Land surface phenology in the highland pastures of montane Central Asia: Interactions with snow cover seasonality and terrain characteristics

Tomaszewska, Monika; Nguyen, Lan H.; Henebry, Geoffrey M.

doi:10.1016/j.rse.2020.111675

Cited by 51 publications

(42 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although we focus on the patterns of the temporal mean phenometrics during the study period, we also examine the patterns during two years with contrasting weather: hotter, drier 2007 versus cooler, wetter 2009. This study leverages the findings from prior studies [25,26] using the same approach to modeling land surface phenology and advances our understanding of the spatio-temporal dynamics of vegetation in the socio-ecological landscapes of montane Central Asia, specifically in the Kyrgyz Republic.…”

Section: Introductionmentioning

confidence: 88%

“…The land surface phenology metrics (or phenometrics) and terrain information used for this study were supplied from our previous work [25], where a detailed description of data, its processing, and phenometrics calculation methodology can be found. We provide an overview here.…”

Section: Geospatial Datamentioning

confidence: 99%

“…These phenometrics can be calculated for each pixel in each year for which there are sufficient, high-quality data and for which the joint time series exhibit sufficient seasonality to enable the modeling of the land surface phenology (LSP). Pasture degradation can be evaluated with phenometrics in a variety of ways, including trends, variation, extremes, and abrupt spatial and/or temporal shifts [12,20,25,26]. However, we are interested here in three questions about patterns that can complicate interpretation of patterns and trends: (1) What are the spatial patterns of the phenometrics as a function of distance from village center?…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Remote Sensing of Pasture Degradation in the Highlands of the Kyrgyz Republic: Finer-Scale Analysis Reveals Complicating Factors

Tomaszewska

Henebry

2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Degradation in the highland pastures of the Kyrgyz Republic, a small country in Central Asia, has been reported in several studies relying on coarse spatial resolution imagery, primarily MODIS. We used the results of land surface phenology modeling at higher spatial resolution to characterize spatial and temporal patterns of phenometrics indicative of the seasonal peak in herbaceous vegetation. In particular, we explored whether proximity to villages was associated with substantial decreases in the seasonal peak values. We found that terrain features—elevation and aspect—modulated the strength of the influence of village proximity on the phenometrics. Moreover, using contrasting hotter/drier and cooler/wetter years, we discovered that the growing season weather can interact with aspect to attenuate the negative influences of dry conditions on seasonal peak values. As these multiple contingent and interactive factors that shape the land surface phenology of the highland pastures may be blurred and obscured in coarser spatial resolution imagery, we discuss some limitations with prior and recent studies of pasture degradation.

show abstract

Section: Introductionmentioning

confidence: 88%

Section: Geospatial Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing of Pasture Degradation in the Highlands of the Kyrgyz Republic: Finer-Scale Analysis Reveals Complicating Factors

Tomaszewska

Henebry

2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…To evaluate the predominance of the direction of a trend within a region, we calculated an asymmetry ratio (AR) of the area of a significant negative trend to the area of the significant positive trend (Tomaszewska and Henebry 2018, Tomaszewska et al 2020) for each variable. We set two thresholds to interpret the AR values: if the AR value was greater than 2.0 (or less than 0.5), it indicated that there was a predominant significant negative (or positive) trend associated with a given variable within the domain of interest.…”

Section: Methods and Datamentioning

confidence: 99%

Trends in land surface phenology across the conterminous United States (1982‐2016) analyzed by NEON domains

