Normalized Difference Vegetation Index Information Research Articles

Spatial and Temporal Variation in Primary Forest Growth in the Northern Daxing’an Mountains Based on Tree-Ring and NDVI Data

We used tree-ring width data of Larix gmelinii and Pinus sylvestris var. mongolica from the northern region of the Daxing’an Mountains, China; normalized difference vegetation index (NDVI) data; and microtopographic information (elevation, slope direction, slope gradient, and topographic location index) to assess spatiotemporal dynamics in the growth of the boreal forest and topographic patterns of forest decline under the background of climate warming. Forest growth trends were determined based on tree growth decline indicators and NDVI time series trends, and topographic patterns of forest decline were analyzed using the C5.0 decision tree model. More climatic information was present in the radial growth of the trees at higher elevations, and P. sylvestris var. mongolica was influenced strongly by climatic factors of the previous year. Since 1759, tree radial growth trends in the study area have experienced two recessions during 1878–1893 and 1935–1943, which were characterized by persistent narrow whorls of tree rings of below-average growth. Changes in NDVI and tree-ring information were similar, and they together indicate a high risk of declining forest growth in the northern Daxing’an Mountains after 2010, especially at higher elevations. The NDVI time series showed that the high temperatures in 2003 negatively affected forest growth in the study area, which was confirmed by the tree-ring data. The decision tree terrain model results had an accuracy of 0.861, and elevation was the most important terrain factor affecting forest decline. The relative importance of elevation, topographic position index, aspect, and slope was 58.41%, 17.70%, 16.81%, and 7.08%, respectively. Classification rule-based decision tree models can be used to quantify the effects of terrain factors on tree growth. This research methodology can aid the management of regional forestry resources and the conservation of forest resources under the background of climate change, which increases the risk of forest decline.

Impacts of Land Cover Changes on Land Surface Temperature using Landsat Imagery with the Supervised Classification Method

Land cover changes occur along with development and an increase in urbanization, affecting the region's ecology. Tarogong Kidul Sub-district, Garut District, experiences land cover changes that change land surface temperature. This study aims to determine the relationship between land cover changes and land surface temperature. This study uses Landsat 8 Surface Reflectance Tier 1 satellite imagery to extract Normalized Difference Vegetation Index information and process soil surface temperatures for three periods, 2014, 2017, and 2020 as well as supervised classification methods. Image collection and processing are done via Google Earth Engine and ArcGIS Pro software. The results were in the form of the distribution of land surface temperature correlated with changes in the value of the land cover vegetation index using simple linear regression and spatial correlation analysis. This study reveals that land cover change is closely related to the increase in land surface temperature indicated by the rise in land surface temperature in areas experiencing changes in land use. The results of linear regression analysis (84.49%) confirm that land cover changes and the greenness index of vegetation are the most critical driving factors for changes in land surface temperature.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Enhancing Animal Movement Analyses: Spatiotemporal Matching of Animal Positions with Remotely Sensed Data Using Google Earth Engine and R

Movement ecologists have witnessed a rapid increase in the amount of animal position data collected over the past few decades, as well as a concomitant increase in the availability of ecologically relevant remotely sensed data. Many researchers, however, lack the computing resources necessary to incorporate the vast spatiotemporal aspects of datasets available, especially in countries with less economic resources, limiting the scope of ecological inquiry. We developed an R coding workflow that bridges the gap between R and the multi-petabyte catalogue of remotely sensed data available in Google Earth Engine (GEE) to efficiently extract raster pixel values that best match the spatiotemporal aspects (i.e., spatial location and time) of each animal’s GPS position. We tested our approach using movement data freely available on Movebank (movebank.org). In a first case study, we extracted Normalized Difference Vegetation Index information from the MOD13Q1 data product for 12,344 GPS animal locations by matching the closest MODIS image in the time series to each GPS fix. Data extractions were completed in approximately 3 min. In a second case study, we extracted hourly air temperature from the ERA5-Land dataset for 33,074 GPS fixes from 12 different wildebeest (Connochaetes taurinus) in approximately 34 min. We then investigated the relationship between step length (i.e., the net distance between sequential GPS locations) and temperature and found that animals move less as temperature increases. These case studies illustrate the potential to explore novel questions in animal movement research using high-temporal-resolution, remotely sensed data products. The workflow we present is efficient and customizable, with data extractions occurring over relatively short time periods. While computing times to extract remotely sensed data from GEE will vary depending on internet speed, the approach described has the potential to facilitate access to computationally demanding processes for a greater variety of researchers and may lead to increased use of remotely sensed data in the field of movement ecology. We present a step-by-step tutorial on how to use the code and adapt it to other data products that are available in GEE.

Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model.

From an agricultural perspective, drought refers to an unusual deficiency of plant available water in the root-zone of the soil profile. This paper focuses on evaluating the benefit of assimilating soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission into the USDA-FAS Palmer model for agricultural drought monitoring. This will be done by examining the standardized soil moisture anomaly index. The skill of the SMAP-enhanced Palmer model is assessed over three agricultural regions that have experienced major drought since the launch of SMAP in early 2015: (1) the 2015 drought in California (CA), USA, (2) the 2017 drought in South Africa, and (3) the 2018 mid-winter drought in Australia. During these three events, the SMAP-enhanced Palmer soil moisture estimates (PM+SMAP) are compared against the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) rainfall dataset and Normalized Difference Vegetation Index (NDVI) products. Results demonstrate the benefit of assimilating SMAP and confirm its potential for improving U.S. Department of Agriculture-Foreign Agricultural Service root-zone soil moisture information generated using the Palmer model. In particular, PM+SMAP soil moisture estimates are shown to enhance the spatial variability of Palmer model root-zone soil moisture estimates and adjust the Palmer model drought response to improve its consistency with ancillary CHIRPS precipitation and NDVI information.

A blended census and multiscale remote sensing approach to probabilistic cropland mapping in complex landscapes

Remote sensing data are used to map the extent of croplands. They are especially useful in sub-Saharan Africa (SSA) where landscapes are complex and farms are small, i.e. less than two ha. In this study, a hierarchical remote sensing approach was developed to estimate field fractions at 30 m spatial resolution in a highly fragmented agricultural region of Ethiopia. The landscape was stratified into crop production system (CPS) zones with ten-day SPOT Proba-V 1 km normalized difference vegetation index (NDVI) composites. The CPS zones were used to disaggregate agricultural census statistics to 1 km field fractions and mask “wet” and “dry” seasons. Long-term average wet-dry season NDVI and topographic information derived from 30 m Landsat-8 (OLI) surface reflectance and the SRTM digital elevation model were combined with 1 km field fractions in a Generalized Additive Model (GAM) to produce the field fractions. Sample dot grids were manually interpreted from very high-resolution DigitalGlobe imagery on the Google Earth platform for training and testing. The model yielded an Area Under the Curve (AUC) of 0.71 and R2 of 0.65 in the holdout sample set. The high AUC reveals the model was effective at classifying 30 m pixels as “crop” or “not crop” while the high R2 indicated leveraging at the extremes (100 and 0% probability), meaning at 30 m resolution, subpixel variations were difficult to discern. The improved model skill compared to previous cropland mapping studies using GAMs can be attributed to the stratification and decomposition of the Landsat time series using CPS-defined phenology. Additional remote sensing model inputs, such as Sentinel-1 radar backscatter and Sentinel-2 red-edge reflectance, could provide additional explanatory power. Wall-to-wall national coverage for agricultural production estimation or other food security related application could be achieved by manually digitizing additional sample data in other regions of Ethiopia or using existing crowd-sourced databases, such as Geo-Wiki.

Complementarity between Textural and Radiometric Indices From Airborne and Spaceborne Multi VHSR Data: Disentangling the Complexity of Heterogeneous Landscape Matrix

The quantitative characterization of landscape structure is critical to assess conservation, and monitor and manage biodiversity. The Mediterranean Basin is a biodiversity hotspot that illustrates the strong relationship between biodiversity and the complexity of the landscape mosaic. Our objective was to test the relevance of two textural indices and one radiometric index (the normalized difference vegetation index (NDVI)) to characterize vegetation structure. These indices could be used as indicators of vegetation composition and organization of four vertical strata when derived from airborne and Pléiades space-borne VHSR imagery. More specifically, we analyzed the influence of the spatial resolution and the radiometric information on the characterization of the landscape structure. Our results indicated that NDVI information at 0.5 m spatial resolution was necessary to be able to incorporate the heterogeneity of vegetation structure. Indices derived from lower resolution NDVI images or different radiometric information than airborne images also proved to be sensitive to vegetation fragmentation and composition. NDVI images brought out details on ligneous/herbs patterns while panchromatic image brought out more details on herbs/bare soil patterns. Combined textural and NDVI indices show strong potential for vegetation structure understanding, allowing detailed mapping. NDVI information shows good potential for applications related to landscape closure dynamics; related habitat degradation indicators caused by shrub encroachment. Panchromatic derived information, on the other hand, provides information relevant in applications focusing grazing management.

