Browsing by Author "Satir, O."

Now showing 1 - 7 of 7

Comparing the Satellite Image Transformation Techniques for Detecting and Monitoring the Continuous Snow Cover and Glacier in Cilo Mountain Chain Turkey
(Elsevier B.V., 2016) Satir, O.
In this paper, satellite image transformations (SIT) for detection and monitoring of the continuous snow covers and glaciers (SCG) were evaluated using Landsat 5 TM (30 m), Landsat 8 OLI (30 m) and RASAT (7.5 m) satellite images at a regional scale. The study was performed in two stages. Firstly, four potential SCG detection indices were produced from the 23rd of August 2013 Landsat 8 image as a Normalized Difference Snow Index (NDSI), a Normalized Difference Snow-Ice Index (NDSII), a Normalized Difference Principle Component Snow Index (NDPCSI) and Tasseled Cap Wetness transformation (WET) as indicators of the SCG. Continuous SCG amount was obtained classifying the August 2013 RASAT satellite image using an object based classification technique as ground truth data. Kappa accuracy co-efficiency of the NDSI, NDSII, NDPCSI and WET were calculated to be 0.74, 0.76, 0.4 and 0.77 respectively. In the second stage, WET SCG maps were produced from August 1984, 2000 and 2015 Landsat images. Changes in the two time periods (1984-2000 and 2000-2015) showed that total SCG loss was 247 ha from August 1984 to August 2015. Almost 47% of the SCG loss recorded in the region in 31 years. The highest loss was observed in the 1st period (198 ha). However, only 49 ha SCG loss was detected in the 2nd period although the 5 year mean temperature changes were found to be similar both in the 1st and 2nd period. Because the most sensitive SCG areas (regions that are located at a lower slope and thus receive more sunlight) had melted in the 1st period. Finally, physical variables were more significant than temperature in the 2nd period for the stability of SCG in the study area. © 2016 Elsevier Ltd. All rights reserved.
Determining the Interactions of Black Pine Net Primary Productivity and Forest Stand Parameters in Northern Turkey
(Aloki Applied Ecological Research and Forensic inst Ltd, 2019) Bulut, S.; Satir, O.; Gunlu, A.
Net primary productivity (NPP) is a vital dataset to assess carbon cycling, carbon budget and interpreting global warming There are many approaches to calculate NPP, and Carnegie-Ames-Stanford approach (CASA) is one of the most popular approaches that was applied in this study. Black pine forest NPP was calculated with the CASA model in a transection zone between humid black sea and dry middle Anatolia region of Turkey for the year of 2016. Model parameters and homogeneity were tested with one-way ANOVA. Results was showed that annual NPP values were varied from 194 to 1213 (g C m(-2) year(-1)) for pure black pine stands. Model validation was made with stand increment, growing stock, and stand carbon values. Correlation co-efficiencies were obtained to be 0.92 and 0.85 respectively. It was found that NPP was higher in young stands where the mass accumulation potential was higher than areas, where crown closure was between 11% and 70%. According to this study, young stands should be established in the forests that were operated with the highest NPP objective. NPP models that can be used on a global scale is required intense data and time consuming In addition, it has been determined that mechanical models which are allowed more practical calculation and can be used with the stand parameters easily.
Land Use/Cover Change Modelling in a Mediterranean Rural Landscape Using Multi-Layer Perceptron and Markov Chain (mlp-Mc)
(Aloki Applied Ecological Research and Forensic inst Ltd, 2018) Mirici, M. E.; Berberoglu, S.; Akin, A.; Satir, O.
Mediterranean land use and land cover (LULC) have a very dynamic structure as a result of continuous transformation process due to anthropogenic effects and environmental gradients. LULC dynamics are important indicator of environmental condition in temporal and spatial scales. The aim of this paper was to simulate the future LULC of a Mediterranean type watershed located at the Eastern Mediterranean Region of Turkey by incorporating multi-layer perceptron (MLP), artificial neural network (ANN) and Markov chain (MC) approaches. Landsat TM/OLI images in 1990, 2003 and 2014 over the study area were classified using hybrid classification approach. The Kappa statistics of the hybrid classification that combines K-means, decision tree and object based classification method for these three images were 0.81, 0.85 and 0.87 respectively. The LULC map of 2014 was simulated using LULC maps of 1990 and 2003 for calibration and validation. The simulation results were compared with the actual 2014 LULC map to assess the accuracy of the simulation, and the rate of overlap was found as 89%. LULC map of 2025 was estimated using LULC maps of 2003 and 2014. These results indicated that, the area of bareground will reduce 13.31% whereas the rate of forest and agricultural area will increase 8.70% and 6.51% respectively.
Mapping Percentage Tree Cover From Envisat Meris Data Using Linear and Nonlinear Techniques
(Taylor & Francis Ltd, 2009) Berberoglu, S.; Satir, O.; Atkinson, P. M.
