Estimation of vegetation aboveground biomass in the Wei-Ku Oasis based on Landsat 8 OLI images

doi:10.11686/cyxb2020569

Abstract

Abstract:

Estimation of vegetation aboveground biomass in the arid oasis can provide important evidence for evaluating the stability of the oasis ecosystem and estimating regional carbon storage. This research targeted the delta oasis of the Weigan-Kuqa Rivers and used ENVI 5.3 software to preprocess Landsat 8 Operational Land Imager （OLI） image data to survey vegetation aboveground biomass in the study area. We extraced vegetation indices and band factors reflecting aboveground biomass information， combined with measured data from sample plots and used conventional statistical models， multiple stepwise regression and partial least square regression methods to establish an optimal model of vegetation aboveground biomass， so as to reveal the spatial distribution characteristics of vegetation aboveground biomass in this oasis. It was found： 1） There was a extremely significant positive correlation between the 20 selected remote sensing factors and the measured aboveground biomass and the values of the correlation coefficients ranged from 0.5-0.7 （P<0.01）. 2） The optimal estimation models for arbors and shrub aboveground biomass were multiple stepwise regression models. The partial least squares regression models were the best models for estimating the aboveground biomass of herbs and crops. The verification determination coefficients of the model were above 0.6， and the root-mean-square error and mean absolute error were both lower. 3）The vegetation aboveground biomass in the study area was typically within the range of 280-1450 g·m^-2，^{with an area of about 6973.82 ha. Land with low lever aboveground biomass （<65 g·m^-2） accounted for about 15.02% of the total land area in the survey area. The ranking of aboveground biomass from high to low for different vegetation categories was： Crops>arbors>shrubs>herbs. For the various vegetation types， the remote sensing estimation model based on the spectral characteristics of ground objects was able to accurately estimate vegetation aboveground biomass in the arid oasis， and carry out remote sensing quantitative inversion of spatial distribution characteristics of its vegetation.}

Key words: vegetation aboveground biomass, estimation model, remote sensing inversion, spatial distribution, the delta oasis of Weigan-Kuqa Rivers

Dian-dai ZHANG, Xue-mei WANG, Mei ZAN. Estimation of vegetation aboveground biomass in the Wei-Ku Oasis based on Landsat 8 OLI images[J]. Acta Prataculturae Sinica, 2021, 30(11): 1-12.

Figures/Tables 15

Fig.1 Distribution of sampling points in study area

Table 1 Calculation formulas of vegetation index

植被指数 Vegetation index	计算公式 Calculation formula
归一化植被指数 NDVI	$N D V I = B N I R - B R E D B N I R + B R E D$
差值植被指数 DVI	$D V I = B N I R - B R E D$
比值植被指数 RVI	$R V I = B N I R / B R E D$
土壤调节植被指数 SAVI	$S A V I = B N I R - B R E D 1 + L B N I R + B R E D + L$
修改型土壤调节植被指数MSAVI	$M S A V I = B N I R + 1 - 2 B N I R + 1 2 - 8 B N I R - B R E D 2$
增强型植被指数 EVI	$E V I = B N I R - B R E D / B N I R + 6 B R E D - 7.5 B B L U E + 1 × 2.5$
大气阻抗植被指数 ARVI	$A R V I = B N I R - 2 B R E D - B B L U E B N I R + 2 B R E D - B B L U E$

Table 1 Calculation formulas of vegetation index

植被指数 Vegetation index	计算公式 Calculation formula
归一化植被指数 NDVI	$N D V I = B N I R - B R E D B N I R + B R E D$
差值植被指数 DVI	$D V I = B N I R - B R E D$
比值植被指数 RVI	$R V I = B N I R / B R E D$
土壤调节植被指数 SAVI	$S A V I = B N I R - B R E D 1 + L B N I R + B R E D + L$
修改型土壤调节植被指数MSAVI	$M S A V I = B N I R + 1 - 2 B N I R + 1 2 - 8 B N I R - B R E D 2$
增强型植被指数 EVI	$E V I = B N I R - B R E D / B N I R + 6 B R E D - 7.5 B B L U E + 1 × 2.5$
大气阻抗植被指数 ARVI	$A R V I = B N I R - 2 B R E D - B B L U E B N I R + 2 B R E D - B B L U E$

