Normalized Difference Vegetation Index Relationship Research Articles

Examining the relationship of major air pollutants with land surface parameters and its monthly variation in Indian cities using satellite data

The relationship of land surface parameters with the air pollutants is worth exploring. The present study investigates the relation of Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) with six air pollutants such as Aerosol Index (AI), Carbon monoxide (CO), formaldehyde (HCHO), Sulphur Dioxide (SO2), Nitrogen dioxide (NO2) and ozone (O3) from January to December month of year 2022 in four cities situated at varying climatic zones of India. Except for O3, which exhibits lower concentration in winter and greater in summer, the air pollutants concentration showed lower values during monsoon season and higher during summer and post-monsoon seasons. The relationship of LST, NDBI and NDVI with different air pollutants were found to vary throughout the year in the four cities. The magnitude of correlation coefficient (R) was found greater for AI and NO2 as compared to other pollutants depicting greater impact of land surface parameters on concentration of AI and NO2. The surface urban cool island effect in Bikaner showed strong negative relation of LST with NO2 with magnitude of R greater than 0.41. The surface urban heat island formation in Varanasi showed strong positive correlation with the air pollutants such as AI, CO, NO2 and O3 with magnitude of R value greater than 0.61, 0.31, 0.59 and 0.32 for AI, CO, NO2 and O3, respectively. Even though, the correlation of LST with air pollutants varied with seasons and cities, NDVI showed negative correlation with most of the air pollutants in the cities except in Bikaner where vegetation content is negligible. Thus, increasing the amount of vegetation in a city can improve the air quality by lowering the quantity of air pollutants there.

Coupling mechanism between vegetation and multi-depth soil moisture in arid–semiarid area: Shift of dominant role from vegetation to soil moisture

Soil moisture (SM) is an essential water source for vegetation, while vegetation also exerts importance on SM during succession. It is crucial to investigate the coupling mechanism between SM and Normalized Difference Vegetation Index (NDVI), which can drive terrestrial carbon sinks while maintaining water sustainability. The Granger causality was used to analyze the unidirectional and bidirectional coupling relationship of SM and NDVI in the 0–289 cm soil layers. The windowed cross correlation was utilized to quantify the bidirectional time lag effect. The findings indicate that the vegetation improved significantly (92.47%) in 2001–2021 in the arid–semiarid area, while the SM in all four layers showed a decreasing trend (>50%). In most areas, NDVI was bidirectional dependent on SM. Vegetation dominated the loss of shallow SM (<100 cm) through root uptake and transpiration, whereas deep SM (>100 cm) in turn dominated the vegetation growth, structure and spatial pattern. At the spatial scale, the top layer of SM in the arid area demonstrated less sensitivity to NDVI due to coupling with the atmosphere. Instead, the influence of NDVI on SM in the semiarid area was dominant, so more attentiveness to the consumption of SM by vegetation is needed. The bidirectional legacy effect could be up to 6 months, attributed to water transport distance, vegetation root systems and physiological mechanisms. The correlation reversed when the lag time was extended or the soil layer was deepened, which means that the greening of vegetation will intensify deep soil drying. Differences in functional traits among plants lead to varying responses. The changes of deep SM need to focus on when implementing artificial meadow construction. This study provides a theoretical basis for optimizing ecological engineering projects, which is essential for restoring global drylands ecosystems.

Wheat Varietal Response to Relative SPAD Index (RSI) and Relative Normalized Difference Vegetation Index (RNDVI) under Variable Nitrogen Application and Terminal Heat Stress along with Yield Repercussion

