LAND-USE AND LAND-COVER MAPPING USING A COMBINATION OF RADAR AND OPTICAL SENSORS IN RORAIMA – BRAZIL

Maffei Valero, Miguel A.; Araújo, Wellington F.; Melo, Valdinar F.; Augusti, Mauricio L.; Fernandes Filho, Elpídio I.

doi:10.1590/1809-4430-Eng.Agric.v42n2e20210142/2022

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Scientific Paper • Eng. agríc. (Online) 42 (2) • 2022 • https://doi.org/10.1590/1809-4430-Eng.Agric.v42n2e20210142/2022 copy

LAND-USE AND LAND-COVER MAPPING USING A COMBINATION OF RADAR AND OPTICAL SENSORS IN RORAIMA – BRAZIL

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Land-use and land-cover (LULC) are important environmental properties of the Earth’s surface. Satellite platforms and state-of-the-art algorithms enable the mapping of large areas, but they still need to be improved. This study aimed to compare free- and open-access images from radar and optical sensors, using the Google Earth Engine™ (GEE) for supervised classification of LULC for five municipalities in Roraima State, Brazil. Sentinel-1 (S1) scenes were used along with Landsat 8 (L8) and Sentinel-2 (S2) ones, resulting in five classification approaches S1 (SD), L8 (ODL), S2 (ODS), S1+L8 (SODL), and S1+S2 (SODS), with an auxiliary ALOS World 3D dataset (DEM≈30m). Accuracy was assessed by an error matrix. The SD approach was significantly different (P ≤ 0.01) from the others using a mean F1-score of 0.80. ODL and ODS had barely perceptible differences (P ≤ 0.1), showing F1-scores of 0.95 and 0.92, respectively. When comparing ODL (F1=0.95) and SODL (F1=0.95) no difference was found (P > 0.1). However, by comparing ODS (F1=0.92) and SODS (F1=0.94), there was a significant classification improvement (P ≤ 0.05). In short, the approaches SODL and SODS had the best pixel-based supervised classification performance.

KEYWORDS
Machine learning; Sentinel-1; Sentinel-2; Landsat 8; Savanna

Input Data	Satellite	Sensors/Spectral Bands/Polarization/Vegetation index	Reference/Source
MapBiomas	Landsat (5,7,8)	TM, ETM+, OLI	Souza et al. (2020)Souza CM, Z. Shimbo J, Rosa MR, Parente LL, A. Alencar A, Rudorff BFT, Hasenack H, Matsumoto M, G. Ferreira L, Souza-Filho PWM, Oliveira SW de, Rocha WF, Fonseca AV, Marques CB, Diniz CG, Costa D, Monteiro D, Rosa ER, Vélez-Martin E, Weber EJ, Lenti FEB, Paternost FF, Pareyn FGC, Siqueira JV, Viera JL, Neto LCF, Saraiva MM, Sales MH, Salgado MPG, Vasconcelos R, Galano S, Mesquita VV, Azevedo T (2020) Reconstructing three decades of Land use and Land cover changes in Brazilian biomes with Landsat archive and earth engine. Remote Sensing 12(17):2735. DOI: https://doi.org/10.3390/rs12172735 https://doi.org/10.3390/rs12172735...
DEM	ALOS World 3D (30m)	AVE_DSM [elevation; slope]	Tadono et al. (2016)Tadono T, Nagai H, Ishida H, Oda F, Naito S, Minakawa K, Iwamoto H (2016) Generation of the 30 m-mesh global digital surface model by ALOS prism. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4:157-162. DOI: https://doi.org/10.5194/isprsarchives-XLI-B4-157-2016 https://doi.org/10.5194/isprsarchives-XL...
	Sentinel-1	VV, VH	ESA/Sentinel-1
Satellite images	Sentinel-2	B02/B, B03/G, B04/R, B05/Red-Edge1, B06/Red-Edge2, B07/Red-Edge3, B08/NIR, B08A/Red-Edge4, B11/SWIR1; B12/SWIR2; NDVI	ESA/Sentinel-2
	Landsat 8	B02/B, B03/G, B04/R, B05/NIR, B06/SWIR1, B07/SWIR2, B10/TIRS, NDVI	USGS/NASA Rouse et al. (1973)Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. In: ERTS Symposium. Washington. p309-317.

