Seasonal Variation In Vegetation Research Articles

Semantic Segmentation-Driven Integration of Point Clouds from Mobile Scanning Platforms in Urban Environments

Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators.

Seasonal variation in vegetation cooling effect and its driving factors in a subtropical megacity

The urban heat island (UHI) effect has become a global issue, attracting widespread attention in recent years. Urban vegetation is an effective way to mitigate UHI through evapotranspiration (ET). However, the seasonal variation in vegetation cooling effect and its driving factors have not been well investigated. Therefore, the seasonal UHI intensity (UHII) on typical clear days in 2021 was calculated for Shenzhen based on the land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The slope between UHII and vegetation coverage was then used to characterize the vegetation cooling effect. Results showed that: (1) there is a significant negative linear correlation between UHII and vegetation coverage, with slopes mostly less than −1 and coefficients of determination (R2) larger than 0.4. (2) The slopes varied seasonally, indicating a larger vegetation cooling effect during summer daytime. A 10 % increase in vegetation coverage can reduce the daytime UHII by 0.16 °C in January and 0.59 °C in June, while at nighttime, the cooling effect varied between 0.12 °C in January and 0.27 °C in June. The daytime and nighttime UHII for the whole year can be reduced by 0.43 °C and 0.24 °C, respectively. (3) The seasonal variation in vegetation cooling effects during the daytime can be largely explained by that of ET, which is mainly driven by leaf area index (LAI).

Seasonal biophysical interactions in tidal marsh evolution: insights from a synchronized dataset in Jiangsu, China

IntroductionTidal marsh wetlands provide essential and valuable services to the wider interconnected marine and coastal environment, although the complex intertwined processes in morphological evolution remain insufficiently understood owing to synchronized data scarcity, limiting the development of numerical models and management strategies.MethodsThis study investigated the hydrodynamic, biological, sediment and morphological processes on the Doulong tidal wetlands, Jiangsu, China, using a one-year field dataset that captured spatial and seasonal variations.Results and discussionOur results indicate that biophysical interactions among multiple processes could result in some overlooked sedimentary behaviours and bio-morphological patterns in tidal marsh wetlands. Firstly, the dominance of alongshore currents caused a rapid alongshore expansion of saltmarsh patches, by which the marsh edge achieved seaward advancing, markedly different from the widely reported cross-shore expansion. Secondly, results showed that the particle size of sediment near the marsh edge coarsened when plants withered and then fined when plants grew, indicating that the seasonal variation trend of sediment grain size in saltmarshes was opposite to the trend of vegetation biomass. Thirdly, the interaction between vegetation and stranded marine debris formed banded debris zones within the saltmarsh, where debris bands could cause a biomass reduction of up to 58%, disrupting the commonly-observed parabolic biomass-elevation relationship. Meanwhile, the seasonal variation of vegetation and hydrodynamics could alter the debris positions and hence result in the formation of multiple parallel debris bands. Overall, this study provides a synchronized dataset and elucidates specific bio-morphological relationships and processes that have thus far not been systematically documented, enhancing the comprehensive understanding of tidal marsh wetland evolution.

The influence of urban trees and total vegetation on asthma development in children.

We aimed to assess whether the influence of urban vegetation on asthma development in children (<13 years) varies by type (e.g., total vegetation, tree type, and grass) and season. We used a cohort of all children born in Montreal, Canada, between 2000 and 2015. Children and cases were identified from linked medico-administrative databases. Exposure to residential vegetation was estimated using the Normalized Difference Vegetation Index (NDVI) for total vegetation and using the total area covered by deciduous and evergreen crowns for trees in 250 m buffers centered on residential postal codes. Seasonal variations in vegetation were modeled by setting values to zero on days outside of pollen and leaf-on seasons. Cox models with vegetation exposures, age as a time axis, and adjusted for sex, material deprivation, and health region were used to estimate hazard ratios (HR) for asthma development. We followed 352,946 children for a total of 1,732,064 person-years and identified 30,816 incident cases of asthma. While annual vegetation (total and trees) measures did not appear to be associated with asthma development, models for pollen and leaf-on seasons yielded significant nonlinear associations. The risk of developing asthma was lower in children exposed to high levels (>33,300 m2) of deciduous crown area for the leaf-on season (HR = 0.69; 95% confidence interval [CI] = 0.67, 0.72) and increased for the pollen season (HR = 1.07; 95% CI =1.02, 1.12), compared with unexposed children. Similar results were found with the Normalized Difference Vegetation Index. The relationship between urban vegetation and childhood asthma development is nonlinear and influenced by vegetation characteristics, from protective during the leaf-on season to harmful during the pollen season.

