A study of grassland aboveground biomass on the Tibetan Plateau using MODIS data and machine learning

doi:10.11686/cyxb2021386

Abstract

Abstract:

The Tibetan Plateau， often referred to as “the third pole of the world”， is located in the Southwest of China， and makes significant contribution to ecology and climate change in China and around the word. Our study evaluated the change in aboveground biomass （AGB） on the Tibetan Plateau from 2000 to 2020. We used multiple machine learning methods combined with MCD43A4 product data to simulate the aboveground biomass and analyzed the temporal and spatial characteristics of AGB in this region. The main results were as follows： 1） Among the constructed machine learning models， Rborist model demonstrated the highest accuracy， with an R² of 0.6484 based on screened variables， and eight variables were found to be highly correlated with biomass： precipitation in May， precipitation in June， average temperature in December， normalized difference phenology index （NDPI）， precipitation in April， maximum temperature in January， precipitation in August and precipitation in December； 2） AGB in the southeast of the Tibetan Plateau was higher than that in the northwest， with a decreasing trend from the southeast to northwest； 3） AGB on the Tibetan Plateau increased steadily from 2000 to 2020， with an overall positive movement. However， 61.38% of Tibetan Plateau grasslands showed a trend of unsustainability， 4.67% showed a slight deterioration trend， 1.19% showed a significant deterioration trend， and 32.76% were stable or showed a recovery trend.

Key words: vegetation index, machine learning, aboveground biomass, spatial and temporal distribution, Tibetan Plateau

Zhe-ren JIN, Qi-sheng FENG, Rui-jing WANG, Tian-gang LIANG. A study of grassland aboveground biomass on the Tibetan Plateau using MODIS data and machine learning[J]. Acta Prataculturae Sinica, 2022, 31(10): 1-17.

Figures/Tables 12

Fig.1 Distribution of grassland types on the Tibetan Plateau

Table 1 The vegetation index used in this study

序号Number	植被指数Vegetation index	名称 Full name	公式 Equation	引用 Reference
1	NDVI	归一化植被指数Normalized difference vegetation index	$N D V I = N I R - R N I R + R$	［22］
2	EVI	增强型植被指数Enhanced vegetation index	$E V I = 2.5 × N I R - R N I R + 6 × R - 7.5 × B + 1$	［23］
3	EVI2	增强型植被指数2 Enhanced vegetation index two	$E V I 2 = 2.5 × N I R - R N I R + 2.4 × R + 1$	［24］
4	DVI	差值植被指数Difference vegetation index	$D V I = N I R - R$	［25］
5	RVI	比值植被指数Ratio vegetation index	$R V I = N I R R$	［26］
6	SAVI	土壤调整植被指数Soil adjusted vegetation index	$S A V I = (N I R - R) × (1 + 0.5) (N I R + R + 0.5)$	［27］
7	MSAVI	修改型土壤调节植被指数Modified soil adjusted vegetation index	$M S A V I = 2 N I R + 1 - (2 N I R + 1) 2 - 8 × (N I R - R) 2$	［28］
8	OSAVI	优化土壤调整植被指数Optimized soil adjusted vegetation index	$O S A V I = N I R - R N I R + R + 0.16$	［29］
9	SATVI	土壤调整总植被指数Soil adjusted total vegetation index	$S A T V I = (S W I R 1 - R) × (1 + 0.5) (S W I R 1 + R + 0.5) - S W I R 2 2$	［30］
10	NDPI	归一化物候指数Normalized difference phenology index	$N D P I = N I R - (0.74 × R + 0.26 × S W I R) N I R + (0.74 × R + 0.26 × S W I R)$	［31］

