Spatiotemporal Analysis of Land Surface Plant Phenology

Digital elevation models (DEMs) have shown much potential for use in the extraction of land surface parameters and analysis of the relationship between land surface units and vegetation cover. However, there is lack of studies on the use of SRTM-3 DEM in vegetation studies of mountainous regions. This study is therefore an attempt to relate land surface parameters to vegetation cover in the Obudu mountain region using SRTM-3 DEM and Landsat data. Geomorphometric classification of the land surface was done using an unsupervised ISOCLUST algorithm while vegetation cover classification was done using the supervised approach based on the Maximum Likelihood algorithm. The resultant land surface units and vegetation cover maps were then related using grid-based statistic within the geographic information systems. The overall measure of difference between the two maps yielded a chi-square (d.f. = 24) = 1.9154, p > 0.05. This implies that there is no significant difference between the land surface units and the vegetation cover in the study area. This findings support the use of SRTM-3 for land surface and vegetation mapping where there is no higher quality data, or the cost of obtaining one is inhibitive; a situation that is faced by many developing economies like Nigeria. However, this results should be interpreted and used within the context of the uncertainty that is contained in the SRTM-3 DEM.

The boreal forest is the second largest biome in the world containing 33% of the Earth's forest cover ([FAO 2001][1]) of which approximately 25% is natural. It is circumpolar and shares similar taxa across its range. It has approximately 20 300 identified species. Along with the tropics, the

Anomalies in global vegetation greenness, SST, land surface air temperature, and precipitation exhibit linked, low-frequency interannual variations. These interannual variations were detected and analyzed for 1982–90 with a multivariate spectral method. The two most dominant signals for 1982–90 had periods of about 2.6 and 3.4 yr. Signals centered at 2.6 years per cycle corresponded to variations in the El Nino–Southern Oscillation index and explained about 28% of the variance in anomalies of SST, land surface air temperature, precipitation, and vegetation; these signals were most pronounced in 1) SST anomalies in the eastern equatorial Pacific Ocean, 2) land surface vegetation and precipitation anomalies in tropical and subtropical regions, and 3) land surface vegetation, precipitation, and temperature anomalies in North America. Signals at 3.4 years per cycle corresponded to variations in the North Atlantic oscillation index and explained 8.6% of the variance in the combined datasets; their occ...

Land surface temperature and vegetation index as a proxy to microclimate

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Recent reports of insect declines have raised concerns about the potential for concomitant losses to ecosystem processes. However, understanding the causes and consequences of insect declines is challenging, especially given the data deficiencies for most species. Needed are approaches that can help quantify the magnitude and potential causes of declines at levels above species. Here we present an analytical framework for assessing broad‐scale plant–insect phenologies and their relationship to community‐level insect abundance patterns. We intentionally apply a species‐neutral approach to analyse trends in phenology and abundance at the macroecological scale. Because both phenology and abundance are critical to ecosystem processes, we estimate aggregate metrics using the overwintering (diapause) stage, a key species trait regulating phenology and environmental sensitivities. This approach can be used across broad spatiotemporal scales and multiple taxa, including less well‐studied groups. Using community (‘citizen’) science butterfly observations from multiple platforms across the Eastern USA, we show that the relationships between environmental drivers, phenology and abundance depend on the diapause stage. In particular, egg‐diapausing butterflies show marked changes in adult‐onset phenology in relation to plant phenology and are rapidly declining in abundance over a 20‐year span across the study region. Our results also demonstrate the negative consequences of warmer winters for the abundance of egg‐diapausing butterflies, irrespective of plant phenology. In sum, the diapause stage strongly shapes both phenological sensitivities and developmental requirements across seasons, providing a basis for predicting the impacts of environmental change across trophic levels. Utilizing a framework that ties thermal performance across life stages in relation to climate and lower‐trophic‐level phenology provides a critical step towards predicting changes in ecosystem processes provided by butterflies and other herbivorous insects into the future. Read the free Plain Language Summary for this article on the Journal blog.

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.

