A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data
Abstract
:1. Introduction
2. Study Site and Data Sets
2.1. Study Site
2.2. Data Sets
Sensor | Date | Sensor | Date | Footprint: Path/Row |
---|---|---|---|---|
Landsat 5 TM | 27 August 1984 | Landsat 8 OLI | 26 July 2013 | 200/36 |
Landsat 5 TM | 27 August 1984 | Landsat 8 OLI | 26 July 2013 | 200/37 |
Landsat 5 TM | 27 August 1984 | Landsat 8 OLI | 26 July 2013 | 200/38 |
Landsat 5 TM | 7 October 1984 | Landsat 8 OLI | 4 August 2013 | 199/36 |
Landsat 5 TM | 7 October 1984 | Landsat 8 OLI | 4 August 2013 | 199/37 |
3. Methods
3.1. Image Pre-Processing
3.2. Spectral Mixture Analysis (Subpixel Approach)
3.3. Image Segmentation (Object-Based Approach)
3.4. Features Vector Used to Carry Out Object-Based Classification
Type of Feature | Name | Description |
---|---|---|
Spectral (Landsat bands) | Mean reflectance for Blue, Green, Red, Nir, Swir1, and Swir2 Landsat bands | Mean value for every object computed from the corresponding reflectance values of all the pixels belonging to the same object [47] |
Standard deviation reflectance for Blue, Green, Red, Nir, Swir1 and Swir2 Landsat bands | Standard deviation (SD) value for every object computed from the corresponding reflectance values of all the pixels belonging to the same object [47] | |
Vegetation indices | NDVI (Normalized Digital Vegetation Index) [52] | |
MSR (Modified Simple Ratio) [53] | ||
NDSVI (Normalized Differential Senescent Vegetation Index) [54] | ||
GVI (Green Vegetation Index) [55] | ||
SMA derived | Fraction PV, Fraction NPV and Fraction Soil | Mean value for every object computed from the corresponding SMA fraction of all the pixels belonging to the same object [47] |
Standard deviation Fraction PV, Fraction NPV and Fraction Soil | Standard deviation (SD) value for every object computed from the corresponding SMA fraction of all the pixels belonging to the same object [47] |
3.5. Random Forest Classifier and Classification Accuracy Assessment
4. Results and Discussion
4.1. Results from Random Forest Classification Including All Object-Based Features
Features | Relative Importance |
---|---|
NDVI_2013 | 100.00 |
Mean Green_2013 | 92.08 |
Mean Swir2_2013 | 90.53 |
MSR_2013 | 86.35 |
Fraction PV 2013 | 86.14 |
Mean Red_2013 | 82.57 |
Fraction Soil 2013 | 79.92 |
Mean Blue_2013 | 71.63 |
Fraction NPV 2013 | 71.57 |
GVI_2013 | 68.37 |
NDSVI_2013 | 68.34 |
Standard deviation Fraction NPV 2013 | 61.10 |
Standard deviation Fraction PV 2013 | 60.36 |
Mean Nir_2013 | 58.42 |
Mean Swir1_2013 | 55.06 |
Standard deviation Swir1_2013 | 45.58 |
Standard deviation Fraction Soil 2013 | 44.91 |
Standard deviation Nir_2013 | 41.44 |
Standard deviation Red_2013 | 37.58 |
Standard deviation Blue_2013 | 32.98 |
Standard deviation Swir2_2013 | 31.66 |
Standard deviation Green_2013 | 24.90 |
Classification Data Predicted by Random Forest Model | Total | |||
---|---|---|---|---|
Forest | Non-Forest | |||
Observed Data (Ground Truth) | Forest | 122 | 22 | 144 |
Non-Forest | 22 | 238 | 260 | |
Total | 144 | 260 | 404 | |
User’s accuracy | Producer’s accuracy | Overall accuracy | ||
Forest | 84.7% (CI: 77.8% to 90.2%) | 84.7% (CI: 77.8% to 90.2%) | 89.1% (CI: 85.7% to 92.0%) | |
Non-Forest | 91.5% (CI: 87.5% to 94.6%) | 91.5% (CI: 87.5% to 94.6%) |
4.2. Results from Random Forest Classification Only Including Object-Based Indices’ Features
Features | Importance |
---|---|
NDVI_2013 | 100.00 |
Fraction PV 2013 | 97.70 |
Fraction Soil 2013 | 83.43 |
GVI_2013 | 77.34 |
MSR_2013 | 75.31 |
NDSVI_2013 | 74.85 |
Fraction_NPV_2013 | 61.54 |
Classification Data Predicted by Random Forest Model | Total | |||
---|---|---|---|---|
Forest | Non-Forest | |||
Observed data (Ground Truth) | Forest | 127 | 15 | 142 |
Non-Forest | 16 | 247 | 263 | |
Total | 143 | 262 | 405 | |
User’s accuracy | Producer’s accuracy | Overall accuracy | ||
Forest | 88.8% (CI: 82.5% to 93.5%) | 89.4% (CI: 83.2% to 94.0%) | 92.3% (CI: 89.3% to 94.7%) | |
Non-Forest | 94.3% (CI: 90.7% to 96.8%) | 93.9% (CI: 90.3% to 96.5%) |
4.3. Forest Cover Change between 1984 and 2013
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Aguilar, F.J.; Nemmaoui, A.; Aguilar, M.A.; Chourak, M.; Zarhloule, Y.; García Lorca, A.M. A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data. Forests 2016, 7, 23. https://doi.org/10.3390/f7010023
Aguilar FJ, Nemmaoui A, Aguilar MA, Chourak M, Zarhloule Y, García Lorca AM. A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data. Forests. 2016; 7(1):23. https://doi.org/10.3390/f7010023
Chicago/Turabian StyleAguilar, Fernando J., Abderrahim Nemmaoui, Manuel A. Aguilar, Mimoun Chourak, Yassine Zarhloule, and Andrés M. García Lorca. 2016. "A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data" Forests 7, no. 1: 23. https://doi.org/10.3390/f7010023
APA StyleAguilar, F. J., Nemmaoui, A., Aguilar, M. A., Chourak, M., Zarhloule, Y., & García Lorca, A. M. (2016). A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data. Forests, 7(1), 23. https://doi.org/10.3390/f7010023