Normalized Difference Vegetation Index Patterns Research Articles

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

An integrated simulation framework for NDVI pattern variations with dual society-nature drives: A case study in Baiyangdian Wetland, North China

Due to climate change and socio-economic development, the growth and distribution of wetland vegetation have been destroyed, resulting in a degradation trend of wetland ecosystem. It is critical to simulate variations in the normalized difference vegetation index (NDVI) patterns under changing environments to guide targeted restoration and protection. Considering that current NDVI pattern simulation focused only on the linear driving forces of nature or society and had a low simulation efficiency, an integrated simulation framework with dual society-nature drives was proposed. In the framework, the spatial and NDVI patterns were simulated by system dynamics-future land use simulation (SD-FLUS) and random forest (RF) models, respectively. Baiyangdian Wetland in North China was taken as a case study, and its typical vegetation—reed—was taken as the simulation object. The NDVI patterns were simulated under the effects of socio-economic development, climate change and ecological protection policies. The results showed that the spatial patterns were affected by social factors, while the NDVI patterns were affected by natural factors. The spatial aggregations of the NDVI were affected by social and natural factors. Besides, the growth space of reeds was easily encroached under the existing development and protection policies. If ecological protection policies were implemented, the reed area would be restored, but the growth status would be poorer, requiring further protection measures and more attention to the growth status of reeds in sensitive areas. This study provides a new research perspective for simulating NDVI patterns and scientific supports for refined management of wetland vegetation.

Spatial and temporal variation in normalized difference vegetation index of vegetation in Liaoning Province from the perspective of ecogeographic zoning

Normalized difference vegetation index (NDVI) is an important indicator reflecting vegetation cover and growth status. It is of significance for regional ecological conservation and natural resource management to investigate its spatial and temporal variation trends and response to ecological factors. We divided Liaoning Province into three ecological geographical regions, including northwest agro-pastoral zone, central agricultural zone, and eastern agroforestry zone. Based on remote sensing, vegetation, climate, topography and human activities, we used trend analysis and geographic probe model to examine the spatial and temporal trends of NDVI in Liaoning Province, and analyzed the intensity and interaction mechanism of each driver on the spatial distribution pattern of NDVI. The results showed that the annual average NDVI in Liaoning Province from 2001 to 2020 was 0-0.92, showing a distribution pattern of high in the east and low in the west, high in the inland and low in the coastal land. The overall trend of vegetation cover was increasing, and the NDVI increasing areas were mainly concentrated in the northwest agro-pastoral zone and the eastern agroforestry zone, the NDVI reduction areas were mainly concentrated at the border between the central agricultural zone and the eastern agroforestry zone, as well as in the coastal area of the eastern agroforestry zone. The annual average NDVI change varied among the three ecological-geographic zones. The NDVI of the northwest agro-pastoral zone from 2001 to 2020 were generally low, but showed a fluctuating trend of slow increase. The NDVI of the eastern agroforestry zone was high overall, and the interannual variation of NDVI was generally stable. The distribution of high and low NDVI in the central agricultural zone was staggered, and the interannual variation of NDVI showed a decreasing trend. Natural factors were the key drivers of NDVI changes in the three ecogeographic zones, with cumulative temperature and precipitation having the greatest influence. The interactions between the factors were all mutually and nonlinearly enhanced.

Spatiotemporal Variation and Pattern Analysis of Air Pollution and Its Correlation with NDVI in Nanjing City, China: A Landsat-Based Study

The rapid socio-economic development and urbanization in China have led to a decline in air quality. Therefore, the spatial and temporal distribution patterns of urban air pollution, as well as its formation mechanisms and influencing factors, have become important areas of research in atmospheric environment studies. This paper focuses on nine monitoring sites in Nanjing, where concentration data for six air pollutants and vegetation index data were collected from 2013 to 2021. The objective of this study is to investigate the changes in air pollutants and vegetation index over time and space, as well as their relationship with each other, and to assess the social and environmental impacts of air pollution. The findings reveal a spatial distribution pattern of air pollution in Nanjing that exhibits significant variability, with pollutant concentrations decreasing from the city center towards the surrounding areas. Notably, the main urban area has lower air quality compared to the peripheral regions. The results obtained from best-fit linear regression models and correlation heatmaps demonstrate a strong correlation (coefficient of determination, R2 > 0.5) between the normalized difference vegetation index (NDVI) and pollutants such as SO2, NO2, PM2.5, PM10, and O3 within a radial distance of 2 km from the air pollutant monitoring sites. These findings indicate that NDVI can be an effective indicator for assessing the distribution and concentrations of air pollutants. Negative correlations between NDVI and socio-economic indicators are observed under relatively consistent natural conditions, including climate and terrain. Therefore, the spatiotemporal distribution patterns of NDVI can provide valuable insights not only into socio-economic growth but also into the levels and locations of air pollution concentrations.

