干旱胁迫下植物抗逆性大尺度遥感监测方法

图1 植被类型随机点位分布

Fig. 1 Random point distribution map of vegetation types

2.2 数据来源与处理

遥感数据用于构建刻画环境和植被状态的指数数据集。主要包括TerraClimate数据集和中分辨率成像光谱仪（MODerate-resolution Imaging Spectroradiometer，MODIS）数据集。TerraClimate数据集^[17]提供的饱和水汽压差数据（Vapor Pressure Deficit，VPD）可以表征空气湿度的变化，时间分辨率为每月，空间分辨率为4 km。2001—2020年8 d最大化合成、500 m空间分辨率的MOD09A1、MOD17A2H数据用于构建遥感指数数据集，每年46期，数据下载地址为：https://ladsweb.nascom.nasa.gov/search/。其中MOD09A1数据集利用QA波段去除云/雪干扰，利用前后两期无云雪影像进行线性插值，利用Whittaker Smoother方法进行数据平滑，完成数据预处理。

2007年全国各省份农作物旱灾受灾面积和绝收面积等灾害统计数据用于方法合理性评估，数据下载地址为：http://data.stats.gov.cn/。2007、2019年MCD12Q1 International Geosphere-Biosphere Programme (IGBP)土地分类数据用于方法合理性评估及不同类型植被抗逆性分析，空间分辨率为500 m，下载地址为：https://ladsweb.modaps.eosdis.nasa.gov/。基于IGBP土地分类数据生成乔木、灌木、草地、农田随机点位450、400、430、360个（图1），用于评估各指数监测植被受干旱影响的有效性。

3 植物抗逆性综合监测方法

本文构建植物抗逆性综合监测方法的技术路线（图2），包括干旱识别、植被异常识别以及植物抗逆性综合监测方法构建、方法合理性评估4个部分。

图2

图2 植物抗逆性综合监测方法技术路线

Fig. 2 Technology roadmap of comprehensive monitoring method of plant stress resistance

3.1 干旱识别

表征环境和植被状态的指标多年年内逐期数据遵循正态分布。基于VPD历年同期均值以及标准差，以与距平值大于两倍标准差为判断依据判定环境干旱，计算公式为：

（1）${{T}_{1}}=\frac{VP{{D}_{i}}-VPD_{i}^{\text{mean}}}{\text{std}\left( VPD_{i}^{\text{start}}-VPD_{i}^{\text{end}} \right)}$

式中：VPD_i为第i期VPD， $V P D_{i}^{m e a n}$ 为第i期VPD历年同期均值，std( $V P D_{i}^{s t a r t}$ - $V P D_{i}^{e n d}$ )为第i期2001—2020年VPD标准差。当T₁>2，判定为环境干旱。

由于植被对水分的需求在年内不同季节有明显差异，其生理活动在不同季节对气候变化同样具有不同的响应^[18]，因此以季节尺度监测我国植物抗逆性。以各季节中VPD相对同期历年均值差异最大的时期计算干旱程度，计算公式为：

（2）${{\rho }_{v}}=\text{Max}\left( \frac{VP{{D}_{i}}}{VPD_{i}^{\text{mean}}} \right)$

式中：ρ_v为干旱程度；VPD_i为干旱时期VPD； $V P D_{i}^{m e a n}$ 为历年同期VPD均值。

3.2 植被异常识别

为了选取能够有效监测干旱胁迫下植被异常状态的遥感指数，在我国干旱发生区域，选取乔木、灌木、草地、农田随机点位450、400、430、360个（图1）。针对常用的表征植被长势、生产力、水分状况以及花青素含量的遥感指数（表1），计算各指数距平百分率，与干旱程度进行拟合，并检验其显著性水平。基于指数历年同期均值以及标准差，以与距平值大于2倍标准差为判断依据判定植被异常，计算公式为：

（3）${{T}_{2}}=\frac{Inde{{x}_{i}}-Index_{i}^{\text{mean}}}{\text{std}\left( Index_{i}^{\text{start}}-Index_{i}^{\text{end}} \right)}$

