A statistical framework for the analysis of long image time series

Coarse spatial resolution satellites are capable of observing large swaths of the planetary surface in each overpass resulting in image time series with high temporal resolution. Many change‐detection strategies commonly used in remote sensing studies were developed in an era of image scarcity and thus focus on comparing just a few scenes. However, change analysis methods applicable to images with sparse temporal sampling are not necessarily efficient and effective when applied to long image time series. We present a statistical framework that gathers together: (1) robust methods for multiple comparisons; (2) seasonally corrected Mann–Kendall trend tests; (3) a testing sequence for quadratic models of land surface phenology. This framework can be applied to long image time series to partition sources of variation and to assess the significance of detected changes. Using a standard image time series, the Pathfinder AVHRR Land (PAL) NDVI data, we apply the framework to address the question of whether the institutional changes accompanying the collapse of the Soviet Union resulted in significant changes in land surface phenologies across the ecoregions of Kazakhstan.     

Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985–2006

Long‐term changes in the Normalized Difference Vegetation Index (NDVI) have been evaluated in several studies but results have not been conclusive due to differences in data processing as well as the length and time of the analysed period. In this research a newly developed 1 km Advanced Very High Resolution Radiometer (AVHRR) satellite data record for the period 1985–2006 was used to rigorously evaluate NDVI trends over Canada. Furthermore, climate and land cover change as potential causes of observed trends were evaluated in eight sample regions. The AVHRR record was generated using improved geolocation, cloud screening, correction for sun‐sensor viewing geometry, atmospheric correction, and compositing. Results from both AVHRR and Landsat revealed an increasing NDVI trend over northern regions where comparison was possible. Overall, 22% of the vegetated area in Canada was found to have a positive NDVI trend based on the Mann–Kendal test at the 95% confidence level. Of these, 40% were in northern ecozones. The mean absolute difference of NDVI measurements between AVHRR and Landsat data was <7%. When compared with results from other studies, similar trends were found over northern areas, while in southern regions the results were less consistent. Local assessment of potential causes of trends in each region revealed a stronger influence of climate in the north compared to the south. Southern regions with strong positive trends appeared to be most influenced by land cover change.     

Detecting the effects of climate change on canopy phenology in coniferous forests in semi-arid mountain regions of China

An increased understanding of the responses of forest phenology to climate on regional scales is critical to the evaluation of biochemical cycles (i.e. carbon, water, heat, and nutrient) under environmental changes. In this study, we aimed to identify climatic constraints on phenological events in an evergreen coniferous forest in semi-arid mountain regions of northern China. We quantified the start of season (SOS), end of season (EOS), and growing season length (GSL) based on satellite-derived data sets (normalized difference vegetation index (NDVI)) and investigated the relationships between these phenological events and climate factors. The results revealed discontinuous trends in phenological events throughout the study period, with neither an obvious extension nor decrement in GSL. We demonstrated that minimum temperatures controlled the dynamics of SOS and EOS, thus providing strong evidence for the need to include minimum temperature as a control on phenology in simulation models. Additionally, precipitation was coupled to the shift in maximum NDVI, as rainfall is a major climatic limitation to vegetation growth in semi-arid regions. It appears that selecting appropriate timescales to analyse the relationships between phenology and climate is critical. We illustrated that NDVI was an effective tool in an effort to gain greater understanding of the effects of environmental change on ecosystem functioning in this forest. Our results may be used as reference to track local changes in the evergreen coniferous forest dynamics under different climate change scenarios for semi-arid mountain regions.     