Exploring vegetation trends and restoration possibilities in Pakistan by using Hurst exponent.

Vegetation cover change and its interaction with climate are significant to study as it has impact on ecosystem stability. We used the Normalized Difference Vegetation Index (NDVI) and climatic factors (temperature and rainfall) for investigating the relationship between vegetation and climate. We also traced spatiotemporal changes in the vegetation in Pakistan from 2000 to 2020; we used the Hurst exponent to estimate future vegetation trends in Pakistan. Our results show an increase in vegetation throughout Pakistan, and the Punjab Province is showing the highest significant vegetation trend at 88.51%. Our findings reveal that the response of vegetation to climate change varies by region and is influenced by local climatic conditions. However, the relationship between rainfall and annual NDVI is stronger than the temperature in the study area-Pakistan. The Hurst exponent value is above 0.5 in all four provinces, that is, the indication of consistent vegetation trends in the future. The highest values are observed in Punjab and Khyber Pakhtunkhwa (KPK). In the Punjab Province, 88.41% of the area showed positive development, with forests in particular showing a significant positive effect on land use classes. On the other hand, the Sindh Province has the highest negative result at 2.87%, with urban areas showing the highest negative development. To sum up, the NDVI pattern and change attribute suggest vegetation restoration in Pakistan.

On classification of Sentinel-2 satellite images by a neural network ResNet-50

Various combinations of neural network parameters and sets of input data for satellite image classification are considered in the article. The training set is completed with a NDVI (normalized difference vegetation index) and local binary patterns. Testing of classifiers created on a different number of epochs and samples is carried out. Values of the neural network hyperparameters are determined that allow a classification accuracy of 0.70 and an F-measure of 0.65 to be achieved. Separation into classes with similar spectral characteristics is shown to offer low classification quality at different parameters and input data sets. Additional information is required. For example, for forests to be divided into more detailed classes, one needs to employ classifiers that use images from different seasons and vegetation periods. In addition, the training set needs to be extended to take into account various natural zones, soils, etc.

Attribution of climate change and human activities to vegetation NDVI in Jilin Province, China during 1998–2020

Vegetation is among the key elements of ecosystems, and the normalized difference vegetation index (NDVI) is among the most frequently used tools for studying changes in regional vegetation dynamics. Studying these changes and their drivers is essential for understanding the interactions between vegetation and ecosystems; therefore, here, we analyzed the spatial and temporal patterns of NDVI in Jilin Province and their influencing factors. The correlation between NDVI and climatic factors was analyzed using Pearson’s correlation analysis. Additionally, we explored the correlation between NDVI and human activity using geographically weighted regression analysis. The results were as follows: (1) From 1998 to 2020, although the temporal trends of NDVI for different vegetation types in Jilin Province showed some differences in general, they all showed an increasing trend. (2) The spatial trends of NDVI in Jilin Province ranged from − 0.031 to 0.046. The NDVI values exhibited a trend toward growth in most regions. (3) Climate change factors are more important than human activity factors for improving vegetation. (4) Among the climatic factors, the negative correlation between temperature and NDVI was stronger than the positive correlation, while the positive and negative correlations between precipitation and NDVI were equally strong. (5) Human activity factors such as GDP and population have mostly negative effects on regional NDVI, whereas land-use type shifts have mostly positive effects. The results of this study contribute to a deeper understanding of the drivers of vegetation change. Additionally, some discussions can provide theoretical references for ecological management and sustainable development in Jilin Province.

