Forest Phenology Research Articles

Impacts of Satellite Revisit Frequency on Spring Phenology Monitoring of Deciduous Broad-Leaved Forests Based on Vegetation Index Time Series

Satellites have different revisit frequencies (i.e., temporal resolutions), ranging from daily to monthly. The satellite revisit frequencies suitable for accurately monitoring the phenology of deciduous broad-leaved forests (DBF) are not well-known. To fill this knowledge gap, this study used MODIS Daily Nadir BRDF-Adjusted images to simulate EVI time series with a wide range of temporal resolutions from daily to 52 days, to investigate the impacts of satellite revisit frequency on monitoring spatial and temporal patterns of spring phenology, i.e., the start of season (SOS), of DBF in North America. Then, these EVI time series were used to extract SOS by two common phenology extraction methods (i.e., relative threshold and curvature methods). Our results reveal that 1 ) low temporal resolutions cannot accurately reconstruct real vegetation growth profile, which generally causes a false early SOS detection, 2 ) the impact of temporal resolutions is nonlinear. The accuracy of SOS detection from data with relatively high frequencies (e.g., 7 days) is only slightly lower than that from daily time series but the accuracy decreases largely with low frequencies, and 3 ) validation with ground observations from PhenoCam Network stations and an experiment using three real satellite datasets (i.e., MODIS, Landsat 8, and Sentinel-2) confirm the findings from our simulation study. This study suggests that satellites with medium temporal resolutions, such as Sentinel-2 and Landsat 8, could extract reliable phenology metrics in non-cloudy regions.

Bamboo phenology and life cycle drive seasonal and long‐term functioning of Amazonian bamboo‐dominated forests

Abstract Bamboo‐dominated forests (BDF) extend over large areas in the drought‐prone Southwestern Amazon, yet little is known about the dynamics of these ecosystems. Here, we investigate the hypothesis that bamboo modulates large‐scale ecosystem dynamics through competition with coexisting trees for water. We examined spatio‐temporal patterns of remotely sensed metrics (Enhanced Vegetation Index [EVI], Normalized Difference Moisture Index [NDMI]) in >300 Landsat images as proxies for canopy leaf phenology and water content at two time scales: (1) a complete bamboo life cycle (~28 years), and (2) the seasonal cycle; and at two spatial scales: (a) comparing adjacent areas of BDF vs. Terra‐firme forests (TFF) to investigate regional dynamics, and (b) comparing the vegetation classes of bamboo, trees in BDF, and trees in TFF to investigate the effects of bamboo on coexisting trees. At the regional scale, BDF showed higher EVI (leaf area density) and lower NDMI (water content) than nearby TFF but these differences disappeared as bamboo died, suggesting a strong influence of bamboo life stage in the functioning of these forests. BDF seasonal cycle showed a bimodal EVI pattern as trees and bamboos had asynchronized leaf production peaks. At the scale of vegetation classes, trees in BDF showed lower NDMI (i.e. water content) than trees in TFF except after bamboo mortality, indicating a release from competition with bamboo for water. Canopy water content of trees in BDF was also reduced during bamboo dry‐season greening (increased EVI ~ leaf production) due to increased water demands. Nevertheless, long‐term and seasonal phenology of trees in BDF did not differ from that of trees in TFF suggesting a potential selection for drought‐tolerant trees in BDF. Synthesis. Bamboo‐dominated forests have received less attention than other Amazonian forests and their functional dynamics are commonly ignored or misinterpreted. Using remote sensing to characterize forest phenology and water content, we show the distinctive seasonal and long‐term dynamics of BDF and coexisting trees and the importance of bamboo competition for water in shaping this ecosystem. Our results suggest a potential selection for drought‐tolerant trees in BDF since they maintain the same EVI as trees in bamboo‐free forests but with lower water content. A better characterization of BDF and their cyclical dynamics is crucial for accurately interpreting Amazonian forests' responses to extreme climatic events such as high temperatures and droughts.

