Analysis of the Spatiotemporal Urban Expansion of the Rome Coastline through GEE and RF Algorithm, Using Landsat Imagery

This study analyzes, through remote sensing techniques and innovative clouding services, the recent land use dynamics in the North-Roman littoral zone, an area where the latest development has witnessed an important reconversion of purely rural areas to new residential and commercial services. The survey area includes five municipalities and encompasses important infrastructure, such as the “Leonardo Da Vinci” Airport and the harbor of Civitavecchia. The proximity to the metropolis, supported by an efficient network of connections, has modified the urban and peri-urban structure of these areas, which were formerly exclusively agricultural. Hereby, urban expansion has been quantified by classifying Landsat satellite images using the cloud computing platform “Google Earth Engine” (GEE). Landsat multispectral images from 1985 up to 2020 were used for the diachronic analysis, with a five-yearly interval. In order to achieve a high accuracy of the final result, work was carried out along the temporal dimension of the images, selecting specific time windows for the creation of datasets, which were adjusted by the information related to the NDVI index variation through time. This implementation showed interesting improvements in the model performance for each year, suggesting the importance of the NDVI standard deviation parameter. The results showed an increase in the overall accuracy, being from 90 to 97%, with improvements in distinguishing urban surfaces from impervious surfaces. The final results highlighted a significant increase in the study area of the “Urban” and “Woodland” classes over the 35-year time span that was considered, being 67.4 km2 and 70.4 km2, respectively. The accurate obtained results have allowed us to quantify and understand the landscape transformations in the area of interest, with particular reference to the dynamics of urban development.

[1]  D. Zema,et al.  What is going on within google earth engine? A systematic review and meta-analysis , 2023, Remote Sensing Applications: Society and Environment.

[2]  O. Mutanga,et al.  Google Earth Engine for Informal Settlement Mapping: A Random Forest Classification Using Spectral and Textural Information , 2022, Remote. Sens..

[3]  G. Modica,et al.  A Multitemporal Fragmentation-Based Approach for a Dynamics Analysis of Agricultural Terraced Systems: The Case Study of Costa Viola Landscape (Southern Italy) , 2022, Land.

[4]  G. Modica,et al.  Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region , 2022, European Journal of Remote Sensing.

[5]  G. Modica,et al.  Characterizing historical transformation trajectories of the forest landscape in Rome's metropolitan area (Italy) for effective planning of sustainability goals , 2021, Land Degradation & Development.

[6]  Q. Cheng,et al.  Review of Spectral Indices for Urban Remote Sensing , 2021, Photogrammetric Engineering & Remote Sensing.

[7]  Marco Vizzari,et al.  Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park , 2021, Remote. Sens..

[8]  Rosa Lasaponara,et al.  Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy , 2021, Sensors.

[9]  Guojin He,et al.  Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest , 2021, Remote. Sens..

[10]  Salvatore Praticò,et al.  Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation , 2021, Remote. Sens..

[11]  Nadhir Al-Ansari,et al.  Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil , 2021 .

[12]  Salvatore Praticò,et al.  Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery: a case study in a citrus orchard and an onion crop , 2021, European Journal of Remote Sensing.

[13]  C. Calzolari,et al.  Assessing soil ecosystem services in urban and peri-urban areas: From urban soils survey to providing support tool for urban planning , 2020 .

[14]  Li Hou,et al.  Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China , 2020, Remote. Sens..

[15]  Maurizio Pollino,et al.  Spatio-Temporal Dynamics of Urban and Natural Areas in the Northern Littoral Zone of Rome , 2020, ICCSA.

[16]  Edyta Wozniak,et al.  SPECIFIC ALPINE ENVIRONMENT LAND COVER CLASSIFICATION METHODOLOGY: GOOGLE EARTH ENGINE PROCESSING FOR SENTINEL-2 DATA , 2020 .

[17]  Lukas W. Lehnert,et al.  Land Cover Classification using Google Earth Engine and Random Forest Classifier - The Role of Image Composition , 2020, Remote. Sens..

[18]  Masoud Mahdianpari,et al.  Google Earth Engine for geo-big data applications: A meta-analysis and systematic review , 2020 .

[19]  Karen C. Seto,et al.  A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change , 2020 .

[20]  Eufemia Tarantino,et al.  Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment , 2020, Remote. Sens..

[21]  J. P. Clemente,et al.  Google Earth Engine: Application Of Algorithms For Remote Sensing Of Crops In Tuscany (Italy) , 2020, 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS).

[22]  Michael E. Meadows,et al.  Mapping Trajectories of Coastal Land Reclamation in Nine Deltaic Megacities using Google Earth Engine , 2019, Remote. Sens..

[23]  K. Seto,et al.  Conceptualizing and characterizing micro-urbanization: A new perspective applied to Africa , 2019, Landscape and Urban Planning.

[24]  Xiao Xiang Zhu,et al.  AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE , 2019, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[25]  R. Congalton,et al.  Assessing the Accuracy of Remotely Sensed Data , 2019 .

[26]  P. C. Pandey,et al.  Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art , 2019, Geocarto International.

