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Land
Special Issues
Digital Earth and Remote Sensing for Land Management
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Digital Earth and Remote Sensing for Land Management

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 4158

Special Issue Editors

Dr. Massimo Fabris

E-Mail Website
Guest Editor
Department of Civil, Environmental and Architectural Engineering-ICEA, University of Padova, 35122 Padova, Italy
Interests: geomatics; digital aerial photogrammetry; digital surface models; deformations monitoring; 3D surveys; land subsidence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Djamil Al-Halbouni

E-Mail Website
Guest Editor
Institute for Earth System Science and Remote Sensing, University of Leipzig, Talstr. 35, Room 0-11, 04103 Leipzig, Germany
Interests: hydrogeophysics; remote sensing; photogrammetry; nat. hazards; numerical modelling; soil; subsidence; karst
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A digital representation of the Earth’s surface is a crucial tool in land management. Remote sensing techniques, in many cases integrated with ground-based data, allow the monitoring of the ground surface with different details, resolutions, and accuracies. These data can be used in the analysis of land use/land cover pattern modifications, urban morphology changes, land surface deformation monitoring, etc.

The goal of this Special Issue is to collect papers (original research articles and/or review papers) to give insights into land management using a digital representation of the Earth’s surface obtained from, but not limited to, remote sensing data. Digital Elevation Models, orthophotomaps, InSAR time-series, GNSS measurements, and various types of information and data (e.g. photogrammetry with optical and/or thermal cameras, LiDAR, multi-beam bathymetry, etc.) acquired from different platforms (e.g. Unmanned Aerial Vehicles (UAVs), as well as Unmanned Surface Vehicles (USVs) or also Unmanned Underwater Vehicles (UUVs)), helicopters and airplanes) can be processed and analysed by means of GIS tools in the field of land management.

This Special Issue will welcome manuscripts that link the following themes by using digital Earth data and land remote sensing techniques:

  • Land management;
  • Land deformation monitoring;
  • Land use and/or land cover changes;
  • Urban and urban–rural morphology modifications.

Papers discussing theoretical models, the results obtained from land monitoring activities, and the evolution in space and time of the digital Earth’s surface and processes are also welcome. We particularly encourage the submission of manuscripts presenting new and/or innovative applications of remote sensing techniques for the monitoring and quantification of land surface deformation, land use/land cover changes, and urban area modifications.

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

Dr. Massimo Fabris
Prof. Dr. Djamil Al-Halbouni
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • digital earth
  • satellite imagery
  • InSAR
  • digital elevation models (DEM)
  • land monitoring
  • land use
  • land cover
  • urban morphology
  • GIS tools

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

Published Papers (3 papers)