Detecting regional GPP variations with statistically downscaled solar-induced chlorophyll fluorescence (SIF) based on GOME-2 and MODIS data

ABSTRACT Solar-Induced chlorophyll Fluorescence (SIF) is associated with vegetation canopy photosynthesis and is potentially used to retrieve Gross Primary Productivity (GPP). However, the coarse resolutions of the currently available SIF satellite data limit their applications. To expand the applicability of the SIF dataset, a framework was developed to disaggregate the Global Ozone Monitoring Experiment-2 (GOME-2) SIF dataset, which was based on statistical relationships between SIF and remotely sensed measurements of the Normalized Difference Vegetation Index (NDVI), the fraction of absorbed photosynthetically active radiation (f PAR), the soil moisture index and Land Surface Temperature (LST). The statistical relationships were established within a zone of n × n pixels (n∈[1, 25]) with a moving window technique. The regression function established within n × n pixels with the smallest Root Mean Square Error (RMSE) and highest coefficient of determination (R 2) was selected for downscaling regression. Compared with the fixed window technique (n = 5) and theglobal regression model, the moving window technique presented low residuals and high R 2 values. Validated with flux-tower eddy covariance measurements, the GPP retrieved within the downscaled SIF data shows the potential to improve vegetation GPP prediction, and the downscaled SIF could trace the seasonal phenology of evergreen forests.

Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.

Does ASCAT observe the spring reactivation in temperate deciduous broadleaf forests?

Scatterometer observations over land are sensitive to the water content in soil and vegetation, but have been rarely used to study seasonal changes in the plant water status and seasonal development of deciduous trees. Here we use Advanced Scatterometer (ASCAT) observations to investigate the sensitivity of C-band backscatter to spring phenology of temperate deciduous broadleaf forests in Austria. ASCAT's multi-angle looking capability enables the observation of backscatter over a large range of incidence angles. The vegetation status affects the slope of the backscatter-incidence angle relationship. We discovered a maximum in the slope around the month April, hereafter referred to as spring peak, predominantly in regions covered by deciduous broadleaf forest. We hypothesized that the spring peak indicates the average timing of leaf emergence in the deciduous trees in the sensor footprint. The hypothesis was tested by comparing the timing of the spring peak to leaf unfolding observations from the PEP725 phenology database, to the increase of leaf area index (LAI) during spring, and to temperature. Our results demonstrate a good agreement between the ASCAT spring peaks, phenology observations and temperature conditions. The steepest increase in LAI however lags behind the ASCAT peak by several days to a few weeks, suggesting that the spring peak in fact marks the timing of maximum woody water content, which occurs right before leaf emergence. Based on these observations, we conclude that the ASCAT signal has a high sensitivity to spring reactivation and in particular water uptake of bare deciduous broadleaf trees. Our findings might provide the basis for novel developments to estimate eco-physiological changes of forests during spring at large scales.

INTRA- AND INTER-ANNUAL TRENDS OF SUN-INDUCED FLUORESCENCE (SIF) FOR CONTRASTING VEGETATION TYPES OF INDIA

Abstract. The photosynthesis governs productivity and health of the forests. Traditionally, remote sensing derived reflectance measures have been used to assess forest phenology, productivity and stress. The chlorophyll pigments absorb solar radiation, and emit fluorescence in far red region of electromagnetic spectrum. Chlorophyll fluorescence directly relates to the photosynthetic activity of the plants. Measurement of chlorophyll fluorescence from space has recently been achieved in the form of Sun-Induced Fluorescence (SIF). But SIF response have been found variable with respect to variation in vegetation type, hence, there is a need to study SIF response of tropical forests of India considering their wide extent, contribution to national carbon cycle and climate resilience. In this study, intra- and inter-annual GOME-2 and OCO-2 SIF responses of contrasting Indian tropical forest types viz., dry deciduous (Betul, Madhya Pradesh), moist deciduous (Kalahandi, Orissa) and wet evergreen forests (Uttara Kannada, Karnataka) has been investigated with respect to rainfall, NDVI and GPP trends. The results show that dry, moist and wet forests of India have differences in photosynthetic activity at intra- and inter-annual scale. GOME-2 SIF observations were more variables than OCO-2 SIF, particularly during green-up and senescence phase. SIF explained higher seasonality for dry deciduous followed by moist deciduous and wet evergreen. Annually integrated SIF (proxy of GPP) was in order: wet evergreen > moist deciduous > dry deciduous.