Assessing the Within-Field Heterogeneity Using Rapid-Eye NDVI Time Series Data

(1) Background: The relation between the sub-field heterogeneity of soil properties and high-resolution satellite time series data might help to explain spatiotemporal patterns of crop growth, but detailed field studies are seldom. (2) Methods: Normalized Difference Vegetation Index (NDVI) data derived from satellite time series images were used to identify changes in the spatial distribution of winter triticale (×Triticosecale), winter rye (Secale cereale) and winter barley (Hordeum vulgare) growth (2015 to 2020) for a field in north-eastern Germany. NDVI patterns (quartiles) that remained persistent over time were identified and it was tested if spatially heterogeneous soil characteristics such as water holding capacity and altitude could explain them. (3) Results: A statistically significant relationship between elevation and soil classes with NDVI values was found in most cases. The lowest NDVI quartiles, considered as representing the poorest growth conditions, were generally found in the depressions with the lowest water holding capacity. These areas showed temporally stable spatial patterns, especially during the pre-harvest period. Over the six-year period, up to 80% of the grid cells with the lowest NDVI values were spatially consistent over time. Differences in the climatic water balance were rather low but could contribute to explaining spatial patterns, such as the lower clustering of values in the wettest year. (4) Conclusions: High-resolution satellite NDVI time series are a valuable information source for precise land management in order to optimize crop management with respect to yield and ecosystem services.

SPATIAL ANALYSIS OF URBAN ECOLOGICAL CONDITION IN DENPASAR CITY, INDONESIA USING LANDSAT 9 IMAGERY

Denpasar City is the capital of the province of Bali where anthropogenic activities are most intensive on the island. The study of urban ecological conditions is vital for planning and monitoring the ideal condition of environmentally friendly settlements in the city of Denpasar. This research aims to 1) examine the temperature-related ecological conditions of micro-scale cities based on urban thermal field variance index (UTFVI) values and 2) assess the distribution pattern of NDVI and LST in the city of Denpasar derived from Landsat 8 satellite data. The land surface temperature (LST) and the normalized difference vegetation index (NDVI) were both derived using data from Landsat 9, which was also utilized (NDVI). Following that, UTFVI was categorized using LST. The results show that the greater the UTFVI value, the worse the state of urban ecological degradation and the greater the intensity of UHI, and vice versa. A low UTFVI value of less than 0.005 is spatially distributed throughout all districts and is characterized as an area with good to very good urban ecological quality. This category predominates in the South and East Denpasar Districts, the northern region of the North Denpasar District, and the southern region of the West Denpasar District.

Vegetation Change and Eco-Environmental Quality Evaluation in the Loess Plateau of China from 2000 to 2020

Vegetation change and ecological quality of the Loess Plateau (LP) are directly related to ecological protection and high-quality development of the Yellow River Basin. Based on LP ecological zoning and multisource remote sensing data, we analyzed vegetation change and its relationship with climate, terrestrial water storage (TWS), and land use/cover change from 2000 to 2020, using the normalized difference vegetation index (NDVI), fraction of vegetation cover (FVC), and net primary productivity (NPP). And ecological environmental quality was evaluated based on the remote sensing ecological index (RSEI). The results showed that the spatial distribution pattern of NDVI, FVC and NPP decreased from southeast to northwest in the LP as a whole. Vegetation in the LP recovered significantly, and NDVI, FVC, and NPP showed significant increases of 35.66%, 34%, and 54.69%, respectively. The average NDVI and FVC in the earth–rocky mountainous region and river valley plain region (Area D) were the highest, but the growth rate was the slowest. The average NDVI, FVC, and growth rates in the loess hilly and gully regions (Area B) were slightly higher than those in the loess sorghum gully region (Area A). The average NDVI, FVC, and NPP in the sandy land and agricultural irrigation regions (Area C) were the lowest but showed significant increase. RSEI in most LP areas changed from poor to medium, increasing by 43.45%. Precipitation is the basic factor affecting vegetation cover pattern, with the increase (40.79 mm/10a) promoting vegetation restoration in the LP. Vegetation restoration lost much TWS (−0.6 mm/month), and Area D had the highest average NDVI, FVC, and NPP but the largest TWS loss. Anthropogenic land use/cover change (LUCC) (decrease in cultivated land and unused land; increase in forest, grassland, and construction land) is the primary factor affecting LP vegetation change. This study provides a scientific reference for further vegetation restoration in the LP.