Liang

Henebry

Liu

et al. 2021

Ecological Applications

Self Cite

View full text Add to dashboard Cite

Tracking phenological change in a regionally explicit context is a key to understanding ecosystem status and change. The current study investigated long‐term trends of satellite‐observed land surface phenology (LSP) in the 17 National Ecological Observatory Network (NEON) domains across the conterminous United States (CONUS). Characterization of LSP trends was based on a high temporal resolution (3‐d) time series of the two‐band enhanced vegetation index (EVI2) derived from a long‐term data record (LTDR) of the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS). We identified significant trend patterns in LSP and their seasonal climate and land use/land cover drivers for each NEON domain. Key findings include (1) the start of season (SOS) predominantly shifted later in 13 out of 17 domains (24.3% of CONUS by area) due potentially to both a lack of spring warming in the eastern United States and changes in agronomic practices over agricultural lands; (2) the end of season (EOS) became predominantly later in nine domains dominated by natural vegetation (14.1% of CONUS by area) in response to widespread warming in autumn; (3) the EOS predominantly shifted earlier in three domains (10.6% of CONUS by area) over primarily agricultural lands as potentially affected by changes in crop growth cycles; and (4) earlier shift in the SOS was mostly found in the Northwest (3.6% of CONUS by area) and was predominant only in the moist Pacific Northwest (27.7% of the domain by area) in response to more pronounced spring warming in the region. The overall patterns of SOS and EOS trends across CONUS appeared constrained by continental‐scale temperature trends as characterized by a west‐east dipole and the distribution of the nation's agricultural lands. In addition, seasonal trend analysis revealed that most NEON domains (15/17) became predominantly greener in part of or throughout the growing season, potentially contributed by both climate change‐induced growth increase and improved agricultural productivity. The domain‐wide LSP trends with their underlying drivers identified here provide important contextual information for NEON science as well as for investigations within CONUS using other distributed observatories (e.g., LTER, LTAR, FLUXNET, USA‐NPN, etc.).

show abstract

“…The annual total precipitation was divided into three classes: 0-400 mm (arid areas), 400-600 mm (semi-arid areas) and >600 mm (dry sub-humid or humid areas) [46]. To identify the predominant direction of the detected significant relationships, the ratios of area with negative correlations to that with positive correlations at both 1% and 5% significant levels were used [47]. The threshold values of > 2.0 (or < 0.5) indicates a strong asymmetry biasing towards significant negative (or positive) correlations [47].…”

Section: Statistical Analysesmentioning

confidence: 99%

Direct and Lagged Effects of Spring Phenology on Net Primary Productivity in the Alpine Grasslands on the Tibetan Plateau

2020

View full text Add to dashboard Cite

As a key biotic factor, phenology exerts fundamental influences on ecosystem carbon sequestration. However, whether spring phenology affects the subsequent seasonal ecosystem productivity and the underlying resource limitation mechanism remains unclear for the alpine grasslands of the Tibetan Plateau (TP). In this study, we investigated the direct and lagged seasonal responses of net primary productivity (NPP) to the beginning of growing season (BGS) along a precipitation gradient by integrating field observations, remote sensing monitoring and ecosystem model simulations. The results revealed distinct response patterns of seasonal NPP to BGS. Specifically, the BGS showed a significant and negative correlation with spring NPP (R = −0.73, p < 0.01), as evidenced by the direct boosting effects of earlier BGS on spring NPP. Moreover, spring NPP was more responsive to BGS in areas with more annual precipitation. The boosting effects of earlier BGS on NPP tended to weaken in summer compared with that in spring. Sequentially, BGS exhibited stronger positive correlation with autumn NPP in areas with less annual precipitation, which suggested the enhanced lagged suppressing effects of earlier spring phenology on ecosystem carbon assimilation during the later growing season under aggravated water stress. Overall, the strengthened NPP in spring was offset by its decrement in autumn, resulting in no obvious relationship between BGS and annual NPP (R = −0.34, p > 0.05) for the entire grasslands on the TP. The findings of this study imply that the lagged effects of phenology on the ecosystem productivity during the subsequent seasons should not be neglected in the future studies.

show abstract

Land surface phenology in the highland pastures of montane Central Asia: Interactions with snow cover seasonality and terrain characteristics

Cited by 51 publications

References 88 publications

Remote Sensing of Pasture Degradation in the Highlands of the Kyrgyz Republic: Finer-Scale Analysis Reveals Complicating Factors

Remote Sensing of Pasture Degradation in the Highlands of the Kyrgyz Republic: Finer-Scale Analysis Reveals Complicating Factors

Trends in land surface phenology across the conterminous United States (1982‐2016) analyzed by NEON domains

Direct and Lagged Effects of Spring Phenology on Net Primary Productivity in the Alpine Grasslands on the Tibetan Plateau

Contact Info

Product

Resources

About