The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests

Satellite remote sensing has the potential to contribute to plant phenology monitoring at spatial and temporal scales relevant for regional and global scale studies. Historically, temporal composites of satellite data, ranging from 8 days to 16 days, have been used as a starting point for satellite-derived phenology data sets. In this study we assess how the temporal resolution of such composites affects the estimation of the start of season (SOS) by: 1) calibrating a relationship between satellite derived SOS with in situ leaf unfolding (LU) of trembling aspen (Populus tremuloides) across Canada and 2) quantifying the sensitivity of calibrated satellite SOS estimates and trends, over Canadian broadleaf forests, to the temporal resolution of NDVI data. SOS estimates and trends derived from daily NDVI data were compared to SOS estimates and trends derived from multiday NDVI composites that retain the exact date of the maximum NDVI value or that assume the midpoint of the multiday interval as the observation date. In situ observations of LU dates were acquired from the PlantWatch Canada network. A new Canadian database of cloud and snow screened daily 1-km resolution National Oceanic and Atmospheric Administration advanced very high resolution radiometer surface reflectance images was used as input satellite data. The mean absolute errors of SOS dates with respect to in situ LU dates ranged between 13 and 40 days. SOS estimates from NDVI composites that retain the exact date of the maximum NDVI value had smaller errors (~ 13 to 20 days). The sensitivity analysis reinforced these findings: SOS estimates from NDVI composites that use the exact date had smaller absolute deviations from the LU date (0 to − 5 days) than the SOS estimates from NDVI composites that use the midpoint (− 2 to − 27 days). The SOS trends between 1985 and 2007 were not sensitive to the temporal resolution or compositing methods. However, SOS trends at individual ecozones showed significant differences with the SOS trends from daily NDVI data (Taiga plains and the Pacific maritime zones). Overall, our results suggest that satellite based estimates of vegetation green-up dates should preferably use sub-sampled NDVI composites that include the exact observation date of the maximum NDVI to minimize errors in both, SOS estimates and SOS trend analyses. For trend analyses alone, any of the compositing methods could be used, preferably with composite intervals of less than 28 days. This is an important finding, as it suggests that existing long-term 10-day or 15-day NDVI composites could be used for SOS trend analyses over broadleaf forests in Canada or similar areas. Future studies will take advantage of the growing in situ phenology networks to improve the validation of satellite derived green-up dates.     

Monitoring and modelling land surface dynamics in Bermejo River Basin,Argentina: time series analysis of MODIS NDVI data

The purpose of this work was to monitor and model land surface phenology over the past ten years in the South American Bermejo River basin using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) product. In order to do this, we evaluated the characteristics of the satellite data and information available on the study area's ecosystem to choose the best model to capture the temporal dynamics of NDVI in local vegetation (sufficiently complex to provide a good fit and simple enough so that each parameter has an immediate ecological meaning). An ecological interpretation of model parameters was provided. Different land surfaces showed distinct fluctuations over time in NDVI values, and this information was used to improve object-oriented classification. A decision tree classification was developed to identify spatial patterns of NDVI functional form and the fluctuations that these patterns presented from 2000 to 2010. We integrated inter-annual information in a final map that distinguishes stable areas from changing sites. Assuming that large inter-annual spatial-scale fluctuations were related to climatic events, we established how vegetated land surfaces within the study area responded to these. Our study was designed to emphasize the interpretation of the spatial and temporal scales of land surface phenology.     

An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska,U.S.A.

Research designed to better define relations between 1-km multitemporal AVHRR-derived NDVI data and selected climatological parameters, soil hydrological properties and land cover characteristics is summarized. Bi-weekly maximum value composite NDVI data and concurrently measured meteorological data acquired in 1990 and 1991 for Nebraska were utilized to study relations between NDVI and accumulated growing degree days,soil temperature, precipitation and potential evapotranspiration. Temporal change in NDVI was found to be closely linked with the temperature regime. NDVI-precipitation and NDVI-potential evapotranspiration relations exhibited time lags, although the length of lag varied with land cover type, precipitation, and soil hydrologic properties. NDVI response to precipitation was stronger in natural grasslands and grassland/wet meadows than in areas of irrigated cropland and mixed crop/ grass. NDVI-climate relations were strongest where vegetation was developed on soils with low root zone available water capacity and high permeability. Relations derived by using NDVI values over 3 pixel by 3 pixel windows showed little difference from those using single 1 km pixel. This may reflect both the relatively homogeneous land cover characteristics of the study area and the effect of off-nadir viewing geometry on AVHRR data acquisition.     

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

We have analysed monthly composites of normalized difference vegetation index (NDVI) calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) for the Amazonian region of northern Brazil across a decade (August 1981 to June 1991) to ascertain if the dominant vegetation types could be differentiated,and to seek inter-annual climatic variation due to changing environmental conditions. The vegetation types observed included dense forest ( submontana and terras baixas ), open forest ( submontana and terras baixas ), transitional forest, seasonal forest ( caatinga ), and two types of savanna ( cerrado ). We found that monthly NDVI composites revealed seasonality in cerrado and especially in caatinga cover types, which can be used in their identification, whilst the phenology of other forest cover types varies little throughout the year. Additionally, yearly composite NDVI values showed a clear and significant reduction ( p 0.95) in dry years, such as those with El Nino Southern Oscillation events. These results indicate the potential use of multi-temporal NDVI data for the environmental characterization and identification of forest ecosystems. Our research found NDVI images from NOAA AVHRR offer a long-term data set that is unequalled for monitoring terrestrial land cover. However, these data have to be used with a degree of caution, especially in regards to atmospheric interference, such as cloud contamination and volcanic eruptions, and post-launch changes in calibration.     