Effects of seasonal variations in vegetation and precipitation on catchment erosion rates along a climate and ecological gradient: insights from numerical modeling

Abstract. Precipitation in wet seasons influences catchment erosion and contributes to annual erosion rates. However, wet seasons are also associated with increased vegetation cover, which helps resist erosion. This study investigates the effect of present-day seasonal variations in rainfall and vegetation cover on erosion rates for four catchments along the extreme climate and ecological gradient (from arid to temperate) of the Chilean Coastal Cordillera (∼ 26–∼ 38∘ S). We do this using the Landlab–SPACE landscape evolution model to account for vegetation-dependent hillslope–fluvial processes and hillslope hydrology. Model inputs include present-day (90 m) topography and a time series (from 2000–2019) of MODIS-derived Normalized Difference Vegetation Index (NDVI) for vegetation seasonality, weather station observations of precipitation, and evapotranspiration obtained from Global Land Data Assimilation System (GLDAS) Noah. The sensitivity of catchment-scale erosion rates to seasonal average variations in precipitation and/or vegetation cover was quantified using numerical model simulations. Simulations were conducted for 1000 years (20 years of vegetation and precipitation observations repeated 50 times). After detrending the results for long-term transient changes, the last 20 years were analyzed. Results indicate that when vegetation cover is variable but precipitation is held constant, the amplitude of change in erosion rates relative to mean erosion rates ranges between 5 % (arid) and 36 % (Mediterranean setting). In contrast, in simulations with variable precipitation change and constant vegetation cover, the amplitude of change in erosion rates is higher and ranges between 13 % (arid) and 91 % (Mediterranean setting). Finally, simulations with coupled precipitation and vegetation cover variations demonstrate variations in catchment erosion of 13 % (arid) to 97 % (Mediterranean setting). Taken together, we find that precipitation variations more strongly influence seasonal variations in erosion rates. However, the effects of seasonal variations in vegetation cover on erosion are also significant (between 5 % and 36 %) and are most pronounced in semi-arid to Mediterranean settings and least prevalent in arid and humid–temperature settings.

Urban classification of the built-up and seasonal variations in vegetation: A framework integrating multisource datasets

The growing emphasis on integrating natural elements into urban environments to counteract adverse impacts has increased research into the role of vegetation within cities. Nonetheless, the inclusion of vegetation in urban classifications remains limited due to the associated challenges, such as time and cost constraints related to its measurement. This study introduces a classification framework that integrates spatio-temporal vegetation characteristics and built-up density variables at the neighbourhood scale using open-access datasets and remote sensing for data retrieval. Through a combination of statistical and spatial autocorrelation analyses and the application of two distinct classification methods to the 15 assessed variables, we identified four primary urban clusters. These clusters are further refined considering vegetation categories, which encompass general vegetation cover, vegetation cover levels, and vegetation cover changes tracked across two different seasons, corresponding to winter and summer. This comprehensive classification not only characterizes the built-up elements but also elucidates the distribution of vegetation showing the nuances in vegetation within similar urban clusters. The resulting classifications aim to bridge the existing gap in urban studies by incorporating the spatio-temporal scale and cover changes of vegetation. This framework serves as a precedent for creating context-specific classifications, which can be subsequently used for conducting further assessments on various urban issues. The outcomes of this study offer an overview of a study area, encompassing urban density and seasonal variations in vegetation. This information facilitates the identification of specific areas where additional attention may be warranted, particularly concerning the provision of ecosystem services and the development of strategies to enhance urban microclimates and thermal comfort, among other aspects.

