Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Data
3.2. Methods
3.2.1. Preprocessing
3.2.2. Processing and Analysis (Classification)
3.2.3. Validation
3.2.4. Feature Space Selection
- The highest KHAT for the Hakea sericea class (with a statistically significant difference; p < 0.05).
- The highest producer’s accuracy the Hakea sericea class (lowest omission error). The classifications will be used to locate Hakea sericea with the aim of eliminating them; therefore it is more important to minimize the omission error than the commission error.
- The highest user’s accuracy for the Hakea sericea class (lowest commission error).
- Minimum volume and complexity of the input data (i.e., feature space, need of derived data like indices), because of their contribution to the processing time.
- The highest overall KHAT (with a statistically significant difference; p < 0.05).
- The highest overall accuracy of the classification.
4. Results
4.1. WorldView-2
4.2. UAV
4.3. Feature Space Selection
4.4. Area Colonized by the Hakea sericea in the Study Area
5. Discussion
- (i)
- identify the areas where invasive Hakea sericea is already installed;
- (ii)
- develop a working protocol in a procedural framework for quick and easy monitoring of the species;
- (iii)
- identify the key areas that need to be controlled for intervention and eradication of the species;
- (iv)
- after controlling the species, promote the recovery or installation of mixed forests in the area, when possible;
- (v)
- maintain and manage the new forest.
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Class | WorldView-2 | UAV | ||
---|---|---|---|---|
Training Areas | Validation Points | Training Areas | Validation Points | |
Forest | 208 | 101 | 111 | 30 |
Hakea sericea | 95 | 76 | 54 | 50 |
Shrubs | 83 | 86 | 17 | 31 |
Infrastructures | 288 | 43 | 12 | 47 |
Bare soil | 98 | 51 | 32 | 54 |
Agriculture | 98 | 46 | - | - |
Water | 6 | 8 | - | - |
Total | 876 | 411 | 226 | 212 |
FS | UAV Orthoimages | WorldView-2 |
---|---|---|
Basic 1 | R + G + B + Nir | R + G + B + Nir1 |
Basic 2 | - | R + G + B + Nir1 + PAN |
Basic 3 | - | R + G + B + Nir1 + CB + Y + RE + Nir2 |
Basic 4 | - | Basic 3 + PAN |
Basic 5 | - | Nir1 + Nir2 + RE + R |
Index 1 | Basic 1 + NDVI-1 | NDVI + NDGI + NDYI + NDREI + NDCBI + NDBI |
Index 2 | Basic 1 + NDVI-2 | Basic 1 + Index 1 |
Texture 1 | Basic 1 + (T3R +T3G + T3B )+ (T5R + T5G + T5B) + (T7R + T7G + T7B) | Basic 3 + T3PAN + T5PAN + T7PAN |
Texture 3a | Index 1 + T3R + T3G + T3B | Index 2 + T3PAN |
Texture 3b | Basic 1 + T3R + T3G + T3B | - |
Texture 5 | Index 1 + T5R + T5G + T5B | Index 2 + T5PAN |
Texture 7 | Index 1+ T7R + T7G + T7B | Index 2 + T7PAN |
Texture A | - | Index 2 + T3PAN + T5PAN + T7PAN |
Feature Space | All Classes | Hakea sericea | |||
---|---|---|---|---|---|
