Minimum Normalized Difference Vegetation Index Research Articles

Vegetation Coverage Changes and Their Response to Meteorological Variables from 2000 to 2009 in Naqu, Tibet, China

To fully understand the effects of climate change on grassland, interannual variation of vegetation coverage and its responses to meteorological variables in Naqu Prefecture of northern Tibet were investigated. This was done based on a Normalized Difference Vegetation Index (NDVI) dataset and historical meteorological data from 2000 to 2009. The NDVI dataset was downloaded from an Earth Observing System (EOS) data center. The meteorological data were retrieved from six meteorological stations of the China Meteorological Administration. Results show that NDVI had a significant increasing trend in the east, but no clear trend in the west. The NDVI in alpine meadows and alpine meadow steppes increased during the observation period. There were significant positive linear correlations between NDVI and water/heat conditions, and correlation between monthly mean minimum temperature (AMIT) and NDVI was the strongest. The strongest correlation between NDVI and heat conditions appeared at alpine meadows. Mean monthly wind velocity (MMWV) was negatively correlated with NDVI (p < 0.001), and the highest correlation was for alpine meadow steppes. There was a lag response of NDVI to water/heat conditions. The lag time of NDVI to precipitation and temperature was one month, but that to accumulated precipitation was two months.

Satellite-derived vegetation indices contribute significantly to the prediction of epiphyllous liverworts

Epiphyllous liverworts form a special group of bryophytes that primarily grow on leaves of understory vascular plants, occurring in constantly moist and warm evergreen forest in tropical and subtropical regions. They are very sensitive to climate change and environmental pollution. Previous studies have focused largely on microhabitat preferences of epiphyllous liverworts and demonstrated the importance of climate factors such as humidity, temperature and light. However, little is known about the relationship between distribution of epiphyllous liverworts and macro-habitat factors at broad spatial scales. Here, we predicated the distribution of epiphyllous liverworts in China based on topographic and bioclimatic variables, as well as satellite-derived vegetation indices at a 1km spatial resolution using presence-only ecological niche models. We used the Area Under the receiver operating characteristic Curve (AUC) and True Skill Statistic (TSS) to validate the models, and then used the Wilcoxon paired test to compare model performances. Furthermore, we applied the jackknife test to identify the important factors affecting predictions. Our results showed that the highest accuracy (i.e., AUC=0.98 and TSS=0.93) in predicting epiphyllous liverworts was achieved by the model that combined climatic and remotely sensed vegetation variables. The satellite-derived annual mean and minimum Normalized Difference Vegetation Index (NDVI) as well as the annual mean and minimum Normalized Difference Water Index (NDWI) emerged as the most important predictors of distribution patterns of epiphyllous liverworts, while climatic variables such as precipitation in the wettest quarter and temperature of the coldest quarter were of ancillary importance. The significant contributions of NDVI and NDWI in defining the distribution range and spatial patterns of epiphyllous liverworts, and the strong relationship between this species and evergreen forest implies that epiphyllous liverworts may be a useful indicator for forest degradation or integrity at broad spatial scales.

Identification of croplands of winter cereals in Rio Grande do Sul state, Brazil, through unsupervised classification of normalized difference vegetation index images

This study aimed to propose methods to identify croplands cultivated with winter cereals in the northern region of Rio Grande do Sul State, Brazil. Thus, temporal profiles of Normalized Difference Vegetation Index (NDVI) from MODIS sensor, from April to December of the 2000 to 2008, were analyzed. Firstly, crop masks were elaborated by subtracting the minimum NDVI image (April to May) from the maximum NDVI image (June to October). Then, an unsupervised classification of NDVI images was carried out (Isodata), considering the crop mask areas. According to the results, crop masks allowed the identification of pixels with greatest green biomass variation. This variation might be associated or not with winter cereals areas established to grain production. The unsupervised classification generated classes in which NDVI temporal profiles were associated with water bodies, pastures, winter cereals for grain production and for soil cover. Temporal NDVI profiles of the class winter cereals for grain production were in agree with crop patterns in the region (developmental stage, management standard and sowing dates). Therefore, unsupervised classification based on crop masks allows distinguishing and monitoring winter cereal crops, which were similar in terms of morphology and phenology.