Nitrogen (N) deficiency and heat stress (HS) are major abiotic stresses that affect the quantity and quality of wheat grains. This study was conducted to examine wheat varietal response to RSI and RNDVI at the anthesis stage and their relationship to yield and yield-related traits under variable N supply and terminal heat stress. Twelve wheat varieties were evaluated in 2016–2017 and 2017–2018 at the National Agricultural Research Centre (NARC), Islamabad, Pakistan. The experiment was divided into three sets, i.e., N120 (120 kg N/ha), N60 (60 kg N/ha) and N0 (0 kg N/ha), based on the nitrogen fertilizer application. The physiological and yield-related parameters were recorded. Mean grain yield for all twelve varieties, averaged from two years of data, ranged between 1655.0 and 3890.1 kg/ha. Maximum RSI (0.99), RNDVI (1.03) and GY (3890.9 kg/ha) were recorded for FSD-08, while AARI-11 showed minimum RSI (0.50), RNDVI (0.56) and GY (1396.40 kg/ha). In the present study, mean CTD was lower, at N0 (3.57 °C), followed by N60 (5.07 °C) and N120 (5.47 °C) on average for the two years of data. The strong positive correlation of RSI and RNDVI with grain yield at R2 = 0.73 and R2 = 0.49 suggest that these parameters can be used as efficient and precise selection criteria for identifying nitrogen-use-efficient wheat varieties under terminal heat-stress conditions. This work will help the researchers to identify and develop nitrogen-use-efficient and thermos-tolerant wheat cultivars by minimizing the negative impacts of heat stress at the anthesis stage.

Land Use Change Impact on Normalized Difference Vegetation Index, Surface Albedo, and Heat Fluxes in Jambi Province: Implications to Rainfall

Jambi covers various land uses with different characteristics related to biogeophysical cycle. Land use plays an important role in the atmosphere-surface interaction and energy balance partition, which influenced rainfall pattern. Two proxies widely used to differentiate various land uses are albedo and normalized difference vegetation index (NDVI). However, study on albedo and NDVI relationship with rainfall in Jambi is still limited. This study aims to analyze the correlation of NDVI and albedo with rainfall and their distribution in Jambi and Muaro Jambi in 2013 and 2017. The research used Landsat 8 OLI TIRS satellite image data to derived NDVI and albedo, and CHIRPS data for rainfall. A simple linear regression was used to calculate the correlation of NDVI and albedo with rainfall. The results showed that the distribution of albedo for each land use class from the lowest to the highest was forest, plantation, cropland, shrubs, and settlements, respectively. On the contrary, the distribution of NDVI and rainfall is the inverse to albedo. Albedo and NDVI had a strong influence on rainfall through surface energy balance partition. This was indicated by the high R-square between albedo and rainfall (0.99) and between NDVI and rainfall (0.97). Increasing upward latent heat flux from the land surface to atmosphere leads to a rainfall increase. In other words, rainfall may also increase with the decrease in albedo, increase in NDVI, or land use change.

Assessing the Contribution of ECa and NDVI in the Delineation of Management Zones in a Vineyard

Precision fertilization implies the need to identify the variability of soil fertility, which is costly and time-consuming. Remotely measured data can be a solution. Using this strategy, a study was conducted, in a vineyard, to delineate different management zones using two indicators: apparent soil electrical conductivity (ECa) and normalized difference vegetation index (NDVI). To understand the contribution of each indicator, three scenarios were used for zone definition: (1) using only NDVI, (2) only ECa, or (3) using a combination of the two. Then the differences in soil fertility between these zones were assessed using simple statistical methods. The results indicate that the most beneficial strategy is the combined use of the two indicators, as it allowed the definition of three distinct zones regarding important soil variables and crop nutrients, such as soil total nitrogen, Mg2+ cation, exchange acidity, and effective cation exchange capacity, and some relevant cation ratios. This strategy also allowed the identification of an ionic unbalance in the soil chemistry, due to an excess of Mg2+, that was harming crop health, as reported by NDVI. This also impacted ECa and NDVI relationship, which was negative in this study. Overall, the results demonstrate the advantages of using remotely sensed data, mainly more than one type of sensing data, and suggest a high potential for differential crop fertilization and soil management in the study area.

NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987–2018

Forests have become increasingly stressed by climate change, including in Romania over recent decades, but their response to climate dynamics has not yet been analysed in this country. This study aims to investigate, for the first time, recent ecological changes in forests across Romania, in relation to climate dynamics that affected the country from 1987 to 2018. To this end, countrywide remote sensing (Landsat) data were processed for forest boundaries over the 32 years, in order to compute annual (summer season data) Normalized Difference Vegetation Index (NDVI) datasets, which were subsequently investigated as trends using the Mann-Kendall test and Sen's slope estimator. Simultaneously, various climatic data (temperature, precipitation and reference evapotranspiration) were processed through interpolation techniques and via the same two statistical tools, and were subsequently used for exploring the impact of climate change on Romanian forestlands after 1987. The results highlighted general greening (increasing NDVI) trends of forests nationally (65% of all NDVI changes, which total over 30.000 km2 across Romania), which were dominated by widespread positive NDVI trends detected in the Carpathians region of Romania. This general ecological dynamic suggests a possible enhancement in vegetation productivity in the country’s high-altitude areas. Contrasting browning (decreasing NDVI) trends were found for 35% of Romanian forestlands marked by NDVI changes, especially apparent in the Extra-Carpathians (lowland) region, which indicates that in these cases forests were degraded or devitalized. However, the statistical significance of both greening and browning trends is limited across the country. The analysis of climatic trends and of correlations between annual NDVI and climate data indicated that recent warming throughout Carpathians may be an important driving force of forest greening in temperature-limited mountain regions. This finding is supported by an at least moderate intensity of air temperature – NDVI relationships (r correlation coefficient values of ∼50%, the highest of all eco-climatic relationships analysed), generally detected throughout mountain environments. At the same time, it seems that evapotranspiration increase accounted at least in part for forest browning in lowland areas, while the impact of precipitation in forest ecological dynamics remains unclear. All these findings can be useful for a better forest management under the future climate change conditions in Romania.

Bogor Botanic Gardens as a nature-based solution for mitigating urban heat island and microclimate regulation

This paper explores the role of Bogor Botanic Gardens (BBG) as a form of Nature-Based Solution (NBS) to mitigate Urban Heat Islands (UHI). Time series analysis of LANDSAT 8 OLI thermal band and Normalized Difference Vegetation Index (NDVI) was done from 2013 to 2020 using Google Earth Engine. Land Surface Temperature (LST) from Bogor and BBG were calculated, compared, and annual UHI areas were derived. The relationship of LST and NDVI were also explored annually to describe the effect of vegetation towards LST with linear regression. Overall, Bogor experiences a decrease of mean LST from 30.67°C and a maximum of 39.14°C in 2013 to 27.07°C and a maximum of 34.35°C in 2020. However, the inside of BBG is cooler with temperature ranging from 28.41°C and a maximum of 35.62°C in 2013 to 24.25°C and a maximum of 29.41°C in 2020. This is an effect of vegetation inside the BBG that regulate microclimate in its surrounding. It can be seen in the negative correlation between NDVI and LST observed with r2 ranging from 0.27 to 0.82. While UHI areas tended to increase from 8220 ha in 2013 to 8926 ha in 2020, BBG consistently acts as an urban cool island in the middle of UHI. Therefore, heat mitigation is proven to be one of the environmental services provided by BBG.

