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Yaban hayatında uydu verilerinin kullanım olanakları üzerine bir çalışma: MaxEnt ile Karaca (Capreolus capreolus L.)' nın habitat uygunluk modellemesi

Year 2018, Volume: 2 Issue: 2, 147 - 156, 10.11.2018
https://doi.org/10.30516/bilgesci.399017

Abstract

Yaban hayatı türlerine yönelik
koruma ve yönetim çalışmalarının etkin bir şekilde gerçekleştirilebilmesi için
türlerin kullandığı habitat büyüklüklerinin belirlenmesi, habitat tercihlerinde
rol oynayan değişkenlerin tespit edilmesi ve izlenmesi gerekmektedir.
Geleneksel arazi envanter yöntemleri ile bu verilerin toplaması hem maliyetli
hem de zaman alıcı bir süreçtir. Bu yöntemlerin aksine, geniş alanlar için
sürekli veri akışı sağlayan uydu görüntülerinin kullanılması hem zaman
açısından hem de maliyet açısından fayda sağlamaktadır. Bu sebeple, Akdağ
(Simav) yöresinde gerçekleştirilen bu çalışmada, Landsat-8 OLI uydu görüntüsü
yardımıyla, Karaca (Capreolus capreolus
L.) türünün tercih ettiği habitat büyüklüğünün ve bu tercihinde rol oynayan
değişkenlerin belirlenmesi amaçlanmıştır. Dolaylı sayım teknikleri kullanılarak
yürütülen arazi çalışmaları esnasında, 32 adet türe ait var verisi
kaydedilmiştir. Definiens yazılımı yardımıyla, Çoklu Çözünürlüklü Segmentasyon
işlemi uygulanılarak, uydu görüntüsü farklı yamalara ayrılmıştır. Bu işlemin
ardından, Satranç Daması Segmentasyonu uygulanılarak, uydu görüntüsü 16, 64,
256 1024 piksele sahip farklı karelajlara ayrılmıştır. Her bir karelaj
içerisinde, Definiens yazılımı yardımıyla 9 farklı algoritma ve ArcGIS yazılımı
ile 6 farklı yama parametresi olmak üzere toplam 15 farklı değişken elde
edilmiştir. MaxEnt yazılımı ile türe ait var verileri ve uydu görüntüsünden
elde edilen değişkenler kullanılarak her bir karelaj boyutu için model
oluşturulmuş ve haritalandırılmıştır. Farklı piksel sayılarına sahip
karelajların modellerine ait eğitim veri seti AUC ve test veri seti AUC
değerleri sırasıyla, 16 (0,712, 0,698) , 64 (0,864, 0, 825), 256 (0,802, 0.795)
1024 (0.792, 0.779) olarak elde edilmiştir. Elde edilen modeller içerisinde, 64
piksele sahip karelaj için oluşturulan model, tür için en uygun model olarak
seçilmiştir. Modeli oluşturan değişkenler, zıtlık(2), kenar zıtlığı ve GLCM
entropi olarak belirlenmiştir. Sonuç olarak, gerçekleştirilen bu çalışma ile
türün tercih ettiği habitat büyüklüğüne ve tercihinde rol oynayan değişkenlere
uydu görüntüsü aracılığıyla erişilebileceği tespit edilmiştir.

