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Olives are a crucial economic crop in Mediterranean countries. Detailed spatial information on the distribution and condition of crops at regional and national scales is essential to ensure the continuity of crop quality and yield efficiency. However, most earlier studies on olive tree mapping focused mainly on small parcels using single-sensor, very high resolution (VHR) data, which is time-consuming, expensive and cannot feasibly be scaled up to a larger area. Therefore, we evaluated the performance of Sentinel-1 and Sentinel-2 data fusion for the regional mapping of olive trees for the first time, using the Izmir Province of Türkiye, an ancient olive-growing region, as a case study. Three different monthly composite images reflecting the different phenological stages of olive trees were selected to separate olive trees from other land cover types. Seven land-cover classes, including olives, were mapped separately using a random forest classifier for each year between 2017 and 2021. The results were assessed using the k-fold cross-validation method, and the final olive tree map of Izmir was produced by combining the olive tree distribution over two consecutive years. District-level areas covered by olive trees were calculated and validated using official statistics from the Turkish Statistical Institute (TUIK). The K-fold cross-validation accuracy varied from 94% to 95% between 2017 and 2021, and the final olive map achieved 98% overall accuracy with 93% producer accuracy for the olive class. The district-level olive area was strongly related to the TUIK statistics (R2 = 0.60, NRMSE = 0.64). This study used Sentinel data and Google Earth Engine (GEE) to produce a regional-scale olive distribution map that can be scaled up to the entire country and replicated elsewhere. This map can, therefore, be used as a foundation for other scientific studies on olive trees, particularly for the development of effective management practices. [ABSTRACT FROM AUTHOR]

This research aims to find crop-based residue burning (CRB) amounts for the Southeastern Anatolia Region of Türkiye, where versatile and diverse of agricultural fields are available. We used the time series of Sentinel-2 images to identify spatial distribution of different crop types and to determine the burned agricultural fields in 2019 using Google Earth Engine platform. We implemented Intergovernmental Panel on Climate Change standards to calculate the emissions from Greenhouse gases and Particulate Matter. We also analyzed Sentinel-5P images to generate Nitrogen Dioxide maps and compared them with crop-based emission calculations. Our analysis illustrated that 14,444.307 Gg of Greenhouse Gases and 117.809 Gg of Particulate Matters were released in 2019 due to CRB practices. Our results can potentially improve the national statistics by providing spatio-temporal field-based CRB emissions and supporting country-level agricultural decision-making processes. Researchers could implement our proposed data and methodology in other regions suffering from the CRB problem. [ABSTRACT FROM AUTHOR]

Determination of the spatio-temporal distribution of Land use and Land cover (LU/LC) is important to understand the dynamics of urbanization, agricultural abandonment, and industrialization. This study aims to create multi-temporal high-resolution LU/LC maps and analyze thematically extensive LU/LC changes using Geographic Object-Based Image Analysis (GEOBIA) and Landscape Metrics for the selected study region in the Istanbul metropolitan city of Turkey. HR SPOT 6/7 images acquired in 2009, 2013, and 2019 were used as main Earth Observation data to create LU/LC maps. Open-source geospatial data were also integrated into classification to better identify some LU/LC classes to increase total classification accuracy. Overall classification accuracy of 2009, 2013, and 2019 dated LU/LC maps are 87.45%, 88.16%, 90.74% respectively. Principal Component Analysis (PCA) and Pearson correlation were used to selecting the landscape metrics and evaluate the results. PCA resulted in three principal components and the total variance was found as 87.3%. [ABSTRACT FROM AUTHOR]

In this research, multicriteria decision analysis with pairwise comparison weighting method was utilized to determine the suitable locations for vineyard plantation in Sarkoy region of Turkey. Soil maps, meteorological measurements, slope, aspect and elevation maps were used as input to conduct spatial analysis. Different methods were compared and pairwise comparison method was identified as the most appropriate method of weighting for this spatial analysis. Current vineyard areas were determined using Worldview-2 imagery and their spatial distribution compared with the resulting suitability map to determine the current suitability. Comparisons showed current vineyards were mostly established in locations where suitability map expresses low capability. Further inspection unveiled that, these low capability lands are closer to the transportation networks and city/county centres that tend to be in sea level elevations as opposed to vine grapes thriving in higher altitudes. Results also enabled providing suggestions on alternative sites for new vineyard plantation. [ABSTRACT FROM AUTHOR]