Assessment of nitrogen diagnosis methods in sunflower

AbstractNitrogen deficiency can severely limit sunflower (Helianthus annuus L.) grain yield and quality. Our objective was to evaluate N diagnosis methods based on: (a) pre‐plant soil nitrate‐nitrogen (NO3––N) test (PPSNT) and soil N mineralized in short‐term anaerobic incubation (Nan), (b) Greenness index (GI) and the normalized difference vegetation index (NDVI) measured at 6 (V6) and 12 (V12) leaves, and (c) grain nitrogen concentration (Nc). Seventeen experiments were carried out between 2010 and 2019 in Argentina, evaluating nine N rates (0, 30, 40, 60, 80, 90, 120, 150, and 160 kg N ha–1). The GI, NDVI, N sufficiency index and relative normalized difference vegetation index (NDVIr) were determined at V6 and V12 growth stages. On average, yield response to N was 492 kg ha–1 and Nc response was 0.25% in 9 and 11 responsive experiments, respectively. The inclusion of Nan improved the PPSNT diagnosis method. The critical N availability (PPSNT + fertilizer N) threshold was 115 kg N ha–1 for experiments with low Nan (&lt;60 mg kg–1), and 90 kg N ha–1 for experiments with high Nan (&gt;60 mg kg–1). The NDVIr at V12 allowed monitoring the crop N status with a 0.95 critical threshold. The Nc adequately diagnosed N deficiencies and the critical threshold was 2.26%. Also, Nc was predicted from the ratio between N availability and grain yield (R2 = .39). Our results would allow to better estimate N availability to recommend adequate N fertilizer rates for sunflower aiming to optimize grain yield and quality, and minimize the economic and environmental cost of fertilization.

NDVI-Based Analysis of the Influence of Climate Changes and Human Activities on Vegetation Variation on Hainan Island

The spatiotemporal characteristics of vegetation variation on Hainan Island were explored with normalized difference vegetation index (NDVI) data from 2001 to 2016 on the basis of a regression-analysis method. The impact of climatic factors and human activities on vegetation variation was assessed in different climate regions by residual analysis. Our study indicated that vegetation regions with significant improvement were primarily located in the coastal part of the semi-arid, central semi-humid, central mountainous humid, and parts of the humid regions. More serious vegetation degradation was observed in the northern part of the semi-humid region, eastern parts of the humid region, and the southern semi-arid and semi-humid regions. The relationship of NDVI with precipitation was stronger than that with temperature in the humid and semi-arid regions, and drought was the critical factor influencing vegetation changes in the semi-arid region due to comprehensive actions of rising temperature and declining rainfall. The findings of residual analysis highlighted that the degradation of vegetation was mainly triggered by anthropogenic factors: the rapid development of urbanization in the northern part of the semi-humid, and the southern semi-arid and semi-humid regions, and excessive aquaculture in the eastern parts of the humid region. The results are expected to provide an effective foundation of formulating environmental and ecological sustainability policies on Hainan Island.

Normalized Difference Vegetation Index and Chlorophyll Content for Precision Nitrogen Management in Durum Wheat Cultivars under Semi-Arid Conditions

To impart sustainability to modern intensive farming systems, environmental pollution caused by nitrogenous fertilizers in needs to be reduced by optimizing their doses. To estimate the grain yield and nutrtional quallity of wheat, the normalized difference vegetation index (NDVI) and chlorophyll content (SPAD) are potential screening tools to identify the N deficiency and screen out the promising cultivars. The two-year field study was comprised with five levels of nitrogen (N) (control, 50, 100, 150 and 200 kg N ha−1) and two durum wheat genotypes (Sena and Svevo). The experimental design was split-plot, in which N levels were placed in the main plots, while wheat genotypes were arranged in sub-plots. To predict the yield and quality traits, the NDVI and SPAD values recorded at heading, anthesis and milky growth stages were taken as response variables. The results revealed that N fertilization significantly influenced the SPAD and NDVI attributed traits of durum wheat, except NDVI at milky stage (NDVI-M) during the first year. The maximum value of NDVI was recorded by 150 kg N ha−1, while control treatment gave the minimum value. The grain yield was increased with the increasing dose of the N up to 100 kg N ha−1 (4121 kg ha−1), and thereafter, it was declined with further increased of N levels. However, the variation between the genotypes was not significant, except NDVI and SPAD values at the milky stage. The genotype Svevo had the highest NDVI values at all growth stages, while the genotype Sena recorded the maximum SPAD values during both years. Similarly, the N levels significantly influenced the quality traits (protein, wet gluten, starch test weight and Zeleny sedimentation) of both genotypes. The highly significant relationship of SPAD and NDVI with the grain yield and yield attributes showed their reliability as indicators for determining the N deficiency and selection of superior wheat genotypes for ensuring food security under climate change scenario.

Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. In this paper, we research the relationship of NDVI with temperature, precipitation, and Aridity Index (AI) in four different arid rangeland areas in Spain’s southeast. We focus on the interphase variability, studying time series from 2002 to 2019 with regression analysis and lagged correlation at two different spatial resolutions (500 × 500 and 250 × 250 m2) to understand NDVI response to meteorological variables. Intraseasonal phases were defined based on NDVI patterns. Strong correlation with temperature was reported in phases with high precipitations. The correlation between NDVI and meteorological series showed a time lag effect depending on the area, phase, and variable observed. Differences were found between the two resolutions, showing a stronger relationship with the finer one. Land uses and management affected the NDVI dynamics heavily strongly linked to temperature and water availability. The relationship between AI and NDVI clustered the areas in two groups. The intraphases variability is a crucial aspect of NDVI dynamics, particularly in arid regions.

Rainfall Trend and Its Relationship with Normalized Difference Vegetation Index in a Restored Semi-Arid Wetland of South Africa

Clearance of terrestrial wetland vegetation and rainfall variations affect biodiversity. The rainfall trend–NDVI (Normalized Difference Vegetation Index) relationship was examined to assess the extent to which rainfall affects vegetation productivity within Nylsvley, Ramsar site in Limpopo Province, South Africa. Daily rainfall data measured from eight rainfall stations between 1950 and 2016 were used to generate seasonal and annual rainfall data. Mann-Kendall and quantile regression were applied to assess trends in rainfall data. NDVI was derived from satellite images from between 1984 and 2003 using Zonal statistics and correlated with rainfall of the same period to assess vegetation dynamics. Mann-Kendall and Sen’s slope estimator showed only one station had a significant increasing rainfall trend annually and seasonally at p &lt; 0.05, whereas all the other stations showed insignificant trends in both rainfall seasons. Quantile regression showed 50% and 62.5% of the stations had increasing annual and seasonal rainfall, respectively. Of the stations, 37.5% were statistically significant at p &lt; 0.05, indicating increasing and decreasing rainfall trends. These rainfall trends show that the rainfall of Nylsvley decreased between 1995 and 2003. The R2 between rainfall and NDVI of Nylsvley is 55% indicating the influence of rainfall variability on vegetation productivity. The results underscore the impact of decadal rainfall patterns on wetland ecosystem change.

Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city

Land surface temperature (LST) and its relationship with normalized difference vegetation index (NDVI) are significantly considered in environmental study. The aim of this study was to retrieve the LST of Raipur City of tropical India and to explore its seasonal relationships with NDVI. Landsat images of four specific seasons for three particular years with fourteen years time interval were analyzed. The result showed a gradual rising (3.63 °C during 1991–2004 and 1.54 °C during 2004–2018) of LST during the whole period of study. The mean LST value of three particular years was the lowest (27.21 °C) on green vegetation and the highest (29.81 °C) on bare land and built-up areas. The spatial distribution of NDVI and LST reflects an inverse relationship. The best (− 0.63) and the least (− 0.17) correlation were noticed in the post-monsoon and winter seasons, respectively, whereas a moderate (− 0.45) correlation were found both in the monsoon and pre-monsoon seasons. This LST-NDVI correlation was strong negative (− 0.51) on vegetation surface, moderate positive on water bodies (0.45), and weak positive on the built-up area and bare land (0.14). In summary, the LST is greatly controlled by surface characteristics. This study can be used as a reference for land use and environmental planning in a tropical city.

