Remote sensing application in plant protection and its usage in smart agriculture to hasten decision making of the farmers

Journal of Plant Diseases and Protection, Journal Year: 2025, Volume and Issue: 132(2)

Published: March 26, 2025

Language: Английский

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Improving monitoring and management methods is of the utmost importance in countries at risk of invasion by the pinewood nematode

Annals of Forest Science, Journal Year: 2024, Volume and Issue: 81(1)

Published: March 27, 2024

Abstract Key message The invasive pine wood nematode is a major threat to forests worldwide, causing extensive tree mortality. Although scientific knowledge and control measures are continuously improving, important gaps remain. We argue that some key questions, notably related early detection pest management, need be urgently tackled in countries at risk of invasion such as France.

Language: Английский

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Early Identification of Vegetation Pest Diseases Using Sentinel 2 NDVI Time Series 2016–2023: The Case of Toumeyella Parvicorvis at Castel Porziano (Italy)

IEEE Geoscience and Remote Sensing Letters, Journal Year: 2024, Volume and Issue: 21, P. 1 - 5

Published: Jan. 1, 2024

The aim of this work was to assess the potential Continuous Change Detection and Classification (CCDC) CCDC trend analysis algorithms on Sentinel 2 NDVI time series (2016-2023) capture estimate subtle internal vegetation anomalies, with a focus disease induced by pests. To explore characterise long-term dynamics, (S2) were analysed using processing chain mainly based three steps (i) segmentation, (ii) linear regression trending, (iii) classification extract map anomalies. pilot site selected in peri-urban area Rome: Castel Porziano heavily affected Toumeyella Parvicorvis recent years. Results from our investigations highlighted effectiveness S2 sense but physically significant degradation signals, reliability LR characterize spatial temporal evolution TP even veiled seasonality annual cycle behaviour, albeit strictly dependent period occurrence event.

Language: Английский

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Evaluating a Novel Approach to Detect the Vertical Structure of Insect Damage in Trees Using Multispectral and Three-Dimensional Data from Drone Imagery in the Northern Rocky Mountains, USA

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(8), P. 1365 - 1365

Published: April 12, 2024

Remote sensing is a well-established tool for detecting forest disturbances. The increased availability of uncrewed aerial systems (drones) and advances in computer algorithms have prompted numerous studies insects using drones. To date, most used height information from three-dimensional (3D) point clouds to segment individual trees two-dimensional multispectral images identify tree damage. Here, we describe novel approach classifying the reflectances assigned 3D cloud into damaged healthy classes, retaining assessment vertical distribution damage within tree. Drone were acquired 27-ha study area Northern Rocky Mountains that experienced recent then processed produce cloud. Using data points on (based depth maps images), random (RF) classification model was developed, which had an overall accuracy (OA) 98.6%, when applied across area, it classified 77.0% with probabilities greater than 75.0%. Based segmented trees, developed evaluated separate trees. For identified severity each based percentages red gray top-kill length continuous treetop. Healthy separated high (OA: 93.5%). remaining different severities moderate 70.1%), consistent accuracies reported similar studies. A subsequent algorithm 91.8%). as (78.3%), exhibited some amount (78.9%). Aggregating tree-level metrics 30 m grid cells revealed several hot spots severe illustrating potential this methodology integrate products space-based remote platforms such Landsat. Our results demonstrate utility drone-collected monitoring structure diseases.

Language: Английский

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Large-scale wall-to-wall mapping of bark beetle damage and forest practices using the distance red swir index and operational harvester data

Ecological Indicators, Journal Year: 2024, Volume and Issue: 162, P. 112036 - 112036

Published: April 16, 2024

Satellite-based inventories of bark beetle attacks are increasingly used for detecting and monitoring infested forest at the landscape scale. The Normalized Distance Red & SWIR index is one few indices that have shown higher accuracies than commonly vegetation indices. In this study, temporal changes distance red swir (ΔDRS) were analyzed, validated applied to multi-temporal Sentinel-2 images covering tile 110 x km2. main purpose was assess applicability a new ΔDRS detect spruce after (Ips typographus) attacks. Harvester data from private company validate method. normalized DRS has previously been developed tested test site level, while study explored demonstrated use in an context on larger Water chlorophyll induced different disturbances effectively identified across landscape. A linear-discriminant analysis classify 274 clusters as attacked healthy forest, with overall accuracy 78%. largest values our (>0.06) corresponded well clear-cuts, all 172 clear-cuts correctly classified. We conclude potential map related water Scandinavian forests it can be useful identify beetle-infested within 1 year clear-cuts.

Language: Английский

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