References

  • Acevedo, P., Delibes‐Mateos, M., Escudero, M. A., Vicente, J., Marco, J., Gortázar, C., (2005). Environmental constraints in the colonization sequence of roe deer (Capreolus capreolus Linnaeus, 1758) across the Iberian Mountains, Spain. Journal of Biogeography, 32(9), 1671-1680.
  • Açar, M., Satil, F., (2014). Flora of Akdag (Balikesir, Dursunbey/Turkey). Biological Diversity and Conservation, 7(2), 38-56.
  • Aksan, Ş., Özdemir, İ., Oğurlu, İ. (2008). Modeling the distributions of some wild mammalian species in Gölcük Natural Park/Turkey.
  • Andren, H., Angelstam, P., (1988). Elevated Predation Rates as an Edge Effect in Habitat Islands: Experimental Evidence. Ecology, 69(2), 544-547.
  • Baldwin, R.A., (2009). Use of Maximum Entropy Modeling in Wildlife Research. Entropy, 11(4), 854-866.
  • Benhaiem, S., Delon, M., Lourtet, B., Cargnelutti, B., Aulagnier, S., Hewison, A. M., Verheyden, H., (2008). Hunting increases vigilance levels in roe deer and modifies feeding site selection. Animal Behaviour, 76(3), 611-618.
  • Beşkardeş, V., Keten, A., Arslangündoğdu, Z. (2008). The Importance of Roe Deer in Wildlife for Turkey. FORESTIST, 58(2), 15-22.
  • Beşkardeş, V., (2016). Large-bodied Mammals and Their Habitat Preferences in Autumn in Yedigöller Wildlife Reserve. Düzce University Journal of Forestry 12(1), 137-144.
  • Chawla, A., Yadav, P. K., Uniyal, S. K., Kumar, A., Vats, S. K., Kumar, S., Ahuja, P. S., (2012). Long-term ecological and biodiversity monitoring in the western Himalaya using satellite remote sensing. Current Science(Bangalore), 102(8):1143-1156.
  • Corsi, F., Duprè, E., Boitani, L. (1999). A large‐scale model of wolf distribution in Italy for conservation planning. Conservation Biology, 13(1), 150-159.
  • Çanakçıoğlu, H. ve T. Mol, (1996). Yaban Hayvanları Bilgisi, yayın no: 3948, O.F. yayın no : 440, ISBN 975-404-424- 4, Istanbul, 550s.
  • Definiens, A. G. (2007) Definiens Imaging Developer 7. eCognition Software. EII Earth. Munich, Germany.
  • Definiens, A. G. (2012). Developer XD 2.0. 4. Reference Book.
  • Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.McC., Peterson, A.T., Phillips, S.J., Richardson, K.S., Scachetti-Pereira, R., Schapire, R.E., Soberon, J., Williams, S., Wisz, M.S., Zimmermann, N.E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129-151.
  • Fahrig, L., Baudry, J., Brotons, L., Burel, F. G., Crist, T. O., Fuller, R. J., ... & Martin, J. L. (2011). Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecology letters, 14(2), 101-112.
  • Gao, T., Nielsen, A. B., Hedblom, M., (2015). Reviewing the strength of evidence of biodiversity indicators for forest ecosystems in Europe. Ecological Indicators, 57, 420-434.
  • Gibbon, J. W., Scott, D. E., Ryan, T. J., Buhlmann, K. A., Tuberville, T. D., Metts, B. S., Winne, C. T. (2000). The Global Decline of Reptiles, Déjà Vu Amphibians: Reptile species are declining on a global scale. Six significant threats to reptile populations are habitat loss and degradation, introduced invasive species, environmental pollution, disease, unsustainable use, and global climate change. BioScience, 50(8), 653-666.
  • Gürdal, M. N. (2008). Studies on wildlife developed and protected areas of Turkey (Doctoral dissertation, SDU graduate school of natural and applied sciences).
  • Hernandez, P.A., Graham, C.H., Master, L.L., Albert, D.L. (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29(5), 773-785.
  • Huş, S., (1974). Av Hayvanları ve Avcılık. İ.Ü. Orman Fakültesi Yayınları. İ.Ü. yayın no:1971, O.F. yayın no : 202, İstanbul, 406s.
  • Mert, A., Aksan, Ş., Özkan, U. Y., Özdemir, İ. (2016). Relationships between the richness of bird species and structural diversity from satellite images of Landsat-8 OLI. Turkish Journal of Forestry, 17(1), 68-72.
  • Mert, A., Yalçınkaya B., (2016). The relation of edge effect on some wild mammals in Burdur-Ağlasun (Turkey) district. Biodicon., 9, 193-201.
  • Mert A., Kıraç A., 2017. Habitat Suitability Mapping of Anatololacerta danfordi (Günter, 1876) in Isparta-Sütçüler District. Bilge International Journal of Science and Technology Research, ISSN 2587-0742, 1(1), 16-22.
  • Mysterud, A., Østbye, E. (2006). Effect of climate and density on individual and population growth of roe deer Capreolus capreolus at northern latitudes: the Lier valley, Norway. Wildlife Biology, 12(3), 321-329.
  • Oğurlu, İ. (2001). Yaban Hayatı Ekolojisi. Süleyman Demirel Üniversitesi, Orman Fakültesi Yayınları, Yayın no: 19, Isparta.
  • Oruç, M.S., Mert, A., Özdemir, İ. (2017). Modelling Habitat Suitability for Red Deer (Cervus elaphus L.) Using Environmental Variables in Çatacık Region, Eskişehir. Bilge International Journal of Science and Technology Research, 1 (2): 135- 142.
  • Özdemir, İ., Norton, D. A., Ozkan, U. Y., Mert, A., Senturk, O. (2008). Estimation of tree size diversity using object oriented texture analysis and aster imagery. Sensors, 8(8), 4709-4724.
  • Phillips, S.J., Dudík, M., Schapire, R.E. (2004). A Maximum Entropy approach to species distribution modeling. Proceedings of The Twenty-First International Conference On Machine Learning, ACM, 83p.
  • Rego, F.L., (2003). Automatic Land-Cover Classification Derived from Highresolution Ikonos Satellite image in the Urban Atlantic Forest in Rio de Janerio, Brasil by means of object oriented approach. PhD dissertation, Department of Remote Sensing and Landscape Information System, University of Freiburg, Germany, 222 p.
  • Tilman, D. (1982) Resource Competition and Community Structure. Princeton University Press, Princeton, NJ.
  • Tufto, J., Andersen, R., Linnell, J., (1996). Habitat use and ecological correlates of home range size in a small cervid: the roe deer. Journal of Animal Ecology, 715-724.
  • Van Horne, B., (1983). Density as A Misleading Indicator of Habitat Quality. J. Wildlife Management, 47: 893-901.
  • Vospernik, S., Reimoser, S. (2008). Modelling changes in roe deer habitat in response to forest management. Forest Ecology and Management, 255(3-4), 530-545.
  • Wisz, M.S., Hijmans, R., Li, J., Peterson, A.T., Graham, C., Guisan, A. (2008). Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5),763-773.
  • Yu, L., Shi, Y., Gong, P., (2015). Land cover mapping and data availability in critical terrestrial ecoregions: A global perspective with Landsat thematic mapper and enhanced thematic mapper plus data. Biological Conservation, 190:34-42.