表1 遥感指数

Tab. 1 Remote sensing index

指数类型	指数名称	计算公式编号
表征植被长势	归一化植被指数^[19] Normalized Difference Vegetation Index（NDVI）	$\frac{(ρ_{N I R} - ρ_{R e d})}{(ρ_{N I R} + ρ_{R e d})}$ （4）
	增强型植被指数^[20] Enhanced Vegetation Index （EVI2）	$2.5 \times \frac{ρ_{N I R} - ρ_{R e d}}{ρ_{N I R} + 2.4 ρ_{R e d} + 1}$ （5）
	优化土壤调整植被指数^[21] Optimized Soil Adjusted Vegetation index（OSAVI）	$\frac{ρ_{N I R} - ρ_{R e d}}{ρ_{N I R} + ρ_{R e d} + 0.16}$ （6）
表征植被生产力	总初级生产力 Gross Primary Productivity (GPP)
表征植被水分状况	地表水体指数^[22] Land Surface Water Index (LSWI)	$\frac{ρ_{N I R} - ρ_{S W I R}}{ρ_{N I R} + ρ_{S W I R}}$ （7）
	短波红外水分胁迫指数^[23] Shortwave Infrared Water Stress Index (SIWSI)	$\frac{ρ_{S W I R 1} - ρ_{N I R}}{ρ_{S W I R 1} + ρ_{N I R}}$ （8）
	归一化多波段干旱指数^[24] Normalized Multi-band Drought Index(NMDI)	$\frac{ρ_{N I R} - (ρ_{S W I R 1} - ρ_{S W I R 2})}{ρ_{N I R} + (ρ_{S W I R 1} - ρ_{S W I R 2})}$ （9）
表征植被花青素含量	调整花青素反射指数^[25] Modified anthocyanin reflectance index(mARI)	$(\frac{1}{ρ_{G r e e n}} - \frac{1}{ρ_{V R E 1}}) \times ρ_{N I R}$ （10）
表征植被花青素含量	修正花青素含量指数^[26] Modified Anthocyanin Content Index (mACI)	$\frac{ρ_{N I R}}{ρ_{G r e e n}}$ （11）

新窗口打开| 下载CSV

式中：Index_i为指数第i期像元值； $I n d e x_{i}^{m e a n}$ 为指数第i期像元值历年同期均值；std( $I n d e x_{i}^{s t a r t}$ - $I n d e x_{i}^{e n d}$ )为第i期2001—2020年各指数标准差。对于 SIWSI，当T₂>2时，判定为植被异常；对于NDVI、GPP、mACI，当T₂<-2时，判定为植被异常。

3.3 抗逆性综合监测方法

根据各指数距平百分率与干旱程度的拟合结果（表2），选取NDVI、GPP、mACI、SIWSI监测植被异常。基于VPD、NDVI、GPP、mACI与SIWSI 5个遥感指数时序数据集，建立滞后时间、抗逆时差、响应程度与恢复能力4个植物抗逆性监测指标，构建植物抗逆性综合监测方法，计算公式为：

（12）

Y = [(a Y_{1} + b Y_{2}) + (c Y_{3} + d Y_{4}) \times ρ_{v}] \times 100

表2 植被指数距平百分比与干旱程度的相关性

Tab. 2 Correlation of the percentage of vegetation index anomalies and the degree of drought

	NDVI	EVI2	OSAVI	GPP	LWSI	SIWSI	NMDI	mARI	mACI
乔木	0.1793^**	0.2347^**	0.1032^**	0.2176^**	0.1728^**	0.4508^**	0.0861	0.1366^**	0.1019^**
灌木	0.5201^**	0.4025^**	0.2258^**	0.3889^**	0.1124^**	0.4014^**	0.1530	0.0981^**	0.1058^**
草地	0.3471^**	0.2710^**	0.1744^**	0.45334^**	0.3212^**	0.1548^**	0.0900	0.1137^**	0.1822^**
农田	0.3371^**	0.2851^**	0.0805	0.1594^**	0.0750	0.1125^**	0.1129	0.3650^**	0.5365^**

