Dynamic Land Research Articles

Spatiotemporal Change Detection of Coastal Wetlands Using Multi-Band SAR Coherence and Synergetic Classification

Synthetic aperture radar (SAR) signal can penetrate clouds and some vegetation canopies in all weather, and therefore, provides an important measurement tool for change detection and sustainable development of coastal wetland environments and ecosystems. However, there are a few quantitative estimations about the spatiotemporal coherence change with multi-band SAR images in complex coastal wetland ecosystems of the Yellow River Delta (YRD). In this study, C-band Sentinel-1 and L-band ALOS-2 PALSAR data were used to detect the spatiotemporal distribution and change pattern of interferometric coherence in the coastal wetlands of the YRD. The results show that the temporal baseline has a greater impact on the interferometric coherence than the perpendicular baseline, especially for short wavelength C-band SAR. Furthermore, the OTSU algorithm was proven to be able to distinguish the changing regions. The coherence mean and standard deviation values of different land cover types varied significantly in different seasons, while the minimum and maximum coherence changes occurred in February and August, respectively. In addition, considering three classical machine learning algorithms, namely naive Bayes (NB), random forest (RF), and multilayer perceptron (MLP), we proposed a method of synergetic classification with SAR coherence, backscatter intensity, and optical images for coastal wetland classification. The multilayer perceptron algorithm performs the best in synergetic classification with an overall accuracy of 98.3%, which is superior to a single data source or the other two algorithms. In this article, we provide an alternative cost-effective method for coastal wetland change detection, which contributes to more accurate dynamic land cover classification and to an understanding of the response mechanism of land features to climate change and human activities.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure.

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO2 exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO2 pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach

Surrogate modelling can alleviate the computational burden of design activities as they rely on multiple evaluations of high-fidelity models. However, the learning task can be adversely affected by the high-dimensionality of the system, complex non-linearities and temporal dependencies, leading to an inaccurate surrogate model. In this paper we present an innovative dual-phase Long-Short Term Memory (LSTM) Autoencoder-based surrogate model applied in an industrial context for the prediction of aircraft dynamic landing response over time, conditioned by an exogenous set of design parameters. The LSTM-Autoencoder is adopted as a dimensionality-reduction tool that extracts the temporal features and the nonlinearities of the high-dimensional dynamical system response, and learns a low-dimensional representation of it. Then, a Fully Connected Neural Network is trained to learn the simplified relationship between the input parameters and the reduced representation of the output. For our application, the results demonstrate that our LSTM-AE based model outperforms both Principal Component Analysis and Convolutional-Autoencoder based surrogate models, in predicting the parameters-dependent high-dimensional temporal system response.

Public perceptions and attitudes toward urban wildlife encounters – A decade of change

Europe is currently undergoing dynamic land use changes causing the expansion of urban habitat, which is driving wildlife species to colonise conurbations, resulting in an increased likelihood of human-wildlife conflict (HWC). Understanding people's attitudes toward wildlife is essential to manage these conflicts. This study assessed people's attitudes toward urban wildlife, the types of conflicts that existed, preferences for managing conflict situations, and determined any changes in perceptions of urban wildlife over a decade. A questionnaire survey of residents of Krakow, the second-largest city in Poland, was conducted in 2010 (n = 721) and repeated in 2020 (n = 887). We found that encounters with certain urban wildlife such as wild boars, red squirrels, roe deer, brown hares, and red foxes had increased significantly in 2020 compared to 2010. Respondents reported that wild boar and beavers did not show fear when encountering humans. Stone martens were considered the most nuisance wildlife species in 2010, while in 2020 wild boar were the most conflictual wildlife species. There were additional reports of conflicts with roe deer and red foxes. The most frequent HWC responses were personal anxiety, intrusion into property and destruction of crops, which increased significantly over the decade, independent of respondents' gender. Respondents preferred nonlethal methods to mitigate conflicts. The study provides valuable information and knowledge on changes in people's attitudes toward urban wildlife that can help with wildlife management in urban areas. Incorporating perception and attitude data from the public, along with a multi-stakeholder approach that includes wildlife professionals, in the planning and design of future urban environments is critical to minimise HWC.

