Land Cover Classification Method Integrating Spaceborne LiDAR Combined with Multispectral Images

Xing Huang; Xuyan Hu; Weiwei Liu; Hong Zhao

doi:10.3788/CJL231063

Chinese Journal of Lasers, Volume. 51, Issue 8, 0810004(2024)

Land Cover Classification Method Integrating Spaceborne LiDAR Combined with Multispectral Images

Xing Huang¹, Xuyan Hu², Weiwei Liu³, and Hong Zhao^4、*

Author Affiliations

¹Lishui Institute of Territorial Spatial Planning and Mapping, Lishui 323000, Zhejiang , China

²Zhejiang South Comprehensive Engineering Survey and Mapping Institute Co., Hangzhou 310030, Zhejiang , China

³Zhejiang Academy of Surveying and Mapping Science and Technology, Hangzhou 311121, Zhejiang , China

⁴College of Aeronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu , China

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Figures & Tables(16)

Fig. 1. Support vector machine (SVM) algorithm principle

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Fig. 2. Sample site distribution in the study area

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Fig. 3. Importance of different features

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Table 1. GEDI L2A extraction parameters list

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Table 1. GEDI L2A extraction parameters list

Variable name	Range of data values	Description
quality_flag	0, 1	Quality identification
degrade_flag	0, 1	Quality identification
sensitivity	0‒1	Sensitivity
lon_lowestmode	118°‒123°	Longitude
lat_lowestmode	27°‒32°	Latitude
elev_lowestmode	0‒2000 m	Ground elevation
elev_highestreturn	0‒50 m	Crown elevation
digital_elevation_mode_srtm	0‒2000 m	SRTM elevation
Rh(0,25,50,75,100)	-10‒40 m	Percentile height
rx_nummodes		Number of modes detected in waveform
lastmodeenergy		Energy in lowest detected mode
smoothwidth		Width of Gaussian function used to smooth noise sections of waveforms
rx_modelocs		Sample numbers of each detected mode (relative to bin 0 of waveform)
rx_modewidths		1 sigma width estimates of each detected mode in waveform
peak		Peak amplitude of raw waveform
pk_sm		Peak amplitude of smoothed waveform

Table 2. GEDI L2A valid footprint screening conditions

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Table 2. GEDI L2A valid footprint screening conditions

Screening parameter	Screening condition	Instruction
degrade_flag	0	The state degradation flag is "1", indicating that the state of the indicating direction and/or positioning information decreases, which affects the accuracy of the GEDI L2A data. Therefore, the light spot with the value of 1 is deleted
sensitivity	≥0.95	Sensitivity parameter: on land, a sensitivity threshold greater than or equal to 0.9 is used to obtain better quality light spots, so the light spots with sensitivity less than 0.95 are deleted
quality_flag	1	A quality mark of "1" indicates that the waveform meets specific criteria based on energy, sensitivity, amplitude, and real-time surface tracking quality and can be represented as a valid waveform
\|elev_lowestmode-SRTM\|	≤50	Because GEDI is susceptible to the influence of clouds in the data collection, delete the light spot whose elev_lowestmode value is significantly different from the SRTM value of the GEDI footprint (\|elev_lowestmode-SRTM\|>50 m)
rx_assess_flag	0	If the value is 1, various errors may occur in the waveform. Therefore, the light spot of rx_assess_flag=1 is deleted

Table 3. Basic information of Landsat spectral band
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Table 3. Basic information of Landsat spectral band
Band Resolution /m Wavelength /nm Description
B2 30 452‒512 Blue
B3 30 533‒590 Green
B4 30 636‒673 Red
B5 30 851‒879 Near infrared
B6 30 1566‒1651 Shortwave infrared 1
B7 30 2107‒2294 Shortwave infrared 2

Table 4. Spectral vegetation index

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Table 4. Spectral vegetation index

Vegetation index	Equation
NDVI	$ζ_{N D V I} = \frac{ρ_{N I R} - ρ_{R E D}}{ρ_{N I R} + ρ_{R E D}}$
NDBI	$ζ_{N D B I} = \frac{ρ_{S N I R} - ρ_{N I R}}{ρ_{S N I R} + ρ_{N I R}}$
NDWI	$ζ_{N D W I} = \frac{ρ_{G R E E N} - ρ_{N I R}}{ρ_{G R E E N} + ρ_{N I R}}$
ARVI	$ζ_{A R V I} = \frac{ρ_{N I R} - (2 ρ_{R E D} - ρ_{B L U E})}{ρ_{N I R} + (2 ρ_{R E D} - ρ_{B L U E})}$
EVI	$ζ_{E V I} = 2.5 (\frac{ρ_{N I R} - ρ_{R E D}}{ρ_{N I R} + 6 ρ_{R E D} - 7.5 ρ_{B L U E} + 1})$
RVI	$ζ_{R V I} = \frac{ρ_{N I R}}{ρ_{R E D}}$

Table 5. Sample size for each land cover category
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Table 5. Sample size for each land cover category
Land cover category
Sample
count
Training sample count Validation sample count
Forest 529 375 154
Shrubland 465 316 149
Grassland 502 350 152
Cropland 464 318 146
Water 456 328 128
Others 486 350 136

