基于5种植被指数的荒漠区植被生物量提取研究

doi:10.13866/j.azr.2023.04.13

Abstract

Abstract:

The aboveground biomass of vegetation in desert areas serves as a crucial indicator for monitoring land desertification and extracting desert vegetation information using remote sensing techniques. In this study, the Minqin County of Gansu Province was selected as the experimental area and Sentinel-2 images were used as the data source. We constructed estimation models (unitary linear, exponential, logarithmic, and binomial models) for the planted index and the aboveground biomass of vegetation, which were measured by us. These models include five vegetation indices: ratio vegetation index (RVI), normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and optimized soil-adjusted vegetation index (OSAVI). The aboveground biomass in the study area was estimated using the selected optimal model. The results demonstrated that SAVI had the highest correlation with the aboveground biomass (r = 0.79) compared with RVI, NDVI, DVI, and OSAVI. The binomial model based on SAVI was the best model (R² = 0.76) for the aboveground biomass estimation in the study area, with higher accuracy (R² = 0.73, RMSE = 0.12). In the Minqin County, the relatively dense areas of vegetation were mainly distributed in the four major irrigation districts (Hongyashan, Huanhe, Changning, and Nanhu), the surrounding area of Qingtu Lake, and the northwest region of Hongshagang Town, whereas the vegetation in other regions was relatively sparse. The proportions of nonvegetation area <[0.005 kg·(100m²)^-1], low vegetation area [0.005-0.2 kg·(100m²)^-1], medium vegetation area [0.2-0.5 kg·(100m²)^-1], and high vegetation area [>0.5 kg·(100m²)^-1] were 66%, 21%, 5%, and 8%, respectively.

Key words: vegetation in desert areas, aboveground biomass, vegetation index, information extraction, Sentinel image data

LIANG Boming, LIU Xin, HAO Yuanyuan, CHU Bin, TANG Zhuangsheng. Extraction of desert vegetation information based on five vegetation indices[J].Arid Zone Research, 2023, 40(4): 647-654.

Figures/Tables 10

Fig. 1

Tab. 1

Species and taxonomic status in quadrat"

科	属	植物名称
豆科 Fabaceae	苦参属Sophora	苦豆子Sophora alopecuroides
	苦马豆属 Sphaerophysa	苦马豆Sphaerophysa salsula
	骆驼刺属 Alhagi	骆驼刺Alhagi camelorum
	锦鸡儿属 Caragana	拧条锦鸡儿Caragana korshinskii
	甘草属 Glycyrrhiza	甘草Glycyrrhiza uralensis
菊科 Asteraceae	花花柴属 Karelinia	花花柴Karelinia caspia
	鸦葱属 Takhtajaniantha	鸦葱Takhtajaniantha austriaca
	漏芦属 Rhaponticum	顶羽菊Rhaponticum repens
	蓝刺头属 Echinops	蓝刺头Echinops sphaerocephalus
	旋覆花属 Inula	蓼子朴Inula salsoloides
禾本科 Poaceae	芦苇属 Phragmites	芦苇Phragmites australis
	虎尾草属 Chloris	虎尾草Chloris virgata
	狗尾草属 Setaria	狗尾草Setaria viridis
	冰草属 Agropyron	冰草Agropyron cristatum
苋科 Amaranthaceae	梭梭属 Haloxylo	梭梭Haloxylon ammodendron
	盐生草属 Halogeton	蛛丝蓬Halogeton arachnoideus
	盐爪爪属 Kalidium	盐爪爪Kalidium foliatum
十字花科 Brassicaceae	独行菜属 Lepidium	宽叶独行菜Lepidium latifolium
十字花科 Brassicaceae	肉叶荠属Braya	蚓果芥Braya humilis
藜科 Chenopodiaceae	雾冰藜属 Bassia	雾冰藜Bassia dasyphylla
藜科 Chenopodiaceae	猪毛菜属 Salsola	猪毛菜Salsola collinal
白刺科 Nitrariaceae	骆驼蓬属 Peganum	骆驼蓬Peganum harmala
白刺科 Nitrariaceae	白刺属 Nitraria	白刺Nitraria tangutorum
紫草科 Boraginaceae	紫丹属 Tournefortia	砂引草Tournefortia sibirica
白花丹科 Plumbaginaceae	补血草属 Limonium	黄花补血草Limonium aureum
石竹科 Caryophyllaceae	石竹属 Dianthus	石竹Dianthus chinensis
旋花科 Convolvulaceae	旋花属 Convolvulus	田旋花Convolvulus arvensis
夹竹桃科 Apocynaceae	鹅绒藤属 Cynanchum	戟叶鹅绒藤Cynanchum acutum
柽柳科 Tamaricaceae	柽柳属 Tamarix	柽柳Tamarix chinensis
胡颓子科 Elaeagnaceae	胡颓子属 Elaeagnus	沙枣Elaeagnus angustifolia
毛茛科 Ranunculaceae	铁线莲属 Clematis	黄花铁线莲Clematis intricata
麻黄科 Ephedraceae	麻黄属 Ephedra	草麻黄Ephedra sinica
茄科 Solanaceae	枸杞属 Lycium	枸杞Lycium chinense