Automatic Detection and Dynamic Analysis of Urban Heat Islands Based on Landsat Images

Given rapid global urban development, increases to impervious surfaces, urban population growth, building construction, and energy consumption result in the urban heat island (UHI) phenomenon. However, the spatial extent of UHIs is not clearly mapped in many UHI studies based on a remote sensing approach. Therefore, we developed a method to extract the spatial extent of the UHI during the period from 2000 to 2021 in Nanjing, China, and explored the impact of urban two- and three-dimensional expansion on UHI spatial extent and UHI intensity. After cropland effects (i.e., bare soil) were eliminated, our proposed method combines the Getis-Ord-Gi* and the standard deviation of the normalized difference vegetation index (NDVI STD) to extract the UHI area from Landsat 5 and Landsat 8 images using land surface temperature (LST) spatial autocorrelation characteristics and the seasonal variation of vegetation. Our results show the following: (1) Bare farmland has a large influence on the extraction results of UHI—combined with the seasonal variation characteristics of NDVI STD, the impact of bare soil on UHI extraction was highly reduced, strongly improving the accuracy of UHI extraction. (2) The dynamics of the UHI area are consistent with the changes in the built-up area in Nanjing at both spatial and temporal scales, but with the increase of the urban green ratio, the UHI area of mature urban areas trends to decrease due to the cooling effect of green space. (3) The accumulation of population and GDP promote the vertical expansion of urban buildings. When the two-dimensional expansion of the city reaches saturation, the UHI intensity is primarily affected by three-dimensional urban expansion.

3D CHANGE DETECTION OF POINT CLOUDS BASED ON DENSITY ADAPTIVE LOCAL EUCLIDEAN DISTANCE

Abstract. With the development of sensors and multi-view stereo matching technology, image-based dense matching point cloud data shares higher geometric accuracy and richer spectral information, and such data is therefore widely used in change detection-related research. Due to the inconsistent position and attitude of the image acquisition for generating two phases of point clouds, as well as the seasonal variation of vegetation, the 3D change detection is often subject to false detection. To improve the accuracy of 3D change detection of point clouds in large fields, a method of 3D change detection of point clouds based on density adaptive local Euclidean distance is proposed. The method consists of three steps: (1) Calculating the local Euclidean distances from each point in the second phase of point clouds to the k nearest neighboring points of the first phase of point clouds; (2) Improving the local geometric Euclidean distance based on the local density and performing 3D change detection according to a given threshold; (3) Clustering the change detection results using Euclidean clustering, and then eliminating the false detection area according to the given threshold. The experiments show that the changed region can be better extracted by the proposed method.

Seasonal Variation of Vegetation and Its Spatiotemporal Response to Climatic Factors in the Qilian Mountains, China

The purpose of this study is to reveal the seasonal difference in vegetation variation and its seasonal response to climate factors in the Qilian Mountains (QM) under the background of global warming. Based on the MOD13 A2 normalized difference vegetation index (NDVI) data and meteorological data, this study analyzed the spatiotemporal dynamics and stability of vegetation in different seasons by using the mean value method, trend analysis and stability analysis method, and discussed their seasonal responses to climatic factors based on the correlation analysis method. The results show that the vegetation cover in the QM experienced a significant upward trend in the past 21 years, but there were obvious spatial differences in vegetation change in different seasons. The growth rate of vegetation in summer was the fastest, and summer vegetation provided the most significant contribution to the growing season vegetation. The order of vegetation stability in the QM among the seasons was growing season &gt; summer &gt; spring &gt; autumn. The vegetation change was obviously affected by temperature in spring, while it was mainly controlled by precipitation in the growing season and summer. The response of vegetation to climatic factors was not significant in autumn. Our results can provide important data support for ecological protection in the QM and socioeconomic development in the Hexi Corridor.