The joint Japan-Mongolia-USA project DUVEX (Dust-Vegetation Interaction Experiment) was designed to develop a biogeophysical model which can simulate dust emission and ecosystem processes over the vegetated land surface. Dust emission processes have been investigated mostly on bare land, and there is very little information about vegetated land. Thus, intensive observations were conducted of a dust event that occurred on the Mongolian steppe on 24 April 2008. Meteorological and dust elements (e.g., saltation flux, visibility, dust concentration) and land-surface parameters (e.g., roughness length, vegetation cover, and the ground-based normalized difference vegetation index) were measured. During the event (from 13:00 to 18:00 LST on 24 April), the threshold wind speed at 1.54 m height, which is the minimum wind speed inducing saltation of particles ranging from 30 to 667 μm in diameter, was 8.9 m s-1 on a land surface with 7.2% vegetation cover with dead brown leaves, a small roughness length (0.0058 m), and a very dry sandy soil at 0-5 mm depth (water content, 0.002 g g-1). For comparison with previous studies, the threshold wind speed value was converted to the values at the heights in each study by using the logarithmic law of wind profile. Our value is close to the SYNOP-derived values for the same area, but larger than ground-observed and SYNOP-derived values for East Asian deserts.

Status of remote sensing algorithms for estimation of land surface state parameters

Several factors are known to affect bird migration timing, but no study has simultaneously compared the effects of temperature, land surface phenology, vegetation greenness, and relative humidity in both spring and fall. In addition, it is unclear whether long-term shifts in migration phenologies have kept pace with changing climates. For example, if migration shifts earlier in the spring, temperatures on migration dates may remain stable over time despite spring warming trends. If the phenologies of birds, plants, and insects shift asynchronously in response to changing climates, then birds may encounter reduced resource availability during migration. We estimated spring and fall 10%, 50%, and 90% cumulative migratory passage dates at 53 weather surveillance radar stations across the US Central Flyway. We determined which conditions (temperature, timing of green-up and dormancy, relative humidity, and enhanced vegetation index [EVI]) explained annual variation in migration phenologies. We also described decadal trends in environmental conditions and whether shifts in migration phenologies were sufficient to compensate for these changes. Annual changes to spring migration phenologies were best explained by anomalies in temperature, with earlier passage in warmer years. Fall migration occurred later on warmer, more humid years with higher EVI and later dormancy. Long-term adjustments in bird migration phenologies did not mitigate their exposure to changing environmental conditions. Although passage dates for all spring migration quantiles advanced significantly (~0.6 days/decade), temperatures on spring 10% passage dates increased, while 50% and 90% passage occurred closer to green-up. In the fall, temperatures increased on50% and 90% passage dates. By contrast, the advancement of 10% passage (~1 day/decade) prevented early migrants from experiencing the cooling late-summer temperature trend. Warmer temperatures in mid to late fall may lead to earlier fruiting phenology and asynchronies with migratory passage, which occurred later in warmer years. Changes in temperature and land surface phenophases experienced by migrants suggest that resource availability during migration has changed and that adjustments to migration phenologies have not compensated for the effects of changing climates.

One possible effect of climate change is the generation of a mismatch in the seasonal timing of interacting organisms, owing to species-specific shifts in phenology. Despite concerns that plants and pollinators might be at risk of such decoupling, there have been few attempts to test this hypothesis using detailed phenological data on insect emergence and flowering at the same localities. In particular, there are few data sets on pollinator flight seasons that are independent of flowering phenology, because pollinators are typically collected at flowers. To address this problem, we established standardized nesting habitat (trap nests) for solitary bees and wasps at sites along an elevational gradient in the Rocky Mountains, and monitored emergence during three growing seasons. We also recorded air temperatures and flowering phenology at each site. Using a reciprocal transplant experiment with nesting bees, we confirmed that local environmental conditions are the primary determinants of emergence phenology. We were then able to develop phenology models to describe timing of pollinator emergence or flowering, across all sites and years, as a function of accumulated degree-days. Although phenology of both plants and insects is well described by thermal models, the best models for insects suggest generally higher threshold temperatures for development or diapause termination than those required for plants. In addition, degree-day requirements for most species, both plants and insects, were lower in locations with longer winters, indicating either a chilling or vernalization requirement that is more completely fulfilled at colder sites, or a critical photoperiod before which degree-day accumulation does not contribute to development. Overall, these results suggest that phenology of plants and trap-nesting bees and wasps is regulated in similar ways by temperature, but that plants are more likely than insects to advance phenology in response to springtime warming. We discuss the implications of these results for plants and pollinators, and suggest that phenological decoupling alone is unlikely to threaten population persistence for most species in our study area.

