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

干旱区研究 ›› 2025, Vol. 42 ›› Issue (1): 141-153.doi: 10.13866/j.azr.2025.01.13 cstr: 32277.14.AZR.20250113

基于高光谱反射特征的荒漠类型可分性评价

刘志飞¹(), 杨雪梅²^,³, 王景瑞³^,⁴, 黄轲盼¹, 徐浩杰¹^,⁵()

1.兰州大学草种创新与草地农业生态系统全国重点实验室，农业农村部草牧业创新重点实验室，草地农业教育部工程研究中心，草地农业科技学院，甘肃兰州 730020
2.兰州文理学院旅游学院，甘肃兰州 730010
3.甘肃省治沙研究所，甘肃兰州 730070
4.兰州大学资源环境学院，甘肃兰州 730000
5.兰州大学寒旱区生态环境遥感研究中心，甘肃兰州 730000

收稿日期:2024-08-14 修回日期:2024-10-21 出版日期:2025-01-15 发布日期:2025-01-17
通讯作者: 徐浩杰. E-mail: xuhaojie@lzu.edu.cn
作者简介:刘志飞（2001-），男，硕士研究生，主要从事干旱区生态环境遥感监测. E-mail: liuzhf2023@lzu.edu.cn
基金资助:
国家重点研发计划(2020YFA0608401);国家自然科学基金(32060373);甘肃省自然科学基金项目(22JR5RA766);甘肃省自然科学基金项目(23JRRA1048);中央高校基本科研业务费专项资金(lzujbky-2022-27)

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

LIU Zhifei¹(), YANG Xuemei²^,³, WANG Jingrui³^,⁴, HUANG Kepan¹, XU Haojie¹^,⁵()

1. State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, School of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, Gansu, China
2. Tourism School, Lanzhou University of Arts and Science, Lanzhou 730010, Gansu, China
3. Gansu Desert Control Research Institute, Lanzhou 730070, Gansu, China
4. School of Resources and Environment, Lanzhou University, Lanzhou 730000, Gansu, China
5. Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, Gansu, China

Received:2024-08-14 Revised:2024-10-21 Published:2025-01-15 Online:2025-01-17

摘要/Abstract

摘要：

本研究采用裸土高光谱反射曲线细节所提取的特征变量评价不同荒漠类型可分性，选择石羊河下游盐漠、砾漠、泥漠和沙漠为研究对象，运用累积差值、一阶微分、包络线去除、植被指数计算、主成分分析法等，辨别不同荒漠类型的高光谱反射特征，提取关键分类变量，量化不同荒漠类型区分度。结果表明：（1）各荒漠类型在446~600 nm和2150~2285 nm处存在差异明显的吸收谷。（2） Carter指数1、绿度指数（GI）、绿色归一化植被指数（GNDVIh2）等在不同荒漠类型间存在显著差异。（3）改进叶绿素吸收指数（MCARI）、土壤调整植被指数（SAVI）、2265 nm与1790~1810 nm反射率在主成分指标构建中的权重值较大。（4）各荒漠类型区分度：沙漠&盐漠>沙漠&泥漠>泥漠&盐漠>砾漠&盐漠>沙漠&砾漠>泥漠&砾漠。研究结果可为西北干旱区荒漠遥感监测提供地面验证和数据支持。

关键词: 荒漠土壤, 高光谱特征波段, 特征提取, 主成分分析, 类别可分性

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

刘志飞, 杨雪梅, 王景瑞, 黄轲盼, 徐浩杰. 基于高光谱反射特征的荒漠类型可分性评价[J]. 干旱区研究, 2025, 42(1): 141-153.

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.

图/表 13

图1

表1

表2

高光谱植被指数及其计算公式"

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	$R 900 R 970$

表2

图2

图3

图4

表3

图5

图6

表4

图7

表5

图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

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

基于高光谱反射特征的荒漠类型可分性评价

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

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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

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