Melis Inalpulat

Greenhouses (GHs) are important elements of agricultural production and help to ensure food security aligning with United Nations Sustainable Development Goals (SDGs). However, there are still environmental concerns due to excessive use of plastics. Therefore, it is important to understand the past and future trends on spatial distribution of GH areas, whereby use of remote sensing data provides rapid and valuable information. The present study aimed to determine GH area changes in an agricultural hotspot, Serik, Türkiye, using 2008 and 2022 Landsat imageries and machine learning, and to predict future patterns (2036 and 2050) via the Markov–FLUS model. Performances of random forest (RF), k-nearest neighborhood (KNN), and k-dimensional trees k-nearest neighborhood (KD-KNN) algorithms were compared for GH discrimination. Accordingly, the RF algorithm gave the highest accuracies of over 90%. GH areas were found to increase by 73% between 2008 and 2022. The majority of new areas were converted from agricultural lands. Markov-based predictions showed that GHs are likely to increase by 43% and 54% before 2036 and 2050, respectively, whereby reliable simulations were generated with the FLUS model. This study is believed to serve as a baseline for future research by providing the first attempt at the visualization of future GH conditions in the Turkish Mediterranean region.

Anthropogenic activities have adverse impacts on productive lands around coastal zones due to rapid developments. Assessment of land use and land cover (LULC) changes provide a better understanding of the process for conservation of such vulnerable ecosystems. Alanya is one of the most popular tourism hotspots on the Mediterranean coast of Turkey, and even though the city faced severe LULC changes after the mid-80s due to tourism-related investments, limited number of studies has been conducted in the area The study aimed to determine short-term and long-term LULC changes and effects of residential development process on agricultural lands using six Landsat imageries acquired between 1984 and 2017, and presented the first attempt of future simulation in the area. Average annual conversions (AAC) (ha) were calculated to assess magnitudes of annual changes in six different periods. AACs were used to calculate area demands for LULC2030 and LULC2050, whereby annual conversions from different periods were multiplied by the number of years between 2017, 2030 and 2050 for each scenario. Finally, optimistic and pessimistic scenarios for agricultural lands are simulated using a future land use simulation model. Accordingly, agricultural lands decreased from 53.9% to 31.4% by 22.5% in 33 years and are predicted to change between 19.50% and 24.63% for 2030, 1.07% and 14.10% for 2050, based on pessimistic and optimistic scenarios, respectively.

Anthropogenic activities have adverse impacts on productive lands around coastal zones due to rapid developments. Assessment of land use and land cover (LULC) changes provides better understanding of the process for conservation of such vulnerable ecosystems. Alanya is one of the most popular tourism hotspots in Mediterranean coast of Turkey, and even though the city faced with severe LULC changes after mid-80s due to tourism-related investments, limited number of studies has conducted in the area The study aimed to determine short-term and long-term LULC changes and effects of residential development process on agricultural lands using six Landsat imageries acquired between 1984 and 2017, and presented the first attempt of future simulation in the area. Average annual conversions (AAC) (ha) calculated to assess magnitudes of annual changes in six different periods. AACs used to calculate area demands for LULC2030 and LULC2050, whereby annual conversions from different periods were multiplied by number of years between 2017, 2030 and 2050 for each scenario. Finally, optimistic and pessimistic scenarios for agricultural lands are simulated using FLUS model. Accordingly, agricultural lands decreased from 53.9% to 31.4% by 22.5% in 33 years, and predicted to change between 19.50% and 24.63% for 2030, 1.07% and 14.10% for 2050, based on pessimistic and optimistic scenarios, respectively.

Natural disasters, especially earthquakes, known to be the most devastating process that threating human life, ecosystems, and land properties including land use and land cover (LULC). Understanding of such changes may help for rehabilitation processes, as well as presentation of baseline to develop management strategies for further steps. Remote sensing technologies have long been used for determination of change directions and magnitudes after earthquakes while development in cloud-based platforms provided users to avoid issues in storage and processing costs, effectively. In present study, it was aimed to determine LULC changes occurred around Antakya city of Hatay after February 06, 2023 and February 20, 2023 earthquakes, which caused serious losses. Moreover changes within 5 km zone from central coordinates were also investigated by considering individual subzones with 1 km width. One of the most widely used machine learning algorithm, random forest (RF), was used classify Sentinel-2 imageries via Google Earth Engine (GEE) platform. Accuracy assessment procedures were implemented to determine reliabilities of LULC2022 and LULC2023, and accuracies were found over 0.85. Investigation of overall changes have revealed that areas of forest (F) and cultivated fields (CF) were considerably decreased while concrete (C), natural vegetation (N) and water (W) areas have increased. Dispersal of collapse buildings resulted in increase of C class not only at city level, but also within each subzone of 5 km buffer zone. Classification of Sentinel-2 imageries through RF algorithm in GEE provided rapid and reliable results for determining changes in Antakya, whereby periodically monitoring of further changes strongly suggested.

Rice is known to be one of the most essential crops in Turkey, as well as many other countries especially in Asia, whereas paddy rice cropping systems have a key role in many processes ranging from human nutrition to environment-related perspectives. Therefore, determination of cultivation area is still a hot topic among researchers from various disciplines, planners, and decision makers. In present study, it was aimed to evaluate performances of three classifications algorithms among most widely used ones, namely, maximum likelihood (ML), random forest (RF), and k-nearest neighborhood (KNN), for paddy rice mapping in a mixed cultivation area located in Biga District of Çanakkale Province, Turkey. Visual, near-infrared and shortwave infrared bands of Landsat 9 acquired in dry season of 2022 year was utilized. The classification scheme included six classes as dense vegetation (D), sparse vegetation (S), agricultural field (A), water surface (W), residential area – base soil (RB), and paddy rice (PR). The performances were tested using the same training samples and accuracy control points. The reliability of each classification was evaluated through accuracy assessments considering 150 equalized randomized control points. Accordingly, RF algorithym could identify PR areas with over 96.0% accuracy, and it was followed by KNN with 92.0%.