Into the woods: Changes in mosquito community composition and presence of key vectors at increasing distances from the urban edge in urban forest parks in Manaus, Brazil

Mosquito-borne Zika virus (ZIKV) was recently introduced into the Americas and now has the potential to spill back into a sylvatic cycle in the region, likely involving non-human primates and Aedes, Haemagogus, and Sabethes species mosquitoes. We investigated potential routes of mosquito-borne virus exchange between urban and sylvatic transmission cycles by characterizing mosquito communities in three urban forest parks that receive heavy traffic from both humans and monkeys in Manaus, Brazil.Parks were stratified by both distance from the urban-forest edge (0, 50, 100, and 500 m) and relative Normalized Difference Vegetation Index (NDVI) (low, medium, or high), and mosquitoes were sampled at randomly selected sites within each stratum using BG-Sentinel traps. Additionally, temperature, relative humidity, and other environmental data were collected at each site.A total of 1,172 mosquitoes were collected from 184 sites sampled in 2018, of which 98 sites were resampled in 2019. Using park as the unit of replication (i.e. 3 replicates per sampling stratum), a two-way ANOVA showed no effect of distance or NDVI on the mean number of identified species (P > 0.05 for both comparisons) or on species diversity as measured by the Shannon-Wiener diversity index (P > 0.10 for both comparisons). However, the Morisita overlap index revealed that mosquito communities changed substantially with increasing distance from edge, with communities at 0 m and 500 m being quite distinct. Aedes albopictus and Ae. aegypti penetrated at least 100 m into the forest, while forest specialists including Haemagogus janthinomys, Sabethes glaucodaemon, and Sa. tridentatus were detected in low numbers within 100 m from the forest edge. Trichoprosopon digitatum and Psorophora amazonica were among the most abundant species collected, and both showed distributions extending from the forest edge to its interior.Our results show overlapping distributions of urban and forest mosquitoes at park edges, which highlights the risk of arbovirus exchange via multiple bridge vectors in Brazilian urban forest parks. These parks may also provide refugia for both Ae. albopictus and Ae. aegypti from mosquito control programs.

An assessment on the relationship between land surface temperature and normalized difference vegetation index

The present study aims to assess the trend of spatiotemporal relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) under different ranges of LST and NDVI values for Raipur City of India using fifteen cloud-free Landsat data sets of the pre-monsoon season from 2002 to 2018. LST maintains a strong negative relationship with NDVI for the whole of the study area. The relationship is quite insignificant for both the high LST zones and low LST zones. The results also indicate that under the positive NDVI values, the LST–NDVI relationships are strong to moderately negative, whereas it is positive and non-consistent under the negative values of NDVI. The results also show that the relationship is stronger in the earlier times, whereas it is weaker in recent times. An increase in heterogeneous landscape inside the city boundary strongly supports the changing pattern of LST–NDVI relationship.

Oil Palm Plantation Monitoring from Satellite Image

Malaysia is one of the largest palm oil producers in the world. To meet the production, its plantation needs to be monitored accordingly. Manual monitoring is no longer feasible due to energy, cost and time consumption. Remote sensing technology such as satellite images can be utilized for palm oil monitoring as they are cost effective, time and energy saving, and able to enhance the possibility of classifying the vegetation through spectral and texture analyses. Due to its impact towards Malaysia economy, an effective monitoring of palm oil plantation can be done by using satellite image to maintain the sustainability of palm oil in Malaysia. This project utilized the computation of Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) to determine the plant health. Relationship of NDVI and SAVI with respect to temperature and precipitation is studied based on the data obtained from Malaysian Meteorology Department. By using regression model technique, the relationship of the plant health based on NDVI analysis, nitrogen content based on SAVI analysis and temperature is found out to be highly correlated while plant health and precipitation is found out to be no or low correlated. The results can be utilized by the plantation operators, especially independent smallholders in monitoring the health status of the palm oil trees to ensure high productivity.

