Spring Normalized Difference Vegetation Index Research Articles

The Response of NDVI to Drought at Different Temporal Scales in the Yellow River Basin from 2003 to 2020

Ecological protection in the Yellow River Basin (YRB) is a major strategy for China’s sustainable development. Amid global warming, droughts have occurred more frequently, severely affecting vegetation growth. Based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) at different time scales from 2003 to 2020, this study employed the linear trend method and the Spearman correlation coefficient method to calculate the trends and correlation coefficients of NDVI and SPEI at different scales at the pixel scale and explored the spatial distribution pattern of the sensitivity of vegetation growth in the YRB to drought. The results show that: (1) NDVI and SPEI are positively correlated in 77% of the area, negatively correlated in 9%, and are positively correlated in the arid and semi-arid areas, while negatively correlated in the humid and subhumid areas. The significant negative correlation between NDVI and drought at high altitudes may be due to the fact that Gramineae vegetation is more sensitive to drought, with heat being more affected than water. (2) Urbanization has a relatively obvious impact on the distribution of drought. Extreme drought mainly occurs in the middle and upper reaches of the Wei River; severe drought mainly occurs in the central area of the Guanzhong Plain centered on Xi’an; the central area of the Loess Plateau; and the surrounding areas of the Zhengzhou-centered Central Plains City Group. (3) The NDVI showed an upward trend from 2003 to 2020, indicating an increase in vegetation density or an expansion of vegetation coverage. From the temporal trend, SPEI decreased at a rate of −0.17/decade, indicating that the entire watershed has a drought trend on an annual scale. (4) Spring NDVI is more sensitive to the water supply provided by SPEI-1, while the positive correlation between SPEI and NDVI begins to rise in June and reaches its peak in July, then starts to decline in August. In autumn and winter, NDVI is more sensitive to 3–6-month accumulated drought. (5) From the dynamic transmission laws of different levels of positive correlation, the positive impact of the 3-month accumulated drought on NDVI is most significant, and the influence of SPEI-1 on the negative correlation between SPEI and NDVI is most significant. This paper aims to clarify the sensitivity of vegetation to different time-scale droughts, provide a basis for alleviating drought in the YRB, and promote sustainable development of ecological environmental protection. The research findings enable us to gain a profound insight into the responsiveness of vegetation growth to drought in the context of global warming and offer a valuable theoretical foundation for devising pertinent measures to alleviate stress on vegetation growth in regions prone to frequent droughts.

Impacts of Climate Change and Human Activities on NDVI Change in Eastern Coastal Areas of China

Based on the datasets of normalized difference vegetation index (NDVI), temperature, precipitation, and solar radiation and the methods of trend, partial correlation, and residual analyses, this study explored the spatiotemporal variation in NDVI and its response to climate change from 1982 to 2019 in eastern coastal areas of China. Then, the effects of climate change and non-climatic factors (e.g., human activities) on NDVI trends were analyzed. The results showed that:① the NDVI trend varied greatly in different regions, stages, and seasons. On average, the growing season NDVI increased faster during 1982-2000 (stage I) than that during 2001-2019 (stage Ⅱ) in the study area. Moreover, NDVI in spring showed a more rapid increase than that in other seasons in both stages. ② For a given stage, the relationships between NDVI and each climatic factor varied in different seasons. For a given season, the major climatic factors associated with NDVI change were different between the two stages. The relationships between NDVI and each climatic factor showed great spatial differences in the study period. In general, the increase in growing season NDVI in the study area from 1982 to 2019 was closely related to the rapid warming. The increase in precipitation and solar radiation in stage Ⅱ also played a positive role. ③ In the past 38 years, climate change played a greater role in the change in growing season NDVI than non-climatic factors, including human activities. Whereas non-climatic factors dominated the increase in growing season NDVI during stage I, climate change played a major role during stage Ⅱ. We suggest that more attention should be paid to the impacts of various factors on vegetation cover variation during different periods to promote the understanding of terrestrial ecosystem changes.