Forest Phenology Shifts in Response to Climate Change over China–Mongolia–Russia International Economic Corridor

Vegetation phenology is a sensitive indicator of climate change. With the intensification of global warming, the changes in growing seasons of various vegetation types have been widely documented across the world. However, as one of the most vulnerable regions in response to the global climate change, the phenological responses and associated mechanisms in mid–high latitude forests are still not fully understood. In this study, long-term changes in forest phenology and the associated relationship with the temperature and snow water equivalent in the China–Mongolia–Russia International Economic Corridor were examined by analyzing the satellite-measured normalized difference vegetation index and the meteorological observation data during 1982 to 2015. The average start date of the growing season (SOS) of the forest ecosystem in our study area advanced at a rate of 2.5 days/decade, while the end date of the growing season (EOS) was delayed at a rate of 2.3 days/decade, contributing to a growing season that was approximately 15 days longer in the 2010s compared to that in 1980s. A higher April temperature is beneficial to the advance in the SOS, and a higher summer temperature has the potential to extend the EOS in the forest ecosystem. However, our results also suggest that a single temperature cannot fully explain the advance of the SOS, as well as the delay in the EOS. The preseason Snow Water Equivalent (SWE) is also an essential factor in influencing the growing season. A higher SWE in February and March and lower SWE in April tend to advance the SOS, while higher SWE in pre-year December and lower SWE in current year October are beneficial to the extension of the EOS.

Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series

Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation.In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).We found strong agreement between SOS estimates from logistic and spline models (rEVI = 0.86; rNDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSDEVI = 3.07, RMSDNDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (rEVI = 0.51, rNDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.

Local and landscape-level diversity effects on forest functioning.

Research of the past decades has shown that biodiversity is a fundamental driver of ecosystem functioning. However, most of this biodiversity-ecosystem functioning (BEF) research focused on experimental communities on small areas where environmental context was held constant. Whether the established BEF relationships also apply to natural or managed ecosystems that are embedded in variable landscape contexts remains unclear. In this study, we therefore investigated biodiversity effects on ecosystem functions in 36 forest stands that were located across a vast range of environmental conditions in managed landscapes of Central Europe (Switzerland). Specifically, we approximated forest productivity by leaf area index and forest phenology by growing-season length and tested effects of tree species richness and land-cover richness on these variables. We then examined the correlation and the confounding of these local and landscape-level diversity effects with environmental context variables related to forest stand structure (number of trees), landscape structure (land-cover edge density), climate (annual precipitation) and topography (mean altitude). We found that of all tested variables tree species richness was among the most important determinants of forest leaf area index and growing-season length. The positive effects of tree species richness on these two ecosystem variables were remarkably consistent across the different environmental conditions we investigated and we found little evidence of a context-dependent change in these biodiversity effects. Land-cover richness was not directly related to local forest functions but could nevertheless play a role via a positive effect on tree species richness.

Sensitivity of Deciduous Forest Phenology to Environmental Drivers: Implications for Climate Change Impacts Across North America

AbstractProjected changes in temperature and precipitation are expected to influence spring and autumn vegetation phenology and hence the length of the growing season in many ecosystems. However, the sensitivity of green‐up and senescence to climate remains uncertain. We analyzed 488 site years of canopy greenness measurements from deciduous forest broadleaf forests across North America. We found that the sensitivity of green‐up to temperature anomalies increases with increasing mean annual temperature, suggesting lower temperature sensitivity as we move to higher latitudes. Furthermore, autumn senescence is most sensitive to moisture deficits at dry sites, with decreasing sensitivity as mean annual precipitation increases. Future projections suggest North American deciduous forests will experience higher sensitivity to temperature in the next 50 years, with larger changes expected in northern regions than in southern regions. Our study highlights how interactions between long‐term and short‐term changes in the climate system influence green‐up and senescence.

Tracking forest biophysical properties with automated digital repeat photography: A fisheye perspective using digital hemispherical photography from below the canopy

In recent years, digital repeat photography has received increasing attention as an inexpensive means by which seasonal changes in vegetation canopies can be monitored. Offering automation and an increased measurement frequency, colour indices derived from above-canopy digital repeat photography have proven a popular alternative to traditional observations of forest phenology. Nevertheless, previous work has demonstrated several features in time-series of colour indices that are unrelated to canopy structure, limiting their utility to track specific biophysical properties such as leaf area index (LAI). Whilst techniques such as digital cover photography and the use of radiometric sensors are better suited to this task, they are restricted by the need for careful calibration of above- and below-canopy reference sensors, ancillary information on canopy leaf angle distribution, and smaller measurement footprints. Using data collected at a deciduous broadleaf forest site in Southern England, we investigated a new method to derive continuous measurements of LAI, making use of automated digital hemispherical photography (DHP) from below the canopy. After applying simple data screening procedures, the LAI observations derived from our automated DHP system demonstrated very close agreement with those obtained from manually acquired DHP images, which were collected under optimal illumination conditions over the surrounding forest plot (r2 = 0.99, RMSE = 0.20, NRMSE = 13%). By combining our automated DHP system with an above-canopy time-lapse digital camera, we then investigated the relationship between the green chromatic coordinate (GCC) and LAI. Distinct hysteresis effects were observed, as were substantial differences between phenological transition dates derived from the GCC and LAI, particularly in the case of the onset of senescence. Our results indicate that phenological transition dates derived from colour indices cannot easily be linked to any one biophysical property. We recommend further investigation of the automated DHP approach, which provides time-series of LAI whose physical interpretation is straightforward, as an alternative to above-canopy digital repeat photography.

