NDVI Variation Research Articles

NDVI Variations and Its Responses to hydrothermal process in the Naqu River Basin, Qinghai-Tibet Plateau-Alpine Region

Extensive studies have been conducted over the whole year and various seasons to characterize the temporal and spatial changes in vegetation NDVI. However, there are few studies on the variations of NDVI in different soil hydrothermal processes of the alpine region. So we studied the spatial-temporal variation of vegetation cover and its responses to hydrothermal process in the Qinghai-Tibet Plateau-alpine region. Based on the GIMMS NDVI remote sensing images from 1982 to 2015, the temporal and spatial variations of NDVI in the Naqu river basin at different time scales were studied by using maximum synthesis method, and the linear regression. Results show that the vegetation has an overall degradation trend in the Naqu river basin, with primarily moderate and mild degradation degrees. Completely freezing period, completely melting period and freezing process period are the main vegetation degradation periods.

Spatiotemporal variations of NDVI of different vegetation types in the Baiyangdian Basin under the background of climate change

Spatiotemporal variations of NDVI of different vegetation types in the Baiyangdian Basin under the background of climate change

Disentangling climatic and anthropogenic contributions to nonlinear dynamics of alpine grassland productivity on the Qinghai-Tibetan Plateau

Alpine grasslands on the Qinghai-Tibetan Plateau are sensitive and vulnerable to climate change and human activities. Climate warming and overgrazing have already caused degradation in a large fraction of alpine grasslands on this plateau. However, it remains unclear how human activities (mainly livestock grazing) regulates vegetation dynamics under climate change. Here, alpine grassland productivity (substituted with the normalized difference vegetation index, NDVI) is hypothesized to vary in a nonlinear trajectory to follow climate fluctuations and human disturbances. With generalized additive mixed modelling (GAMM) and residual-trend (RESTREND) analysis together, both magnitude and direction of climatic (in terms of temperature, precipitation, and radiation) and anthropogenic impacts on NDVI variation were examined across alpine meadows, steppes, and desert-steppes on the Qinghai-Tibetan Plateau. The results revealed that accelerating warming and greening, respectively, took place in 76.2% and 78.8% of alpine grasslands on the Qinghai-Tibetan Plateau. The relative importance of temperature, precipitation, and radiation impacts was comparable, between 20.4% and 24.8%, and combined to explain 66.2% of NDVI variance at the pixel scale. The human influence was strengthening and weakening, respectively, in 15.5% and 14.3% of grassland pixels, being slightly larger than any sole climatic variable across the entire plateau. Anthropogenic and climatic factors can be in opposite ways to affect alpine grasslands, even within the same grassland type, likely regulated by plant community assembly and species functional traits. Therefore, the underlying mechanisms of how plant functional diversity regulates nonlinear ecosystem response to climatic and anthropogenic stresses should be carefully explored in the future.

Evaluation of four image fusion NDVI products against in-situ spectral-measurements over a heterogeneous rice paddy landscape

Satellite image fusion methods that improve spatial and temporal resolution have significant potential to advance understanding of ecosystem dynamics in space and time. However, systematic evaluations of image fusion methods against in situ spectral data are lacking. Here, we used a suite of in situ spectral data collected at 60 elementary sampling units (10 × 10 m) covering 15 Landsat pixel (30 × 30 m) plots and one Moderate Resolution Imaging Spectroradiometer (MODIS) pixel (250 × 250 m) throughout the entire growing season in a heterogeneous rice paddy landscape to evaluate four state-of-the-art image fusion NDVI products. They include the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), SaTellite dAta IntegRation (STAIR), and the CubeSat Enabled Spatio-Temporal Enhancement Method (CESTEM); the former three blended Landsat and MODIS data, whereas the latter combined CubeSats, Landsat, and MODIS observations. All fusion products showed strong linear relationships against in situ data when combining all spatial and temporal observations (R2: 0.73 to 0.93) although there were partly negative biases (–1% to –9%). These biases resulted from forcing data to image fusion algorithms, such as Landsat (–4%) and MODIS (–7%). Performance difference between fusion methods were considerably larger for spatial than for temporal variation. Furthermore, Landsat NDVI explained only 17–22% of spatial variation against in situ spectral data, which can be translated into weak performance of image fusion products to predict spatial variability in NDVI. Image fusion products that relied on spatial interpolation showed large biases (–15% to –30%) for a vegetation plot surrounded by mixed land cover plots. Our results highlight key sources of uncertainty and will be instrumental in improving satellite image fusion methods to monitor land surface phenology in space and time.