Land use and land cover (LULC) classification is known to be one of the most widely used indicators of environmental change and degradation all over the world. There are various algorithms and methods for LULC classification, whereby reliability of the classification maps presents the principal concern. The study focused on evaluation of accuracies of LULC maps produced from original bands of Sentinel-2 imageries together with Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Principal Component Analysis (PCA) using Google Earth Engine (GEE) platform to identify best enhancing method for agricultural land classification. Moreover, short-term LULC changes aimed to be identified in the specified area. To achieve the aims, all available imageries acquired in the same month of different years with less than 10% cloud contamination were used to compose averaged images for May 2018 and May 2022 for generating LULC2018 and LULC2022 maps. The area has separated into seven main classes, namely, olive (O), perennial cultivation (P), non-perennial cultivation (NP), forest (F), natural vegetation (N), settled area-bare land (S), and water surface (W) via random forest algorithym. Reliabilities of LULC maps were evaluated through accuracy assessment procedures considering stratified randomized control points. Transitions between each LULC classes were identified.

The study presents the first attempt of determination and mapping of recent biogas potentials (BP) at different scales from province to village level in Çanakkale using Geographic Information Systems (GIS). The BP of different scales was calculated based on animal waste amounts from bovine, ovine and poultry farming. The study area covers the ten districts of Çanakkale province with the exception of Imbros and Tenedos Islands. The inventory records of different animal types were obtained from of Republic of Turkey Ministry of Agriculture and Forestry Çanakkale Directorate of Provincial Agriculture and Forestry. GIS procedures are conducted in ArcGIS (10.3) software. Findings revealed that the annual biogas production potential of the whole province is almost 6.4×107 m3. Biga district seemed to include 39 % of overall BP whereas Eceabat district presented a slight percentage of the potential production with the value of approximately 1 %. Moreover, the highest and lowest subscale-level potentials have found in Yukarıdemirci (Biga) and Bahçedere (Ayvacık), with approximately 154×104 m3 and 137 m3 BP, respectively. The overall BP of the province have concluded to be promising, and present study believed to serve as a baseline for future studies related to determination of new biogas plant suitable lands.

The Present study focused on investigation of Land Use Land Cover (LULC) changes within three basins located in North-West part of Anatolia region, Turkey. The study area consisted of six provinces with 41 districts. Visible, near-infrared and shortwave-infrared bands of Landsat imageries, acquired in production seasons of 1984, 1999, and 2014, served as main data source. The area is covered by six Landsat scenes. Supervised classification maximum likelihood algorithm was adopted whereas LULC classes were considered as forest (F), grazing-other vegetation (G-O), agriculture (A), water surface (W), and residential area-bare soil (R-B). Prior to the classification process, scenes were subset according to district boundaries, and district-level images were classified to improve the classification precision, and then mosaicked to obtain LULC1984, LULC1999 and LULC2014 maps. The reliabilities of maps were evaluated by accuracy assessments using 900 stratified randomized control points with minimum of 90 points per class. LULC changes were evaluated in two terms; changes within study area (WSA) (ha, %) and changes within class area (WCA) (%). Moreover, distributions of class areas along three basins were presented. Finally, past statuses of each class were compared, particularly, impacts of urbanization on agriculture were highlighted, and population change patterns were evaluated as main underlying reason.

Land cover is one of the key terrestrial variables used for monitoring and as input for modelling in support of achieving the United Nations Strategical Development Goals. Global and Continental Land Cover Products (GCLCs) aim to provide the required harmonized information background across areas; thus, they are not being limited by national or other administrative nomenclature boundaries and their production approaches. Moreover, their increased spatial resolution, and consequently their local relevance, is of high importance for users at a local scale. During the last decade, several GCLCs were developed, including the Global Historical Land-Cover Change Land-Use Conversions (GLC), the Globeland-30 (GLOB), Corine-2012 (CLC) and GMES/ Copernicus Initial Operation High Resolution Layers (GIOS). Accuracy assessment is of high importance for product credibility towards incorporation into decision chains and implementation procedures, especially at local scales. The present study builds on the collaboration of scientists participating in the Global Observations of Forest Cover—Global Observations of Land Cover Dynamics (GOFC-GOLD), South Central and Eastern European Regional Information Network (SCERIN). The main objective is to quantitatively evaluate the accuracy of commonly used GCLCs at selected representative study areas in the SCERIN geographic area, which is characterized by extreme diversity of landscapes and environmental conditions, heavily affected by anthropogenic impacts with similar major socio-economic drivers. The employed validation strategy for evaluating and comparing the different products is detailed, representative results for the selected areas from nine SCERIN countries are presented, the specific regional differences are identified and their underlying causes are discussed. In general, the four GCLCs products achieved relatively high overall accuracy rates: 74–98% for GLC (mean: 93.8%), 79–92% for GLOB (mean: 90.6%), 74–91% for CLC (mean: 89%) and 72–98% for GIOS (mean: 91.6%), for all selected areas. In most cases, the CLC product has the lower scores, while the GLC has the highest, closely followed by GIOS and GLOB. The study revealed overall high credibility and validity of the GCLCs products at local scale, a result, which shows expected benefit even for local/regional applications. Identified class dependent specificities in different landscape types can guide the local users for their reasonable usage in local studies. Valuable information is generated for advancing the goals of the international GOFC-GOLD program and aligns well with the agenda of the NASA Land-Cover/Land-Use Change Program to improve the quality and consistency of space-derived higher-level products.

Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.