Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data
2.1. Study Area
2.2. Definitions of the Twelve Land Cover Classes
2.3. Image Sources and Pre-Processing
- The four image tiles intersecting with the area of interest (55HCD, 55HDD, 55HCC and 55HDC) from 1 May 2018 to 30 April 2019 were collected. This included 579 tiles, giving an average of 48 tiles per month, or 12 acquisition dates per month.
- Clouds in each tile were masked using the quality band metadata.
- Dark Object Subtraction (DOS) haze correction  was applied to each tile to obtain psuedo surface reflectance values.
- NDVI = (NIR − R)/(NIR + R) was computed.
- The four 10 m resolution bands, six 20 m resolution bands (see Table 2) and NDVI were selected, giving 11 bands per tile.
- A single mosaic was generated for each month. As multiple images are available for each month, the monthly images were generated using a quality mosaic method, selecting the image per-pixel with the highest NDVI, similar to . This produces twelve images, each with eleven bands.
- The bands were renamed with the band name followed by mosaic month with the format YYMM. For example, the NIR band from May 2018 was named NIR_1805. The twelve image mosaics were flattened into a single 12 month × 11 band = 132 band image.
2.4. Segmentation Optimization for Object-Based Image Analysis
- The total area error, which is the sum of two components:
- Omission errors, that is the area of the test geometries that are not covered by the intercepting segments (green area in Figure 4).
- Commission errors, that is the area of the intercepting segments that overlap the test geometries (red area in the figure).
- The total number of segments intercepting the 75 test objects (3 in the example shown in Figure 4).
2.5. Training Data Collection and Cleaning
- Points marking new perennial plantings, where the canopy cover is small relative to the row and tree spacing. The 10 m Sentinel pixels contain more soil than canopy in these areas, and thus, are not able to classify these areas accurately.
- Points marking annual cropping areas that were fallow in the study year.
2.6.1. Supervised Classifier Optimization
- Classification and regression tree (CART), a decision tree algorithm, which classifies data points using successive decisions based on feature values.
- Random forest (RF), which uses the average of multiple decision trees, each trained on different subsets of training data and features, to arrive at a classification for each data point.
- Support vector machine (SVM), which classifies data points based on finding hyperplanes (surfaces defined by combinations of input features) that optimally separate the classes based on training data. SVM with linear and radial basis function (RBF) kernels were assessed.
2.6.2. Classified Map Generation
- The classifier was run pixel-by-pixel on the un-segmented original image, producing a pixel-based classified map.
- The classifier was run segment-by-segment on the segmented image, producing an object-based map.
- The pixel-based classified map was transformed into an object-based map by finding the majority pixel class within each segment, producing the refined object-based map.
2.6.3. Classified Map Accuracy Assessment
3.1. Segmentation Optimization
3.2. Training Data
- Cluster 0 includes most of the perennial crop and Forest points, as well as the majority of the Annual points and some Other points. The NDVI time series shows that the cluster includes areas with dominant growth over the late spring to early autumn months (October to April) and areas with evergreen growth.
- Cluster 1 includes points with low NDVI, corresponding to areas with little vegetation. The Annual points in this cluster are annual cropping areas that were fallow in the study year. The perennial points in this cluster were young or very sparse plantings (which is the case in much of the Hazelnut growing area), with insufficient canopy to be detectable using the 10 m imagery. The majority of the Other points are in this cluster, corresponding to unplanted areas, built up areas and water bodies.
- Cluster 2 has high NDVI over the late winter to early spring months (July to November). This cluster is dominated by a significant number of Annual points. Therefore, this cluster identifies winter-grown annual crops.
3.3. Classifier Optimization Using the Filtered Samples
3.4. Classified Map Generation and Comparison
Final Map and Accuracy Assessment
Conflicts of Interest
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|Perennial crops||Citrus||Includes common oranges (mainly the Valencia) and navel oranges Citrus sinensis. There are also some Grapefruit Citrus paradisi, Lemon Citrus limon and Mandarin Citrus reticulata orchards.|
|Plum||Prunus domestica, which in this area are mainly used to produce dried prunes.|
|Stonefruit||Other stonefruit, which includes small areas of Nectarines Prunus persica var. nucipersica, Peaches Prunus persica and Apricots Prunus armeniaca.|
|Vineyard||Vitis vinifera, mainly used to grow grapes for wine production in this area.|
|Other areas||Annual||Annual crops, which includes those mainly grown over the summer (such as rice, cotton and maize) and those grown over the winter (such as barley, canola and wheat) as well as melons and vegetables.|
|Forest||Trees other than those used to produce a crop, such as native forest areas, mainly consisting of evergreen Eucalyptus.|
|Other||All other areas, including water, buildings, grass and cropping areas not planted during the study year.|
|Grouping||Band||Abbreviation||Approx. Band Center||Resolution (m)|
|Near infrared||NIR||830 nm||10|
|Red edge 1||RE1||700 nm||20|
|Red edge 2||RE2||740 nm||20|
|Red edge 3||RE3||780 nm||20|
|Near infrared narrowband||NIRN||860 nm||20|
|Short wave infrared 1||SWIR1||1610 nm||20|
|Short wave infrared 2||SWIR2||2200 nm||20|
|S1(2) TS||VV, VH||1805, 1806, …, 1904||24|
|S2(4) 1 month||B, G, R, NIR||1806||4|
|S2(5) 1 month||B, G, R, NIR, NDVI||1806||5|
|S2(10) 1 month||B, G, R, NIR, RE1, RE2, RE3, NIRN, SWIR1, SWIR2||1806||10|
|S2(5) 2 month||B, G, R, NIR, NDVI||1809, 1810||10|
|NDVI TS||NDVI||1805, 1806, …, 1904||12|
|S2(4) TS||B, G, R, NIR||1805, 1806, …, 1904||48|
|S2(10) TS||B, G, R, NIR, RE1, RE2, RE3, NIRN, SWIR1, SWIR2||1805, 1806, …, 1904||120|
|S2(10) + S1(2) Agg||B, G, R, NIR, RE1, RE2, RE3, NIRN, SWIR1, SWIR2, VV, VH||Min, Mean, Max||36|
|S2(10) + S1(2) TS||B, G, R, NIR, RE1, RE2, RE3, NIRN, SWIR1, SWIR2, VV, VH||1805, 1806, …, 1904||144|
|Size||Compactness||Merging||Area Error Per Field (ha)||Segments Per Field|
|20||0.4||1 × 0.05||0.31||44.65|
|40||0.4||2 × 0.05||0.38||9.88|
|80||0.2||1 × 0.1||0.94||3.48|
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Brinkhoff, J.; Vardanega, J.; Robson, A.J. Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data. Remote Sens. 2020, 12, 96. https://doi.org/10.3390/rs12010096
Brinkhoff J, Vardanega J, Robson AJ. Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data. Remote Sensing. 2020; 12(1):96. https://doi.org/10.3390/rs12010096Chicago/Turabian Style
Brinkhoff, James, Justin Vardanega, and Andrew J. Robson. 2020. "Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data" Remote Sensing 12, no. 1: 96. https://doi.org/10.3390/rs12010096