Predicting future urban waterlogging-prone areas by coupling the maximum entropy and FLUS model

• We aim to develop a robust method for predicting future waterlogging-prone areas. • Maximum entropy algorithm and Future Land Use Simulation model were combined. • Dominant driver of waterlogging (land use change) was considered in the prediction. • A high proportion of impervious surfaces may face huge waterlogging risks in 2030. • This method could support future urban design and waterlogging risk prevention. Urban waterlogging is a severe hazard that can directly damage environmental quality and human well-being. It would be desirable for hazard mitigation planning and sustainable urban design if potential waterlogging-prone areas under dynamic land use change could be appropriately predicted. However, previous related studies did not simultaneously consider the reliability of negative samples and the future influence of fine-scale land use change. To fill the knowledge gap, this research has developed a robust method for predicting future waterlogging-prone areas by coupling the maximum entropy (MAXENT) and the Future Land Use Simulation (FLUS) model. The former can ensure that no extra sampling bias will be introduced, while the latter can accurately forecast the spatio-temporal pattern of land use. This case study has confirmed the accuracy and feasibility of this method. It was found that the proportion of impervious surfaces, population density, and proportion of green areas are key spatial drivers behind urban waterlogging issues. In addition, the future hazard potential map provided by the MAXENT and FLUS implies that a large proportion of impervious surfaces will face huge waterlogging risks. Therefore, policymakers should focus more on places with a higher probability of urban waterlogging. In summary, this research is expected to offer a practical tool for future urban design and waterlogging risk prevention.

Assessing the performance of MODIS and VIIRS active fire products in the monitoring of wildfires: a case study in Turkey

MODIS and VIIRS fire products have been widely used to detect and monitor fire activity at a global scale, as they provide highly relevant information on fire events, on their spatial and seasonal trends. Although these products have some limitations in detecting fires in forested areas due to closed canopy and smoke, they have been widely used to monitor and assess forest fires in many scientific studies. This study analyzes the performance of MODIS (MCD14ML) and VIIRS S-NPP (VNP14IMG) active fire/hotspot products in fire detection in five different land cover types (closed and open forests, shrublands, herbaceous vegetation and croplands) and compares the results to the ground-based fire database from 2015 to end of the 2019 in Turkey. Detected fires with a confidence value above 30% (nominal and high confidence) were used in the study. The land cover was assessed using the European Space Agency (ESA) Copernicus Global Land Service (CGLS) Dynamic Land Cover Layers at 100 m resolution in the study area. The performance assessment of two fire/hotspot products were conducted in three fire size classes, namely: fire size <1 ha, 1 to 10 ha, and >10 ha in five different land cover types. The results indicated that the overall accuracy of MODIS ranged from 0.6% to 16.6% and VIIRS S-NPP ranged from 1.3% to 25.6% of all ground-based fires in five different land cover types. The detection rates increased as the fire size increased. This study indicates that some limitations still exist to use MODIS and VIIRS S-NPP active fire/hotspot data in the assessment of wildfires.

Unsteady load assessment of a scaled-helicopter model in a ship airwake

The paper presents the results of wind tunnel tests to measure unsteady aerodynamic loads on a scaled-helicopter operating in the airwake of a generic frigate model. Rotor loads were measured by a six-axis dynamic balance in a stern landing maneuver simulated by trimming the rotor along a sloped descent trajectory towards the landing spot. The measures allowed to identify not only the average loads but also the frequency spectrum in the range of interest for flight mechanics. The results indicate that for the three tested wind directions, including headwind, 30∘ and 60∘ from the port-side, moving towards the landing spot the unsteadiness is increased. The rate of amplification of the loads for the trajectories not aligned with the wind is significantly higher compared with those measured in the headwind test. Furthermore, a fully dynamic landing maneuver was compared with the sequence of trimmed measures to evaluate the effect of the approach velocity of the helicopter on unsteady loads. A notable reduction in the unsteadiness of the pitch moment was found, while the unsteady thrust and roll moment were more comparable between static and dynamic tests.