Extracting grassland vegetation phenology in North China based on cumulative SPOT-VEGETATION NDVI data

Plant phenology is one of the main indicators of climate or other environmental processes. This paper assesses the detection accuracy of start of season (SOS) and end of season (EOS) for grassland vegetation in north China from 2001 to 2010 using SPOT-VEGETATION normalized difference vegetation index (NDVI) data sets and in situ observations. The cumulative NDVI is calculated and fitted using a logistic model to identify phenological transition dates. The curvature of the fitted logistic models predicts phenological transition dates that correspond to the times at which the curvature in the yearly integrated NDVI exhibits local minimums or maximums. Validating with in situ observations, phenological dates are extracted from satellite time series data and are accurate to within 10 days. The spatial trends of SOS and EOS are very similar for 2001–2010. SOS mainly occurs from the day of year (DOY) 110 to DOY 170, and EOS occurs from DOY 240 to DOY 300. SOS displays a marked delay from south to north, while EOS gradually advances, indicating regional differences in climate and terrain. However, the effect of latitude and longitude on the average EOS of alpine grasslands is not significantly different, while SOS at low latitude and high longitude is 10 days earlier than at high-latitude and high-longitude regions. We detected an overall advance in SOS of 3.1 days over 10 years, and a 1.3-day delay in EOS. However, the amplitude is low (about 5 days) and the changes in most regions are not significant (close to zero). The results in this paper are concordant with many reported studies that explored the phenology of grasslands in North China, which is an important component of global grasslands science.     

Phenological change detection while accounting for abrupt and gradual trends in satellite image time series

A challenge in phenology studies is understanding what constitutes phenological change amidst background variation. The majority of phenological studies have focused on extracting critical points in the seasonal growth cycle, without exploiting the full temporal detail. The high degree of phenological variability between years demonstrates the necessity of distinguishing long-term phenological change from temporal variability. Here, we demonstrate the phenological change detection ability of a method for detecting change within time series. BFAST, Breaks For Additive Seasonal and Trend, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change. We tested BFAST by simulating 16-day NDVI time series with varying amounts of seasonal amplitude and noise, containing abrupt disturbances (e.g. fires) and long-term phenological changes. This revealed that the method is able to detect the timing of phenological changes within time series while accounting for abrupt disturbances and noise. Results showed that the phenological change detection is influenced by the signal-to-noise ratio of the time series. Between different land cover types the seasonal amplitude varies and determines the signal-to-noise ratio, and as such the capacity to differentiate phenological changes from noise. Application of the method on 16-day NDVI MODIS images from 2000 until 2009 for a forested study area in south eastern Australia confirmed these results. It was shown that a minimum seasonal amplitude of 0.1 NDVI is required to detect phenological change within cleaned MODIS NDVI time series using the quality flags. BFAST identifies phenological change independent of phenological metrics by exploiting the full time series. The method is globally applicable since it analyzes each pixel individually without the setting of thresholds to detect change within a time series. Long-term phenological changes can be detected within NDVI time series of a large range of land cover types (e.g. grassland, woodlands and deciduous forests) having a seasonal amplitude larger than the noise level. The method can be applied to any time series data and it is not necessarily limited to NDVI.     

