Modeling litter mass using satellite NDVI images and environmental variables in a brutian pine forest located in the southwest of Turkey
Litter mass (LM) on the forest floor is an important component of the carbon (C) cycle in forest ecosystems. A considerably amount of LM is accumulated in the conifer forests in the Mediterranean Region of Turkey. Therefore, LM should be considered to estimate above ground biomass (AGB) more accurat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-05-01
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| Series: | Carbon Management |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17583004.2020.1735917 |
| _version_ | 1797678397975953408 |
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| author | Ibrahim Ozdemir Salih Yilmaz |
| author_facet | Ibrahim Ozdemir Salih Yilmaz |
| author_sort | Ibrahim Ozdemir |
| collection | DOAJ |
| description | Litter mass (LM) on the forest floor is an important component of the carbon (C) cycle in forest ecosystems. A considerably amount of LM is accumulated in the conifer forests in the Mediterranean Region of Turkey. Therefore, LM should be considered to estimate above ground biomass (AGB) more accurately in these forests. Rapid and affordable methods are necessary for LM inventory. Satellite remote sensing data and GIS-based environmental variables, which cover broad geographic regions, are alternative data sources in this scope. In this work, the LM was modeled by means of generalized additive model (GAM) using the predictor variables including the Normalized Difference Vegetation Index (NDVI) images (derived from RapidEye, SPOT-5, Aster) and environmental data (heat index-HI and radiation index-RI). According to the cross validation test, the best model using NDVIASTER and HI as predictor variables explained a total variation of 49% of the LM as response variable. We may conclude that the LM on the floor of brutian pine stands can be moderately predicted using satellite data and environmental variables. We consider that the results of this study may contribute to estimation of AGB and C storages more accurately in pure brutian pine stands. |
| first_indexed | 2024-03-11T22:59:08Z |
| format | Article |
| id | doaj.art-03a452f5e8504eefba9ac797b06e617b |
| institution | Directory Open Access Journal |
| issn | 1758-3004 1758-3012 |
| language | English |
| last_indexed | 2024-03-11T22:59:08Z |
| publishDate | 2020-05-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Carbon Management |
| spelling | doaj.art-03a452f5e8504eefba9ac797b06e617b2023-09-21T15:09:06ZengTaylor & Francis GroupCarbon Management1758-30041758-30122020-05-0111320521210.1080/17583004.2020.17359171735917Modeling litter mass using satellite NDVI images and environmental variables in a brutian pine forest located in the southwest of TurkeyIbrahim Ozdemir0Salih Yilmaz1Isparta University of Applied SciencesAntalya Forest Regional DirectorateLitter mass (LM) on the forest floor is an important component of the carbon (C) cycle in forest ecosystems. A considerably amount of LM is accumulated in the conifer forests in the Mediterranean Region of Turkey. Therefore, LM should be considered to estimate above ground biomass (AGB) more accurately in these forests. Rapid and affordable methods are necessary for LM inventory. Satellite remote sensing data and GIS-based environmental variables, which cover broad geographic regions, are alternative data sources in this scope. In this work, the LM was modeled by means of generalized additive model (GAM) using the predictor variables including the Normalized Difference Vegetation Index (NDVI) images (derived from RapidEye, SPOT-5, Aster) and environmental data (heat index-HI and radiation index-RI). According to the cross validation test, the best model using NDVIASTER and HI as predictor variables explained a total variation of 49% of the LM as response variable. We may conclude that the LM on the floor of brutian pine stands can be moderately predicted using satellite data and environmental variables. We consider that the results of this study may contribute to estimation of AGB and C storages more accurately in pure brutian pine stands.http://dx.doi.org/10.1080/17583004.2020.1735917littercarbonremote sensingbiomassclimate change |
| spellingShingle | Ibrahim Ozdemir Salih Yilmaz Modeling litter mass using satellite NDVI images and environmental variables in a brutian pine forest located in the southwest of Turkey Carbon Management litter carbon remote sensing biomass climate change |
| title | Modeling litter mass using satellite NDVI images and environmental variables in a brutian pine forest located in the southwest of Turkey |
| title_full | Modeling litter mass using satellite NDVI images and environmental variables in a brutian pine forest located in the southwest of Turkey |
| title_fullStr | Modeling litter mass using satellite NDVI images and environmental variables in a brutian pine forest located in the southwest of Turkey |
| title_full_unstemmed | Modeling litter mass using satellite NDVI images and environmental variables in a brutian pine forest located in the southwest of Turkey |
| title_short | Modeling litter mass using satellite NDVI images and environmental variables in a brutian pine forest located in the southwest of Turkey |
| title_sort | modeling litter mass using satellite ndvi images and environmental variables in a brutian pine forest located in the southwest of turkey |
| topic | litter carbon remote sensing biomass climate change |
| url | http://dx.doi.org/10.1080/17583004.2020.1735917 |
| work_keys_str_mv | AT ibrahimozdemir modelinglittermassusingsatellitendviimagesandenvironmentalvariablesinabrutianpineforestlocatedinthesouthwestofturkey AT salihyilmaz modelinglittermassusingsatellitendviimagesandenvironmentalvariablesinabrutianpineforestlocatedinthesouthwestofturkey |