Seasonal Spatiotemporal Changes in the NDVI and Its Driving Forces in Wuliangsu Lake Basin, Northern China from 1990 to 2020

In the context of global climate change, many studies have focused on the interannual vegetation variation trends and their response to precipitation and temperature, but ignored the effects of seasonal variability. This study explored the relationship between normalized difference vegetation index (NDVI) and seasonal climate elements in the Wuliangsu Lake Basin area from 1990 to 2020, and quantified the impacts of human activities on vegetation dynamics. We used Landsat series data to analyze the spatial and temporal variation of the NDVI using the trend analysis method, the Theil–Sen median, the Mann–Kendall test, and the Hurst index. Then, we used meteorological data and land use data to quantify the effects of human activities using residual analysis, and correlation methods to determine the driving forces of NDVI variations. The results showed that the NDVI changes presented obvious regional characteristics, with a decreasing trend from southeast to northwest in Wuliangsu Lake Basin. Due to global warming, the start of the growing season (SOS) is 4.3 days (2001 to 2010) and 6.8 days (2011 to 2020) earlier compared with 1990 to 2000. The end of the season (EOS) is advanced by 3.6 days (2001 to 2010), and delayed by 8.9 days (2011 to 2020). Seasonal (spring, summer, autumn, and winter) NDVIs with precipitation and temperature show spatial heterogeneity. Further, changes in grasslands and woodlands were vulnerable to climate change and human activities. Since the beginning of the 21st century, human activity was the driving force for vegetation improvement in the Dengkou, west-central, north and southwest regions, where ecological instability is weak. This finding can provide a theoretical basis for the implementation of the same type of ecological restoration projects and the construction of ecological civilization, and contribute to the regional green and sustainable development.

Linkage of Spring Vegetation Dipole Pattern in Mid‐High Latitude Asia to Preceding Autumn Sea Ice Over the Barents‐Laptev Seas

AbstractBased on the normalized difference vegetation index (NDVI) and empirical orthogonal function analysis, a “northeast‐southwest” dipole pattern of spring vegetation in mid‐high latitude Asia was detected. This pattern was revealed to be associated with the autumn sea ice concentration (SIC) anomalies in the Barents‐Laptev Seas on the interannual variability. Increased autumn SIC over the Barents‐Laptev Seas tends to increase spring NDVI over the west of Lake Baikal and decrease spring NDVI in the Russian Far East. Further analyses indicated that the sea surface temperature (SST) anomalies persisting from winter to spring over the Northeast Atlantic and the Northwest Pacific play an important role in such a relationship. Increased autumn SIC over the Barents‐Laptev Seas is associated with a teleconnection pattern over the mid‐high latitudes in the following winter, accompanied by anomalous anticyclonic circulations over the Northeast Atlantic and the Northwest Pacific. Such changes in the large‐scale circulations induce downward net heat flux anomalies over the Northeast Atlantic and the Northwest Pacific, thereby contributing to the warming of SST which may persist to spring and in turn influence the atmospheric circulations. The Northeast Atlantic SST warming in spring may evoke a downstream wave train and cause positive geopotential height anomalies over the west of Lake Baikal, which increases local temperature and hence the vegetation. The Northwest Pacific SST warming in spring may lead to negative geopotential height anomalies over the Russian Far East through a meridional wave train, consequently resulting in a decrease in local temperature and hence vegetation.

Concurrent Climate Extremes and Impacts on Ecosystems in Southwest China

Global warming and its associated changes in temperature and precipitation have significantly affected the ecosystem in Southwest China, yet studies that integrate temperature and precipitation changes are inadequate for quantitatively assessing the impacts of extreme events on ecosystems. In this study, the return period of concurrent climate extremes characterized by precipitation deficit and extreme temperature and the spatial and temporal dynamic patterns of their impacts on ecosystems were assessed by using high-precision temperature and precipitation data, as well as NDVI and NPP data collected for the 1985–2015 period. The results show that the 2009 concurrent event had a return period of about 200 years. The return periods of individual climate factors are significantly overestimated or underestimated. Concurrent events significantly reduced the spring and annual Normalized Difference Vegetation Index (NDVI) and net primary productivity (NPP) in Southwest China. The magnitude of the reduction in vegetation greenness and productivity increased with the intensity of concurrent events. Concurrent events beginning in autumn 2009 reduced spring NDVI and NPP by 8.8% and 23%, and annual NDVI and NPP by 2.23% and 7.22%, respectively. Under future climate scenarios, the return period of concurrent events could be significantly shortened, which would have a more severe impact on regional ecosystems.

