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Special Issue "UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation and Agricultural Crops"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2014)

Special Issue Editors

Guest Editor
Dr. Arko Lucieer

School of Land and Food, Discipline of Geography and Spatial Sciences, University of Tasmania, Private Bag 76, Hobart, TAS 7001, Australia
Website | E-Mail
Phone: +61362262140
Fax: +61 (0)3 6226 2989
Interests: environmental and quantitative remote sensing; unmanned aerial vehicles (UAVs); UAV sensor integration; hyperspectral, multispectral, and thermal image processing; image texture measures; classification and machine learning; object-based image analysis; change detection; terrain analysis techniques
Guest Editor
Dr. Pablo J. Zarco-Tejada

QuantaLab Remote Sensing Laboratory, Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo, s/n, E-14004 Córdoba, Spain
Website | E-Mail
Fax: +(34) 957 499 252
Interests: hyperspectral remote sensing imagery for vegetation stress monitoring, water stress detection with thermal imagery; pre-visual indicators of stress; chlorophyll fluorescence; precision agriculture
Guest Editor
Prof. Dr. Uwe Rascher

IBG-2: Plant Sciences, Institute of Bio- und Geosciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Website | E-Mail
Fax: +49-(0)2461-61 2492
Interests: ecophysiology of photosynthesis; plant stress physiology; field phenotyping; optical remote sensing; understanding of sun-induced fluorescence; high resolution imaging spectroscopy
Guest Editor
Prof. Dr. Georg Bareth

GIS & RS Group, Institute of Geography, University of Cologne, D-50923 Cologne, Germany
Website | E-Mail
Fax: +49-221-470-1638
Interests: low-weight UAVs; hyperspectral and multisepctral remote sensing; field spectrometry; terrestrial lasercanning; 3D analysis; plant biomass; plant nitrogen; change detection; matter fluxes; precision agriculture; spatial data management

Special Issue Information

Dear Colleagues,

Recent developments in Unmanned Aerial Vehicle (UAV) platforms, sensors, and image processing techniques have resulted in an increasing uptake of this technology in the remote sensing science community. Several studies have successfully demonstrated UAV operations using small platforms equipped with sensors for RGB, multispectral, hyperspectral, and thermal imaging, as well as with laser scanning capabilities. While regulations may still hinder the broader usage of UAVs, technological development - especially in miniaturizing sensors - is progressing rapidly. UAVs’ capacity for near-continuous acquisition of ultra-high resolution imagery has provided both opportunities and challenges in the area of vegetation mapping and monitoring; specific applications of this technology exist in the areas of forestry, agriculture, and ecosystem research. There are significant research opportunities in detecting tree and crop species, physiological and structural traits, plant communities, biophysical and biochemical characteristics of canopies, and vegetation stress. Novel UAV-based imaging techniques and the development of specialized processing workflows will allow us to address novel and important science questions in vegetation monitoring. This Special Issue focuses on UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation and Agricultural Crops. The scope of this issue includes, but is not limited to, significant improvements in sensor technology, descriptions of processing schemes and algorithms, improved understandings of vegetation processes, and the interpretation of spatio-temporal vegetation dynamics using time series. Contributions should focus on UAV-based approaches that are aimed at improving our scientific understanding of vegetation processes. Studies may span the spatial scale from millimeters to the regional, and time scales from seconds to seasons. Prospective authors are invited to contribute to this Special Issue of Remote Sensing (Impact Factor 2012: 2.1; 5-Year Impact Factor 2012: 2.2) by submitting an original manuscript. Contributions may focus on, but are not limited to:

  • UAV-based stereo imaging of vegetation
  • UAV-based structure from motion (SfM) imaging of vegetation
  • UAV-based laser scanning of vegetation
  • UAV-based multispectral imaging of vegetation
  • UAV-based hyperspectral imaging of vegetation
  • UAV-based thermal imaging of vegetation
  • UAV-based change detection of vegetation
  • Methodological developments for information extraction from UAV-based imaging: i.e., vegetation index development and automatic feature extraction
  • Accuracy and precision evaluations of UAV-based vegetation imaging techniques
  • Real-time georeferencing for UAV-based imaging of vegetation
  • Radiometric and spectral calibration of UAV instruments
  • Linking field samples to UAV imagery and image derivatives

Dr. Arko Lucieer
Dr. Pablo J. Zarco-Tejada
Prof. Dr. Uwe Rascher
Prof. Dr. Georg Bareth
Guest Editors

Submission

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. Papers will be published continuously (i.e., as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles, and 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 refereed through a peer-review process. A guide for authors and other relevant information for the submission of manuscripts is available on the Instructions for Authors page. Remote Sensing 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 1000 CHF (Swiss Francs).

