Phenology is the study of recurring plant and animal life cycle stages, especially their timing and relationships with weather and climate [1
]. Plant phenology focuses on the timing of annually recurring plant growth and reproductive phenomena, as well as the drivers of these events associated with endogenous and exogenous forces [2
]. Plant phenological variation is highly sensitive to climate change and has strong feedbacks to the climate system [3
]. Since the Qinghai–Tibet Plateau (QTP) has experienced a rapid warming that is twice as high as the global average level in recent decades [4
], climate change and its impacts on alpine vegetation phenology have been the source of much concern [5
]. To date, most land surface phenological studies on the QTP were based on Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI) data from the Advanced Very High Resolution Radiometer (AVHRR) [5
], Moderate-Resolution Imaging Spectroradiometer (MODIS) [6
], and Satellite Pour l’Observation de la Terre (SPOT) [6
]. The spatial resolutions of these data range normally from 500 m to 8 km. Thus, the extracted phenological metrics generally represent the composite of various vegetation types in a relatively large terrain unit and have been used to detect general phenological response to climate change at regional scales. By contrast, detailed spatial phenological patterns and their response to localized climatic factors remain poorly understood, which might lead to an inaccurate estimate of carbon budgets and ecosystem productivity on the QTP. To investigate spatial phenological responses to localized climatic factors, topographic features (such as slope, aspect, and elevation) at higher spatial resolution (such as 30 m) that control vegetation properties and growth conditions at local areas [22
] can be used as a proxy for localized climatic factors. This is based on the fact that the spatial resolutions of gridded climate datasets currently available are normally coarser than 0.05 degrees [25
], which is unable to represent the microclimate differentiation on the highly heterogeneous QTP.
Landsat sensors observe the land surface at a spatial resolution of 30 m with a repeat cycle of 16 days. However, Landsat observations with low cloud coverage are very limited within a year for most areas on the Earth [26
]. Therefore, multiyear Landsat NDVI/EVI data have been combined to improve the temporal resolution for phenological detection [27
]. This kind of phenological detection can capture multiyear mean vegetation dynamics at higher spatial resolution than that with satellite data from other sensors with more frequent repeat cycles. In addition, interannual variation of vegetation phenology could be derived by computing the annual anomaly in the timing (day of year) of the spring onset and autumn onset relative to the long-term average [29
]. This approach can produce reasonable interannual phenological variation in North American temperate and boreal deciduous forests [28
], and mixed and conifer-dominated forests [31
]. Alternatively, the temporal resolution of Landsat data can also be improved by fusing Landsat streams with daily MODIS data for detailed analysis of vegetation phenology [32
]. The basic assumption in current data fusion methods is that the vegetation growth rate is the same within a land cover type [35
]. It should be noted that although these approaches may roughly retrieve multiyear mean and yearly land surface phenology from Landsat data, accurate vegetation phenology timing metrics should be derived from Landsat observations in the same year. Therefore, our research developed a new approach to estimate the grassland LSP using a single-year NDVI curve on the QTP.
The overall objectives of this study are to (1) extract vegetation phenology at each pixel along a transect across the middle of the QTP based on the Landsat observations in the same year; (2) quantify the impacts of topographic features on spatial heterogeneity of vegetation phenological metrics; and (3) explain the climatic cues of terrain effects. To achieve these goals, we combined Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat 8 OLI (Operational Land Imager) at the overlapping zones of adjacent images across the middle of the QTP from 2013 to 2015. This combination could provide as many as 92 observations within a year, which is potentially able to produce fairly accurate phenological timings [36
Our research investigated a new approach to detecting vegetation phenology from the Landsat observations in the same year and then quantified the impacts of topographic features at 30 m resolution on the spatial heterogeneity of vegetation phenological metrics. It improved the understanding of vegetation phenology variation on the QTP. Combining Landsat ETM+/OLI observations in overlapping zones of adjacent images offered a high frequency of data for the extraction of land surface phenology. This increase in scene number allowed us to investigate the spatial patterns of vegetation phenology in the same year at a spatial resolution of 30 m in a region spanning 2600 km of the QTP. The reliability of LSP extractions is strongly dependent on the quality of Landsat observations, which is influenced mainly by the number of Landsat overpasses, frequency of cloud contamination, and the Scan Line Corrector (SLC) failure in ETM+. Therefore, to reveal spatial variations of LSP precisely, the evaluation of Landsat observations and phenological extractions is necessary. As the quality of Landsat-derived NDVI data can be measured by PGQ—namely, the higher the PGQ score, the higher the quality of NDVI data—NDVI data in western zones and the central area of each zone should be better than in eastern zones and the edge areas of each zone on the QTP. In this study, we removed NDVI data with the PGQ less than 25% because the LSP with PGQ above 25% is closer to ground-based phenological date observations acquired from the limited ground stations on the QTP. Our study confirmed that PGQ could be used as an important measure of data quality in Landsat-based vegetation phenology extractions. However, PGQ provides an evaluation of overall quality for an EVI2 time series, but it is less likely able to quantify the detection quality of a specific phenological transition date. Indeed, it has been demonstrated that large uncertainties in phenology detections are produced when missing Landsat observations occur around phenological transition dates [48
]. Thus, the local proportion of good-quality (LPGQ) observations has been proposed to evaluate the detection quality for each phenological transition date in a vegetation growing cycle [36
], which is calculated based on the good-quality observations around the given phenological date. Certainly, LPGQ provides a better evaluation of NDVI data quality around each phenological date in an individual pixel than does PGQ. In this study, however, as topographic impacts on spatial heterogeneity of vegetation phenological metrics were derived by analyzing a large number of pixels at a spatial resolution of 30 m, NDVI data quality around each phenological date is assumed to have only marginal influences on the phenology timing determination.