Spatiotemporal analysis of vegetation cover changes around surface water based on NDVI: a case study in Korama basin, Southern Zinder, Niger

In the modern era, vegetation dynamics is an important aspect of climate change studies. The present study examined spatiotemporal changes of (NDVI) normalized difference vegetation index in the Korama basin (Southern Zinder of Niger) from 2000 to 2018, and their correlation with climatic factors was predicted. To analyze the change of vegetation cover, geographical information system, MODIS_NDVI, remote sensing, and climate variables (e.g., temperature and precipitation) datasets were used. Further, the correlation was performed for different years of vegetation types during the growing season (June–October). Our results show an increasing trend in average maximum annual NDVI across the Korama River Basin in the years 2000 and 2018. Conversely, significantly increasing trends in most of the areas were reported. Moreover, in downstream the vegetation cover is increased in Matameye and Magaria, but with a smaller increase in the upstream rate in Mirriah. Furthermore, a decrease in the surface water was observed in the Tessaoua, Matameye, and Magaria sections of the study region in 2000 and 2018, while a rise in water surface area was observed in Matameye and Magaria in the years 2006 and 2012. During rainy and dry seasons, NDVI correlated differently with temperature and precipitation with strong seasonal variations, while the mean vegetation period of NDVI does not show any significant change. In addition, moderate increase was observed in years 2000 and 2012 (r: 0.22; P: 0.50; R2: 0.05; r: 0.31; P: 0.34, R2: 0.10, respectively), and weak decrease in 2006 and 2018 (r: 0.61; P: 0.04; R2: 0.37; r: 0.58; P: 0.06, R2:0.33, respectively). The analysis indicates that climatic parameters such as precipitation and temperature are the main limiting factors affecting the vegetation growth. Indeed, the trends calculated by the correlation analysis showed that as climate factors increased (July, August, and September), the NDVI value increased at a rate of 0.16, reflecting the best growth in vegetation and rise in water bodies, although significantly decreased during years. This study would be highly useful in choice-making for sustainable water resource management in the Korama watershed in Southern Zinder, Niger.

Differential response of oak and beech to late frost damage: an integrated analysis from organ to forest

Intense spring freezing events can kill the recently produced cohort of leaves, forcing trees to expend additional carbon and nutrient stocks to produce a second cohort of leaves. The future trends in the frequency of late spring frosts will affect the adaptation of some tree species to their current habitats. Here we studied the effect of a late frost which occurred in 2017 on a mixed beech (Fagus sylvatica)-oak (Quercus petraea) forest located in central Spain, where these species reach their southernmost distribution limits. We followed a multi-scale approach from organ to forest levels. At the organ level, leaf and stem morphological and biochemical traits were compared between frost-damaged and non-damaged trees. At the tree level, we compared the 2017 radial growth between damaged and non-damaged trees. At the stand level, the 2017 Leaf Area Index (LAI) and daily variations of NDVI during 2017 were compared with those of average years in areas dominated by beech and oak. Finally, at the forest scale, daily NDVI dynamics during 2017 were compared with those of the three previous years. According to our results, beech trees damaged by late frost kept non-structural carbohydrate (NSC) concentrations stable by drastically reducing wood production. This growth reduction could compensate for the drop in carbon inputs due to the death of the first leaf cohort, the need to form a second leaf cohort, the one-month delay between both leaf flushes and the smaller photosynthetic surface of the second leaf cohort. In contrast, although the frost-damaged oaks lost the first cohort of leaves and formed a second one, no differences in leaf individual area and phenology, nor stem NSC concentrations and radial growth were found when comparing damaged and non-damaged oaks. The differential response between both tree species seems to provide oak with a competitive advantage over beech.