Seasonal relationship between normalized difference vegetation index and abundance of the Phlebotomus kala-azar vector in an endemic focus in Bihar, India

Remote sensing was applied for the collection of spatio-temporal data to increase our understanding of the potential distribution of the kala-azar vector Phlebotomus argentipes in endemic areas of the Vaishali district of Bihar, India. We produced monthly distribution maps of the normalized difference vegetation index (NDVI) based on data from the thematic mapper (TM) sensor onboard the Landsat-5 satellite. Minimum, maximum and mean NDVI values were computed for each month and compared with the concurrent incidence of kala-azar and the vector density. Maximum and mean NDVI values (R2 = 0.55 and R2 = 0.60, respectively), as well as the season likelihood ratio (X2 = 17.51; P <0.001), were found to be strongly associated with kala-azar, while the correlation with between minimum NDVI values and kala-azar was weak (R2 = 0.25). Additionally, a strong association was found between the mean and maximum NDVI values with seasonal vector abundance (R2 = 0.60 and R2 = 0.55, respectively) but there was only a marginal association between minimum NDVI value and the spatial distribution of kala-azar vis-à-vis P. argentipes density.

Quantifying Impacts of Land Ownership on Regional Forest NDVI Dynamics

Identifying the impacts of human activities on local and regional ecological processes is important for protecting essential ecological functions. In this study, the spatial and temporal dynamics of Normalized Difference Vegetation Index (NDVI) at three adjacent areas with different proportions of private land (6 percent, 20 percent and 55 percent) in the Bankhead National Forest in Alabama, USA from 1998 to 2004 were examined. The phase coupling, synchrony, and information entropy of NDVI at multiple temporal scales for each of these areas were examined. A higher proportion of private land (e.g., 55 percent) resulted in decrease of annual mean, coefficient of variance, seasonal maximum, and absolute value of rate of increase/ rate of decrease for NDVI values as well as increase in seasonal minimum NDVI and decrease in spatial coupling and synchrony of NDVI dynamics. Thus, a higher proportion of private land could affect regional NDVI dynamics in complex and ecologically significant ways.

Population dynamics of two rodent species in agro‐ecosystems of central Argentina: intra‐specific competition, land‐use, and climate effects

AbstractUnderstanding the role of feedback structure (endogenous processes) and exogenous (climatic and environmental) factors in shaping the dynamics of natural populations is a central challenge within the field of population ecology. We attempted to explain the numerical fluctuations of two sympatric rodent species in agro‐ecosystems of central Argentina using Royama's theoretical framework for analyzing the dynamics of populations influenced by exogenous climatic forces. We found that both rodent species show a first‐order negative feedback structure, suggesting that these populations are regulated by intra‐specific competition (limited by food, space, or enemy‐free space). In Akodon azarae endogenous structure seems to be very strongly influenced by human land‐use represented by annual minimum normalized difference vegetation index (NDVI), with spring and summer rainfall having little influence upon carrying capacity. Calomys venustus’ population dynamics, on the other hand, seem to be more affected by local climate, also with spring and summer rainfall influencing the carrying capacity of the environment, but combined with spring mean temperature.

Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran

In order to evaluate the capability of NOAA-AVHRR data for drought monitoring in the northwest of Iran having cold semi-arid climate, a study plan was designed involving the production of normalized difference vegetation index (NDVI) and vegetation condition index (VCI) indices and correlating their values to precipitation data. Raw AVHRR images were processed and geometric and radiometric corrections were performed. Seven-day maximum NDVI maps were produced and VCI was calculated using the maximum and minimum NDVI values for the same time period. Precipitation statistics from 19 synoptic meteorological stations were collected. The study covered a five-year time period with three consecutive months in the growing season. Pearson correlation was performed to correlate NDVI and VCI values to precipitation data. Different time lag schemes were tried and the highest correlation coefficients (r values) were obtained while correlating NDVI and VCI to three-month (current plus last two months) precipitation. Better agreement was observed between NDVI and precipitation as compared with that between VCI and precipitation in individual stations. Good correlations were also obtained between average NDVI and VCI of the study area and average three-month precipitation. The results indicated that NOAA-AVHRR derived NDVI well reflects precipitation fluctuations in the study area promising a possibility for early drought awareness necessary for drought risk management.