Dynamic land use implications of rapidly expanding and evolving wind power deployment

The expansion of wind power poses distinct and varied geographic challenges to a sustainable energy transition. However, current knowledge of its land use impacts and synergies is limited by reliance on static characterizations that overlook the role of turbine technology and plant design in mediating interactions with the environment. Here, we investigate how wind technology development and innovation have shaped landscape interactions with social and ecological systems within the United States and contribute to evolving land area requirements. This work assesses trends in key land use facets of wind power using a holistic set of metrics to establish an evidence base that researchers, technology designers, land use managers, and policymakers can use in envisioning how future wind-intensive energy systems may be jointly optimized for clean energy, social, and environmental objectives. Since 2000, we find dynamic land occupancy patterns and regional trends that are driven by advancing technology and geographic factors. Though most historical U.S. wind deployment has been confined to the temperate grassland biome in the nation’s interior, regional expansion has implicated diverse land use and cover types. A large percentage of the typical wind plant footprint (∼96% to 99%) is not directly impacted by permanent physical infrastructure, allowing for multiple uses in the spaces between turbines. Surprisingly, turbines are commonly close to built structures. Moreover, rangeland and cropland have supported 93.4% of deployment, highlighting potential synergies with agricultural lands. Despite broadly decreasing capacity densities, offsetting technology improvements have stabilized power densities. Land use intensity, defined as the ratio of direct land usage to lifetime power generation of wind facilities, has also trended downwards. Although continued deployment on disturbed lands, and in close proximity to existing wind facilities and other infrastructure, could minimize the extent of impacts, ambitious decarbonization trajectories may predispose particular biomes to cumulative effects and risks from regional wind power saturation. Increased land-use and sustainability feedback in technology and plant design will be critical to sustainable management of wind power.

Introducing dynamic land subsidence index based on the ALPRIFT framework using artificial intelligence techniques

Introducing dynamic land subsidence index based on the ALPRIFT framework using artificial intelligence techniques

Modeling dynamic land use changes in the eastern portion of the hexi corridor, China by cnn-gru hybrid model

ABSTRACT Deep learning models have been extensively employed to simulate dynamic Land Use Changes (LUC) at the regional level. However, existing studies were found to be limited by the inadequate extraction of Spatiotemporal Neighborhood Features (SNF) and ignored obvious Long-Term Dependence (LTD) in time series data, which low simulation accuracies. Consequently, it remains to be explored whether deep learning should be employed to initially extract spatial or temporal features in LUC simulations to achieve better results. We propose here a deep learning model of a Convolutional Neural Network – Gated Recurrent Unit (CNN-GRU), coupled with SNF learning to simulate LUC. Land use data for the Eastern Portion of the Hexi Corridor (EPHC) (2000–2020) were utilized to verify the effectiveness of our model, which was compared with four other models (GRU, CNN, CNN-LSTM, and GRU-CNN). We found that CNN-GRU the highest simulation accuracy (OA = 0.9346), whereas the kappa coefficient increased by 1.22%-4.57%. These results indicated that: (1) EPHC urbanization showed a trend of contraction, rapid expansion, and slow growth. (2) The CNN-GRU model effectively extracted SNF and LTD, which improved the accuracy of the simulation. (3) An enhanced effect could be achieved when spatial features were initially extracted during the SNF deep learning extraction process. (4) Variable neighborhood unit sizes influenced the simulation results, where a 3 × 3 window was most suitable for the study area.

Impaired one-legged landing balance in young female athletes with previous ankle sprain: a cross-sectional study.

Ankle sprain is the most common type of sports injury, especially in team sports. Standing and dynamic landing balance, as an indicator of ankle instability, were investigated using varying experimental approaches. In the present cross-sectional study, 81 adolescent female elite handball and football players were divided into two groups based on previous ankle sprain injury (PI) or not (C). At time of test, all players were fully returned to elite-level sport. Subjects were tested during a one-legged landing (OLL) and in a one-legged static standing balance test (OLBT). In the OLL CoP trajectory displacement was calculated in 200 ms time epochs for evaluation of the initial stages of dynamic landing balance. OLBT was evaluated by calculating total displacement of the CoP trajectory. CoP displacement was greater in PI than C during the first 200 milliseconds epoch after landing (P=0.001, 252 mm [44], vs. 223 mm [28]), respectively) and in the subsequent 200 ms epoch (P=0.021, 72 mm [20], vs. 61 mm [16], respectively). No significant differences between PI and C were observed in time epochs from 400 to 1000 milliseconds or in OLBT. Adolescent elite athletes with a history of previous ankle sprain demonstrate impaired OLL balance in the first 400 milliseconds following jump landing compared to non-injured controls. Consequently, although athletes with previous ankle sprain may return to sport, dynamic postural control may not be fully restored. Future prospective studies are needed to decide, if the OLL test could be considered a relevant criterion tool for safe return-to-sport.