Rewilding of large herbivore communities in high elevation Puna: geographic segregation and no evidence of positive effects on peatland productivity

During the past decades, the Argentine Puna (a subtropical high elevation desert of c. 14 million hectares) has been going through a process of “rewilding” of large herbivore communities. In response to rural population outmigration and resulting livestock reduction accompanied by protection measures, large wild herbivores such as vicunas increased. To explore the ecological consequences of this transition, we analysed the spatial patterns of wild herbivores and livestock in relation to human settlements, and we estimated the changes in vegetation productivity of peatlands, using NDVI derived from MODIS satellite images between 2001 and 2012. Peatlands are key ecosystems in high elevation deserts; they regulate water and carbon fluxes and provide most forage for livestock and wildlife. A field survey of 50 peatlands revealed that travel time from human settlements was positively correlated with the wild herbivore abundance, and negatively correlated with livestock abundance (especially sheep and goats). Thus, it is a good indicator of livestock use. At the regional scale, after controlling for the physical and climatic variables of the 8134 peatlands, accessibility from human settlements explained less than 0.1% of mean and interannual NDVI variability. These results provide no evidence of livestock-induced degradation. Furthermore, likely due to rainfall increase, peatlands showed an overall positive NDVI trend between 2001 and 2012, but isolated peatlands showed comparatively more negative NDVI trends during the same period, possibly associated with increased wildlife herbivory. Similar rewilding processes could occur in many mountain regions undergoing decreasing human population and marginal land uses. Our study suggests that traditional management could contribute to the productivity, stability, and resilience of peatlands.

Lithology, topography, and spatial variability of vegetation moderate fluvial erosion in the south-central Andes

Understanding how tectonics, climate and lithology interact to control fluvial erosion is complicated because these factors are spatially-variable and they may not be well-represented by mean values. We address these complications using eight new and 54 published 10Be catchment-wide fluvial erosion rates from the south-central Andes. We assess how tectonics, climate, lithology, and topography control erosion through bivariate and multivariate Bayesian regression analysis. We first compare catchment-wide mean values of independent variables compared to other summary statistics and find that metrics that capture extreme values (e.g., 90th percentile) and spatial variability (e.g., 90th minus 10th percentile) produce stronger correlations. This suggests that catchment-wide means may oversimplify the roles of tectonics, climate, and lithology in influencing erosion rates. We find that the overall variability of erosion rates in the south-central Andes is best explained by a combination of lithologic resistance and spatial variability in both vegetation (using the normalized difference vegetation index, NDVI) and topography (using specific stream power). Despite poor bivariate correlations, both lithologic resistance and spatial variability of specific stream power are significant regressors in our multivariate modeling. Lithology influences the relationship (i.e., linearity) between topography and erosion rates. Spatial variability of NDVI produces the strongest correlation with erosion rates of any of the variables we consider. Hence, spatial variability of NDVI both accounts for potential non-uniform vegetation responses to climate and also incorporates the role of both humid climates (high 90th percentile) and large bare regions (low 10th percentile) within a single catchment.

