Vegetation greening in Spain detected from long term data (1981–2015)

ABSTRACT

This study describes a newly developed high-resolution (1.1 km) Normalized Difference Vegetation Index dataset for the peninsular Spain and the Balearic Islands (Sp_1km_NDVI). This dataset is developed based on National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA–AVHRR) afternoon images, spanning the past three decades (1981–2015). After a careful pre-processing procedure, including calibration with post-launch calibration coefficients, geometric and topographic corrections, cloud removal, temporal filtering, and bi-weekly composites by maximum NDVI-value, we assessed changes in vegetation greening over the study domain using Mann-Kendall and Theil-Sen statistics. Our trend results were compared with those derived from some widely recognized global NDVI datasets [e.g. the Global Inventory Modelling and Mapping Studies 3rd generation (GIMMS3g), Smoothed NDVI (SMN) and Moderate-Resolution Imaging Spectroradiometer (MODIS)]. Results demonstrate that there is a good agreement between the annual trends based on Sp_1km_NDVI product and other datasets. Nonetheless, we found some differences in the spatial patterns of the NDVI trends at the seasonal scale. Overall, in comparison to the available global NDVI datasets, Sp_1km_NDVI allows for characterizing changes in vegetation greening at a more-detailed spatial and temporal scale. In specific, our dataset provides relatively long-term corrected satellite time series (>30 years), which are crucial to understand the response of vegetation to climate change and human-induced activities. Also, given the complex spatial structure of NDVI changes over the study domain, particularly due to the rapid land intensification processes, the spatial resolution (1.1 km) of our dataset can provide detailed spatial information on the inter-annual variability of vegetation greening in this Mediterranean region and assess its links to climate change and variability.     

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem-atmosphere CO₂ exchange in the Amazon region.     

Time-series analysis of NDVI from AVHRR data over the Hindu Kush–Himalayan region for the period 1982–2006

The Hindu Kush–Himalayan (HKH) region with its surrounding mountains in central Asia is a region that has been warming at an alarming rate and is sensitive to climate change due to its heterogeneous terrain and high altitude. In a region where research is limited due to the paucity of field-based biophysical observations, analysis of remotely sensed data such as the normalized difference vegetation index (NDVI) can provide invaluable information on spatio-temporal patterns in linkages among land use, climate and vegetative phenological cycles, and trends in vegetative cover. In this study, NDVI data with 8 km spatial resolution for each 15 day composite period from 1982 to 2006 were analysed using a seasonal trend analysis technique, where the first step determines the annual mean and seasonal NDVI patterns across the HKH region. The second step analyses the non-parametric trends in magnitude and timing of the annual mean and seasonal NDVI cycle. The seasonal vegetation cycles were compared for the first and last ten years of the time series and were also analysed across areas undergoing significant change. Results indicated an overall greening trend in NDVI magnitude in most areas, particularly over open shrubland, grassland and cropland. Trends in the annual seasonal timing of NDVI indicated an earlier green-up for most parts of this region. Results also confirmed deforestation trends observed in a few states in northeastern India and Myanmar (Shan state) within the HKH region.     

The application of polynomial analyses to detect global vegetation dynamics during 1982–2012

Use of the normalized difference vegetation index (NDVI) to build long-term vegetation trends is one of the most effective techniques for identifying global environmental change. Trend identification can be achieved by ordinary least squares (OLS) analysis or the Theil–Sen (TS) procedure with a Mann–Kendall (MK) significance test, and these linear regression approaches have been widely used. However, vegetation changes are not linear, and thus the response of vegetation to global climate change may follow non-linear trends. In this article, a polynomial trend-fitting method, which uses stepwise regression and expands on previous research, is presented. With an improved fitting ability, this procedure may reveal trends that were concealed by linear fitting methods. Globally, the traditional TS-MK method reveals significant greening trends for 37.27% of vegetated land, and significant browning trends for 7.98%. Using the polynomial analysis, 34.62% of pixels were fitted by high-order trends. The significant greening trends covered up to 30% of cultivated land, thus indicating that cultivated vegetation may be increasing faster than natural vegetation. Significant vegetation browning mostly occurred in sparse vegetation areas, which suggests that vegetation growth may be more sensitive to climate change in arid regions. Our results show that use of polynomial analysis can help further elucidate global NDVI trends.     

Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation

Interannual trends in annual and seasonal vegetation activities from 1982 to 1990 on a global scale were analysed using the Pathfinder AVHRR Land NDVI data set corrected by utilising desert and high NDVI areas. Climate effects on interannual variations in NDVI were also investigated using temperature and precipitation data compiled from stational observations. In the northern middlehigh latitudes, vegetation activities increased over broad regions because of a gradual rise in temperature. NDVI increases were also detected in the tropical regions, such as western Africa and south-eastern Asia. Plant photosynthetic activities on the other hand, decreased remarkably in some arid and semi-arid areas in the Southern Hemisphere, because annual rainfall decreased during this period.     

Automatic detection of forest fire disturbance based on dynamic modelling from MODIS time-series observations

Forest disturbances provide an important reference and a basis for studying the carbon cycle, biodiversity, and eco-social development. Remote sensing is a promising data source for monitoring forest ecosystem dynamics and detecting disturbance areas. This research used a seasonal trend method to model Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series from 2007 to 2011 recursively with a fixed-size temporal sliding window and a step length of 1 (i.e. 16 days). Model parameter variations were monitored to detect changes in the structure of the time-series data. Significant changes in the time-series structure were captured as disturbance signals. The method was applied to the 2009 Minto Flats fire in Alaska, USA, and the forest-disturbance detection results obtained using the proposed method essentially agreed with the Monitoring Trends of Burned Severity data set. This result indicates that the proposed method can reliably reveal the occurrence of forest fire disturbances. Moreover, because the model parameter variations reflect the disturbance signal, and the modelling and detection process requires only MODIS NDVI time-series data without any other ancillary ground information, the disturbance area can be detected effectively and automatically.     

Biological mechanisms underlying recent increases in the NDVI of Mediterranean shrublands

Long-term satellite observations of normalized difference vegetation index (NDVI) for Mediterranean shrublands suggest an increase in vegetation activity during the 1980s, caused by climatic warming. However, whether this was due to artificial trends in the satellite data remains in question. We used a mechanistic model of vegetation growth and a database of observed climate to test whether the observed increase in NDVI could have been caused by changes in canopy structure driven by changes in climate. The model reproduced the longterm upward trend in maximum seasonal NDVI between 1981 and 1991, indicating that a change in vegetation structure could feasibly explain the satellite observations. The model indicated that the NDVI trend was caused by a 12% increase in leaf area index (LAI), mainly owing to changes in precipitation and rising atmospheric CO₂. By contrast, climatic warming during the 1980s exerted little control over this variation in LAI. Simulated trends in canopy structure exerted significant impacts on canopy function, being associated with a 15% rise in net primary productivity and a 30% increase in transpiration. From this analysis, we conclude that trends in historical satellite observations of NDVI have a plausible biological basis.     

