Feedbacks of Vegetation on Summertime Climate Variability over the North American Grasslands. Part I: Statistical Analysis

2006 ◽  
Vol 10 (17) ◽  
pp. 1-27 ◽  
Author(s):  
Weile Wang ◽  
Bruce T. Anderson ◽  
Nathan Phillips ◽  
Robert K. Kaufmann ◽  
Christopher Potter ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Climate Variability ◽  
Time Scales ◽  
North American ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Growing Season ◽  
Statistical Technique ◽  
The North

Abstract Feedbacks of vegetation on summertime climate variability over the North American Grasslands are analyzed using the statistical technique of Granger causality. Results indicate that normalized difference vegetation index (NDVI) anomalies early in the growing season have a statistically measurable effect on precipitation and surface temperature later in summer. In particular, higher means and/or decreasing trends of NDVI anomalies tend to be followed by lower rainfall but higher temperatures during July through September. These results suggest that initially enhanced vegetation may deplete soil moisture faster than normal and thereby induce drier and warmer climate anomalies via the strong soil moisture–precipitation coupling in these regions. Consistent with this soil moisture–precipitation feedback mechanism, interactions between temperature and precipitation anomalies in this region indicate that moister and cooler conditions are also related to increases in precipitation during the preceding months. Because vegetation responds to soil moisture variations, interactions between vegetation and precipitation generate oscillations in NDVI anomalies at growing season time scales, which are identified in the temporal and the spectral characteristics of the precipitation–NDVI system. Spectral analysis of the precipitation–NDVI system also indicates that 1) long-term interactions (i.e., interannual and longer time scales) between the two anomalies tend to enhance one another, 2) short-term interactions (less than 2 months) tend to damp one another, and 3) intermediary-period interactions (4–8 months) are oscillatory. Together, these results support the hypothesis that vegetation may influence summertime climate variability via the land–atmosphere hydrological cycles over these semiarid grasslands.

scholarly journals Vegetation greenness response to water availability in northern China from 1982 to 2006

2016 ◽  
Author(s):  
Zhang Fengtai ◽  
An Youzhi
Keyword(s):  
Soil Moisture ◽  
Water Availability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Northern China ◽  
Entire Study ◽  
Vegetation Greenness ◽  
Spatio Temporal ◽  
Using Data

Abstract. An evaluation of the relationship between satellite-observed Normalized Difference Vegetation Index (NDVI) data as a proxy for vegetation greenness and water availability (rainfall and soil moisture) can greatly improve our understanding of how vegetation greenness responds to water availability fluctuations. Using Sen and Pearson’s correlation methods, we analyzed the spatio-temporal variation of vegetation greenness for both the entire year and the growing season (GS,4-10) in northern China from 1982 to 2006. Although, vegetation greenness and soil moisture during the study period changed significantly for the entire study area, there was no change in rainfall. Linear correlation analysis between NDVI and rainfall revealed higher correlations using data for all seasons. Higher correlations for NDVI and soil moisture were obtained using growing season data. This study highlights how strongly vegetation greenness responds to water availability dynamics, especially in the growing season period.

scholarly journals Does Earlier and Increased Spring Plant Growth Lead to Reduced Summer Soil Moisture and Plant Growth on Landscapes Typical of Tundra-Taiga Interface?

2019 ◽  
Vol 11 (17) ◽  
pp. 1989 ◽  
Author(s):  
Alemu Gonsamo ◽  
Michael T. Ter-Mikaelian ◽  
Jing M. Chen ◽  
Jiaxin Chen
Keyword(s):  
Soil Moisture ◽  
Plant Growth ◽  
High Latitude ◽  
Boreal Forests ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Root Zone ◽  
Terrestrial Ecosystems ◽  
Growing Season ◽  
Northern High Latitude

