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: 2 August 2024 | Viewed by 958

Special Issue Editors


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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
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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

Published Papers (1 paper)

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Research

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 541
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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