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Research

25 pages, 6737 KiB  
Article
Integration of VIS–NIR Spectroscopy and Multivariate Technique for Soils Discrimination Under Different Land Management
by Mohamed S. Shokr, Abdel-rahman A. Mustafa, Talal Alharbi, Jose Emilio Meroño de Larriva, Abdelbaset S. El-Sorogy, Khaled Al-Kahtany and Elsayed A. Abdelsamie
Land 2024, 13(12), 2056; https://doi.org/10.3390/land13122056 - 30 Nov 2024
Viewed by 697
Abstract
Proximal sensing has become increasingly popular due to developments in soil observation technologies and the demands of timely information gathering through contemporary methods. By utilizing the morphological, physical, and chemical characteristics of representative pedogenetic profiles established in various soils of the Sohag governorate, [...] Read more.
Proximal sensing has become increasingly popular due to developments in soil observation technologies and the demands of timely information gathering through contemporary methods. By utilizing the morphological, physical, and chemical characteristics of representative pedogenetic profiles established in various soils of the Sohag governorate, Egypt, the current research addresses the characterization of surface reflectance spectra and links them with the corresponding soil classification. Three primary areas were identified: recently cultivated, old cultivated, and bare soils. For morphological analysis, a total of 25 soil profiles were chosen and made visible. In the dark room, an ASD Fieldspec portable spectroradiometer (350–2500 nm) was used to measure the spectrum. Based on how similar their surface spectra were, related soils were categorized. Ward’s method served as the basis for the grouping. Despite the fact that the VIS–NIR spectra of the surface soils from various land uses have a similar reflectance shape, it is still possible to compare the soil reflectance curves and the effects of the surface soils. As a result, three groups of soil curves representing various land uses were observed. Cluster analysis was performed on the reflectance data in four ranges (350–750, 751–1150, 1151–1850, and 1851–2500 nm). The groups derived from the soil surface ranges of 350–750 nm and 751–1150 nm were not the same as those derived from the ranges of 1151–1850 nm and 1851–2500 nm. The last two categories are strikingly comparable to various land uses with marginally similar features. Based on the ranges of 1151–1850 nm and 1851–2500 nm in surface spectral data, the dendrogram effectively separated and combined the profiles into two separate clusters. These clusters matched different land uses exactly. The results can be used to promote the widespread usage of in situ hyperspectral data sets for the investigation of various soil characteristics. Full article
(This article belongs to the Special Issue Digital Earth and Remote Sensing for Land Management)
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23 pages, 10725 KiB  
Article
Leveraging Geospatial Information to Map Perceived Tenure Insecurity in Urban Deprivation Areas
by Esaie Dufitimana, Jiong Wang and Divyani Kohli-Poll Jonker
Land 2024, 13(9), 1429; https://doi.org/10.3390/land13091429 - 4 Sep 2024
Viewed by 976
Abstract
Increasing tenure security is essential for promoting safe and inclusive urban development and achieving Sustainable Development Goals. However, assessment of tenure security relies on conventional census and survey statistics, which often fail to capture the dimension of perceived tenure insecurity. This perceived tenure [...] Read more.
Increasing tenure security is essential for promoting safe and inclusive urban development and achieving Sustainable Development Goals. However, assessment of tenure security relies on conventional census and survey statistics, which often fail to capture the dimension of perceived tenure insecurity. This perceived tenure insecurity is crucial as it influences local engagement and the effectiveness of policies. In many regions, particularly in the Global South, these conventional methods lack the necessary data to adequately measure perceived tenure insecurity. This study first used household survey data to derive variations in perceived tenure insecurity and then explored the potential of Very-High Resolution (VHR) satellite imagery and spatial data to assess these variations in urban deprived areas. Focusing on the city of Kigali, Rwanda, the study collected household survey data, which were analysed using Multiple Correspondence Analysis to capture variations of perceived tenure insecurity. In addition, VHR satellite imagery and spatial datasets were analysed to characterize urban deprivation. Finally, a Random Forest regression model was used to assess the relationship between variations of perceived tenure insecurity and the spatial characteristics of urban deprived areas. The findings highlight the potential of geospatial information to estimate variations in perceived tenure insecurity within urban deprived contexts. These insights can inform evidence-based decision-making by municipalities and stakeholders in urban development initiatives. Full article
(This article belongs to the Special Issue Digital Earth and Remote Sensing for Land Management)
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15 pages, 5883 KiB  
Article
Estimating the Aboveground Fresh Weight of Sugarcane Using Multispectral Images and Light Detection and Ranging (LiDAR)
by Charot M. Vargas, Muditha K. Heenkenda and Kerin F. Romero
Land 2024, 13(5), 611; https://doi.org/10.3390/land13050611 - 1 May 2024
Viewed by 1566
Abstract
This study aimed to develop a remote sensing method for estimating the aboveground fresh weight (AGFW) of sugarcane using multispectral images and light detection and ranging (LiDAR). Remotely sensed data were acquired from an unmanned aerial vehicle (drone). Sample plots were harvested and [...] Read more.
This study aimed to develop a remote sensing method for estimating the aboveground fresh weight (AGFW) of sugarcane using multispectral images and light detection and ranging (LiDAR). Remotely sensed data were acquired from an unmanned aerial vehicle (drone). Sample plots were harvested and the AGFW of each plot was measured. Sugarcane crown heights and volumes were obtained by isolating individual tree crowns using a LiDAR-derived digital surface model of the area. Multiple linear regression (MLR) and partial least-squares regression (PLSR) models were tested for the field-sampled AGFWs (dependent variable) and individual canopy heights and volumes, and spectral indices were used as independent variables or predictors. The PLSR model showed more promising results than the MLR model when predicting the AGFW over the study area. Although PLSR is well-suited to a large number of collinear predictor variables and a limited number of field samples, this study showed moderate results (R2 = 0.5). The visual appearance of the spatial distribution of the AGFW map is satisfactory. The limited no. of field samples overfitted the MLR prediction results. Overall, this research highlights the potential of integrating remote sensing technologies in the sugarcane industry, thereby improving yield estimation and effective crop management. Full article
(This article belongs to the Special Issue Digital Earth and Remote Sensing for Land Management)
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