Table 2 Calculation formulas of Landsat 8 band combination

波段组合Band combination	计算公式Calculation formula
波段值和波段倒数Band value and band reciprocal	B₁，B₂，B₃，B₄，B₅，B₆，B₇ 1/B₂，1/B₃，1/B₄，1/B₅，1/B₆，1/B₇
2个波段比值Ratio of 2 bands	B₂₃=B₂/B₃，B₂₄=B₂/B₄，B₂₅=B₂/B₅ B₃₄=B₃/B₄，B₃₅=B₃/B₅，B₄₅=B₄/B₅
3个波段比值Ratio of 3 bands	B₂₃₄=（B₂+B₃）/B₄， B₂₃₅=（B₂+B₃）/B₅， B₂₄₃=（B₂+B₄）/B₃ B₂₄₅=（B₂+B₄）/B₅， B₂₅₃=（B₂+B₅）/B₃， B₂₅₄=（B₂+B₅）/B₄ B₃₄₂=（B₃+B₄）/B₂， B₃₄₅=（B₃+B₄）/B₅， B₃₅₂=（B₃+B₅）/B₂ B₃₅₄=（B₃+B₅）/B₄， B₄₅₂=（B₄+B₅）/B₂， B₄₅₃=（B₄+B₅）/B₃
4个波段比值Ratio of 4 bands	B₂₃₄₅=（B₂+B₃）/（B₄+B₅），B₂₄₃₅=（B₂+B₄）/（B₃+B₅） B₂₅₃₄=（B₂+B₅）/（B₃+B₄），B₃₄₂₅=（B₃+B₄）/（B₂+B₅） B_234/5=（B₂+B₃+B₄）/B₅，B_345/2=（B₃+B₄+B₅）/B₂

Table 3 Function expressions conventional statistical model

序号 Serial number	常规统计模型函数 Function of conventional statistical models	表达式 Expression
1	线性函数 Linear function	$y = b 0 + b 1 x$
2	幂函数 Power function	$y = b 0 x b 1$
3	逆函数 Inverse function	$y = b 0 + b 1 / x$
4	对数函数 Logarithmic function	$y = b 0 + b 1 l n x$
5	指数函数 Exponential function	$y = b 0 e b 1 x$
6	S型曲线函数 S-curve function	$y = e b 0 + b 1 / x$
7	二次项函数 Quadratic term function	$y = b 0 + b 1 x + b 2 x 2$
8	三次项函数 Cubic term function	$y = b 0 + b 1 x + b 2 x 2 + b 3 x 3$

Table 3 Function expressions conventional statistical model

序号 Serial number	常规统计模型函数 Function of conventional statistical models	表达式 Expression
1	线性函数 Linear function	$y = b 0 + b 1 x$
2	幂函数 Power function	$y = b 0 x b 1$
3	逆函数 Inverse function	$y = b 0 + b 1 / x$
4	对数函数 Logarithmic function	$y = b 0 + b 1 l n x$
5	指数函数 Exponential function	$y = b 0 e b 1 x$
6	S型曲线函数 S-curve function	$y = e b 0 + b 1 / x$
7	二次项函数 Quadratic term function	$y = b 0 + b 1 x + b 2 x 2$
8	三次项函数 Cubic term function	$y = b 0 + b 1 x + b 2 x 2 + b 3 x 3$

Table 4 Correlation analysis of remote sensing characteristic factors and aboveground biomass of vegetation

