Assessment of the Spread of Chestnut Ink Disease From 1995 to 2005 Using Aerial Photography and Geostatistical Methods

Martins, Luís; Castro, João Paulo; Marques, Catarina; Abreu, Cláudia

doi:10.17660/actahortic.2009.844.48

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Chestnut experts from University of Trás-os-Montes e Alto Douro have consistently monitored Padrela's region regarding chestnut trees cultural practices, environmental context, phytosanitary conditions, and an evolution, in recent decades. This monitoring activity resulted in several publications [1, 7,19,[25][26][27][28][29][30], which conclude that both cultural practices and environmental context are identical throughout the region. Therefore, the 12 trees selected from plantation #2-the selection was made based on the main characteristics of an adult chestnut tree, well represented in this Plantation-can be considered representative for the entire area under study.…”

Section: Tree Height and Crow Diameter Estimationmentioning

confidence: 99%

UAV-Based Automatic Detection and Monitoring of Chestnut Trees

et al. 2019

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Unmanned aerial vehicles have become a popular remote sensing platform for agricultural applications, with an emphasis on crop monitoring. Although there are several methods to detect vegetation through aerial imagery, these remain dependent of manual extraction of vegetation parameters. This article presents an automatic method that allows for individual tree detection and multi-temporal analysis, which is crucial in the detection of missing and new trees and monitoring their health conditions over time. The proposed method is based on the computation of vegetation indices (VIs), while using visible (RGB) and near-infrared (NIR) domain combination bands combined with the canopy height model. An overall segmentation accuracy above 95% was reached, even when RGB-based VIs were used. The proposed method is divided in three major steps: (1) segmentation and first clustering; (2) cluster isolation; and (3) feature extraction. This approach was applied to several chestnut plantations and some parameters—such as the number of trees present in a plantation (accuracy above 97%), the canopy coverage (93% to 99% accuracy), the tree height (RMSE of 0.33 m and R2 = 0.86), and the crown diameter (RMSE of 0.44 m and R2 = 0.96)—were automatically extracted. Therefore, by enabling the substitution of time-consuming and costly field campaigns, the proposed method represents a good contribution in managing chestnut plantations in a quicker and more sustainable way.

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Section: Tree Height and Crow Diameter Estimationmentioning

confidence: 99%

UAV-Based Automatic Detection and Monitoring of Chestnut Trees

et al. 2019

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Temporal Analysis of Sweet Chestnut Decline in Northeastern Portugal Using Geostatistical Tools

Castro¹,

Azevedo²,

Martins³

2010

Acta Hortic.

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The rising demand for sweet chestnut (Castanea sativa) in Portugal and elsewhere in Europe has led to more intensive management practices to increase nut production. This intensification has potentially increased the widespread of ink and chestnut blight diseases, causing decline in sweet chestnut orchards health and production and limiting the establishment of new planted areas. In this study we estimated chestnut decline along the last twenty years (1986 to 2006) in the northern part of Portugal using 1986, 1995 and 2006 aerial photography to quantify the damage at the tree level within fixed sample plots according to a categorical scale. Mean damage and damage variance in each date, however, were not significantly different. Geostatistical analyses indicated, however, changes in the spatial distribution of damaged and undamaged areas over time. The spread of decline in the region of study was estimated using Kriging based on the spherical model. During the examined period we observed spread of chestnut decline and increasing damage levels in regions where damage is systematically high. The chestnut productive surface in the region has increased in the last twenty years because new plantations exceeded mortality areas. The spatial analyses applied here have made clearer the relations between the spread of chestnut decline and geographical variables.

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Multi-Temporal Analysis of Forestry and Coastal Environments Using UASs

et al. 2017

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Due to strong improvements and developments achieved in the last decade, it is clear that applied research using remote sensing technology such as unmanned aerial vehicles (UAVs) can provide a flexible, efficient, non-destructive, and non-invasive means of acquiring geoscientific data, especially aerial imagery. Simultaneously, there has been an exponential increase in the development of sensors and instruments that can be installed in UAV platforms. By combining the aforementioned factors, unmanned aerial system (UAS) setups composed of UAVs, sensors, and ground control stations, have been increasingly used for remote sensing applications, with growing potential and abilities. This paper's overall goal is to identify advantages and challenges related to the use of UAVs for aerial imagery acquisition in forestry and coastal environments for preservation/prevention contexts. Moreover, the importance of monitoring these environments over time will be demonstrated. To achieve these goals, two case studies using UASs were conducted. The first focuses on phytosanitary problem detection and monitoring of chestnut tree health (Padrela region, Valpaços, Portugal). The acquired high-resolution imagery allowed for the identification of tree canopy cover decline by means of multi-temporal analysis. The second case study enabled the rigorous and non-evasive registry process of topographic changes that occurred in the sandspit of Cabedelo (Douro estuary, Porto, Portugal) in different time periods. The obtained results allow us to conclude that the UAS constitutes a low-cost, rigorous, and fairly autonomous form of remote sensing technology, capable of covering large geographical areas and acquiring high precision data to aid decision support systems in forestry preservation and coastal monitoring applications. Its swift evolution makes it a potential big player in remote sensing technologies today and in the near future.

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Assessment of the Spread of Chestnut Ink Disease From 1995 to 2005 Using Aerial Photography and Geostatistical Methods

Cited by 3 publications

References 17 publications

UAV-Based Automatic Detection and Monitoring of Chestnut Trees

UAV-Based Automatic Detection and Monitoring of Chestnut Trees

Temporal Analysis of Sweet Chestnut Decline in Northeastern Portugal Using Geostatistical Tools

Multi-Temporal Analysis of Forestry and Coastal Environments Using UASs

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