Century-long changes and drivers of soil nitrous oxide (N2 O) emissions across the contiguous United States.

The atmospheric concentration of nitrous oxide (N2O) has increased by 23% since the pre‐industrial era, which substantially destructed the stratospheric ozone layer and changed the global climate. However, it remains uncertain about the reasons behind the increase and the spatiotemporal patterns of soil N2O emissions, a primary biogenic source. Here, we used an integrative land ecosystem model, Dynamic Land Ecosystem Model (DLEM), to quantify direct (i.e., emitted from local soil) and indirect (i.e., emissions related to local practices but occurring elsewhere) N2O emissions in the contiguous United States during 1900–2019. Newly developed geospatial data of land‐use history and crop‐specific agricultural management practices were used to force DLEM at a spatial resolution of 5 arc‐min by 5 arc‐min. The model simulation indicates that the U.S. soil N2O emissions totaled 0.97 ± 0.06 Tg N year−1 during the 2010s, with 94% and 6% from direct and indirect emissions, respectively. Hot spots of soil N2O emission are found in the US Corn Belt and Rice Belt. We find a threefold increase in total soil N2O emission in the United States since 1900, 74% of which is from agricultural soil emissions, increasing by 12 times from 0.04 Tg N year−1 in the 1900s to 0.51 Tg N year−1 in the 2010s. More than 90% of soil N2O emission increase in agricultural soils is attributed to human land‐use change and agricultural management practices, while increases in N deposition and climate warming are the dominant drivers for N2O emission increase from natural soils. Across the cropped acres, corn production stands out with a large amount of fertilizer consumption and high‐emission factors, responsible for nearly two‐thirds of direct agricultural soil N2O emission increase since 1900. Our study suggests a large N2O mitigation potential in cropland and the importance of exploring crop‐specific mitigation strategies and prioritizing management alternatives for targeted crop types.

Culinary Center in Dalegan Gresik, East Java with a Wave Metaphor Architecture Approach

Tourism sector has potential of development as it belongs to one of regional revenue sources for Gresik Regency. One of tourism sectors that has great potential is culinary tourism. To Gresik Regency, improving tourism is possible to do because total visits of culinary tourism get increasing from year to year. Unfortunately, a specific culinary site which can accommodate typical food of Gresik has not been available yet. Therefore, the researcher planned and designed Culinary Centre in Dalegan Gresik, East Java under the theme of metaphoric architecture. To collect the research data, several methods were employed such as field study, literature study, and descriptive analysis. The macro concept of recreational is implemented by adjusting the title in the tourism site through the micro concept of dynamic land order having relaxed elements. The micro concept of shape accommodates wavy metaphor of sea water. Thus, this design is expected to be used as the basis for Gresik development so that it becomes a tourism object which can provide food-based recreational service and entertainment to all tourists.

Experimental and theoretical studies on the dynamic landing of water striders on water.

As a species of insects living on water, water striders jump from the water surface to avoid predation and then steadily land without piercing the surface. This spectacular property has attracted extensive interests since it provides bio-inspirations for designing functional microrobots moving on water. In this work, we investigate the landing dynamics of water striders by using artificial striders with different masses and leg lengths. It is found that once a water strider has landed, it oscillates on the water surface and the amplitude decays gradually, triggering a sequence of surface waves. Through scaling analysis, we relate the depth of the dimple that the strider leg displaces to its landing velocity, as well as its leg length and body mass. The subsequent time evolution of the interface where the strider lands is modeled as a damped oscillator, and its energy is exhausted by the surface waves. Moreover, we discuss the maximum depth of the dimple excited by the landing and find that the dynamic process can store more energy than the statically deforming process. Finally, we put forward a criterion of piercing the water surface from the energy point of view. These findings should be of great importance for understanding the locomotion of insects on water and for designing robust water-walking bionic robots.