Based on combined analysis of LSP and elevation, greenup onset is delayed but dormancy onset advances as the elevation increases on the QTP, which is consistent with the results derived from coarse-spatial-resolution satellite data (1–8 km) [10
]. However, the changing rates in our study are significantly larger than those in the previous studies (Table 1
) because scaling effects may induce larger changing rates at the spatial resolution of 30 m [51
]. In other words, the coarser-resolution satellite data may smooth and attenuate the changing rates of LSP. Thus, the elevation dependence of change in land surface phenology detected by Landsat data should be more precise than that derived by AVHRR, MODIS, and SPOT data.
Moreover, the elevational gradient of change in greenup onset date was nearly four times as large as that of dormancy onset (Figure 6
), which confirms the conclusion that spring phenology variation is very sensitive to air temperature variation [9
]. By contrast, the smaller elevational gradient of change in dormancy onset date is likely associated with the strong impacts of both temperature and precipitation [15
], because precipitation is less dependent on elevation than air temperature. Meanwhile, interruption of phenological progression at about 3400 m might be associated with abrupt elevation change from Zone 1 to Zone 2 (Figure A2
), while the delayed tendency of the dormancy onset date above 5600 m (Figure 6
b) may be induced by disturbance of snow cover (see Figure 4
The elevation dependence of vegetation LSP on the QTP has been the research focus for terrain effects; whereas, influences of aspect and slope on LSP have usually been neglected. However, the topographic controls of local vegetation phenology in the mountainous region are integrated by elevation, aspect, and slope. Landsat data with the higher spatial resolution allowed us to detect the impact of terrain on phenology through aspect and slope. To quantify the separate effects of the aspect or slope on the LSP, we used the single variable principle to eliminate the other factors which controlled the LSP. The results confirm that aspect and slope do have strong controls on LSP and the climatic cues are diverse. In the meadow area, greenup onset on shaded slopes was usually later than that on sunlit slopes, which can be explained by lower temperature and later snowmelt timing on the shaded slopes than on the sunlit slopes [50
]. Contrarily, the lower temperature on the shaded slopes triggered an earlier occurrence date of dormancy onset, while the higher temperature on the sunlit slopes induced a later occurrence date of dormancy onset (Figure 7
a,b). In the steppe area, greenup onset was earlier on both shaded and sunlit slopes (Figure 7
c), which indicates that greenup onset may not be mainly affected by air temperature but by precipitation and soil moisture regimes [8
]. As the dependence of precipitation on aspect may be smaller than that of temperature in the steppe area where slopes are usually smaller than 30° (Figure 1
, Figure A2
), the differences in greenup onset dates between shaded and sunlit slopes are not significant. However, similar to the dormancy onset response to aspect in the meadow area, the dormancy onset in the steppe area was also earlier on shaded slopes than on sunlit slopes (Figure 7
d), which may be induced by lower temperature. Moreover, the effect of aspect on phenological occurrence dates was larger in the steppe area than in the meadow area, especially for dormancy onset. This finding implies that the response of steppe dormancy onset to temperature regimes might be more sensitive than that of meadow dormancy onset on the QTP, possibly due to the lower levels of moisture in the steppe.
