Mapping of understory infested boxwood trees using high resolution imagery

Esmaili, Rohollah; Shataee, Shaban; Soosani, Javad; Naghavi, Hamed

doi:10.1016/j.rsase.2020.100289

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…Health monitoring and preservation of date palm trees was carried using VHR-UAV images to map individual trees [34]. Another study [35] used VHR satellite images for pest detection in mixed hardwood forests. An analysis of diseases on permanent crops was also carried using VHR satellite images [36].…”

Section: Introductionmentioning

confidence: 99%

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Al-Mulla,

Ali,

Parimi

2023

Sustainability

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Many insects attack date palm trees but date palm trees in the Sultanate are particularly under threat due to the spread of pests and the Dubas bug (Db). Date palm productivity in Oman has been reduced by 28% due to Db infestation. The manual field detection of these pests requires huge efforts and costs, making field surveys time consuming and difficult. In this context, remote sensing integrated with deep learning techniques can help in the early detection of Db infestation. A total of 240 date palms with corrected geospatial locations and coordinates and their health status were systematically recorded throughout the 66-square-kilometer study area. We used advanced remote sensing tools and deep learning techniques to detect individual palm trees and their health levels in terms of Db infestation. Very-high-resolution (50 cm) satellite images rendered in visible and NIR bands were used as datasets to delineate and identify individual tree positions and determine their health condition. Our proposed method resulted in an overall accuracy of 87% for the detection of date palm trees and 85% for the detection of health levels of the plants. The overall detection accuracy of high and low infestation levels was observed with high precision at 95% and 93%, respectively. Hence, we can conclude with confidence that our technique performed well by accurately detecting individual date palm trees and determining their level of Db infestation. The approach used in this study can also provide farmers with useful knowledge regarding the Db risk and damage control for better management of Db. Moreover, the model used in this study may also lay the foundations for other models to detect infested plants and trees other than date palms.

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Section: Introductionmentioning

confidence: 99%

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Al-Mulla,

Ali,

Parimi

2023

Sustainability

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“…in Europe, which has been spreading and establishing across the continent during the last decade (Strachinis et al 2015). In Iran, this disease has rapidly spread from west to east throughout the Hyrcanian boxwood stands and approximately 40 000 ha of the original boxwood stands were infested and damaged by this disease at different intensities (Esmaili et al 2020). Cydalima perspectalis larvae feed on the leaves of different species of Buxus, seriously damaging these ornamental plants in private and public gardens as well as in semi-natural box-tree https://doi.org/10.17221/56/2020-JFS forests.…”

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confidence: 99%

Satellite image processing of the Buxus hyrcana Pojark dieback in the Northern Forests of Iran

Ghavidel¹,

Bayat²,

Farashiani³

2021

J. For. Sci.

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Pests and diseases can cause a variety of reactions in plants. In recent years, the boxwood dieback has become one of the essential concerns of practitioners and natural resources managers in Iran. To control the boxwood dieback spread, the early detection and disease distribution maps are required. The boxwood dieback causes a range of changes in colour, shape and leaf size with respect to photosynthesis and transpiration. Through remote sensing techniques, e.g. satellite image processing data, the variation of thermal and visual characteristics of the plant could be used to measure and illustrate the symptoms of the disease. In this study, five common vegetation indices like difference vegetation index (DVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), simple ratio (SR), and plant health index (PHI) were extracted and calculated from Landsat 8 satellite image data from six regions in the Gilan province, located in the northern part of Iran out of 150 maps over the time period 2014‒2018. It turned out that among the aforementioned indices, based upon the results of the models, SR and NDVI indices were more useful for the disease spread, respectively. Our disease progression model fitting criteria showed that this technique could probably be used to assess the extent of the affected areas and also the disease progression in the investigated regions in future.

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Mapping of understory infested boxwood trees using high resolution imagery

Cited by 2 publications

References 46 publications

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Satellite image processing of the Buxus hyrcana Pojark dieback in the Northern Forests of Iran

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