自然资源遥感, 2023, 35(4): 226-235 doi: 10.6046/zrzyyg.2022284

技术应用

土壤盐渍化对滨海湿地生态环境质量的影响——以黄河三角洲为例

张治梅,1, 樊彦国,1, 矫志军2, 管青春1

1.中国石油大学(华东)海洋与空间信息学院,青岛 266580

2.中南大学地球科学与信息物理学院,长沙 410012

Impact of soil salinization on the eco-environment quality of coastal wetlands:A case study of Yellow River Delta

ZHANG Zhimei,1, FAN Yanguo,1, JIAO Zhijun2, GUAN Qingchun1

1. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China

2. School of Geosciences and Info-physics, Central South University, Changsha 410012, China

通讯作者: 樊彦国(1965-),男,博士,教授,主要研究方向为3S技术在数字国土、城市及海岸带方向的教学与研究工作。Email:ygfan@upc.edu.cn。

责任编辑: 陈昊旻

收稿日期: 2022-07-5   修回日期: 2022-10-31  

基金资助: 国家自然科学青年基金项目“基于生态系统服务的海岸带韧性评估及驱动机制研究——以黄河三角洲为例”(42106215)
自主创新项目-战略专项项目“退化生态系统土壤典型指标在线监测技术”(22CX01004A-3)

Received: 2022-07-5   Revised: 2022-10-31  

作者简介 About authors

张治梅(1998-),女,硕士研究生,主要研究方向为湿地环境演变与遥感应用。E-mail: zmzhang98@163.com。

摘要

土壤盐渍化是土地退化和沙漠化的重要原因之一,会对生态环境造成巨大的影响。滨海湿地属于典型的生态环境脆弱地区且盐渍化特征突出,迫切需要研究土壤盐渍化对湿地生态环境的影响。该文提出基于基线的盐渍化指数(baseline-based soil salinity index,BSSI),有效抑制了复杂地物对地表盐渍化监测带来的影响,盐渍化土壤提取精度高于其他盐分指数模型10%; 随后通过改进顾及水效益的生态环境质量指数(water benefit-based ecological index,WBEI)提出优化的顾及水效益的生态环境质量指数(optimized water benefit-based ecological index,OWBEI),使生态环境质量评估精度有效提高至87%; 最后,以黄河三角洲为例获取其土壤盐渍化分布和生态环境质量分布,探讨土壤盐渍化对生态环境质量的影响机制。结果表明,随着盐渍化程度的不断加深,滨海湿地的土壤脆弱性不断增加,间接导致生态环境质量持续变差。尽管生态环境保护措施不断提出,但针对盐渍化土壤的保护措施较少,导致生态质量状况恶化并负反馈于土壤,最终形成恶性循环,不利于当地生产生活和社会发展。

关键词： 滨海湿地; 土壤盐渍化; 生态环境质量; 虚拟盐渍化基线; 植被指数

Abstract

Soil salinization is an important reason for land degradation and desertification and has a huge impact on the eco-environment. Coastal wetlands are typical areas subjected to a weak eco-environment and severe salinization, and there is an urgent need to investigate the impact of soil salinization on their eco-environment. This study proposed the baseline-based soil salinity index (BSSI), which can effectively suppress the influence of complex features on surface salinization monitoring and improve the accuracy of saline soil extraction by 10% compared to other salinity index models. Furthermore, this study proposed the optimized water benefit-based ecological index (OWBEI) by optimizing the water benefit-based ecological index (WBEI), which can effectively increase the accuracy of eco-environment quality assessment to 87%. Finally, this study explored the mechanical processes of the influence of soil salinization on the eco-environment quality based on the distribution of soil salinization and eco-environment quality obtained from the Yellow River Delta. The results show that the deterioration of soil salinization has led to an increase in the soil vulnerability of coastal wetlands, indirectly resulting in a continuous decrease in eco-environment quality. Although eco-environment protection measures have been continuously proposed, few of them are tailored to the solving of salinization. This leads to the deterioration of the ecological quality, which then yields negative feedback to the soil and eventually forms a vicious circle. This adversely affects local production, life, and social development.