Monitoring land surface phenology (LSP) is important for understanding both the responses and feedbacks of ecosystems to the climate system, and for representing these accurately in terrestrial biosphere models. Moreover, by shedding light on phenological trends at a variety of scales, LSP provides the potential to fill the gap between traditional phenological (field) observations and the large-scale view of global models. In this study, we review and evaluate the variability and evolution of satellite-derived growing season length (GSL) globally and over the past three decades. We used the longest continuous record of Normalized Difference Vegetation Index data available to date at global scale to derive LSP metrics consistently over all vegetated land areas and for the period 1982-2012. We tested GSL, start- and end-of-season metrics (SOS and EOS, respectively) for linear trends as well as for significant trend shifts over the study period. We evaluated trends using global environmental stratification information in place of commonly used land cover maps to avoid circular findings. Our results confirmed an average lengthening of the growing season globally during 1982-2012 - averaging 0.22-0.34 days yr(-1), but with spatially heterogeneous trends. About 13-19% of global land areas displayed significant GSL change, and over 30% of trends occurred in the boreal/alpine biome of the Northern Hemisphere, which showed diverging GSL evolution over the past three decades. Within this biome, the 'Cold and Mesic' environmental zone appeared as an LSP change hotspot. We also examined the relative contribution of SOS and EOS to the overall changes, finding that EOS trends were generally stronger and more prevalent than SOS trends. These findings constitute a step towards the identification of large-scale phenological drivers of vegetated land surfaces, necessary for improving phenological representation in terrestrial biosphere models.

Lake surface temperatures have increased globally in recent decades. Climate change can affect lake biota directly via enhanced water temperatures, shorter ice cover duration and prolonged stratification, and indirectly via changes in species interactions. Changes in the seasonal dynamics of phytoplankton and zooplankton can further affect whole lake ecosystems. However, separating the effects of climate change from the more direct and dominating effects of nutrients is a challenge. Our aim was to explore the ecological effects of climate change while accounting for the effects of re-oligotrophication in Lake Mjøsa, the largest lake in Norway. While restoration measures since the 1970s have resulted in strongly reduced nutrient levels, the surface water temperature has increased by almost 0.4°C decade-1 during the same period. We analysed long-term trends and abrupt changes in environmental and biological time series as well as changes in the seasonal dynamics of individual plankton taxa. The general long-term trends in phenology were diverging for phytoplankton (later peaks) vs. zooplankton (earlier peaks). However, individual taxa of both phytoplankton and zooplankton displayed earlier peaks. Earlier peaks of the phytoplankton group Cryptophyceae can be explained by increased spring temperature or other climate-related changes. Earlier onset of population growth of certain zooplankton species (Limnocalanus macrurus and Holopedium gibberum) can also be explained by climatic change, either directly (earlier temperature increase) or more indirectly (earlier availability of Cryptophyceae as a food source). In the long run, climate-related changes in both phytoplankton and zooplankton phenology may have implications for the fish communities of this lake.

Aerodynamic roughness length (z0) is one of the important parameters that influence energy exchange at the land–atmosphere interface in numerical models, so it is of significance to accurately parameterize the land surface. To parameterize the z0 values of China’s land surface vegetation using remote sensing data, we parameterized the vegetation canopy area index using the leaf area index and land cover products of moderate resolution imaging spectroradiometer data. Then we mapped the z0 values of different land cover types based on canopy area index and vegetation canopy height data. Finally, we analyzed the intra-annual monthly z0 values. The conclusions are: (1) This approach has been developed to parameterize large scale regional z0 values from multisource remote sensing data, allowing one to better model the land–atmosphere flux exchange based on this feasible and operational scheme. (2) The variation of z0 values in the parametric model is affected by the vegetation canopy area index and its threshold had been calculated to quantify different vegetation types. In general, the z0 value will increase during the growing season. When the threshold in the dense vegetation area or in the growing season is exceeded, the z0 values will decrease but the zero-plane displacement heights will increase. This technical scheme to parameterize the z0 can be applied to large-scale regions at a spatial resolution of 1 km, and the dynamic products of z0 can be used in high resolution land or atmospheric models to provide a useful scheme for land surface parameterization.