Keywords

  • UAV
  • UAS
  • vegetation
  • forestry
  • agriculture
  • ecosystem
  • change detection
  • phenology
  • vitality
  • plant physiology
  • plant structure
  • stereo imaging
  • structure from motion
  • 3D
  • RGB
  • multispectral
  • hyperspectral
  • laser scanning
  • thermal
  • vegetation indicies
  • object extraction
  • spatio-temporal patterns
  • GPS/DGPS/RTK
  • accuracy and precision

Published Papers (13 papers)

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Research

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Open AccessArticle High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials
Remote Sens. 2015, 7(4), 4213-4232; doi:10.3390/rs70404213
Received: 17 December 2014 / Revised: 6 March 2015 / Accepted: 26 March 2015 / Published: 8 April 2015
Cited by 15 | PDF Full-text (14261 KB) | HTML Full-text | XML Full-text
Abstract
The development of reliable methods for the estimation of crown architecture parameters is a key issue for the quantitative evaluation of tree crop adaptation to environment conditions and/or growing system. In the present work, we developed and tested the performance of a method
[...] Read more.
The development of reliable methods for the estimation of crown architecture parameters is a key issue for the quantitative evaluation of tree crop adaptation to environment conditions and/or growing system. In the present work, we developed and tested the performance of a method based on low-cost unmanned aerial vehicle (UAV) imagery for the estimation of olive crown parameters (tree height and crown diameter) in the framework of olive tree breeding programs, both on discontinuous and continuous canopy cropping systems. The workflow involved the image acquisition with consumer-grade cameras on board a UAV and orthomosaic and digital surface model generation using structure-from-motion image reconstruction (without ground point information). Finally, geographical information system analyses and object-based classification were used for the calculation of tree parameters. Results showed a high agreement between remote sensing estimation and field measurements of crown parameters. This was observed both at the individual tree/hedgerow level (relative RMSE from 6% to 20%, depending on the particular case) and also when average values for different genotypes were considered for phenotyping purposes (relative RMSE from 3% to 16%), pointing out the interest and applicability of these data and techniques in the selection scheme of breeding programs. Full article
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Open AccessArticle Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images
Remote Sens. 2015, 7(4), 4026-4047; doi:10.3390/rs70404026
Received: 24 November 2014 / Revised: 5 February 2015 / Accepted: 23 March 2015 / Published: 2 April 2015
Cited by 9 | PDF Full-text (62909 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying
[...] Read more.
Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications. Full article
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Open AccessArticle Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture
Remote Sens. 2015, 7(3), 2971-2990; doi:10.3390/rs70302971
Received: 14 November 2014 / Revised: 15 January 2015 / Accepted: 17 February 2015 / Published: 13 March 2015
Cited by 16 | PDF Full-text (19918 KB) | HTML Full-text | XML Full-text
Abstract
Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to
[...] Read more.
Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to low operational costs, high operational flexibility and high spatial resolution of imagery. In order to optimize the use of these technologies for precision viticulture, their technical, scientific and economic performances need to be assessed. The aim of this work is to compare NDVI surveys performed with UAV, aircraft and satellite, to assess the capability of each platform to represent the intra-vineyard vegetation spatial variability. NDVI images of two Italian vineyards were acquired simultaneously from different multi-spectral sensors onboard the three platforms, and a spatial statistical framework was used to assess their degree of similarity. Moreover, the pros and cons of each technique were also assessed performing a cost analysis as a function of the scale of application. Results indicate that the different platforms provide comparable results in vineyards characterized by coarse vegetation gradients and large vegetation clusters. On the contrary, in more heterogeneous vineyards, low-resolution images fail in representing part of the intra-vineyard variability. The cost analysis showed that the adoption of UAV platform is advantageous for small areas and that a break-even point exists above five hectares; above such threshold, airborne and then satellite have lower imagery cost. Full article
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Open AccessArticle UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis
Remote Sens. 2015, 7(1), 1074-1094; doi:10.3390/rs70101074
Received: 31 October 2014 / Accepted: 12 January 2015 / Published: 19 January 2015
Cited by 11 | PDF Full-text (86113 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs.
[...] Read more.
Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB) images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1) Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2) the inclusion of texture features improved classification accuracy significantly; (3) classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time. Full article
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Open AccessArticle Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer
Remote Sens. 2015, 7(1), 725-746; doi:10.3390/rs70100725
Received: 27 October 2014 / Accepted: 4 January 2015 / Published: 12 January 2015
Cited by 11 | PDF Full-text (1561 KB) | HTML Full-text | XML Full-text
Abstract
In this study we present a hyperspectral flying goniometer system, based on a rotary-wing unmanned aerial vehicle (UAV) equipped with a spectrometer mounted on an active gimbal. We show that this approach may be used to collect multiangular hyperspectral data over vegetated environments.
[...] Read more.
