Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 280, P. 113198 - 113198

Published: Aug. 4, 2022

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools increase our understanding stress-related physiological processes. Therefore, this study aimed provide an overview main spectral retrieval approaches detecting crop Firstly, we present integrated views on: i) biotic abiotic factors, phases stress, respective plant responses, ii) affected traits, appropriate domains corresponding methods measuring traits remotely. Secondly, representative results a systematic literature analysis are highlighted, identifying current status possible future trends monitoring. Distinct occurring under short-term, medium-term or severe chronic exposure can be captured with due specific light interaction processes, such as absorption scattering manifested reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, emitted solar-induced fluorescence thermal (TIR). From 96 research papers, following observed: increasing usage satellite unmanned aerial vehicle data parallel shift from simpler parametric towards more advanced physically-based hybrid models. Most designs were largely driven by sensor availability practical economic reasons, leading common VIS-NIR-TIR combinations. The majority reviewed studies compared proxies calculated single-source rather than using synergistic way. We identified new ways forward guidance improved detection: (1) combined acquisition multiple sensors analysing simultaneously (holistic view); (2) simultaneous combining multi-domain radiative transfer models machine learning methods; (3) assimilation estimated distinct into growth As outlook, recommend streams model schemes build up Digital Twins agroecosystems, which may most efficient way detect diversity environmental stresses thus enable management decisions.

Language: Английский

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Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)

Remote Sensing of Environment, Journal Year: 2021, Volume and Issue: 255, P. 112240 - 112240

Published: Jan. 20, 2021

The European spruce bark beetle (Ips typographus [L.]) is one of the most damaging pest insects forests. A crucial measure in control removal infested trees before beetles leave bark, which generally happens end June. However, stressed tree crowns do not show any significant color changes visible spectrum at this early-stage infestation, making early detection difficult. In order to detect related forest stress an stage, we investigated differences radar and spectral signals healthy trees. How characteristics changed over time was analyzed for whole vegetation season, covered period attacks (April), infestation (‘green-attacks’, May July), middle late-stage (August October). results that already existed beginning attacks. separability between samples did change significantly during ‘green-attack’ stage. indicate were had signatures differed from ones. These stress-induced could be more efficient indicators infestations than symptoms. study used Sentinel-1 2 images a test site southern Sweden April October 2018 2019. red SWIR bands Sentinel-2 showed highest samples. backscatter additional contributed only slightly Random Forest classification models. We therefore propose Normalized Distance Red & (NDRS) index as new based on our observations linear relationship bands. This identified with accuracies 0.80 0.88 attacks, 0.82 0.81 0.91 middle- infestations. are higher those attained by established indices aimed detection, such Difference Water Index, Ratio Drought Disease Stress Index. By using proposed method, highlight potential NDRS estimate vulnerability season.

Language: Английский

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Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 287, P. 113484 - 113484

Published: Feb. 3, 2023

Detecting disease- or insect-infested forests as early possible is a classic application of remote sensing. Under conditions climate change and global warming, outbreaks the European spruce bark beetle (Ips typographus, L.) are threatening related timber industry across Europe, detection infestations important for damage control. Infested trees without visible discoloration (green attack) have been identified using multispectral images, but how green attacks can be detected still unknown. This study aimed to determine when infested start show an abnormal spectral response compared with healthy trees, quantify detectability during infestation process. Pheromone bags were used attract beetles in controlled experiment, subsequent assessed field on weekly basis. In total, 977 monitored, including 208 attacked trees. Multispectral drone images obtained before insect attacks, representing different periods infestation. Individual tree crowns (ITC) delineated by marker-controlled watershed segmentation, average reflectance ITCs was analyzed based duration The driving factors examined. We propose new Multiple Ratio Disease–Water Stress Indices (MR-DSWIs) vegetation indices (VI) detecting infestations. defined VI range 5–95% tree, value outside that tree. Detection rates always higher than observed field, newly proposed MR-DSWIs more established VIs. Infestations detectable at 5 10 weeks after attack rate 15% 90%, respectively, from images. Weeks 5–10 therefore represent suitable period methodology map stage.

