Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Li, Hua; Wang, Huidong; Sui, Haigang; Wardlow, Brian D.; Hayes, Michael J.; Wang, Jianxun

doi:10.3390/rs11161873

Cited by 41 publications

(23 citation statements)

References 73 publications

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“…The analysis of the spatial distribution of time lag between the emergence of meteorological drought and response of vegetation covers to it shows that the late response times (i.e., time lags more than two months) are mostly associated with tree cover land use types (particularly forests consisting evergreen trees), while there is also a positive correlation between the response time of vegetated covers to the agricultural droughts and the density of their green leaves. The late response time of trees to drought events is mostly because of the root system of trees that allows them to have access to more stable water resources and meanwhile supports them by nutrient uptakes during drought events [65,66]. These findings are consistent with previous studies that showed similar results in different case studies that vegetation covers of forests may respond to meteorological droughts with a time lag and also remain for a longer period after their termination from a meteorological point of view [67,68].…”

Section: Discussionsupporting

confidence: 90%

Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe

2021

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Agricultural droughts impose many economic and social losses on various communities. Most of the effective tools developed for agricultural drought assessment are based on vegetation indices (VIs). The aim of this study is to compare the response of two commonly used VIs to meteorological droughts—Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and Soil Moisture and Ocean Salinity (SMOS) vegetation optical depth (VOD). For this purpose, meteorological droughts are calculated by using a standardized precipitation index over more than 24,000 pixels at 0.25° × 0.25° spatial resolution located in central Europe. Then, to evaluate the capability of VIs in the detection of agricultural droughts, the average values of VIs anomalies during dry and wet periods obtained from meteorological droughts are statistically compared to each other. Additionally, to assess the response time of VIs to meteorological droughts, a time lag of one to six months is applied to the anomaly time series of VIs during their comparison. Results show that over 35% of the considered pixels NDVI, over 22% of VOD, and over 8% of both VIs anomalies have a significant response to drought events, while the significance level of these differences and the response time of VIs vary with different land use and climate conditions.

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Section: Discussionsupporting

confidence: 90%

Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe

2021

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“…The behavior patterns of the VIs in the forests respond to summer rains, where the highest values were observed and that correspond to the growing season, NDVI, SAVI, MSAVI, and EVI increase appreciably during the beginning and the end of the season of growth [50]. Forests have a longer response of lag to other types of vegetation, when they face extreme weather; their own responses to stress and their adaptability make them less influenced by climatic anomalies and have a lag response to prolonged drought due to their deep root system [51]. In the case of grasslands, there is a tendency to decrease in the values of the VIs, for the dry seasons, seasonality of precipitation, and more specifically, the proportion of total precipitation in summer, is related to the productivity of grasses [52], the lack of water can cause the grass to appear dry and straw, which affects the sensitivity of the VIs to the color.…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

et al. 2020

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In this research, vegetation indices (VIs) were analyzed as indicators of the spatio-temporal variation of vegetation in a semi-arid region. For a better understanding of this dynamic, interactions between vegetation and climate should be studied more widely. To this end, the following methodology was proposed: (1) acquire the NDVI, EVI, SAVI, MSAVI, and NDMI by classification of vegetation and land cover categories in a monthly period from 2014 to 2018; (2) perform a geostatistical analysis of rainfall and temperature; and (3) assess the application of ordinary and uncertainty least squares linear regression models to experimental data from the response of vegetation indices to climatic variables through the BiDASys (bivariate data analysis system) program. The proposed methodology was tested in a semi-arid region of Zacatecas, Mexico. It was found that besides the high values in the indices that indicate good health, the climatic variables that have an impact on the study area should be considered given the close relationship with the vegetation. A better correlation of the NDMI and EVI with rainfall and temperature was found, and similarly, the relationship between VIs and climatic factors showed a general time lag effect. This methodology can be considered in management and conservation plans of natural ecosystems, in the context of climate change and sustainable development policies.

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“…With the rapid advancement of remote sensing technology, global-scale land surface parameters, derived from long time series of remote sensing observations, have provided a good description of vegetation changes [11][12][13]. Leaf area index (LAI), defined as onehalf the total developed area of green leaves per unit horizontal surface area, can serve as a proxy for detecting vegetation greenness and serves as a measure of the amount of vegetation that is vertically distributed [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

et al. 2021

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Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran’s I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources.

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Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Cited by 41 publications

References 73 publications

Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe

Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe

Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

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