Land use and cover change (LUCC) and migration in Sukoharjo, Indonesia

Purpose This study aims to provide descriptive analysis on urbanization pattern in Sukoharjo Regency and also its correlation to land use and cover change (LUCC) issue. This becomes more relevant because the authors find that there are few studies conducted regarding the topic. Consider again the importance role of Sukoharjo Regency as an area that provides supporting food supply, LUCC particularly in agriculture land became even more crucial. Design/methodology/approach This study uses literature reviews and descriptive analysis. Data for this study are obtained from previous studies and statistical data from Central Statistical Bureau. Findings Sukoharjo Regency proved as an interesting migration destination for some group of population. The population group particularly come from Surakarta city (growth core) as a form of urban sprawl or urban expansion. Migrant population live in Sukoharjo Regency are mostly a population group with a productive age range and generally with high-school or vocational school level. Moreover, their main reason to migrate is the availability of job opportunities. Regarding LUCC, migrant populations that come to Sukoharjo Regency apparently have property with ownership certification. Housing area development, particularly concentrated in the district, was located close to the borderline Surakarta city. Originality/value This research contributes to the analysis of land use change trends in peri-urban areas caused by migration. The results of this study can be used for further policy making to overcome the dilemma of land use change, especially those that occur on agricultural land.

Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning

Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural resources within the Okavango Basin is undoubtedly a complex issue. It is often difficult to accurately map land use and cover using remote sensing in heterogeneous landscapes. This study investigates the combined value of climate-based regionalization and integration of spectral bands with spectral indices to enhance the accuracy of multi-temporal land use/cover classification using deep learning and machine learning approaches. Two experiments were set up, the first entailing the integration of spectral bands with spectral indices and the second involving the combined integration of spectral indices and climate-based regionalization based on Koppen–Geiger climate zones. Landsat 5 TM and Landsat 8 OLI images, machine learning classifiers (random forest and extreme gradient boosting), and deep learning (neural network and deep neural network) classifiers were used in this study. Supervised classification using a total of 5140 samples was conducted for the years 1996, 2004, 2013, and 2020. Average overall accuracy and Kappa coefficients were used to validate the results. The study found that the integration of spectral bands with indices improves the accuracy of land use/cover classification using machine learning and deep learning. Post-feature selection combinations yield higher accuracies in comparison to combinations of bands and indices. A combined integration of spectral indices with bands and climate-based regionalization did not significantly improve the accuracy of land use/cover classification consistently for all the classifiers (p < 0.05). However, post-feature selection combinations and climate-based regionalization significantly improved the accuracy for all classifiers investigated in this study. Findings of this study will improve the reliability of land use/cover monitoring in complex heterogeneous TDBs.

Temporal analysis of land use and cover in an urban watershed in the city of Londrina / PR

This study analyzed the changes in land use and cover of Ribeirão Cambé watershed (Londrina /PR), between 1975 and 2015, and evaluated how these changes impact on the runoff volume. For the classification of soil use and cover were used satellite images from the Landsat series (1-MMS, 5-TM and 8-OLI), which were acquired for free from the INPE/DGI website. The classification was made by the SPRING program, it was used to establish four themes of soil use and cover: urban, dense vegetation, underbrush and exposed soil. The CN value was obtained from the CN tables of SCS for urban and suburban basins. Morphological characterization of Ribeirão Cambé basin indicates low probability of flooding. Using satellite images, it was possible to affirm that significant changes happened in the soil use and cover of this basin, having grown 150% in 40 years, with the highest growth rates occurring in the first analyzed decades, 42%, 33%, 18% and 11%, respectively. Thus, the conclusion is that changes in soil use and cover in river basins reflect on the runoff, evidentiating the need of discussion about urban planning and flood control.

Land use and cover change in Northeast China and its impacts on the Xing'an permafrost in 1980-2020

Vegetation plays important roles in the development and protection of permafrost; it is one of the main local and ecosystemic factors that affect the thermal stability of the underlying soil strata. Multi-period land use and cover change (LUCC) data and long-time series of air temperature were chosed. Based on these data, spatiotemporal changes in mean annual air temperature (MAAT) were simulated by the Ordinary Least Squares (OLS) method and Ordinary Kriging (OK) model in the 1980s-2010s in Northeast China. The influences of LUCC on MAAT in Northeast China and distribution of the Xing’an permafrost were analyzed and the results showed that: (1) Decadal average of MAAT increased from 4.60oC (1980s) to 5.38oC (2010s) in Northeast China, with an upward trend of 0.25oC/10a. (2) During the 1980s to 2010s, the total permafrost area showed a decreasing trend (3.668×104 km2/10a). (3) In permafrost regions, LUCC had undergone significant structural changes: forested land showed a consistent decreasing trend and other lands showed an overall increasing trend. (4) The effects of different LUCC on MAAT in the permafrost region varied substantially. The mean MAAT of forested land was the lowest (2.33oC), and; that of unused land, the highest (0.37oC). The change rate in MAAT of cultivated land was the highest (0.37oC/10a), and; that of unused land, the lowest (0.28oC/10a). (5) The degradation rates of permafrost in forested land (1.822×104 km2/10a) and grassland (1.397×104 km2/10a) were the largest from 1980s to 2010s.

Impacts of land use and land cover change and reforestation on summer rainfall in the Yangtze River basin

Land use and cover have been significantly changed all around the world during the last decade. In particular, the Grain for Green (GG) program has resulted in significant changes in regional land use and cover, especially in China. Land use and cover change (LULCC) may lead to changes in regional climate. In this study, we take the Yangtze River basin as a case study and analyze the impacts of LULCC and reforestation on summer rainfall amounts and extremes based on the Weather Research and Forecasting model. Firstly, two observed land use and cover scenarios (1990 and 2010) were chosen to investigate the impacts of LULCC on summer rainfall during the last decade. Secondly, two hypothetical reforestation scenarios (i.e., scenarios of 20 % and 50 % cropland changed to forest) were taken based on the control year of 2010 to test the sensitivity of summer rainfall (amounts and extremes) to reforestation. The results showed that average summer rainfall and extreme summer daily rainfall decreased in the Yangtze River basin between 1990 and 2010 due to LULCC. Reforestation could increase summer rainfall amount and extremes, and the effects were more pronounced in populated areas than over the whole basin. Moreover, the effects of reforestation were influenced by the reforestation proportion. In addition, the summer rainfall increased less conversely, with the transform proportion of cropland to forest increased from 20 % to 50 %. By analyzing the changes in water vapor mixing ratio, upward moisture flux, and 10 m wind, it is suggested that this result might be caused by the horizontal transportation processes of moisture. Although a comprehensive assessment of the impacts of LULCC on summer rainfall amounts and extremes was conducted, further studies are needed to investigate the uncertainty better.