Analysis of phenological change patterns using 1982–2000 Advanced Very High Resolution Radiometer (AVHRR) data

Using the National Oceanic & Atmospheric Administration (NOAA) National Aeronautics & Space Administration (NASA) Pathfinder Land dataset (PAL data) from 1982–2000, vegetation phenology (onset, peak and offset) was defined and analysed with climate data. In areas of precipitation-dependent phenology such as Central Africa, it was found that Normalized Difference Vegetation Index (NDVI) is affected approximately 20–40 days after the occurrence of precipitation, depending on land cover types. In areas of temperature-dependent phenology such as Siberia, the relationship of phenology and latitude/elevation was investigated. Using temporal NDVI data of 1982–2000, changes in seasonal NDVI pattern were classified into 11 classes and mapped in the Northern Hemisphere. From this analysis, increasing trends of the annual sum of NDVI were found in Siberia, NE Europe and the northern part of North America where good correspondence with the increasing trend of air temperature was recognized. In contrast, some areas such as the east of the Aral Sea showed a decreasing trend of the annual sum of NDVI. It was found that, in the Northern Hemisphere, the area with increasing trend of the annual sum of NDVI is approximately 12 times larger than the area with the decreasing trend. Also, it was found that areas of increasing/decreasing trend of the annual sum of NDVI correspond roughly to areas with increasing/decreasing trend of air temperature from 1982 to 1995.     

Automatic Classification of Time Series (ACTS): A new clustering method for remote sensing time series

Automatic classification methods have often been used as a first step for land cover mapping. The principle of such methods is to determine clusters of pixels with similar radiometric temporal behaviour based on their statistical properties. This allows a segmentation of the image into regions with similar radiometric properties. Most automatic classification of remote sensing data are based on the K-mean or dynamical clustering method. The latter method has two limitations. (i) It is necessary to fix the number of clusters, but this parameter is, in general, unknown. (ii) It is very slow and does not work correctly when the dimension of the problem and the number of samples become large which is typically the case for classification of remote sensing data at large scale. To avoid these limitations we have developed a new method called ACTS (Automatic Classification of Time Series), which is based on both hierarchical and dynamical clustering principles. First of all, the method is really 'automatic' since it determines, automatically, the number of clusters. Secondly, the method is very fast and does not show a degradation of the results with large dimensions or data sets. Application to synthetic data sets shows that in most cases ACTS is able to retrieve all the clusters of the image independently of the dimensions of the problem. Comparison of classifications based on actual global 8 km NDVI (Normalized Differential Vegetation Index) composites using both ACTS and a K-mean method show very similar results but the convergence for ACTS is 20 times faster than the K-mean method using '10-day' composites. The ability of ACTS to work with problems of large dimensions enables clustering of multi-year time series of NDVI. ACTS is here applied to the clustering of a 12-year time series of 10-day composites (1982-1993). The results show that the seasonal signal is dominant. The clusters are mainly representative of seasonal land cover regions. Moreover, the regions are more clearly delineated in comparison with the classification based on only one year of data. Such improved clustering can help avoid some confusion between biomes. Finally, ACTS is applied to 'deseasonalized' time series to investigate the interannual variability of the NDVI. The areas of higher variability are located in the tropical regions with a strong influence of El Nino events. A small positive trend in NDVI is visible in high latitudes. However, several problems linked to the quality of the data are clearly visible. For instance, the decrease of NDVI following the Pinatubo eruption in 1991 and the drift in calibration with the change from NOAA 9 to NOAA 11 in 1988 are visible in most of the regions. This unfortunately limits the possible interpretation of the signal and emphasizes the need to improve the preprocessing of AVHRR (Advanced Very High Resolution Radiometer) data for interannual variability applications.     

AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005

The Sahel region of Africa has experienced a decrease in rainfall from the early 1960s to mid 1990s. Recent studies have detected an increased in NDVI amplitude and growing season integrated NDVI for the region since 1982. However, these studies have not examined how plant phenology has changed. Phenology examines life cycle events such as bud burst and leaf senescence. Using the software TIMESAT to estimate phenological parameters from the GIMMS AVHRR NDVI dataset, we have found significant positive trends for the length of the growing and end of the growing season for the Soudan and Guinean regions, but significant trends in the Sahel could not be detected. The geographical extent of these trends contrasts with the more northern extent of positive trends of NDVI amplitude and growing season integrated NDVI. Results suggest two types of “greening” trends associated with rainfall change since the drought in the early 1980s.     