Vegetation Browning Trends in Spring and Autumn over Xinjiang, China, during the Warming Hiatus

Satellite-derived vegetation records (GIMMS3g-NDVI) report that climate warming promotes vegetation greening trends; however, the climate impacts on vegetation growth during the global warming hiatus period (1998–2012) remain unclear. In this study, we focused on the vegetation change trend in Xinjiang in spring and autumn before and during the recent warming hiatus period, and their climate-driving mechanisms, which have not been examined in previous studies. Based on satellite records, our results indicated that the summer normalized difference vegetation index (NDVI) in Xinjiang experienced a greening trend, while a browning trend existed in spring and autumn during this period. The autumn NDVI browning trend in Xinjiang was larger than that in spring; however, the spring NDVI displayed a higher correlation with climatic factors than did the autumn NDVI. During the warming hiatus, spring climatic factors were the main controlling factors of spring NDVI, and spring vapor pressure deficit (VPD) had the highest positive correlation with spring NDVI, followed by spring temperature. The larger increase in air temperature in spring than in autumn resulted in increased VPD differences in spring and autumn. In autumn, summer climatic factors (e.g., VPD, WS, RH, and precipitation) were significantly correlated with the autumn NDVI during the warming hiatus. However, the autumn temperature was weakly correlated with the autumn NDVI. Our results have significant implications for understanding the response of vegetation growth to recent and future climatic conditions.

Effects of Climate Change on Vegetation Growth in the Yellow River Basin from 2000 to 2019

A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors and climate data (precipitation and temperature) derived from the national meteorological stations were employed to examine the spatiotemporal differences in vegetation growth and its reaction to climate changes in the YRB from 2000–2019, using several sophisticated statistical methods. The results showed that both NDVI and climatic variables exhibited overall increasing trends during this period, and positive correlations at different significant levels were found between temperature/precipitation and NDVI. Furthermore, NDVI in spring had the strongest response to temperature/precipitation, and the correlation coefficient of NDVI with temperature and precipitation was 0.485 and 0.726, respectively. However, an opposite situation was detected in autumn (September to November) since NDVIs exhibited the weakest responses to temperatures/precipitation, and the NDVI’s correlation with both temperature and precipitation was 0.13. This indicated that, compared to other seasons, increasing the temperature and precipitation has the most significant effect on NDVI in spring (March to May). Except for a few places in the northern, southern, and southwestern regions of the YRB, NDVI was positively correlated with precipitation in most areas. There was an inverse relationship between NDVI and temperature in most parts of the central YRB, especially in summer (June to August) and growing season (May to September); however, there was a positive correlation in most areas of the YRB in spring. Finally, continuous attention must be given to the influence of other factors in the YRB.

Implications of landscape configuration on understory forage productivity: a remote sensing assessment of native forests openings

Sound management of native forests used for cattle grazing requires understanding the dynamics of forage productivity in the openings. Despite their importance, forage productivity drivers in highly heterogeneous forested landscapes, or their variability over the year, are still unclear. The aim of this work is to find predictors of Normalized Difference Vegetation Index (NDVI) variation in the openings of native temperate forests and to evaluate how these predictors change within the growing season. We used high spatial resolution remote sensing imagery from NW Patagonia to separate forest openings from tree dense canopy. We obtained data of each opening related with herbaceous and shrub forage productivity and calculated landscape metrics. We estimated a multiple linear regression model for predicting NDVI in each season. Beyond known variables related with forage productivity (altitude, precipitation, etc.), the shape of forest’ openings appeared as relevant in predicting NDVI. Higher values of forest opening perimeters were related with a decrease in NDVI in spring when soil water content is not limiting and conversely with an increase in NDVI in summer when water is limiting growth. These results suggest that environmental drivers such as temperature and soil moisture inside the opening, and competition or facilitation process between trees and grasses are mediated by the shape of the opening. Management of heterogeneous native forests for cattle raising requires considering the shape of the openings to maximize forage productivity.

