Minimum Normalized Difference Vegetation Index Research Articles

Acquisitions and applications of forest canopy hyperspectral imageries at hotspot and multiview angle using unmanned aerial vehicle platform

The accurate retrieval of forest functional and structural parameters is of great significance in the scientific research of ecosystem, global change, and carbon and nitrogen cycles. Recently, an unmanned aerial vehicle (UAV) hyperspectral imaging system provides a cost-effective way to capture the hyperspectral imageries from any points of the hemisphere above a forest canopy. However, compared with single-angle hyperspectral images, the multiangle hyperspectral images provide more information about forest functional and structural characteristics. We developed a semiautomatic multiangle observation method using a UAV hyperspectral imaging system and successfully collected the multiangle hyperspectral imageries with clear hotspots of broadleaf and coniferous forest canopies. Our results indicated that the hotspot of a forest canopy had a great effect on the reflectance, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) of forests. The maximum values of canopy reflectance and EVI were found at the hotspot position, while the minimum NDVI was at the hotspot. Moreover, the hotspot effect was similar in both broadleaf and coniferous forests. Although the hotspot had no obvious effects on the photochemical reflectance index (PRI), different view zenith angles had a great effect on PRI. Our findings provide a solid foundation for retrieving forest structural parameters using fully automatic multiangle hyperspectral imaging system at both aerial and satellite platforms.

Compositing the Minimum NDVI for Daily Water Surface Mapping

Capturing high frequency water surface dynamics via optical remote sensing is important for understanding hydro-ecological processes over seasonally flooded wetlands. However, it is a difficult task due to the presence of clouds on satellite images. This study proposed the MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Minimum Value Composite (MinVC) algorithm to generate daily water surface data at a 250-m resolution. The algorithm selected pixelwise minimum values from the combined daily Terra and Aqua MODIS NDVI data within a 15-day moving window. Consisting mainly of cloud and water surface information, the MinVC NDVI data were segmented for water surfaces over the Poyang Lake, China (2000–2017) by using an edge detection model. The water surface mapping result was strongly correlated with the Landsat based result (R2 = 0.914, root mean square error, RMSE = 223.7 km2), the cloud free MODIS image based result (R2 = 0.824, RMSE = 356.7 km2), the recent Landsat-MODIS image fusion based result (R2 = 0.765, RMSE = 403 km2), and the hydrodynamic modeling result (R2 = 0.799). Compared to the equivalent eight-day MOD13 NDVI based on the Constraint View-Angle Maximum Value Composite (CV-MVC) algorithm, the daily MinVC NDVI highlighted water bodies by generating spatially homogenous water surface information. Consequently, the algorithm provided spatially and temporally continuous data for calculating water submersion times and trends in water surface area, which contribute to a better understanding of hydro-ecological processes over seasonally flooded wetlands. Within the framework of sensor intercalibration, the algorithm can be extended to incorporate multiple sensor data for improved water surface mapping.

Exposure to the natural environment and rurality is protective of ADHD in a large birth cohort of New Zealand children

OPS 29: Green space and neurological effects, Room 411, Floor 4, August 28, 2019, 1:30 PM - 3:00 PM Background: Several small experimental studies and cross-sectional observational studies have found that exposure to the natural environment may protect against Attention Deficit Hyperactivity Disorder (ADHD), or can moderate symptoms in children diagnosed with ADHD. Methods: We test whether exposure to the natural environment protects against ADHD and whether this hypothesized protective effect varies across a child’s life course. The sample consisted of all children born in New Zealand in 1998 (n=49,923). We used Statistics New Zealand’s Integrated Data Infrastructure (IDI) to identify children with ADHD and define covariates. ADHD was defined using hospitalization and pharmacy records. Exposure to the natural environment (or “green space”), from prenatal to age 18, was measured using the Normalized Difference Vegetation Index (NDVI) and land-use data from Landcare Research New Zealand. We estimated logit models of ADHD incidence controlling for gender, ethnicity, education, income, smoking status, birth order, and premature or underweight birth. Results: Children who lived in a rural area after age 2 were less likely to develop ADHD (OR 0.67; 95% CI 0.46-0.98), as were those with increased minimum NDVI exposure after age 2 (standardized OR for exposure quartile 2 (Q2) 0.84, 95%CI 0.7-1.0; Q3 OR 0.81, 95%CI 0.68-0.96; and Q4 OR 0.66, 95%CI 0.55-0.81). In early life (prenatal to age 2), neither rural living nor NDVI were protective. Conclusions: In this first longitudinal study of the relationship between the natural environment and ADHD, we found that rurality and increased minimum greenness were strongly and independently associated with a reduced risk of ADHD.

