Mapping invasive alien plant species with very high spatial resolution and multi-date satellite imagery using object-based and machine learning techniques: A comparative study

Figures & data

Figure 1. Study area limits and reference data locations of invasive alien plant species (giant hogweed, Japanese knotweed, and phragmites).

Figure 2. Field photography of invasive alien plant species at two zoom levels: (a, b) Japanese knotweed; (c, d) Giant hogweed; and (e, f) phragmites.

Table 1. Characteristics of satellite images used in this study.

Image	Band	Wavelength range (center) (nm)	Spatial resolution (m)	Acquisition date
WV-3	Coastal	400–450 (425)	1.24	2020/07/06
	Blue	450–510 (480)
	Green	510–580 (545)
	Yellow	585–625 (605)
	Red	630–690 (660)
	Red Edge	705–745 (725)
	Near-infrared 1	770–895 (832.5)
	Near-infrared 2	860–1040 (950)
	Panchromatic	450–800 (625)	0.31
SPOT-7	Blue	450–520 (485)	6	2019/10/26
	Green	530–590 (560)
	Red	625–695 (660)
	Near-infrared	760–890 (825)
	Panchromatic	450–745 (597.5)	1.5

WV-3 : WorldView-3, SPOT-7 : Satellite Pour l’Observation de la Terre-7.

Figure 3. Methodological flowchart of the study.

Table 2. ERGAS (Relative Dimensionless Global Error) values for the fusion methods applied to the WorldView-3 image.

	Zhang	Bayesian fusion	RCS	LMVM	GS
Tile 1	2.80	2.31	2.03	2.03	2.87
Tile 2	4.24	3.72	3.40	3.08	4.31
Tile 3	6.17	7.43	4.80	4.34	5.40
Tile 4	3.98	3.13	2.95	2.59	3.53
Mean ± SD	4.30 ± 0.94	4.15 ± 1.64	3.30 ± 0.81	3.01 ± 0.70	4.03 ± 0.83

RCS: Ratio Component Substitution; LMVM: Local Mean and Variance Matching.

GS: Gram-Schmidt; SD: Standard Deviation.

Table 3. Extracted features (spectral, textural, and geometric) for classification modeling.

Category	Feature	Definition	Reference
Spectral	Mean	All
	Maximum	All
	Standard deviation	All
	95^th percentile	All
	Skewness	All
	NDVI (Normalized Difference Vegetation Index)	(NIR1 - red)/ (NIR1 + Red)	Carlson and Ripley (1997)
	RENDVI (Red-Edge Normalized Difference Vegetation Index)	(NIR1 - RE)/ (NIR1 + RE)	Waser et al. (2014)
	VARI (Visible Atmospherically Resistant Index)	(green - red) / (green + red -blue)	Gitelson et al. (2002)
	RGR (Red Green Ratio)	red/green	Zhou et al. (2021)
	SR (Simple Ratio)	NIR1 /red	Zhou et al. (2021)
	BTBR (Bi-Temporal Band Ratio)	(red, green)*	Dorigo et al. (2012)
	HSI (Hue, Saturation, Intensity)	red, green, blue*	Chaudhary et al. (2012)
	CI (Colour Index)	(red - green) / (red + green)	Escadafal and Huete (1991)
	BI (Brightness Index)	(red^2 + NIR1^2)^0.5	Zhou et al. (2018)
	NDWI (Normalized Difference Water Index)	(green - NIR1) / (green + NIR1)	Zhou et al. (2018)
	NIR-RE ratio	(NIR1–3.06 × RE)/(1.71× NIR1–2.42 × RE − 4.14)	Developed by the research team
Textural	GLCM (Gray Level Co-occurrence Matrix): contrast, correlation, entropy, homogeneity, Angular second moment, mean, standard-deviation) GLDV (Gray Level Difference Vector): contrast, entropy, mean, angular second moment	All	Haralick (1979)
Geometric	Width, length, compactness	-

NIR: Near-infrared, RE: Red-Edge.

*Because of limited space, only the bands used are indicated (see references for detailed definition).

Table 4. Performance measures of classifiers. The values in bold correspond to the maximum performance values.

		WV-3			WV-3 + BTBR			SPOT-7			SPOT-7 + BTBR			WV-3 + SPOT-7 + BTBR
		RF	SVM	XGBoost	RF	SVM	XGBoost	RF	SVM	XGBoost	RF	SVM	XGBoost	RF	SVM	XGBoost
	Kappa	0.77	0.75	0.81	0.85	0.80	0.81	0.44	0.48	0.41	0.64	0.65	0.49	0.85	0.80	0.81
	OA	0.87	0.85	0.89	0.91	0.88	0.89	0.70	0.72	0.66	0.80	0.80	0.70	0.91	0.88	0.89
Japanese knotweed	UA	0.90	0.87	0.90	0.88	0.83	0.90	0.67	0.74	0.47	0.77	0.81	0.58	0.88	0.85	0.85
	PA	0.97	0.94	0.97	0.97	0.97	1.00	0.50	0.47	0.58	0.67	0.72	0.64	0.97	0.92	0.94
	F1-score	0.93	0.91	0.93	0.92	0.90	0.95	0.57	0.58	0.52	0.72	0.76	0.61	0.92	0.88	0.89
Phragmites	UA	1.00	0.75	0.92	1.00	0.84	0.88	0.77	0.75	0.80	0.93	0.88	0.72	0.94	0.84	1.00
	PA	0.52	0.57	0.57	0.76	0.76	0.67	0.48	0.57	0.38	0.67	0.71	0.62	0.81	0.76	0.76
	F1-score	0.69	0.65	0.71	0.86	0.80	0.76	0.59	0.65	0.52	0.78	0.79	0.67	0.87	0.80	0.86
Giant hogweed	UA	0.78	0.82	0.90	0.82	0.82	0.80	1.00	0.75	0.33	0.80	0.60	0.50	0.82	0.83	0.80
	PA	0.70	0.90	0.90	0.90	0.90	0.80	0.20	0.30	0.20	0.40	0.30	0.20	0.90	1.00	0.80
	F1-score	0.74	0.86	0.90	0.86	0.86	0.80	0.33	0.43	0.25	0.53	0.40	0.29	0.86	0.91	0.80
Absence	UA	0.85	0.86	0.88	0.92	0.91	0.90	0.69	0.71	0.76	0.78	0.80	0.76	0.93	0.90	0.90
	PA	0.93	0.87	0.93	0.92	0.86	0.91	0.89	0.91	0.81	0.93	0.92	0.80	0.91	0.87	0.91
	F1-score	0.89	0.87	0.90	0.92	0.88	0.90	0.78	0.79	0.78	0.85	0.85	0.78	0.92	0.89	0.90
Mean (F1-score)		0.81	0.82	0.86	0.89	0.86	0.85	0.57	0.61	0.52	0.72	0.70	0.58	0.89	0.87	0.87
SD (F1-score)		0.10	0.09	0.08	0.03	0.03	0.07	0.12	0.11	0.13	0.09	0.15	0.15	0.03	0.03	0.03