A Breakpoints based Spatio-temporal Analysis of Tibetan Plateau Vegetation

Due to the fragile ecosystem and unique geographical environment on the TP，the vegetation strongly responds to climatic shifts.Therefore，it is of great significance to discuss the spatiotemporal trend shift of vegetation，to evaluate the climate change of the plateau and to predict regional ecological development.Using the GIMMS NDVI3g dataset from 1982 to 2012 to extract the NDVI information of the TP，as well as establishing seasonal trend model to classify research through the seasonal trend analysis and breakpoints detection method，reveals the spatiotemporal pattern of the trend shifts of plateau vegetation at both ends of the breakpoints combining the classification of land cover.The results shows that conclusions.(1) The seasonal trend model can effectively identify the breakpoints of vegetation time series，moreover the time span of the breakpoints were large and the spatial heterogeneity were strong.(2) The trend of vegetation degeneration in the western part of the Tibetan Plateau was small，vegetation degeneration in the south and northeast regions was obvious，and vegetation in the central and eastern regions has improved.58.93% of the vegetation status tends to be stable.The area where the vegetation status changes significantly accounts for about 32.3% of the entire plateau.(3) In the area where the vegetation status is generally or significantly changed，the vegetation improvement of monotonous trend and interruption trend were more than that of degradation，and the degenerative situation in the reverse trend were more than the improvement.Monotonous trend changed in 3.14% of the regions，58.36% of the regions occurred interruption trend changes，and 38.50% of regions occurred reverse trends.The time distribution of the monotonous trend and the interruption trend were more concentrated，while the reverse trend covered the entire time series.(4) The vegetation improvement and degradation in different land cover types were various conditions.The type with the highest rate of improvement was desert(53.30%)，and the type with the highest rate of degradation was sparse vegetation(60.14%).Overall，the vegetation in Tibetan plateau tends to be greening，but the spatial heterogeneity remains significant.     

Natural vegetation responses to warming climates in Qaidam Basin 1982–2003

In this paper, we quantified vegetation variations in the Qaidam Basin from 1982 to 2003 by using growing-season NDVI sequences, which were defined as the summation of monthly NDVI values from May to September, and were calculated pixel-by-pixel from a successive 8-km NDVI dataset. We adopt linear regressions to examine the trends in growing-season NDVI and the trends in climate (temperature, precipitation and sunshine duration) during this period in an attempt to depict their temporal and spatial variability. Our results indicate that climate in the Qaidam Basin has homogeneously warmed at a rate of about 0.6°C/decade during the study period, with significant trends in monthly mean temperatures in April–September. However, there were no statistically significant trends observed in precipitation and sunshine duration. We found positive growing-season NDVI trends in 31.6% of the vegetated lands in 1982–2003 and in 24.1% over the first half period, 1982–1992. In addition, few areas were shown to have negative trends during these periods. In 1993–2003, however, the percentage of land with a positive trend decreased to 13.1%, and the percentage of vegetated land with a negative trend increased to 10.2%. Growing-season NDVI trends show both temporal and spatial variability. Areas with negative trends are distributed mostly at lower elevations and near oasis boundaries, and areas with positive trends at higher elevations. Using correlation analyses we estimated the relationship between growing-season NDVI and the climatic factors with the consideration of duration and lagging effects. The results suggest that growing-season NDVI trends are more correlated to temperature increases in growing-season months when compared to variations in precipitation and sunshine duration; however increased precipitation amounts within May–August can also facilitate vegetation growth in some of this arid basin. However, we found no significant correlations between growing-season NDVI and temperature in the non-trend areas, which account for the majority of the vegetated land. We suggest that the variability in vegetation responses to the observed warming climates results from the differences in background thermal condition and moisture availability, which depend on elevation and other factors, such as hydrological conditions.     

Quantitative mapping of global land degradation using Earth observations

Land degradation is a global issue on par with climate change and loss of biodiversity, but its extent and severity are only roughly known and there is little detail on the immediate processes – let alone the drivers. Earth-observation methods enable monitoring of land degradation in a consistent, physical way and on a global scale by making use of vegetation productivity and/or loss as proxies. Most recent studies indicate a general greening trend, but improved data sets and analysis also show a combination of greening and browning trends. Statistically based linear trends average out these effects. Improved understanding may be expected from data-driven and process-modelling approaches: new models, model integration, enhanced statistical analysis and modern sensor imagery at medium spatial resolution should substantially improve the assessment of global land degradation.     