OA (%) | KHAT | PA (%) | UA (%) | KHAT | |
Basic 1 (B1) | 75.61 | 0.70 a | 76.92 (66.97–85.63) | 89.55 (80.96–96.03) | 0.88 ab |
Basic 2 (B2) | 79.02 | 0.74 ab | 89.74 (81.92–95.74) | 92.11 (84.79–97.43) | 0.91 ab |
Basic 3 (B3) | 80.98 | 0.77 b | 93.59 (86.84–98.33) | 94.81 (88.42–99.17) | 0.95 b |
Basic 4 (B4) | 81.22 | 0.77 b | 93.59 (86.84–98.33) | 93.59 (86.84–98.33) | 0.93 b |
Basic 5 (B5) | 78.05 | 0.73 ab | 89.74 (78.05–93.17) | 88.61 (80.58–94.88) | 0.87 ab |
Index 1 (I1) | 70.00 | 0.63 c | 51.28 (40.29–62.22) | 83.33 (71.46–92.74) | 0.81 a |
Index 2 (I2) | 74.63 | 0.69 c | 76.92 (66.97–85.63) | 89.55 (80.96–96.03) | 0.88 ab |
Texture1 (T1) | 82.20 | 0.78 b | 93.59 (86.84–98.33) | 93.59 (86.84–98.33) | 0.93 b |
Texture3a (T3a) | 80.00 | 0.75 ab | 88.46 (80.34–94.81) | 93.24 (86.16–98.23) | 0.93 b |
Texture5 (T5) | 80.00 | 0.75 ab | 89.74 (81.92–95.74) | 93.33 (86.33–98.26) | 0.93 b |
Texture7 (T7) | 80.24 | 0.76 ab | 91.03 (83.52–96.64) | 93.42 (86.50–98.28) | 0.93 b |
TextureAll (TA) | 81.46 | 0.77 b | 91.03 (83.52–96.64) | 94.67 (88.12–99.07) | 0.94 b |
Feature Space | All Classes | Hakea sericea | |||
---|---|---|---|---|---|
OA (%) | KHAT | PA (%) | UA (%) | KHAT | |
Basic 1 (B1) | 75.47 | 0.68 a | 76.92 (64.59–87.41) | 72.90 (60.44–83.87) | 0.51 |
Index 1 (I1) | 75.00 | 0.68 a | 76.92 (64.59–87.41) | 72.90 (60.44–83.87) | 0.51 |
Index 2 (I2) | 75.47 | 0.68 a | 76.92 (64.59–87.41) | 72.90 (60.44–83.87) | 0.51 |
Texture1 (T1) | 66.98 | 0.57 bc | 80.77 (68.98–90.46) | 66.94 (55.44–77.75) | 0.43 |
Texture3a (T3a) | 69.34 | 0.61 b | 76.92 (64.59–87.41) | 70.93 (58.68–81.96) | 0.48 |
Texture3b (T3b) | 69.81 | 0.61 b | 76.92 (64.59–87.41) | 70.93(58.68–81.96) | 0.48 |
Texture5 (T5) | 67.45 | 0.58 c | 71.15 (58.24–82.62) | 67.14 (54.85–78.61) | 0.43 |
Texture7 (T7) | 65.57 | 0.56 c | 73.08 (60.33–84.24) | 71.66 (59.00–82.93) | 0.49 |
Feature Space | WorldView-2 | UAV |
---|---|---|
Name | Basic 3 | Basic 1 |
KHAT (Hakea sericea) | 0.95 | 0.51 |
Producer’s accuracy (%) | 93.59 | 76.92 |
User’s accuracy (%) | 94.81 | 72.90 |
Bands | CB, B, G, Y, R, RE, Nir1, Nir2 | R, G, B, Nir |
Overall accuracy (%) | 80.98 | 75.47 |
KHAT (global) | 0.77 | 0.68 |
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Alvarez-Taboada, F.; Paredes, C.; Julián-Pelaz, J. Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach. Remote Sens. 2017, 9, 913. https://doi.org/10.3390/rs9090913
Alvarez-Taboada F, Paredes C, Julián-Pelaz J. Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach. Remote Sensing. 2017; 9(9):913. https://doi.org/10.3390/rs9090913
Chicago/Turabian StyleAlvarez-Taboada, Flor, Claudio Paredes, and Julia Julián-Pelaz. 2017. "Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach" Remote Sensing 9, no. 9: 913. https://doi.org/10.3390/rs9090913