Optimized Stratification for Mapping Riparian Vegetation in Arid and Semiarid Environments

This paper describes a method of mapping riparian vegetation in semi-arid to arid environments using the Landsat normalized difference vegetation index (NDVI). The method successfully identified a range of riparian community types across the entire state of Nevada, USA, which spans 7 degrees of latitude and almost 4000 m of elevation. The landscape was stratified into units of similar elevation and solar exposure, and riparian areas were identified as having anomalously high NDVI within a local neighborhood. Thousands of calibration points were used in a simplex optimization to select the spatial neighborhood, the elevation and insolation strata, the minimum NDVI to be considered as potentially riparian, the number of standard deviations from the mean for an anomaly to be classified as riparian, and a limit on upslope position. Mapping of subpixel riparian corridors was improved by applying a directional high-pass filter to the NDVI data. Irrigated areas in agricultural and urban areas were removed based on land ownership maps and manual editing. The final map was tested with 400 independent test points: producer’s accuracy was 84.6% and user’s accuracy was 93.5%. This method should be broadly useful for mapping riparian features across large and complex regions with arid to semi-arid environments.

The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability.

Stay-green associates with low water soluble carbohydrates at flowering in oat

High throughput phenotyping of canopy senescence based on Normalized Difference Vegetation Index (NDVI) could be useful in plant breeding. The aim of this paper was to assess the association between grain yield and canopy senescence quantified with NDVI in oat lines selected for grain yield and milling quality.Sixteen lines were grown in four winter-rainfall environments where logistic curves between NDVI and thermal time from anthesis (GS60) were fitted to return five traits: maximum NDVI, NDVImax; minimum NDVI, NDVImin; thermal time to 50% senescence, x50; rate of senescence, rate; and the area under the NDVI curve, area. Across sources of variation, residuals of yield after removing the effect of phenology correlated with area (r = 0.69), NDVImax (r = 0.67), x50 (r = 0.57), NDVImin (r = 0.51) and rate (r = −0.30), all significant at P < 0.05. All five senescence traits correlated negatively with concentration of water soluble carbohydrates at anthesis, particularly area (r = −0.75, P < 0.0001). There was no correlation for yield between environments in 5 out of 6 comparisons (broad sense heritability = 0.39) and water soluble carbohydrates correlated between environments in 6 out of 6 comparisons (broad sense heritability = 0.89). Correlations between environments were irregular for senescence traits. Selection for low concentration of water soluble carbohydrates could increase oat grain yield by improving both grain number per m2 and leaf area duration.

Analysis of vegetation condition and its relationship with meteorological variables in the Yarlung Zangbo River Basin of China

Abstract. The Yarlung Zangbo River Basin is located in the southwest border of China, which is of great significance to the socioeconomic development and ecological environment of Southwest China. Normalized Difference Vegetation Index (NDVI) is an important index for investigating the change of vegetation cover, which is widely used as the representation value of vegetation cover. In this study, the NDVI is adopted to explore the vegetation condition in the Yarlung Zangbo River Basin during the recent 17 years, and the relationship between NDVI and meteorological variables has also been discussed. The results show that the annual maximum value of NDVI usually appears from July to September, in which August occupies a large proportion. The minimum value of NDVI appears from January to March, in which February takes up most of the percentage. The higher values of NDVI are generally located in the lower elevation area. When the altitude is higher than 3250 m, NDVI began to decline gradually, and the NDVI became gradual stabilization as the elevation is up to 6000 m. The correlation coefficient between NDVI and precipitation in the Yarlung Zangbo River Basin is greater than that with temperature. The Hurst index of the whole basin is 0.51, indicating that the NDVI of the Yarlung Zangbo River Basin shows a weak sustainability.

