Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA

Land use and land cover (LULC) changes through built-up area expansion always increases linearly with land demand as a consequence of population growth and urbanization. Cirebon City is a center for Ciayumajakuning Region that continues to grow and exceeds its administrative boundaries. This phenomenon has led to peri-urban regions which show urban and rural interactions. This study aims to analyze (1) the dynamics of LULC changes using cellular automata (CA), artificial neural network (ANN), and ANN-CA; (2) the influential factors (drivers); and (3) change probability in the period 2030 and 2045 for Cirebon’s peri-urban. We used logistic regression as quantitative approach to analyze the interaction of drivers and LULC changes. The LULC data derived from Landsat series satellite imagery in 1999-2009 and 2009-2019, validation of dynamic spatial model refers to 100 LULC samples. This research shows that LULC changes are dominated by built-up area expansion which causes plantations and agricultural land to decrease. The drivers have a simultaneous effect on LULC changes with r-square of 0.43, where land slope, distance from existing built-up area, distance from CBD, and accessibility are significant triggers. LULC simulation of CA algorithm is the best model than ANN and ANN-CA based on overall accuracy and overall accuracy (0.96, 0.75, 0.73 and 0.95, 0.66, 0.66 respectively), it reveals urban sprawl through the ribbon and compact development. The average probability of built-up area expansion is 0.18 (2030) and 0.19 (2045). If there is no intervention in spatial planning, this phenomenon will decrease productive agricultural lands in Cirebon's peri-urban

Assessing the impact of villagization program on land use land cover dynamics in Benishangul-Gumuz, Western Ethiopia

Planning for continuing natural resource management requires current information on the dynamics of land use and land cover. The aim of this paper was to analyze the impacts of the villagization program on land use land cover dynamics in Benishangul-Gumuz region, western Ethiopia. The study has employed a mixed-method research design using both primary and secondary sources. Multispectral LANDSAT satellite images with a 30 m resolution were acquired for land use land cover change detection between the years 1999, 2009, and 2022. Arc GIS 10.8, QGIS 3.28, ERDAS Imagine 2014, and Microsoft Excel software were used for image classification, accuracy assessment, and change detection. Six different land use land cover types: forest land, shrub and grassland, cultivated land, residential, bare land, and water bodies were identified between 1999 and 2022. The trends indicated a dramatic decrease at the rate of 27.2 ha of forestland, 17.1 ha of shrub and grassland, and 4.6 ha of water bodies per year, while the share of cultivated land, residential, and bare land has expanded at an average rate of 34.3 ha, 11.7 ha, and 2.9 ha per year respectively between 1999 and 2022. The phenomenon was caused by added population pressure due to villagization program, which in turn triggered farmland expansion and deforestation. It is recommended that raising local community awareness, reforestation, practicing land use plans, and promoting successful livelihood diversification could help to alleviate the issue and reroute the course of events in order to achieve sustainable natural resource management

Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images

Mangroves are valuable contributors to coastal ecosystems, and remote sensing is an indispensable way to obtain knowledge of the dynamics of mangrove ecosystems. Due to the similar spectral features between mangroves and other land cover types, challenges are posed since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this paper, we propose a classification method named the multi-feature joint sparse algorithm (MF-SRU), in which spectral, topographic, and textural features are integrated as the decision-making features, and sparse representation of both center pixels and their eight neighborhood pixels is proposed to represent the spatial correlation of neighboring pixels, which can make good use of the spatial correlation of adjacent pixels. Experiments are performed on Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in Southeastern China, and the results show that the proposed method can effectively improve the extraction accuracy of mangroves