Over the past four decades, satellite observations have shown intensified global greening. At the same time, widespread browning and reversal of or stalled greening have been reported at high latitudes. One of the main reasons for this browning/lack of greening is thought to be warming-induced water stress, i.e., soil moisture depletion caused by earlier spring growth and increased summer evapotranspiration. To investigate these phenomena, we use MODIS collection 6, Global Inventory Modeling and Mapping Studies third-generation (GIMMS) normalized difference vegetation index (NDVI3g), and Global Land Evaporation Amsterdam Model (GLEAM) satellite-based root-zone soil moisture data. The study area was the Far North of Ontario (FNO), 453,788 km2 of heterogeneous landscape typical of the tundra-taiga interface, consisting of unmanaged boreal forests growing on mineral and peat soils, wetlands, and the most southerly area of tundra. The results indicate that the increased plant growth in spring leads to decreased summer growth. Lower summer soil moisture is related to increased spring plant growth in areas with lower soil moisture content. We also found that earlier start of growing season leads to decreased summer and peak season maximum plant growth. In conclusion, increased spring plant growth and earlier start of growing season deplete summer soil moisture and decrease the overall summer plant growth even in temperature-limited high latitude ecosystems. Our findings contribute to evolving understanding of changes in vegetation dynamics in relation to climate in northern high latitude terrestrial ecosystems.

scholarly journals Persistent Vegetation Greening and Browning Trends Related to Natural and Human Activities in the Mount Elgon Ecosystem

2020 ◽  
Vol 12 (13) ◽  
pp. 2113
Author(s):  
Dan Wanyama ◽  
Nathan J. Moore ◽  
Kyla M. Dahlin
Keyword(s):  
Climate Variability ◽  
Time Scales ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Local Climate ◽  
Sen’S Slope ◽  
Mount Elgon

Many developing nations are facing severe food insecurity partly because of their dependence on rainfed agriculture. Climate variability threatens agriculture-based community livelihoods. With booming population growth, agricultural land expands, and natural resource extraction increases, leading to changes in land use and land cover characterized by persistent vegetation greening and browning. This can modify local climate variability due to changing land–atmosphere interactions. Yet, for landscapes with significant interannual variability, such as the Mount Elgon ecosystem in Kenya and Uganda, characterizing these changes is a difficult task and more robust methods have been recommended. The current study combined trend (Mann–Kendall and Sen’s slope) and breakpoint (bfast) analysis methods to comprehensively examine recent vegetation greening and browning in Mount Elgon at multiple time scales. The study used both Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data and attempted to disentangle nature- versus human-driven vegetation greening and browning. Inferences from a 2019 field study were valuable in explaining some of the observed patterns. The results indicate that Mount Elgon vegetation is highly variable with both greening and browning observable at all time scales. Mann–Kendall and Sen’s slope revealed major changes (including deforestation and reforestation), while bfast detected most of the subtle vegetation changes (such as vegetation degradation), especially in the savanna and grasslands in the northeastern parts of Mount Elgon. Precipitation in the area had significantly changed (increased) in the post-2000 era than before, particularly in 2006–2010, thus influencing greening and browning during this period. The greenness–precipitation relationship was weak in other periods. The integration of Mann–Kendall and bfast proved useful in comprehensively characterizing vegetation greenness. Such a comprehensive description of Mount Elgon vegetation dynamics is an important first step to instigate policy changes for simultaneously conserving the environment and improving livelihoods that are dependent on it.

Relationship of soil moisture with solar-induced chlorophyll fluorescence and normalized difference vegetation index in different phenological stages

2020 ◽  
Author(s):  
Qiu Shen ◽  
Jianjun Wu ◽  
Leizhen Liu ◽  
Wenhui Zhao
Keyword(s):  
Soil Moisture ◽  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Maturity Stage ◽  
Phenological Stages ◽  
Active Radiation ◽  
Relationship Of ◽  
The Relationship