Varying climate response across the tundra, forest-tundra and boreal forest biomes in northern West Siberia

Satellite studies using the normalized difference vegetation index (NDVI) have revealed changes in northern Eurasian vegetation productivity in recent decades, including greening in tundra and browning in the boreal forests. However, apparent NDVI changes and relationships to climate depend on the temporal and spatial sampling and the biome and forest-land cover type studied. Here we perform a consistent analysis of NDVI and climate across four bioclimatic zones (tundra, forest-tundra, northern and middle taiga) in northern West Siberia (NWS), further stratified into eight forest-land cover types. We utilize NDVI data from the Moderate Resolution Imaging Spectroradiometer and climate reanalysis data from 2000 to 2016, a period including the record warm anomaly in 2016 (+2 °C–5 °C June–July surface air temperature (SAT) across NWS). Statistically significant (α = 0.05) correlations were found for two bivariate relationships at the biome level: between NDVImax and June–July surface air temperature (SAT)(r ∼ +0.79), and between middle taiga NDVImax and July precipitation (r ∼ +0.48). No significant statistical relationships were found for the northern taiga and forest-tundra biomes. However, within these biomes we found that deciduous needle-leaf (larch) NDVImax is significantly correlated with July temperature (r ∼ +0.48). Qualitatively, spatial composites of NDVI and climate variables were effective for revealing insights and patterns of these relationships at the sub-regional scale. The spatial heterogeneity of NDVI patterns indicates divergent reactions of specific types of vegetation, as well as local effects that are clearly important on the background of a regional climate response.

Vegetation Phenological Characterization of Alluvial Plain Shorea robusta-dominated Tropical Moist Deciduous Forest of Northeast India Using MODIS NDVI Time Series Data

The current study attempts to extract the phenological variables of alluvial plain Shorea robusta-dominated tropical moist deciduous forest of Northeast India using threshold-based method. A total of 230 Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 time series normalized difference vegetation index (NDVI) datasets (year 2006–2015) has been used. Time series NDVI datasets were fitted to an adaptive Savitzky–Golay filter for smoothing. Important phenological matrices, namely start of season (SOS), end of season (EOS), length of season (LOS), peak of season (POS), seasonal amplitude, seasonally integrals (large and small integral), were evaluated. SOS varied from 106 to 120 day of year (DOY) (average of 110 ± 17.6), and EOS varied from 425 to 441 DOY (average of 431 ± 14.33). POS reaches in the month of September and October (average 262 ± 15). In the current study, a mean amplitude of 0.35, lower value of small integral (4.70 ± 0.34) and higher value of large integral (16.24 ± 0.25) signify that the studied forest is highly productive ecosystem, with semievergreen or moist deciduous canopy. Strong linear relationship of NDVI with temperature and rainfall was witnessed, particularly with a 1–2 month time lag. Also, NDVI-temperature correlation was found stronger than NDVI-precipitation correlation, suggesting that the area being a humid subtropical region, temperature plays a greater role in the timing of phenological events than rainfall and can act as a crucial factor for growth of the species under the climate changing scenario.

Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

Relationship of NDVI and oak (Quercus) pollen including a predictive model in the SW Mediterranean region

Techniques of remote sensing are being used to develop phenological studies. Our goal is to study the correlation among the Normalized Difference Vegetation Index (NDVI) related with oak trees included in three set data polygons (15, 25 and 50 km to aerobiological sampling point as NDVI-15, 25 and 50), and oak (Quercus) daily average pollen counts from 1994 to 2013. The study was developed in the SW Mediterranean region with continuous pollen recording within the mean pollen season of each studied year. These pollen concentrations were compared with NDVI values in the locations containing the vegetation under a study based on two cartographic sources: the Extremadura Forest Map (MFEx) of Spain and the Fifth National Forest Inventory (IFN5) from Portugal. The importance of this work is to propose the relationship among data related in space and time by Spearman and Granger causality tests. 9 out of 20 studied years have shown significant results with the Granger causality test between NDVI and pollen concentration, and in 12 years, significant values were obtained by Spearman test. The distances of influence on the contribution of Quercus pollen to the sampler showed statistically significant results depending on the year. Moreover, a predictive model by using Artificial Neural Network (ANN) was applied with better results in NDVI25 than for NDVI15 or NDVI50. The addition of NDVI25 with the lag of 5 days and some weather parameters in the model was applied with a RMSE of 4.26 (Spearman coefficient r = 0.77) between observed and predicted values. Based on these results, NDVI seems to be a useful parameter to predict airborne pollen.