Satellites and Specifications	Radar Imagery		Optical Imagery
Satellites and Specifications	Sentinel-1A/B		Sentinel-2A/B	Landsat 8
Sensor	SAR C-Band		MSI	OLI
Spatial resolution (m)	10		10/20	30
Number of bands (used)	1		9	7
Revisit frequency (days)	<6		<6	16
Swath (km)	250		290	180
	Central frequency 5.405 GHz		Spectral bands
	Orbit, [relative number] and incident angle range		B, G, R, Red-ege(1,2,3), NIR, SWIR(1,2)	Ultra-Blue, B, G, R, NIR, SWIR(1,2)
	Descending	Ascending	MGRS Tile	Path/Row
*Normandia*	[164] 34.22° - 41.25°	[83] 34.33° - 41.42°	20NQJ, 20NQK, 20NRJ, 20NRK, 21NTE	231/57, 231/58, 232/57, 232/58
Number of scenes	118	82	377	126
*Alto Alegre*	[54] 30.89° - 37.70° [156] 30.83° - 46.13° [83] 42.12° - 46.19°	[134] 35.91° - 46.05° [135] 35.92° - 46.05° [62] 30.78° - 44.06°	20NLH, 20NLJ, 20NMH, 20NMJ, 20NMK, 20NNH, 20NNJ, 20NPH, 20NPJ, 20NQH, 20NQJ	232/57, 232/58, 233/57/, 233/58, 1/57, 1/58,
Number of scenes	238	192	1460	142
*Boa Vista*	[83] 39.16° - 43.55° -	[62] 43.37° - 46.02° [164] 30.75° - 35.05°	20NQH, 20NQJ, 20NRJ, 20NQK	232/57, 232/58
Number of scenes	118	179	303	73
*Bonfim*	[83] 32.60° - 39.94°	[164] 32.51° - 39.02° [163] 33.36° - 37.45°	20NQH, 20NQJ, 20NRH, 20NRJ, 20NRK	231/58, 231/59, 232/57
Number of scenes	118	122	376	149
*Cantá*	[83] 35.98° - 42.11° -	[62] 41.94° - 46.01° [163] 35.04° - 30.74° [164] 30.75° - 34.22°	20NQG, 20NQH, 20NQJ, 20NRH, 20NRJ	231/58, 231/59
Number of scenes	118	122	375	111

Code	Image type	Numbers of bands	Platform	Bands composite
SD	SAR, DEM	4	Sentinel-1, ALOS World 3D	VH, VV, elev, slope
ODL	Optical, DEM	10	Landsat 8, ALOS World 3D	B2, B3, B4, B5, B6, B7, B10, NDVI, elev, slope
SODL	SAR, Optical, DEM	14	Sentinel-1, Landsat 8, ALOS World 3D	VH, VV, B2, B3, B4, B5, B6, B7, B10, NDVI, elev, slope,
ODS	Optical, DEM	13	Sentinel-2, ALOS World 3D	B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12, NDVI, elev, slope
SODS	SAR, Optical, DEM	15	Sentinel-1, Sentinel-2, ALOS World 3D	VH, VV, B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12, NDVI, elev, slope

ID	Class Name	Description
1	Forest Formation	Vegetation types dominated by tree species with high-density continuous canopy, and forest resulting from natural regrowth
2	Forest Plantation	Planted tree species for commercial use
3	Non-Forest Formation in Wetland	Floodplain with fluvial and lake influence, located along watercourses and in lowlands areas that accumulate water
4	Grassland Formation	Vegetation type with a predominance of herbaceous stratum, shrub-herbaceous stratum, natural grasslands
5	Pasture	Pasture areas, related to farming activity. Part of the area classified as grassland formation also includes pasture areas
6	Agriculture	Areas predominantly occupied by annual and perennial crops where it was not possible to distinguish between pasture and agriculture
7	Beach^* * Classification modified by the authors	Sandy areas, with bright white color, where there is no vegetation predominance of vegetation of any kind, river sandbank
8	Savanna^* * Classification modified by the authors	Savanna (lavrado, cerrado), open vegetation, dominated by grass and grasses, where trees and shrubs may or may not be present
9	Urban Infrastructure	Urban areas with a predominance of non-vegetated surfaces, including roads, highways, and constructions
10	Water	River, lake, and other water bodies.

	SD							ODL
Code	UA (%)	PA (%)	F1	CE (%)	OE (%)	Area (km²)	%	UA (%)	PA (%)	F1	CE (%)	OE (%)	Area (km²)	%
1	8.79	99.60	0.99	0.40	1.21	28148.45	52.28	99.94	99.99	1	0.01	0.06	29728.80	55.21
2	76.73	79.22	0.78	20.78	23.27	1962.26	3.64	98.54	96.73	0.98	3.27	1.46	1173.41	2.18
3	85.71	55.56	0.67	44.44	14.29	689.65	1.28	97.14	85.00	0.91	15.00	2.86	1160.31	2.15
4	95.78	97.21	0.96	2.79	4.22	4396.38	8.16	97.94	98.83	0.98	1.17	2.06	6737.28	12.51
5	71.47	47.18	0.57	52.82	28.53	1873.58	3.48	98.31	97.15	0.98	2.85	1.69	3881.10	7.21
6	87.64	90.07	0.89	9.93	12.36	4747.70	8.82	98.00	99.22	0.99	0.78	2.00	749.79	1.39
7	90.53	86.87	0.89	13.13	9.47	86.89	0.16	100	99.07	1	0.93	0	14.84	0.03
8	83.31	80.00	0.82	20.00	16.69	11224.21	20.85	95.55	96.57	0.96	3.43	4.45	9272.63	17.22
9	67.12	40.50	0.51	59.50	32.88	488.70	0.91	81.32	59.68	0.69	40.32	18.68	1021.91	1.90
10	91.39	93.24	0.92	6.76	8.61	227.98	0.42	99.28	100	1	0	0.72	103.55	0.19
					Total	53845.80	100					Total	53843.63	100
AO (%)					97.18%							99.56%
Kappa					0.88							0.98