序号 Serial number	变量 Variables	乔木 Arbors	灌木 Shrubs	草本 Herbs	农作物 Crops	序号 Serial number	变量 Variables	乔木 Arbors	灌木 Shrubs	草本 Herbs	农作物 Crops
1	NDVI	0.716**	0.720**	0.711**	0.602**	11	B₃₅₄	0.660**	0.617**	0.628**	0.673**
2	RVI	0.701**	0.716**	0.707**	0.675**	12	B₄₅₂	0.657**	0.519**	-	0.669**
3	SAVI	0.716**	0.720**	0.711**	0.602**	13	B₄₅₃	0.737**	0.601**	-	0.686**
4	ARVI	0.662**	0.543**	-	0.589**	14	B_345/2	0.634**	0.497**	-	0.666**
5	DVI	0.730**	0.670**	0.609**	0.488**	15	B₂₅₃₄	0.711**	0.660**	0.598**	0.679**
6	EVI	0.634**	0.567**	0.555**	0.542**	16	1/B₃	-	0.587**	-	0.756**
7	1/B₂	0.590**	0.596**	0.536**	0.718**	17	1/B₄	-	0.531**	-	0.742**
8	B₂₅₃	0.755**	0.701**	0.588**	0.682**	18	1/B₆	-	-	-	0.572**
9	B₂₅₄	0.650**	0.508**	-	0.672**	19	1/B₇	-	-	-	0.669**
10	B₃₅₂	0.677**	0.571**	-	0.670**	20	B₃₄	-	-	-	0.557**

Table 5 Evaluation of different estimation models for aboveground biomass of arbors

模型 Model	表达式 Expression	建模Modeling			验证Verification			P
模型 Model	表达式 Expression	R2	RMSE	MAE	R²	RMSE	MAE	P
二次项Quadratic term	$Y = 131535 B 453 2 - 55681 B 453 + 6032.2$	0.686	4.517	0.067	0.893	9.521	0.118	0.000
多元逐步回归MSR	$Y = 12444.188 B 253 - 2583.234$	0.607	5.050	0.066	0.812	8.176	0.095	0.000
偏最小二乘回归PLSR	$Y = 78279.023 B 253 - 35702.875 B 453 + 2641.657 N D V I ????? - 72808.192 B 254 - 2275.642$	0.713	4.589	0.069	0.745	7.753	0.107	0.000

Table 5 Evaluation of different estimation models for aboveground biomass of arbors

模型 Model	表达式 Expression	建模Modeling			验证Verification			P
模型 Model	表达式 Expression	R2	RMSE	MAE	R²	RMSE	MAE	P
二次项Quadratic term	$Y = 131535 B 453 2 - 55681 B 453 + 6032.2$	0.686	4.517	0.067	0.893	9.521	0.118	0.000
多元逐步回归MSR	$Y = 12444.188 B 253 - 2583.234$	0.607	5.050	0.066	0.812	8.176	0.095	0.000
偏最小二乘回归PLSR	$Y = 78279.023 B 253 - 35702.875 B 453 + 2641.657 N D V I ????? - 72808.192 B 254 - 2275.642$	0.713	4.589	0.069	0.745	7.753	0.107	0.000

Fig.2 Verification map of arbors optimal model in the study area

Table 6 Effect evaluation of different estimation models for aboveground biomass of shrubs

模型 Model	表达式 Expression	建模Modeling			验证Verification			P
模型 Model	表达式 Expression	R2	RMSE	MAE	R²	RMSE	MAE	P
S型曲线S-curve	$Y = e 24.469 - 12.474 / N D V I$	0.560	0.832	0.689	0.394	0.826	0.642	0.000
多元逐步回归MSR	$Y = 753.726 N D V I - 907.902 E V I + 25.979$	0.542	10.525	6.577	0.637	5.810	4.125	0.000
偏最小二乘回归 PLSR	$Y = - 121910.662 N D V I + 123789.485 S A V I - 5723.928 R V I + ?????????? 762.62 B 253 - 400.296 D V I + 3537.964 B 2534 + 9181.916 B 354 - ?????????? 3966.025 E V I - 4253.029$	0.648	9.233	5.783	0.721	6.198	4.910	0.000

Table 6 Effect evaluation of different estimation models for aboveground biomass of shrubs

模型 Model	表达式 Expression	建模Modeling			验证Verification			P
模型 Model	表达式 Expression	R2	RMSE	MAE	R²	RMSE	MAE	P
S型曲线S-curve	$Y = e 24.469 - 12.474 / N D V I$	0.560	0.832	0.689	0.394	0.826	0.642	0.000
多元逐步回归MSR	$Y = 753.726 N D V I - 907.902 E V I + 25.979$	0.542	10.525	6.577	0.637	5.810	4.125	0.000
偏最小二乘回归 PLSR	$Y = - 121910.662 N D V I + 123789.485 S A V I - 5723.928 R V I + ?????????? 762.62 B 253 - 400.296 D V I + 3537.964 B 2534 + 9181.916 B 354 - ?????????? 3966.025 E V I - 4253.029$	0.648	9.233	5.783	0.721	6.198	4.910	0.000