Bermudagrass Seasonal Responses to Nitrogen Fertilization and Irrigation Detected Using Optical Sensing

The objective of this study was to evaluate seasonal differences in bermudagrass response to N fertilization and irrigation by using optical sensing. A second objective was to determine if optical sensing could measure N status when the turf response to N was confounded by differences in moisture status. Bermudagrasses (Cynodon dactylon L.) ‘Rivera’ and ‘Yukon’ were managed under three irrigation treatments and six N treatments during the growing seasons in 2003 and 2004. Turf quality, normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red light reflectance in relation to near infrared reflectance (R/NIR), and green light reflectance in relation to near infrared reflectance (G/NIR) were measured. Bermudagrass demonstrated a noticeable third‐order polynomial seasonal trend in response to N and irrigation treatment, and this trend was characterized as early‐, peak‐, mid‐ and late‐season responses. Normalized difference vegetation index and GNDVI demonstrated a better relationship with turf quality and N status than R/NIR and G/NIR. A comparison among the four indices showed NDVI to be more closely correlated with irrigation, N fertilization, and turf quality. Minimum acceptable and target NDVI were developed by seasonal period based on visual turf quality assessment. It was also found that NDVI response to N fertilization was not strongly affected by irrigation treatment and could be used as an indicator of N status and need regardless of irrigation treatment.

Mapping wildfire danger at regional scale with an index model integrating coarse spatial resolution remote sensing data

Wildfires are a prevalent natural hazard in the south of France. Planners need a permanent fire danger assessment valid for several years over a territory as large and heterogeneous as Midi‐Pyrénées region. To this end, we developed an expert knowledge‐based index model adapted to the specific features of the study area. The fire danger depends on two complementary elements: spatial occurrence and fire intensity. Among the GIS layers identified as input variables for modeling, vegetation fire susceptibility is one of the most influent. However, the main difficulty at this scale is the scarcity or the lack of exhaustiveness of the data. In this respect, remote sensing imagery is capable of providing relevant information. We proposed to calculate an annual relative greenness index (annual RGRE) that reflects vegetation dryness in summer. We processed times series of Normalized Difference Vegetation Index (NDVI) from SPOT‐VEGETATION images over the last six available years (1998 to 2003). The first step was to verify that these images characterize vegetation types and highlight intraannual and interannual response variability. It is then possible to identify phenological stages corresponding to the maximum NDVI (and therefore to maximum photosynthetic activity) during the growing season, the minimum NDVI at the end of the growing season and the minimum NDVI during winter period. These phenology metrics ground the annual RGRE calculation. Values obtained for each observation year show significant correlation (r2 = 0.70) with the De Martonne aridity index calculated for the same period. A synthesis of yearly index was integrated in the model as a variable that expresses fire susceptibility.

Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor

This study presents first results on Normalized Difference Vegetation Index (NDVI), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the geostationary satellite Meteosat Second Generation (MSG) covering the African continent. With a temporal resolution of 15 min MSG offers complementary information for NDVI monitoring compared to vegetation monitoring based on polar orbiting satellites. The improved temporal resolution has potential implications for accurate NDVI assessment of the African continent; e.g. the increased amount of available scenes are expected to help overcome problems related to cloud cover which makes the MSG data particularly well suited for early warning systems. Time series of 2004 MSG NDVI was compared to MODIS (Moderate Resolution Imaging Spectroradiometer) Terra and Aqua NDVI for the Dahra site in the Senegalese Sahel, West Africa. It was found that NDVI was available for 82 days with multiple cloud free acquisitions per day during the growing season as compared to 47 days with information from either MODIS Terra or Aqua for that particular site. Differences in MSG SEVIRI and MODIS BRDF on a seasonal scale were found to influence the time series of NDVI for the test site; MSG NDVI being higher than MODIS in July–August and lower in October–November. Preliminary composite analysis suggests that the period of compositing to produce continent scale cloud free products can be reduced to ∼5 days using MSG NDVI as compared to polar orbiting data. With the availability of diurnal reflectance information the significance of differences between the red and near-infrared wavelengths due to anisotropy become evident, causing diurnal variations in observed NDVI. Diurnal MSG NDVI was compared to in situ measured MSG NDVI at the test site in Senegal and the same “bowl-shaped” diurnal curve was found for a medium dense cover of annual grasses. The range in observed NDVI and time of diurnal minimum was different due to different viewing geometry. Daily minimum of in situ measured NDVI was around solar noon whereas minimum MSG NDVI occurs one hour prior to noon due to the test site location 12° west of the satellite sensor. Diurnal variation in observed NDVI was studied for a number of pixels characterized by different sensor view zenith angles and vegetation types. This analysis illustrated the diurnal NDVI dependency of illumination conditions, view angle and vegetation intensity and pinpoints the importance of proper BRDF modeling to produce daily values of MSG NDVI normalized for acquisition time, which will be the subject of a forthcoming paper.