Revisiting dry season vegetation dynamics in the Amazon rainforest using different satellite vegetation datasets

There has been a debate regarding whether the Amazon rainforest is greening during the dry season. This is partially because of the great uncertainty associated with the ability of different vegetation indices to accurately assess tropical vegetation status. This paper, revisit this issue by comprehensively examining the seasonal variations in vegetation recorded in various satellite-based vegetation datasets, namely, the leaf area index (LAI), contiguous solar-induced fluorescence (CSIF), enhanced vegetation index (EVI), and vegetation optical depth (VOD). All four of these vegetation datasets show an increase in vegetation during the dry season in most parts of the Amazon; however, the vegetation changes are not only spatially variable, but also differ among the datasets. This may be attributable in part to the different physical characteristics captured by each of the datasets. For example, the seasonal maximum value occurs first in the LAI, followed by the CSIF, EVI, and VOD, in that order. The seasonal cycle of the LAI agrees reasonably well with in-situ observations of leaf flush and leaf fall. As new leaf production offsets senescence and abscission, the dry-season vegetation increases in most parts of the Amazon rainforest. Partial correlation analysis was used to further investigate the potential climatic cues (i.e., precipitation, temperature and radiation) associated with the seasonal changes recorded in the vegetation data. We found that precipitation and radiation were the dominant potential cues for seasonal VOD (48%) and LAI (59%) changes, respectively. However, CSIF appears to be associated more closely with temperature and precipitation, with significant correlations observed across ∼x223C 37% of the Amazon rainforest area for both with CSIF. Finally, variations in the EVI showed similar sensitivity to all three climatic variables considered. The findings presented here will greatly improve our understanding of vegetation dynamics and the carbon cycle in the Amazon rainforest ecosystem.

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.

Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model

Urban heat island (UHI), driving by urbanization, plays an important role in urban sustainability under climate change. However, the quantification of UHI's response to urbanization is still challenging due to the lack of robust and continuous temperature and urbanization datasets and reliable quantification methods. This study proposed a framework to quantify the response of surface UHI (SUHI) to urban expansion using the annual temperate cycle model. We built a continuous annual SUHI series at the buffer level from 2003 to 2018 in the Jing-Jin-Ji region of China using MODIS land surface temperature and imperviousness derived from Landsat. We then investigated the spatiotemporal dynamic of SUHI under urban expansion and examined the underlying mechanism. Spatially, the largest SUHI interannual variations occurred in suburban areas compared to the urban center and rural areas. Temporally, the increase in SUHI under urban expansion was more significant in daytime compare to nighttime. We found that the seasonal variation of SUHI was largely affected by the seasonal variations of vegetation in rural areas and the interannual variation was mainly attributed to urban expansion in urban areas. Additionally, urban greening led to the decrease in summer daytime SHUI in central urban areas. These findings deepen the understanding of the long-term spatiotemporal dynamic of UHI and the quantitative relationship between UHI and urban expansion, providing a scientific basis for prediction and mitigation of UHI.

Assessing the suitability of FROM-GLC10 data for understanding agricultural ecosystems in China: Beijing as a case study

ABSTRACTThe first 10 m resolution global land-cover map, FROM-GLC10 was released in early March 2019. Due to its high spatial resolution and reliable global accuracy, we evaluated the suitability of FROM-GLC10 data for understanding agricultural ecosystems in Beijing using a comparable vector data set, Google Earth images and field survey data. The overall accuracy (OA) for FROM-GLC10 based on three data sets was 71.08%, 79.63% and 80.36%, respectively. Meanwhile, there were notable misclassifications between cropland, grassland and forest. The limited accuracy for these vegetation types might be attributed to the spatially mixed vegetation structures, seasonal variations of vegetation and the temporal inconsistence between Landsat and Sentinel data set. Due to its satisfactory OA, FROM-GLC10 data have the potential to be widely employed for evaluating the progress of large-scale ecological restoration projects. On the other hand, the data producers, who can pre-set and classify some customized land-cover types, consider time-series analysis and big data fusion methods and conduct large-scale verification, and data users, who can integrate different data sources, should work together to enhance the suitability and reliability of FROM-GLC10 data for understanding agricultural ecosystems in China.

Season of birth affects juvenile survival of giraffe

AbstractVariation in timing of reproduction and subsequent juvenile survival often plays an important role in population dynamics of temperate and boreal ungulates. Tropical ungulates often give birth year round, but survival effects of birth season for tropical ungulate species are unknown. We used a population of giraffe in the Tarangire Ecosystem of northern Tanzania, East Africa to determine whether calf survival varied by season of birth. Variation in juvenile survival according to season of birth was significant, with calves born during the dry season experiencing the highest survival probability. Phenological match may confer a juvenile survival advantage to offspring born during the dry season from greater accumulated maternal energy reserves in mothers who conceive in the long rainy season, high‐protein browse in the late dry‐early short rainy seasons supplementing maternal and calf resources, reduced predation due to decreased stalking cover, or some combination of these. Asynchrony is believed to be the ancestral state of all ungulates, and this investigation has illustrated how seasonal variation in vegetation can affect juvenile survival and may play a role in the evolution of synchronous births.

