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IntroductionMonitoring LULC changes is crucial for developing strategies for natural resource management, assessing the current potential of a region, and addressing global environmental issues. In this context, this study examines land use and land cover (LULC) changes in forest and non-forest areas of Anamur district, located in the Mediterranean Region of Türkiye, between 2000 and 2020.MethodsUsing the intensity analysis method, which offers a detailed and efficient approach to understanding LULC changes, the study analyzes transitions at interval, category, and transition levels. LULC maps were generated through supervised classification of Landsat satellite images, focusing on seven classes: Coniferous, Broad-Leaved, Mixed, Treeless Gap, Settlement, Agriculture, and Water. The analysis evaluated changes within and between these categories, interpreting the results through graphical outputs. The driving forces behind these changes were also explored, and their underlying causes were discussed.Results and DiscussionResults at the interval level revealed that the most significant changes occurred during the 2000-2010 period. At the category level, the Coniferous category exhibited the highest degree of change in both intervals. During 2000-2010, Coniferous gains predominantly replaced non-forest areas (Agriculture, Settlement, and Water), while this pattern was less evident in 2010-2020. In contrast, Treeless Gap gains primarily replaced Coniferous areas during 2010-2020, while no significant losses in Treeless Gap were targeted by other categories. Broad-Leaved species were found to heavily target Water losses, likely due to their higher water demands compared to Coniferous species, as supported by prior studies. This research highlights the advantages of intensity analysis in LULC studies, offering insights into spatial changes and their intensity across categories. It aims to promote its adoption and underscores the importance of targeted conservation and land management strategies to mitigate the impacts of forest loss, land use changes, and water resource pressures.
IntroductionMonitoring LULC changes is crucial for developing strategies for natural resource management, assessing the current potential of a region, and addressing global environmental issues. In this context, this study examines land use and land cover (LULC) changes in forest and non-forest areas of Anamur district, located in the Mediterranean Region of Türkiye, between 2000 and 2020.MethodsUsing the intensity analysis method, which offers a detailed and efficient approach to understanding LULC changes, the study analyzes transitions at interval, category, and transition levels. LULC maps were generated through supervised classification of Landsat satellite images, focusing on seven classes: Coniferous, Broad-Leaved, Mixed, Treeless Gap, Settlement, Agriculture, and Water. The analysis evaluated changes within and between these categories, interpreting the results through graphical outputs. The driving forces behind these changes were also explored, and their underlying causes were discussed.Results and DiscussionResults at the interval level revealed that the most significant changes occurred during the 2000-2010 period. At the category level, the Coniferous category exhibited the highest degree of change in both intervals. During 2000-2010, Coniferous gains predominantly replaced non-forest areas (Agriculture, Settlement, and Water), while this pattern was less evident in 2010-2020. In contrast, Treeless Gap gains primarily replaced Coniferous areas during 2010-2020, while no significant losses in Treeless Gap were targeted by other categories. Broad-Leaved species were found to heavily target Water losses, likely due to their higher water demands compared to Coniferous species, as supported by prior studies. This research highlights the advantages of intensity analysis in LULC studies, offering insights into spatial changes and their intensity across categories. It aims to promote its adoption and underscores the importance of targeted conservation and land management strategies to mitigate the impacts of forest loss, land use changes, and water resource pressures.
Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns’ evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract the spatial distribution data of major grain crops in northeast China for the year 2022. Using crop classification data from 2000 to 2022, the spatial overlay analysis method identified cropping pattern types based on spatial and temporal changes. The primary cropping patterns identified were continuous maize cropping, maize–soybean rotation, mixed cropping, and continuous soybean cropping. Simultaneously, this research constructed three distinct crop periods: Period I (2000–2002), Period II (2010–2012), and Period III (2020–2022). Over three periods, these patterns covered 94.73%, 88.76%, and 86.39% of the area, respectively. The evolution of the dominant cropping pattern from Period I to Period II involved the transition from continuous soybean cropping to continuous maize cropping, while from Period II to Period III, the main shift was from continuous maize cropping to maize–soybean mixed cropping. From a spatial perspective, since Period I, maize has increasingly replaced soybean as the dominant crop, with continuous maize cropping expanding northward and continuous soybean cropping contracting. The maize–soybean rotation area also migrated northward, particularly in the core area of the Songnen Plain, evolving mostly into continuous maize cropping. Maize cropping areas exhibited significant regional characteristics, being densely distributed in the Sanjiang Plain and Liaohe Plain, and along major tributaries in northeast China. Consequently, the interplay of the natural environment, economic policies, and agricultural technologies drove these changes. The findings offer valuable insights for optimizing cropping patterns and developing rotation systems in northeast China.
The Aral Sea is an indispensable component of the socio-economic progress of Central Asia but has undergone substantial ecological transformations over the last few decades, primarily due to global warming and human activities. Among these changes, the basin area has decreased, and water levels have dropped. This paper focuses on a comprehensive analysis of the spatial variation of key climate parameters, such as temperature, precipitation, and potential evapotranspiration over the Aral Sea. Moreover, we examined the transformation of seasonal water areas in the Aral Sea during the growing and non-growing seasons between 2002 and 2017 and the influence of climate and human factors on these changes using Landsat satellite data. Our results indicate that the western section of the Aral Sea has experienced a reduction in water area by 2.41 km2 and 1.83 km2 during the warm (R2 = 0.789) and cold (R2 = 0.744) seasons, respectively, over the investigated period. The decrease in lake water volume during the warm season can be attributed to local climate variations, as a strong negative correlation exists between seasonal water storage change and temperature (potential evapotranspiration). The correlation analysis shows that the water change in the northern part of the Aral Sea during the growing season has a significant positive correlation with temperature (R = 0.52) and an insignificant negative correlation with precipitation (R = −0.22). On the contrary, in the west and east parts of the Aral Sea, there is a significant negative correlation with temperature (R = −0.71 and −0.62) and a high positive correlation with precipitation (R = 0.71 and 0.55) during the growing season.
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