Attribution of climate and human activities to vegetation change in China using machine learning techniques

A series of policies and laws have been implemented to address climate change impacts in China since the 1980s. One of the most notable policies is ecological restoration engineering. However, there are many environmental factors that affect vegetation in the ecological restoration engineering zones. The relationships among different factors cannot be explained well by traditional statistical methods due to the existence of hidden non-linear features. Moreover, it is difficult to adopt threshold methods to accurately define vegetation areas fully, or to quantitatively analyze and assess the effects of climate factors and human activities on vegetation changes. The objective of this study was to determine vegetation area and distribution using Landsat TM/ETM/OLI images combined with a support vector machine (SVM) classification model. We analyzed the dynamic characteristics of vegetation area and greenness (NDVI, Normalized Difference Vegetation Index) in China's ecological restoration engineering zones from 1990 to 2015. Based on random forest regression (RFR) with a residual analysis method, the contributions of meteorological factors and human activities to vegetation greenness changes were quantitatively evaluated. Vegetation area and NDVI changed significantly in the study areas, increasing by more than 50% and 40%, respectively, from 1990 to 2015. Temperature, sunshine hours, and precipitation impacted vegetation greenness, which caused NDVI fluctuations in specific years. However, the NDVI increase was difficult to explain fully with meteorological factors. Using cross-validation, we predicted about 80% of the observed NDVI variation occurring from 1984 to 1994. Nine meteorological factors were related to vegetation growth, of which the average temperature, minimum temperature, maximum temperature, and average relative humidity were most critical. The combined effect of the nine climatic factors contributed less to NDVI increase than human activities. Human activity was the most important factor associated with NDVI increase, with contributions of more than 100% in most study areas. Human activities derived from national or local policies had large impacts on vegetation changes. The methods and results of this study can help to understand vegetation changes observed in ecological zones and provide guidance for evaluating ecological restoration policies.

Evaluating the Migration Mortality Hypothesis Using Monarch Tagging Data

The decline in the eastern North American population of the monarch butterfly population since the late 1990s has been attributed to the loss of milkweed during the summer breeding season and the consequent reduction in the size of the summer population that migrates to central Mexico to overwinter (milkweed limitation hypothesis). However, in some studies the size of the summer population was not found to decline and was not correlated with the size of the overwintering population. The authors of these studies concluded that milkweed limitation could not explain the overwintering population decline. They hypothesized that increased mortality during fall migration was responsible (migration mortality hypothesis). We used data from the long-term monarch tagging program, managed by Monarch Watch, to examine three predictions of the migration mortality hypothesis: 1) that the summer population size is not correlated with the overwintering population size, 2) that migration success is the main determinant of overwintering population size and 3) that migration success has declined over the last two decades. As an index of the summer population size, we used the number of wild-caught migrating individuals tagged in the U.S. Midwest from 1998-2015. As an index of migration success we used the recovery rate of Midwest tagged individuals in Mexico. With regard to the three predictions: 1) the number of tagged individuals in the Midwest, explained 74% of the variation in the size of the overwintering population. Other measures of summer population size were also correlated with overwintering population size. Thus, there is no disconnection between late summer and winter population sizes. 2) Migration success was not significantly correlated with overwintering population size, and 3) migration success did not decrease during this period. Migration success was correlated with variation in NDVI, a measure of greenness of the southern part of the U.S. used by migrating butterflies for nectar. Thus, the main determinant of yearly variation in overwintering population size is summer population size with migration success being a minor determinant. Consequently, increasing milkweed habitat, which has the potential of increasing the summer monarch population, is the conservation measure that will have the greatest impact.

THE METHOD OF FORECASTING YIELDS ON THE BASIS OF REMOTE SENSING DATA OF HIGH RESOLUTION ON THE EXAMPLE OF WHEAT HAS BEEN SUGGESTED