A research on usage possibilities of satellite data in wildlife: Modeling habitat suitability of Roe deer(Capreolus capreolus L.) with MaxEnt

Year 2018, Volume: 2 Issue: 2, 147 - 156, 10.11.2018
https://doi.org/10.30516/bilgesci.399017

Abstract

Determining
and monitoring habitat size preferred by species and variables that play a role
in their habitat preferences are important to carry out conservation and
management activities for wildlife species. The collection of these data with
traditional field inventory methods is a process both costly and time
consuming. In contrast to these methods, the use of satellite data providing
continuous data for large areas provides both time and cost benefits. For this
reason, in this study carried out in Akdağ (Simav) region, it was aimed to
determine the habitat size preferred by Roe deer (Capreolus capreolus L.) and the variables playing a role in this
preference with Landsat-8 OLI satellite image. During field studies conducted
using indirect inventory techniques, 32 presence data of species were recorded.
By using Definiens software, multiresolution segmentation was applied and the
satellite image was divided into different patches. Following this, the
Chessboard Segmentation was applied and the satellite image was divided into
different grids with 16, 64, 256 and 1024 pixels. Within each grids, 15
different variables (9 different algorithms with Definiens software, 6
different patch parameters with ArcGIS software) were obtained. By using
MaxEnt, models are created and mapped for each grids size using the presence
data of species and the variables obtained from the satellite image. The
training data set AUC and the test data set AUC values for models of grids size
with different pixel numbers were obtained as 16 (0.712, 0.698), 64 (0.864, 0,
825), 256 (0.802, 0.795) 1024 (0.792, 0.779). Among the obtained models, the
model created for grid with 64 pixels was selected as the most suitable model
for the species. The variables forming the model are determined as Contrast to
Neighbor Pixels (2), Edge Contrast of Neighbor Pixels and GLCM entropy. Finally,
with this study, it was determined that habitat size preferred by species and
variables that play a role in their habitat preferences can be accessed by the
satellite data

