Remote Sensing for Sustainable Development: Multi-Temporal Landsat Analysis of Land-Use Change and Urbanization in the Rejoso Watershed (2005–2024)

Changes in built up area which are increasingly rapid consequence of the latest data requirements for urban planning. On the other side the process of expansion of built up area without control can impact the destruction of lands that have ecological functions so that they have an effect on the onset of social and environmental disasters. Of the many cities in Indonesia, the City of Tasikmalaya is one of the most significant cities that develops the built-up area. Effective technology that can be used to predict the development of built up area in the City of Tasikmalaya is to utilize multitemporal landsat imagery. This study aims are 1) Identify changes in built up area in Tasikmalaya City and its surroundings based on multitemporal landsat imagery, 2) Predict the development of built up area in Tasikmalaya City. The research method uses remote sensing technology, multitemporal recording landsat imagery in 2002 and 2019, through landuse change modeler modeling. The results of this study indicate an increase in built up area from 2002 to 2019, predicted results of the development land built up in 2034 produce a probability of expansion 0.229 with the direction development from northeast to southwest.

Model testing is a step that must be done before operational activities. This testing aimed to test rice growth phase models based on MODIS in Lombok using multitemporal LANDSAT imagery and field data. This study was carried out by the method of analysis and evaluation in several stages, these are: evaluation of accuracy by multitemporal Landsat 8 image analysis, then evaluation by using field data, and analysis of growth phase information to calculate model consistency. The accuracy of growth phase model was calculated using Confusion Matrix. The results of stage I analysis for phase of April 30 and July 19 showed the accuracy of the model is 58-59%, while the evaluation of stage II for phase of period July 19 with survey data indicated that the overall accuracy is 53%. However, the results of model consistency analysis show that the resulting phase of the smoothed MODIS imagery shows a consistent pattern as well as the EVI pattern of rice plants with an 86% accuracy, but not for pattern data without smoothing. This testing give conclusion is the model is good, but for operational MODIS input data must be smoothed first before index value extraction.

A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery

Predicting temperate conifer forest successional stage distributions with multitemporal Landsat Thematic Mapper imagery

This study assessed land cover (LC) changes in Kahramanmaraş (K.Maraş) and its environs by using multitemporal Landsat and ASTER imagery, respectively belong to 1989, 2000 and 2004. A priori defined nine land cover classes in the classification scheme were urban and built-up, forest, sparsely vegetated areas, grassland, vegetated stream beds, unvegetated stream beds, bare areas, crop fields, and water bodies. Individual classifications were employed using the combination of both unsupervised and supervised classification methods. Iterative Self Organizing Data Analysis (ISODATA) was used to reduce spectral variation in the scenes arising from complex pattern of crop fields. Maximum Likelihood classifier was used in the LC classification of the individual images. Image pairs of consecutive dates were compared by overlaying the thematic LC maps and cross-tabulating the LC statistics. Urbanization and expansion of agriculture were the major reasons for the dramatic LC conversions. The amount of conversion from crop fields to water occurred as large as 927.67 ha, accounting for 73% of the total land-to-water conversion. Conversions to agriculture have mainly been occurred from grasslands and sparsely vegetated areas as large as 1,314.95 and 1,325.84 ha, respectively. Urban coverage doubled in this period as a result of 1,443.45 ha of increase. Urban area increased in the second period from 2,920 to 3,526 ha. Conversions to agriculture occurred at high amounts. A total of 1,075.79 ha area changed from sparsely vegetated areas to crop fields. A landscape-level environmental monitoring scheme based on satellite remote sensing was proposed for effective environmental resource management.

This research is carried out for the land cover change detection in the Nakdong River basin before and after the 4 major rivers restoration project using the LiDAR DEM(Digital Elevation Model) and the multi-temporal Landsat imagery. Firstly the river basin polygon is generated by using the levee boundaries extracted from the LiDAR DEM, and the four river basin imagery are generated from the multi-temporal Landsat-5 TM(Thematic Mapper) and Landsat-8 OLI(Operational Land Imager) imagery by using the generated river basin polygon. Then the main land covers such as river, grass and bare soil are separately generated from the generated river basin imagery by using the image classification method, and the ratio of each land cover in the entire area is calculated. The calculated land cover changes show that the areas of grass and bare soil in the entire area have been significantly changed because of the seasonal change, while the area of the river has been significantly increased because of the increase of the water storage. This paper contributes to proposing an efficient methodology for the land cover change detection in the Nakdong River basin using the LiDAR DEM and the multi-temporal satellite imagery taken before and after the 4 major rivers restoration project.