Fig.3 Verification of shrubs optimal model in the study area

Table 7 Accuracy evaluation of estimation model for herbs aboveground biomass in the study area

模型 Model	表达式 Expression	建模Modeling			验证Verification			P
模型 Model	表达式 Expression	R2	RMSE	MAE	R²	RMSE	MAE	P
二次项Quadratic term	$Y = - 33287 R V I 2 + 5455.5 R V I - 198.02$	0.467	5.931	4.939	0.696	5.204	4.695	0.000
多元逐步回归MSR	$Y = 323.65 N D V I - 167.376$	0.462	5.958	4.901	0.687	5.271	4.824	0.000
偏最小二乘回归 PLSR	$Y = - 2281832.994 N D V I + 2285265.977 S A V I - 11772.656 R V I + ????????? 10756.369 B 354 - 302.516 D V I + 4400.234 B 2534 - 3794.417 E V I - ????????? 5623.736$	0.632	5.018	4.071	0.728	6.119	5.011	0.000

Table 7 Accuracy evaluation of estimation model for herbs aboveground biomass in the study area

模型 Model	表达式 Expression	建模Modeling			验证Verification			P
模型 Model	表达式 Expression	R2	RMSE	MAE	R²	RMSE	MAE	P
二次项Quadratic term	$Y = - 33287 R V I 2 + 5455.5 R V I - 198.02$	0.467	5.931	4.939	0.696	5.204	4.695	0.000
多元逐步回归MSR	$Y = 323.65 N D V I - 167.376$	0.462	5.958	4.901	0.687	5.271	4.824	0.000
偏最小二乘回归 PLSR	$Y = - 2281832.994 N D V I + 2285265.977 S A V I - 11772.656 R V I + ????????? 10756.369 B 354 - 302.516 D V I + 4400.234 B 2534 - 3794.417 E V I - ????????? 5623.736$	0.632	5.018	4.071	0.728	6.119	5.011	0.000

Fig.4 Verification of herbs optimal model in the study area

Table 8 Accuracy evaluation of crops aboveground biomass estimation model in the study area

模型 Model	表达式 Expression	建模Modeling			验证Verification			P
模型 Model	表达式 Expression	R2	RMSE	MAE	R²	RMSE	MAE	P
S型曲线S-curve	$Y = e 10.115 - 1.359 1 / B 7$	0.623	22.027	16.058	0.513	18.648	14.360	0.000
多元逐步回归MSR	$Y = 1761.667 1 B 3 - 911.79$	0.571	19.860	13.008	0.677	17.638	13.648	0.000
偏最小二乘回归 PLSR	$Y = 7622.851 1 B 3 + 783.59 1 B 4 + 6615.782 1 B 2 + 1530.927 B 453 ?????????? - 3540.965 B 352 + 349.606$	0.613	18.863	13.158	0.626	17.659	13.671	0.000

Table 8 Accuracy evaluation of crops aboveground biomass estimation model in the study area

模型 Model	表达式 Expression	建模Modeling			验证Verification			P
模型 Model	表达式 Expression	R2	RMSE	MAE	R²	RMSE	MAE	P
S型曲线S-curve	$Y = e 10.115 - 1.359 1 / B 7$	0.623	22.027	16.058	0.513	18.648	14.360	0.000
多元逐步回归MSR	$Y = 1761.667 1 B 3 - 911.79$	0.571	19.860	13.008	0.677	17.638	13.648	0.000
偏最小二乘回归 PLSR	$Y = 7622.851 1 B 3 + 783.59 1 B 4 + 6615.782 1 B 2 + 1530.927 B 453 ?????????? - 3540.965 B 352 + 349.606$	0.613	18.863	13.158	0.626	17.659	13.671	0.000