Developed a method of interpreting the results of remote monitoring in the form of values of vegetation (stress) indices in the expected yields has been developed. The studies were carried out in 2019 in the Kiev region at the production site of winter wheat crops in the separate division of the National Research and Training Institute of Ukraine “Agronomic Experimental Station”. Remote monitoring was performed on 06/25/2019 using the SlantRange 3p multispectral sensor complex mounted on a UAV. The flight altitude was 100 m. Both standard indices such as the NDVI and Chlorophyll index variations as well as the stresses of own production proposed by Slantrange were calculated: Stress, Vegetation fraction, Yield potential. Separately, we examined the output directly from the spectral channels, which were obtained from a wreath of images of SlantView’s proprietary software. The calculations were performed in MathCad software, where the image was considered in the form of a matrix. Harvest accounting was carried out using John Deere combines, which monitored every second with the establishment of positioning by satellite navigation system. False results were removed from the results of ground monitoring due to errors in the sensor equipment, incomplete use of the header width. It was found that a comparison of the yield of winter wheat and the conditions of these plants 2 months before the indicated procedure according to the results of spectral analysis using UAVs made it possible to establish a relationship between the quantitative characteristics of yield and divisions of stress indices. Of the indices studied, the best result of a linear approximation of the experimental dependence with a determination coefficient of 0.845 between yield and the numerical value of the spectral characteristic was shown by the Stress index developed by SlantRange. The best sensitivity was obtained using the Vegetation index. Fraction, also proposed by SlantRange, making it also promising for crop forecasting.

Associations detected between measures of neighborhood environmental conditions and human microbiome diversity

While emerging research suggests urban green space revegetation increases soil microbiota diversity and native plant species affect skin microbiome diversity, there is still a paucity of knowledge on relationships between neighborhood environmental conditions and the human microbiome. This study leveraged data on human microbiome samples (nose, mouth, rectum) taken at autopsy at the Wayne County Medical Examiner's Office (2014–2015). We evaluated relationships between the microbiome and five measures of environmental conditions (NDVI standard deviation, NDVI mean, percent trees, percent grassland and soil type) near the home of 126 decedents. For the rectum microbiome, NDVI sd had negative, significant associations with diversity (ASVs β = −0.20, p = 0.045; Faith PD β = −0.22, p = 0.026). In contrast, while insignificant, there were consistent, positive associations between diversity and NDVI sd for the mouth microbiome (ASVs β = 0.09, p = 0.337, Faith PD β = 0.14, p = 0.149, Shannon diversity β = 0.14, p = 0.159, Heip's evenness β = 0.11, p = 0.259) and a significant association for the nose microbiome (eigenvalues 3 β = 0.18, p = 0.057). We found consistent, significant, negative associations between percent grassland and diversity of the nose microbiome (ASVs β = −0.25, p = 0.008, Faith PD β = −0.25, p = 0.009, Shannon diversity β = −0.17, p = 0.062). For the mouth microbiome, we found a small effect of percent trees on diversity (eigenvalues 1 β = −0.08, p = 0.053). Clay loam soil was negatively (eigenvalues 2 β = −0.47, p = 0.053) and positively associated (eigenvalues 3 β = 0.65, p = 0.008) with rectum microbiome diversity, compared to loam soil. There was no potential indicator taxon among NDVI quartiles. These findings may be relevant for urban planning and management of urban outdoor spaces in ways that may support healthy human microbiomes. Still, future research is needed to link variation in NDVI, vegetation, plant and/or soil microbe diversity and to confirm or negate our findings that environmental conditions may have contrasting influence on the microbiome of the rectum versus the nose and mouth and that grasslands affect the nose microbiome.

Monitoring city green zones using GIS technologies: An example of Tashkent city, Uzbekistan

Increasing green zones can be a suitable option to meet the challenges of growing cities. City authorities in developing countries face a lack of reliable data on the status of urban trees and forestry. The objective of this study was to carry out a spatio-temporal analysis of the dynamics of urban green zones of Tashkent City using Landsat satellite images taking into account climatic changes. For this, we used methods of laying test plots in assessing tree-shrubbery vegetation, a technique for expert interpretation of land cover objects from satellite images of medium spatial resolution, and mathematical statistics. The statistical analyses showed that the temperature regime has the greatest degree of influence on the dynamics of NDVI, i.e. the onset of the growing season corresponds to an increase in temperature. Precipitation, in view of its unstable and uneven distribution in the study area, has a weak negative relationship with the phytomass of green zones. Spatial analysis revealed an average degree of variability of NDVI in the green areas of the city for the studied period of 2000-2011.

