Efficiency of sample-based indices for spatial pattern recognition of wild pistachio (Pistacia atlantica) trees in semi-arid woodlands

Erfanifard, Yousef; Saborowski, Joachim; Wiegand, Kerstin; Meyer, Katrin

doi:10.1007/s11676-015-0205-y

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…4) showed that trees are more likely to spatially interact at smaller scales, and the significance of the effect (i.e., the departure from null models) is weaker at large scales. This result is consistent with other reports that tree spatial correlation diminishes with distance (Miao et al 2014;Erfanifard et al 2016;Moreno-Fernández et al 2020). In fact, all the proposed univariate and bivariate patterns, including the quantitatively and vectorially marked related patterns, could be further expanded to trivariate patterns, which incorporate spatial correlations for three species and also need corresponding summary statistics and mull models (Velázquez et al 2016).…”

Section: Discussionsupporting

confidence: 90%

Spatial pattern analysis of forest trees based on the vectorial mark

et al. 2021

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Analysis of spatial patterns to describe the spatial correlation between a tree location and marks (i.e., structural variables), can reveal stand history, population dynamics, competition and symbiosis. However, most studies of spatial patterns have concentrated on tree location and tree sizes rather than on crown asymmetry especially with direct analysis among marks characterizing facilitation and competition among of trees, and thus cannot reveal the cause of the distributions of tree locations and quantitative marks. To explore the spatial correlation among quantitative and vectorial marks and their implication on population dynamics, we extracted vertical and horizontal marks (tree height and crown projection area) characterizing tree size, and a vectorial mark (crown displacement vector characterizing the crown asymmetry) using an airborne laser scanning point cloud obtained from two forest stands in Oxfordshire, UK. Quantitatively and vectorially marked spatial patterns were developed, with corresponding null models established for a significance test. We analyzed eight types of univariate and bivariate spatial patterns, after first proposing four types. The accuracy of the pattern analysis based on an algorithm-segmented point cloud was compared with that of a truly segmented point cloud. The algorithm-segmented point cloud managed to detect 70–86% of patterns correctly. The eight types of spatial patterns analyzed the spatial distribution of trees, the spatial correlation between tree size and facilitated or competitive interactions of sycamore and other species. These four types of univariate patterns jointly showed that, at smaller scales, the trees tend to be clustered, and taller, with larger crowns due to the detected facilitations among trees in the study area. The four types of bivariate patterns found that at smaller scales there are taller trees and more facilitation among sycamore and other species, while crown size is mostly homogeneous across scales. These results indicate that interspecific facilitation and competition mainly affect tree height in the study area. This work further confirms the connection of tree size with individual facilitation and competition, revealing the potential spatial structure that previously was hard to detect.

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Section: Discussionsupporting

confidence: 90%

Spatial pattern analysis of forest trees based on the vectorial mark

et al. 2021

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“…Despite all limitations, the great advantage of the indices discussed in this study is the simplicity of sampling and interpretation when describing the spatial distribution pattern through a simple value (Erfanifard et al, 2016; Szmyt, 2014). This easiness very likely accounts for the great amount of works found in recent scientific literature that use at least one of the dispersion indices and coefficients that were analyzed in this study (Bastos et al, 2018; Bidarnamani & Shabanipoor, 2018; Butturi‐Gomes & Petrere Júnior, 2020; dos Santos et al, 2020; Hernández et al, 2018; Park et al, 2018; Piroozi et al, 2018; Sasaki et al, 2019; Siahaan, Hilwan, & Setiawan, 2019; Sousa & Cunha, 2018; Utami et al, 2019).…”

Section: About the Use Or Disuse Of Dispersion Indicesmentioning

confidence: 99%

Why do traditional dispersion indices used for analysis of spatial distribution of plants tend to become obsolete?