Tab. 1

Tab. 2

Tab. 3

Tab. 4

Tab. 5

Tab. 6

Tab. 7

Fig. 2

Fig. 3

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波段号	哨兵2号波段	中心波长/nm	空间分辨率/m
1	Coastal aerosol	433	60
2	Blue	490	10
3	Green	560	10
4	Red	665	10
5	Vegetation Red Edge	705	20
6	Vegetation Red Edge	740	20
7	Vegetation Red Edge	783	20
8	NIR	842	10
8A	Vegetation Red Edge	865	20
9	Water vapour	945	60
10	SWIR-Cirrus	1375	60
11	SWIR	1610	20
12	SWIR	2190	20

影像编号	成像日期	处理基线编号	相对轨道编号	拼接地域编号
1	2020-07-26	N0214	R004	T48SUJ
2	2020-07-26	N0214	R004	T48SVH
3	2020-07-26	N0214	R004	T48STJ
4	2020-07-26	N0214	R004	T48SUH
5	2020-08-05	N0214	R004	T48STH
6	2020-08-25	N0214	R004	T48SVJ

植被指数	计算公式
比值植被指数RVI	RVI=NIR/RED
归一化植被指数NDVI	NDVI=(NIR-RED)/(NIR+RED)
差值植被指数DVI	DVI=NIR-RED
土壤调节植被指数SAVI	SAVI=(1＋L)(NIR-RED)/(NIR+RED+L)
优化型土壤调节植被指数OSAVI	OSAVI=(NIR-RED)/(NIR+RED+X)

植被划分	植被地上生物量 /[kg·(100m²)^-1]	覆被特征
无植被区	<0.005	沙地、盐碱地和裸土地等
低植被区	0.005~0.2	低产草地、撂荒地、疏林地以及中度沙化、盐渍化土地等
中植被区	0.2~0.5	中产草地、低郁闭林地、灌木林地以及轻度沙化、盐渍化土地等
高植被区	>0.5	高产草地、耕地和密林等

植被指数	估算模型	R²
RVI	y = 0.8487x - 0.0043	0.59
	y = 0.226ln(x) + 0.558	0.44
	y = 0.05e^3.5318^x	0.49
	y = 0.3182x² + 0.5621x + 0.0385	0.60
NDVI	y = 0.57x + 0.0196	0.46
	y = 0.0475ln(x) + 0.2385	0.15
	y= 0.0655e^2.2292^x	0.60
	y = 1.2093x² - 0.2675x + 0.0998	0.63
DVI	y = 2691.5x + 166.29	0.44
	y = 219.99ln(x) + 1190.7	0.14
	y = 380.99e^1.9669^x	0.57
	y = 5736.5x²- 1280.9x + 546.96	0.62
SAVI	y = 0.8396x + 0.0364	0.62
	y = 0.0707ln(x) + 0.3604	0.21
	y = 0.101e^2.3966^x	0.73
	y = 1.3554x² - 0.099x + 0.1263	0.76
OSAVI	y = 0.689x + 0.017	0.45
	y = 0.0578ln(x) + 0.2823	0.15
	y = 0.0707e^2.3733^x	0.59
	y = 1.412x² - 0.2888x + 0.1107	0.61

Extraction of desert vegetation information based on five vegetation indices

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