Temporal annual and seasonal NDVI variation of Sagarmatha national park, Nepal and its relationship with precipitation and soil moisture

Temporal annual and seasonal NDVI variation of Sagarmatha national park, Nepal and its relationship with precipitation and soil moisture

Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin

Abstract The detection and attribution of vegetation changes is a prerequisite for vegetation restoration and management. In arid and semi-arid areas, natural and anthropogenic factors interact to influence vegetation change, making it challenging to disentangle the contributions of driving forces. Here we used NDVI as an indicator of vegetation condition and analyzed its spatial and temporal changes in the middle reaches of the Heihe River Basin from 2000 to 2015. Then we applied the Geodetector method, a robust spatial statistics approach, to quantify the effects of natural and anthropogenic factors on NDVI changes. NDVI across the study area showed a significant increasing trend from 2000 to 2015. Both natural and anthropogenic factors were identified as significant driving forces of NDVI change, and the factors, land use conversion type, mean annual precipitation and soil type, caused the greatest influence. The explanatory power of a single factor was often enhanced when it interacted with other factors. We also found that influencing factors often correlated with NDVI changes in a non-linear way. Our research highlights that the Geodetector method is an effective way to disentangle the complicated driving factors of vegetation change, and our results is useful for projecting vegetation change under future environmental change and taking measures to prevent and mitigate land degradation in drylands.

Land Cover Mapping in Cloud-Prone Tropical Areas Using Sentinel-2 Data: Integrating Spectral Features with Ndvi Temporal Dynamics

Accurate remote sensing and mapping of land cover in the tropics remain difficult tasks since data gaps and a heterogenic landscape make it challenging to perform land cover classification. In this paper, we proposed a multi-feature classification method to integrate temporal statistical features with spectral and textural features. This method is designed to improve the accuracy of land cover classification in cloud-prone tropical regions. Sentinel-2 images were used to construct an NDVI stack for a time-series statistical analysis to characterize the temporal variance of land cover. Two statistical indices were calculated and used to represent the variation in annual vegetation. These indices included the mean (NDVI_mean) and coefficient of variation (NDVI_cv) for the NDVI time series. The temporal statistical features were then integrated with spectral and textural features extracted from high-quality Sentinel-2 imagery for Random Forest classification. The performance and contribution of different combinations were assessed based on their classification accuracies. Our results show that the time-series statistical analysis is an effective way to represent land cover category information contained in annual NDVI variance. The method uses clear pixels from dense low-quality images to obtain the NDVI statistical characteristics, thus, to reduce the influence of random factors such as weather conditions on single-date image. The addition of NDVI_mean and NDVI_cv can improve the separability among most types of land cover. The overall accuracy and the kappa coefficient reached values of 0.8913 and 0.8514 when NDVI_mean and NDVI_cv were integrated. Furthermore, the time-series statistical analysis has less stringent requirements regarding image quality and features a high computational efficiency, which shows its great potential to improve the overall accuracy of land cover classification at regional scales in cloud-prone tropical regions.

Response of Vegetation to Changes in Temperature and Precipitation at a Semi-Arid Area of Northern China Based on Multi-Statistical Methods

Hydrothermal and climatic conditions determine vegetation productivity and its dynamic changes. However, the legacy effect and the causal relationships between these climatic variables and vegetation growth are still unclear, especially in the dry regions. Based on multi-statistical methods, including bivariate correlation analysis and composite Granger causality tests, we investigated the correlation, causality, and lag length between temperature/precipitation and the vegetation growth (Normalized Difference Vegetation Index, NDVI) in three typical sub-watersheds in the Luanhe River Basin, China. The results show that: (1) Precipitation and temperature are the Granger causes of NDVI variation in the study catchment; (2) temperature and precipitation are not strictly positively correlated with NDVI during growing seasons along with the whole sequence, and excessive warmth and precipitation inhibits vegetative growth; (3) the lag length of vegetation growth in response to temperature/precipitation was shorter in agriculture areas (~2 months) than the forest-dominant area, which have indicated 3–4 months lag length; and (4) anthropogenic disturbance did not result in notable negative effects on vegetation growth at the Luanhe River Basin. Our study further suggests that use of these multi-statistical methods could be a valuable approach for comprehensively understanding the correlation between vegetation growth and climatic variations. We have also provided an avenue to bridge the gaps between stationary and non-stationary sequence, as well as to eliminate pseudo regression problems. These findings provide critical information for developing cost-efficient policies and land use management applications for forest conservation in arid and semi-arid area.