Spatial-temporal NDVI pattern of global mangroves: A growing trend during 2000–2018

Mangroves are coastal vegetation with high ecological and economic value that are mainly distributed in tropical and subtropical intertidal zones. In the past, they have been degraded by extensive deforestation for agricultural and aquatic land. In recent years, mangroves have been protected and sustainably used through considerable measures of conservation, restoration and afforestation, but the health trends of mangroves during this process are not clear. To identify the mangrove health conditions and dynamics, we investigated the spatial-temporal trends of global mangroves using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) dataset during 2000–2018. The results illustrated that 1) Asian mangroves had the highest NDVI values, especially in Southeast Asia (0.80), while the average NDVI of African mangroves was the lowest (0.67). NDVI values higher than 0.80 were mainly located in Southeast Asia and South America, which accounted for 24.0 % and 7.1 % of the global mangrove area, respectively. 2) Globally, the proportion of mangrove forests that increased significantly (23.6 %, p value < 0.05) was approximately twice as large as the significant decrease (10.7 %, p value < 0.05). Asia, where mangroves are widespread, accounts for nearly half of the world's significant increase (10.8 %) and decrease (4.6 %). Generally, the annual average NDVI for global mangroves exhibited a slow increasing trend from 2000 to 2018 (p value = 0.13). 3) The global mangrove NDVI showed a positive correlation with precipitation (Rprep = 0.79, p value < 0.01) and temperature (Rtemp = 0.37, p value < 0.01), while it was inhibited by sea surface salinity (Rsss = −0.45, p value < 0.01) on a scale of 1° of latitude. 4) The results of the overall growth trend of mangroves indicated that global mangrove conservation appeared to achieve initial success, but direct or potential factors, such as salinity stress, natural disasters, small-scale deforestation, construction of coastal facilities, and sea level rise, still threaten the survival of mangroves, leading to a decline in their health status. This study provides information on the health status of mangrove ecosystems and can assist in formulating subsequent conservation and management measures.

Effect of rainfall variability on tree phenology in moist tropical deciduous forests.

Plants in their life cycle go through a series of life processes. These phenological changes are influenced by different climatic conditions. Abiotic factors like temperature, precipitation, and photoperiodism affect the onset and offset of particular phenophase in the plant periodic cycle. In this study, we tested the influence of precipitation on the forest phenology at two sites of Dudhwa National Park (DNP), Uttar Pradesh and Simlipal National Park (SNP), Odisha, India. DNP and SNP receive an annual average rainfall of 1093.5mm and 1500mm, respectively, of which most rainfall (~ 90%) occurs during June-September. Normalized Difference Vegetation Index (NDVI) was measured for 2years 2015 and 2018, with 2015 being a drought year and 2018 being a normal rainfall year. NDVI was analyzed at different temporal scales of months, season, and years using the t test (Welch's two-tailed) and General Linear Mixed Model (GLMM). Effect of drought (2015) and normal (2018) rainfall year was not significant at both the sites, whereas season, year*season interaction, season*rainfall interaction, and year*season*rainfall interaction were found significant at DNP (P < 0.05, ICC = 0.68, marginal R2 = 0.81; conditional R2 = 0.94). At SNP, rainfall, year, season, and their interaction were non-significant, whereas several months showed a significant effect on the NDVI values for both sites. Winter and monsoon season in DNP, and post-monsoon season in SNP, showed a significant effect on the NDVI patterns. Thus, the effect of precipitation stress in the deciduous forests was evident at small intervals of observation. Tree phenology compensated for differences when observed from a higher temporal scale of a year. There existed a mechanism in trees to tide over adverse conditions and maintain the phenology over longer intervals of time. The resilience and vulnerability of such forest ecosystems against abiotic factors and extreme events would be instrumental in climate change adaptation strategies. Tree phenology can be used as an indicator of forest health and resilience.

LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data

Vegetation index time-series analysis of multitemporal satellite data is widely used to study vegetation dynamics in the present climate change era. This paper proposes a systematic methodology to predict the Normalized Difference Vegetation Index (NDVI) using time-series data extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS). The key idea is to obtain accurate NDVI predictions by combining the merits of two effective computational intelligence techniques; namely, fuzzy clustering and long short-term memory (LSTM) neural networks under the framework of dynamic time warping (DTW) similarity measure. The study area is the Lesvos Island, located in the Aegean Sea, Greece, which is an insular environment in the Mediterranean coastal region. The algorithmic steps and the main contributions of the current work are described as follows. (1) A data reduction mechanism was applied to obtain a set of representative time series. (2) Since DTW is a similarity measure and not a distance, a multidimensional scaling approach was applied to transform the representative time series into points in a low-dimensional space, thus enabling the use of the Euclidean distance. (3) An efficient optimal fuzzy clustering scheme was implemented to obtain the optimal number of clusters that better described the underline distribution of the low-dimensional points. (4) The center of each cluster was mapped into time series, which were the mean of all representative time series that corresponded to the points belonging to that cluster. (5) Finally, the time series obtained in the last step were further processed in terms of LSTM neural networks. In particular, development and evaluation of the LSTM models was carried out considering a one-year period, i.e., 12 monthly time steps. The results indicate that the method identified unique time-series patterns of NDVI among different CORINE land-use/land-cover (LULC) types. The LSTM networks predicted the NDVI with root mean squared error (RMSE) ranging from 0.017 to 0.079. For the validation year of 2020, the difference between forecasted and actual NDVI was less than 0.1 in most of the study area. This study indicates that the synergy of the optimal fuzzy clustering based on DTW similarity of NDVI time-series data and the use of LSTM networks with clustered data can provide useful results for monitoring vegetation dynamics in fragmented Mediterranean ecosystems.

Mapping stressed wheat plants by soil aluminum effect using C-band SAR images: implications for plant growth and grain quality

Under toxic aluminum (Al) levels in the soil, wheat (Triticum aestivum L.) suffers stress and plant growth is affected. A method for diagnosis of plants is proposed that includes the following as a strategy: to analyze total Al in the soil, employ satellite radar imagery and calculate a vegetation index. The objective of this research, conducted at the field scale, was to explore how radar backscattering coefficients from a winter wheat canopy, combined with the normalized difference vegetation index (NDVI) and geographic information system (GIS) technology, can be used as a mapping tool for the variability of Al-stressed canopies. As a result, an analysis of covariance showed significant differences, and the lowest plant height was obtained at a high level of soil Al, as well as the minimum grain weight and magnesium content. It was found that a simple model could be used to estimate plant height from the backscattering coefficient of vertical transmit-vertical receive polarization (σ°VV), with a strong correlation (r − 0.84). In turn, a third-order polynomial regression model (R2 0.70) was proposed to estimate the NDVI from σ°VV. This model provided a good estimate of the NDVI at the stem elongation stage of growth (50 days after sowing). Detected NDVI patterns were associated with variation in canopy stress depending on polarimetric information, which, in turn, was related to soil Al levels. Thus, the maps derived from the model can monitor spatial variability, where NDVI values < 0.68 indicate stressed areas. This study provides guidance for in-season stress spatial variability caused by Al.

Understanding the influence of COVID-19 induced lockdown on urban thermal environment of Ranchi city, India

ABSTRACT The objective of the study is to understand the pattern of land surface temperature (LST) and normalized difference vegetation index (NDVI) developed in Ranchi city during Covid-19-induced lockdown (2020) and its comparison with previous years. The study incorporated Landsat 8 (Operational land imager) data from United States Geological Survey and air temperature and relative humidity data from power.larc.nasa.gov for the years 2017, 2019 and 2020. The results exposed a drastic change in the LST and NDVI pattern of the city. The mean LST of the city during April has declined from 39.80°C in 2017 to 32.38°C in 2020. Similarly, the mean LST of May also declined from 38.41°C in 2017 to 34.84°C in 2020. On the contrary, the city experienced an ascending growth of NDVI from 0.24 to 0.26 in April and May 2017 to 0.349 and 0.37 in 2020, respectively. Additionally, the city portrays declining air temperature with enhanced relative humidity. Ranchi city also exhibited relatively maximum area under ecologically excellent category in the year 2020 and reduced area under ecologically the worst category based on urban thermal field variance index. Thus, reduced temperature with augmented humidity and NDVI developed a healthy urban environment.