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

Vegetation Change and Its Response to Climate Change in Yunnan Province, China

The impact of global climate change on vegetation has become increasingly prominent over the past several decades. Understanding vegetation change and its response to climate can provide fundamental information for environmental resource management. In recent years, the arid climate and fragile ecosystem have led to great changes in vegetation in Yunnan Province, so it is very important to further study the relationship between vegetation and climate. In this study, we explored the temporal changes of normalized difference vegetation index (NDVI) in different seasons based on MOD13Q1 NDVI by the maximum value composite and then analyzed spatial distribution characteristics of vegetation using Sen’s tendency estimation, Mann–Kendall significance test, and coefficient of variation model (CV) combined with terrain factors. Finally, the concurrent and lagged effects of NDVI on climate factors in different seasons and months were discussed using the Pearson correlation coefficient. The results indicate that (1) the temporal variation of the NDVI showed that the NDVI values of different vegetation types increased at different rates, especially in growing season, spring, and autumn; (2) for spatial patterns, the NDVI, CV, and NDVI trends had strong spatial heterogeneity owning to the influence of altitudes, slopes, and aspects; and (3) the concurrent effect of vegetation on climate change indicates that the positive effect of temperature on NDVI was mainly in growing season and autumn, whereas spring NDVI was mainly influenced by precipitation. In addition, the lag effect analysis results revealed that spring precipitation has a definite inhibition effect on summer and autumn vegetation, but spring and summer temperature can promote the growth of vegetation. Meanwhile, the precipitation in the late growing season has a lag effect of 1-2 months on vegetation growth, and air temperature has a lag effect of 1 month in the middle of the growing season. Based on the above results, this study provided valuable information for ecosystem degradation and ecological environment protection in the Yunnan Province.

Divergent negative spring vegetation and summer runoff patterns and their driving mechanisms in natural ecosystems of northern latitudes

The reported wide change in natural vegetation growth in the Northern Hemisphere could have altered hydrological cycles in the past 30 years. However, it is hard to understand the underlying mechanisms for substantial variations in the magnitude and direction of runoff response to vegetation change. Models fail to clearly identify the underlying mechanisms due to limitations in accommodating all the driving factors of the extremely complex reality. This study used a data-driven approach to determine the linkages between Normalized Difference Vegetation Index (NDVI) and runoff in the spring and summer seasons of 1982–2015 in 3600 + northern mid/high-latitude (≥30 °N) catchments. The results suggested that a significant (p < 0.05) negative correlation existed between spring NDVI and summer runoff in the Rocky Mountains, the Great Lakes region, the northern Scandinavia and the Alps. In the southern Rocky Mountains, there was a significant increase in spring NDVI and a decrease in summer runoff due to loss of snowpack. In the Great Lakes region, significant decrease in spring NDVI was accompanied by increase in summer runoff. Earlier spring onset and consequent withering vegetation in late spring significantly reduced spring NDVI while slightly increased summer runoff in Northern Rocky Mountain. The warmer and wetter winter could have increased spring NDVI owning to better water/energy environment, and insignificantly reduced summer runoff in northern Scandinavia and the Alps. Both anthropogenic warming and Arctic amplification might influence the patterns of vegetation-runoff in the region. Our findings can help improving the understanding on how vegetation change affects runoff and enhancing the forecasting abilities of hydrological models.

Impact of climatic factors on vegetation dynamics in the upper Yangtze River basin in China