Monitoring Sahelian floodplains using Fourier analysis of MODIS time‐series data and artificial neural networks

Fourier analysis of Moderate Resolution Image Spectrometer (MODIS) time‐series data was applied to monitor the flooding extent of the Waza‐Logone floodplain, located in the north of Cameroon. Fourier transform (FT) enabled quantification of the temporal distribution of the MIR band and three different indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Enhanced Vegetation Index (EVI). The resulting amplitude, phase, and amplitude variance images for harmonics 0 to 3 were used as inputs for an artificial neural network (ANN) to differentiate between the different land cover/land use classes: flooded land, dry land, and irrigated rice cultivation. Different combinations of input variables were evaluated by calculating the Kappa Index of Agreement (KIA) of the resulting classification maps. The combinations MIR/NDVI and MIR/EVI resulted in the highest KIA values. When the ANN was trained on pixels from different years, a more robust classifier was obtained, which could consistently separate flooded land from dry land for each year.     

Effect of climate change on vegetation phenology of different land-cover types on the Tibetan Plateau

Research in vegetation phenology change has been one heated topic of current ecological and climate change study. The Tibetan Plateau, as the highest plateau of the earth, is more vulnerable and sensitive to climate change than many other regions. In this region, shifts in vegetation phenology have been intensively studied during recent decades, primarily based on satellite-retrieved data. In this study, we explored the spatiotemporal changes of vegetation phenology for different land-cover types in the Tibetan Plateau and characterized their relationship with temperature and precipitation by using long-term time-series datasets of normalized difference vegetation index (NDVI) from 1982 to 2014. Diverse phenological changes were observed for different land-cover types, with an advancing start of growing season (SOS), delaying end of growing season (EOS) and increasing length of growing season (LOS) in the eastern Tibetan Plateau where meadow was the dominant vegetation type, but with the opposite changes in the steppe and sparse herbaceous or sparse shrub regions which are mostly located in the northwestern and western edges of the Tibetan Plateau. Correlation analysis indicated that sufficient preseason precipitation may delay the SOS of evergreen forests in the southeastern Plateau and advance the SOS of steppe and sparse herbaceous or sparse shrub in relatively arid areas, while the advance of SOS in meadow areas could be related to higher preseason temperature. For EOS, because it is less sensitive to climate change than SOS, the response of EOS for different land-cover types to precipitation and temperature were more complicated across the Tibetan Plateau.     

North American vegetation dynamics observed with multi-resolution satellite data

We investigated normalized difference vegetation index data from the NOAA series of Advanced Very High Resolution Radiometers and found regions in North America that experienced marked increases in annual photosynthetic capacity at various times from 1982 to 2005. Inspection of these anomalous areas with multi-resolution data from Landsat, Ikonos, aerial photography, and ancillary data revealed a range of causes for the NDVI increases: climatic influences; severe drought and subsequent recovery; irrigated agriculture expansion; insect outbreaks followed by logging and subsequent regeneration; and forest fires with subsequent regeneration. Vegetation in areas in the high Northern Latitudes appear to be solely impacted by climatic influences. In other areas examined, the impact of anthropogenic effects is more direct. The pattern of NDVI anomalies over longer time periods appear to be driven by long-term climate change but most appear to be associated with climate variability on decadal and shorter time scales along with direct anthropogenic land cover conversions. The local variability of drivers of change demonstrates the difficulty in interpreting changes in NDVI and indicates the complex nature of changes in the carbon cycle within North America. Coarse scale analysis of changes could well fail to identify the important local scale drivers controlling the carbon cycle and to identify the relative roles of disturbance and climate change. Our results document regional land cover land use change and climatic influences that have altered continental scale vegetation dynamics in North America.     

Vegetation phenology and its variations in the Tibetan Plateau,China

Understanding the vegetation phenology and its variations in the Tibetan Plateau is critical to the study of ecological responses to global climate change. In this study, several pre-processed methods or techniques were applied to filter the Global Inventory Modelling and Mapping Study’s Normalized Difference Vegetation Index (GIMMS NDVI) data from 1982 to 2006, and construct the daily NDVI series. Firstly, vegetation and non-vegetation were determined by NDVI quantity contour, and cloud-covered pixels were also eliminated by NDVI change characteristics in a year. Then, the NDVI series were filtered by three-standard deviation and Savitzky-Golay method. Finally, the Savitzky–Golay method was employed to fit and construct the daily NDVI series. These methods guarantee a more reliable subsequent calculation of subsequent vegetation phenology. The vegetation phenology parameters including the start of growth season (SOG), the end of growth season (EOG), the lengths of growth season (LOG) and the absolute increase in vegetation (AIV), defined as the difference between the maximum NDVI and the NDVI for SOG in a year, were derived from the daily NDVI series based on the maximum ratio threshold method and their variations were analysed. The results showed that the SOGs were gradually delayed from the southeast to the northwest of the Tibetan Plateau, but the distribution pattern of the EOGs was opposite to that of the SOGs. From 1982 to 2006, SOGs were advanced approximately 3–18 days and EOGs delayed around 0–24 days in the southeast, whereas AIVs decreased around 0–0.3. In the northwest, these phenology parameters followed inverse trends compared with those of the southeast. Over the 25-year period, LOG changes had no constructive or active effects on the vegetation absolute increase. These complex phenological shifts were mainly due to the spatial differences in the environmental changes. However, in some extent, they might be related to the vegetation itself, such as its fractional cover. These findings may help to understand the alpine vegetation responds to climate change in the Tibetan Plateau.     

