Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Y. Erfanifard"'
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-4-W1-2022, Pp 287-293 (2023)
Crown area is one of the key parameters in determining tree growth and an important basis for estimation of biophysical characteristics at single-tree levels in natural and man-made forests. Therefore, the present study was aimed to improve the estim
Externí odkaz:
https://doaj.org/article/eebe801994e74d8290829ab762ea4d23
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-4-W1-2022, Pp 315-320 (2023)
In recent years, the forests of northern Iran, which have a very high value, have been changed and turned into other uses due to various human reasons. Meanwhile, residential and road construction is more visible in these forests. Recognizing the loc
Externí odkaz:
https://doaj.org/article/65fff3c9720d45d6b4369c72f7c72925
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-4-W4, Pp 43-49 (2017)
Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over differe
Externí odkaz:
https://doaj.org/article/cfaf0dc618414c87a734c029aba7ca41
Autor:
Y. Erfanifard, F. Aali Beiranvand
Publikováno v:
Iranian Journal of Applied Ecology, Vol 5, Iss 15, Pp 15-26 (2016)
Positive and negative (facilitative and competitive) interactions of plants are important issues in autecology and can be evaluated by the spatial pattern analysis in plant ecosystems. This study investigates the intraspecific interactions of Indian
Externí odkaz:
https://doaj.org/article/18bd370a28084c04a61ec5b127af2c05
Autor:
Y. Erfanifard, E. Khosravi
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XL-1-W5, Pp 163-168 (2015)
Evaluating the interactions of woody plants has been a major research topic of ecological investigations in arid ecosystems. Plant-plant interactions can shift from positive (facilitation) to negative (competition) depending on levels of environmenta
Externí odkaz:
https://doaj.org/article/e4f02c9353044a2983fdf4381bbca6b5
Autor:
Y. Erfanifard, F. Rezayan
Publikováno v:
Iranian Journal of Applied Ecology, Vol 3, Iss 9, Pp 81-91 (2014)
Spatial pattern of trees in forests reveals how trees interact with each other and their environment. Spatial structure of trees in forest ecosystems is affected by environmental heterogeneity that leads to their heterogeneous distribution. This stud
Externí odkaz:
https://doaj.org/article/146a9e7bc8c944aba1b81e67d1de1367
Autor:
Y. Erfanifard, F. Rezayan
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XL-2/W3, Pp 109-114 (2014)
Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation
Externí odkaz:
https://doaj.org/article/76e0e398c32d49a390c831dda059ed68
Publikováno v:
Iranian Journal of Applied Ecology, Vol 3, Iss 7, Pp 83-93 (2014)
Distance methods and their estimators of density may have biased measurements unless the studied stand of trees has a random spatial pattern. This study aimed at assessing the effect of spatial arrangement of wild pistachio trees on the results of de
Externí odkaz:
https://doaj.org/article/5fdec998273b45ab880cf2858c56a327
Publikováno v:
Iranian Journal of Applied Ecology, Vol 2, Iss 5, Pp 15-25 (2014)
The ecological relationship between trees is important in the sustainable management of forests. Studying this relationship in spatial ecology, different indices are applied that are based on distance to nearest neighbor. The aim of this research was
Externí odkaz:
https://doaj.org/article/15dfcd18e028492c8caf990d98ca3d09
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XL-1/W3, Pp 153-158 (2013)
The Support Vector Machine (SVM) is a theoretically superior machine learning methodology with great results in classification of remotely sensed datasets. Determination of optimal parameters applied in SVM is still vague to some scientists. In this
Externí odkaz:
https://doaj.org/article/efb5e0f891354f79b05056cdf06cf09b