注：**表示p<0.05。

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式中：Y为植物抗逆性综合评分；Y₁为滞后时间；Y₂为抗逆时差；Y₃为响应程度；Y₄为恢复能力；ρ_v为干旱程度；a、b、c和d为各项监测指标的权重，以随机点位（图1）为样本，利用熵值法确定各权重分别为0.15、0.15、0.4和0.3。Y越大，表明植物抗逆性越强。为了减少干旱程度对抗逆性综合评估结果的影响，对响应程度和恢复能力乘以干旱程度ρ_v。

滞后时间指植被响应干旱的滞后时间，不同类型植被在干旱胁迫下生理结构发生变化的速度不同。以NDVI、GPP、mACI与SIWSI中最先出现异常的时间为植物开始抗逆的时间，计算植物开始抗逆与干旱开始的时间差为滞后时间。计算公式为：

（13）${{Y}_{1}}=\left[ Min\left( {{T}_{\text{SNDVI}}},{{T}_{\text{SGPP}}},{{T}_{\text{SmACI}}},{{T}_{\text{SSIWSI}}} \right)-{{T}_{\text{SVPD}}} \right]/3$

式中：Y₁为滞后时间，T_SNDVI、T_SGPP、T_SACI、T_SSIWSI分别代表NDVI、GPP、mACI与SIWSI开始异常的时刻，单位为月，T_SVPD代表VPD开始异常的时刻。当滞后时间大于3个月，即Y₁大于1时，认为植物的抗逆性最强。

抗逆时差指干旱持续时间与植物抗逆持续时间的差，植物自身对干旱的适应能力以及自我修复能力的差异导致不同类型植被在同一干旱条件下抗逆持续时间不同。计算公式为：

（14）${{T}_{\text{DVPD}}}={{T}_{\text{SVPD}}}-{{T}_{\text{EVPD}}}$

（15）${{T}_{\text{D} ind \ \ { ex}}}=\text{Max}\left( {{T}_{\text{DNDVI}}},{{T}_{\text{DGPP}}},{{T}_{\text{DmACI}}},{{T}_{\text{DSIWSI}}} \right)$

（16）${{Y}_{2}}=\left\{ \begin{matrix} \begin{matrix}\left( {{T}_{\text{DVPD}}}-{{T}_{\text{D} ind \ \ {ex}}} \right)/1 & {} & {{T}_{\text{DVPD}}}>{{T}_{\text{D } ind \ \ {ex}}} \\ \end{matrix} \\ \begin{matrix} 0 & {} & {{T}_{\text{DVPD}}}\le {{T}_{\text{D} ind\ \ {ex}}} \\ \end{matrix} \\ \end{matrix} \right.$

式中：T_DVPD与T_Dindex分别为干旱和植被异常持续时间；T_SVPD与T_EVPD分别为干旱开始与结束的时刻，即VPD异常的开始与结束时刻；T_DNDVI、T_DGPP、T_DACI与T_DSIWSI分别为NDVI、GPP、mACI与SIWSI异常持续的时间；Y₂为抗逆时差，单位为月，当Y₂大于等于1时，认为植物抗逆性最强。

响应程度是指干旱胁迫下植被各指数数值和处于正常环境下指数数值的比值，这一指标最能直观反映干旱对植被的影响。计算公式为：

（17）${{Y}_{3}}=\left[ \begin{array}{*{35}{l}} {{a}_{1}}\text{Min}\left( \frac{NDV{{I}_{i}}}{NDVI_{i}^{\text{mean}}} \right)+{{b}_{1}}\text{Min}\left( \frac{mAC{{I}_{i}}}{mACI_{i}^{\text{mean}}} \right)+ \\ {{c}_{1}}\text{Min}\left( 1-\frac{SIWS{{I}_{i}}-SIWSI_{i}^{\text{mean}}}{\left| SIWSI_{i}^{\text{mean}} \right|} \right)+{{d}_{1}}\frac{\sum GP{{P}_{\text{dry}}}}{\sum GP{{P}_{\text{mean}}}} \\ \end{array} \right]$

式中：Y₃为响应程度；NDVI_i、mACI_i、SIWSI_i为干旱胁迫下第i期植被指数像元值； $N D V I_{i}^{m e a n}$ 、 $m A C I_{i}^{m e a n}$ 、 $S I W S I_{i}^{m e a n}$ 为第i期指数历年同期均值；∑GPP_dry为干旱发生年份总初级生产力；∑GPP_mean为2001—2020年历年总初级生产力的均值。a₁、b₁、c₁、d₁为各个指数的权重系数，以随机点位（图1）为样本，利用熵值法确定各权重分别为0.3、0.2、0.2、0.3。

