MAPPING TREE DENSITY OF ZAGROS OAK FORESTS USING KRIGING AND WORLDVIEW-2 SATELLITE IMAGES FROM GOOGLE EARTH DATABASE

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MAHDAVI A.; AZIZ J.; AKHAVAN R.

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Journal: Journal of Wood and Forest Science and Technology Year:2017 | Volume:23 | Issue:4 Page(s): 87-110

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Title

MAPPING TREE DENSITY OF ZAGROS OAK FORESTS USING KRIGING AND WORLDVIEW-2 SATELLITE IMAGES FROM GOOGLE EARTH DATABASE

Author(s)

MAHDAVI A. | AZIZ J. | AKHAVAN R. | Issue Writer Certificate

Keywords

GEOSTATISTICSQ2

MAPPINGQ2

DENSITYQ2

ZAGROS OAK FORESTQ1

WORLDVIEW-2Q1

Abstract

Background and objectives: DENSITY (i.e. number of trees per hectare) in a forest stand shows status of the stand and its monitoring is important to assess the stand’s changes. In addition, understanding about DENSITY trees of forest stands is one of the primary proceedings in forest management. In this study for estimation and MAPPING DENSITY trees of Oak Forest used GEOSTATISTICS (Variogram modeling and Kriging interpolation) and WORLDVIEW-2 satellite images in Google's earth database. Study area was a 450 hectares forest area in Ilam province, southwest of Iran.Materials and Methods: Field's sampling was performed based on a 100 ’ 250 meter systematic network inventory using 2500 m2 square samples on the Google Earth image. The image used in this study, was received from Google ’ s earth server to high spatial resolution from the WORLDVIEW-2 sensors (0.46 meter). The image was geo-referenced using the ground control point (collected with GPS) in the Universal Transverse Mercator (UTM) system. Overall, 180 sample plots were established and measured on the geo-referenced image received from Google's earth server. For validation of DENSITY measuring on Google's earth image, 30 sample plots were randomly selected and measured in the field, which result of t-test showed that no significant differences. Experimental variograms for forest stem DENSITY was calculated and plotted using the geo-referenced inventory plots. Then deferent theoretical variogram model (Circular, Spherical, Exponential and Gaussian Models) fitted to Experimental variograms for forest stem DENSITY and selected best theoretical variogram model for estimation stem DENSITY based on good of fitness.Results: The calculated variogram of stem DENSITY showed strong and anisotropic spatial autocorrelation, which best fitted by exponential theoretical model. Estimations were made by ordinary block kriging method (block size 50 ’ 50 meter).Cross-validation results showed that all the estimations are unbiased. Thus, kriging and WORLDVIEW-2 images from Google's earth database together have the potential to estimate and map the DENSITY trees of this kind of oak forests, accurately. The results of this study show that the Google earth images sampling absence of significant differences with ground sampling, so that the results of this study can decrease the cost sampling and resolve many restrictions of fieldwork.Conclusion: Consequently, MAPPING DENSITY tree using Kriging and Google Earth Images was more efficient for the oak DENSITY estimate in the study area. Also according to the results, we suggested that in the other area of Zagro's forest is used from MAPPING DENSITY tree using Kriging and Google Earth Images to be more results that are reliable.

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APA: Copy

MAHDAVI, A., AZIZ, J., & AKHAVAN, R.. (2017). MAPPING TREE DENSITY OF ZAGROS OAK FORESTS USING KRIGING AND WORLDVIEW-2 SATELLITE IMAGES FROM GOOGLE EARTH DATABASE. JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY, 23(4), 87-110. SID. https://sid.ir/paper/156907/en

Vancouver: Copy

MAHDAVI A., AZIZ J., AKHAVAN R.. MAPPING TREE DENSITY OF ZAGROS OAK FORESTS USING KRIGING AND WORLDVIEW-2 SATELLITE IMAGES FROM GOOGLE EARTH DATABASE. JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY[Internet]. 2017;23(4):87-110. Available from: https://sid.ir/paper/156907/en

IEEE: Copy

A. MAHDAVI, J. AZIZ, and R. AKHAVAN, “MAPPING TREE DENSITY OF ZAGROS OAK FORESTS USING KRIGING AND WORLDVIEW-2 SATELLITE IMAGES FROM GOOGLE EARTH DATABASE,” JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY, vol. 23, no. 4, pp. 87–110, 2017, [Online]. Available: https://sid.ir/paper/156907/en

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