[27]  H. Mahmoud,et al.  Spatiotemporal variation analysis of urban land expansion in the establishment of new communities in Upper Egypt: A case study of New Asyut city , 2019, The Egyptian Journal of Remote Sensing and Space Science.

[28]  Onisimo Mutanga,et al.  Google Earth Engine Applications Since Inception: Usage, Trends, and Potential , 2018, Remote. Sens..

[29]  Bo Huang,et al.  Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery , 2018, Remote Sensing of Environment.

[30]  G. Caneva,et al.  Safeguarding natural and cultural heritage on Etruscan tombs (La Banditaccia, Cerveteri, Italy) , 2018, Rendiconti Lincei. Scienze Fisiche e Naturali.

[31]  Francesca Bozzano,et al.  Imaging Multi-Age Construction Settlement Behaviour by Advanced SAR Interferometry , 2018, Remote. Sens..

[32]  K. Seto,et al.  Time series analysis of satellite data to characterize multiple land use transitions: a case study of urban growth and agricultural land loss in India , 2018 .

[33]  Amy L. Griffin,et al.  Impacts on the Urban Environment: Land Cover Change Trajectories and Landscape Fragmentation in Post-War Western Area, Sierra Leone , 2018, Remote. Sens..

[34]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[35]  G. Modica,et al.  Abandonment of traditional terraced landscape: A change detection approach (a case study in Costa Viola, Calabria, Italy) , 2017 .

[36]  B. Murgante,et al.  The Dynamics of Urban Land Rent in Italian Regional Capital Cities , 2017 .

[37]  Fan Yang,et al.  Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China , 2017 .

[38]  Lu Liang,et al.  Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record , 2017, Remote. Sens..

[39]  Wei You,et al.  Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine , 2016, Remote. Sens..

[40]  G. McCord,et al.  Geographic determinants of China's urbanization , 2016 .

[41]  Lizhe Wang,et al.  A Comparison of Machine Learning Algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery , 2016, Remote. Sens..

[42]  Joanne C. White,et al.  Optical remotely sensed time series data for land cover classification: A review , 2016 .

[43]  Doreen S. Boyd,et al.  Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach , 2016, Remote. Sens..

[44]  Francesco Solano,et al.  An index for the assessment of degraded Mediterranean forest ecosystems , 2015 .

[45]  Albert Y. Zomaya,et al.  Remote sensing big data computing: Challenges and opportunities , 2015, Future Gener. Comput. Syst..

[46]  L. Salvati,et al.  In-between sprawl and fires: long-term forest expansion and settlement dynamics at the wildland–urban interface in Rome, Italy , 2015 .

[47]  Divine Odame Appiah,et al.  Application of Geo-Information Techniques in Land Use and Land Cover Change Analysis in a Peri-Urban District of Ghana , 2015, ISPRS Int. J. Geo Inf..

[48]  A. Thomson,et al.  A global map of urban extent from nightlights , 2015 .

[49]  A. Tatem,et al.  Detecting Change in Urban Areas at Continental Scales with MODIS Data , 2015 .

[50]  S. Gedam,et al.  Monitoring land use changes associated with urbanization: An object based image analysis approach , 2015 .

[51]  Yifang Ban,et al.  Unsupervised Change Detection in Multitemporal SAR Images Over Large Urban Areas , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[52]  L. Salvati “A Chronicle of a Death Foretold”: Urban Expansion and Land Consumption in Rome, Italy , 2013 .

[53]  Giuseppe Modica,et al.  Free Web Mapping Tools to Characterise Landscape Dynamics and to Favour e-Participation , 2013, ICCSA.

[54]  Simon D. Jones,et al.  The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification , 2013, Remote. Sens..

[55]  J. Townshend,et al.  Urban growth of the Washington, D.C.–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover , 2013 .

[56]  G. Modica,et al.  Spatio-temporal analysis of the urban–rural gradient structure: an application in a Mediterranean mountainous landscape (Serra San Bruno, Italy) , 2012 .

[57]  G. Modica,et al.  Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics , 2012 .

[58]  E. Pranzini,et al.  Human Impact on Shoreline Evolution Along the Follonica Gulf (Southern Tuscany): How Tourism May Kill the Goose that Lays the Golden Eggs , 2011 .

[59]  L. Salvati,et al.  Exploring long-term land cover changes in an urban region of southern Europe , 2011 .

[60]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[61]  Q. Mcnemar Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

[62]  R. Lasaponara,et al.  On the Use of Google Earth Engine and Sentinel Data to Detect “Lost” Sections of Ancient Roads. The Case of Via Appia , 2022, IEEE Geoscience and Remote Sensing Letters.

[63]  C. Lippitt,et al.  Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review , 2022, Remote. Sens..

[64]  Giovana Mira de Espindola,et al.  Urban Land Mapping Based on Remote Sensing Time Series in the Google Earth Engine Platform: A Case Study of the Teresina-Timon Conurbation Area in Brazil , 2021, Remote. Sens..

[65]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[66]  L. Facioni,et al.  The Forest Vegetation of the Tolfa-Ceriti Mountains (Northern Latium - Central Italy) , 2010 .

[67]  L. Breiman Random Forests , 2001, Machine Learning.

[68]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .