Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets
Abstract
:1. Introduction
2. Experimental Section
2.1. Eucalyptus Plantations
2.2. Study Area and Construction of the NDVI Time Series
Sensor Type | Acquisition Date (Year/Day) | Sensor Type | Acquisition Date | ||
---|---|---|---|---|---|
1 | ETM 7+ | 2000/258 | 9 | TM 5 | 2008/320 |
2 | TM 5 | 2001/251 | 10 | TM 5 | 2009/290 |
3 | ETM 7+ | 2002/311 | 11 | HJ-1A CCD1 | 2010/278 |
4 | TM 5 | 2003/338 | 12 | HJ-1B CCD2 | 2011/296 |
5 | TM 5 | 2004/325 | 13 | HJ-1B CCD2 | 2012/274 |
6 | TM 5 | 2005/346 | 14 | HJ-1A CCD2 | 2013/287 |
7 | TM 5 | 2006/266 | 15 | HJ-1A CCD2 | 2014/285 |
8 | ETM 7+ | 2007/293 |
Sensor Type | Path/Row | Band Setting (μm) | Spatial Resolution | Map Projection |
---|---|---|---|---|
ETM 7+/TM 5 | 122/43 | 0.45~0.52, 0.52~0.60, 0.63~0.69, 0.76~0.90, 1.55~1.75, 10.4~12.5, 2.08~2.35, (0.5~0.9, ETM 7+ only) | 30 m | UTM 49N, Datum WGS84 |
HJ-1A/B CCD1/CCD2 | 895/164 | 0.43~0.52, 0.52~0.60, 0.63~0.69, 0.76~0.90 | 30 m | UTM 50N, Datum WGS84 |
2.3. Eucalyptus Classification Methodology
2.3.1. Eucalyptus Classification Steps
2.3.2. Building a Reference NDVI Time Series Sub-Sequence
2.3.3. Eucalyptus Classification Algorithm
2.3.4. Estimation of the Presence and Planting Date of Eucalyptus Plantations
2.4. Comparison with Three Other Classification Algorithms Using a High-Resolution Photograph
2.4.1. Acquisition of the Validation Photograph
2.4.2. Comparison of Four Discriminant Functions
Discriminant Functions | Formulas |
---|---|
ITA: Inverted Triangle Area | ; ; , and are the respective ITAs for case one and two. |
CTB: City Block | was NDVI average value of the ith time step of reference NDVI time series (the same below). |
SED: Standardized Euclidian Distance | |
BE: Bounding Envelope | ; , if ; , if ; , elseif |
2.5. Validation of Eucalyptus Classification Results
3. Results and Discussion
3.1. Comparison of the Four Different Classification Algorithms
3.1.1. Determination of two Threshold Coefficients
3.1.2. Comparison of Our Results with the Other Classification Algorithms
Truth Class (from the Mosaic Photograph) | User Accuracy | |||
---|---|---|---|---|
Eucalyptus Pixels (Case 1/Case 2) | Not-Eucalyptus Pixels (Case 1/Case 2) | |||
ITA discriminant function | Eucalyptus pixels | 126/106 | 35/25 | 79% |
Not-eucalyptus pixels | 60 | 926 | 94% | |
Producer accuracy | 79% | 94% | Overall acc. 91% | |
BE discriminant function | Eucalyptus pixels | 169 | 123 | 58% |
Not-eucalyptus pixels | 123 | 863 | 88% | |
Producer accuracy | 58% | 88% | Overall acc. 81% | |
SED discriminant function | Eucalyptus pixels | 145 | 147 | 50% |
Not-eucalyptus pixels | 147 | 839 | 85% | |
Producer accuracy | 50% | 85% | Overall acc. 77% | |
CTB discriminant function | Eucalyptus pixels | 143 | 149 | 49% |
Not-eucalyptus pixels | 149 | 837 | 85% | |
Producer accuracy | 49% | 85% | Overall acc. 77% |
3.2. Further Validation of the Classification Results Using a High-Resolution GF Image
Truth Class (Interpreted from GF-1 Image) | User Accuracy | |||
---|---|---|---|---|
Eucalyptus Pixels | Not-Eucalyptus Pixels | |||
Classification result of sub-region A | Eucalyptus pixels | 62636 | 20198 | 75.62% |
Not-eucalyptus pixels | 19141 | 182114 | 90.49% | |
Producer accuracy | 76.59% | 90.02% | Overall acc 86.15% | |
Classification result of sub-region B | Eucalyptus pixels | 51319 | 14756 | 77.67% |
Not-eucalyptus pixels | 15623 | 202391 | 92.83% | |
Producer accuracy | 76.66% | 93.20% | Overall acc.89.31% |
3.3. Estimation of the Eucalyptus Planting Date
3.4. Eucalyptus Plantation Map Classified with the ITA Algorithm
3.5. Assessment of Eucalyptus Classification
- (1)
- Errors due to different data sources. When building the NDVI time series, we used NDVI from ETM 7+/TM 5 from 2000 to 2009 and NDVI from HJ-1A/B from 2010 to 2014. Although the difference of NDVI from these three sensors was so small (as discussed in Section 2.2) that a process of normalizing NDVI was left out, the classification accuracy of 2008, 2009, 2010 and 2011 (the biggest length of the reference NDVI time series sub-sequence was three time steps) was likely somewhat lower due to the lack of normalization between sensors. For validation, we used the UAV photographs and the GF-1 image. Although all images were pre-processed, many errors, especially those caused by different flight angles and attitudes of satellites could not be avoided [36,37].
- (2)
- Errors due to mixed pixels. In the application of remote sensing data, one pixel necessarily includes information from many different features in addition to the one of interest [6,38]. For example, on the borders of eucalyptus plantations, a eucalyptus pixel may be classified as a not-eucalyptus pixel because of interference from extraneous features (water, bamboo, etc.), thereby decreasing accuracy in these areas. As shown in Figure 8, within the eucalyptus class, numerous vacant regions existed, where the coverage of eucalyptus plantations was low and even some bare soil was exposed, resulting in mixed pixels. Therefore, although the spatial resolution was high in this study, errors from the effect of mixed pixels could not be avoided.
- (3)
- Errors caused by the determination of the reference NDVI time series sub-sequence and threshold coefficients. The reference NDVI time series sub-sequence and threshold coefficients are the basis of the classification algorithms. When determining these coefficients, we considered as many significant factors as we could to build a representative reference time series. However, artificial and systemic factors still existed. We selected a mosaic photograph region to determine threshold coefficients and worked to balance commission and omission errors. Nevertheless, as further validation showed, there remained a small difference between these errors. Our study area was relatively small, so the same threshold coefficients could be shared. Larger study areas will require additional investigations and the adjustment of threshold coefficients for different sub-regions.
3.6. Potential of the ITA Methodology
3.6.1. Potential Improvement of the ITA Methodology
3.6.2. Application Potential of the ITA Methodology
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Qiao, H.; Wu, M.; Shakir, M.; Wang, L.; Kang, J.; Niu, Z. Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets. Remote Sens. 2016, 8, 117. https://doi.org/10.3390/rs8020117
Qiao H, Wu M, Shakir M, Wang L, Kang J, Niu Z. Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets. Remote Sensing. 2016; 8(2):117. https://doi.org/10.3390/rs8020117
Chicago/Turabian StyleQiao, Hailang, Mingquan Wu, Muhammad Shakir, Li Wang, Jun Kang, and Zheng Niu. 2016. "Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets" Remote Sensing 8, no. 2: 117. https://doi.org/10.3390/rs8020117