Alves

Santana

2021

Population Ecology

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It is common to characterize the spatial distribution of plant patterns as random, aggregate, or uniform. In this context, a major challenge for the researcher is the choice of the method to identify the spatial pattern correctly as well as the factors related to it. The vast literature on the subject is not recent, especially regarding the dispersion indices. The aim of this review was to conduct a critical and temporal analysis of these dispersion indices and test their effectiveness in determining the spatial distribution of Paepalanthus chiquitensis Herzog (Eriocaulaceae). This species is a meaningful model due to its occurrence in specific sites. The Lexis, Charlier, dispersion, relative variance, aggregation, Green, inverse of k of the negative binomial, Morisita, and standardized Morisita indices were limited to indicating that the individuals of the species are aggregate and did not provide information on neither spatial dimension (scale) where the aggregation occurs, nor the factors related to this aggregation. Although they have distinct magnitudes, the algebraic expressions of dispersion, relative variance, aggregation, Green, inverse of k, Morisita, and standardized Morisita indices exhibited a close relationship with each other and little progress from their precursors Lexis and Charlier. By disregarding the possibility of spatial dependence, these indices make it impossible to generate important hypotheses for the investigation of factors related to spatial structure. Therefore, they became obsolete and are falling into disuse. It should be noted that these measurements accomplished their role and contributed to science in times of limited technologies for spatial data.

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“…Furthermore, point clouds produced from RGB images might significantly be influenced by different parameters such as quality of camera, overlapping rate of sequential images, limitation of software and algorithms. CHMs constructed based on such point clouds are not applicable to height estimation within all forest ecosystems as previous studies (Erfanifard et al 2019, Selim et al 2020Safonova et al 2021) measured tree heights on UAV images. Therefore, it is necessary to develop methods that can be directly used on VHR images obtained from airborne platforms such as UVAs.…”

Section: Introductionmentioning

confidence: 97%

Height estimation of pine (Pinus eldarica) single trees using slope corrected shadow length on unmanned aerial vehicle (UAV) imagery in a plantation forest

Hosingholizade,

Erfanifard,

Alavipanah

et al. 2023

AFR

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Tree height is one of the key parameters in forest plantations that plays a crucial role in estimation of above-ground biomass (AGB) of trees and stands. The parameter may be obtained by different methods from airborne remotely sensed datasets such shadow length of each tree individual or crown height models (CHMs). However, tree height estimation based on shadow length might be biased considering diverse topography of forest sites. Therefore, this study aims to develop a reliable method to estimate tree height in a plantation forest using shadow length on UAV imagery. First, heights of 151 pine (Pinus eldarica) trees were precisely measured in Pardisan Park, North Khorasan province, Iran. Additionally, a collection of images was captured by a Phantom 4Pro UAV in order to illustrate the study area. Then, two different approaches were considered to estimate the height of trees. In the first approach, tree heights were estimated based on shadow length on the UAV orthomosaic and correcting the effect of slope. The second approach considered the UAV-based CHM and height estimation using CHM segmentation and local maximum filtering. The results showed that tree heights estimated by the first approach were not significantly different from the insitu data (p=0.298). Furthermore, the heights estimated by the slope corrected shadow length showed higher precision compared to the heights estimated by the shadow length without slope correction (Relative root mean squared error, RRMSE 5.6% and 8.2% respectively). However, the heights obtained from the first approach were less precise than the second approach (RRMSE 5.6% and 4.2% respectively). In general, it was concluded that height estimation of pine trees based on shadow length after correction of slope effects can be considered as a reliable approach, although CHM is more efficient in estimating the tree heights. The findings of this study are applicable for height estimation of pine trees within plantation forests on UAV imagery.

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Efficiency of sample-based indices for spatial pattern recognition of wild pistachio (Pistacia atlantica) trees in semi-arid woodlands

Cited by 6 publications

References 44 publications

Spatial pattern analysis of forest trees based on the vectorial mark

Spatial pattern analysis of forest trees based on the vectorial mark

Why do traditional dispersion indices used for analysis of spatial distribution of plants tend to become obsolete?

Height estimation of pine (Pinus eldarica) single trees using slope corrected shadow length on unmanned aerial vehicle (UAV) imagery in a plantation forest

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