Micro-spatial distribution of malaria cases and control strategies at ward level in Gwanda district, Matabeleland South, Zimbabwe

BackgroundAlthough there has been a decline in the number of malaria cases in Zimbabwe since 2010, the disease remains the biggest public health threat in the country. Gwanda district, located in Matabeleland South Province of Zimbabwe has progressed to the malaria pre-elimination phase. The aim of this study was to determine the spatial distribution of malaria incidence at ward level for improving the planning and implementation of malaria elimination in the district.MethodsThe Poisson purely spatial model was used to detect malaria clusters and their properties, including relative risk and significance levels at ward level. The geographically weighted Poisson regression (GWPR) model was used to explore the potential role and significance of environmental variables [rainfall, minimum and maximum temperature, altitude, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rural/urban] and malaria control strategies [indoor residual spraying (IRS) and long-lasting insecticide-treated nets (LLINs)] on the spatial patterns of malaria incidence at ward level.ResultsTwo significant clusters (p < 0.05) of malaria cases were identified: (1) ward 24 south of Gwanda district and (2) ward 9 in the urban municipality, with relative risks of 5.583 and 4.316, respectively. The semiparametric-GWPR model with both local and global variables had higher performance based on AICc (70.882) compared to global regression (74.390) and GWPR which assumed that all variables varied locally (73.364). The semiparametric-GWPR captured the spatially non-stationary relationship between malaria cases and minimum temperature, NDVI, NDWI, and altitude at the ward level. The influence of LLINs, IRS and rural or urban did not vary and remained in the model as global terms. NDWI (positive coefficients) and NDVI (range from negative to positive coefficients) showed significant association with malaria cases in some of the wards. The IRS had a protection effect on malaria incidence as expected.ConclusionsMalaria incidence is heterogeneous even in low-transmission zones including those in pre-elimination phase. The relationship between malaria cases and NDWI, NDVI, altitude, and minimum temperature may vary at local level. The results of this study can be used in planning and implementation of malaria control strategies at district and ward levels.

Assessing Seasonal and Inter-Annual Variations of Lake Surface Areas in Mongolia during 2000-2011 Using Minimum Composite MODIS NDVI.

A minimum composite method was applied to produce a 15-day interval normalized difference vegetation index (NDVI) dataset from Moderate Resolution Imaging Spectroradiometer (MODIS) daily 250 m reflectance in the red and near-infrared bands. This dataset was applied to determine lake surface areas in Mongolia. A total of 73 lakes greater than 6.25 km2in area were selected, and 28 of these lakes were used to evaluate detection errors. The minimum composite NDVI showed a better detection performance on lake water pixels than did the official MODIS 16-day 250 m NDVI based on a maximum composite method. The overall lake area detection performance based on the 15-day minimum composite NDVI showed -2.5% error relative to the Landsat-derived lake area for the 28 evaluated lakes. The errors increased with increases in the perimeter-to-area ratio but decreased with lake size over 10 km2. The lake area decreased by -9.3% at an annual rate of -53.7 km2 yr-1 during 2000 to 2011 for the 73 lakes. However, considerable spatial variations, such as slight-to-moderate lake area reductions in semi-arid regions and rapid lake area reductions in arid regions, were also detected. This study demonstrated applicability of MODIS 250 m reflectance data for biweekly monitoring of lake area change and diagnosed considerable lake area reduction and its spatial variability in arid and semi-arid regions of Mongolia. Future studies are required for explaining reasons of lake area changes and their spatial variability.

Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China

Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Accurate estimates of FVC are crucial to the use in land surface models. The dimidiate pixel model is the most widely used method for retrieval of FVC. The normalized difference vegetation index (NDVI) of bare soil endmember (NDVIsoil) is usually assumed to be invariant without taking into account the spatial variability of soil backgrounds. Two NDVIsoil determining methods were compared for estimating FVC. The first method used an invariant NDVIsoil for the Northeast China. The second method used the historical minimum NDVI along with information on soil types to estimate NDVIsoil for each soil type. We quantified the influence of variations of NDVIsoil derived from the second method on FVC estimation for each soil type and compared the differences in FVC estimated by these two methods. Analysis shows that the uncertainty in FVC estimation introduced by NDVIsoil variability can exceed 0.1 (root mean square error—RMSE), with the largest errors occurring in vegetation types with low NDVI. NDVIsoil with higher variation causes greater uncertainty on FVC. The difference between the two versions of FVC in Northeast China, is about 0.07 with an RMSE of 0.07. Validation using fine-resolution FVC reference maps shows that the second approach yields better estimates of FVC than using an invariant NDVIsoil value. The accuracy of FVC estimates is improved from 0.1 to 0.07 (RMSE), on average, in the croplands and from 0.04 to 0.03 in the grasslands. Soil backgrounds have impacts not only on NDVIsoil but also on other VIsoil. Further focus will be the selection of optimal vegetation indices and the modeling of the relationships between VIsoil and soil properties for predicting VIsoil.