植被生态系统的恢复能力指系统发生变化后恢复到原来状态的能力。初级生产力作为生态系统最重要的生物状态变量，其动态受环境驱动变量变化的影响。恢复能力较强的植被，当生态系统驱动变量恢复到正常水平，其生物状态变量也能近似恢复到正常水平^[27]。以干旱后第二年植被有机物累积量作为监测植被恢复情况的依据，相比监测植被恢复到原始状态所需的时间，这一方法不需要长时间的跟踪且工作量较小。计算公式为：

（18）${{Y}_{4}}=\frac{\sum GP{{P}_{\text{next}}}}{\sum GP{{P}_{\text{mean}}}}$

式中：Y₄为恢复能力；∑GPP_next为干旱后第二年总初级生产力累加和；∑GPP_mean为2001—2020年历年总初级生产力均值。

3.4 方法合理性评估

在持续干旱情况下，植被有机物合成减少、自身有机物持续消耗，植被有机物总量降低，严重时甚至会导致植被死亡。这种影响在作物产量上的体现最为直观，持续干旱会造成作物减产或绝收。因此本文以基于抗逆性综合监测方法得到的作物抗逆性综合评分与绝收比例即绝收面积和受灾面积的比值作相关性分析，并对比NDVI响应程度与绝收比例的相关性以检验方法的合理性。

4 结果与分析

4.1 植被指数距平百分比与干旱程度的相关性

从各指数距平百分率与干旱程度的拟合结果上看，NDVI、GPP、SIWSI、mACI在监测植被受干旱影响的效果上优于同类型指数，其距平百分率与干旱程度在0.05水平上均显著相关，且不同指数在反映不同植被类型受干旱影响的效果上有显著差异（表2）。NDVI可以有效监测灌木受干旱的影响，在4种指数中效果最优。乔木SIWSI距平百分率与干旱程度的相关性明显优于NDVI，这是由于乔木光合速率高，伴随较大的蒸腾失水。我国草地主要分布在内蒙古高原东部、青藏高原的东部和南部。由于区域低温低蒸发量的特征，土壤持水能力强，在干旱条件下能保持较为稳定的蒸散，因此相比于NDVI，草地GPP距平百分率与干旱程度有更强的相关性。对于农田来说，病虫害、农业管理措施同样会引起作物绿度、水分含量和生产力的明显变化，干旱胁迫下农田mACI距平百分率与干旱程度的相关性明显优于其他指数，可以作为表征植物受干旱胁迫的指标。

4.2 植物抗逆性的季节差异和空间异质性

不同季节干旱胁迫下植物抗逆性有明显差异（图3），从春、夏、秋、冬四个季节干旱胁迫下我国植物抗逆性综合评分整体分布来看，我国春夏两季植物抗逆性综合评分普遍低于秋冬两季。夏季干旱对植被影响最大，植物抗逆性综合评分最低。春季大部分植被开始生长，需要吸收大量的水分和营养物质，对水分变化比较敏感。夏季是植被生长盛期，植被各项生理指标均处于高峰期，加上夏季气温高，植被吸水和蒸腾速率大大增加，加剧了夏季干旱对植被的影响。秋冬两季气温下降，水分减少，植被的适应特性使其生长缓慢，在冬季甚至停止生长，进入休眠期，对环境变化敏感性降低。因此秋冬两季植物抗逆性整体较强。

图3

图3 不同季节植物抗逆性综合评分空间分布

Fig. 3 Spatial distribution map of comprehensive scoring of plant stress resistance in different seasons

