基于高光谱反射特征的荒漠类型可分性评价

doi:10.13866/j.azr.2025.01.13

Abstract

Abstract:

Few studies have used the characteristic variables extracted from the details of the hyperspectral reflectance curves of bare soil to evaluate the separability of various desert types. In this study, salt desert, gravel desert, mud desert, and desert in the lower reaches of the Shiyang River were used as the research objects, and cumulative difference, first-order differentiation, continuum removal, vegetation index calculation and principal component analysis were used to identify the hyperspectral reflectance features of various desert types, extract the key categorical variables, and quantify the degree of differentiation of various desert types. The results showed that (1) the absorption valleys at 446-600 nm and 2150-2285 nm differed significantly among the desert types. (2) the Carter index 1, Greenness Index, and Green NDVI hyper 2 differed significantly among the desert types. (3) The Modified Chlorophyll Absorption Ratio Index, Soil Adjusted Vegetation Index, and 2265 nm and 1790-1810 nm reflectance had larger weight values in constructing the principal component indexes; and (4) the differentiation index of each desert type: desert & salty desert>desert & muddy desert>muddy & salty desert>gravelly & salty desert>desert & gravelly desert>mud & gravelly desert. These findings provide ground verification and data support for the remote sensing monitoring of deserts in the northwest Arid Zone.

Key words: desert soil, hyperspectral feature band, feature extraction, principal component analysis, class separability

LIU Zhifei, YANG Xuemei, WANG Jingrui, HUANG Kepan, XU Haojie. Class separability evaluation of desert types based on the hyperspectral reflectance characteristics[J].Arid Zone Research, 2025, 42(1): 141-153.

Figures/Tables 13

Fig. 1

Tab. 1

Tab. 2

Hyperspectral vegetation indices and their formulas"

光谱指数全称	缩写	中文名称	计算公式
Carter Index 1	Ctr1	Carter指数1	$R 695 R 420$
Carter Index 2	Ctr2	Carter指数2	$R 695 R 760$
Enhanced Vegetation Index	EVI	增强型植被指数	$2.5 (R 800 - R 670) R 800 + 6 R 670 - 7.5 R 450 + 1$
Greenness Index	GI	绿度指数	$R 554 R 677$
Green NDVI hyper 2	GNDVIh2	绿色归一化差值植被指数	$R 801 - R 550 R 801 + R 550$
Gitelson-Merzlyak Index	GMI	Gitelson-Merzlyak指数	$R 750 R 700$
hyperspectral NDVI	hNDVI	高光谱归一化差值植被指数	$R 900 - R 685 R 900 + R 685$
Renormalized Difference Vegetation Index	RNDVI	仿归一化差值植被指数	$R 842 - R 740 R 842 + R 740$
Modified Chlorophyll Absorption Ratio Index	MCARI	改进叶绿素吸收指数	$(R 700 - R 670) - 0.2 (R 700 - R 550) R 700 R 670$
Normalized Difference Index 1	NDI1	归一化差值指数1	$R 800 - R 680 R 800 + R 680$
Normalized Difference Index 2	NDI2	归一化差值指数2	$R 570 - R 539 R 570 + R 539$
Normalized Difference Index 3	NDI3	归一化差值指数3	$R 550 - R 531 R 550 + R 531$
Normalized Difference Index 4	NDI4	归一化差值指数4	$R 750 - R 705 R 750 + R 705$
Normalized Difference Index 5	NDI5	归一化差值指数5	$R 750 - R 715 R 715 - R 670$
Normalized Difference Chlorophyll	NDchl	归一化差异叶绿素指数	$R 935 - R 705 R 935 + R 705$
Photochemical Reflectance Index	PRI	光化学反射指数	$R 531 - R 570 R 531 + R 570$
Pigment Specific Normalized Difference	PSND	色素归一化差异指数	$R 810 - R 674 R 810 + R 674$
Structure Intensive Pigment Index	SIPI	结构强度色素指数	$R 800 - R 450 R 800 + R 650$
Soil Adjusted Vegetation Index	SAVI	土壤调节植被指数	$(1 + 0.15) (R 800 - R 670) (R 800 + R 670 + 0.15)$
Optimized Soil Adjusted Vegetation Index	OSAVI	优化土壤调节植被指数	$(1 + 0.16) (R 800 - R 670) R 800 + R 670 + 0.16$
Reflectance at the inflexion point	Rre	红边位置指数	$R 670 - R 780 2$
Pigment Specific Simple Ratio (Cholophyll a)	PSSRa	色素简单比值指数（叶绿素a）	$R 800 R 680$
Pigment Specific Simple Ratio (Cholophyll b)	PSSRb	色素简单比值指数（叶绿素b）	$R 800 R 635$
Simple Ratio Index 1	SR1	简单比值指数1	$R 810 R 674$
Simple Ratio Index 2	SR2	简单比值指数2	$R 774 R 677$
Simple Ratio Index 3	SR3	简单比值指数3	$R 710 R 760$
Simple Ratio Index 4	SR4	简单比值指数4	$R 850 R 710$
Simple Ratio Index 5	SR5	简单比值指数5	$R 750 R 710$
Simple Ratio Index 6	SR6	简单比值指数6	$R 850 R 715$
Simple Ratio Index 7	SR7	简单比值指数7	$R 900 R 680$
Simple Ratio Index 8	SR8	简单比值指数8	$R 715 R 550$
Vogelmann Red Edge Index 1	VOG1	Vogelmann红边指数1	$R 740 R 720$
Vogelmann Red Edge Index 2	VOG2	Vogelmann红边指数2	$R 734 - R 747 R 715 + R 726$
Vogelmann Red Edge Index 3	VOG3	Vogelmann红边指数3	$R 734 - R 747 R 715 + R 720$
Water Band Index	WBI	水带指数	$R 950 R 900$
Water Index	WI	水体指数	$R 900 R 970$