With respect to slope effects on LSP, we detected opposite patterns in meadow and steppe areas (Figure 8
and Figure 9
). To analyze the climatic attribution of slope effects on LSP, relationships between slope steepness and microclimatic factors should be identified. Unfortunately, there are no specified meteorological observations on different aspects and slopes of the QTP. Thus, we can only roughly evaluate possible climatic cues of slope effects on LSP. According to microclimatic observations on different aspects and slopes of mountainous areas in southern China [53
], soil temperature and evaporation decrease with the increase of slope on the north slope but increase on the south slope. Statistical analysis and process-based modeling of long-term phenological observation data on the QTP showed that herbaceous plant green leaves may not grow until the ground has been warmed above 0 °C after snow melt. Hence, snow-melt water is a crucial factor for triggering greenup in addition to temperature in snow-dominated regions of the QTP [9
]. That is, more snowfall during late winter and early spring can translate into more soil moisture storage as spring temperature increases, and induce earlier greenup; whereas, less snowfall during late winter and early spring can limit soil moisture availability under rapid spring temperature increase, and force later greenup. In addition, statistical analysis based on remote sensing data on the QTP indicated that the end date of the growing season (EGS) was also controlled by thermal–moisture conditions. Namely, the higher the monthly mean air temperature and the more the monthly precipitation, the later the EGS [15
]. Based on the above microclimatic characteristics and plant phenological responses to climatic factors, we attempt to explain slope effects on LSP.
In the meadow area, the range of slope steepness with effective vegetation phenological data was between 0 and 50 degrees. On the south slope, although soil temperature might be high on the steep slope, strong evaporation may induce soil water deficit and limit the advancing effect of high temperature on greenup onset and delaying effect of high temperature on dormancy onset. Due to predominant effects of soil water deficit on plant phenology on the south slope, later greenup onset and earlier dormancy onset occurred on the steep slope (Figure 8
c,d). On the north slope, however, lower soil temperature on the steep slope may play a vital role and result in later greenup onset and earlier dormancy onset (Figure 8
a,b). In the steppe area, the climate is drier than in the meadow area. The plant community is mainly composed of meso-xerophytes and xerophytes, such as Stipa
. As the range of slope steepness with effective vegetation phenological data was less than in the meadow area (between 0 and 40 degrees), the combination of high soil temperature and strong evaporation on the steep south slope may not induce significant soil water shortage. Under the leading effect of the soil temperature gradient change on plant phenology, earlier greenup onset and later dormancy onset appeared on the steep slope (Figure 9
c,d). On the north slope, the range of soil temperature and evaporation decrease with the increase of slope might be also less than that in the meadow area, and result in a slightly lower soil temperature but relatively higher soil moisture on the steep slope. Under the leading effect of the soil moisture gradient change on plant phenology, greenup onset shifted to an earlier date and dormancy onset was postponed slightly with the increase of slope (Figure 9
This study has realized high-precision extraction of vegetation phenology through combining Landsat ETM+/OLI observations in overlapping zones of adjacent images from 2013 to 2015, and revealed terrain effects on land surface phenology along a 2600 km belt transect on the QTP. This approach is of crucial importance for accurately estimating phenological performance under complex terrain conditions. The main conclusions are as follows.
Within a 12 × 12 km steppe area, spatial patterns of greenup onset and dormancy onset dates were controlled mainly by elevation and aspect. At regional scales, the greenup onset date showed a delayed tendency with the increase of elevation at a mean rate of 1.52 days/100 m, while the dormancy onset date indicated an advanced tendency at a mean rate of −0.59 days/100 m. The elevation dependence of change in land surface phenology detected by Landsat data in our study is significantly larger and more precise than those derived from AVHRR, MODIS, and SPOT data in the previous studies. In the meadow area, greenup onset date was usually later but dormancy onset date was commonly earlier on shaded slopes than on sunlit slopes. The main attribution of phenological timing distribution on different aspects might be temperature regimes. By contrast, greenup onset date in the steppe area did not significantly depend on aspects. Precipitation and soil moisture regimes may play a role in causing greenup onset timing distribution on different aspects. However, dormancy onset date in the steppe area indicated a similar response to aspects with that in the meadow area. With regard to the effect of slope on vegetation phenology in the meadow area, greenup onset date was significantly delayed but dormancy onset date significantly advanced with the increase of slope on both north and south slopes. Either delaying rate in greenup onset or advancing rate in dormancy onset was more rapid on the north slope than on the south slope. In the steppe area, however, greenup onset significantly shifted to an earlier date with the increase of slope on both north and south slopes, while dormancy onset was significantly postponed with the increase of slope only on the south slopes. The advancing rate in greenup onset and delaying rate in dormancy onset were more rapid on the south slope than on the north slope. The coordination status of temperature and moisture on different aspects with the increase of slope controls gradient changes of spring and autumn phenology.