我国植物抗逆性存在显著的空间异质性，主要由植被的空间分布决定。已有研究表明，干旱对雨养农田的影响最大，其次是自然植被，对灌溉农田的影响最小^[28]。春季、夏季植物抗逆性综合评分低于70分的区域主要位于山西、陕西北部，冬季抗逆性综合评分低于70分的区域主要位于河南、山东、湖北一带。这些地区年降雨量在400~800 mm，处于亚湿润区向半干旱区的过渡地带，主要植被类型是农田，由于灌溉水源缺乏，依靠天然降水发展雨养农业成为发展农业生产的基本方略，受干旱影响最大；春季评分高于90分的区域主要集中在内蒙古东北部以及云南的南部地区，夏季评分高于90分的区域主要集中在内蒙古北部、西藏西南部以及黑龙江北部地区。这些地区主要以自然植被为主。内蒙古东北部以及西藏西南部地区以及黑龙江北部地区分布有大面积的多年冻土。由于其埋深大、水分补给少的特点，植被生长环境旱化，植被根系发达、耐旱性强。

4.3 不同植被类型抗逆性差异

从我国不同植被类型的抗逆性综合评分分布来看（图4），落叶针叶林综合评分的分布最为集中，且在夏季其抗逆性明显优于其他植被类型。我国落叶针叶林主要分布在50 °N以上地区，这些地区降雨量常年在400~600 mm之间。落叶针叶林适宜生长在较干旱的环境中，蒸腾作用弱，水分利用效率高。因此相对于别的植被，尤其在夏季干旱胁迫下，落叶针叶林表现出更强的抗逆性。我国常绿阔叶林主要分布在长江以南地区，夏季生长尤为旺盛，蒸腾作用强，对水分缺失更为敏感，导致了夏季干旱胁迫下其抗逆性低于落叶阔叶林。不同植被类型抗逆性存在显著差异，同一植被类型抗逆性同样存在显著的类内异质性。落叶针叶林综合评分分布相比其他类型植被更为集中，我国落叶针叶林集中分布在大兴安岭北部山地，因此抗逆性类内差异很小。春秋两季草地的抗逆性优于落叶针叶林但抗逆性类内差异大，综合评分分布相对分散。我国草种资源种类繁多，各类型草种广泛分布，不同类型草种的生理耐旱性的差异可达十倍^[29]，故其具有较大的抗逆性类内差异。

图4

图4 不同植被类型抗逆性综合评分分布

Fig. 4 Distribution map of the comprehensive scores for the resistance of different vegetation types

4.4 抗逆性综合评分与作物绝收比例的相关性分析

利用各省份作物抗逆性综合评分与绝收比例作相关性分析，并对比NDVI响应程度与绝收比例的拟合结果。发现作物绝收比例与抗逆性综合评分呈显著负相关（图5(a)），与NDVI响应程度相关性较弱（图5(b)），说明本文提出的抗逆性综合监测方法具有较高的可信度。

图5

DOI:10.11867/j.issn.1001-8166.2009.04.0428 [本文引用: 1]

图5 2017年作物抗逆性综合评分、NDVI响应程度与绝收比例的相关性

Fig. 5 Correlation of the comprehensive stress resistance score, NDVI response degree and the percentage of crop failure in 2017

5 结论

本文基于不同类型植被各遥感指数距平百分率与干旱程度相关性差异，选取NDVI、GPP、mACI与SIWSI监测植被变化。综合考虑植物抗逆过程，建立滞后时间、抗逆时差、响应程度与恢复能力4个植物抗逆性监测指标，构建植物抗逆性综合监测方法。利用各省份作物抗逆性综合评分与绝收比例进行相关性分析，两者呈显著负相关。运用该方法对2001—2020年我国不同植被类型在季节性干旱胁迫下的抗逆性进行监测和分析，得到以下结论：

（1）我国不同季节植物抗逆性差异较大，空间异质性显著。由于植被年内生长和气候变化具有明显的季节性规律，对环境变化的敏感性相应地呈现出季节差异，植物抗逆性夏季最强，冬季最弱。从我国植物抗逆性空间分布上看，春季植物抗逆性综合评分低于70分的区域主要位于山西、陕西北部，植物抗逆性较弱；综合评分高于90分的区域主要集中在内蒙古东北部以及云南的南部地区，植物抗逆性较强。

（2）不同类型植被的抗逆性有明显差异。夏季干旱胁迫下落叶针叶林抗逆性最强，且抗逆性类内差异最小；春秋两季干旱胁迫下草地抗逆性最强，但由于草种繁多，分布广泛，抗逆性类内差异最大。

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