Fig.5 Verification map of crops optimal model in the study area

Fig.6 Estimation of vegetation aboveground biomass in the study area

Table 9 Statistics of vegetation aboveground biomass in study area

等级 Grade	地上生物量 AGB （g·m^-2）	乔木Arbors		灌木Shrubs		草本Herbs		农作物Crops
等级 Grade	地上生物量 AGB （g·m^-2）	面积 Area （km²）	百分比 Proportion（%）	面积 Area （km²）	百分比 Proportion（%）	面积 Area （km²）	百分比 Proportion（%）	面积 Area （km²）	百分比 Proportion（%）
Ⅰ	0<AGB≤65	78.04	16.74	0.17	0.01	1245.46	75.21	45.11	0.92
Ⅱ	65<AGB≤280	175.23	37.58	0.31	0.01	410.43	24.79	182.80	3.73
Ⅲ	280<AGB≤950	212.98	45.68	2087.26	99.98	-	-	1436.62	29.31
Ⅳ	950<AGB≤1450	-	-	-	-	-	-	3236.96	66.04
合计Total	-	466.25	100.00	2087.74	100.00	1655.89	100.00	4901.49	100.00

References 32

1	Tao Y， Zhang Y M. Multi-scale biomass estimation of desert shrubs： A case study of Haloxylon ammodendron in the Gurbantunggut Desert， China. Acta Prataculturae Sinica， 2013， 22（6）： 1-10.
	陶冶，张元明.荒漠灌木生物量多尺度估测——以梭梭为例. 草业学报， 2013， 22（6）： 1-10.
2	Wu M Y， Dong G， Wang Y J， et al. Estimation of forest aboveground carbon storage in Sichuan Miyaluo Nature Reserve based on remote sensing. Acta Ecologica Sinica， 2020， 40（2）： 621-628.
	巫明焱，董光，王艺积，等. 川西米亚罗自然保护区森林地上碳储量遥感估算研究. 生态学报， 2020， 40（2）： 621-628.
3	Guan J R， Shang T Q， Yi L T， et al. Biomass change and community succession characteristics of dominant species in evergreen and deciduous broad-leaved mixed forests in Tianmu Mountain. Acta Ecologica Sinica， 2017， 37（20）： 6761-6772.
	管杰然，商天其，伊力塔，等. 天目山常绿落叶阔叶混交林优势种生物量变化及群落演替特征. 生态学报， 2017， 37（20）： 6761-6772.
4	Zhou X， Zuo X A， Zhao X Y， et al. Effect of change in semiarid sand dune habitat on aboveground plant biomass， carbon and nitrogen. Acta Prataculturae Sinica， 2014， 23（6）： 36-44.
	周欣，左小安，赵学勇，等. 半干旱沙地生境变化对植物地上生物量及其碳、氮储量的影响. 草业学报， 2014， 23（6）： 36-44.
5	Ren K， Guo K， Zheng J M， et al. Ecological benefits of different vegetation restoration modes along the Xining to Golmud section of Qinghai-Tibet Railway. Chinese Journal of Ecology， 2019， 38（3）： 627-636.
	任康，郭坤，郑景明，等. 青藏铁路西格段沿线不同植被恢复模式的生态效益. 生态学杂志， 2019， 38（3）： 627-636.
6	Chu D， Deji Y Z， Ji Q M， et al. Aboveground biomass estimate methods for typical grassland types in the Tibetan Plateau. Remote Sensing for Land and Resources， 2013， 25（3）： 43-50.
	除多，德吉央宗，姬秋梅，等. 西藏高原典型草地地上生物量遥感估算. 国土资源遥感， 2013， 25（3）： 43-50.
7	Li L， Chen E X， Li Z Y， et al. A review on forest height and above-ground biomass estimation based on synthetic aperture radar. Remote Sensing Technology and Application， 2016， 31（4）： 625-633.
	李兰，陈尔学，李增元，等. 合成孔径雷达森林树高和地上生物量估测研究进展. 遥感技术与应用， 2016， 31（4）： 625-633.
8	Ghosh S M， Behera M D. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography， 2018， 96： 29-40.
9	Ren Y， Wang H B， Xu D P. Estimation of aboveground biomass of arbor forest based on Landsat 8 image. Forest Resources Management， 2018（6）： 38-44.
	任怡，王海宾，许等平. 基于Landsat 8影像的乔木林地上生物量估算. 林业资源管理， 2018（6）： 38-44.
10	Qiao Z N， Geng Q H， Xu Y N. Estimating Poplar plantation biomass using satellite remote sensing data. Journal of Northeast Forestry University， 2019， 47（5）： 66-71.