References

  • Acevedo, P., Delibes‐Mateos, M., Escudero, M. A., Vicente, J., Marco, J., Gortázar, C., (2005). Environmental constraints in the colonization sequence of roe deer (Capreolus capreolus Linnaeus, 1758) across the Iberian Mountains, Spain. Journal of Biogeography, 32(9), 1671-1680.
  • Açar, M., Satil, F., (2014). Flora of Akdag (Balikesir, Dursunbey/Turkey). Biological Diversity and Conservation, 7(2), 38-56.
  • Aksan, Ş., Özdemir, İ., Oğurlu, İ. (2008). Modeling the distributions of some wild mammalian species in Gölcük Natural Park/Turkey.
  • Andren, H., Angelstam, P., (1988). Elevated Predation Rates as an Edge Effect in Habitat Islands: Experimental Evidence. Ecology, 69(2), 544-547.
  • Baldwin, R.A., (2009). Use of Maximum Entropy Modeling in Wildlife Research. Entropy, 11(4), 854-866.
  • Benhaiem, S., Delon, M., Lourtet, B., Cargnelutti, B., Aulagnier, S., Hewison, A. M., Verheyden, H., (2008). Hunting increases vigilance levels in roe deer and modifies feeding site selection. Animal Behaviour, 76(3), 611-618.
  • Beşkardeş, V., Keten, A., Arslangündoğdu, Z. (2008). The Importance of Roe Deer in Wildlife for Turkey. FORESTIST, 58(2), 15-22.
  • Beşkardeş, V., (2016). Large-bodied Mammals and Their Habitat Preferences in Autumn in Yedigöller Wildlife Reserve. Düzce University Journal of Forestry 12(1), 137-144.
  • Chawla, A., Yadav, P. K., Uniyal, S. K., Kumar, A., Vats, S. K., Kumar, S., Ahuja, P. S., (2012). Long-term ecological and biodiversity monitoring in the western Himalaya using satellite remote sensing. Current Science(Bangalore), 102(8):1143-1156.
  • Corsi, F., Duprè, E., Boitani, L. (1999). A large‐scale model of wolf distribution in Italy for conservation planning. Conservation Biology, 13(1), 150-159.
  • Çanakçıoğlu, H. ve T. Mol, (1996). Yaban Hayvanları Bilgisi, yayın no: 3948, O.F. yayın no : 440, ISBN 975-404-424- 4, Istanbul, 550s.
  • Definiens, A. G. (2007) Definiens Imaging Developer 7. eCognition Software. EII Earth. Munich, Germany.
  • Definiens, A. G. (2012). Developer XD 2.0. 4. Reference Book.
  • Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.McC., Peterson, A.T., Phillips, S.J., Richardson, K.S., Scachetti-Pereira, R., Schapire, R.E., Soberon, J., Williams, S., Wisz, M.S., Zimmermann, N.E. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129-151.
  • Fahrig, L., Baudry, J., Brotons, L., Burel, F. G., Crist, T. O., Fuller, R. J., ... & Martin, J. L. (2011). Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecology letters, 14(2), 101-112.
  • Gao, T., Nielsen, A. B., Hedblom, M., (2015). Reviewing the strength of evidence of biodiversity indicators for forest ecosystems in Europe. Ecological Indicators, 57, 420-434.
  • Gibbon, J. W., Scott, D. E., Ryan, T. J., Buhlmann, K. A., Tuberville, T. D., Metts, B. S., Winne, C. T. (2000). The Global Decline of Reptiles, Déjà Vu Amphibians: Reptile species are declining on a global scale. Six significant threats to reptile populations are habitat loss and degradation, introduced invasive species, environmental pollution, disease, unsustainable use, and global climate change. BioScience, 50(8), 653-666.
  • Gürdal, M. N. (2008). Studies on wildlife developed and protected areas of Turkey (Doctoral dissertation, SDU graduate school of natural and applied sciences).
  • Hernandez, P.A., Graham, C.H., Master, L.L., Albert, D.L. (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29(5), 773-785.
  • Huş, S., (1974). Av Hayvanları ve Avcılık. İ.Ü. Orman Fakültesi Yayınları. İ.Ü. yayın no:1971, O.F. yayın no : 202, İstanbul, 406s.