It is necessary to understand vegetation dynamics and their climatic controls for sustainable ecosystem management. This study examines the vegetation dynamics and the effect of climate change on vegetation growth in the pristine conditions of 58 woodland National Nature Reserves (NNRs) located in the upper Yangtze River basin (UYRB) in China which are little influenced by human activities. Changes in the normalized difference vegetation index (NDVI), precipitation, and temperature in the selected NNRs were observed and analyzed for the period between 1999 and 2015. The relationship between time-lag effect of climate and changes in the NDVI were assessed using Pearson correlations. The results showed three major trends. 1) The NDVI increased during the study period; this indicates an increase in the amount of green vegetation, especially due to the warmer climate during the growing season. The NDVIs in March and September were significantly affected by the temperature of the previous months. Spring temperatures increased significantly (P < 0.05) and there was a delay between climatic factors and their effect on vegetation, which depended on the previous season. In particular, the spring temperature had a delayed effect on the NDVI in summer. 2) The way in which vegetation responds to climatic factors varied significantly across the seasons. Temperature had a greater effect on the NDVI in spring and summer and the effect was greater at higher altitudes. A similar trend was observed for precipitation, except for altitudes of 1000–2000 m. 3) Temperature had a greater effect on the NDVI in spring and autumn at higher altitudes. The same trend was observed for precipitation in summer. These findings suggest that the vegetation found in NNRs in the upper reaches of the Yangtze River was in good condition between 1999 and 2015 and that the growth and development of vegetation in the region has not been adversely affected by climate change. This demonstrates the effectiveness of nature reserves in protecting regional ecology and minimizing anthropogenic effects.

Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data

Earth observation satellite technologies allow the status of crops to be assessed at various geographical scales. The most popular vegetation index for analyzing this status is the Normalized Difference Vegetation Index (NDVI). This article studies the relationship between the NDVI and cereal-grain yield in selected regions (corresponding to NUTS2 levels) of four Central European countries: Poland, Germany, the Czech Republic, and Slovakia. The remote sensing data cover the vegetation seasons (from the beginning of March until the end of June) of 2012–2016, and were obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor of NASA's Terra satellite. The average NDVI of the data was calculated for both the entire area and the arable-land area of the regions (according to the Corine Land Cover program). Statistical analysis includes correlation and linear regression analyses between cereal grain yield and mean NDVI for entire area and arable land of the regions in each year of the study. They are strongly correlated from the beginning of March until mid-May (most of the correlation coefficients for arable land are between 0.50 and 0.85), the strongest relationship being around the beginning of April in the Czech Republic and Slovakia (most of the correlation coefficients are between 0.80 and 1.00). In most regions of Poland, the relationships are quite strong, but in German regions they are weak and inconsistent. Regression coefficients (slopes) for relationships between the NDVI in the beginning of April and the grain yield of cereals range from 10.8 to 26.2. This means that an increase in the NDVI in early spring by 0.1 unit increases the grain yield of cereals by about 1.1–2.6 t/ha. The obtained results are promising because they prove the possibility of forecasting cereal grain yield at the regional level, three–four months before the harvest, which is important for planning food policy.

Climate Prediction of Satellite-Based Spring Eurasian Vegetation Index (NDVI) using Coupled Singular Value Decomposition (SVD) Patterns

Satellite-based normalized difference vegetation index (NDVI) data are widely used for estimating vegetation greenness. Seasonal climate predictions of spring (April–May–June) NDVI over Eurasia are explored by applying the year-to-year increment approach. The prediction models were developed based on the coupled modes of singular value decomposition (SVD) analyses between Eurasian NDVI and climate factors. One synchronous predictor, the spring surface air temperature from the NCEP’s Climate Forecast System (SAT-CFS), and three previous-season predictors (winter (December–January–February) sea-ice cover over the Barents Sea (SICBS), winter sea surface temperature over the equatorial Pacific (SSTP), and winter North Atlantic Oscillation (NAO) were chosen to develop four single-predictor schemes: the SAT-CFS scheme, SICBS scheme, SSTP scheme, and NAO scheme. Meanwhile, a statistical scheme that involves the three previous-season predictors (i.e., SICBS, SSTP, and NAO) and a hybrid scheme that includes all four predictors are also proposed. To evaluate the prediction skills of the schemes, one-year-out cross-validation and independent hindcast results are analyzed, revealing the hybrid scheme as having the best prediction skill. The results indicate that the temporal correlation coefficients at 92% of grid points over Eurasia are significant at the 5% significance level in the hybrid scheme, which is the best among all the schemes. Furthermore, spatial correlation coefficients (SCCs) of the six schemes are significant at the 1% significance level in most years during 1983–2015, with the averaged SCC of the hybrid scheme being the highest (0.60). The grid-averaged root-mean-square-error of the hybrid scheme is 0.04. By comparing the satellite-based NDVI value with the independent hindcast results during 2010–2015, it can be concluded that the hybrid scheme shows high prediction skill in terms of both the spatial pattern and the temporal variability of spring Eurasian NDVI.