<p>As an important part of water cycle in terrestrial ecosystem, soil moisture (SM) provides essential raw materials for vegetation photosynthesis, and its changes can affect the photosynthesis process and further affect vegetation growth and development. Thus, SM is always used to detect vegetation water stress and agricultural drought. Solar-induced chlorophyll fluorescence (SIF) is signal with close ties to photosynthesis and the normalized difference vegetation index (NDVI) can reflect the photosynthetic characteristics and photosynthetic yield of vegetations. However, there are few studies looking at the sensitivity of SIF and NDVI to SM changes over the entire growing season that includes multiple phenological stages. By making use of GLDAS-2 SM products along with GOME-2 SIF products and MODIS NDVI products, we discussed the detailed differences in the relationship of SM with SIF and NDVI in different phenological stages for a case study of Northeast China in 2014. Our results show that SIF integrates information from the fraction of photosynthetically active radiation (fPAR), photosynthetically active radiation (PAR) and SIF<sub>yield</sub>, and is more effective than NDVI for monitoring the spatial extension and temporal dynamics of SM on a short time scale during the entire growing season. Especially, SIF<sub>PAR_norm</sub> is the most sensitive to SM changes for eliminating the effects of seasonal variations in PAR. The relationship of SM with SIF and NDVI varies for different vegetation cover types and phenological stages. SIF is more sensitive to SM changes of grasslands in the maturity stage and  rainfed croplands  in the senescence stage than NDVI, and it has significant sensitivities to SM changes of forests in different phenological stages. The sensitivity of SIF and NDVI to SM changes in the senescence stages stems from the fact that vegetation photosynthesis is relatively weaker at this time than that in the maturity stage, and vegetations in the reproductive growth stage still need much water. Relevant results are of great significance to further understand the application of SIF in SM detection.</p>

scholarly journals Vegetation greenness response to water availability in northern China from 1982 to 2006

Solid Earth ◽  
2016 ◽  
Vol 7 (3) ◽  
pp. 995-1002 ◽  
Author(s):  
Fengtai Zhang ◽  
Youzhi An
Keyword(s):  
Soil Moisture ◽  
Water Availability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Northern China ◽  
Entire Study ◽  
Vegetation Greenness ◽  
Using Data ◽  
Response To Water

Abstract. Vegetation and moisture are two key factors of soil genesis and development. An evaluation of the relationship between satellite-observed normalized difference vegetation index (NDVI) data as a proxy for vegetation greenness and water availability (rainfall and soil moisture) can greatly improve our understanding of how vegetation greenness responds to water availability fluctuations. Using Sen and Pearson's correlation methods, we analyzed the spatiotemporal variation of vegetation greenness for both the entire year and the growing season (GS, 4–10) in northern China from 1982 to 2006. Although vegetation greenness and soil moisture during the study period changed significantly for the entire study area, there was no change in rainfall. Linear correlation analysis between NDVI and rainfall revealed higher correlations using data for all seasons. Higher correlations for NDVI and soil moisture were obtained using growing season data. This study highlights how strongly vegetation greenness responds to water availability dynamics, especially in the growing season period.

Feedbacks of Vegetation on Summertime Climate Variability over the North American Grasslands. Part II: A Coupled Stochastic Model

2006 ◽  
Vol 10 (16) ◽  
pp. 1-30 ◽  
Author(s):  
Weile Wang ◽  
Bruce T. Anderson ◽  
Dara Entekhabi ◽  
Dong Huang ◽  
Robert K. Kaufmann ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
North American ◽  
Power Spectra ◽  
Region Of Interest ◽  
Precipitation Variability ◽  
The North ◽  
Monte Carlo Experiments ◽  
Local Soil