ANALYSIS OF THE DYNAMICS OF LAND USE AND COVER IN THE DISTRICT OF INHARRIME (1980-2020)

The analysis of the dynamic use and land cover in the District of Inharrime in the period 1980- 2020 consisted of the interpretation of the images obtained to partir of the sensors on board in satellites of different times. In this work, landsat 5 images were classification for the years 1980, 2000 and 2020, followed by validation (confusion matrix) that shows the quality of the generated model. The methodology used was the bibliographic review, use of Remote Detection techniques and Geographic Information Systems that on the one hand allowed the acquisition and spatial analysis and on the other facilitated the classification of images from the study area, the handling of the existing database (BDCOV250) of the National Center for Mapping and Remote Sensing of the National Directorate of Land and Forests. As a final product, maps of land use and land cover were elaborated for the three years under analysis. Based on these results, it was possible to make the detection of changes that allow verifying the classes that changed over time and those that remained unchanged. The mapping of land use and coverage based on satellite images will allow planners to become aware of the current situation of the district, for actions that allow the creation of means for conservation, rational and conscious use of existing forest resources in the district.

FlexBRDF: A Flexible BRDF Correction for Grouped Processing of Airborne Imaging Spectroscopy Flightlines.

Bidirectional reflectance distribution function (BRDF) effects are a persistent issue for the analysis of vegetation in airborne imaging spectroscopy data, especially when mosaicking results from adjacent flightlines. With the advent of large airborne imaging efforts from NASA and the U.S. National Ecological Observatory Network (NEON), there is increasing need for methods that are flexible and automatable across images with diverse land cover. Flexible bidirectional reflectance distribution function (FlexBRDF) is built upon the widely used kernel method, with additional features including stratified random sampling across flightline groups, dynamic land cover stratification by normalized difference vegetation index (NDVI), interpolation of correction coefficients across NDVI bins, and the use of a reference solar zenith angle. We demonstrate FlexBRDF using nine long (150–400 km) airborne visible/infrared imaging spectrometer (AVIRIS)‐Classic flightlines collected on 22 May 2013 over Southern California, where diverse land cover and a wide range of solar illumination yield significant BRDF effects. We further test the approach on additional AVIRIS‐Classic data from California, AVIRIS‐Next Generation data from the Arctic and India, and NEON imagery from Wisconsin. Comparison of overlapping areas of flightlines show that models built from multiple flightlines performed better than those built for single images (root mean square error improved up to 2.3% and mean absolute deviation 2.5%). Standardization to a common solar zenith angle among a flightline group improved performance, and interpolation across bins minimized between‐bin boundaries. While BRDF corrections for individual sites suffice for local studies, FlexBRDF is an open source option that is compatible with bulk processing of large airborne data sets covering diverse land cover needed for calibration/validation of forthcoming spaceborne imaging spectroscopy missions.

Using Saildrones to Assess the SMAP Sea Surface Salinity Retrieval in the Coastal Regions

Remote sensing of sea surface salinity (SSS) near land is difficult due to land contamination. In this study, we assess SSS retrieved from the Soil Moisture Active Passive (SMAP) mission in coastal region. SMAP SSS products from the Jet Propulsion Laboratory (JPL), and from the Remote Sensing Systems (RSS) are collocated with in situ data collected by saildrones during the North American West Coast Survey. Satellite and saildrone salinity measurements reveal consistent large-scale features: the fresh water (low SSS) assocciated with the Columbia River discharge, and the relatively salty water (high SSS) near Baja California associated with regional upwelling. The standard deviation of the difference (stdD) for collocations with SMAP Level 3 (8 days average) between 40 and 100km from land is 0.51 (0.56) psu for JPL V5 (RSS V4 70km). This is encouraging for the potential application of SMAP SSS in monitoring coastal zone freshwater particularly where there exists large freshwater variance. We analyze the different land correction approaches independently developed at JPL and RSS using SMAP Level 2 matchups. We found that JPL&#x0027;s land correction method is more promising in pushing SMAP SSS retrieval towards land. For future improvement, we suggest implementing dynamic land correction versus the current climatology based static land correction to reduce uncertainty in estimating land contribution. In Level 2 to Level 3 processing, a more rigorous quality control may help to eliminate outliers and deliver reliable Level 3 products without over-smoothing, which is important in resolving coastal processes such as fronts or upwelling.