Table 1 The vegetation index used in this study

序号Number	植被指数Vegetation index	名称 Full name	公式 Equation	引用 Reference
1	NDVI	归一化植被指数Normalized difference vegetation index	$N D V I = N I R - R N I R + R$	［22］
2	EVI	增强型植被指数Enhanced vegetation index	$E V I = 2.5 × N I R - R N I R + 6 × R - 7.5 × B + 1$	［23］
3	EVI2	增强型植被指数2 Enhanced vegetation index two	$E V I 2 = 2.5 × N I R - R N I R + 2.4 × R + 1$	［24］
4	DVI	差值植被指数Difference vegetation index	$D V I = N I R - R$	［25］
5	RVI	比值植被指数Ratio vegetation index	$R V I = N I R R$	［26］
6	SAVI	土壤调整植被指数Soil adjusted vegetation index	$S A V I = (N I R - R) × (1 + 0.5) (N I R + R + 0.5)$	［27］
7	MSAVI	修改型土壤调节植被指数Modified soil adjusted vegetation index	$M S A V I = 2 N I R + 1 - (2 N I R + 1) 2 - 8 × (N I R - R) 2$	［28］
8	OSAVI	优化土壤调整植被指数Optimized soil adjusted vegetation index	$O S A V I = N I R - R N I R + R + 0.16$	［29］
9	SATVI	土壤调整总植被指数Soil adjusted total vegetation index	$S A T V I = (S W I R 1 - R) × (1 + 0.5) (S W I R 1 + R + 0.5) - S W I R 2 2$	［30］
10	NDPI	归一化物候指数Normalized difference phenology index	$N D P I = N I R - (0.74 × R + 0.26 × S W I R) N I R + (0.74 × R + 0.26 × S W I R)$	［31］

Table 2 Survey sample data and basic statistics of different grassland type

草地类型Grassland type	风干重Dry weight （kg·hm^-2）				盖度Cover degree （%）				高度Height （cm）
草地类型Grassland type	最小值Minimum value	最大值Maximum value	平均值Mean	标准偏差Standard deviation	最小值Minimum value	最大值Maximum value	平均值Mean	标准偏差Standard deviation	最小值Minimum value	最大值Maximum value	平均值Mean	标准偏差Standard deviation
A	104.00	2420.00	808.42	604.83	15.00	99.00	55.10	31.58	4.26	52.00	26.12	14.66
B	106.00	2384.00	604.35	486.25	9.00	100.00	52.70	31.08	2.00	16.20	4.74	3.35
C	57.00	5858.00	1533.51	922.10	1.00	100.00	83.15	16.27	0.86	92.00	11.62	8.85
D	50.00	4154.00	588.59	519.72	5.00	100.00	55.60	23.31	1.00	53.00	8.74	5.89
E	11.00	1585.00	406.76	280.79	2.00	94.00	29.16	19.51	1.50	17.00	4.92	3.14
F	140.00	500.00	304.06	89.62	15.00	99.00	53.09	17.18	1.00	85.00	12.98	13.91
G	179.00	2927.00	1276.86	812.90	10.00	95.00	52.00	33.77	4.70	34.20	13.80	7.80
H	790.00	1633.00	1153.33	433.37	61.00	71.00	66.33	5.03	10.10	11.40	10.93	0.72
I	281.00	4816.00	2001.48	928.60	15.00	100.00	87.13	13.37	1.60	96.70	18.61	11.88
J	143.00	969.00	456.15	242.65	15.00	60.00	35.62	13.90	3.00	28.00	10.00	7.56
K	113.00	1771.00	672.83	306.59	4.00	80.00	50.23	19.24	3.00	33.20	11.87	5.89
L	210.00	3009.00	831.73	462.44	7.00	97.00	61.01	19.83	1.00	81.00	15.66	9.82
M	8.00	1096.00	393.85	281.47	1.00	83.00	25.24	20.01	0.95	48.00	14.85	9.21
N	883.00	3684.00	1812.50	966.50	48.00	99.00	75.50	21.92	0.95	25.13	8.25	8.27
总计Total	8.00	5858.00	1359.28	950.42	1.00	100.00	76.16	22.53	0.86	96.70	12.18	9.44

