Integration of WorldView-2 and airborne laser scanning data to classify defoliation levels in Quercus ilex L. Dehesas affected by root rot mortality: Management implications

Navarro-Cerrillo, Rafael M.; Varo-Martínez, Ma Ángeles; Acosta, Cristina; Palacios-Rodríguez, Guillermo; Sánchez‐Cuesta, Rafael; Gómez, Francisco J. Ruiz

doi:10.1016/j.foreco.2019.117564

Cited by 22 publications

(15 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ground truth was manually mapped and can be summarized as follows: the training data set contained 704 oak trees, of which 479 were mapped as healthy, 207 as sick, and 18 as dead; the validation data set included 354 trees, of which 218 were mapped as healthy, 118 as sick, and 18 as dead. (Godinho et al, 2018(Godinho et al, , 2016Navarro-Cerrillo et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…For a more precise determination of the tree contours, the ALS data could be valuable. As Navarro-Cerrillo et al (2019) showed, a tree segmentation approach based on a region growing algorithm can also be applied to determine the shape of oaks trees from ALS data in savannah-like dehesa ecosystems. The principle idea is that a local maxima filter is used to determine the highest point of a tree and then a region growing segmentation algorithm (Erikson and Olofsson, 2005) is used to determine the area of the tree crown.…”

Section: Discussionmentioning

confidence: 99%

“…The finally corrected image was transferred to ArcGIS Pro, which was used for the calculation of the vegetation indices (VIs) and the vitality classification. Ten VIs were selected from studies on tree canopy cover and vitality in dehesa systems (Godinho et al, 2018(Godinho et al, , 2016Navarro-Cerrillo et al, 2019). Table 1 shows the VIs and their formulas.…”

Section: Classification Of Tree Vitalitymentioning

confidence: 99%

“…To protect the ecosystems, dehesas have been included in the European Nature 2000 network, they are protected by the EU directive 92/43, and the autonomous community Extremadura introduced the dehesa law, which restricts the pruning of trees (European Union, 1992;Moreno and Pulido, 2009;Plieninger and Schaar, 2008). Nevertheless, dehesas still undergo a significant land use change, reinforced by climate change and hence they are regarded as a highly vulnerable ecosystem in the Mediterranean (Costa et al, 2016;Navarro-Cerrillo et al, 2019;Rolo and Moreno, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

First Investigation of Mediterranean Oak Tree Vitality With High-Resolution Worldview-3 Satellite Data: Comparing Ten Vegetation Indices and Three Machine Learning Classifiers

Tilly

Reddig

Lussem

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Oak trees are the primary component in Mediterranean agro-silvopastoral systems. Since the second half of the 20th century, however, a severe oak decline has been observed. Climate change reinforces this problem, which is consistent with worldwide observable tree dieback. As the trees have significant ecological and socio-economic functions, their observation and assessment of vitality are increasingly researched. Satellite remote sensing is very well suitable for large-scale surveys of the extensive and sometimes hardly accessible areas. This study investigates the usability of high-resolution WorldView-3 data for the classification of tree vitality. The ground truth was collected on an Andalusian dehesa at the end of September 2019, timely corresponding with the satellite data acquisition. After customary post-processing of the WorldView-3 data, 10 vegetation indices (ARVI, CIgreen, CSI, DPI, EVI, GNDVI, NDVI, PSRI, RENDVI, and RGI) were calculated from the multispectral image. Three machine learning classifiers (Maximum Likelihood, Random Forest, and Support Vector Machine) were then used for a supervised image classification with three vitality classes (healthy, sick, and dead). Independent ground truth data were used for the validation. The best results were achieved with the red edge normalized difference vegetation index (RENDVI) and the Support Vector Machine classifier (F1 scores between 0.27 and 0.72). A maximal overall accuracy of around 0.6 is, however, improvable. Further studies should focus on other classification methods, more reliable ground truth, and combined analyses of spectral and structural data.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Classification Of Tree Vitalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

First Investigation of Mediterranean Oak Tree Vitality With High-Resolution Worldview-3 Satellite Data: Comparing Ten Vegetation Indices and Three Machine Learning Classifiers

Tilly

Reddig

Lussem

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Although crown defoliation is usually the main factor assessed when studying holm oak health status in relation to root rot [45,46], this factor was not considered in our work. This analysis would have required different experimental design, considering a plot with a wider range of crown defoliation and health status, a healthy plot with similar soil conditions as a control, and more biological repetitions for each defoliation level.…”

Section: Discussionmentioning

confidence: 99%

Small-Scale Abiotic Factors Influencing the Spatial Distribution of Phytophthora cinnamomi under Declining Quercus ilex Trees

Sánchez‐Cuesta

Navarro-Cerrillo

Quero

et al. 2020

Forests

Self Cite

View full text Add to dashboard Cite

Phytophthora root rot is considered one of the main factors associated with holm oak (Quercus ilex L.) mortality. The effectiveness and accuracy of soilborne pathogen and management could be influenced by soil spatial heterogeneity. This factor is of special relevance in many afforestation of southwestern Spain, which were carried out without phytosanitary control of the nursery seedlings. We selected a study area located in a 15 year-old afforestation of Q. ilex, known to be infested by Phytophthora cinnamomi Rands. Soil samples (ntotal = 132) were taken systematically from a grid under 4 trees, and analysed to quantify 12 variables, the colony forming units (cfu) of P. cinnamomi plus 11 physical and chemical soil properties. The combined analysis of all variables was performed with linear mixed models (GLMM), and the spatial patterns of cfu were characterised using an aggregation index (Ia) and a clustering index (ν) by SADIE. Cfu values ranged from 0 to 211 cfu g−1, and the GLMM built with the variables silt, P, K and soil moisture explained the cfu distribution to the greatest extent. The spatial analysis showed that 9 of the 12 variables presented spatial aggregation (Ia > 1), and the clustering of local patches (νi ≥ 1.5) for organic matter, silt, and Ca. The spatial patterns of the P. cinnamomi cfu under planted holm oak trees are related to edaphic variables and canopy cover. Small-scale spatial analysis of microsite variability can predict which areas surrounding trees can influence lower oomycetes cfu availability.

show abstract

Do soil properties and ecophysiological responses of oak (Quercus brantii Lindl.) correlate with the rate of dieback?

Nejad

Badehian

Nejad

et al. 2021

Trees

View full text Add to dashboard Cite

Integration of WorldView-2 and airborne laser scanning data to classify defoliation levels in Quercus ilex L. Dehesas affected by root rot mortality: Management implications

Cited by 22 publications

References 58 publications

First Investigation of Mediterranean Oak Tree Vitality With High-Resolution Worldview-3 Satellite Data: Comparing Ten Vegetation Indices and Three Machine Learning Classifiers

First Investigation of Mediterranean Oak Tree Vitality With High-Resolution Worldview-3 Satellite Data: Comparing Ten Vegetation Indices and Three Machine Learning Classifiers

Small-Scale Abiotic Factors Influencing the Spatial Distribution of Phytophthora cinnamomi under Declining Quercus ilex Trees

Do soil properties and ecophysiological responses of oak (Quercus brantii Lindl.) correlate with the rate of dieback?

Contact Info

Product

Resources

About