Language: Английский

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Early detection of bark beetle (Ips typographus) infestations by remote sensing – A critical review of recent research

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 556, P. 121595 - 121595

Published: Feb. 16, 2024

Bark beetle disturbances increasingly threaten structure and functionality of temperate boreal forests globally. The early detection bark beetle-infested trees, i.e. before beetles' emergence from the breeding tree, is essential for an effective outbreak mitigation. Terrestrial control surveys as traditionally employed infestation detection, however, are resource-intensive approach their limits in difficult terrain during mass outbreaks. Developments remote sensing algorithms giving hope that early-infested trees will be detectable remotely, thereby improving success management efficacy. Yet, a comprehensive quantitative evaluation approaches currently being developed lacking to date. This review synthesises state-of-the-art recent research on (or green-attack) by sensing, places it context with underlying biological constraints, technical opportunities potential applications. Since each beetle-host tree system has specific characteristics detectability, we focus greatest impact European forests, spruce (Ips typographus), which attacks Norway (Picea abies). By screening published within period 2000–2022, included 26 studies our analyses. All reviewed were purely exploratory, testing variety data and/or classification relatively limited spatial temporal coverage. Among tested platforms sensor types, satellite multispectral imagery most frequently investigated. Promising spectral wavelength range or index highly varied among regions. Timeliness accuracy found insufficient efficient management, regardless platform, type, resolution applied. main reasons preventing better performance include rapid development I. typographus combination delayed variable vitality response crown, frequent cloud cover spruce-dominated regions across Europe. In conclusion, current survey methods cannot yet replace terrestrial timely management. Nevertheless, they might supportive either back-up regular surveys, situations, e.g. detect hibernation accessibility, extensively managed without sufficient capacity. We suggest term 'early detection' used consistently synonym 'pre-emergence avoid ambiguity. Finally, provide recommendations future based lessons learned analysed, namely use more rigorous targeted study design, ensure interdisciplinarity, communicate results explicitly.

Language: Английский

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Tracking changes in aquaculture ponds on the China coast using 30 years of Landsat images

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2021, Volume and Issue: 102, P. 102383 - 102383

Published: June 8, 2021

Although coastal aquaculture ponds provide high-quality fish protein for billions of people, they are threatened by urbanization, pollution, and climate change. Moreover, colossal pose damages such as natural wetland shrinkage water quality deterioration. However, understanding the trends drivers on a national scale remains challenge. In this study, decision-tree classifier was used to quantify spatiotemporal distribution over last 30 years in 12 provinces located coast China. we analyzed their drivers, including geographical conditions, socioeconomic factors, development policies. The key results study include following: (1) from 1990 2020, cumulative area holding reached 21997.90 km2 9613.66 km2, representing 3.7-fold 1.6-fold increase, respectively, than values. Based most tend be plains bays following low-lying land. (2) Influenced different levels changed policies, experienced "rapidly increasing period" 2011, growing 246 per year; "stable between 2011 2017; "sharply shrinking after 2017 declining 417 km2/year. (3) Coastal land reclamation played critical role expansion cumulatively contributing approximately 22% resource past years. future, result competition, extent China tends decrease continually.

Language: Английский

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Early detection of bark beetle infestation in Norway spruce forests of Central Europe using Sentinel-2

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2021, Volume and Issue: 100, P. 102335 - 102335

Published: April 20, 2021

In the past decade, massive outbreaks of bark beetles (Ips spp.) have caused large-scale decline coniferous-dominated, prevailingly managed forests Central Europe. Timely detection newly infested trees is important for minimizing economic losses and effectively planning forest management activities to stop or at least slow outbreaks. With advancement Copernicus services, a pair Sentinel-2 satellites provides unique remote sensing data source multi-temporal observations in high spatial resolution on scale individual stands (although not allowing tree detection). This study investigates potential using seasonal trajectories bands selected vegetation indices early beetle infestation (so–called green-attack stage detection) Norway spruce monoculture Czech Republic. Spectral nine six were constructed 2018 season 14 satellite from April November distinguish four classes. We used random algorithm classify healthy (i.e., infested) with various decay. The separated classes better than did bands. Among most promising we identified tasselled cap wetness (TCW) component normalized difference index near shortwave infrared Analysing inter-annual change was more single-date classification. It achieved 96% classification accuracy day year 291 tested set. based assessment changes TCW applied time series 2019 its outputs verified field conditions conducted 80 plots (located stands). overall 78% separation Our highlights great use wavelengths by infestation.