Abstract A coupled linear model is derived to describe interactions between anomalous precipitation and vegetation over the North American Grasslands. The model is based on biohydrological characteristics in the semiarid environment and has components to describe the water-related vegetation variability, the long-term balance of soil moisture, and the local soil–moisture–precipitation feedbacks. Analyses show that the model captures the observed vegetation dynamics and characteristics of precipitation variability during summer over the region of interest. It demonstrates that vegetation has a preferred frequency response to precipitation forcing and has intrinsic oscillatory variability at time scales of about 8 months. When coupled to the atmospheric fields, such vegetation signals tend to enhance the magnitudes of precipitation variability at interannual or longer time scales but damp them at time scales shorter than 4 months; the oscillatory variability of precipitation at the growing season time scale (i.e., the 8-month period) is also enhanced. Similar resonance and oscillation characteristics are identified in the power spectra of observed precipitation datasets. The model results are also verified using Monte Carlo experiments.

scholarly journals Vegetation Phenological Changes in Multiple Landforms and Responses to Climate Change

2020 ◽  
Vol 9 (2) ◽  
pp. 111 ◽  
Author(s):  
Hongzhu Han ◽  
Jianjun Bai ◽  
Gao Ma ◽  
Jianwu Yan
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Vegetation Phenology ◽  
The South ◽  
Response Characteristics ◽  
The North ◽  
Spatio Temporal

Vegetation phenology is highly sensitive to climate change, and the phenological responses of vegetation to climate factors vary over time and space. Research on the vegetation phenology in different climatic regimes will help clarify the key factors affecting vegetation changes. In this paper, based on a time-series reconstruction of Moderate-Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data using the Savitzky–Golay filtering method, the phenology parameters of vegetation were extracted, and the Spatio-temporal changes from 2001 to 2016 were analyzed. Moreover, the response characteristics of the vegetation phenology to climate changes, such as changes in temperature, precipitation, and sunshine hours, were discussed. The results showed that the responses of vegetation phenology to climatic factors varied within different climatic regimes and that the Spatio-temporal responses were primarily controlled by the local climatic and topographic conditions. The following were the three key findings. (1) The start of the growing season (SOS) has a regular variation with the latitude, and that in the north is later than that in the south. (2) In arid areas in the north, the SOS is mainly affected by the temperature, and the end of the growing season (EOS) is affected by precipitation, while in humid areas in the south, the SOS is mainly affected by precipitation, and the EOS is affected by the temperature. (3) Human activities play an important role in vegetation phenology changes. These findings would help predict and evaluate the stability of different ecosystems.

scholarly journals The Relationship between NDVI and Climate Factors at Different Monthly Time Scales: A Case Study of Grasslands in Inner Mongolia, China (1982–2015)

2019 ◽  
Vol 11 (24) ◽  
pp. 7243 ◽  
Author(s):  
Zhifang Pei ◽  
Shibo Fang ◽  
Wunian Yang ◽  
Lei Wang ◽  
Mingyan Wu ◽  
...  
Keyword(s):  
Time Scales ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Growing Season ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Climate Factors ◽  
The Relationship

There are currently only two methods (the within-growing season method and the inter-growing season method) used to analyse the normalized difference vegetation index (NDVI)–climate relationship at the monthly time scale. What are the differences between the two methods, and why do they exist? Which method is more suitable for the analysis of the relationship between them? In this study, after obtaining NDVI values (GIMMS NDVI3g) near meteorological stations and meteorological data of Inner Mongolian grasslands from 1982 to 2015, we analysed temporal changes in NDVI and climate factors, and explored the difference in Pearson correlation coefficients (R) between them via the above two analysis methods and analysed the change in R between them at multiple time scales. The research results indicated that: (1) NDVI was affected by temperature and precipitation in the area, showing periodic changes, (2) NDVI had a high value of R with climate factors in the within-growing season, while the significant correlation between them was different in different months in the inter-growing season, (3) with the increase in time series, the value of R between NDVI and climate factors showed a trend of increase in the within-growing season, while the value of R between NDVI and precipitation decreased, but then tended toward stability in the inter-growing season, and (4) when exploring the NDVI–climate relationship, we should first analyse the types of climate in the region to avoid the impacts of rain and heat occurring during the same period, and the inter-growing season method is more suitable for the analysis of the relationship between them.

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

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