Evaluating autumn phenology derived from field observations, satellite data, and carbon flux measurements in a northern mixed forest, USA.

Common approaches currently used to monitor forest phenology include direct field observation and indirect approaches such as satellite remote sensing and carbon flux measurements. However, differences in both temporal and spatial scales of these methods make direct comparison challenging. In order to evaluate the reliability of indirect measures of autumn phenology in estimating direct observations, we compared the timing of three transition dates and the rate of autumn progression derived from (i) satellite data (MOD13Q1 006 enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) products, 2000-2017), (ii) carbon flux measurements (net ecosystem exchange (NEE) and gross primary production (GPP), 1997-2016), and (iii) field observation (2010, 2012 for the north site and 2010, 2012, and 2013 for the south site) from a mixed forest in northern Wisconsin, USA. Overall, the transition dates and progression rates derived from NDVI were closest to that of field observations. Furthermore, the start of autumn derived from satellite data was earlier than directly observed leaf coloration (LC), which resulted from species-specific canopy senescence patterns and the sensitivity of the vegetation indices. Even after full leaf fall was reached, EVI continued to detect coloring which was likely due to the presence of understory plant species. Finally, NEE and GPP changes tended to start before LC as a result of tree physiological and environmental changes and continued after full leaf fall possibly due to understory and coniferous activity. These results highlight the need for long-term field observations of both trees and understory species, information on species-specific canopy senescence patterns, and species composition in understanding the efficiency of indirect approaches in estimating autumn forest phenology.

Change point estimation of deciduous forest land surface phenology

Dramatic phenological shifts and ecosystem responses of deciduous forests to global climate change have been reported around the world. Land Surface Phenology (LSP) derived from satellite imagery is useful to estimate the phenological responses of vegetation to climate variability and inform terrestrial ecosystem models at landscape to global scales. However, there is a large (and unquantified) uncertainty in estimated phenological dates due to the relatively coarse temporal resolution of typical data and methodological limitations. To assess responses of phenology and related ecological function and services, it is essential to decrease the temporal uncertainty of estimated phenological processes. In this study, we developed a new LSP estimation method using linear change point models to determine four phenological transitions using twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) from 2000 to 2015. We evaluated the approach using long-term phenological ground observations and compare performance of four LSP estimations generated from two data sources (i.e. 8-day and twice daily EVI time series) and two methods (i.e. double logistic and change point estimation). We found that the LSP generated from change point estimation with twice daily EVI time series had the highest accuracy (i.e. lower Root Mean Square Error (RMSE), mean bias, and Mean Absolute Error (MAE)) for both spring and fall phenology evaluated by Harvard Forest phenology observations and a large citizen science database of phenological observations from the National Phenology Network. For example, change point estimation reduced the estimation error for fall senescence date from over 40 days in the standard MODIS phenology product (version 005) to 11.5–24 days of RMSE, −2.6 to −5.8 days of mean bias, and 7.9–20.1 days of MAE. The change point methodology also enables calculation of additional metrics to describe the biophysical process of vegetation, including rates of greenup, green-down, and senescence, EVI values at each phenological transition, and the estimation uncertainties for each transition date. Our LSP estimations will improve more comprehensive investigations of landscape phenology of deciduous forest and the associated ecosystem processes at regional to global scales.