Changes in vegetation spring dates in the second half of the twentieth century

This study aims at estimating trends in spring phenology from vegetation index and air temperature at 2?m height. To this end, we have developed a methodology to infer spring phenological dates from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) time-series, which are then extrapolated to the period 1948–2006 with the help of Reanalysis data, using its 2?m height air temperature parameter. First, yearly NDVI is fitted to a double-logistic function for the whole extent of the GIMMS database (1981–2003). This fitting procedure allows us to describe, on a yearly basis, the NDVI evolution for each pixel through the estimation of six parameters which include the spring date. Retrieved spring date time-series are then upscaled to Reanalysis database resolution and compared to degree-day amounts. Those degree-day amounts are estimated for various thresholds in order to determine the best thresholds for their calculations on a pixel-by-pixel basis. Once the correct thresholds are identified by correlation with corresponding GIMMS spring date time-series, spring dates are estimated for the whole extent of the Reanalysis database (1948–2006). Finally, Mann–Kendall trend tests are conducted on degree-day-retrieved spring date time-series and trends are estimated only for those pixels that show statistically significant trends. These trends in spring occurrence have an average value of –0.03 days per year, but range between –0.9 and?+0.9 days per year, depending on the considered areas. Since the approach is based only on air temperature, retrieved spring dates for vegetation whose growth is limited by water are unreliable, as correlation analysis confirms. The obtained spring date trends show good coherence with previous studies and could be used for climate change impact studies, especially in polar and temperate areas, where the model is more reliable.     

基于GIMMS、VGT和MODIS的中国东部植被指数对比分析

GIMMS NDVI、VGT NDVI和MODIS NDVI/EVI是目前在植被变化有关研究中经常使用的植被遥感数据,它们之间的差异也得到了广泛关注。然而,在分析这些数据之间的差异时,较少有研究注意到植被本身固有的季节循环可能夸大了各数据间的相关关系。应用2000~2006年GIMMS NDVI、VGT NDVI、MODIS NDVI/EVI等不同植被遥感数据,对比了基于这些数据集的中国东部植被年际变化的差异,探讨了植被季节循环对不同遥感数据之间相关性的影响。结果表明:由不同遥感数据提取的植被年际变化特征具有明显的一致性,然而,植被本身固有的季节循环特征掩盖了不同数据集的差异。季节循环去除前,各数据集之间具有显著的相关性;季节循环去除后,各数据集的相关性明显降低,但不同数据集在北部区域依然具有较好的一致性,其差异主要出现在南部区域,差异最明显的是GIMMS与MODIS数据,二者在淮河以南的区域几乎不存在显著相关。     

Spatial–temporal changes in vegetation coverage in the global coastal zone based on GIMMS NDVI3g data

ABSTRACT

In this paper, we used the Global Inventory Modelling and Mapping Studies (GIMMS) third-generation Normalized Difference Vegetation Index (NDVI) (GIMMS NDVI3g) dataset. Based on GIMMS NDVI3g data over the global coastal zone from 1982 to 2014, the spatial–temporal characteristics of vegetation coverage were analysed by plotting the spatial pattern and monthly calendar of NDVI; furthermore, historical trends and future evolutions of vegetation coverage change at the pixel scale were studied by performing the Mann-Kendall trend test and calculating the trend slope (β) and Hurst index (H) of NDVI. The main findings are as follows: 1) Vegetation density exhibits dramatic differences in the global coastal zone. Specifically, desert belts mostly have perennial non-vegetation or low vegetation coverage, and tundra belts principally have moderate or high vegetation coverage; additionally, forest belts mainly have dense vegetation coverage. 2) In the global coastal zone, intra-annual variations in vegetation coverage show a ‘∩’-shaped curve with an obvious peak from June to September (maximum in July or August), while inter-annual variations show a fluctuating but generally slowly increasing trend over the entire study period; accordingly, variations in different subregions show significant differences. 3) At monthly, seasonal and annual scales, the overall vegetation coverage increases in the global coastal zone, while there are relatively few areas with decreasing vegetation coverage; furthermore, change trends of vegetation coverage in most areas will demonstrate relatively strong positive persistence in the future. 4) The increasing trend in high-latitude coastal tundra is extremely significant in the growing season because vegetation in the tundra belts is highly sensitive to climate change. 5) Areas with a decreasing trend of vegetation coverage exhibit spatial patterns of aggregation in the ‘circum urban agglomeration’ and ‘nearby desert belt’ regions, that is, the decreasing trend of vegetation coverage is relatively high in coastal urban agglomeration areas and desert belt peripheries. This paper is expected to provide knowledge to support vegetation conservation, ecosystem management, integrated coastal zone management and climate change adaptation in coastal areas.     

