UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas
"> Figure 1
<p>UAV experiments classification by vegetation type (colors), airframe (symbols), altitude, size, and endurance.</p> "> Figure 2
<p>Visible mosaic of Robison Ridge site in Antarctica (left), moss health derived from MTVI2 vegetation index (upper right), and moss surface temperature at ultra-high spatial resolution (lower right); the red circle highlights a thermal shadow (reprinted from [<a href="#B85-remotesensing-06-11051" class="html-bibr">85</a>]).</p> "> Figure 3
<p>Very high-resolution (<span class="html-italic">≈</span>7 cm/pixel) RGB images showing delineated gaps in two different regions in Germany (<b>a</b>, <b>b</b>), and the gap map obtained for the same plot as (b) with a manned LIDAR flight (<b>c</b>) (reprinted from [<a href="#B87-remotesensing-06-11051" class="html-bibr">87</a>]).</p> "> Figure 4
<p>Interpolated DEMs of Constitution Hill in Wales, UK, using (<b>a</b>) TLS and (<b>b</b>) SfM, (<b>c</b>) aerial photograph of the site, and (<b>d</b>) point density map.‘A’ and ‘B’ labels correspond to the headwall at the highest point and near-vertical faces respectively. ‘VF’ and ‘DF’ labels refer to vegetation-free and desenly vegetated sub-regions respectively (reprinted from [<a href="#B91-remotesensing-06-11051" class="html-bibr">91</a>]).</p> "> Figure 5
<p>Imaging sensors used in UAV-based systems for vegetation remote sensing.</p> "> Figure 6
<p>Different UAV platforms used in vegetation remote sensing.</p> ">
Abstract
:1. Introduction
2. Taxonomy of UAV Experiments in the Remote Sensing of Vegetation
Category | Subcategory |
---|---|
1-Area of mobility | Air Ground Water |
2-Area of application | Military Industry Surveying Agriculture Aquaculture Forestry |
3-Application 4-Processing | Mapping Monitoring Scouting Applying |
5-Data analysis | Online Offline |
6-Level of data fusion | Regression Classification Data mining Low Intermediate High |
7-Software architect. | Modular Blackboard Control Multi-agent Component Dataflow Redundant Complementary Cooperative |
8-Sensors set 9-Sensing activity | Active Passive |
10-Method 11-Platform size 12-Propulsion | Optical Thermal Electrical Magnetic Acoustic Mechanical Chemical Small / Light Medium Large / Heavy |
13-Automation degree | Electric Combustion Manual Automated Autonomous |
Category | Subcategory | |
---|---|---|
(2) | A-Vegetation | Wildland Agricultural field |
(3) | B-Application | Passive Proactive Rective |
(4–7) | C-Processing | Pre-processing Vegetation indices Segmentation 3D reconstruction |
(8–10) | D-Payload | Laser Spectral Thermal Chemical |
(11–13) | E-Platform | Fixed-wing Rotorcraft Unpowered |
2.1. Vegetation
2.2. Application
2.3. Processing
2.4. Payload
2.5. Platform
3. Vegetation Types
4. Applications
Application area | Use case |
---|---|
Passive applications | |
Climate change monitoring | Mapping moss beds in Antarctica [42] |
Monitoring of biodiversity in the Fonttainebleau forest [41] | |
Rangeland health monitoring | Determining canopy cover and gap sizes [35] |
Monitoring changes in the canopy structure [28] | |
Dead wood identification [11] | |
Rangeland/forest inventory | Differentiating rangeland vegetation [36] |
Mapping and characterization of riparian forests [11] | |
Topographic surveying and mapping | Mapping the substrate and vegetation in rivers [43] |
Assessing ephemeral gully erosion in agricultural fields [61] | |
Proactive applications | |
Wildfire fighting | Forest fire detection and monitoring [62,63] |
Precission Agriculture | Modeling canopy structure [48] |
Ripeness monitoring [53] | |
Water stress detection [45,46,50,51] | |
Estimation of nitrogen level [22,59,64] | |
Pathogen detection [44,55] | |
Aerobiological sampling [29] | |
Plant health monitoring [54] | |
Mapping invasive weeds [53] | |
Monitoring herbicide applications [65] | |
Reactive applications | |
Spraying | Spraying chemicals on crops [66] |
5. Data Processing
5.1. Image Pre-Processing
5.2. Vegetation Indices
Index | Description | Bands | Uses |
---|---|---|---|
Thermal indices | |||
CWSI | Crop Water Stress Index | TIR | Water stress detection [44,50,51] |
Ig, I3 | Stomatal Conductance indices | TIR | Water stress detection [50,51] |
Spectral indices | |||
GI | Greenness Index | VIS | Chlorophyll concentration [22,44,45,51] |
GNDVI | Green Normalized Difference VI | NIR, VIS | Nitrogen concentration [22] |
Water stress detection [51] | |||
LAI estimation [60] | |||
NDVI | Normalized Difference VI | NIR, VIS | LAI estimation [22,44–46,54–56,58,72] |
Water stress detection [51] | |||
PRI | Photochemical Reflectance Index | VIS | Water stress detection [44,45,46,51] |
SAVI | Soil-Adjusted VI | NIR, VIS | LAI estimation [22,44,51] |
TCARI/OSAVI | Transformed Chlorophyll Absorption in Reflectance/Optimized Soil-Adjusted VI | NIR, VIS | Chlorophyll concentration [44,[4546] |
Water stress detection [51] |
5.3. Segmentation
5.4. 3D Reconstruction
6. Payload
7. Aerial Platforms and Flight Characteristics
8. Discussion
9. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Salamí, E.; Barrado, C.; Pastor, E. UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas. Remote Sens. 2014, 6, 11051-11081. https://doi.org/10.3390/rs61111051
Salamí E, Barrado C, Pastor E. UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas. Remote Sensing. 2014; 6(11):11051-11081. https://doi.org/10.3390/rs61111051
Chicago/Turabian StyleSalamí, Esther, Cristina Barrado, and Enric Pastor. 2014. "UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas" Remote Sensing 6, no. 11: 11051-11081. https://doi.org/10.3390/rs61111051