A simple method to extract tropical monsoon forests using NDVI based on MODIS data: A case study in South Asia and Peninsula Southeast Asia

Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation Index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps (MODMF) from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products (MCD12Q1). The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMF which can be identified for 7 to 9 times between 2001 and 2009 account for 53.1%, while only 7.9% of MCD12Q1 pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000 (GLC2000), MCD12Q1 and University of Maryland (UMD) products are compared with the statistical dataset at national level, which reveals that MODMF has the highest R 2 of 0.95 and the lowest RMSE of 14 014 km2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques.

Optimum Monitoring Time for the Normalized Difference Vegetation Index of Crops

<abstract> Influenced by such factors as air temperature, humidity, solar radiation, and soil moisture, the normalized difference vegetation index (NDVI) for remote sensing of vegetation always changes dynamically within a day. Therefore, choosing the proper time to monitor the index is very important so as to minimize the effects of these factors. In this study, the NDVIs of soybean, cotton, pasture, regular grass, and weeds were measured with a GreenSeeker handheld NDVI sensor for six consecutive days. Furthermore, SPSS statistical software was used to test the differences in the NDVI values between different times for the five types of vegetation. The optimum monitoring time was determined as the period with minimum NDVI change by investigating the NDVI changes within a day and during the days after rainfall. The results showed that the most suitable time within a day for monitoring the NDVI was from 10:00 to 12:00 in the morning, and the best time was from 10:00 to 11:00. The most suitable time for monitoring the NDVI after saturating rainfall was from the 2nd day to the 4th day, and the best time was from the 3rd day to the 4th day.

Evaluating the potential of MODIS satellite data to track temporal dynamics of autumn phenology in a temperate mixed forest

Autumn phenology plays a critical role in regulating growing season duration and can be estimated from satellite remote sensing. However, to date, little work has been undertaken to evaluate the performance of remotely sensed autumn phenology, mainly due to a lack of spatiotemporally compatible field observations. To address this limitation, we conducted intensive ground observations of leaf coloration and leaf fall from 610 deciduous trees at two 625×625m study areas within a mixed forest in northern Wisconsin, USA during 2010 and 2012. We derived landscape phenology (LP) indices by upscaling these plot-level observations to facilitate spatially compatible comparisons with coarse resolution satellite measures. The satellite-derived land surface phenology (LSP) was based on 250m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data from both standard 16-day composite (MOD13Q1) and daily Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products. The results revealed that LSP dormancy onset differed from the observed date of full leaf coloration by 5.25days on average (ranging from 0 to 12days). Furthermore, progression of the autumn season as determined from LSP and LP showed close agreement as increasing LP leaf coloration corresponded to declining NDVI and EVI values. In addition, the end of the leaf coloring phase was marked by a simultaneous stabilizing of both NDVI and EVI time series whereas the timing of the end of the growing season (leaf fall) closely corresponded to minimum NDVI values. These findings clearly support the use of satellite measurements to effectively monitor temporal dynamics of autumn phenology in a temperate mixed forest.

Can temporal and spatial NDVI predict regional bird-species richness?

Understanding the distribution of the species and its controls over biogeographic scales is still a major challenge in ecology. National Park Networks provide an opportunity to assess the relationship between ecosystem functioning and biodiversity in areas with low human impacts. We tested the productivity–biodiversity hypothesis which states that the number of species increases with the available energy, and the ​variability–biodiversity hypothesis which states that the number of species increases with the diversity of habitats. The available energy and habitat heterogeneity estimated by the normalized difference vegetation index (NDVI) was shown as a good predictor of bird-species richness for a diverse set of biomes in previously published studies. However, there is not a universal relationship between NDVI and bird-species richness. Here we tested if the NDVI can predict bird species richness in areas with low human impact in Argentina. Using a dataset from the National Park Network of Argentina we found that the best predictor of bird species richness was the minimum value of NDVI per year which explained 75% of total variability. The inclusion of the spatial heterogeneity of NDVI improved the explanation power to 80%. Minimum NDVI was highly correlated with precipitation and winter temperature. Our analysis provides a tool for assessing bird-species richness at scales on which land-use planning practitioners make their decisions for Southern South America.