OA: Overall Accuracy; PA: Producer Accuracy; UA: User Accuracy; SD: Standard Deviation; RF: Random Forest; SVM: Support Vector Machine.

XGBoost: Extreme Gradient Boosting

Figure 4. Examples of invasive alien plant species detected in four types of environment: (a) riverbanks; (b) roadsides; (c) agriculture ditches; and (d) residential areas. Species are indicated in yellow (giant hogweed); orange (phragmites); and purple (Japanese knotweed).

Figure 5. Examples of undetected phragmites stands. (a) Phragmites at low density and (b) a phragmite stand before the flowering stage (field photography from July 30, 2020).

Figure 6. Examples of non-invasive alien plant species classified as Japanese knotweed Field photography of (a) staghorn sumac (Rhus typhina) from July 30, 2020, and (b) wild red raspberry (Rubus idaeus) from July 29, 2020.

Table

Imagery source	Type of image	Used spectrum range	References
Landsat	Multispectral	Visible and NIR	Yu et al. (2020); Labonté et al. (2020); Becker, Zmijewski, and Crail (2013); Wilfong, Gorchov, and Henry (2009)
Sentinel-1	Radar	C band	Kattenborn et al. (2019)
Sentinel-2	Multispectral	Visible – NIR	Kattenborn et al. (2019)
SPOT-7	Multispectral	Visible – NIR	Abutaleb et al. (2020)
Ikonos	Multispectral	Visible – NIR	Lawrence, Wood, and Sheley (2006); Casady, Hanley, and Seelan (2005)
WorldView-2	Multispectral	Visible – NIR	Rizaludin Mahmud, Numata, and Hosaka (2020); Abutaleb et al. (2020); Paz-Kagan et al. (2019); Fernandes et al. (2014)
WorldView-3	Multispectral	Visible-NIR-SWIR	Shendryk et al. (2020)
Aerial imagery	Hyperspectral and LiDAR	Visible-NIR-SWIR	Ewald et al. (2020) Kattenborn et al. (2019); Marvin, Asner, and Schnitzer (2016); Ishii and Washitani (2013); Asner et al. (2008)
UAV	Multispectral	Visible – NIR	Qian et al. (2020); Kattenborn et al. (2019); Müllerová et al. (2017) Fernandes et al. (2014); Dorigo et al. (2012)
Terrestrial imagery	Hyperspectral	Visible-NIR – SWIR	Rizaludin Mahmud, Numata, and Hosaka (2020)

UAV: Unmanned Aerial Vehicle; SPOT-7: Satellite Pour l’Observation de la Terre-7; NIR: Near Infrared; SWIR: Short Waves Infra-red; LiDAR: Light Detection and Ranging

Table

	Number of polygons	Area (m^2)
		Min	Max	Mean	Median	SD	Total area
Japanese knotweed	128	13	919	124	93	115	41.235
Phragmites	76	24	417	160	146	86	16.934
Giant hogweed	32	29	203	84	74	47	2.707
Absence	283	3	1466	143	100	149	12.181
Total	519	-	-	-	-	-	73.057

Note: SD: Standard Deviation.

Table

Classifier	Hyperparameter	Optimal Value
		WV-3	SPOT-7	WV-3 + SPOT-7 + BTBR
XGBoost	Nrounds	250	100	400
	Max depth	6	10	5
	Learning rate	0.4	0.4	0.3
	Gamma	0	0	0
	Colsample_bytree	0.6	0.6	0.6
	min_child_weight	1	1	1
	Subsample	0.55	0.55	0.66
	Number of features selected	35	15	35
RF	Mtry	7	7	7
	Ntree	400	400	400
	Number of features selected	25	15	25
SVM	Sigma	0.05	0.07	0.03
	Cost	2	2	4
	Number of features selected	25	15	35

Table

	Producer accuracy	Omission error (1- producer accuracy)	User accuracy	Commission error (1-user accuracy)
Japanese knotweed	0.97	0.03	0.88	0.12
Phragmites	0.81	0.19	0.94	0.06
Giant hogweed	0.90	0.10	0.82	0.18
Absence	0.91	0.09	0.93	0.07

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Data availability statement

The data that support the findings of this study are openly available in the Open Science Framework data repository (link: https://osf.io/b3ufr/?view_only=b02f51fddf5f4c11bd7c457fe85c2710).