In this study we present a hyperspectral flying goniometer system, based on a rotary-wing unmanned aerial vehicle (UAV) equipped with a spectrometer mounted on an active gimbal. We show that this approach may be used to collect multiangular hyperspectral data over vegetated environments. The pointing and positioning accuracy are assessed using structure from motion and vary from σ = 1° to 8° in pointing and σ = 0.7 to 0.8 m in positioning. We use a wheat dataset to investigate the influence of angular effects on the NDVI, TCARI and REIP vegetation indices. Angular effects caused significant variations on the indices: NDVI = 0.83–0.95; TCARI = 0.04–0.116; REIP = 729–735 nm. Our analysis highlights the necessity to consider angular effects in optical sensors when observing vegetation. We compare the measurements of the UAV goniometer to the angular modules of the SCOPE radiative transfer model. Model and measurements are in high accordance (r2 = 0.88) in the infrared region at angles close to nadir; in contrast the comparison show discrepancies at low tilt angles (r2 = 0.25). This study demonstrates that the UAV goniometer is a promising approach for the fast and flexible assessment of angular effects. Full article
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Open AccessArticle Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle
Remote Sens. 2014, 6(12), 12815-12836; doi:10.3390/rs61212815
Received: 19 August 2014 / Revised: 9 December 2014 / Accepted: 15 December 2014 / Published: 22 December 2014
Cited by 9 | PDF Full-text (9712 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A passive method for remote sensing of the nuisance green algae Cladophora glomerata in rivers is presented using an unmanned aerial vehicle (UAV). Included are methods for UAV operation, lens distortion correction, image georeferencing, and spectral analysis to support algal cover mapping. Eighteen
[...] Read more.
A passive method for remote sensing of the nuisance green algae Cladophora glomerata in rivers is presented using an unmanned aerial vehicle (UAV). Included are methods for UAV operation, lens distortion correction, image georeferencing, and spectral analysis to support algal cover mapping. Eighteen aerial photography missions were conducted over the summer of 2013 using an off-the-shelf UAV and three-band, wide-angle, red, green, and blue (RGB) digital camera sensor. Images were post-processed, mosaicked, and georeferenced so automated classification and mapping could be completed. An adaptive cosine estimator (ACE) and spectral angle mapper (SAM) algorithm were used to complete the algal identification. Digital analysis of optical imagery correctly identified filamentous algae and background coverage 90% and 92% of the time, and tau coefficients were 0.82 and 0.84 for ACE and SAM, respectively. Thereafter, algal cover was characterized for a one-kilometer channel segment during each of the 18 UAV flights. Percent cover ranged from <5% to >50%, and increased immediately after vernal freshet, peaked in midsummer, and declined in the fall. Results indicate that optical remote sensing with UAV holds promise for completing spatially precise, and multi-temporal measurements of algae or submerged aquatic vegetation in shallow rivers with low turbidity and good optical transmission. Full article
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Open AccessArticle Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV
Remote Sens. 2014, 6(12), 12037-12054; doi:10.3390/rs61212037
Received: 29 May 2014 / Revised: 18 November 2014 / Accepted: 19 November 2014 / Published: 3 December 2014
Cited by 9 | PDF Full-text (1244 KB) | HTML Full-text | XML Full-text
Abstract
The development of low-cost unmanned aerial vehicles (UAVs) and light weight imaging sensors has resulted in significant interest in their use for remote sensing applications. While significant attention has been paid to the collection, calibration, registration and mosaicking of data collected from small
[...] Read more.
The development of low-cost unmanned aerial vehicles (UAVs) and light weight imaging sensors has resulted in significant interest in their use for remote sensing applications. While significant attention has been paid to the collection, calibration, registration and mosaicking of data collected from small UAVs, the interpretation of these data into semantically meaningful information can still be a laborious task. A standard data collection and classification work-flow requires significant manual effort for segment size tuning, feature selection and rule-based classifier design. In this paper, we propose an alternative learning-based approach using feature learning to minimise the manual effort required. We apply this system to the classification of invasive weed species. Small UAVs are suited to this application, as they can collect data at high spatial resolutions, which is essential for the classification of small or localised weed outbreaks. In this paper, we apply feature learning to generate a bank of image filters that allows for the extraction of features that discriminate between the weeds of interest and background objects. These features are pooled to summarise the image statistics and form the input to a texton-based linear classifier that classifies an image patch as weed or background. We evaluated our approach to weed classification on three weeds of significance in Australia: water hyacinth, tropical soda apple and serrated tussock. Our results showed that collecting images at 5–10 m resulted in the highest classifier accuracy, indicated by F1 scores of up to 94%. Full article
Open AccessArticle A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles
Remote Sens. 2014, 6(11), 11013-11030; doi:10.3390/rs61111013
Received: 3 June 2014 / Revised: 20 October 2014 / Accepted: 3 November 2014 / Published: 10 November 2014
Cited by 17 | PDF Full-text (846 KB) | HTML Full-text | XML Full-text
Abstract
During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs.
[...] Read more.
During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. In this article we present a lightweight hyperspectral mapping system (HYMSY) for rotor-based UAVs, the novel processing chain for the system, and its potential for agricultural mapping and monitoring applications. The HYMSY consists of a custom-made pushbroom spectrometer (400–950 nm, 9 nm FWHM, 25 lines/s, 328 px/line), a photogrammetric camera, and a miniature GPS-Inertial Navigation System. The weight of HYMSY in ready-to-fly configuration is only 2.0 kg and it has been constructed mostly from off-the-shelf components. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model (DSM) and provides high accuracy orientation of the system over the DSM. The pushbroom data is georectified by projecting it onto the DSM with the support of photogrammetric orientations and the GPS-INS data. Since an up-to-date DSM is produced internally, no external data are required and the processing chain is capable to georectify pushbroom data fully automatically. The system has been adopted for several experimental flights related to agricultural and habitat monitoring applications. For a typical flight, an area of 2–10 ha was mapped, producing a RGB orthomosaic at 1–5 cm resolution, a DSM at 5–10 cm resolution, and a hyperspectral datacube at 10–50 cm resolution. Full article