Interannual linkage between wintertime sea-ice cover variability over the Barents Sea and springtime vegetation over Eurasia

The rapid loss of Arctic sea-ice could cause climate changes in the Northern Hemisphere, especially in the middle and high latitudes. In this study, the interannual linkage between winter (December–January–February) sea-ice cover over the Barents Sea (SICBS) and spring (April–May–June) vegetation greenness over Eurasia, as well as the possible mechanisms of this teleconnection are investigated. The Normalized Difference Vegetation Index (NDVI) derived from the advanced very high resolution radiometer is used as the proxy of vegetation greenness. During 1982–2015, the winter SICBS is significantly correlated with the spring NDVI over Eurasia (NDVIEA). The positive (negative) winter SICBS anomalies tend to increase (decrease) the spring NDVIEA. The results show that the increased winter SICBS corresponds to higher winter surface air temperature and soil temperature over most parts of Eurasia, and in turn, corresponds to less winter snow cover and less snow water equivalent. The persistent less and thinner snow cover from winter to spring over Eurasia, especially over Western and Central Siberia, tends to induce increased surface air temperature through decreased surface albedo and less snowmelt latent heat. Subsequently, the increased surface air temperature corresponding to increased SICBS contributes to higher vegetation greenness over Eurasia in spring and vice versa. The ensemble simulations from the Community Atmosphere Model version 5 further confirm the associated possible physical mechanism. Meanwhile, the variability of the SICBS shows good seasonal persistence from winter to spring. The increased spring SICBS also leads to the changes in climate conditions that are favorable for increased NDVI near Lake Baikal.

Long-term spatiotemporal variations in satellite-based soil moisture and vegetation indices over Iran

Soil moisture plays a crucial role in vegetation growth. However, the long-term influence of soil moisture on vegetation growth was not sufficiently understood in many regions, especially in developing countries, due to the lack of ground measurements. Remote sensing data provide a promising way to overcome this limitation. In this study, a long-term spatiotemporal variation in remote sensing surface soil moisture (SSM) and vegetation indices (VIs) and their relationship during 1988–2015 were analyzed over Iran. Trend-free pre-whitening Mann–Kendall test was applied for the detection of the trends in soil moisture and VIs time series. Also, the validity of the “dry gets drier, wet gets wetter” (DGDWGW) paradigm was examined throughout the country. Finally, the consistency between SSM and vegetation indices trends was investigated using homogeneity Chi-squared test. The results showed that the monthly average of the SSM over 45% of Iran is lower than 0.15 m3/m3. Seasonal average of SSM is 0.17 and 0.12 m3/m3 in spring and winter, respectively. On average, monthly SSM trends are downward in 70% of Iran, which 30% of those is statistically significant. Over the past 28 years, about 50% and 35% of Iran got drier with rates of 0.24 × 10−2 (spring) and 0.78 × 10−3 (summer) m3/m3 per year. According to DGDWGW paradigm examining, 14% of Iran follows the DGDWGW paradigm. Summer and spring normalized difference vegetation index (NDVI) and enhanced vegetation index values are less than 0.1 in about 45% and 65% of the areas, respectively. The NDVI values are decreased in 40% of Iran in the last 28 years, of which half of those are statistically significant. SSM trends were consistent with vegetation indices using a homogeneity test. About 45% of SSM trends agree in sign with NDVI values. Life zone in the Southeast of Iran is arid desert scrubs, and in this area with sparse vegetation cover, mixing the spectral of soil and vegetation causes a serious problem in vegetation indices representing therefore the prominent mismatch between SSM and vegetation indices trends which were observed in Southeast of Iran.

Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.).

Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with unmanned aerial systems were used to assess phenotypes in spring and winter pea breeding plots. The Green Red Vegetation Index (GRVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), plot volume, canopy height, and canopy coverage were extracted from RGB and multispectral information at five imaging times (between 365 to 1948 accumulated degree days/ADD after 1 May) in four winter field pea experiments and at three imaging times (between 1231 to 1648 ADD) in one spring field pea experiment. The image features were compared to ground-truth data including AGBM, lodging, leaf type, days to 50% flowering, days to physiological maturity, number of the first reproductive node, and seed yield. In two of the winter pea experiments, a strong correlation between image features and seed yield was observed at 1268 ADD (flowering). An increase in correlation between image features with the phenological traits such as days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in these winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height (r = 0.83) at 1231 ADD. In two other winter pea experiments and the spring pea experiment, the GRVI and NDVI features were significantly correlated with AGBM at flowering. When selected image features were used to develop a least absolute shrinkage and selection operator model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.60 and 0.84 in the winter and spring pea experiments, respectively. A SPOT-6 satellite image (1.5 m resolution) was also evaluated for its applicability to assess biomass and seed yield. The image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, however, the trend was not consistent. In summary, the study supports the potential of using unmanned aerial system-based imaging techniques to estimate biomass and crop performance in pea breeding programs.

Assessing the effects of drought and “Grain for Green” Program on vegetation dynamics in China's Loess Plateau from 2000 to 2014

China's Loess Plateau (CLP) has experienced severe water loss and soil erosion over the past few decades. To prevent water loss and soil erosion and improve ecological environment, the Grain for Green Project (GGP) was launched in 1999. Meanwhile, the frequency and severity of drought in CLP has shown an increasing trend in the recent years. However, few studies have addressed the coupled effects of drought and the GGP on vegetation dynamics in this area. Based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI), this study assesses the change trends of drought and vegetation dynamics and the role of droughts and GGP on trends in vegetation dynamics. Results show that: (1) At the regional scale, annual and seasonal NDVI show a significant increase from 2000 to 2014; however, drought change trends were insignificant. (2) At the pixel scale, annual, spring, summer, and autumn NDVI increased in the CLP from 2000 to 2014, with 51.92%, 50.04%, 74%, and 62.69% of the study area showing a significant increase, respectively, with p < 0.05. Summer drought had strongest effect on vegetation dynamics. (3) Drought was one of the primary reasons for the decreasing trend of NDVI in the northern of the CLP. The severe and extreme drought (SPEI<−1.5) in summer of 2001 and 2005 reduced the NDVI by 22.44% and 9.98%, respectively. (4) The residual analysis results indicate that 21.54% of the CLP was affected by drought. 43.64% and 34.82% of the CLP experienced human induced improvement and degradation, respectively. (5) The GGP had an important influence on vegetation dynamics, strong correlations between the cumulative afforestation area and annual NDVI in Yan'an and Yulin from 2000 to 2013 were found, with r = 0.96 and 0.65. The spatial pattern of the cumulative afforestation area ratio matched quite well with the NDVI change trend.

Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps—An Earth Observation-Based Analysis

Alpine ecosystems are particularly sensitive to climate change, and therefore it is of significant interest to understand the relationships between phenology and its seasonal drivers in mountain areas. However, no alpine-wide assessment on the relationship between land surface phenology (LSP) patterns and its climatic drivers including snow exists. Here, an assessment of the influence of snow cover variations on vegetation phenology is presented, which is based on a 17-year time-series of MODIS data. From this data snow cover duration (SCD) and phenology metrics based on the Normalized Difference Vegetation Index (NDVI) have been extracted at 250 m resolution for the entire European Alps. The combined influence of additional climate drivers on phenology are shown on a regional scale for the Italian province of South Tyrol using reanalyzed climate data. The relationship between vegetation and snow metrics strongly depended on altitude. Temporal trends towards an earlier onset of vegetation growth, increasing monthly mean NDVI in spring and late summer, as well as shorter SCD were observed, but they were mostly non-significant and the magnitude of these tendencies differed by altitude. Significant negative correlations between monthly mean NDVI and SCD were observed for 15–55% of all vegetated pixels, especially from December to April and in altitudes from 1000–2000 m. On the regional scale of South Tyrol, the seasonality of NDVI and SCD achieved the highest share of correlating pixels above 1500 m, while at lower elevations mean temperature correlated best. Examining the combined effect of climate variables, for average altitude and exposition, SCD had the highest effect on NDVI, followed by mean temperature and radiation. The presented analysis allows to assess the spatiotemporal patterns of earth-observation based snow and vegetation metrics over the Alps, as well as to understand the relative importance of snow as phenological driver with respect to other climate variables.