Reconstructing past forest composition and abundance by using archived Landsat and national forest inventory data

ABSTRACTEffective modelling of forest susceptibility to defoliating insect outbreaks requires a better understanding of outbreak dynamics, which includes detailed knowledge of the pre- and post-outbreak forest status as well as subsequent feedback mechanisms. In this paper, we strive to fill the forest status need by combining archived Landsat sensor data (pre- and post-outbreak) with different formats and dates of the U.S. Forest Service’s Forest Inventory and Analysis (FIA) data (periodic [1970s, 1990s] and annual [2003–2006]). Specifically, we explore the utility of these FIA ground data for calibrating models of forest species and type abundance for mapping past forest composition in the Border Lakes Ecoregion (BLE) of Upper Midwest of the US. Model calibration results between Landsat reflectance and FIA ground data for both total forest basal area and balsam fir (Abies balsamea) relative basal area, a preferred host of the spruce budworm (SBW, Choristoneura fumiferana), were poor to moderate (R2adj 0.39 and 0.48, respectively). Results for aspen (Populous tremuloides) and spruce (Picea glauca and P. mariana) abundance yielded substantially better accuracies (R2adj 0.64 and 0.78; RMSE 15.56 and 10.65 m2 ha−1, respectively). Groupings of tree species into broadleaved and conifers substantially improved model calibration result (R2adj range: 0.72–0.91), except for the SBW host group (A. balsamea, P. glauca, and P. mariana). Periodic FIA ground data from the early 1990s generated stronger models compared to other FIA-Landsat date combinations tested. A paired t-test of abundance differences between undisturbed forest from the older 1977 and 1990 periodic inventories was significant (p-value < 0.0001), suggesting possible effects of variable FIA sampling protocol or ground plot positional accuracy through time. However, a similar paired t-test of abundance difference between periodic FIA (1990) and annual FIA (2003–2006) was not significant (p-value = 0.249). We posit four potential factors that may have contributed to weak Landsat-FIA calibration results for species abundance: 1) variation in FIA subplot arrangement and sampling protocols through time, 2) variability in species abundance and heterogeneity among FIA sampling across adjacent Landsat orbital paths, 3) understory species (balsam fir) that are largely hidden from remote detection, and 4) cloud cover and orbital phase mismatches preventing capture of key forest phenology aids. While past and present FIA sampling protocols were not specifically designed for integration with 30-meter satellite sensor data, careful pairing of FIA ground data (past or present) with Landsat sensor data can facilitate reasonable estimates, of forest abundance for generalized forest types, and possibly forest species when heterogeneity is low. Nevertheless, we recommend that FIA subplot sampling protocols be augmented to include measurements of forest conditions that are more amenable to integration with 30-meter Landsat sensor data.

ИСТОРИЧЕСКИЙ ПУТЬ РАЗВИТИЯ БОТАНИКИ КАК ФУНДАМЕНТА ЛЕСНОГО ОБРАЗОВАНИЯ И НАУКИ В РОССИИ

The Department of Botany and Dendrology of the oldest educational forestry institution of Russia, the present St. Petersburg State Forest Technical University named after S.M. Kirov, is celebrating its centenary. At the time of its foundation it was the first specialized department of this kind both in Russia and in the world. In the course of its historical development, the Department of Botany and Dendrology has been formed not only as a large educational and methodological center for botanical, dendrological and general biology training. It has also become a major scientific center providing a wide range of research in the field of biology and ecology of woody plants, their introduction, intraspecific polymorphism, selection, phenological biorhythm, forest science, floristics and taxonomy, anthropogenic dynamics of plant cover and biodiversity conservation of forest ecosystems, bioindication, bioclimatology, forest phenology, and dendrophenological forecasting. It is impossible to list scientific publications of the Department, for only V.N.Sukachov alone was the author of about 500 papers. Under the leadership of V.N.Sukachev, his students and followers, provided research training and supervision for about 50 doctors and candidates of sciences, most of whom specialized in dendrology. Already in itself this fact testifies that creating the first in Russia Department of dendrology, V.N.Sukachev looked far ahead into addressing botanical and dendrological problems of Russia. Only within the last 10-15 years, the staff of the Department published 10 monographs, 2 textbooks, more than 10 teaching manuals. The geography of scientific research is constantly expanding. The scientific school “Dendrology and Biogeocenology named after V.N.Sukachev” has been established and is functioning at the Department.

Elucidating the phenology of the Sundarbans mangrove forest using 18-year time series of MODIS vegetation indices

Elucidating the phenology of the Sundarbans mangrove forest using 18-year time series of MODIS vegetation indices

Butterfly Fauna and Phenology in a Dry Forest of the Motagua Valley, Guatemala

Field surveys of butteflies were conducted during 14 months in a Guatemalan dry forest, which yielded 103 species in 79 genera from 18 subfamilies of six families, with Cyanophrys miserabilis (Clench, 1946) (Lycaenidae: Theclinae) as a new country record. A seasonal pattern was shown in which species richness reached a peak at the end of the rainy season and declined greatly in the dry season. Species composition also differed significantly between the two seasons. This is the first quantitative study on the butterfly phenology of a Guatemalan dry forest, which will provide a scientific baseline for future entomological and ecological research and for biodiversity conservation in these forests

Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

Forest ecosystems in an ecotone and their dynamics to climate change are growing ecological and environmental concerns. Phenology is one of the most critical biological indicators of climate change impacts on forest dynamics. In this study, we estimated and visualized the spatiotemporal patterns of forest phenology from 2001 to 2017 in the Qinling Mountains (QMs) based on the enhanced vegetation index (EVI) from MODerate-resolution Imaging Spectroradiometer (MODIS). We further analyzed this data to reveal the impacts of climate change and topography on the start of the growing season (SOS), end of the growing season (EOS), and the length of growing season (LOS). Our results showed that forest phenology metrics were very sensitive to changes in elevation, with a 2.4 days delayed SOS, 1.4 days advanced EOS, and 3.8 days shortened LOS for every 100 m increase in altitude. During the study period, on average, SOS advanced by 0.13 days year−1, EOS was delayed by 0.22 days year−1, and LOS increased by 0.35 day year−1. The phenological advanced and delayed speed across different elevation is not consistent. The speed of elevation-induced advanced SOS increased slightly with elevation, and the speed of elevation-induced delayed EOS shift reached a maximum value of 1500 m from 2001 to 2017. The sensitivity of SOS and EOS to preseason temperature displays that an increase of 1 °C in the regionally averaged preseason temperature would advance the average SOS by 1.23 days and delay the average EOS by 0.72 days, respectively. This study improved our understanding of the recent variability of forest phenology in mountain ecotones and explored the correlation between forest phenology and climate variables in the context of the ongoing climate warming.

Forest dynamics in relation to meteorology and soil in the Gulf Coast of Mexico

Forest dynamics is complex, and the complexity could be a synthetic result of climate change. Specifically studying 11 forest type groups of the Gulf of Mexico coast region defined, we intended to explore and model the direct and indirect impacts of climate change on underlying forest dynamics. This study utilized normalized difference of vegetation index (NDVI) as a measurement indicator of forest dynamics, referring to the dynamics of canopy structure and phenology of forests, and for a given type of forests, seasonal and yearly NDVI values were applied to the quantification of its growth across the Gulf Coast. By utilizing geographically weighted regression (GWR) method, we related normalized difference vegetation index (NDVI) to precipitation, temperature, and silt and clay fractions in the soil. This study demonstrated an explanatory power of soil, besides the common macroclimate factors of precipitation, temperature, on explaining forest dynamics, which also revealed that the presence of spatiotemporal heterogeneity would affect model performance. Our results indicated that the model performance varied by forest type groups and seasons. The meteorology-soil model presented the best overall fit performance for White/Red/Jack Pine forests concerning R2 (0.952), adjusted R2 (0.905), Akaike information criterion (AIC, -1100) and residual sum of squares (RSS, 0.053) values. The comparative analysis of model performance also indicated that the meteorology-soil model has the best fit of data in summer. This study advanced the understanding of forests dynamics under conditions of climate change by highlighting the significance of soil, which is a significant confounding variable influencing forest activities but is often missed in forest-climate dynamics analysis.

Metabarcoding reveals diet diversity in an ungulate community in Thailand

AbstractThe diverse large mammal communities found in Asian dry forests and savannas should segregate based on their diet selection. We examined the diet composition of sympatric ungulate species using metabarcoding to determine whether their diet was segregated and whether obvious attributes (i.e., body size, phylogeny, ecology) explained the structure. We collected fecal samples from eight ungulate species in Huai Kha Khaeng Wildlife Sanctuary in the western forest complex of Thailand. The fecal collections occurred around a plot where all woody species were codified within a genetic barcode library, and this library was supplemented with samples from plant species known to be consumed by these species. Of 273 plant species tested, at least 93 were found within the fecal samples. Over half of the identified species were not previously known by experts as forage species. All ungulate species showed a strong consumption of grasses and forbs. For the three species with sufficient sample size (sambar, banteng, and guar), there were seasonal differences in their diet, with each showing increased occurrence of woody plants during the dry season. The pattern of forage consumption did not follow obvious paradigms of body size or taxonomy, with significant diet differences found in two similar‐sized bovids (gaur, banteng), while the diet of sambar was more similar to bovids than to the other deer species. Asian ungulates differ in their forage consumption and metabarcoding should allow for testing of diet shifts in response to seasonal rains and fires which dominate the phenology of Asian dry forests and savannas.