Language: Английский

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Comparison of field survey and remote sensing techniques for detection of bark beetle-infested trees

Forest Ecology and Management, Journal Year: 2022, Volume and Issue: 506, P. 119984 - 119984

Published: Jan. 6, 2022

Language: Английский

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Near-real-time monitoring of land disturbance with harmonized Landsats 7–8 and Sentinel-2 data

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 278, P. 113073 - 113073

Published: May 13, 2022

Land disturbance can increase carbon emissions, cause detrimental environmental impacts, and threaten human life property. Monitoring land in near-real-time is essential to mitigate their negative effects prevent future losses. However, rapid timely monitoring of at a high spatial resolution its infancy. Here, we developed an algorithm for Near-Real-Time MOnitoring laNd dIsturbance based on Time-series harmOnized Reflectance (NRT-MONITOR) from Landsats 7–8 Sentinel-2 data 30-m resolution. It incorporates online recursive called Forgetting Factor improve efficiency the determination get fast detection harmonized data. This validated by using 1200 samples created time series 2015 2019 within conterminous United States (CONUS). An overall accuracy 70% has been achieved variety types. NRT-MONITOR improves processing (11.5 times faster) compared COLD (Zhu et al., 2020). The mean lag NRT-MONITOR, defined as delta days confirming after occurrence, only 35 days, which observations reduced number clear (from six four) needed confirm disturbance. Finally, be integrated into alerting system provide potential probability maps that are updated every three days.

Language: Английский

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Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(13), P. 3135 - 3135

Published: June 29, 2022

Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored detection European spruce bark beetle (Ips typographus, L.) at individual tree crown level using multispectral satellite images. Moreover, possibility tracking progression outbreak over time multitemporal data. Sentinel-2 data acquired during summer 2020 a beetle–infested area in Italian Alps were used for mapping time, while airborne lidar to automatically detect crowns classify species. Mapping carried out support vector machine classifier with input vegetation indices extracted from The results showed that it was possible two stages (i.e., early, late) an overall accuracy 83.4%. how is technically track evolution almost bi-weekly period crowns. outcomes paper are useful both management ecological perspective: allows forest managers map different spatial accuracy, maps describing could be further studies related behavior beetles.

Language: Английский

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Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations

GIScience & Remote Sensing, Journal Year: 2023, Volume and Issue: 60(1)

Published: June 23, 2023

Bark beetle infestations are among the most substantial forest disturbance agents worldwide. Moreover, as a consequence of global climate change, they have increased in frequency and size number affected areas. Controlling bark outbreaks requires consistent operational monitoring, is possible using satellite data. However, while many satellite-based approaches been developed, full potential dense, multi-sensor time series has yet to be fully explored. Here, for first time, we used all available multispectral data from Landsat Sentinel-2, Sentinel-1 SAR data, combinations thereof detect Bavarian Forest National Park. Based on multi-year reference dataset annual infested areas, assessed separability between healthy forests various vegetation indices calculated We two compute infestation probability different datasets: Bayesian conditional probabilities, based best-separating index each type, random regression, type. Five sensor configurations were tested their detection capabilities: alone, Sentinel-2 combined, types combined. The best overall results terms spatial accuracy achieved with (max. accuracy: 0.93). detections also closest onset estimated year. detected areas larger contiguous patches higher reliability compared smaller patches. somewhat inferior those 0.89). While yielding similar results, combination did not provide any advantages over or alone 0.87), was unable 0.62). combined three achieve satisfactory either 0.67). Spatial accuracies typically probabilities than forest-derived but latter resulted earlier detections. approach presented herein provides flexible pipeline well-suited monitoring outbreaks. Furthermore, it can applied other types.

Language: Английский

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