The use of regression tree method for Sentinel-2 satellite data to mapping percent tree cover in different forest types.

Quantifying forest systems is of importance for ecological services and economic benefits in ecosystem models. This study aims to map the percent tree cover (PTC) of various forest stands in the Buyuk Menderes Basin, located in the western part of Turkey with different characteristics in the Mediterranean and Terrestrial transition regions Sentinel-2 data with 10-m spatial resolution. In recent years, some researches have been carried out in different fields to show the capabilities and potential of Sentinel-2 satellite sensors. However, the limited number of PTC researches conducted with Sentinel-2 images reveals the importance of this study. This study aimed to demonstrate reliable PTC data in landscape planning or ecosystem modeling by introducing an advanced approach with high spatial, spectral, and temporal resolution and more cost-effective. In this study, a regression tree algorithm, one of the popular machine learning techniques for ecological modeling, was used to estimate the tree cover's dependent variable based on high-resolution monthly metrics' spectral signatures. Six frames of TripleSat images were used as training data in the regression tree. Monthly Sentinel-2 bands and produced metrics including NDVI, LAI, fCOVER, MSAVI2, and MCARI were almost the first time used as predictor variables. Stepwise linear regression (SLR) was applied to select these predictor bands in the regression tree and a correlation coefficient of 0.83 was obtained. Result PTC maps were produced and the results were evaluated based on coniferous and broadleaf. The results were tested using high spatial resolution TripleSat images and higher model accuracy was determined in both forest types. The high correlation is due to the Sentinel 2 satellite's band characteristics and the metrics are directly related to the tree cover. As a result, the high-accuracy availability of the Sentinel2 satellite is seen to map the PTC on a regional scale, including complex forest types between the Mediterranean and terrestrial transition climates.

Selection of renewable energy systems sites using the MaxEnt model in the Eastern Mediterranean region in Turkey.

Global warming has become the center of worldwide environmental concerns, especially in recent years. One of the ways to deal with global warming that causes climate change is to adopt the renewable energy power technique. Different renewable energy sources such as solar, wind, hydro, ocean, geothermal, and bioenergy are currently the backbone of green and sustainable economic growth. However, renewable energy sites are directly or indirectly dependent on environmental, social, and technical criteria.The main objective of this paper is to identify potential best renewable energy site alternatives using the maximum entropy model (MaxEnt) and Geographical Information systems (GIS). Thus, the framework formed by the findings will guide investors in the renewable energy sector. The results showed that suitable areas for solar and wind were mainly located in the Hatay and Mersin of the Eastern Mediterranean Region in Turkey. The energy suitability site maps indicate that 8% (3.42 km2) and 3.39% (1554 km2) of the total study area have suitability and very suitability for solar and wind energy respectively. Moreover, it is seen that 44.82% (20,689km2) of the regions are the same when suitable and very suitable regions are overlaid for the installation of solar and wind energy sites. The receiver operating characteristic (ROC) curve was used to evaluate model performance. The area under the curve (AUC) values are calculated 0.87 and 0.95 for solar and wind energy, respectively. Relying on realistic data, this paper proposes an innovative method to identify suitable areas for solar and wind power plants. The maps obtained to contribute to renewable energy production will be useful for creating future strategies in the Mediterranean region.

Modelling climate change impacts on regional net primary productivity in Turkey.

This study projects and models the terrestrial net primary productivity (NPP) considering the representative concentration pathways (RCPs) scenarios of Turkey using remote-sensing-based biogeochemical modelling techniques. Changes in annual NPP between 2000-2010 and 2070-2080 were projected with the biogeochemical ecosystem model NASA-Carnegie Ames Stanford Approach (CASA). A multi-temporal data set, including 16-day MODIS composites with a spatial resolution of 250 m, was used within the CASA model. The 5th Assessment Report (AR5) of the IPCC presented several scenarios for RCPs named RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 that laid the foundation for the future climate projections. The futuristic NPP modelling was based on the assumptions of maintaining CO2 level in the range of 421 to 936 ppm and a rise in temperature from 1.1 to 2.6 °C. The NPP in Turkey averaged 1232 g C m2 year-1 as per the model results. Considering 2000-2010 as the baseline period, the NPP was modelled within the range of 9.6 and 316 g C m2 year-1. Modelled average NPP was 1332.4 g C m2 year-1 per year between 2061 and 2080. The forest productivity was also estimated to be increased up to 113 g C m-2 year-1 under the climate change scenarios. However, there were minor differences in the projected average NPP under the baseline period covering years from 2000 to 2080 from those under RCPs. It appeared that variation in temperature and precipitation as a result of climate change affected the terrestrial NPP. The regional environmental and socio-economic consequences of climate change on diverse landscapes such as Turkey were properly modelled and analysed to understand the spatial variation of climate change impacts on vegetation. Changes in NPP imply that forests in Turkey could be carbon sinks in the future as their current potential that would profile Turkey's climate mitigation. This is one of the pioneering studies to estimate the future changes of regional NPP in Turkey by integrating various spatial inputs and a biogeochemical model.

Spatial and temporal evaluation of soil erosion in Turkey under climate change scenarios using the Pan-European Soil Erosion Risk Assessment (PESERA) model.