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Open AccessArticle Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System
Remote Sens. 2014, 6(11), 10335-10355; doi:10.3390/rs61110335
Received: 19 May 2014 / Revised: 26 September 2014 / Accepted: 10 October 2014 / Published: 27 October 2014
Cited by 16 | PDF Full-text (4940 KB) | HTML Full-text | XML Full-text
Abstract
Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models
[...] Read more.
Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three early- to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients \({R}^{2}\) of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04m\(\cdot\)px\(^{-1}\)).Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction. Full article
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Open AccessArticle Using Unmanned Aerial Vehicles (UAV) to Quantify Spatial Gap Patterns in Forests
Remote Sens. 2014, 6(8), 6988-7004; doi:10.3390/rs6086988
Received: 12 May 2014 / Revised: 22 July 2014 / Accepted: 22 July 2014 / Published: 29 July 2014
Cited by 14 | PDF Full-text (7768 KB) | HTML Full-text | XML Full-text
Abstract
Gap distributions in forests reflect the spatial impact of man-made tree harvesting or naturally-induced patterns of tree death being caused by windthrow, inter-tree competition, disease or senescence. Gap sizes can vary from large (>100 m2) to small (<10 m2),
[...] Read more.
Gap distributions in forests reflect the spatial impact of man-made tree harvesting or naturally-induced patterns of tree death being caused by windthrow, inter-tree competition, disease or senescence. Gap sizes can vary from large (>100 m2) to small (<10 m2), and they may have contrasting spatial patterns, such as being aggregated or regularly distributed. However, very small gaps cannot easily be recorded with conventional aerial or satellite images, which calls for new and cost-effective methodologies of forest monitoring. Here, we used an unmanned aerial vehicle (UAV) and very high-resolution images to record the gaps in 10 temperate managed and unmanaged forests in two regions of Germany. All gaps were extracted for 1-ha study plots and subsequently analyzed with spatially-explicit statistics, such as the conventional pair correlation function (PCF), the polygon-based PCF and the mark correlation function. Gap-size frequency was dominated by small gaps of an area <5 m2, which were particularly frequent in unmanaged forests. We found that gap distances showed a variety of patterns. However, the polygon-based PCF was a better descriptor of patterns than the conventional PCF, because it showed randomness or aggregation for cases when the conventional PCF showed small-scale regularity; albeit, the latter was only a mathematical artifact. The mark correlation function revealed that gap areas were in half of the cases negatively correlated and in the other half independent. Negative size correlations may likely be the result of single-tree harvesting or of repeated gap formation, which both lead to nearby small gaps. Here, we emphasize the usefulness of UAV to record forest gaps of a very small size. These small gaps may originate from repeated gap-creating disturbances, and their spatial patterns should be monitored with spatially-explicit statistics at recurring intervals in order to further insights into forest dynamics. Full article
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Open AccessArticle Spatial Co-Registration of Ultra-High Resolution Visible, Multispectral and Thermal Images Acquired with a Micro-UAV over Antarctic Moss Beds
Remote Sens. 2014, 6(5), 4003-4024; doi:10.3390/rs6054003
Received: 17 January 2014 / Revised: 15 April 2014 / Accepted: 15 April 2014 / Published: 2 May 2014
Cited by 22 | PDF Full-text (2308 KB) | HTML Full-text | XML Full-text
Abstract
In recent times, the use of Unmanned Aerial Vehicles (UAVs) as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel) and higher temporal resolution data. For
[...] Read more.
In recent times, the use of Unmanned Aerial Vehicles (UAVs) as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel) and higher temporal resolution data. For the purposes of vegetation monitoring, the use of multiple sensors such as near infrared and thermal infrared cameras are of benefit. Collecting data with multiple sensors, however, requires an accurate spatial co-registration of the various UAV image datasets. In this study, we used an Oktokopter UAV to investigate the physiological state of Antarctic moss ecosystems using three sensors: (i) a visible camera (1 cm/pixel), (ii) a 6 band multispectral camera (3 cm/pixel), and (iii) a thermal infrared camera (10 cm/pixel). Imagery from each sensor was geo-referenced and mosaicked with a combination of commercially available software and our own algorithms based on the Scale Invariant Feature Transform (SIFT). The validation of the mosaic’s spatial co-registration revealed a mean root mean squared error (RMSE) of 1.78 pixels. A thematic map of moss health, derived from the multispectral mosaic using a Modified Triangular Vegetation Index (MTVI2), and an indicative map of moss surface temperature were then combined to demonstrate sufficient accuracy of our co-registration methodology for UAV-based monitoring of Antarctic moss beds. Full article
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Open AccessArticle Vicarious Radiometric Calibration of a Multispectral Camera on Board an Unmanned Aerial System
Remote Sens. 2014, 6(3), 1918-1937; doi:10.3390/rs6031918
Received: 16 December 2013 / Revised: 7 February 2014 / Accepted: 19 February 2014 / Published: 28 February 2014
Cited by 16 | PDF Full-text (1008 KB) | HTML Full-text | XML Full-text
Abstract
Combinations of unmanned aerial platforms and multispectral sensors are considered low-cost tools for detailed spatial and temporal studies addressing spectral signatures, opening a broad range of applications in remote sensing. Thus, a key step in this process is knowledge of multi-spectral sensor calibration
[...] Read more.
Combinations of unmanned aerial platforms and multispectral sensors are considered low-cost tools for detailed spatial and temporal studies addressing spectral signatures, opening a broad range of applications in remote sensing. Thus, a key step in this process is knowledge of multi-spectral sensor calibration parameters in order to identify the physical variables collected by the sensor. This paper discusses the radiometric calibration process by means of a vicarious method applied to a high-spatial resolution unmanned flight using low-cost artificial and natural covers as control and check surfaces, respectively. Full article

