Estimating quantitative forest attributes using Pleiades satellite data and non-parametric algorithms in Darabkola forests, Mazandaran
Knowledge on quantitative forest attributes is a prerequisite for forest stand management. The aim of this study was to evaluate high resolution Pleiades data in estimating the standing volume and basal area using non-parametric algorithms in Darabkola forest of Sari, Mazandaran province. A sampling...
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Research Institute of Forests and Rangelands of Iran
2015-09-01
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| Series: | تحقیقات جنگل و صنوبر ایران |
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| Online Access: | http://ijfpr.areeo.ac.ir/article_105652_7aa78153c4348bd3ede36a120f2b94f6.pdf |
| _version_ | 1818204367092711424 |
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| author | Mojgan Zahriban Asghar Fallah Shaban Shataee Siavash Kalbi |
| author_facet | Mojgan Zahriban Asghar Fallah Shaban Shataee Siavash Kalbi |
| author_sort | Mojgan Zahriban |
| collection | DOAJ |
| description | Knowledge on quantitative forest attributes is a prerequisite for forest stand management. The aim of this study was to evaluate high resolution Pleiades data in estimating the standing volume and basal area using non-parametric algorithms in Darabkola forest of Sari, Mazandaran province. A sampling design of 144 plots each with area of 1000 m2 was established using a systematic random sampling method. In each plot, information including as position of plot center, diameter at breast height of all trees within sample plot and height of selected trees were recorded, based on which the standing volume and basal area per ha were derived. The Pleiades data was preprocessed, and the pixel grey values corresponding to the ground samples were extracted from spectral bands. These were further considered as the independent variables to predict the standing volume and basal area per ha. Modeling was carried out based on 70% of sample plots as training set using K-Nearest Neighbor, support vector machine, and random forest methods. The predictions were cross-validated using the left-out 30% samples. Support vector machine comparatively retuned the best estimates for stand basal area with root mean square error of 38.75% and relative bias of 3.12, while it predicted the stand volume with root mean square error of 45.13% and relative bias of -3.21 as well. The results of study proved the average spectral and spatial capability of Pleiades data to estimate these two main, where the caveats are concluded to be mainly due to the heterogeneity and the density of forest stands across the study area. |
| first_indexed | 2024-12-12T03:40:06Z |
| format | Article |
| id | doaj.art-09b94bb30bd84ffc8d25b1644fc33fbf |
| institution | Directory Open Access Journal |
| issn | 1735-0883 2383-1146 |
| language | fas |
| last_indexed | 2024-12-12T03:40:06Z |
| publishDate | 2015-09-01 |
| publisher | Research Institute of Forests and Rangelands of Iran |
| record_format | Article |
| series | تحقیقات جنگل و صنوبر ایران |
| spelling | doaj.art-09b94bb30bd84ffc8d25b1644fc33fbf2022-12-22T00:39:42ZfasResearch Institute of Forests and Rangelands of Iranتحقیقات جنگل و صنوبر ایران1735-08832383-11462015-09-0123346547710.22092/ijfpr.2015.105652105652Estimating quantitative forest attributes using Pleiades satellite data and non-parametric algorithms in Darabkola forests, MazandaranMojgan Zahriban0Asghar Fallah1Shaban Shataee2Siavash Kalbi3M.Sc. Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resources University, Sari, Iran.Associate Prof., Department of Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resources University, Sari, IranAssociate Prof., Department of Forestry, Faculty of Natural Resources, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, IranPh.D. Student Forestry, Department of Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resources University, Sari, Iran.Knowledge on quantitative forest attributes is a prerequisite for forest stand management. The aim of this study was to evaluate high resolution Pleiades data in estimating the standing volume and basal area using non-parametric algorithms in Darabkola forest of Sari, Mazandaran province. A sampling design of 144 plots each with area of 1000 m2 was established using a systematic random sampling method. In each plot, information including as position of plot center, diameter at breast height of all trees within sample plot and height of selected trees were recorded, based on which the standing volume and basal area per ha were derived. The Pleiades data was preprocessed, and the pixel grey values corresponding to the ground samples were extracted from spectral bands. These were further considered as the independent variables to predict the standing volume and basal area per ha. Modeling was carried out based on 70% of sample plots as training set using K-Nearest Neighbor, support vector machine, and random forest methods. The predictions were cross-validated using the left-out 30% samples. Support vector machine comparatively retuned the best estimates for stand basal area with root mean square error of 38.75% and relative bias of 3.12, while it predicted the stand volume with root mean square error of 45.13% and relative bias of -3.21 as well. The results of study proved the average spectral and spatial capability of Pleiades data to estimate these two main, where the caveats are concluded to be mainly due to the heterogeneity and the density of forest stands across the study area.http://ijfpr.areeo.ac.ir/article_105652_7aa78153c4348bd3ede36a120f2b94f6.pdfDarabkola Forest of Sarinon-parametric methodsPleiades satelliteforest quantitative characteristics |
| spellingShingle | Mojgan Zahriban Asghar Fallah Shaban Shataee Siavash Kalbi Estimating quantitative forest attributes using Pleiades satellite data and non-parametric algorithms in Darabkola forests, Mazandaran تحقیقات جنگل و صنوبر ایران Darabkola Forest of Sari non-parametric methods Pleiades satellite forest quantitative characteristics |
| title | Estimating quantitative forest attributes using Pleiades satellite data and
non-parametric algorithms in Darabkola forests, Mazandaran |
| title_full | Estimating quantitative forest attributes using Pleiades satellite data and
non-parametric algorithms in Darabkola forests, Mazandaran |
| title_fullStr | Estimating quantitative forest attributes using Pleiades satellite data and
non-parametric algorithms in Darabkola forests, Mazandaran |
| title_full_unstemmed | Estimating quantitative forest attributes using Pleiades satellite data and
non-parametric algorithms in Darabkola forests, Mazandaran |
| title_short | Estimating quantitative forest attributes using Pleiades satellite data and
non-parametric algorithms in Darabkola forests, Mazandaran |
| title_sort | estimating quantitative forest attributes using pleiades satellite data and non parametric algorithms in darabkola forests mazandaran |
| topic | Darabkola Forest of Sari non-parametric methods Pleiades satellite forest quantitative characteristics |
| url | http://ijfpr.areeo.ac.ir/article_105652_7aa78153c4348bd3ede36a120f2b94f6.pdf |
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