Global patterns of drought deciduous phenology in semi‐arid and savanna‐type ecosystems

Seasonal variations in vegetation have direct and indirect impacts on biogeochemical cycling, net primary productivity, and the global climate. While leaf phenology in many high latitude ecosystems can be predicted based on a four‐season cycle, semi‐arid and savanna‐type (SAST) ecosystems are thought to be much less consistent and interannual variability can be quite large. While leaf phenology in SAST ecosystems is often attributed to soil moisture availability, different plants appear to respond to different signals, and predicting drought deciduous phenology, especially at large scales, as in land surface models (LSMs), continues to be a challenge. Here we undertook a meta‐analysis of published research on leaf phenology in SAST ecosystems to assess whether any consistent patterns would be revealed across the SAST regions of the world. After reviewing 188 papers and correlating green‐up and brown‐down dates with a number of possible drivers, including latitude, temperature, precipitation, continent, and plant functional type (PFT), we found that latitude, then continent and PFT, were the best predictors of phenological timing. This result suggests that solar radiation may be an important driver of phenology even in the low latitudes, and that biogeographic differences among continents and PFTs may be critical to improving our predictions of SAST phenology. All global LSMs lump plant species into functional groups, and these groups typically span continents despite known physiological differences between species on different continents. Our review suggests that more research is needed into SAST phenology at both field and remotely sensed scales, but that LSM predictions could be improved by simply developing continent‐ and latitude‐specific model parameterizations. With a large fraction of the world's human population living in and relying on services from SAST ecosystems, it is critical that we focus more attention on these understudied systems and improve their representations in global models.

Replicability of data collected for empirical estimation of relative pollen productivity

The effects of repeated survey and fieldwork timing on data derived from a recently proposed standard field methodology for empirical estimation of relative pollen productivity (RPP) have been tested. Seasonal variations in vegetation and associated pollen assemblages were studied in three contrasting cultural habitat types; semi-natural ancient woodlands, lowland heaths, and unimproved, traditionally managed hay meadows. Results show that in woodlands and heathlands the standard method generates vegetation data with a reasonable degree of similarity throughout the field season, though in some instances additional recording of woodland canopy cover should be undertaken, and differences were greater for woodland understorey taxa than for arboreal taxa. Large differences in vegetation cover were observed over the field season in the grassland community, and matching the phenological timing of surveys within and between studies is clearly important if RPP estimates from these sites are to be comparable. Pollen assemblages from closely co-located moss polsters collected on different visits are shown to be variable in all communities, to a greater degree than can be explained by the sampling error associated with pollen counting, and further study of moss polsters as pollen traps is recommended.

Ranking pollen from bee plants according to their protein contribution to honey bees

The existing literature classifies bee pollen as “excellent” or “poor” according to protein content. In this research, we ranked bee pollen according to its contribution to bee nutrition, taking into consideration the seasonal variation. We found that the richness in protein content of each single taxon alone is not enough to classify it, as a “good” pollen source, and seasonal variation in vegetation and colony needs should be taken into account. In many cases, bees collected more pollen from plants poorer in protein, which were in total more beneficial to the colony compared to pollen rich in protein, but collected in smaller amounts. We concluded that the amount of the collected pollen was the most important factor and this is relative to the population of the plants at the surrounding area, the flowering period, and the season. The attractiveness of pollen, as indicated by the amount of pollen collected by the bees, was not correlated to the protein content of different taxa. Bees collected pollen from a large number of taxa, but only few of those contributed significantly to their nutritional requirements. We found that only 14 out of 46 pollen taxa included the 88.8% of the total proteins that were available for bees. The crude protein of these “selected” pollen sources ranged from 13.9 to 25.5%. Pollen from plants blooming in spring had higher protein content (20–24.7%) than those from summer (15.1–19.9%) and autumn (19.3–23.1%). The great amount of pollen that honey bees collected in spring and its richness in proteins could explain the strong growth of brood and population during this period.