融合QA-SDS的MODIS NDVI时序数据重构

基于云南省MOD13Q1时序数据,对比分析了不同质量设置(UI5、UI5-CSS、UI3、UI3-CSS)和不同时序重构方法(简单线性插值、Savitzky-Golay滤波、非对称高斯函数拟合法和双逻辑函数拟合法)组合下NDVI时序重构效果。结果表明:NDVI时序中无效像元数和最大间隙长度在时间和地域上的分布差异受气候干、雨季影响显著。非对称高斯函数拟合法和双逻辑函数拟合法的稳健性和拟合效果较优。NDVI时序中无效像元最大间隙长度是衡量数据质量优劣和时序重构可行性的重要指标,雨季降水和多云天气过于集中是影响云南省境内部分地区时序重构质量提升的关键。基于重构NDVI时序,云南省全境NDVI时空分布呈现雨季大于干季、西部大于东部、南部高于北部、河谷大于山地的特征。     

Vegetation dynamics from NDVI time series analysis using the wavelet transform

A multi-resolution analysis (MRA) based on the wavelet transform (WT) has been implemented to study NDVI time series. These series, which are non-stationary and present short-term, seasonal and long-term variations, can be decomposed using this MRA as a sum of series associated with different temporal scales. The main focus of the paper is to check the potential of this MRA to capture and describe both intra- and inter-annual changes in the data, i.e., to discuss the ability of the proposed procedure to monitor vegetation dynamics at regional scale. Our approach concentrates on what wavelet analysis can tell us about a NDVI time series. On the one hand, the intra-annual series, linked to the seasonality, has been used to estimate different key features related to the vegetation phenology, which depend on the vegetation cover type. On the other hand, the inter-annual series has been used to identify the trend, which is related to land-cover changes, and a Mann-Kendall test has been applied to confirm the significance of the observed trends. NDVI images from the MEDOKADS (Mediterranean Extended Daily One-km AVHRR Data Set) imagery series over Spain are processed according to a per-pixel strategy for this study. Results show that the wavelet analysis provides relevant information about vegetation dynamics at regional scale, such as the mean and minimum NDVI value, the amplitude of the phenological cycle, the timing of the maximum NDVI and the magnitude of the land-cover change. The latter, in combination with precipitation data, has been used to interpret the observed land-cover changes and identify those subtle changes associated to land degradation.     

1982～2000年黄淮海地区植被覆盖变化特征分析

利用1982～2000年8km NOAA-AVHRR数据,采用均值法、差值法和一元线性回归模拟法,分析了中国黄淮海地区植被的动态变化及其空间分布特征,模拟了年NDVI均值的变化趋势,并对不同植被类型的NDVI年内和年际变化规律进行了分析。结果表明:黄淮海地区植被覆盖总体上呈增加趋势,20世纪90年代末相对80年代初平均NDVI值增加了近0.03。从空间分布上看,大部分地区NDVI都呈增加的趋势,其中NDVI极显著增加的区域主要分布在山东的西北部和西部、河南的东部、河北的北部及江苏的北部地区,F检验的显著性水平达到了99%。NDVI呈减少趋势的地区很少,主要分布在北京、天津、山西中部。各植被类型的NDVI在年内的变化呈很强的季节性,年际变化规律大致相同,呈波动上升的趋势。     

Differences in monitoring vegetation dynamics between Moderate Resolution Imaging Spectroradiometer Collection 5 and Collection 6 vegetation index products on the Loess Plateau,China