	ODS							SODL
Code	UA (%)	PA (%)	F1	CE (%)	OE (%)	Area (km²)	%	UA (%)	PA (%)	F1	CE (%)	OE (%)	Area (km²)	%
1	99.87	99.93	1	0.09	0.13	30555.67	56.75	99.97	99.99	1	0.01	0.03	30457.31	56.57
2	94.32	88.87	0.88	10.40	7.25	1070.10	1.99	99.56	98.28	0.99	1.72	0.44	1370.85	2.55
3	100	78.72	0.87	16.95	2.00	1126.74	2.09	97.50	82.98	0.90	17.02	2.50	521.85	0.97
4	97.17	98.14	0.98	1.88	2.67	9160.83	17.01	99.04	99.72	0.99	0.28	0.96	5680.11	10.55
5	94.27	96.81	0.95	4.03	4.67	4897.22	9.10	98.42	99.65	0.99	0.35	1.58	3469.72	6.44
6	98.24	99.70	0.99	0.60	1.93	896.61	1.67	99.37	99.84	1	0.16	0.63	375.66	0.70
7	99.06	100	1	0	1.02	6.83	0.01	100	100	1	0	0	2.97	0.01
8	93.66	96.27	0.95	2.70	5.64	4674.30	8.68	96.10	98.40	0.97	1.60	3.90	11859.07	22.03
9	81.13	36.13	0.59	52.27	14.86	1350.48	2.51	89.19	53.66	0.67	46.34	10.81	80.13	0.15
10	100	100	1	0.74	0	104.86	0.19	100	99.35	1	0.65	0	25.96	0.05
					Total	53843.63	100					Total	53843.63	100
AO (%)					99.33%							99.71%
Kappa					0.97							0.99

SODS
Code	UA (%)	PA (%)	F1	EC (%)	EO (%)	Area (km²)	%	Code	Class name
1	99.91	99.96	1	0.05	0.09	30967.13	57.51	1	Forest Formation
2	98.25	91.99	0.94	5.90	3.04	1021.88	1.90	2	Forest Plantation
3	100	85.71	0.89	28.95	3.57	613.90	1.14	3	Wetland
4	98.89	99.48	0.99	0.20	1.76	7042.96	13.08	4	Grassland
5	95.58	97.33	0.97	2.55	4.97	4822.75	8.96	5	Pasture
6	99.06	99.85	0.99	0.46	0.92	793.79	1.47	6	Agriculture
7	100	100	1	0	0	2.37	0.00	7	Beach
8	95.40	97.34	0.96	3.09	3.69	8360.04	15.53	8	Savanna
9	91.30	50.00	0.64	44.80	16.87	209.28	0.39	9	Urban
10	100	100	1	0	0	9.53	0.02	10	Water
					Total	53843.63	100
AO (%)					99.53%
Kappa					0.98

	SD	ODL	SODL	ODS	SODS
SD	-	0.001953^* ***** = P ≤ 0.01	0.001953^* ***** = P ≤ 0.01	0.001953^* ***** = P ≤ 0.01	0.001953^* ***** = P ≤ 0.01
ODL	0.001953^* ***** = P ≤ 0.01	-	0.4531 ^NS NS = Not Significant.	0.0625^* * = P ≤ 0.1	0.375 ^NS NS = Not Significant.
SODL	0.001953^* ***** = P ≤ 0.01	0.4531 ^NS NS = Not Significant.	-	0.01563^ = P ≤ 0.05	0.03125^ = P ≤ 0.05
ODS	0.001953^* ***** = P ≤ 0.01	0.0625^* * = P ≤ 0.1	0.01563^ = P ≤ 0.05	-	0.03125^ = P ≤ 0.05
SODS	0.001953^* ***** = P ≤ 0.01	0.375 ^NS NS = Not Significant.	0.03125^ = P ≤ 0.05	0.03125^ = P ≤ 0.05	-

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[1] *Corresponding author. University of Los Andes, Agricultural Engineering, Trujillo, Trujillo, Venezuela. E-mail: migmaffei@gmail.com