Green Infrastructure Planning Using Modern Technologies, LiDAR and 3D Urban Models

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 883

Special Issue Editors


E-Mail Website
Guest Editor
Department of Cartography and Photogrammetry, Faculty of Geodesy, University of Zagreb, Zagreb, Croatia
Interests: cartography; geoinformatics; GIS; SDI; smart cities

E-Mail Website
Guest Editor
Department of Computing and Control, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
Interests: geoinformatics; GIS; machine learning in GIS; remote sensing; LiDAR; SDI; geospatial services and geoportals

E-Mail Website
Guest Editor
Department of Architecture, Faculty of Engineering, Alexandria University, Alexandria, Egypt
Interests: urban planning; climate change; smart cities; spatial data analysis; GIS

E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

Rapid urbanisation and climate change have bolstered the need for sustainable approaches to urban development, and green infrastructure (GI) networks of natural and semi-natural spaces have come to play crucial roles in enhancing urban resilience, improving air quality, regulating temperature, and supporting biodiversity.

Recent advances in LiDAR technology and 3D urban modelling offer powerful tools for mapping, visualising, and analysing urban environments, enabling detailed assessments of vegetation structure, surface permeability, and spatial patterns, and thereby supporting evidence-based planning and decision-making in green infrastructure design.

The aim of this Special Issue is to collect original research articles and review papers that provide insights into innovative research and practical applications at the intersection of remote sensing, urban modelling, and sustainable planning.

This Special Issue welcomes manuscripts addressing the following themes:

  • Integration of LiDAR and 3D urban models for green infrastructure mapping;
  • Urban change modelling and analysis;
  • 3D modelling and metrics for ecosystem services and climate change adaptation;
  • Data fusion approaches combining LiDAR, drones, and satellite imagery;
  • Smart city applications and digital twins for green infrastructure planning;
  • Tools and workflows for 3D spatial analysis and visualisation;
  • Implications of 3D green infrastructure assessment for policy and planning;
  • Modernisation of land and urban management education.

We look forward to receiving your original research articles and reviews.

Dr. Vesna Poslončec-Petrić
Prof. Dr. Dubravka Sladić
Dr. Dina M. Saadallah
Prof. Dr. Joep Crompvoets
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • LiDAR 
  • 3D urban modelling 
  • green infrastructure 
  • smart city 
  • urban sustainability 
  • ecosystem services 
  • evidence-based planning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 1319 KB  
Article
A Machine Learning-Validated Comparison of LAI Estimation Methods for Urban–Agricultural Vegetation Using Multi-Temporal Sentinel-2 Imagery in Tashkent, Uzbekistan
by Bunyod Mamadaliev, Nikola Kranjčić, Sarvar Khamidjonov and Nozimjon Teshaev
Land 2026, 15(2), 232; https://doi.org/10.3390/land15020232 - 29 Jan 2026
Viewed by 522
Abstract
Accurate estimation of Leaf Area Index (LAI) is essential for monitoring vegetation structure and ecosystem services in urban and peri-urban environments, particularly in small, heterogeneous patches typical of semi-arid cities. This study presents a comparative assessment of four empirical LAI estimation methods—NDVI-based, NDVI-advanced, [...] Read more.
Accurate estimation of Leaf Area Index (LAI) is essential for monitoring vegetation structure and ecosystem services in urban and peri-urban environments, particularly in small, heterogeneous patches typical of semi-arid cities. This study presents a comparative assessment of four empirical LAI estimation methods—NDVI-based, NDVI-advanced, SAVI-based, and EVI-based methods—applied to atmospherically corrected Sentinel-2 Level-2A imagery (10 m spatial resolution) over a 0.045 km2 urban–agricultural polygon in the Tashkent region, Uzbekistan. Multi-temporal observations acquired during the 2023 growing season (June–August) were used to examine intra-seasonal vegetation dynamics. In the absence of field-measured LAI, a Random Forest regression model was implemented as an inter-method consistency analysis to assess agreement among index-derived LAI estimates rather than to perform external validation. Statistical comparisons revealed highly systematic and practically significant differences between methods, with the EVI-based approach producing the highest and most dynamically responsive LAI values (mean LAI = 1.453) and demonstrating greater robustness to soil background and atmospheric effects. Mean LAI increased by 66.7% from June to August, reflecting irrigation-driven crop phenology in the semi-arid study area. While the results indicate that EVI provides the most reliable relative LAI estimates for small urban–agricultural patches, the absence of ground-truth data and the influence of mixed pixels at 10 m resolution remain key limitations. This study offers a transferable methodological framework for comparative LAI assessment in data-scarce urban environments and provides a basis for future integration with field measurements, higher-resolution imagery, and LiDAR-based 3D vegetation models. Full article
Show Figures

Figure 1

Back to TopTop