Due to its negative impacts on land productivity and plant growth, soil salinization is a significant problem particularly in arid and semi-arid regions of the world. Among the natural factors of soil salinization, parent materials including high amount of salts and minerals in soil structure, known as primary salinization, can be addressed. Conventional irrigation techniques and poor drainage systems are predominant human-induced activities that result in secondary salinization. Countries with especially dryland environment, face difficulty in providing adequate food for their rapidly increasing population since each year lots of cultivated land are abandoned due to adverse effects of primary and secondary soil salinization. Therefore, monitoring, mapping and predicting soil salinization is of utmost importance regarding lessening and/or preventing further increase in soil salinity through some protective measures. The subject of concern becomes even more pronounced particularly in agricultural lands as is the case in the vicinity of Tuz (Salt) Lake Region in Turkey. This research focuses mainly on multi-temporal monitoring of Tuz Lake Region in order to track changes in areas of salty spots in years 1990, 2002, 2006, 2011 and 2015. A total number of 25 Landsat-5 TM and Landsat-8 images obtained between 1990 and 2015 were analysed in this study. Field electrical conductivity (EC) measurements for 322 soil samples in year 2002 were checked; and 28 of these samples were selected for generating salinity maps representing areas in the vicinity of the lake. All satellite images were radiometrically and atmospherically corrected prior to classification. Following the pre-processing step, five soil salinity indices were applied on all satellite images. 28 soil samples were then overlaid on images in order to extract the exact index values related to soil samples and regression approach was used to relate satellite image-derived salinity indices and field measurements. Linear and exponential regression analyses were conducted separately as the next step for all indices based on data gathered in 2002. Salinity index (SI) 1 = B 1 * B 3 showed the best result with R 2 value of 0.93 and 0.83 for exponential and linear regression analysis, respectively. Salinity maps for years 1990, 2006, 2011 and 2015 were further produced utilizing the exponential and linear regression expressions attained for year 2002. Besides, this study detected land cover changes in the area from year 2000–2006, and from 2006 to 2012 by using CORINE land cover data to analyse the possible relationships between land cover change and salinity changes. [ABSTRACT FROM AUTHOR]

On 30 May 2013, a forest fire occurred in Izmir, Turkey causing damage to both forest and fruit trees within the region. In this research, pre- and post-fire SPOT-6 images obtained on 30 April 2013 and 31 May 2013 were used to identify the extent of forest fire within the region. SPOT-6 images of the study region were orthorectified and classified using pixel and object-based classification (OBC) algorithms to accurately delineate the boundaries of burned areas. The present results show that for OBC using only normalized difference vegetation index (NDVI) thresholds is not sufficient enough to map the burn scars; however, creating a new and simple rule set that included mean brightness values of near infrared and red channels in addition to mean NDVI values of segments considerably improved the accuracy of classification. According to the accuracy assessment results, the burned area was mapped with a 0.9322 kappa value in OBC, while a 0.7433 kappa value was observed in pixel-based classification. Lastly, classification results were integrated with the forest management map to determine the effected forest types after the fire to be used by the National Forest Directorate for their operational activities to effectively manage the fire, response and recovery processes. [ABSTRACT FROM PUBLISHER]

The article presents a study which analyzed the land cover data's effects on simulations of regional climate. The study introduced a precise and up-to-date land cover data for the Marmara Region in Turkey which was gained from Landsat Enhanced Thematic Mapper (ETM+) images into the model of Weather Research and Forecasting (WRF). Results show that more exact regional temperature simulations are produced by the updated land cover dataset, suggesting how important the precise land cover data is.

A devastating earthquake with a magnitude of Mw 7.4 occurred on the North Anatolian Fault Zone (NAFZ) of Turkey on August 17, 1999 at 00:01:39 UTC (3:01 a.m. local time). The aim of this study is to propose a new approach to automatically identify earthquake induced damage areas which can provide valuable information to support emergency response and recovery assessment procedures. This research was conducted in the Adapazari inner city, covering a 3 × 3 km area, where 11,373 buildings collapsed as a result of the earthquake. SPOT high resolution visible infrared (HRVIR) Pan images obtained before (25 June 1999) and after (4 October 1999) the earthquake were used in the study. Five steps were employed to conduct the research and these are: (i) geometric and radiometric correction of satellite images, (ii) Fast Fourier Transform (FFT) of pre- and post-earthquake images and filtering the images in frequency domain, (iii) generating difference image using Inverse Fast Fourier Transform (IFFT) pre- and post- earthquake images, (iv) application of level slicing to difference image to identify the earthquake-induced damages, (v) accuracy assessment of the method using ground truth obtained from a 1/5,000 scale damage map. The total accuracy obtained in the research is 80.19 %, illustrating that the proposed method can be successfully used to automatically identify earthquake-induced damage areas. [ABSTRACT FROM AUTHOR]

In this study, an integrated approach of remote sensing and Geographic Information Systems (GIS) was used to investigate the spatial and temporal characteristics of the Kizilirmak Delta. It is a major wetland complex on the Black Sea coast, which has a very rich biodiversity and critical habitat for globally endangered bird species, and was announced as an RAMSAR protection site in Turkey. Main objectives of this study were: (1) determination of the land use/cover changes, (2) examination of the effects of these changes on environment, and (3) assessment of the human impacts on the environment. 1987 Landsat TM and 2004 Aster images, topographic maps, field survey, and other ancillary data have been used to perform the study objectives. The obtained results delineated that the Kizilirmak Delta has been considerably changed as a result of human activities between 1987 and 2004. Analysis of satellite images and other data sources in the GIS environment introduced the changes in the delta and their impacts on the environment. [ABSTRACT FROM AUTHOR]

Scanned historical maps are available from different sources in various scales and contents. Automatic geographical feature extraction from these historical maps is an essential task to derive valuable spatial information on the characteristics and distribution of transportation infrastructures and settlements and to conduct quantitative and geometrical analysis. In this research, we used the Deutsche Heereskarte 1:200,000 Türkei (DHK 200 Turkey) maps as the base geoinformation source to construct the past transportation networks using the deep learning approach. Five different road types were digitized and labeled to be used as inputs for the proposed deep learning-based segmentation approach. We adapted U-Net++ and ResneXt50_32×4d architectures to produce multi-class segmentation masks and perform feature extraction to determine various road types accurately. We achieved remarkable results, with 98.73% overall accuracy, 41.99% intersection of union, and 46.61% F1 score values. The proposed method can be implemented in DHK maps of different countries to automatically extract different road types and used for transfer learning of different historical maps. [ABSTRACT FROM AUTHOR]

Highlights: [•] We estimate crop cover fraction in cultivated areas with digital camera pictures. [•] We develop a light use efficiency based model for yield estimation. [•] We include meteorological yield limiting factors in model calculation. [•] We derive relational equations between crop cover and spectral vegetation indexes. [•] We reach 5% estimation error in test parcels and 10% in region based analysis. [Copyright &y& Elsevier]

Land surface of the Earth has been changing as a result of human induced activities and natural processes. Accurate representation of landscape characteristics and precise determination of spatio-temporal changes provide valuable inputs for environmental models, landscape and urban planning, and historical land cover change analysis. This study aims to determine historical land use and land cover (LULC) changes using multi-modal geospatial data, which are the cadastral maps produced in 1858, monochrome aerial photographs obtained in 1955, and multi-spectral WorldView-3 satellite images of 2020. We investigated two pilot regions, Aksu and Kestel towns in Bursa/Turkey, to analyze the long-term LULC changes quantitatively and to understand the driving forces that caused the changes. We propose methods to facilitate the preparation of historical datasets for the LULC change detection and present an object-oriented joint classification scheme for multi-source datasets to accurately map the spatio-temporal changes. Our approach minimized the amount of manual digitizing required for the boundary delineation of LULC classes from historical geospatial data. Also, our quantitative analysis of LULC maps indicates diverging developments for the selected locations in the long period of 162 years. We observed rural depopulation and gradual afforestation in Aksu; whereas, agricultural land abandonment and deforestation in Kestel., (© 2022. The Author(s).)