Keywords： coastal wetlands; soil salinization; eco-environment quality; virtual salinization baseline; vegetation index

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本文引用格式

张治梅, 樊彦国, 矫志军, 管青春. 土壤盐渍化对滨海湿地生态环境质量的影响——以黄河三角洲为例[J]. 自然资源遥感, 2023, 35(4): 226-235 doi:10.6046/zrzyyg.2022284

ZHANG Zhimei, FAN Yanguo, JIAO Zhijun, GUAN Qingchun. Impact of soil salinization on the eco-environment quality of coastal wetlands:A case study of Yellow River Delta[J]. Remote Sensing for Land & Resources, 2023, 35(4): 226-235 doi:10.6046/zrzyyg.2022284

0 引言

湿地被誉为“地球之肾”,是地球上生物生产力最高的生态系统之一,在物质保障、能量循环和生物多样性维持等诸多方面扮演着重要角色[1]。在全球快速城市化过程中,资源开发、农田开垦、填海造陆等人为活动不可避免地导致滨海湿地土壤盐渍化问题突出,严重影响了当地的生态环境[2-3]。

几十年来,人们为准确评估土壤盐渍化做出了巨大努力。现有文献中土壤盐渍化评价方法的发展可分为3个主要阶段: 样品分析方法、地球物理勘探测试方法和遥感(remote sensing,RS)技术[4-5]。遥感技术的优势在于长期对地观测过程中形成的有价值的历史数据的存在,为长期、大范围的土壤盐渍化动态监测提供数据支撑[6]。其中,利用光学遥感影像获得光谱指数建立线性非线性和盐渍化反演模型,与深度学习[7]、特征提取[8]、波段选择[9]等相比,具有简单、高效、成本低等优点,因此得到了广泛应用。目前,许多盐分指数已被提出并应用于土壤盐渍化监测,如盐度指数(salinity index,SI)[10-11]、归一化盐度指数(normalized difference salinity index,NDSI)[12-13]、亮度指数(brightness index,BI)[10]、强度指数(intensity index,Int)[14]及植被土壤盐度指数(vegetation soil salinity index,VSSI)[15]等。

然而,滨海湿地复杂的地貌和土地类型很容易与盐渍化土壤混淆,如不透水面和滩涂等。由于盐渍化土壤的所处环境条件复杂,高度异质的光谱和空间特征导致土壤盐分反演困难[16⇓-18]。现有盐分指数对环境和观测条件的变化过于敏感,如气溶胶、太阳耀斑的类型和厚度[19]等。因此,他们难以适应复杂的环境,无法准确提取盐渍化土壤。目前,需要构造新的盐分指数来准确监测复杂环境下的土壤盐渍化状况。

此外,对生态环境质量进行准确评价,是生态保护的核心,也是目前湿地生态研究的热点。根据纳入评价指标的数量,现有方法一般可分为单指标法和多指标法2种[20⇓-22]。基于单一指标的方法通常关注生态状况的一个方面。例如,归一化植被指数(normalized difference vegetation index,NDVI)[23-24]、地表温度(land surface temperature, LST)[25]、永久性植被丰度(permanent vegetation fraction, PVF)[26]、叶面积指数(leaf area index, LAI)[27]等,被认为是有效的生态监测方法。虽然单指标法可以实现对生态环境质量快速评价,但由于滨海湿地环境复杂多样,导致其无法全面评价生态环境质量。

