Special Issue "Unmanned Aerial Vehicles in Geomatics"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 April 2016)

Special Issue Editor

Guest Editor
Prof. Dr. Gonzalo Pajares Martinsanz

Department Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040 Madrid, Spain
Website | E-Mail
Phone: +34.1.3947546
Interests: computer vision; image processing; pattern recognition; 3D image reconstruction, spatio-temporal image change detection and track movement; fusion and registering from imaging sensors; superresolution from low-resolution image sensors

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) offer an interesting opportunity to acquire geographic information that can be conveniently processed for subsequent data analysis. This can be achieved by sensing technologies installed onboard UAVs and specific tools. The huge amount of data collected from UAVs represents a new challenge regarding developments of processing, storage, and transmission techniques, where the confluence of multidisciplinary technologies is always welcome.

Therefore, UAVs, and the sensory technologies onboard such platforms, enable different applications for efficient Earth observation. An overview of sensory technologies and of UAVs, in collaboration with relevant geomatics applications, is provided in Pajares (2015), which can be used by authors as a guide.

This Special Issue will publish papers covering a broad variety of perspectives based on geo-information. Topics include, but are not limited, to UAVs for:

  • Photogrammetry
  • Agriculture and forestry
  • Disaster monitoring
  • Surveillance
  • Environmental monitoring
  • Land coverage and vegetation
  • Atmospheric observation
  • Cultural
  • Wildlife
  • Urban environments

Pajares, G. An Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs). Photogrammetry Engineering and Remote Sensing, 2015 Vol. 81, No. 4, April 2015, pp. 281-329; doi: 10.14358/PERS.81.4.197.

Prof. Dr. Gonzalo Pajares Martinsanz
Guest Editor

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 papers will be 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. ISPRS International Journal of Geo-Information 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 900 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.


Published Papers (5 papers)

View options order results:
result details:
Displaying articles 1-5
Export citation of selected articles as:

Editorial

Jump to: Research

Open AccessEditorial Unmanned Aerial Vehicles in Geomatics
ISPRS Int. J. Geo-Inf. 2016, 5(8), 147; doi:10.3390/ijgi5080147
Received: 17 August 2016 / Accepted: 19 August 2016 / Published: 22 August 2016
PDF Full-text (155 KB) | HTML Full-text | XML Full-text
Abstract
Geomatics as a geospatial science, including technologies and processes, has experienced a boost in recent years with the development of Unmanned Aerial Vehicles (UAVs) equipped with sensing instruments [1].[...] Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Geomatics)