Remote sensing based drought monitoring in Zimbabwe

Purpose – Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years. Design/methodology/approach – The Vegetation Condition Index (VCI), which shows how close the Normalized Difference Vegetation Index of the current time is to the minimum Normalized Difference Vegetation Index calculated from the long-term record for that given time, was used to monitor drought occurrence in Zimbabwe. A time series of dekadal Normalized Difference Vegetation Index, calculated from SPOT images, was used to compute seasonal VCI maps from 2005 to 2010. The VCI maps were then classified into three drought severity classes (severe, moderate, and mild) based on the relative changes in the vegetation condition from extremely bad to optimal. Findings – The results showed that droughts occur annually in Zimbabwe though, on average, the droughts are mostly mild. The occurrence and the spatial distribution of drought in Zimbabwe was also found to be random affecting different places from season to season thus the authors conclude that most parts of the country are drought prone. Originality/value – Remote sensing technologies utilising such indices as the VCI can be used for drought monitoring in Zimbabwe.

Analyses on phenological and morphological variations of mangrove forests along the southwest coast of Bangladesh

Drastic changes in river discharge and salinity levels are threatening the phenology and morphology of the coastal mangrove forests of the Sundarbans of Bangladesh. We have used AVHRR GIMMS (1985–2006) and MODIS (2005–2010) satellite Normalized Difference Vegetation Index (NDVI) data to identify the temporal variation of the phenology of the mangroves. Linear interpolation and Fourier-based adjustment were applied to remove noise from the NDVI time series. Then linear regression analysis on a single area (8 km ✕ 8 km) and a composite of 36 areas for three NDVI statistics the annual minimum, annual average, and annual maximum were performed--over the time periods 1985–1990, 1990–2000, 2000–2006 and 2005–2010 to identify possible functional changes in NDVI time series around the Sundarbans. Furthermore, we used fourteen LANDSAT images spanning the period 1989–2010 to estimate the spatiotemporal rate of shoreline changes over the three time periods 1989–2000, 2000–2006, and 2006–2010. A decreasing trend in the annual minimum NDVI was observed in most of the areas of the Sundarbans for the period 1990–2000. During the years 2000–2006, the trends of the three NDVI statistics became significantly positive, indicating an improvement of the mangrove phenology. In the period 2005–2010, a decreasing trend in all the NDVI variables was again dominant. The coast underwent rapid erosion from 1989–2000 and 2006–2010. However, the rate substantially declined between 2000 and 2006, when accretion was dominant. The advent of the upstream Farakka barrage caused a significant reduction in the Ganges-Gorai River discharge and increased the salinity in and around the Sundarbans. Our study concludes that this may be responsible for the degradation of mangrove phenology and accelerated erosion in the earlier and recent periods. In the interim, 2000–2006, improved river discharge and salinity levels due to the Ganges water sharing agreement (1996) and dredging of the Gorai River bed (1998–1999) enhanced the mangrove phenology and helped the coast to gain land.

Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel

The co-existence of trees and grasses is a defining feature of savannah ecosystems and landscapes. During recent decades, the combined effect of climate change and increased demographic pressure has led to complex vegetation changes in these ecosystems. A number of recent Earth observation (EO)-based studies reported positive changes in biological productivity in the Sahelian region in relation to increased precipitation, triggering an increased amount of herbaceous vegetation during the rainy season. However, this ‘greening of the Sahel’ may mask changes in the tree–grass composition, with a potential reduction in tree cover having important implications for the Sahelian population. Large-scale EO-based evaluation of changes in Sahelian tree cover is assessed by analysing long-term trends in dry season minimum normalized difference vegetation index (NDVImin) derived from three different satellite sensors: Système Pour l’Observation de la Terre (SPOT)-VEGETATION (VGT), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) dataset. To evaluate the reliability of using NDVImin as a proxy for tree cover in the Sahel, two factors that could potentially influence dry season NDVImin estimates were analysed: the total biomass accumulated during the preceding growing season and the percentage of burned area observed during the dry season. Time series of dry season NDVImin derived from low-resolution satellite time series were found to be uncorrelated to dry grass residues from the preceding growing season and to seasonal fire frequency and timing over most of the Sahel (88%), suggesting that NDVImin can serve as a proxy for assessing changes in tree cover. Good agreement (R2 = 0.79) between significant NDVImin trends (p < 0.05) derived from VGT and MODIS was found. Significant positive trends in NDVImin were registered by both MODIS and VGT dry season NDVImin time series over the Western Sahel, whereas trends based on GIMMS data were negative for the greater part of the Sahel. EO-based trends were generally not confirmed at the local scale based on selected study cases, partly caused by a temporal mismatch between data sets (i.e. different periods of analysis). Analysis of desert area NDVImin trends indicates less stable values for VGT and GIMMS data as compared with MODIS. This suggests that trends in dry season NDVImin derived from VGT and GIMMS should be used with caution as an indicator for changes in tree cover, whereas the MODIS data stream shows a better potential for tree-cover change analysis in the Sahel.