	乔正年，耿庆宏，徐雁南. 运用卫星遥感数据对杨树人工林生物量的估算. 东北林业大学学报， 2019， 47（5）： 66-71.
11	Yang G Q， Ma Y， Wang J B， et al. AGB of Tamarix remote sensing estimation research based on GF-1 image-Take Changyi Tamarix national special marine reserves as an example. Marine Environmental Science， 2018， 37（1）： 78-85， 94.
	杨国强，马毅，王建步，等. 基于高分一号卫星影像的柽柳地上生物量遥感估算研究-以昌邑柽柳国家海洋特别保护区为例. 海洋环境科学， 2018， 37（1）： 78-85， 94.
12	Ding Z D， Sun Y J， Sun Z. Estimation of tree biomass with GF-2. Journal of Beijing Normal University （Natural Science）， 2021， 57（1）： 135-141.
	丁志丹，孙玉军，孙钊. 基于GF-2的乔木生物量估测模型研究. 北京师范大学学报（自然科学版）， 2021， 57（1）： 135-141.
13	Yang X F， Zan M， Munire∙M M T. Estimation of above ground biomass of Populus euphratica forest using UAV and satellite remote sensing. Transactions of the Chinese Society of Agricultural Engineering， 2021， 37（1）： 77-83.
	杨雪峰，昝梅，木尼热∙买买提. 基于无人机和卫星遥感的胡杨林地上生物量估算. 农业工程学报， 2021， 37（1）： 77-83.
14	Zhao H F， Li X D， Zhang D， et al. Aboveground biomass in grasslands in Qinghai Province estimated from MODIS data and its influencing factors. Acta Prataculturae Sinica， 2020， 29（12）： 5-16.
	赵慧芳，李晓东，张东，等. 基于MODIS数据的青海省草地地上生物量估算及影响因素研究. 草业学报， 2020， 29（12）： 5-16.
15	Zhou W， Li H， Xie L， et al. Remote sensing inversion of grassland aboveground biomass based on high accuracy surface modeling. Ecological Indicators， 2021， 121： 107215.
16	Zhang Y， Yin X J， Wang W Q， et al. Estimation of grassland aboveground biomass using Landsat 8 OLI satellite image in the Northern hillside of Tianshan Mountain. Remote Sensing Technology and Application， 2017， 32（6）： 1012-1021.
	张雅，尹小君，王伟强，等. 基于Landsat 8 OLI遥感影像的天山北坡草地地上生物量估算. 遥感技术与应用， 2017， 32（6）： 1012-1021.
17	Zhang A W， Zhang S， Guo C F， et al. Grass biomass inversion based on Landsat 8 spectral derived data classification system. Spectroscopy and Spectral Analysis， 2020， 40（1）： 239-246.
	张爱武，张帅，郭超凡，等. Landsat 8光谱衍生数据分类体系下的牧草生物量反演. 光谱学与光谱分析， 2020， 40（1）： 239-246.
18	Sun S Z， Wang C J， Yin X J， et al. Estimating aboveground biomass of natural grassland based on multispectral images of unmanned aerial vehicles. Journal of Remote Sensing， 2018， 22（5）： 848-856.
	孙世泽，汪传建，尹小君，等. 无人机多光谱影像的天然草地生物量估算. 遥感学报， 2018， 22（5）： 848-856.
19	Xu K， Su Y， Liu J， et al. Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data. Ecological Indicators， 2020， 108： 105747.
20	Kang X Y， Zhang A W， Pang H Y. Estimation of grassland aboveground biomass from UAV-mounted hyperspectral image by optimized spectral reconstruction. Spectroscopy and Spectral Analysis， 2021， 41（1）： 250-256.
	康孝岩，张爱武，庞海洋. 基于光谱重建优化的无人机高光谱影像估算牧草生物量. 光谱学与光谱分析， 2021， 41（1）： 250-256.
21	Li B， Xu X， Zhang L， et al. Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing， 2020， 162： 161-172.
22	Tao H， Feng H， Xu L， et al. Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data. Sensors， 2020， 20（5）： 1296.
23	Liu Y Q， Yan F， Chen J H. Applicating Landsat 8 OLI to estimate biomass in Pisha sandstone area. Research of Soil and Water Conservation， 2021， 28（2）： 135-140， 148.