Tab. 2

Fig. 2

Fig. 3

Fig. 4

Tab. 3

Fig. 5

Fig. 6

Tab. 4

Fig. 7

Tab. 5

Fig. 8

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荒漠类型	植被类型	物质组成
砾漠	猪毛蒿、红砂、盐爪爪、驼蹄瓣、茴香、白刺	砾石、卵石、粗砂粒
泥漠	细叶骆驼蓬	细砂粒、粉粒、黏粒
沙漠	沙米、雾冰藜、驼蹄瓣、砂蓝刺头、猪毛蒿	中砂粒
盐漠	驼蹄瓣、碱蓬、猪毛蒿、芦苇、白刺、盐爪爪、黑果枸杞	细砂粒、黏粒

荒漠类型	波段范围/nm
砾漠	420~520、900~970、1700~1800、2040~2140
泥漠	440~510、790~910、1680~1800、2050~2150、2260~2310
沙漠	400~460、900~970、1790~1810、2040~2150、2270~2310
盐漠	430~530、820~920、980~1100、1160~1270、1610~1700、2050~2170

高光谱植被指数	荒漠类型
高光谱植被指数	砾漠	泥漠	沙漠	盐漠
Ctr1	2.418~2.623	2.232~2.401	3.242~3.662	1.444~1.667
GI	0.727~0.746	0.770~0.793	0.655~0.683	0.887~0.928
GNDVIh2	0.198~0.221	0.173~0.193	0.251~0.282	0.069~0.104
PRI	-0.112~-0.103	-0.094~-0.086	-0.157~-0.139	-0.045~-0.031
NDI2	0.083~0.091	0.070~0.076	0.114~0.128	0.024~0.035
NDI3	0.049~0.053	0.041~0.045	0.065~0.073	0.016~0.022
SIPI	0.289~0.311	0.269~0.289	0.346~0.370	0.144~0.195
SR8	1.413~1.462	1.333~1.382	1.546~1.639	1.092~1.154

变量编号	含义	主成分一线性组合系数	主成分二线性组合系数	权重值	排名
变量1-5	1610~1700 nm平均反射率	-0.044	0.278	0.0366	8
变量1-6	1790~1810 nm平均反射率	0.053	0.278	0.0376	4
变量1-7	2050~2150 nm平均反射率	0.045	0.272	0.0360	12
变量2-4	1110 nm处反射率	-0.040	0.277	0.0359	13
变量2-5	1126 nm处反射率	-0.031	0.278	0.0348	17
变量2-8	1443 nm处反射率	0.009	0.279	0.0322	24
变量2-9	1455 nm处反射率	0.001	0.282	0.0315	26
变量2-10	1474 nm处反射率	-0.013	0.283	0.0331	23
变量2-11	1495 nm处反射率	-0.035	0.281	0.0357	16
变量2-12	4790 nm处反射率	0.037	0.281	0.0360	10
变量2-13	2190 nm处反射率	0.020	0.280	0.0337	21
变量2-14	2220 nm处反射率	0.020	0.279	0.0336	22
变量2-15	2265 nm处反射率	0.058	0.272	0.0376	3
变量3-1	446~600 nm吸收谷深度	0.261	0.014	0.0343	19
变量3-2	446~600 nm吸收谷面积	0.260	0.029	0.0358	15
变量3-3	2150~2285 nm吸收谷深度	0.240	0.038	0.0344	18
变量3-4	2150~2285 nm吸收谷面积	0.250	0.041	0.0360	11
变量4-1	Ctr1	0.264	0.025	0.0359	14
变量4-3	GNDVIh2	0.263	-0.030	0.0364	9
变量4-4	MCARI	-0.238	-0.091	0.0400	1
变量4-6	NDchl	0.217	0.028	0.0304	27
变量4-7	OSAVI	0.250	-0.056	0.0376	5
变量4-9	NDI3	0.264	-0.010	0.0342	20
变量4-15	SAVI	0.250	-0.057	0.0377	2
变量4-16	SIPI	0.262	-0.039	0.0373	6
变量4-17	SR3	-0.252	-0.047	0.0369	7
变量4-19	VOG3	-0.177	-0.013	0.0237	28
变量4-20	VOG2	-0.177	-0.014	0.0237	29
变量4-21	WBI	0.232	-0.022	0.0316	25

Class separability evaluation of desert types based on the hyperspectral reflectance characteristics

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