基于多指标的方法已广泛应用于生态评估。这一方法通常基于多个指标进行综合考虑[23-24]。基于P-S-R模型框架的生态环境质量评价方法主要考虑3类因素: 景观变化、景观生态系统状态和人类响应[28-29]。这类方法重点考虑了与人类活动相关的指标,如数字高程、地表温度、道路密度等,但很少涉及生态指标。Zhou等[30],Xu等[31],Firozjaei等[32]提出了基于遥感的生态指数(remote sensing-based ecological index,RSEI),从绿度、湿度、干度和热度4个方面评估生态。此外,Firozjaei等基于植被-不透水面-土壤(vegetation-impervious surface-soil,V-I-S)模型,从绿度、湿度、干度、热度和不透水度5个方面评价了洛杉矶和芝加哥等6个城市的地表生态环境状况[32]。上述方法虽然从不同的角度考虑了多个指标,但忽略了开放水域的影响。顾及水效益的生态环境质量评价指数(water benefit-based ecological index,WBEI)除了包含水生态因子外,还定义了与气候资源相对应的热环境以及土地覆盖[19]。但由于湿地生态系统组成复杂,动态性高,现有的湿地生态环境质量评价方法在植被要素精确监测中面临难题。

滨海湿地属于典型的生态环境脆弱地区,同时湿地土壤变化剧烈,迫切需要研究土壤盐渍化对湿地生态环境的影响。Bless等[33]通过调查了沿海景观中的土壤盐分,提出了盐分影响土地的水通量概念模型,确定了景观演变与实际盐度之间的关系。Kingwell等[34]通过研究农场的景观形状及其土壤分布对旱地盐度的影响,得到景观像圆柱形部分且土壤均质的地方可为盐分减少或遏制提供额外动力。然而,现有的研究更倾向于关注土壤盐分与地表景观的相互影响[4],很少分析土壤盐渍化与生态环境间的相互作用。

本研究以黄河三角洲为研究区,通过分析Landsat和实测数据光谱特征,提出虚拟盐渍化基线,构建基于基线的盐渍化指数(baseline-based soil salinity index,BSSI),抑制了不透水面和滩涂等复杂环境因素的影响,提高了复杂环境下土壤盐渍化的监测精度。通过将新型高光谱植被指数(hyperspectral image-based vegetation index,HSVI)改进应用到多光谱卫星,提高对植被要素的精确监测能力,结合WBEI构造优化的顾及水效益的生态环境质量评价指数(optimized water benefit-based ecological index,OWBEI),提高湿地区域的生态质量反演精度。基于本文构建的BSSI和OWBEI,建立土壤盐渍化对滨海湿地生态环境质量影响分析体系,探究土壤盐渍化现象对生态环境质量的影响机制。

1 研究区概况与数据源

1.1 研究区概况

黄河三角洲地处莱州湾与渤海湾之间,是中国面积最大、最年轻、生物多样性最丰富的河口三角洲之一[35],覆盖区域包括东营市河口区、垦利区和部分利津县(N37°28'32″~38°9'46″, E118°34'20″~119°19'0″),如图1所示(http://bzdt.ch.mnr.gov.cn/)。

图1

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图1   研究区位置

Fig.1   Location of the study area


黄河三角洲属暖温带季风大陆性气候,气候温和,四季分明,雨热同期,降水约70%发生在夏季,年平均温度约为12.1 ℃,年平均降水量约为551.6 mm,但年平均蒸发量约为1 928.2 mm[36]。受海水侵蚀的影响,土壤盐碱化严重。

1.2 遥感数据

本文采用Landsat TM影像和OLI影像作为基础数据,该数据由美国地质调查局(United States Geological Survey,USGS)(https://glovis.usgs.gov/)提供(表1)。各年份均根据影像质量和云量筛选,为保证影像数据质量,选取目视清晰且云量小于5%的影像数据[37-38]。将获取的Landsat影像进行辐射定标、大气校正、去条带以及裁剪操作[39-40]。