This study investigates the influence of hydroclimatic variability on shoreline dynamics along two contrasting coastal systems in East Java, Indonesia, Surabaya’s engineered urban coast and Sidoarjo’s natural tidal-flat coast, during 2015–2025. Shorelines were extracted from multi-temporal Landsat imagery using NDWI, shoreline change rates were calculated with DSAS, and rainfall data from BMKG were correlated with mean End Point Rate (EPR) to assess hydroclimatic impacts on coastal morphodynamics. Results reveal contrasting shoreline trajectories driven by coastal typology. Surabaya exhibits semi-cyclic shoreline adjustments controlled by reclamation barriers and restricted sediment pathways, resulting in localized accretion despite declining rainfall. In contrast, Sidoarjo shows rainfall-responsive erosion–accretion cycles, where open tidal channels enable rapid sediment redistribution during high-rainfall periods. Rainfall–EPR correlations are strong at the local scale (r = 0.76 in Surabaya; r = 0.80 in Sidoarjo) but weak when aggregated (r ≈ 0.10). This indicates that integrating hydroclimatic indicators with geomorphic context does not increase overall correlation strength, but improves interpretation of typology-specific shoreline responses obscured in aggregated analyses. The NDWI–DSAS–rainfall workflow provides a scalable framework for monitoring shoreline dynamics in tropical monsoonal environments.

<strong>Dynamics and Evolution of the Coast of Probolinggo, East Java.</strong> The coastal plain of Probolinggo extends from Tongas Subdistrict in the western part to Kraksaan Subdistrict in the eastern part. The south side is the fertile rice field area, while the north side is the fish pond area. The phenomena of coastal changes in Probolinggo are strongly influenced by the materials supplied from Mount Bromo. This research was conducted in April–May 2012, February 2013, and August 2014 and aimed to gain insight into the coastal dynamics and evolution as well as changes and land cover convertion in Probolinggo. The analysis of coastal changes in Probolinggo from 1973 until 2013 was done using multitemporal landsat imageries, the measurement and drawing of coastal profile were carried out using shokiza geodetic instrument and SeaBird Electronic tide gauge. The results showed that in the western part, the accretion process is still ongoing, while in the eastern part, the accretion speed since 1989 has continued to decline and even some places experienced erosion. The new coastal plain formed by the accretion of marine processes is utilized by the residents as fish ponds, whereas accretion formed by fluvial processes is utilized as rice fields. <br /><br />

Information of paddy rice distribution is essential for food production and methane emission calculation. Phenology-based algorithms have been utilized in the mapping of paddy rice fields by identifying the unique flooding and seedling transplanting phases using multi-temporal moderate resolution (500 m to 1 km) images. In this study, we developed simple algorithms to identify paddy rice at a fine resolution at the regional scale using multi-temporal Landsat imagery. Sixteen Landsat images from 2010-2012 were used to generate the 30 m paddy rice map in the Sanjiang Plain, northeast China-one of the major paddy rice cultivation regions in China. Three vegetation indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI), were used to identify rice fields during the flooding/transplanting and ripening phases. The user and producer accuracies of paddy rice on the resultant Landsat-based paddy rice map were 90% and 94%, respectively. The Landsat-based paddy rice map was an improvement over the paddy rice layer on the National Land Cover Dataset, which was generated through visual interpretation and digitalization on the fine-resolution images. The agricultural census data substantially underreported paddy rice area, raising serious concern about its use for studies on food security.

With the dual pressures of accelerating urbanization and global climate warming, understanding the evolution and connectivity of cold island networks has become crucial for managing urban thermal risks. This study explores the spatiotemporal dynamics, driving mechanisms, and scenario-based projections of cold island networks in a rapidly urbanizing region of Southwest China. Using multi-temporal Landsat imagery (2000–2024), ecological resistance surface modeling, and least-cost path analysis, the study evaluated historical changes and simulated future scenarios for 2035 and 2050 under both Natural Development (ND) and Park City (PC) planning interventions. The findings reveal that: (1) Between 2000 and 2024, rapid urbanization significantly expanded high-temperature areas, fragmented cooling sources, and reshaped cold island connectivity into a hierarchical corridor network centered on a dominant ventilation axis; (2) Since 2019, ecological restoration measures have notably enhanced the structural cohesion and connectivity of cooling corridors, partially mitigating previous fragmentation; (3) Scenario simulations indicate that proactive ecological planning could reduce the extent of high-temperature zones by approximately 20% by 2050, demonstrating strong potential for mitigating future thermal risks. Overall, the results emphasize the necessity of incorporating continuous cold island corridors and connectivity principles into urban spatial planning to enhance climate resilience and support sustainable development.

As a flagship city of China’s reform and opening-up policy and the core engine of the Guangdong–Hong Kong–Macao Greater Bay Area, Shenzhen faces dual challenges of economic development and ecological conservation during its rapid urbanization. This study systematically investigates the relationship between urbanization and ecological quality in this high-density megacity over the past three decades (1990–2020) using multi-temporal Landsat imagery, incorporating an enhanced Remote Sensing Ecological Index (RSEI), impervious surface extraction techniques, and a Coupling Coordination Degree (CCD) model. Key findings include: (1) Impervious surfaces expanded from 458.15 km2 to 709.23 km2, showing a tri-phase pattern of rapid expansion, steady infill, and slight contraction, with an annual growth rate of 1.47%; (2) Ecological quality exhibited a “decline-recovery” trajectory, with RSEI values decreasing from 0.477 (1990) to 0.429 (2000) before rebounding to 0.491 (2020), demonstrating phased ecological fluctuations and restoration; (3) The CCD between urbanization and ecological environment improved significantly from “marginal coordination” (0.548) to “primary coordination” (0.636), forming a distinct “west-high-east-low” spatial pattern with significant clustering effects. This study reveals a novel three-dimensional synergistic pathway (“industrial upgrading-spatial optimization-ecological restoration”) for sustainable development in megacities, establishing the “Shenzhen Paradigm” for ecological governance in rapidly urbanizing regions worldwide.