The aim of this study was to predict percentage tree cover from Envisat Medium Resolution Imaging Spectrometer (MERIS) imagery with a spatial resolution of 300 m by comparing four common models: a multiple linear regression ( MLR) model, a linear mixture model (LMM), an artificial neural network ( ANN) model and a regression tree (RT) model. The training data set was derived from a fine spatial resolution land cover classification of IKONOS imagery. Specifically, this classification was aggregated to predict percentage tree cover at the MERIS spatial resolution. The predictor variables included the MERIS wavebands plus biophysical variables (the normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of photosynthetically active radiation (fPAR), fraction of green vegetation covering a unit area of horizontal soil (fCover) and MERIS terrestrial chlorophyll index (MTCI)) estimated from the MERIS data. An RT algorithm was the most accurate model to predict percentage tree cover based on the Envisat MERIS bands and vegetation biophysical variables. This study showed that Envisat MERIS data can be used to predict percentage tree cover with considerable spatial detail. Inclusion of the biophysical variables led to greater accuracy in predicting percentage tree cover. This finer-scale depiction should be useful for environmental monitoring purposes at the regional scale.
Modelling Long Term Forest Fire Risk Using Fire Weather Index Under Climate Change in Turkey
(Aloki Applied Ecological Research and Forensic inst Ltd, 2016) Satir, O.; Berberoglu, S.; Cilek, A.
Fire weather indices (FWIs) are among the most effective techniques to define real time or long term forest fire risk using meteorological data. In this research, long term forest fire risk of Turkey was modelled using a fire weather index called F index for present (1990 - 2010) and future (2061 - 2080) periods. Dry bulb temperature, relative humidity and maximum wind speed were mapped using 945 meteorological stations in Turkey, with a spatial resolution of 250 m. Long term mean F index values (from 1990 to 2010 and from 2061 to 2080) were calculated for 7 months representing fire seasons from April to October. Average fire occurrence of each month and monthly mean F index values of the forestlands were correlated using Pearson correlation statistic and determination coefficiency (R-2) was 0.82. Additionally, projected annual mean temperature and humidity based on HadGEM2-ES model RCP 4.5 scenario were used to derive future F index. Mean F index values of the forestlands were shown that forest fire risk of Turkey will have an increase of 21.1% in 2070s.
Monitoring the Wheat, Corn and Cotton Areas in an Eastern Mediterranean Agricultural Basin Between 2007 and 2013
(International Society for Photogrammetry and Remote Sensing, 2016) Satir, O.; Yeler, O.
Detecting the seasonal agricultural crop pattern accurately is a vital part of the agricultural planning. In this extent, Cukurova Region that is located in Eastern Mediterranean Region of Turkey was evaluated on agricultural landscape pattern. This region is the most productive agricultural region of Turkey also crop variability and yield are higher than many parts of the world. The main agricultural part of the area is called Lower Seyhan Plane (LSP) and it has been formed by the Seyhan, Ceyhan and Berdan rivers. The purpose of the study was to define the wheat, corn and cotton crop pattern using multi-temporal Landsat satellite images and object based classification approach for 2007 and 2013 cropping years. Three main crop's areal difference were evaluated and changes were monitored between 2007 and 2013. The accuracy of the classifications were obtained by the spatial kappa statistics. Overall kappa accuracy was derived to be 0.9. Classification results were shown that wheat areas were decreased 35% and corn and cotton areas were increased 49% and 69% respectively. Particularly, government subventions and market demands were impacted cropping pattern in the region significantly. In addition, multi-temporal Landsat images and object based classification were a great combination to define regional agricultural crop pattern with very good accuracy (>90%). © 2018 International Society for Photogrammetry and Remote Sensing. All Rights Reserved.
A Simplified Method for the Determination and Monitoring of Green Areas in Urban Parks Using Multispectral Vegetation Indices
(Scibulcom Ltd, 2014) Bilgili, B. C.; Satir, O.; Muftuoglu, V.; Ozyavuz, M.
In this article, a simple method to be used in determining and monitoring existing urban green areas was examined from an ecological point of view. To this end, Altinpark, which is located in the city of Ankara in Turkey, was studied as a pilot area. Normalised difference vegetation index (NDVI), soil adjusted vegetation index (SAW) and tasselled cap green vegetation index (GVI) values of Altinpark were used as ecological indicators. Vegetation cover size and temporal growing rates in the park area in Ankara were determined by employing Remote Sensing Technology. The results were assessed using Kappa statistics. Green area detection accuracies from NDVI, SAW and GVI were 71, 73 and 56%, respectively. Additionally, the 1989 and 2006 green areas of Altinpark were compared with each other to see the temporal growth of green cover in the park area. Green cover change was detected to be 43.9 ha.