表1   Landsat影像数据

Tab.1  Landsat datasets

影像获取时间传感器分辨率/m
2006-05-03TM30
2012-05-03TM30
2017-04-23OLI30
2020-05-01OLI30

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1.3 实测数据

为了准确应用土壤样品建立含盐量和光谱反射率之间的关系,土壤样品的野外采集时间要求与遥感卫星的成像时间一致。因此数据采集时间为2006年5月3日,采样点的深度为20~30 cm,在每个采样点共设置5次重复采样,共采样70个样本点,数据来自国家科技基础设施平台国家地球系统科学数据中心(http://www.geodata.cn/)。

2 研究方法

建立土壤盐渍化对滨海湿地生态环境质量影响机制分析体系,如图2所示。

图2

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图2   土壤盐渍化对滨海湿地生态影响机制分析体系

Fig.2   The mechanism analysis system of soil salinization on coastal wetland ecology


首先,使用预处理的Landsat影像及实测数据,利用指数法建立回归模型获取土壤盐渍化状况; 其次,构造OWBEI获得生态质量状况; 最后,分析土壤盐渍化对黄河三角洲湿地生态环境质量的影响机制。

2.1 土壤盐分反演

本文依据土壤盐分分类标准,将研究区域土壤盐分含量(soil salinity content,SSC)分为5个等级(表2): 盐土、重度盐渍土、中度盐渍土、轻度盐渍土和非盐渍土[41]。

表2   盐渍土分类等级

Tab.2  Saline soil classification

等级SSC%
非盐渍土[0.0, 0.3]
轻度盐渍土(0.3, 0.5]
中度盐渍土(0.5, 1.0]
重度盐渍土(1.0, 2.0]
盐土(2.0, +∞)

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指数法已经被证实可以很好地提取土壤盐渍化状况[10⇓⇓-13]。通过研究盐渍土与其他地类以及盐渍土不同等级之间的光谱曲线图,建立虚拟盐渍化基线,构建新型盐分指数BSSI,来表征地表土壤盐分的分布。

图3显示了BSSI的构建原则,分为2个步骤: 首先,构建虚拟盐渍化基线; 然后,计算短波红外-1(first short-wave infrared,SWIR1)和虚拟盐渍化基线之间的距离。

图3

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图3   指数构造示意图

Fig.3   Schematic diagram of index structure


如图3所示,BSSI计算公式为:

BSSI=RSWIR1-RSWIR1'
RSWIR1'=[(λSWIR2-λSWIR1)RNIR+(λSWIR1-λNIR)RSWIR2]/(λSWIR2-λNIR)

式中: λNIR,λSWIR1和λSWIR2分别为波长为 0.835 μm,1.648 μm和2.206 μm 的光谱带; RNIR,RSWIR1和 RSWIR2分别为近红外波段、短波红外-1波段和短波红外-2波段的反射率。

2.2 生态环境质量评估

本研究针对传统生态环境质量评价方法繁琐、低效的指标融合框架,构建一种新的简单、高效的生态环境质量评价方法(OWBEI),包括生态指标计算和多生态指标融合2部分。

2.2.1 生态指标计算

1)水环境生态指标构建。Jiao等[19]通过研究构建了一个新的指标,即地表潜在水丰度指数(surface potential water abundance index,SPWI)来表征地表潜在水含量的空间分布。SPWI可以有效提高水因子区分效果,让结果更符合实际,其计算方法为:

SPWI=RNIR-RSWIR2+RBRNIR+RSWIR2+RB

式中: RNIR,RSWIR2和RB分别为近红外波段、短波红外-2波段和蓝光波段的反射率。

潜热强度可以反映空气湿度,归一化木质素指数(normalized difference lignin index,NDLI)可以反映地表潜热强度[42-43]。因此,本文采用NDLI表示空气湿度指标,同时更可以表示水要素的生态功能,构建计算公式为:

NDLI=RG-RRRG+RR+RSWIR1

式中: RG,RR和RSWIR1分别为绿光波段、红光波段和短波红外波段-1波段的反射率。

2)土地覆盖生态指标构建。HSVI在绿地精细化检测中具有优异表现,既可以有效抑制阴影、蓝色彩钢瓦等因素的影响,又可以有效解决植被指数饱和问题[44]。本文摒弃了HSVI中复杂的阴影抑制项,选择了更为有效的指数函数结构和非归一化差值指数结构,并将其应用到植被指数的构造中,构建了基于多光谱数据的HSVI,其计算公式为:

HSVI=[(2RNIR-1)-RR]RB+RR 。

人类活动的另一个直接反映是土地开发状况。NDSI[45-46]对不透水表面反应良好,同时对裸露土壤敏感[47]。因此本文选择NDSI作为生态指标之一,其计算公式为:

NDSI=RSWIR1-RNIRRSWIR1+RNIR 。

3)热环境生态指标构建。为了更全面地反映生态环境状况,本文选择LST作为热环境指标, 使用辐射传输方程法反演LST[48]。

通过辐射传输方程变形可知,卫星传感器接收到的热红外辐射亮度值LZ的计算公式为[49]:

LZ=[εB(TS)+(1-ε)Ld]τ+Lu

式中: ε为地表比辐射率(land surface emissivity, LSE); TS为地表真实温度(K); B(TS)为黑体热辐射亮度; τ为大气在热红外波段的透过率; Lu和Ld分别为大气上行辐射亮度和大气下行辐射亮度。黑体在热红外波段的辐射亮度B(TS)为:

B(TS)=[LZ-Lu-τ(1-ε)Ld]/τε 。

利用普朗克公式计算获取地物表面实际温度TS,公式为:

TS=K2/ln(K1/B(TS)+1)

式中,K1和K2为热红外波段特定的热转换系数。

2.2.2 多生态指标融合

基于熵的融合方法不仅可以消除人为主观因素的干扰,而且可以解释生态指标与生态环境之间的交互关系[50-51]。通过计算各评估指标之间蕴含的信息量差异程度,并据此为每个指标赋予适当的权重[52-53]。结合基于熵计算的指标权重,融合多指标获取OWBEI。

首先,计算每个指标的权重。定义ej为第j个评价指标的熵值,计算公式为:

ej=1ln(n)×∑i=1nfijln(1fij)
fij=xij∑i=1nxij

式中: fij为第i个像素在第j个指标中所占的比例; xij为第j个指标中像素i的反射率; n为第j个指标的像素个数。

其次,定义wj为第j个评价指标的权重,计算公式[54]为:

wj=1-ejm-∑i=1mei, j=1,2,3,…,m

式中m为指标个数。

最后,在确定各指标及其权重后,采用线性叠加的方法对各指标进行融合,以确保融合结果的准确性,计算公式为:

OWBEI=w1NNDLI+w2NHSVI+w3NSPWI-w4NLST-w5NNDSI

式中: w1,w2,w3,w4和w5分别为OWBEI中NDLI,HSVI,SPWI,LST和NDSI的权重; NNDLI,NHSVI,NSPWI,NLST和NNDSI分别为NDLI,HSVI,SPWI,LST和NDSI归一化后的结果。

2.3 精度验证

2.3.1 土壤盐渍化验证方法

本文通过与4种常用的方法做对比分析(表3)。并选择了5个参数进行验证,包括整体精度(overall accuracy,OA)、Kappa系数(Kappa)、漏分误差(omission error,EO)和错分误差(commission error,EC)[44]。

表3   对比方法

Tab.3  Classification method

方法公式
SI[1]SI=(B×R)
SI1[2]SI1=(G×R)
SI2[2]SI2=(G2+R2+NIR2)
VSSI[3]VSSI=2G-5R+NIR

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2.3.2 生态环境质量评价验证方法

本文利用中国生态环境状况公报给出的各区县级生态环境质量结果生态环境指数(ecological index,EI)(https://www.mee.gov.cn/)验证反演结果的准确性。EI的计算公式为:

EI=0.35BRI+0.25VCI+0.15WNDI+0.15(100-LSI)+0.1(100-PLI)+ERI

式中: BRI,VCI,WNDI,LSI,PLI和ERI分别为生物丰富度指数、植被覆盖指数、水网密度指数、土地压力指数、污染负荷指数和环境限制指数。

3 结果与分析

3.1 黄河三角洲土壤盐渍化状况

3.1.1 土壤盐渍化结果验证

盐渍化土提取结果的精度统计于表4中。通过表4可以得知,SI,SI1和SI2具有最低的OA和Kappa系数,较高EC和EO,其效果较差。相较于其他4种方法BSSI的OA和Kappa最高,表明该方法的精度最高,并且该方法的EC和EO较低。因此,BSSI在土壤盐渍化反演中显示出其优势。

表4   盐渍土分类精度

Tab.4  Classification accuracy of saline soil

方法OA/%Eo/%Ec/%Kappa
SI67.8858.0612.900.376 2
SI167.1551.6120.970.354 2
SI270.0764.521.610.427 2
VSSI81.0241.940.000.630 4
BSSI91.2417.741.610.825 7

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3.1.2 土壤盐渍化空间分布

基于上述土壤盐渍化反演方法,得到2006年,2012年,2017年和2020年土壤盐渍化的空间分布(图4和图5)。并统计这4年的土壤盐渍化不同等级所占面积,结果如表5所示。

图4

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图4   地表假彩色图像

Fig.4   False color images of the surface


图5

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图5   土壤盐渍化等级图

Fig.5   Soil salinization grade maps


表5   黄河三角洲土壤盐渍化面积和比例

Tab.5  Area and proportion of soil salinization in the Yellow River Delta

等级年份
2006201220172020
非盐
渍化		面积/km22 921.672 965.703 060.772 976.00
占比/%58.1559.1060.9459.20
轻度
盐渍化		面积/km2298.39203.46111.2797.99
占比/%5.944.052.221.95
中度
盐渍化		面积/km2709.58442.31288.55210.44
占比/%14.128.815.744.19
重度
盐渍化		面积/km2753.27789.58707.17428.03
占比/%14.9915.7314.088.51
盐土面积/km2341.85617.44854.981 314.54
占比/%6.8012.3017.0226.15

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如表5所示,土壤盐渍化主要分布在黄河入海口旧址和现黄河入海口,与已有对于黄河三角洲盐渍化研究分布相符合[53-54]。黄河流量减少,海水倒灌,并且黄河下游地下水的水位埋藏较浅,这些都是导致其土壤盐渍化严重的主要原因[36]。另外,人类活动(如,建造盐池,城市化)造成滨海地区的土壤盐渍化严重[54]。

计算得到2006年研究区非盐渍化(2 921.67 km2,占总面积的58.15%)占主导地位(图6)。然而,近40%的面积受到土壤盐渍化的威胁,其中轻度盐渍化占5.94%,中度盐渍化占14.12%,重度盐渍化占14.99%。同样地,2012年,2017年和2020年非盐渍化也都占据主导地位,分别为59.10%,60.94%和59.20%。2012年轻度盐渍化占4.05%,中度盐渍化占8.81%,重度盐渍化占15.73%,盐土占12.30%。2017年轻度盐渍化占2.22%,中度盐渍化占5.74%,重度盐渍化占14.08%,盐土占17.02%。2020年轻度盐渍化占1.95%,中度盐渍化占4.19%,重度盐渍化占8.51%,盐土占26.15%,土壤盐渍化面积高达2 051.00 km2。

图6

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图6   现黄河入海口地表真彩色图像

Fig.6   True color images of the surface of the Yellow River Estuary


受自然因素(如降水、气温、土壤条件)和社会经济因素(如人口、城市化、政策等)的影响不同,不同地区土壤盐渍化程度不同。2006—2020年非盐渍化面积较稳定,保持在60%左右,轻度盐渍化和中度盐渍化呈现下降趋势。然而,随着人类活动的增加,该区的荒地被不透水表面所取代,从而导致盐土的面积呈现上升趋势,年增长率约1.5%。