Extremes of ground-based and satellite measurements in the vegetation period for the Carpathian Basin

Decadal change in temperature and precipitation time series of the 20th century has been analyzed based on monthly climatological datasets for the Carpathian Basin. Beyond extreme value analysis of precipitation, variability and fluctuation properties have been determined. IPCC TAR suggests that the Carpathian Basin could become highly vulnerable to global warming. Our investigations support these findings, especially, in case of two subregions: (1) Great Hungarian Plain, (2) watershed of the Lake Balaton. Therefore detailed investigations have been carried out for these areas. Besides temperature and precipitation, potential agricultural activity can be characterized by the normalized difference vegetation index (NDVI) derived from remote measurements of NOAA satellites (1981–2001). In our investigations NDVI values for the European continent and the Carpathian Basin have been statistically analyzed during the last two decades. Variation analysis focus on month-by-month fluctuations and possible tendencies. Annual maximum and minimum NDVI fields have been determined for both areas. Then, we have focused on systematic changes of NDVI both in the Carpathian Basin and the entire European region. The main objectives of our research include detailed analysis of regions in these areas, whether or not local consequences of the global climate warming can be detected. Therefore, tendencies in NDVI time series have been analyzed for selected subregions comparing different type of lands, especially hilly and plain areas.

DISTRIBUTION OF ANOPHELINE MOSQUITOES IN ERITREA

The spatial distribution of anopheline mosquito species was studied throughout Eritrea during the 1999-2001 malaria transmission seasons from October to December for the highlands and western lowlands and February to April for the coastal region. Of the 302 villages sampled, 59 were visited in both the first and second year. Overall, 13 anopheline species were identified, with the Anopheles gambiae complex predominating during the first year (75.6%, n = 861) and the second year (91.9%, n = 1,262). Intrazonal variation accounted for 90% of the total variation in mosquito distribution. Polymerase chain reaction results indicated that 99% (n = 1,309) of the An. gambiae s.l. specimens were An. arabiensis, indicating that this was the only member of the gambiae complex present. There was a high degree of aggregation of anophelines within zones and villages, with more than 80% of the total anophelines being collected from less than 20% of the villages and from only 10% of the houses sampled. At least 80% of the anopheline mosquitoes were collected from grass-thatched Agudo-type housing. Vector abundance showed an inverse relationship with elevation, with highest densities in the low-lying western lowlands. Multiple regression analysis of log-transformed mean density of An. arabiensis with rainfall and the normalized difference vegetation index (NDVI) (average NDVI, minimum NDVI, and maximum NDVI) showed that these independent variables were not significantly associated with mosquito densities (R2 = 0.058). Our study contributes to the basic understanding of the ecology and distribution of malaria vectors with respect to species composition and spatial heterogeneities both that could be used to guide vector control operations in Eritrea.

LAND USE IMPACTS ON THE NORMALIZED DIFFERENCE VEGETATION INDEX IN TEMPERATE ARGENTINA

We studied the impacts of land use in temperate Argentina on the normalized difference vegetation index (NDVI), which has often been used to estimate the interception of photosynthetically active radiation and carbon uptake by terrestrial vegetation. The NDVI was derived from the National Oceanic and Atmospheric Administration (NOAA) satellites. We incorporated land use, climate, soil, and NDVI data into a geographic information system with a county-level spatial resolution. Land use was characterized in terms of the proportion of different crop types at the county level. Three attributes were derived from the seasonal dynamics of the NDVI: the annual integral (NDVI-I), the difference between the maximum and minimum NDVI divided by the integral (RREL), and the date of maximum NDVI (DMAX). The environmental controls of the NDVI attributes for the counties showing the lowest proportion of croplands (“low-impacted vegetation” areas, LIV) were analyzed using stepwise multiple regressions with the three NDVI a...

30-second degree grid land cover classification of Asia

With the development of the global economy, environmental research has become more important than ever, especially in the Asian region. The objective of this study is to produce a land cover classification dataset for the whole of Asia using the NOAA AVHRR 1-km dataset. Ground data were mainly collected from existing thematic maps which were obtained from members of the Land Cover Working Group (LCWG) of the Asian Association of Remote Sensing (AARS). Classification was mainly based on cluster analysis of the monthly ratio of surface temperature and Normalized Difference Vegetation Index (NDVI) for seven months from April to October 1992. Additional variables, such as DEM, the maximum monthly composite NDVI in a year, and the minimum monthly composite NDVI in a year were also used in the classification processing. In order to add and improve ground data in the future, collected ground data will be published with the developed land cover dataset.