Treeline vegetation composition and change in Canada's western Subarctic from AVHRR and canopy reflectance modeling

Climate change is expected to have significant impacts on northern vegetation, particularly along transition zones such as the treeline. Studies of vegetation composition and change in this ecotone have largely focussed on local analysis of individual trees using labour intensive stand reconstruction techniques, which are spatially limited and do not consider vegetation types other than trees. Remote sensing may be well suited to monitoring recent changes across the treeline because it captures integrated changes of all vegetation life forms over large spatial extents. This research examines treeline vegetation composition and change along the western subarctic treeline mapped by Timoney et al. (1992) using a 1 km resolution, 22-year AVHRR archive from 1985-2006. While most remote sensing studies on vegetation change in arctic and subarctic regions only exploit information contained in the Normalized Difference Vegetation Index (NDVI), we examine long-term reflectance trends in AVHRR bands 1 and 2 in addition to NDVI. The GeoSail canopy reflectance model is used to map treeline composition by combining information from 22-year summertime and early springtime composite images. A set of spectral change vectors are then generated from GeoSail simulations and used to classify trends in AVHRR along the treeline to estimate vegetation change. Evaluation of vegetation composition against the MODIS Vegetation Continuous Fields (VCF) product that has been recently validated along the treeline reveals good spatial correspondence. Temporal trends are shown to agree with literature on tundra-taiga vegetation dynamics in recent decades. Evidence is presented that suggests replacement of bare surfaces with herb, conifer decline along the southern treeline, increased shrubiness, and increased conifer recruitment and growth in the north.     

Analysing the forcing mechanisms for net primary productivity changes in the Heihe River Basin,north‐west China

Most of the inland river basins in north‐west China have experienced ecosystem degradation and even desertification in the last few decades. As a case study, we estimated the net primary productivity (NPP) of the Heihe river basin and analysed its difference between 2002 and 1998 by using the C‐Fix, a Monteith type parametric NPP model. The data used include the normalized difference vegetation index (NDVI) derived from the 1‐km SPOT/VEGETATION sensor and other environmental records. By obtaining the spatiotemporal patterns of NPP change as well as land use changes from higher resolution imagery in the basin, we identified its forcing factors in terms of climate change and human activities. We suggest that a decline in rainfall over the five years was one reason for NPP decrease in the basin. Other factors, such as irrational reclamation upstream and intensive development of irrigated farmland in the midstream play more important roles. They reinforce water competition between artificial and natural ecosystems over the whole basin. It is also found that human activities can produce very different NPP changes in a short time in mountainous regions. The NPP decreased in the east Qilian Mountains due to farmland reclamation and overgrazing but increased in the west, according to the ecosystem preserve project.     

Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China

Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI.     

Interfacing NOAA/ANHRR NDVI and soil truth maps for monitoring vegetation phenology at a local scale in a heterogeneous landscape of Southern Italy

This paper discusses the preprocessing, clustering, and labelling steps of data supplied from NOAA Advanced Very High Radiometers (AVHRR) to monitor vegetation phenology in a complex area (Vulture Basin, Italy). Time cluster maps of Normalized Difference Vegetation Index (NDVI) are compared with a land use map and a Digital Elevation Model of the region. This study results show that AVHRR/NDVI well discriminates forested areas whatever the altitude may be; whereas the phenology of cultivated fields must be distinguished between plain and mountain phenology. The pixels not fitting into this picture mostly account for three peculiar microclimatic situations (two long and narrow valleys and a smooth, sunny mountain area).