COMPARISON OF REMOTE SENSING AND GEOSTATISTICS TECHNIQUES IN FOREST TREE DENSITY ESTIMATION, CASE STUDY LOVEH FORESTS, GORGAN

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MOHAMMADI JAHANGIR; SHATAEI JOUYBARI SH.; HABASHI H.; YAGHMAEI FARHAD

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Journal: Journal of Agricultural Sciences and Natural Resources Year:2008 | Volume:15 | Issue:1 (SPECIAL ISSUE ON NATURAL RESOURCES) Page(s): 10-21

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Information Journal Paper

Title

COMPARISON OF REMOTE SENSING AND GEOSTATISTICS TECHNIQUES IN FOREST TREE DENSITY ESTIMATION, CASE STUDY LOVEH FORESTS, GORGAN

Author(s)

MOHAMMADI JAHANGIR | SHATAEI JOUYBARI SH. | HABASHI H. | YAGHMAEI FARHAD | Issue Writer Certificate

Keywords

TREE DENSITYQ3

REMOTE SENSINGQ2

GEOSTATISTICSQ2

KRIGINGQ3

LOVEH FORESTSQ3

Abstract

Awareness of forest TREE DENSITY is one of priorities that managers for assessment forest resources, timing treatment silviculture and planning must access to it. This information generally are produced through fieldwork, which in large areas takes much more time and cost. Also, REMOTE SENSING and interpolation methods can be suitable ways to estimate forest TREE DENSITY. In this study, 99 plots with 60m×60 m were set down with systematic cluster sampling method at the study area. In each plot, information of TREE DENSITY and geographic coordinates of each plot center were recorded. The regression model with ETM4 and ETM5 as independent variables were better predictor of TREE DENSITY (RMSE=170.13) than other combinations of ETM+ bands and vegetation indices. In GEOSTATISTICS methods estimation was preformed by ordinary KRIGING using spherical model cross validation results indicated that KRIGING could make a precise estimation (RMSE=201.768). The results of this research showed that in TREE DENSITY estimation, the regression model was lower RMSE than ordinary KRIGING. The difference RMSE between regression model and ordinary KRIGING in local scale not significant but in large area regression model has good results. Generally, estimation of TREE DENSITY using satellite data offers advantages such as using in large area, a reasonable cost and decreasing sampling.

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APA: Copy

MOHAMMADI, JAHANGIR, SHATAEI JOUYBARI, SH., HABASHI, H., & YAGHMAEI, FARHAD. (2008). COMPARISON OF REMOTE SENSING AND GEOSTATISTICS TECHNIQUES IN FOREST TREE DENSITY ESTIMATION, CASE STUDY LOVEH FORESTS, GORGAN. JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES, 15(1 (SPECIAL ISSUE ON NATURAL RESOURCES)), 10-21. SID. https://sid.ir/paper/9301/en

Vancouver: Copy

MOHAMMADI JAHANGIR, SHATAEI JOUYBARI SH., HABASHI H., YAGHMAEI FARHAD. COMPARISON OF REMOTE SENSING AND GEOSTATISTICS TECHNIQUES IN FOREST TREE DENSITY ESTIMATION, CASE STUDY LOVEH FORESTS, GORGAN. JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES[Internet]. 2008;15(1 (SPECIAL ISSUE ON NATURAL RESOURCES)):10-21. Available from: https://sid.ir/paper/9301/en

IEEE: Copy

JAHANGIR MOHAMMADI, SH. SHATAEI JOUYBARI, H. HABASHI, and FARHAD YAGHMAEI, “COMPARISON OF REMOTE SENSING AND GEOSTATISTICS TECHNIQUES IN FOREST TREE DENSITY ESTIMATION, CASE STUDY LOVEH FORESTS, GORGAN,” JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES, vol. 15, no. 1 (SPECIAL ISSUE ON NATURAL RESOURCES), pp. 10–21, 2008, [Online]. Available: https://sid.ir/paper/9301/en

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