Accurately monitoring vegetation dynamics on the Loess Plateau (LP) is critical for evaluating the benefits of ecological restoration projects. The Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) product has been a primary data source for monitoring vegetation dynamics. However, MODIS Collection 5 (C5) VI products are known to be affected by sensor degradation, which has been addressed in the newly released MODIS Collection 6 (C6) VI products. Herein, we compared the spatiotemporal differences in vegetation dynamics between the Terra MODIS C5 and C6 data products and among different annual value retrieval methods for the LP during 2001–2016. Our results indicated a lower magnitude but a greener trend in the normalized difference vegetation index (NDVI), and areas with significant greening (p < 0.050) were found to increase by about 13%–16% from C5 to C6, depending on the retrieval method. Regions with either no particular trend or a downward trend in vegetation derived from the Terra-C5 NDVI mostly showed significant increasing trends based on the Terra-C6 NDVI. Moreover, the different retrieval methods also exhibited differences in the evaluation of vegetation dynamics, with the largest differences in terms of both magnitude and trend being identified with the annual maximum value method. This highlighted a compelling need to choose suitable methods in different regions for the retrieval of annual VI values, in order to facilitate more robust and comparable conclusions. Additionally, discrepancies also existed in the response of vegetation to climate variations between the Terra-C5 and C6 products for all three annual VI retrieval methods. Our findings, based on multiple products and analysis methods, may lead to improved understanding of both vegetation dynamics and their linkage to climate variables. The results suggest that caution be utilized when using only MODIS Terra-C5 products to evaluate vegetation dynamics and calibrate ecosystem models.     

Interannual growth dynamics of vegetation in the Kuparuk River watershed,Alaska based on the Normalized Difference Vegetation Index

Interannual above-ground production patterns are characterized for three tundra ecosystems in the Kuparuk River watershed of Alaska using NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. NDVI values integrated over each growing season (SINDVI) were used to represent seasonal production patterns between 1989 and 1996. Spatial differences in ecosystem production were expected to follow north-south climatic and soil gradients, while interannual differences in production were expected to vary with variations in seasonal precipitation and temperature. It was hypothesized that the increased vegetation growth in high latitudes between 1981 and 1991 previously reported would continue through the period of investigation for the study watershed. Zonal differences in vegetation production were confirmed but interannual variations did not covary with seasonal precipitation or temperature totals. A sharp reduction in the SINDVI in 1992 followed by a consistent increase up to 1996 led to a further hypothesis that the interannual variations in SINDVI were associated with variations in stratospheric optical depth. Using published stratospheric optical depth values derived from the SAGE and SAGE-II satellites, it is demonstrated that variations in these depths are likely the primary cause of SINDVI interannual variability.     

利用遥感监测长江三角洲森林植被物候对气候变化的响应

植物的物候与气候等环境因素息息相关,是指示气候与自然环境变化对生态影响的重要指标。目前,气候变暖日益为人所关注,使用遥感技术研究植物物候与气候变化之间的关系具有重要的意义。监测人口密度高和城市经济发达地区的植物物候对气候变暖的响应,可以揭示区域热环境变化及其产生的生态效应。本研究选取长江三角洲地区为研究区域,使用SPOT卫星VGT传感器的长时间NDVI数据序列,对经济发达区域森林植被的NDVI序列进行非对称性高斯函数拟合法平滑处理,并提取与研究其物候特征,发现①NDVI与气温具有较强相关性,随气候变暖,森林植被NDVI年均值有增加趋势;②森林植被生长活跃期起始日期提前,终止日期延后,时长有明显的延长趋势,生长活跃期内NDVI有所增加;③森林植被NDVI极大值与极小值出现日期均明显提前,NDVI极大值有增大趋势,而极小值呈下降趋势,年内极差增加,NDVI增长期缩短,衰落期延长;④森林植被在春、夏两季NDVI均值有所增长,秋季无明显变化,冬季略有降低。