Use of climatic data and satellite imagery to model the abundance of Culicoides imicola, the vector of African horse sickness virus, in Morocco.

African horse sickness (AHS) is a vector-borne, infectious disease of equids caused by African horse sickness virus (AHSV). The only proven field vector of the virus is the biting midge Culicoides imicola. Following a recent epizootic (1989-91) of AHS in Morocco, light traps and automatic weather stations were operated for 2 years at twenty-two sites distributed over much of the country. The annually-averaged mean daily trap catch of C. imicola at these sites was negatively correlated with wind speed, and positively correlated with the average and mean annual minimum NDVI (Normalized Difference Vegetation Index, a remotely sensed measure of vegetation activity). There were no significant correlations between the mean daily trap catch and air temperature, soil temperature, relative humidity, saturation deficit, rainfall, altitude or the mean annual maximum or range of NDVI. The best two-variable model, which combined WindspeedMnAvMn (the average daily minimum wind speed of the least windy month) and NDVImin (the average annual minimum NDVI) as predictors, explained over 50% of the variance in the annually-averaged mean daily trap catch of C. imicola. There was a significant, positive correlation between minimum wind speed at night and the daily mortality rate of adult female C. imicola and it is suggested that the relationship between wind speed and the abundance of C. imicola arises from effects on adult mortality or dispersal. Considering several climatic variables, in North Africa NDVImin was most significantly correlated with total annual rainfall. It is suggested that the relationship between NDVImin and the abundance of C. imicola arises from the impact of soil moisture on both. It is proposed that areas of Morocco with higher levels of soil moisture in late summer or autumn provide more, larger and/or more enduring breeding sites for C. imicola, as well as supporting more photosynthetically active vegetation and hence having higher NDVI.

Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models

We tested the potential of artificial neural networks (ANNs) as predictive tools in ecology. We compared the performance of ANNs and regression models (RM) in predicting ecosystems attributes, with special emphasis on temporal (interannual) predictions of functional attributes of the ecosystem at regional scales. We tested the predictive power of ANNs and RMs using simulated data for six functional traits derived from the seasonal course of the normalized difference vegetation index (NDVI): the annual integral of the NDVI curve (NDVI-I), the maximum (MAX) and minimum (MIN) NDVI, the date of the MAX NDVI (DM) and the date of start (SGS) and end (EGS) of the growing season. For one of these traits (NDVI-I), we also generated a set of data that incorporated the effects of the state of the system in previous years (inertial effects). Even simple non-linearities in the actual functional form of the relationship between environmental variables and ecosystem attributes preclude a precise prediction of these attributes when the rules are not explicit. That was evident for predictions based on both ANNs and RMs under absolutely deterministic conditions (error-free scenario). Non-linearities in the simulated traits of the NDVI curve derive from multiplicative terms in the models. Under the presence of these non-linear terms, a different aggregation of the driving variables (monthly vs. annual or quarterly climatic data) reduce substantially the ability of both RMs and ANNs to predict the independent variable. For the six traits analyzed, the ANNs were able to make better predictions than RMs. The correlation between observed and predicted values of each of the six traits considered was higher for the ANNs than for the RMs. ANNs showed clear advantages to capture inertial effects. The ANN used was able to use previous year information on climate to estimate current year NDVI-I much better than the RM that used the same input information.

Regional Patterns of Normalized Difference Vegetation Index in North American Shrublands and Grasslands

We characterized the seasonal pattern of Normalized Difference Vegetation Index (NDVI) of 49 grassland and shrubland sites corresponding to areas with low human impact. Each site was characterized as a vector of 21 elements, where each element corresponded to the average NDVI for one date. We used the integral of the NDVI as an estimate of the total production, and the difference between the maximum and minimum NDVI over the year as a measure of the seasonality. Based on a Principal Component Analysis, the integral of the NDVI accounted for 63% of the variability of the SITE x DATE matrix. The difference between the maximum and minimum NDVI throughout the year accounted for 19%. Mean annual precipitation explained 67% of the variability of the integral of the NDIV curves (P &lt; 0.01). The slope of the relationship between precipitation and integrated NDVI was significantly steeper for grasslands than for shrublands. The difference between the extreme values of NDVI increased with mean annual precipitation and the thermal amplitude, and decreased with mean annual temperature (P &lt; 0.01).