Research

Jump to: Editorial

Open AccessArticle Evaluation of Different Irrigation Methods for an Apple Orchard Using an Aerial Imaging System
ISPRS Int. J. Geo-Inf. 2016, 5(6), 79; doi:10.3390/ijgi5060079
Received: 9 April 2016 / Revised: 23 May 2016 / Accepted: 26 May 2016 / Published: 1 June 2016
Cited by 3 | PDF Full-text (5451 KB) | HTML Full-text | XML Full-text
Abstract
Regular monitoring and assessment of crops is one of the keys to optimal crop production. This research presents the development of a monitoring system called the Crop Monitoring and Assessment Platform (C-MAP). The C-MAP is composed of an image acquisition unit which is
[...] Read more.
Regular monitoring and assessment of crops is one of the keys to optimal crop production. This research presents the development of a monitoring system called the Crop Monitoring and Assessment Platform (C-MAP). The C-MAP is composed of an image acquisition unit which is an off-the-shelf unmanned aerial vehicle (UAV) equipped with a multispectral camera (near-infrared, green, blue), and an image processing and analysis component. The experimental apple orchard at the Parma Research and Extension Center of the University of Idaho was used as the target for monitoring and evaluation. Five experimental rows of the orchard were randomly treated with five different irrigation methods. An image processing algorithm to detect individual trees was developed to facilitate the analysis of the rows and it was able to detect over 90% of the trees. The image analysis of the experimental rows was based on vegetation indices and results showed that there was a significant difference in the Enhanced Normalized Difference Vegetation Index (ENDVI) among the five different irrigation methods. This demonstrates that the C-MAP has very good potential as a monitoring tool for orchard management. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Geomatics)
Open AccessArticle Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data
ISPRS Int. J. Geo-Inf. 2016, 5(6), 75; doi:10.3390/ijgi5060075
Received: 12 February 2016 / Revised: 5 May 2016 / Accepted: 9 May 2016 / Published: 24 May 2016
Cited by 2 | PDF Full-text (9737 KB) | HTML Full-text | XML Full-text
Abstract
Spatial data acquisition is a critical process for the identification of the coastline and coastal zones for scientists involved in the study of coastal morphology. The availability of very high-resolution digital surface models (DSMs) and orthophoto maps is of increasing interest to all
[...] Read more.
Spatial data acquisition is a critical process for the identification of the coastline and coastal zones for scientists involved in the study of coastal morphology. The availability of very high-resolution digital surface models (DSMs) and orthophoto maps is of increasing interest to all scientists, especially those monitoring small variations in the earth’s surface, such as coastline morphology. In this article, we present a methodology to acquire and process high resolution data for coastal zones acquired by a vertical take off and landing (VTOL) unmanned aerial vehicle (UAV) attached to a small commercial camera. The proposed methodology integrated computer vision algorithms for 3D representation with image processing techniques for analysis. The computer vision algorithms used the structure from motion (SfM) approach while the image processing techniques used the geographic object-based image analysis (GEOBIA) with fuzzy classification. The SfM pipeline was used to construct the DSMs and orthophotos with a measurement precision in the order of centimeters. Consequently, GEOBIA was used to create objects by grouping pixels that had the same spectral characteristics together and extracting statistical features from them. The objects produced were classified by fuzzy classification using the statistical features as input. The classification output classes included beach composition (sand, rubble, and rocks) and sub-surface classes (seagrass, sand, algae, and rocks). The methodology was applied to two case studies of coastal areas with different compositions: a sandy beach with a large face and a rubble beach with a small face. Both are threatened by beach erosion and have been degraded by the action of sea storms. Results show that the coastline, which is the low limit of the swash zone, was detected successfully by both the 3D representations and the image classifications. Furthermore, several traces representing previous sea states were successfully recognized in the case of the sandy beach, while the erosion and beach crests were detected in the case of the rubble beach. The achieved level of detail of the 3D representations revealed new beach characteristics, including erosion crests, berm zones, and sand dunes. In conclusion, the UAV SfM workflow provides information in a spatial resolution that permits the study of coastal changes with confidence and provides accurate 3D visualizations of the beach zones, even for areas with complex topography. The overall results show that the presented methodology is a robust tool for the classification, 3D visualization, and mapping of coastal morphology. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Geomatics)
Open AccessArticle Potential of UAVs for Monitoring Mudflat Morphodynamics (Application to the Seine Estuary, France)
ISPRS Int. J. Geo-Inf. 2016, 5(4), 50; doi:10.3390/ijgi5040050
Received: 23 November 2015 / Revised: 29 March 2016 / Accepted: 8 April 2016 / Published: 13 April 2016
Cited by 3 | PDF Full-text (4703 KB) | HTML Full-text | XML Full-text
Abstract
