bernhard; Hilmers, Torben; Hochrein, Sophia; Jacobs, Martin; Leroy, Benjamin M. L.; Pretzsch, Hans; Weisser, Wolfgang W.; Mitesser, Oliver

doi:10.1111/2041-210x.13726

Cited by 22 publications

(24 citation statements)

References 51 publications

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“…Nevertheless, some other satellite applications are not discussed in the following sections but are only listed here because of little published data on the specific applications. These applications include but are not limited to wild animal tracking [39], cloud tracking, tree defoliation tracking [40], low-salinity pool tracking [41], deep convective cloud tracking [42], crop phenology tracking [43], etc. Furthermore, traffic object tracking is one of most interest within the field of satellite video-based visual tracking due to its promising application potential and performance.…”

Section: Methodology and Overview Of Taxonomy In Satellite Video Trac...mentioning

confidence: 99%

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on wpafb videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower ds are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-ds group and the Hard-ds group, respectively.

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Section: Methodology and Overview Of Taxonomy In Satellite Video Trac...mentioning

confidence: 99%

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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“…A study of insect induced defoliation using C-band SAR was presented in [6], which calculated the correlation between defoliation risk and smoothed time series of backscatter values averaged over five hectare (ha) plots. In a precursor to this work, we discriminated between live and dead canopy based on an accurate estimation of polarimetric covariance from a single, full-polarimetric C-band image [16].…”

Section: A Remote Sensing Of Insect Induced Canopy Defoliationmentioning

confidence: 99%

“…For our AOI, we do not have SAR imagery before the geometrid moth outbreak, and we thus have to use Landsat data for the pre-event image. Furthermore, we do not have enough data to use time series for smoothing or monitoring gradual changes, as in the studies based on low-resolution MODIS NDVI [8], [9], [2], [10], [11] or the approach in [6]. Hence, we must rely on bi-temporal change detection using pairs of images.…”

Section: B Heterogeneous Change Detectionmentioning

confidence: 99%

“…The available cloudfree optical images are relatively few and far between and consecutive images are often from different sensors. Synthetic aperture radar (SAR) is an active sensor largely uninfluenced by clouds, which can be utilised to monitor defoliation and deforestation [5], [6]. However, planned acquisition of SAR images of the Polmak study site did not start until after the outbreak.…”

Section: Introductionmentioning

confidence: 99%

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Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping

Agersborg¹,

Luppino²,

Anfinsen³

et al. 2022

Preprint

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In this paper we develop a method for mapping forest mortality in the forest-tundra ecotone using satellite data from heterogeneous sensors. We use medium resolution imagery in order to provide the complex pattern of forest mortality in this sparsely forested area, which has been induced by an outbreak of geometrid moths. Specifically, Landsat-5 Thematic Mapper images from before the event are used, with RADARSAT-2 providing the post-event images. We obtain the difference images for both multispectral optical and synthetic aperture radar (SAR) by using a recently developed deep learning method for translating between the two domains. These differences are stacked with the original pre-and post-event images in order to let our algorithm also learn how the areas appear before and after the change event. By doing this, and focusing on learning only the changes of interest with one-class classification (OCC), we obtain good results with very little training data.

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“…As very high-resolution (VHR) satellite imagery is currently only provided by commercial companies, or can only be accessed through open data programs for scientific use (i.e., the European Space Agency Third Party Missions [12]), aerial imagery remains a crucial dataset in multiple operational forestry applications on a local scale [13][14][15]. Indeed, VHR imagery provided in at least yearly cycles would be necessary to provide the capability to monitor changes and disturbances (i.e., wind fall), whereas intra-annual data in sufficient detail would be necessary for capturing dynamic changes caused by biotic and abiotic drivers (i.e., insect calamities, drought effects) [16,17]. EO systems such as Sentinel-2, which provide image data in short time intervals with increased spectral resolution, thus offer the potential to capture these dynamics, but might not deliver the spatial detail needed.…”

Section: Introductionmentioning

confidence: 99%

Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2

2022

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Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some information needs, spatially very high-resolution imagery (i.e., aerial imagery, ≤2 m) is needed to generate maps at a scale suitable for regional and local applications. In this study, we present the development, implementation, and evaluation of a Geographic Object-Based Image Analysis (GEOBIA) framework to stratify forests (needleleaf, broadleaved, non-forest) in Luxembourg. The framework is exclusively based on open data and free and open-source geospatial software. Although aerial imagery is used to derive image objects with a 0.05 ha minimum size, Sentinel-2 scenes of 2020 are the basis for random forest classifications in different single-date and multi-temporal feature setups. These setups are compared with each other and used to evaluate the framework against classifications based on features derived from aerial imagery. The highest overall accuracies (89.3%) have been achieved with classification on a Sentinel-2-based vegetation index time series (n = 8). Similar accuracies have been achieved with classification based on two (88.9%) or three (89.1%) Sentinel-2 scenes in the greening phase of broadleaved forests. A classification based on color infrared aerial imagery and derived texture measures only achieved an accuracy of 74.5%. The integration of the texture measures into the Sentinel-2-based classification did not improve its accuracy. Our results indicate that high resolution image objects can successfully be stratified based on lower spatial resolution Sentinel-2 single-date and multi-temporal features, and that those setups outperform classifications based on aerial imagery only. The conceptual framework of spatially high-resolution image objects enriched with features from lower resolution imagery facilitates the delivery of frequent and reliable updates due to higher spectral and temporal resolution. The framework additionally holds the potential to derive additional information layers (i.e., forest disturbance) as derivatives of the features attached to the image objects, thus providing up-to-date information on the state of observed forests.

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Tracking the temporal dynamics of insect defoliation by high‐resolution radar satellite data

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 22 publications

References 51 publications

Object Tracking Based on Satellite Videos: A Literature Review

Object Tracking Based on Satellite Videos: A Literature Review

Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping

Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2

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