Rapid urbanization and industrialization drive profound land use and land cover (LULC) transformations across India, placing unprecedented pressure on groundwater resources. This study presents a two-decade (2003–2023) spatio-temporal assessment of LULC dynamics and groundwater quality in the industrialized Muvattupuzha River Basin, Kerala. Multi-temporal Landsat imagery was classified using the Support Vector Machine (SVM) algorithm, achieving high classification performance (overall accuracy 89%, Kappa 0.86). Results reveal a striking 32.09% expansion of built-up areas, accompanied by a 17.91% decline in forest cover and a 4% reduction in agricultural land, reflecting accelerated urban sprawl and landscape conversion. The Entropy-based Groundwater Quality Index (EGWQI) exhibited a strong inverse relationship with built-up areas (r = − 0.91) and a highly positive association with forests and water bodies (r ≥ 0.98), underscoring the buffering role of natural ecosystems. Although 86.7% of wells remain in the ‘Excellent’ category, persistent contamination hotspots were identified near industrial and agricultural clusters, with risks amplified during monsoonal runoff events. Proximity and correlation analyses confirmed that industrial zones and quarries are critical drivers of localized groundwater degradation. These findings highlight the urgent need for integrated land–water governance, implementation of green infrastructure, and strict effluent management protocols to mitigate anthropogenic impacts and safeguard long-term groundwater sustainability in rapidly urbanizing tropical basins.

Rapid urbanisation is a major driver of land use and land cover (LULC) transformation in urban lake catchments, often threatening the long-term sustainability of lake ecosystems. This study aimed to quantify and analyse long-term spatio-temporal LULC dynamics within the Shahpura Lake catchment, Bhopal, India, over 40 years (1980–2021). The lake catchment was delineated using a DEM-based hydrological approach derived from Shuttle Radar Topography Mission (SRTM) data. Multi-temporal Landsat imagery was analysed using supervised classification with a Maximum Likelihood algorithm to generate LULC maps for five reference years. Temporal changes were assessed using area statistics, linear trend analysis, and correlation analysis at the catchment scale. The results reveal a statistically significant expansion of built-up land, increasing by 220.7% (+0.062 km²/year; R² = 0.94; p < 0.01), accompanied by substantial declines in vegetation (−40.1%) and barren land (−39.6%). A strong inverse correlation between built-up and vegetation cover (r = −0.96; p < 0.05) confirms urban expansion as the dominant driver of land transformation. The surface area of Shahpura Lake remained relatively stable throughout the study period, showing no significant long-term trend. The study emphasises the importance of catchment-scale assessment for understanding urban lake dynamics. It also presents a remote sensing and GIS-based framework to support sustainable planning and management of urban lake ecosystems in rapidly urbanising regions.

Natural deltas can provide human beings with flat and fertile land to be cultivated. It is important to monitor cropland dynamics to provide policy-relevant information for regional sustainable development. This paper utilized Landsat imagery to study the cropland dynamics of the Yellow River Delta during the last three decades. Multi-temporal Landsat data were used to account for the phenological variations of different plants. Several spectral and textural features were adopted to increase the between-class separability. The robust random forest classifier was used to generate the land cover maps of the Yellow River Delta for 1986, 1995, 2005 and 2015. Experimental results indicated that the proposed methodology showed good performance with an average classification accuracy of 89.44%. The spatial-temporal analysis indicated that the cropland area increased from 467.6 km2 in 1986 to 718.5 km2 in 2015 with an average growth rate of 8.65 km2/year. The newly created croplands were mainly due to the reclamation of grassland and bare soil while the losses of croplands were due to abandoned cultivation and urban sprawl. The results demonstrate that a sustainable perspective should be adopted by the decision makers in order to simultaneously maintain food security, industrial development and ecosystem safety.

A map of the vegetation of the Catskill Park, NY, was created using multi-temporal Landsat Thematic Mapper TM data and ancillary spatial data to support ecological studies in Catskill watersheds. The map emphasizes forest types defined by dominant tree species and depicts 24 vegetation classes. Mapping included a series of supervised classifications in a decision tree framework that allowed forest types to be distinguished using spectral characteristics and other environmental relationships (e.g., landscape position, elevation). Traditional contingency table analysis (based on limited ground sampling) suggests overall map accuracy ranging from 28% to 90%, depending on the level of aggregation of the original 24 map classes. Fuzzy accuracy assessment based on the same ground data suggests a 71% level of acceptable classification. The map indicates that maple-dominated forests are predominant in the Catskill region, but that beech and birch-dominated forests become more important at higher elevations. Oak-dominated forests are very important along the eastern side of the Catskills, and conifer-dominated forests are largely restricted to mountaintops and stream bottoms.