Vegetation dynamics based on NDVI in Yangtze River Basin (China) during 1982-2015

Knowledge of vegetation dynamics is important for the sustainability of natural resources and understanding the changes in ecosystems and its impact to earth’s environment. We have carried out analysis of GIMMS (Global inventory modelling and mapping studies) NDVI3g (third generation normalized difference vegetation data for the period 1982-2015. Based on the Linear Regression (LR) analysis, the Mann-Kendall (MK) test with Sen’s slope estimator and Kriging interpolation method, we have investigated the spatiotemporal variations of vegetation NDVI in the Yangtze River Basin (YRB). The results show pronounced increase in the annual mean NDVI at the rate of 0.01/10yr during 1982-2015, with significant turning point (TP) around 1994. The spatial distribution of the annual mean NDVI reasonably increased in the northern, eastern and south-western YRB, while decreased in the Yangtze River Delta (YRD) and parts of the southern YRB during 1982-2015. Pronounced change in NDVI trend is found in different seasons, for example, the increasing trend during spring (0.02/10yr) and in autumn (0.02/10yr) was higher compared to winter (0.006/10yr) and summer (0.002/10yr) seasons.

西南山地流域NDVI变化特征及降水敏感性——以贵州沅江流域为例

西南山地流域NDVI变化特征及降水敏感性——以贵州沅江流域为例

Assessment of climate impact on vegetation dynamics over East Africa from 1982 to 2015

Located across the equator, the East Africa region is among regions of Africa which have previously known the severe vegetation degradation. Some known reasons are associated with the climate change events and unprofessional agricultural practices. For this purpose, the Advanced Very High Resolution Radiometer (AVHRR) version 3 NDVI (NDVI3g) and Climate Research Unit (CRU) datasets for precipitation and temperature were used to assess the impact of climate factors on vegetation dynamics over East Africa from 1982 to 2015. Pearson correlation of NDVI and climate factors were also explored to investigate the short (October - December) rainy seasons. The phenological metrics of the region was also extracted to understand the seasonal cycle of vegetation. The results show that a positive linear trend of 14.50 × 10−4 for mean annual NDVI before 1998, where as a negative linear trend of −9.64 × 10−4 was found after 1998. The Break Point (BP) was obtained in 1998, which suggests to nonlinear responses of NDVI to climate and non-climate drivers. ENSO-vegetation in El-nino years showed a weak teleconnection between ENSO and vegetation growth changes of croplands. Also, the analyzed correlations on NDVI data resulted to the higher correlation between NDVI and precipitation than that with temperature. The Hurst exponent result showed that about, 18.63% pixels exhibited a behavior, typical of random walk (H = 0.5) suggesting that NDVI growth changes may eventually persist, overturn or fluctuate randomly in the future depending on the drivers. Vegetation trends with sustainable (unsustainable) trends were 36.8% (44.6%). Strikingly, about 20% of the total vegetated area showed unsustainable trend from degradation to amelioration. More so, results reveal that the vegetation of the croplands (non-croplands) over East Africa changed insignificantly by 6.9 × 10−5/yr (5.16 × 10−4/yr), suggesting that non-croplands are fast getting reduced Nonetheless, the NDVI growth responses to monthly and seasonal changes in climate were adjudged to be complex and dynamic. Seasonally, the short rainy season showed the higher variability in NDVI than the long rainy season. Also, the DJF, MAM and SON seasons are strongly driven by precipitation variation effect of ENSO versus NDVI series.