基于MODIS NDVI的辽河保护区成立前后植被覆盖时空动态研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于MODIS NDVI的辽河保护区成立前后植被覆盖时空动态研究 DOI: 10.5846/stxb201709041594 作者: 作者单位: 北京林业大学,中国环境科学研究院,北京林业大学,中国环境科学研究院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学青年基金项目（41201187）；三峡库区生态环境教育部工程研究中心开放基金（KF2016-03） Spatial-temporal dynamics of vegetation cover before and after establishment of Liaohe River Reserve based on MODIS NDVI Author: Affiliation: Beijing Forestry University,Chinese Research Academy of Environmental Sciences,Beijing Forestry University,Chinese Research Academy of Environmental Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以2010年成立以自然生态恢复为主的辽河保护区为研究对象，基于中分辨率成像光谱仪（MODIS）获取的500米分辨率的归一化植被指数（NDVI）数据，研究了辽河保护区成立前（2000-2009年）和成立后（2010-2015年）植被覆盖时空动态。结果表明：（1）2000-2015年NDVI呈现持续增加趋势；2000-2009年NDVI的年际增加主要发生在夏季，2010-2015年NDVI的年际增加主要发生在春季和冬季。辽河保护区成立后春季NDVI的逐年增加有利于该区域防风固沙功能的提升。（2）季节尺度上，2000-2009年春季降水量与春季和夏季NDVI显著正相关，夏季气温与夏季NDVI显著负相关；2010-2015年仅夏季NDVI动态受春季气温和夏季日照时数变化的影响。（3）月尺度上，气温是影响辽河保护区NDVI变化的主要气候因素，2000-2015年NDVI动态对气温变化的响应存在1个月的滞后期；2000-2009年NDVI动态对降水变化的响应存在1个月的滞后期，2010-2015年则无滞后效应。（4）2000-2009年影响NDVI动态的人类活动主要为农田耕作；辽河保护区成立后，河岸带封育区内的自然恢复和小型人工湿地建设促进了NDVI增加，大型人工湿地建设和人工经营牧草地对NDVI动态无显著影响。 Abstract:Liaohe River Reserve was established in 2010, and recovered primarily by natural ecological restoration. Based on the 500 metres normalized difference vegetation index (NDVI) extracted from the moderate resolution imaging spectroradiometer (MODIS) images, we examined the spatial-temporal dynamics of NDVI before (2000-2009) and after (2010-2015) establishment of Liaohe River Reserve. The results showed that:(1) annual NDVI increased from 2000 to 2015. The interannual increase in NDVI mainly occurred in the summers of 2000-2009, and in springs and winters of 2010-2015. The significant increase of spring NDVI from 2010 to 2015 promoted the improvement of the ecosystem sand fixation service. (2) At the seasonal scale, during 2000-2009, spring precipitation was positively correlated with spring and summer NDVI. Summer temperature was negatively correlated with summer NDVI. During 2010-2015, only the variation in summer NDVI was associated with spring temperature and summer sunshine duration. (3) At the monthly scale, temperature was the dominant factor affecting NDVI variation of the Liaohe River Reserve, and temperature change had a lag effect of one month on NDVI variation during 2010-2015. NDVI variation during 2000-2009 was significantly linked to precipitation change with a lag time of one month, but variation in 2010-2015 had no time lag effect. (4) Farming practices were the dominant anthropogenic factors affecting NDVI dynamics during 2000-2009. After the establishment of Liaohe River Reserve, natural restoration and construction of small artificial wetlands promoted the increase of NDVI, whereas the construction of large artificial wetlands and artificial pastures had no significant influence on NDVI dynamics. 参考文献 相似文献 引证文献