	刘雨晴，闫峰，陈俊翰. 基于Landsat 8 OLI数据的砒砂岩区生物量遥感估算. 水土保持研究， 2021， 28（2）： 135-140， 148.
24	Ye J Y， Wu B， Liu M H， et al. Estimation of aboveground biomass of vegetation in the desert-oasis ecotone on the Northeastern edge of the Ulan Buh Desert. Acta Ecologica Sinica， 2018， 38（4）： 1216-1225.
	叶静芸，吴波，刘明虎，等. 乌兰布和沙漠东北缘荒漠-绿洲过渡带植被地上生物量估算. 生态学报， 2018， 38（4）： 1216-1225.
25	Wang X M， Dong J J. Estimation of the aboveground biomass of desert steppe and typical steppe in Inner Mongolia using generalized linear model. Acta Agrestia Sinica， 2020， 28（6）： 1711-1718.
	王秀梅，董建军. 基于广义线性模型估算内蒙古荒漠草原及典型草原地上生物量变化. 草地学报， 2020， 28（6）： 1711-1718.
26	He B Z， Ding J L， Liu B H， et al. Spatiotemporal variation of soil salinization in Weigan-Kuqa river delta oasis. Scientia Silvae Sinicae， 2019， 55（9）： 185-196.
	何宝忠，丁建丽，刘博华，等. 渭库绿洲土壤盐渍化时空变化特征. 林业科学， 2019， 55（9）： 185-196.
27	Huang Y， Wang X M. Analysis of species diversity of typical plant communities in oasis-desert transition zone of Tarim basin northern margin. Southwest China Journal of Agricultural Sciences， 2018， 31（3）： 605-610.
	黄晔，王雪梅. 塔里木盆地北缘绿洲-荒漠过渡带典型植物群落物种多样性分析. 西南农业学报， 2018， 31（3）： 605-610.
28	Jia X Q， Feng M C， Yang W D， et al. Hyperspectral estimation of aboveground dry biomass of winter wheat based on the combination of vegetation indices. Chinese Journal of Ecology， 2018， 37（2）： 424-429.
	贾学勤，冯美臣，杨武德，等. 基于多植被指数组合的冬小麦地上干生物量高光谱估测. 生态学杂志， 2018， 37（2）： 424-429.
29	Ali I， Cawkwell F， Dwyer E， et al. Satellite remote sensing of grasslands： From observation to management-A review. Journal of Plant Ecology， 2016， 9（6）： 649-671.
30	Nesha M K， Hussin Y A， van Leeuwen L M， et al. Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan， Indonesia. International Journal of Applied Earth Observation and Geoinformation， 2020， 91： 102158.
31	Sun L， Wang M， Fan X. Spatial pattern and driving factors of biomass carbon density for natural and planted coniferous forests in mountainous terrain， Eastern Loess Plateau of China. Forest Ecosystems， 2020， 7（1）： 104-116.
32	Wu J Z， Sun F， Cui Y， et al. Relationship between vegetation biomass and soil bulk density on unstable slopes in different climatic regions： A case study of Jiangjiagou watershed in Dongchuan District of Kunming City， Yunnan Province of Southwestern China. Journal of Beijing Forestry University， 2020， 42（3）： 24-35.
	吴建召，孙凡，崔羽，等. 不同气候区失稳性坡面植被生物量与土壤密度的关系-以云南省昆明市东川区蒋家沟流域为例. 北京林业大学学报， 2020， 42（3）： 24-35.

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[2]	MA Tao, LV Wen-qiang, LI Ze-xia, CHEN Ai-hua, DONG Yan-li. Water distribution characteristics of soil profiles in five land use types under rotational cropping in the Hill and Gully Regions on the Loess Plateau [J]. Acta Prataculturae Sinica, 2020, 29(7): 30-39.
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[4]	NI Lu, WU Jing, LI Chun-bin, QIN Ge-xia, LI Zheng, KONG Jie. Temporal and spatial variations in natural grassland phenology in China over the last 30 years [J]. Acta Prataculturae Sinica, 2020, 29(1): 1-12.
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