陕西省植被覆盖时空变化及其对极端气候的响应

基于2001-2018年MODIS NDVI数据，从生态分区视角分析陕西省归一化植被指数（NDVI）的时空变化特征，并结合该地区31个气象站点日值数据，探讨NDVI对极端气温和极端降水指数的响应特征。结果表明：（1）陕西省及其各生态区的NDVI变化均显著上升，整体呈南高北低的分布特点，其中秦巴山地落叶与阔叶林生态区（IV）NDVI值最高为0.86，陕北北部典型草原生态区（I）NDVI值最低为0.38。（2）年际尺度上，陕西省NDVI与极端气温暖极值（暖夜日数）和极端降水指数总体呈显著正相关（P<0.05），在陕西省北部NDVI变化主要受极端降水的影响，南部则对极端气温的敏感度更高。（3）多年月尺度上，各生态区NDVI对极端气温冷极值（最低气温、日最低气温的极低值和日最高气温的极低值）和极端气温暖极值（最高气温、日最低气温的极高值和日最高气温的极高值）存在明显的滞后性，滞后时间多为3个月；与极端降水指数（单日最大降水量和连续5日最大降水量）的滞后时间为2个月，说明陕西省内NDVI对极端气候的响应具有显著的滞后效应。;Vegetation plays an important role in the terrestrial ecosystem. Under the background of global warming, the response of vegetation cover to climate change has become an important research field. Normalized Difference Vegetation Index (NDVI) is commonly used to study the response of vegetation change to temperature and precipitation on a regional scale. In recent years, the frequent occurrence of extreme climate events has increased the risk and vulnerability of terrestrial ecosystems and endangered ecological security. Shaanxi province is located in the hinterland of China, with a large north-south span and obvious regional differences in hydrothermal conditions and surface ecological environment. Therefore, it is necessary to study the dynamic change of NDVI and its response to extreme climate, which can provide theoretical evidence of ecosystem evolution and ecological environment conservation in Shaanxi Province. Based on MODIS NDVI data from 2001 to 2018, the spatio-temporal variation pattern of NDVI was analyzed from the perspective of different ecological areas in Shaanxi Province. The response characteristics of NDVI to extreme temperature and precipitation indices were also discussed by using the daily meteorological observations from 31 stations in this region. The results showed that (1) the variation of NDVI increased significantly and the spatial difference of NDVI value was obvious in Shaanxi Province and its ecological areas. The high value of NDVI distributed in the south, whereas the low value of NDVI distributed in the north. The decreasing order of the value of NDVI of the different ecological areas was as follows:ecological region of deciduous and broad-leaved forest in Qinba Mountain (IV) > agricultural ecological area of Fenwei Basin (III) > agricultural and grassland ecological region of the Loess Plateau (II) > typical grassland ecological area in northern Shaanxi (I). Meanwhile, the lowest value of NDVI was 0.38 and the highest value was 0.86. (2) The correlation between NDVI and extreme climate indices showed that annual NDVI in each ecological area was significantly positively correlated with TN90p and extreme precipitation indices. However, extreme precipitation had an important impact on vegetation growth in the north of Shaanxi Province, and extreme temperature was the main factor in the south. (3) On the monthly scale, the NDVI had a significant lag time of 3 months with extreme temperature indices, including TMIN_mean, TN_n, TX_n, TMAX_mean, TN_x, and TX_x, while had a lag time of 2 months with extreme precipitation indexes in each ecological area. In general, the results effectively revealed the response of vegetation to climate fluctuations in Shaanxi Province.

Influence of flight hours on spectral behavior of rice (Oryza Sativa L.) and soybean (Glycine Max L.) cultivars in images obtained by remotely piloted aircraft system

The research aimed to evaluate the spectral behavior of remotely piloted aircraft system (RPAS) images taken of agricultural crops during the day to answer the following hypotheses: (1) there are no significant differences between spectral reflectance information obtained through RPAS images and data from spectroradiometry; (2) the survey time can be defined as a limiting factor in obtaining accurate reflectance data; and (3) it is possible to characterize homogeneous observation windows throughout the day. With regular intervals of 1 h, 12 surveys were made in rice and soybean cultivars, using a Sequoia® camera, embedded in RPAS, and the FieldSpec®3 spectroradiometer, to validate the image spectra. Normalized Difference Vegetation Index (NDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Transformed Chlorophyll Absorption Reflectance Index (TCARI) derived from aerial images were subjected to Kruskal–Wallis and hierarchical cluster tests to define homogeneous observation times. The Sequoia® system made it possible to obtain reflectance data that met the validation criteria, even operating under varying interaction factors during the day, so the flight schedule did not result in loss of information about the vegetation. The hierarchical cluster analysis allowed the detection of temporal patterns of NDVI, OSAVI, and TCARI throughout the day. For rice and soybeans, three more homogeneous observation windows were characterized. In terms of temporal pattern of NDVI, OSAVI, and TCARI, the Kruskal–Wallis analysis was quite sensitive to the inherent variation of the crops throughout the day, so a hypothesis of homogeneous monitoring windows input throughout the day could not be proven.