  • Mert, A., Aksan, Ş., Özkan, U. Y., Özdemir, İ. (2016). Relationships between the richness of bird species and structural diversity from satellite images of Landsat-8 OLI. Turkish Journal of Forestry, 17(1), 68-72.
  • Mert, A., Yalçınkaya B., (2016). The relation of edge effect on some wild mammals in Burdur-Ağlasun (Turkey) district. Biodicon., 9, 193-201.
  • Mert A., Kıraç A., 2017. Habitat Suitability Mapping of Anatololacerta danfordi (Günter, 1876) in Isparta-Sütçüler District. Bilge International Journal of Science and Technology Research, ISSN 2587-0742, 1(1), 16-22.
  • Mysterud, A., Østbye, E. (2006). Effect of climate and density on individual and population growth of roe deer Capreolus capreolus at northern latitudes: the Lier valley, Norway. Wildlife Biology, 12(3), 321-329.
  • Oğurlu, İ. (2001). Yaban Hayatı Ekolojisi. Süleyman Demirel Üniversitesi, Orman Fakültesi Yayınları, Yayın no: 19, Isparta.
  • Oruç, M.S., Mert, A., Özdemir, İ. (2017). Modelling Habitat Suitability for Red Deer (Cervus elaphus L.) Using Environmental Variables in Çatacık Region, Eskişehir. Bilge International Journal of Science and Technology Research, 1 (2): 135- 142.
  • Özdemir, İ., Norton, D. A., Ozkan, U. Y., Mert, A., Senturk, O. (2008). Estimation of tree size diversity using object oriented texture analysis and aster imagery. Sensors, 8(8), 4709-4724.
  • Phillips, S.J., Dudík, M., Schapire, R.E. (2004). A Maximum Entropy approach to species distribution modeling. Proceedings of The Twenty-First International Conference On Machine Learning, ACM, 83p.
  • Rego, F.L., (2003). Automatic Land-Cover Classification Derived from Highresolution Ikonos Satellite image in the Urban Atlantic Forest in Rio de Janerio, Brasil by means of object oriented approach. PhD dissertation, Department of Remote Sensing and Landscape Information System, University of Freiburg, Germany, 222 p.
  • Tilman, D. (1982) Resource Competition and Community Structure. Princeton University Press, Princeton, NJ.
  • Tufto, J., Andersen, R., Linnell, J., (1996). Habitat use and ecological correlates of home range size in a small cervid: the roe deer. Journal of Animal Ecology, 715-724.
  • Van Horne, B., (1983). Density as A Misleading Indicator of Habitat Quality. J. Wildlife Management, 47: 893-901.
  • Vospernik, S., Reimoser, S. (2008). Modelling changes in roe deer habitat in response to forest management. Forest Ecology and Management, 255(3-4), 530-545.
  • Wisz, M.S., Hijmans, R., Li, J., Peterson, A.T., Graham, C., Guisan, A. (2008). Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5),763-773.
  • Yu, L., Shi, Y., Gong, P., (2015). Land cover mapping and data availability in critical terrestrial ecoregions: A global perspective with Landsat thematic mapper and enhanced thematic mapper plus data. Biological Conservation, 190:34-42.
There are 35 citations in total.

Details

Primary Language Turkish
Subjects Forest Industry Engineering
Journal Section Research Articles
Authors

Sibel Tekin This is me

Berna Yalçınkaya

Ahmet Acarer This is me

Ahmet Mert

Publication Date November 10, 2018
Acceptance Date June 22, 2018
Published in Issue Year 2018 Volume: 2 Issue: 2

Cite

APA Tekin, S., Yalçınkaya, B., Acarer, A., Mert, A. (2018). Yaban hayatında uydu verilerinin kullanım olanakları üzerine bir çalışma: MaxEnt ile Karaca (Capreolus capreolus L.)’ nın habitat uygunluk modellemesi. Bilge International Journal of Science and Technology Research, 2(2), 147-156. https://doi.org/10.30516/bilgesci.399017