极干旱区稀疏荒漠植被地上生物量遥感估算

doi:10.12118/j.issn.1000–6060.2021.177

Abstract

Abstract:

The aboveground biomass (AGB) is an essential indicator of desert ecosystem health evaluation and desertification monitoring in arid areas. The estimation accuracy of the general remote sensing model is reduced because of the influence of small and sparse desert vegetation, low AGB per unit area, and background soil information. Improving biomass estimation accuracy of sparse desert vegetation can provide essential parameters for terrestrial ecosystem carbon storage estimation and carbon cycle research. It can also provide technical support for desertification monitoring. Herein, the Gobi area in front of the Altun Mountains in the south of Kumtag Desert, northwest China with an extremely arid climate is considered as the research area. The vegetation coverage area was accurately extracted using UAV image data. The estimation results based on the high-spatial resolution WorldView-3 remote sensing data were used to correct the estimation results based on the medium spatial resolution Landsat-OLI remote sensing data. Then, stepwise linear regression (SLR), LASSO regression, and ridge regression (RR) models were used to estimate the AGB of sparse desert vegetation. The results showed that: (1) High-spatial resolution satellite remote sensing data can accurately describe the spatial and temporal distribution characteristics of vegetation in arid areas. The RMSErel mean value of WorldView-3 remote sensing data was 44.68%. (2) The RR model had a better regression effect than SLR and LASSO models. The RMSErel of the WorldView-3 RR model was 44.68%, better than the SLR model (58.33%) and LASSO model (45.26%). Additionally, the RMSErel of the Landsat-OLI RR model was 114.34%, better than the SLR model (134.94%) and LASSO model (115.81%). (3) Using the WorldView-3 remote sensing data as an intermediate medium between ground and Landsat-OLI remote sensing data, the AGB estimation results of desert vegetation based on Landsat-OLI remote sensing data were corrected, and the RMSErel decreased by 31.18%. The spatial resolution of remote sensing data is the main factor affecting the estimation accuracy of the sparse desert vegetation AGB regression model. The main reason for the decrease in the estimation accuracy of AGB is the decrease in the sensitivity of Landsat-OLI remote sensing data to sparse desert vegetation information. The nonlinear multiple regression methods, SLR, LASSO, and RR, are used to build the regression model between remote sensing characteristic parameters and ground sample data, effectively improving model stability. When extracting sparse desert vegetation information, using high-spatial resolution remote sensing data as a medium between ground and medium spatial resolution remote sensing data can effectively improve the extraction accuracy of sparse desert vegetation information from medium spatial resolution remote sensing data.

Key words: remote sensing eigenvalue, sparse desert vegetation, aboveground biomass, regression model

YE Jingyun,WU Bo,JIA Xiaohong,FEI Bingqiang,GAO Junliang,CHENG Long,PANG Yingjun,YAO Bin,KONG Deyong. Estimation of aboveground biomass of sparse desert vegetation based on remote sensing techniques in hyper-arid area[J].Arid Land Geography, 2022, 45(2): 478-487.

Figures/Tables 9

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物种	预测变量	异速生长方程	R²	MAE/g	植株个数
泡泡刺（Nitraria sphaerocarpa）	V	y=131.27V^0.61	0.87	29.16	n=15（P<0.0001）
旱蒿（Artemisia xerophytica）	V	y=393.99V^0.95	0.86	87.54	n=14（P<0.0001）
梭梭（Haloxylon ammodendron）	V	y=688.38V^0.83	0.78	235.25	n=15（P<0.0001）
柽柳（Tamarix ramosissima）	V	y=533.09V^0.75	0.73	355.42	n=15（P<0.0001）
沙拐枣（Calligonum mongolicum）	A	y=18.70A-2.71	0.99	2.89	n=15（P<0.005）
膜果麻黄（Ephedra przewalskii）	A	y=21.49A-12.49	0.97	8.42	n=13（P<0.005）
裸果木（Gymnocarpos przewalskii）	A	y=12.84A-2.36	0.99	2.07	n=9（P<0.005）
红砂（Reaumuria soongarica）	A	y=11.76A-1.10	0.74	5.34	n=23（P<0.005）
合头藜（Sympegma regelii）	A	y=7.94A-1.58	0.83	4.80	n=28（P<0.005）
霸王（Zygophyllum xanthoxylon）	A	y=7.93A-1.84	0.96	1.25	n=10（P<0.005）

遥感特征参数	Landsat-OLI						WorldView-3
遥感特征参数	RED	NIR	RVI	NDVI	MSAVI		RED	NIR	RVI	NDVI	MSAVI
Kappa系数	0.72	0.72	0.77	0.70	0.75		0.82	0.85	0.90	0.95	0.93

模型类型	RMSE/g·m^-2	SD/g·m^-2	RMSErel/%
Landsat-OLI LASSO	95.36	42.84	115.81
WorldView-3 LASSO	32.99	10.27	45.26
Landsat-OLI RR	94.15	40.99	114.34
WorldView-3 RR	33.42	9.81	44.68
Landsat-OLI SLR	95.92	43.39	134.94
WorldView-3 SLR	43.07	21.93	58.33

Estimation of aboveground biomass of sparse desert vegetation based on remote sensing techniques in hyper-arid area

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