Intertidal mudflats play a critical role in estuarine exchange, connecting marine and continental supplies of nutrients and sediments. However, their complex morphodynamics, associated with a wide range of physical and biological processes, are still poorly understood and require further field investigation. In addition,
[...] Read more.
Intertidal mudflats play a critical role in estuarine exchange, connecting marine and continental supplies of nutrients and sediments. However, their complex morphodynamics, associated with a wide range of physical and biological processes, are still poorly understood and require further field investigation. In addition, mudflats are challenging areas for Structure-from-Motion (SfM) photogrammetric surveys. Indeed, the mudflats generally hold back residual tidal water, which can make stereo restitution particularly difficult because of poor correlations or sun-glint effects. This study aims to show the potential of light UAVs (Unmanned Aerial Vehicles) for monitoring sedimentary hydrodynamics at different spatial scales in a silty estuary. For each UAV mission an orthophotograph and a Digital Elevation Model (DEM) are computed. From repeated surveys the diachronic evolution of the area can be observed via DEM differencing. Considering the ground texture in such a context, the stereo restitution process is made possible because of the high spatial resolution of the UAV photographs. Providing a synoptic view as well as high spatial resolution (less than 4 cm), the UAV dataset enables multi-scale approaches from the study of large areas to the morphodynamics of smaller-scale sedimentary structures and the morphodynamics impact of plant ground cover. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Geomatics)
Open AccessArticle Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches
ISPRS Int. J. Geo-Inf. 2015, 4(4), 2792-2820; doi:10.3390/ijgi4042792
Received: 17 August 2015 / Revised: 23 November 2015 / Accepted: 30 November 2015 / Published: 10 December 2015
Cited by 9 | PDF Full-text (1656 KB) | HTML Full-text | XML Full-text
Abstract
Grassland ecosystems cover around 40% of the entire Earth’s surface. Therefore, it is necessary to guarantee good grassland management at field scale in order to improve its conservation and to achieve optimal growth. This study identified the most appropriate statistical strategy, between partial
[...] Read more.
Grassland ecosystems cover around 40% of the entire Earth’s surface. Therefore, it is necessary to guarantee good grassland management at field scale in order to improve its conservation and to achieve optimal growth. This study identified the most appropriate statistical strategy, between partial least squares regression (PLSR) and narrow vegetation indices, for estimating the structural and biochemical grassland traits from UAV-acquired hyperspectral images. Moreover, the influence of fertilizers on plant traits for grasslands was analyzed. Hyperspectral data were collected from an experimental field at the farm Haus Riswick, near Kleve in Germany, for two different flight campaigns in May and October. The collected image blocks were geometrically and radiometrically corrected for surface reflectance. Spectral signatures extracted for the plots were adopted to derive grassland traits by computing PLSR and the following narrow vegetation indices: the MERIS Terrestrial Chlorophyll Index (MTCI), the ratio of the Modified Chlorophyll Absorption in Reflectance and Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI) modified by Wu, the Red-edge Chlorophyll Index (CIred-edge), and the Normalized Difference Red Edge (NDRE). PLSR showed promising results for estimating grassland structural traits and gave less satisfying outcomes for the selected chemical traits (crude ash, crude fiber, crude protein, Na, K, metabolic energy). Established relations are not influenced by the type and the amount of fertilization, while they are affected by the grassland health status. PLSR is found to be the best strategy, among the approaches analyzed in this paper, for exploring structural and biochemical features of grasslands. Using UAV-based hyperspectral sensing allows for the highly detailed assessment of grassland experimental plots. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Geomatics)

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Monitoring of an Apple Orchard Using Aerial Imaging System
Author: Duke Bulanon
Abstract: The uniform distribution of water and nutrients to fields of crops neglects the needs and health of individual plants, often resulting in waste. A system that can easily analyze and address the differing needs of each plant can help farmers save resources and produce healthier crops. The purpose of this research is to develop engineering technologies that will improve crop production efficiency. The Crop Monitoring and Assessment Platform (C-MAP) proposed in this research utilizes a multispectral camera attached to an unmanned aerial vehicle (UAV) designed to collect pictures of entire fields from a bird’s-eye view. An image processing algorithm based on vegetation indices is used in post processing to analyze these pictures and look for correlation between plant health and image color bands. One of the most promising correlations results from the Enhanced Normalized Difference Vegetation Index (ENDVI), which has helped detect variances in crop water and nitrogen levels. In the future this platform will help farmers decrease nutrient waste and cost while increasing crop yield.

Journal Contact

MDPI AG
IJGI Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to IJGI Edit a special issue Review for IJGI
logo
loading...
Back to Top