Study of temporal variation of vegetation indices and phenology of tropical deciduous broadleaf forest in eastern India

Abstract. In the recent years study of vegetation variation has taken significance as a quintessential part of a bigger research agenda of climate change. This study has attempted to (a) Analyse the inter-annual and seasonal variation of vegetation (b) Observe the variation in phenological transition dates of tropical deciduous broadleaf forest (DBF) in Eastern India from 2003 to 2012. The study was conducted on 1500 sq. km area of dense forest in the districts of West Singhbhum and Sundargarh. MODIS EVI data of 250 meter resolution was used. Land cover mask was used to study only the DBF pixels. Least squares convolution method proposed by Savitzky and Golay was used for noise reduction and smoothing. For the determination of phenological parameters iterative Savitzky-Golay method followed by threshold method was used in TIMESAT software. The results showed that (i) EVI varied between 0.41 in early April and 0.71 in mid October (ii) The overall trend was decreasing with a slope of 0.0022, representing a degradation trend (iii) EVI of summer season was found to be more stable than of rainy and winter season (iv) Summer and rainy months showed a decreasing trend whereas the winter months showed an overall increase (v) Day of start of season varied between 2nd May and 20th June whereas the day of end of season varied between 1st February and 7th March (vi) Length of season was longest for 2007 at 302 days and shortest for 2010 at 235 days and showed an overall decreasing trend.

Seasonal variation of vegetation productivity over an alpine meadow in the Qinghai–Tibet Plateau in China: modeling the interactions of vegetation productivity, phenology, and the soil freeze–thaw process

AbstractPhenology controls the seasonal activities of vegetation on land surfaces and thus plays a fundamental role in regulating photosynthesis and other ecosystem processes. Therefore, accurately simulating phenology and soil processes is critical to ecosystem and climate modeling. In this study, we present an integrated ecosystem model of plant productivity, plant phenology, and the soil freeze–thaw process to (1) improve the quality of simulations of soil thermal regimes and (2) estimate the seasonal variability of plant phenology and its effects on plant productivity in high‐altitude seasonal frozen regions. We tested different model configurations and parameterizations, including a refined soil stratification scheme that included unfrozen water in frozen soil, a remotely sensed diagnostic phenology scheme, and a modified prognostic phenology scheme, to describe the seasonal variation in vegetation. After refined soil layering resolution and the inclusion of unfrozen water in frozen soil, the results show that the model adequately reproduced the soil thermal regimes and their interactions observed at the site. The inclusion of unfrozen water in frozen soil was found to have a significant effect on soil moisture simulation during the spring but only a small effect on soil temperature simulation at this site. Moreover, the performance of improved phenology schemes was good. The phenology model accurately predicted the start and end of phenology, and its precise prediction of phenology variation allows an improved simulation of vegetation production.

NDVI variation and its relation to climate in Canadian ecozones

Parks Canada began the Northern Satellite Monitoring Program in 1997, with the objective of tracking large‐scale vegetation variation in Canadian ecosystems and helping land managers to develop appropriate management practices in response to climate change. Under this program, a sequence of 10‐day composite Advanced Very High Resolution Radiometer (AVHRR)‐derived Normalized Difference Vegetation Index (NDVI) data from 1985 to 2007 was examined to study seasonal and inter‐annual relationships between vegetation and climate data over Canadian ecosystems using statistical and wavelet analysis. Statistical analysis showed that temperature was the principal driver for seasonal variability in greenness, explaining more than 70 percent of seasonal variation in vegetation for most Canadian ecozones. In comparison with temperature, the relationships between NDVI and precipitation were weaker but still significant. Maximum annual NDVI showed increasing trends in Canadian ecozones during the study period, although increasing rates were spatially heterogeneous. Wavelet analysis confirmed that inter‐annual variation in NDVI was different at two ecozones in Canada. NDVI variation in the Northern Arctic was significant at scales of 3–4 years from 1997 to 2001, which was associated with temperature and precipitation variation. Comparatively, NDVI variation in the Boreal Shield was significant at scales of 5–8 years from 1991 to 1999, but did not correspond with climate variation.