Fig.2 Model performance values from forward feature selection

Fig.3 Correlation coefficient among the variables

Fig.4 Filter variables before and after the model runs

Table 3 Parameters of different machine learning methods

方法Method	筛选变量前的参数Parameters of variables before filtering	筛选变量后的参数Parameters of variables after filtering
Rborist	predFixed=34 and minNode=3	predFixed=2 and minNode=3
RRF	mtry=34， coefReg=0.505 and coefImp=0.5	mtry=2， coefReg=1 and coefImp=0.5
ranger	mtry=34， splitrule=variance and min.node.size=5	mtry=2， splitrule=variance and min.node.size=5
RRFglobal	mtry=67 and coefReg=0.505	mtry=2 and coefReg=0.01
parRF	mtry=34	mtry=2
rf	mtry=34	mtry=2
qrf	mtry=34	mtry=2
kknn	kmax=9， distance=2 and kernel=optimal	kmax=9， distance=2 and kernel=optimal
cforest	mtry=67	mtry=8
xgbTree	nrounds=150， max_depth=3， eta=0.3， gamma=0， colsample_bytree=0.6， min_child_weight=1 and subsample=1	nrounds=150， max_depth=3， eta=0.4， gamma=0， colsample_bytree=0.8， min_child_weight=1 and subsample=1
xgbDART	nrounds=150， max_depth=3， eta=0.3， gamma=0， subsample=1， colsample_bytree= 0.8， rate_drop=0.5， skip_drop=0.95 and min_child_weight=1	nrounds=150， max_depth=3， eta=0.3， gamma=0， subsample=1， colsample_bytree= 0.8， rate_drop=0.5， skip_drop=0.95 and min_child_weight=1
bstTree	mstop=150， maxdepth=3 and nu=0.1	mstop=150， maxdepth=3 and nu=0.1
svmRadialSigma	sigma=0.03387348 and C=1	sigma=0.2790061 and C=1
gaussprRadial	-	-
monmlp	hidden1=5 and n.ensemble=1	hidden1=5 and n.ensemble=1
brnn	neurons=3	neurons=3
bam	select=TRUE and method=GCV.Cp	select=FALSE and method=GCV.Cp
spls	K=24， eta=0.5 and kappa=0.5	K=7， eta=0.9 and kappa=0.5
glmnet	alpha=0.1 and lambda=1.112425	alpha=1 and lambda=1.112425
enet	fraction=0.525 and lambda=0.0001	fraction=1 and lambda=0.0001
ridge	lambda=0.0001	lambda=0.0001
leapForward	nvmax=4	nvmax=4
nnls	-	-

Fig.5 The spatial distribution of grassland above-ground biomass on the Tibetan Plateau from 2000 to 2020

Fig.6 Trends of grassland above-ground biomass on the Tibetan Plateau from 2000 to 2020

Fig.7 Spatial distribution of grassland above-ground biomass on the Tibetan Plateau from 2000-2020 based on trend and Hurst index

Table 4 The change characteristic analysis of different grassland from 2000-2020 on the Tibetan Plateau （%）