Review

Jump to: Research

Open AccessReview UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas
Remote Sens. 2014, 6(11), 11051-11081; doi:10.3390/rs61111051
Received: 3 June 2014 / Revised: 21 October 2014 / Accepted: 22 October 2014 / Published: 11 November 2014
Cited by 17 | PDF Full-text (9784 KB) | HTML Full-text | XML Full-text
Abstract
The miniaturization of electronics, computers and sensors has created new opportunities for remote sensing applications. Despite the current restrictions on regulation, the use of unmanned aerial vehicles equipped with small thermal, laser or spectral sensors has emerged as a promising alternative for assisting
[...] Read more.
The miniaturization of electronics, computers and sensors has created new opportunities for remote sensing applications. Despite the current restrictions on regulation, the use of unmanned aerial vehicles equipped with small thermal, laser or spectral sensors has emerged as a promising alternative for assisting modeling, mapping and monitoring applications in rangelands, forests and agricultural environments. This review provides an overview of recent research that has reported UAV flight experiments on the remote sensing of vegetated areas. To provide a differential trend to other reviews, this paper is not limited to crops and precision agriculture applications, but also includes forest and rangeland applications. This work follows a top-down categorization strategy and attempts to fill the gap between application requirements and the characteristics of selected tools, payloads and platforms. Furthermore, correlations between common requirements and the most frequently used solutions are highlighted. Full article
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