3.2 黄河三角洲湿地生态环境质量状况

本文选取的2006年到2020年河东区和垦利区的EI与OWBEI对比结果如表6所示。由表6可知,河口区2006年到2020年EI结果与OWBEI结果一致都为较差。垦利区2006年到2017年的EI结果与OWBEI一致都为一般,但2020年EI结果为一般,OWBEI结果为较差存在误差。综上,EI的结果与OWBEI结果总体一致。

表6   2006年到2020年EI与OWBEI对比结果

Tab.6  Comparison results of EI and OWBEI from 2006 to 2020

研究区方法2006年2012年2017年2020年
河口区EI较差
(0.20~
0.35)		较差
(0.20~
0.35)		较差
(0.20~
0.35)		较差
(0.20~
0.35)
OWBEI较差
(0.31)		较差
(0.33)		较差
(0.34)		较差
(0.28)
垦利区EI一般
(0.35~
0.55)		一般
(0.35~
0.55)		一般
(0.35~
0.55)		一般
(0.35~
0.55)
OWBEI一般
(0.37)		一般
(0.48)		一般
(0.46)		较差
(0.25)

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3.3 黄河三角洲土壤盐渍化对生态环境质量的影响

土壤盐渍化的变化会产生一系列的物理和化学作用。以现黄河入海口为例(图6、图7和图8),结合各生态指标变化情况(图9)分析土壤盐渍化的变化对生态环境质量状况的影响机制。

图7

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图7   土壤盐渍化等级图

Fig.7   Soil salinization grade maps


图8

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图8   OWBEI结果

Fig.8   The result of OWBEI


图9

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图9   现黄河入海口各生态指标的平均值

Fig.9   Average values of various ecological indicators at the Yellow River estuary


如图7所示,2006—2020年非盐渍化面积波动不大,轻度盐渍化、中度盐渍化和重度盐渍化面积不断减少。然而,盐土面积却在增加,相应位置的生态环境质量也较周围较差(图8)。2009年以来国家相继出台各种政策,对黄河三角洲地区的盐渍化进行治理,有计划地对荒碱地进行开发治理及改造中低产田。从图9可知,经过治理该区的2006—2012年该区的植被数量得到了升高,植被的显著增加导致地表水分蒸发减缓并且空气湿度也显著升高,总体生态环境质量明显上升。因此,由于土地盐渍化使得多数生态指标都朝向有利的方向发展,如LST和NDLI,最终导致整体生态环境改善。

然而,2012—2020年该区由于盐土面积的增加,植被数量骤减,导致地表水分蒸发加快并且空气湿度也显著降低,最终导致黄河入海口的总体生态质量状况下降。图9中的NDSI和OWBEI也证实了这一结论。

综上可知,土壤盐渍化作为滨海湿地的突出特征对生态环境质量影响明显,在未来湿地治理方面应更加注重对土壤盐渍化的问题的解决,以期间接改善滨海湿地的生态状况。

4 结论

本文通过构造虚拟盐渍化基线,增强了盐渍化土壤的光谱特征,有效抑制滨海湿地不透水面、裸土等复杂地物的影响。BSSI的构建有效提高了地表盐渍化反演精度,为土壤盐渍化监测提供了一种新的可靠指数。此外,本文将HSVI应用到多光谱领域,解决了WBEI在湿地区域植被监测不准确问题,建立新型湿地生态评价方法OWBEI,提高湿地区域生态环境反演精度。

BSSI和OWBEI的结合形成了一整套土壤盐渍化对滨海湿地生态环境质量影响机制分析体系。分析掌握土壤盐渍化对滨海湿地生态环境质量的影响机制,为滨海湿地的区域治理提供科学的数据支撑。未来将在此工作的基础上将研究内容迁移到鄱阳湖湿地以验证不同湿地在该体系上的适用性。

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