行政区划 Administrative divisions	草地类型 Grassland type	变化趋势不稳定Trend instability	持续性轻微降低 Sustained insignificant decrease	持续性明显降低 Sustained significant decrease	持续性稳定不变 Continuous stability	持续性轻微增加 Sustained insignificant increase	持续性明显增加Sustained significant increase
甘肃省 Gansu Province	A	68.10	0.46	0.04	10.30	2.49	18.61
	C	51.06	3.61	0.97	7.99	25.00	11.38
	D	22.07	0.00	0.00	22.90	18.67	36.36
	F	31.64	0.00	0.00	21.55	25.03	21.78
	K	53.02	2.22	0.42	4.42	21.12	18.79
	M	40.54	0.00	0.00	21.06	11.41	26.98
	O	30.68	0.00	0.00	13.57	12.62	43.13
	P	43.37	0.00	0.00	22.06	6.57	27.99
	Q	61.59	6.54	0.37	7.40	19.11	4.99
	小计Total	43.70	1.99	0.48	11.92	20.71	21.20
青海省 Qinghai Province	A	65.06	1.04	0.88	14.37	6.96	11.68
	B	0.00	0.00	0.00	100.00	0.00	0.00
	C	53.26	6.01	1.45	13.94	18.42	6.92
	D	40.10	2.72	1.55	22.69	18.60	14.34
	E	26.20	0.00	0.00	19.38	30.78	23.64
	F	49.28	0.01	0.00	21.65	19.30	9.76
	G	0.00	0.00	0.00	100.00	0.00	0.00
	K	55.03	7.53	1.67	7.44	18.08	10.26
	M	73.30	0.00	0.00	3.28	3.28	20.13
	N	50.82	0.46	0.28	8.67	13.04	26.73
	P	55.90	0.44	0.37	25.97	6.72	10.60
	Q	24.96	1.05	1.59	13.42	23.16	35.83
	小计Total	50.72	4.41	1.31	16.36	17.11	10.09
四川省 Sichuan Province	A	41.74	2.57	0.98	2.08	42.35	10.28
	C	56.24	10.71	1.32	11.26	16.26	4.21
	G	68.78	2.16	0.00	2.94	8.81	17.31
	I	75.56	3.58	0.25	2.47	7.65	10.49
	J	62.72	0.73	0.00	1.88	13.35	21.32
	K	59.16	7.39	1.23	7.67	16.53	8.02
	N	100.00	0.00	0.00	0.00	0.00	0.00
	Q	58.08	8.00	0.70	6.67	17.89	8.65
	小计Total	57.14	9.66	1.27	10.10	16.37	5.45
西藏自治区 Tibet Autonomous Region	A	71.53	3.69	1.41	7.92	7.97	7.48
	D	66.97	2.14	0.84	25.61	2.10	2.34
	E	55.60	0.16	0.05	38.09	2.02	4.08
	F	52.75	0.16	0.09	39.32	2.06	5.61
	G	71.46	10.42	3.97	5.46	7.94	0.74
	H	81.37	7.12	1.71	6.69	2.57	0.54
	I	76.61	4.44	0.00	4.03	6.85	8.06
	J	94.64	2.82	0.47	1.69	0.38	0.00
	K	59.33	15.57	3.79	14.00	6.50	0.80
	L	52.50	23.98	9.44	12.45	1.46	0.16
	M	79.63	0.19	0.00	10.42	4.39	5.36
	N	70.30	10.60	5.02	8.84	3.58	1.65
	O	76.71	0.11	0.00	7.84	10.58	4.76
	P	61.43	0.70	0.34	26.64	2.96	7.93
	Q	55.84	8.04	0.79	27.76	3.47	4.10
	小计Total	63.61	2.30	0.89	27.98	2.30	2.93
新疆维吾尔自治区 Xinjiang Uygur Autonomous Region	A	57.58	0.18	0.68	19.66	2.95	18.95
	C	59.28	4.55	1.69	23.21	5.79	5.49
	D	58.78	2.56	0.70	28.41	4.12	5.42
	E	69.46	3.34	1.40	19.38	2.73	3.70
	F	65.48	1.36	0.63	27.55	2.04	2.94
	K	61.40	3.49	0.62	21.36	8.42	4.72
	M	55.24	0.08	0.07	21.74	8.97	13.90
	N	73.08	0.33	0.09	8.66	6.70	11.14
	O	67.54	0.39	0.12	14.21	7.42	10.32
	P	75.68	0.09	0.03	9.14	4.76	10.31
	小计Total	66.31	1.93	0.72	20.08	4.32	6.65
云南省 Yunnan Province	A	91.82	6.36	0.00	0.91	0.91	0.00
	C	91.80	1.27	0.03	3.65	2.90	0.36
	G	96.45	0.16	0.00	0.81	1.62	0.97
	H	92.66	1.74	0.13	2.94	2.54	0.00
	I	89.41	1.91	0.00	1.04	4.77	2.86
	N	100.00	0.00	0.00	0.00	0.00	0.00
	小计Total	91.84	1.31	0.03	3.38	2.94	0.50

Table 4 The change characteristic analysis of different grassland from 2000-2020 on the Tibetan Plateau （%）