The impacts of climate change on soil erosion are mainly caused by the changes in the amount and intensity of rainfall and rising temperature. The combination of rainfall and temperature change is likely to be accompanied by negative or positive variations in agricultural and forest management. Turkey contains vast fertile plains, high mountain chains and semi-arid lands, with a climate that ranges from marine to continental and therefore is susceptible to soil erosion under climate change, particularly on high gradients and in semi-arid areas. This study aims to model the soil erosion risk under climate change scenarios in Turkey using the Pan-European Soil Erosion Assessment (PESERA) model, predicting the likely effects of land use/cover and climate change on sediment transport and soil erosion in the country. For this purpose, PESERA was applied to estimate the monthly and annual soil loss for 12 land use/cover types in Turkey. The model inputs included 128 variables derived from soil, climate, land use/cover and topography data. The total soil loss from the land surface is speculated to be approximately 285.5 million tonnes per year. According to the IPCC 5th Assessment Report of four climate change scenarios, the total soil losses were predicted as 308.9, 323.5, 320.3 and 355.3 million tonnes for RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios respectively from 2060 to 2080. The predicted amounts of fertile soil loss from agricultural land in a year were predicted to be 55.5 million tonnes at present, and 62.7, 59.9, 61.7 and 58.1 under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 respectively. This confirms that approximately 30% of the total erosion occurs over the agricultural lands. In this respect, degraded forests, scrub and arable lands were subjected to the highest erosion rate (68%) of the total, whereas, fruit trees and berry plantations reflected the lowest erosion rates. Low soil organic carbon, sparse vegetation cover and variable climatic conditions significantly enhanced the erosion of the cultivated lands by primarily removing the potential food for organisms. Finally, process-based models offer a valuable resource for decision-makers when improving environmental management schemes and also decrease uncertainty when considering risks.

Implementation of fuzzy, Simos and strengths, weaknesses, opportunities and threats analysis for municipal solid waste landfill site selection: Adana City case study.

The selection of suitable landfill locations for municipal solid waste has become a top priority, especially in developed countries as a result of rapid population growth, unplanned urbanisation, increasing waste production and the limited area available. However, determining the location of landfill sites is a complex decision-making problem for municipalities and depends on social, environmental, technical and economic factors and regulations. In this study, we combined a geographic information system (GIS), multi-criteria decision-analysis techniques and fuzzy logic to determine the best location for landfill sites in Adana, Turkey, in four steps. Firstly, the threshold values and the coefficient weights of 15 criteria, grouped into environmental and socio-economic factors, were determined by a literature review and expert opinion to select suitable landfill locations. Secondly, selection criteria were standardised using fuzzy logic. Thirdly, we assessed the criteria weights based on their effectiveness on the selection of potential landfill sites using the Simos method. According to the weight coefficients, environmental factors are more important than socio-economic factors. Final maps for each criterion were calculated and overlaid by a GIS. As a result, the final suitability results were divided into four discrete categories: very high, high, moderate and low suitability areas, representing 1%, 76%, 17% and 6% of the location options, respectively. Finally, four different alternative areas were identified as being very highly suitable for landfill locations, which were evaluated in detail using a strengths, weaknesses, opportunities and threats analysis. Three key aspects affect the final decision of a landfill site, in decreasing order of importance: environmental protection, minimising the negative impact on urban life quality and economic issues. Consequently, these results can guide decision-makers (ministries, municipalities, planners, etc.) during the selection of suitable landfill sites in both national and international studies.

Response of the regression tree model to high resolution remote sensing data for predicting percent tree cover in a Mediterranean ecosystem.

Percent tree cover is the percentage of the ground surface area covered by a vertical projection of the outermost perimeter of the plants. It is an important indicator to reveal the condition of forest systems and has a significant importance for ecosystem models as a main input. The aim of this study is to estimate the percent tree cover of various forest stands in a Mediterranean environment based on an empirical relationship between tree coverage and remotely sensed data in Goksu Watershed located at the Eastern Mediterranean coast of Turkey. A regression tree algorithm was used to simulate spatial fractions of Pinus nigra, Cedrus libani, Pinus brutia, Juniperus excelsa and Quercus cerris using multi-temporal LANDSAT TM/ETM data as predictor variables and land cover information. Two scenes of high resolution GeoEye-1 images were employed for training and testing the model. The predictor variables were incorporated in addition to biophysical variables estimated from the LANDSAT TM/ETM data. Additionally, normalised difference vegetation index (NDVI) was incorporated to LANDSAT TM/ETM band settings as a biophysical variable. Stepwise linear regression (SLR) was applied for selecting the relevant bands to employ in regression tree process. SLR-selected variables produced accurate results in the model with a high correlation coefficient of 0.80. The output values ranged from 0 to 100 %. The different tree species were mapped in 30 m resolution in respect to elevation. Percent tree cover map as a final output was derived using LANDSAT TM/ETM image over Goksu Watershed and the biophysical variables. The results were tested using high spatial resolution GeoEye-1 images. Thus, the combination of the RT algorithm and higher resolution data for percent tree cover mapping were tested and examined in a complex Mediterranean environment.