Development of a smartphone-based information platform for rural land sublease in China

This paper compares the current situation of development ,clarifies the problems that need to be solved by the information platform of rural land sublease, and then constructs a smartphone-based information platform and draws conclusions and suggestions, firstly, based on the analysis of rural land sublease policy and platform as a realistic basis, the design of rural land sublease information platform is carried out by using comparative analysis method and object-oriented analysis and design method. By constructing and analyzing the rural land sublease information platform, we deepen the informationization of land sublease government services and enhance the dynamic land title registration function. Based on the construction of the rural land sublease information platform, the government is promoted to explore the orderly opening and utilization of information resources, and provide all-in-one land sublease governmental information services in line with the needs and user habits of the new era. In today's new era of mobile network, the cell phone platform can better serve to realize the rights and obligations of land sublease regulated by the Land Contract Law of the People's Republic of China, improve the efficiency of rural land resources, and promote rural revitalization. With the gradual establishment of land sublease management information systems in various places, government information services such as land title registration inquiries are gradually standardized. However, there are still problems such as fragmentation of information on land sublease management and transactions, asymmetry of land sublease information, and low technical level.

Synergy of remotely sensed data in spatiotemporal dynamic modeling of the crop and cover management factor

Soil erosion is a threat to the water quality constituents of sediments and nutrients and can cause long-term environmental damages. One important parameter to quantify the risk of soil loss from erosion is the crop and cover management factor (C-factor), which represents how cropping and management practices affect the rates and potential risk of soil erosion. We developed remotely sensed data-driven models for dynamic predictions of C-factor by implementing dynamic land cover modeling using the SWAT (Soil and Water Assessment Tool) model on a watershed scale. The remotely sensed processed variables included the enhanced vegetation index (EVI), the fraction of photosynthetically active radiation absorbed by green vegetation (FPAR), leaf area index (LAI), soil available water content (AWC), slope gradient (SG), and ratio of area (AR) of every hydrologic response unit (HRU) to that of the total watershed, comprising unique land cover, soil type, and slope gradient characteristics within the Fish River catchment in Alabama, USA between 2001 and 2014. Linear regressions, spatial trend analysis, correlation matrices, forward stepwise multivariable regression (FSMR), and 2-fold cross-validation were conducted to evaluate whether there were possible associations between the C-factor and EVI with the successive addition of remotely sensed environmental factors. Based on the data analysis and modeling, we found a significant association between the C-factor and EVI with the synergy of the environmental factors FPAR, LAI, AWC, AR, and SG (predicted R 2 ( R 2 pred ) = 0.51; R 2 = 0.68, n = 3 220, P < 0.15). The results showed that the developed FSMR model constituting the non-conventional factors AWC ( R 2 pred = 0.32; R 2 = 0.48, n = 3 220, P < 0.05) and FPAR ( R 2 pred = 0.13; R 2 = 0.28, n = 3 220, P = 0.31) was an improved fit for the watershed C-factor. In conclusion, the union of dynamic variables related to vegetation (EVI, FPAR, and LAI), soil (AWC), and topography (AR and SG) can be utilized for spatiotemporal C-factor estimation and to monitor watershed erosion.

ANALYSIS OF SPATIO-TEMPORAL URBAN DYNAMICS IN 11 SMART CITIES OF UTTAR PRADESH, INDIA

Abstract. Urban planning in smart cities needs to be done in a “Smart” way. One way is to analyze the urbanisation pattern by spatio-temporal change detection techniques. Classified data such as, for years 1985, 1995 and 2005 Decadal Land use data for India and for year 2015, Copernicus Global Land service Dynamic Land Cover layers (CGLS-LC100 products) are used to perform multi-temporal analysis of the 11 smart cities of Uttar Pradesh state of India namely "Agra", "Aligarh", "Bareilly", "Jhansi", "Kanpur", "Lucknow", "Moradabad", "Prayagraj", "Rampur", "Saharanpur" and "Varanasi". Dynamics of Urban expansion are studied utilizing concepts of Landscape Metrics calculated by FRAGSTATS and also Shannon’s Entropy Values (Hn) over the 11 smart cities. Largest Patch Index (LPI), Landscape Shape Index (LSI), Aggregation Index (AI) and Mean Euclidean Nearest Neighbor Distance (ENN_MN) are metrics used to characterize urbanisation. Results indicate rise in value of LSI over the years from 1985 and with sudden increase in year 2015 for Built-up patches, corroborating more complexity in shapes of Built-up patches in all 11 cities. Kanpur, showing large values of LPI indicates the sudden increase of Built-up land use class over the years. The decreasing value of ENN_MN over the years indicates less centrality for built-up pixels in urbanisation. AI is unchanged for Built-up patches for 1985–1995 but decrease in year 2015 indicates less compactness which is due to dispersion of built-up pixels. High values of Hn over the years indicating dispersion of urbanisation in all 11 smart cities except Agra, also validates results.