行政区划 Administrative divisions	草地类型 Grassland type	变化趋势不稳定Trend instability	持续性轻微降低 Sustained insignificant decrease	持续性明显降低 Sustained significant decrease	持续性稳定不变 Continuous stability	持续性轻微增加 Sustained insignificant increase	持续性明显增加Sustained significant increase
甘肃省 Gansu Province	A	68.10	0.46	0.04	10.30	2.49	18.61
	C	51.06	3.61	0.97	7.99	25.00	11.38
	D	22.07	0.00	0.00	22.90	18.67	36.36
	F	31.64	0.00	0.00	21.55	25.03	21.78
	K	53.02	2.22	0.42	4.42	21.12	18.79
	M	40.54	0.00	0.00	21.06	11.41	26.98
	O	30.68	0.00	0.00	13.57	12.62	43.13
	P	43.37	0.00	0.00	22.06	6.57	27.99
	Q	61.59	6.54	0.37	7.40	19.11	4.99
	小计Total	43.70	1.99	0.48	11.92	20.71	21.20
青海省 Qinghai Province	A	65.06	1.04	0.88	14.37	6.96	11.68
	B	0.00	0.00	0.00	100.00	0.00	0.00
	C	53.26	6.01	1.45	13.94	18.42	6.92
	D	40.10	2.72	1.55	22.69	18.60	14.34
	E	26.20	0.00	0.00	19.38	30.78	23.64
	F	49.28	0.01	0.00	21.65	19.30	9.76
	G	0.00	0.00	0.00	100.00	0.00	0.00
	K	55.03	7.53	1.67	7.44	18.08	10.26
	M	73.30	0.00	0.00	3.28	3.28	20.13
	N	50.82	0.46	0.28	8.67	13.04	26.73
	P	55.90	0.44	0.37	25.97	6.72	10.60
	Q	24.96	1.05	1.59	13.42	23.16	35.83
	小计Total	50.72	4.41	1.31	16.36	17.11	10.09
四川省 Sichuan Province	A	41.74	2.57	0.98	2.08	42.35	10.28
	C	56.24	10.71	1.32	11.26	16.26	4.21
	G	68.78	2.16	0.00	2.94	8.81	17.31
	I	75.56	3.58	0.25	2.47	7.65	10.49
	J	62.72	0.73	0.00	1.88	13.35	21.32
	K	59.16	7.39	1.23	7.67	16.53	8.02
	N	100.00	0.00	0.00	0.00	0.00	0.00
	Q	58.08	8.00	0.70	6.67	17.89	8.65
	小计Total	57.14	9.66	1.27	10.10	16.37	5.45
西藏自治区 Tibet Autonomous Region	A	71.53	3.69	1.41	7.92	7.97	7.48
	D	66.97	2.14	0.84	25.61	2.10	2.34
	E	55.60	0.16	0.05	38.09	2.02	4.08
	F	52.75	0.16	0.09	39.32	2.06	5.61
	G	71.46	10.42	3.97	5.46	7.94	0.74
	H	81.37	7.12	1.71	6.69	2.57	0.54
	I	76.61	4.44	0.00	4.03	6.85	8.06
	J	94.64	2.82	0.47	1.69	0.38	0.00
	K	59.33	15.57	3.79	14.00	6.50	0.80
	L	52.50	23.98	9.44	12.45	1.46	0.16
	M	79.63	0.19	0.00	10.42	4.39	5.36
	N	70.30	10.60	5.02	8.84	3.58	1.65
	O	76.71	0.11	0.00	7.84	10.58	4.76
	P	61.43	0.70	0.34	26.64	2.96	7.93
	Q	55.84	8.04	0.79	27.76	3.47	4.10
	小计Total	63.61	2.30	0.89	27.98	2.30	2.93
新疆维吾尔自治区 Xinjiang Uygur Autonomous Region	A	57.58	0.18	0.68	19.66	2.95	18.95
	C	59.28	4.55	1.69	23.21	5.79	5.49
	D	58.78	2.56	0.70	28.41	4.12	5.42
	E	69.46	3.34	1.40	19.38	2.73	3.70
	F	65.48	1.36	0.63	27.55	2.04	2.94
	K	61.40	3.49	0.62	21.36	8.42	4.72
	M	55.24	0.08	0.07	21.74	8.97	13.90
	N	73.08	0.33	0.09	8.66	6.70	11.14
	O	67.54	0.39	0.12	14.21	7.42	10.32
	P	75.68	0.09	0.03	9.14	4.76	10.31
	小计Total	66.31	1.93	0.72	20.08	4.32	6.65
云南省 Yunnan Province	A	91.82	6.36	0.00	0.91	0.91	0.00
	C	91.80	1.27	0.03	3.65	2.90	0.36
	G	96.45	0.16	0.00	0.81	1.62	0.97
	H	92.66	1.74	0.13	2.94	2.54	0.00
	I	89.41	1.91	0.00	1.04	4.77	2.86
	N	100.00	0.00	0.00	0.00	0.00	0.00
	小计Total	91.84	1.31	0.03	3.38	2.94	0.50

Fig.8 Correlation coefficient between VIs （vegetation indexes） and biomass

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