Remotely sensed vegetation greening along a restoration gradient of a tropical forest, Kibale National Park, Uganda

AbstractRestoration has now emerged as a global priority, with international initiatives such as the “UN Decade on Ecosystem Restoration (2021–2030)”. To fulfill the large‐scale global restoration ambitions, an essential step is the monitoring of vegetation recovery after restoration interventions. This study aimed to evaluate the utility of remotely sensed vegetation indices, using the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), to monitor the progress of forest regeneration across a tropical forest restoration project area in Kibale National Park, Uganda. Using the chronosequence approach, results indicated non‐linear patterns in NDVI and EVI across the first 25 years of recovery. Both NDVI and EVI increased for the first 10 years of forest regeneration. This 'greening' phase could be used as the indicator of the successful onset of forest recovery. In particular, the decline of elephant grass, and the consequent arrival of shrubs and trees, can be detected as an increase in NDVI. Primary forests differed from the 25‐year‐old regenerating forests based on the unique combination of low mean and low seasonal variation in EVI. Our results, therefore, suggest that the long‐term success of forest restoration could be monitored by evaluating how closely the combination of mean, and degree of seasonal variation in EVI, resembles that observed in the primary forest.

Evaluating Vegetation Response to Climate Variability over Japan

Vegetation plays a significant role in the exchange of energy, water and carbon between the atmosphere and land surface, understanding its response to climate variability is of great importance for climate adaptation studies. This study examined Seasonal June-July- August, and December-January-February(JJA and DJF) vegetation response to Temperature(T) and Rainfall(R) variability. Vegetation response to climate dynamics over Japan are still poorly understood, in other to quantify these response spatio-temporal distribution of T and R were investigated, vegetations changes was also accessed utilizing MODIS Normalized Difference Vegetation Index (NDVI) data from 2007-2016(10 years) along with T and R datasets from 1987-2017 (31 years), The NDVI patterns show a checked heterogeneity relating to seasonal variations in climates, our findings further reveals Northern region record an increasing trend in T and R, standard deviation of 0.48, 9.66, with CV of 6.63%, 9.25% respectively were recorded. Also, an increasing trend in T and R was equally observed in the southern region with standard deviation of 0.43, 28.5, by a CV of 2.47% and 15.05. Further analysis revealed critical patterns in the NDVI during DJF months and then afterward NDVI was seen with critical expanding values during the JJA month and diminishing NDVI patterns were seen over similar districts. The result further made it clear that NDVI changes were highly connected to different T and R patterns over the region while seasonal mean NDVI showed a critical increment for JJA in the North and DJA in the south.

Relationships between NDVI, river discharge and climate in the Okavango River Basin region

AbstractThe Okavango River Basin (ORB) is a highly sensitive and biodiverse region in southern Africa whose climate, vegetation and river discharge characteristics are not well understood. This study investigated relationships between rainfall, temperature, Normalized Difference Vegetation Index (NDVI) and river discharge over the region as well as their trends and interannual variability. It is found that spatial patterns of NDVI are closely related to those of rainfall, but less so with temperature at monthly and seasonal time scales. The relationships between NDVI and rainfall/temperature differ north of 18.9°S where rainfall is higher than to its south. Typically, there are lags of 1–2‐months between NDVI and either rainfall or temperature. Also, there are large areas across the region that show significant warming trends in all seasons as well as wetting (mainly in the north). This increasing trend in surface temperature may act to worsen the impacts of extreme events such as severe drought and fire in the region. There is also pronounced interannual variability with significant correlations found with El Niño‐Southern Oscillation (ENSO), the subtropical South Indian Ocean Dipole and the Botswana High for rainfall and temperature, and for NDVI with ENSO. For the Southern Annular Mode, significant correlations were found with rainfall and temperature only in December and April. On longer time scales, focus was also placed on the wet 2006–2013 period relative to much drier 1999–2005 epoch for October–December. The wetter conditions during 2006–2013 appear related to La Niña Modoki conditions and warmer sea surface temperature near Angola as well as regional circulation differences.