Phenological characteristics of the main vegetation types on the Tibetan Plateau based on vegetation and water indices

Plant phenology is considered one of the most sensitive and easily observable natural indicators of climate change, though few studies have focused on the heterogeneities of phenology across the different vegetation types. In this study, we tried to find the phenological characteristics of the main vegetation types on the Tibetan Plateau. MCD12Q1 images over the Tibetan Plateau from 2001 to 2010 were used to extract the main vegetation types. The Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) were calculated using surface reflectance values from the blue, red, near-infrared, short-wave infrared (SWIR) 6 (for LSIW6), and SWIR7 (for LSIW7) bands derived from MOD09A1 and used to explore the phenological characteristics of the main vegetation types on the Tibetan Plateau. The results showed that there were eight constant vegetation types on the Tibetan Plateau from 2001 to 2010 demonstrating multiple phenological characteristics. Evergreen needleleaf forest, evergreen broadleaf forest, and permanent wetland had the minimum NDVI values during the summer season, while open shrubland and grassland had the maximum NDVI/EVI values during this period. NDVI and EVI of cropland/natural vegetation had two peaks for their seasonal variations. EVI showed a more significant correlation with LSWI6/LSWI7 than NDVI. Compared to LSWI7, larger EVI values occurred in evergreen needleleaf forest, evergreen broadleaf forest, mixed forest, and permanent wetland, while smaller values occurred in shrubland and barren or sparsely vegetated cover, and nearly equal values occurred in grassland and cropland.

Remote sensing reveals long-term effects of caribou on tundra vegetation

Declining use and abandonment of traditional ranges by migratory caribou (Rangifer tarandus) have often been related to density-dependent depletion of summer forage. The Pen Islands caribou herd (R. t. caribou), Ontario and Manitoba, Canada, numbered in the thousands on its traditional summer tundra range during the 1980s, but then declined in that region. We postulated that increased caribou abundance over three decades negatively affected phytomass, given that under the exploitation ecosystem hypothesis (EEH), grazers limit the amount of primary production if few predators are present. We tested this prediction using the Normalized Difference Vegetation Index (NDVI), as a proxy for phytomass available to caribou. We lagged caribou abundance in the explanatory model by the number of years (4–7) between peak caribou abundance and minimum NDVI. NDVI was negatively related to caribou abundance lagged by 6 years, and growing degree days explained much of the annual variation in NDVI. Precipitation was not an important predictor in the model. Our study is the first to apply NDVI to support the EEH for caribou. We propose that this method could be used over broad scales to shed light on limiting factors for migratory caribou across the circumpolar North.

Spatial and statistical analyses of the relations between vegetation cover and incidence of cutaneous leishmaniasis in an endemic province, northeast of Iran

ObjectiveTo investigate the relations between the vegetation cover and occurrence of cutaneous leishmaniasis (CL) in Golestan province during 2010–2012. MethodsData of monthly CL incidence and remote sensing normalized difference vegetation index (NDVI) of moderate resolution imaging spectroradiometer sensor were used in the study. Pearson's correlation analysis was conducted to examine the type and strength of relationships between the spatially averaged NDVI variables (i.e. minimum, maximum and mean NDVI values) and CL incidence in township level. Spearman rank correlation was carried out to explain associations between the monthly CL incidence and NDVI variables at 0, 1, 2, 3 and 4 months lagged periods. Locations of CL cases at village level were geocoded and compared with frequency of CL cases in each NDVI-derived vegetation categories. ResultsPearson's analysis revealed significant associations between the NDVI variables and CL incidence at the township level (P<0.05). Results of Spearman analysis indicated that the highest correlations between NDVI variables and monthly CL incidence were established when the NDVI time-series lagged the CL incidence series NDVI variables. Purely spatial analysis demonstrated that despite majority of cases occurred at partially-vegetated areas, the possibility of CL occurrence was higher in bare soil areas by taking into account area of each vegetation zones. Conclusionslack or low vegetation cover is a major determinant of CL frequencies in Golestan province. The decrease of the mean vegetation density in townships is accompanied by increase of CL occurrence and vice versa.