Table 6. Different groups of features

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Table 6. Different groups of features

Feature group number	A feature group contains features
1	All waveform features （47）
2	All spectral features （13）
3	Selected waveform features （9）
4	Selected spectral features （11）
5	Selected waveform and spectral features （20）
6	Selected regions sample （6 regions）

Table 7. Confusion matrix of feature group 1

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Table 7. Confusion matrix of feature group 1

SVM	Ground reality data						UA /%	Kappa	OA /%
SVM	Forest	Shrubland	Grassland	Cropland	Water	Others	UA /%	Kappa	OA /%
Forest	96	20	15	12	9	2	62.34	0.59	65.10
Shrubland	38	78	17	5	4	7	52.35
Grassland	35	15	84	12	3	3	55.26
Cropland	8	8	10	102	4	20	67.11
Water	4	1	7	2	111	3	86.72
Others	9	3	3	15	10	96	70.59
PA /%	50.53	62.40	61.76	68.92	78.72	73.28

Table 8. Confusion matrix of feature group 2

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Table 8. Confusion matrix of feature group 2

SVM	Ground reality data						UA /%	Kappa	OA /%
SVM	Forest	Shrubland	Grassland	Cropland	Water	Others	UA /%	Kappa	OA /%
Forest	134	0	1	0	1	18	87.01	0.75	78.825
Shrubland	15	91	20	18	2	3	61.07
Grassland	8	9	127	8	0	0	83.55
Cropland	11	5	36	91	0	3	62.33
Water	1	0	1	0	123	3	96.09
Others	13	2	2	2	3	114	83.82
PA /%	73.63	85.05	67.91	76.47	95.35	80.85

Table 9. Confusion matrix of feature group 3

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Table 9. Confusion matrix of feature group 3

SVM	Ground reality data						UA /%	Kappa	OA /%
SVM	Forest	Shrubland	Grassland	Cropland	Water	Others	UA /%	Kappa	OA /%
Forest	120	19	7	2	3	3	77.92	0.68	72.90
Shrubland	23	84	25	9	2	6	56.38
Grassland	15	20	94	8	5	10	61.84
Cropland	2	6	12	108	3	21	71.05
Water	2	3	2	4	116	1	90.63
Others	7	7	2	4	3	113	83.09
PA /%	71.01	60.43	66.20	80.00	87.88	73.38

Table 10. Confusion matrix of feature group 4

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Table 10. Confusion matrix of feature group 4

SVM	Ground reality data						UA /%	Kappa	OA /%
SVM	Forest	Shrubland	Grassland	Cropland	Water	Others	UA /%	Kappa	OA /%
Forest	136	1	0	1	0	16	88.31	0.79	81.75
Shrubland	13	100	17	16	1	2	67.11
Grassland	5	8	130	9	0	0	85.53
Cropland	9	4	30	107	0	2	70.39
Water	1	0	1	0	126	0	98.44
Others	13	2	3	1	4	113	83.09
PA /%	76.84	86.96	71.82	79.85	96.18	84.96

Table 11. Confusion matrix of feature group 5

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Table 11. Confusion matrix of feature group 5

SVM	Ground reality data						UA /%	Kappa	OA /%
SVM	Forest	Shrubland	Grassland	Cropland	Water	Others	UA /%	Kappa	OA /%
Forest	143	6	0	2	0	3	92.86	0.89	90.68
Shrubland	13	126	4	6	0	0	84.56
Grassland	5	8	134	4	0	1	88.16
Cropland	1	6	6	137	0	0	91.33
Water	2	0	0	1	122	3	95.31
Others	3	2	1	2	2	126	92.65
PA /%	85.63	85.14	92.41	90.13	98.39	94.74

Table 12. The number of samples of land cover categories in the subregion

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Table 12. The number of samples of land cover categories in the subregion

Category	The number of samples
Category	Region 1	Region 2	Region 3	Region 4	Region 5	Region 6
Forest	100	99	105	103	99	99
Shrubland	61	93	96	103	105	28
Grassland	98	103	99	102	102	53
Cropland	102	97	95	98	98	95
Water	104	3	32	59	104	66
Others	95	26	95	103	96	97

Table 13. The overall accuracy of sub-region prediction results

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Table 13. The overall accuracy of sub-region prediction results

Model	OA /%
Model	Region 1	Region 2	Region 3	Region 4	Region 5	Region 6
Region 1 model	88.50	86.29	84.23	84.39	86.44	87.04
Region 2 model	85.50	87.27	85.29	86.69	84.39	86.65
Region 3 model	85.43	84.21	85.96	84.93	83.89	83.31
Region 4 model	84.25	83.01	85.25	86.97	84.70	83.19
Region 5 model	87.71	85.84	83.52	83.41	88.54	86.24
Region 6 model	87.35	84.41	86.31	85.28	85.42	89.21

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Xing Huang, Xuyan Hu, Weiwei Liu, Hong Zhao. Land Cover Classification Method Integrating Spaceborne LiDAR Combined with Multispectral Images[J]. Chinese Journal of Lasers, 2024, 51(8): 0810004

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