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau.

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study.

Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI) information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD) always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters.

Assessing Field-Specific Risk of Soybean Sudden Death Syndrome Using Satellite Imagery in Iowa.

Moderate resolution imaging spectroradiometer (MODIS) satellite imagery from 2004 to 2013 were used to assess the field-specific risks of soybean sudden death syndrome (SDS) caused by Fusarium virguliforme in Iowa. Fields with a high frequency of significant decrease (>10%) of the normalized difference vegetation index (NDVI) observed in late July to middle August on historical imagery were hypothetically considered as high SDS risk. These high-risk fields had higher slopes and shorter distances to flowlines, e.g., creeks and drainages, particularly in the Des Moines lobe. Field data in 2014 showed a significantly higher SDS level in the high-risk fields than fields selected without considering NDVI information. On average, low-risk fields had 10 times lower F. virguliforme soil density, determined by quantitative polymerase chain reaction, compared with other surveyed fields. Ordinal logistic regression identified positive correlations between SDS and slope, June NDVI, and May maximum temperature, but high June maximum temperature hindered SDS. A modeled SDS risk map showed a clear trend of potential disease occurrences across Iowa. Landsat imagery was analyzed similarly, to discuss the ability to utilize higher spatial resolution data. The results demonstrated the great potential of both MODIS and Landsat imagery for SDS field-specific risk assessment.

Agricultural risk management using NDVI pasture index-based insurance for livestock producers in south west Buenos Aires province

Purpose – Based on the Normalized Difference Vegetation Index (NDVI)-based insurance developed by the Ministry of Agriculture, Livestock and Fisheries of Argentina (MAGyP) with technical assistance of the World Bank (WB), the purpose of this paper is to evaluate the out-of-sample performance of the NDVI-based insurance in Bahía Blanca Department of the south west of Buenos Aires (SWBA) Province in Argentina, by calculating the technical premium with the methodology developed by MAGyP-WB and NDVI information up to 2007, and analyzing the results that would have been obtained in 2008 and 2009. Design/methodology/approach – With the available NDVI information (1982-2009), the authors uses the out-of-sample method to analyze the rating of the contract and the reasonability of the premium payment through a comparison of the frequency and severity of payouts in the NDVI coverage with the losses suffered by droughts in SWBA. Specifically, it has been taken the data until 2006 to set the Triggers and Exits values and to calculate the premium rates, and the payout and loss ratio in 2007 was then analyzed. A similar analysis was done for years 2007-2008 and 2008-2009. Findings – According with the rating methodology described in this paper, payouts determined by the NDVI-based insurance fit with falls in forage production and reduced meat production yields, which confers reasonability to this tool as a coverage option for cattle and fodder producers. Definite technical premiums based on 1982-2007 period, capture the occurrence of severe drought events, defining a risk profile that is consistent with the used information. Adding more observations to the sample, this profile is redefined, showing the sensitivity of the results to the quality and quantity of data used in the analysis. Research limitations/implications – There is a subjective assessment in the determination of the sample and the weighting of this information. The election of the period to be considered, how to incorporate the changes in the patterns of climate behavior in the medium- and long-term and the expected effects in the NDVI, etc. will impact on the values of resulting technical premiums. In the specific case of this analysis, the fact of not having considered in the sample two extreme years (2008 and 2009) has a concrete implication in the obtained premiums. In this paper, only the Bahía Blanca Department was considered. Rainfall pattern and extensive grazing of natural grassland in the SWBA area might vary from Department to Department, so the results and payouts might change according to these circumstances. Practical implications – The situation shown in the findings could be seen as an underestimation of the risk to which the producer is exposed and should be taken into account for further researches and insurance products to be designed. When the considered data series shows atypical or extreme values and these are incorporated into the sample, significant changes can be registered in the triggers and premiums. Originality/value – This paper analyzes how a risk profile is redefined depending on the sample considered and shows the sensitivity of the results to used data when using index-based insurance.

A model of West African millet prices in rural markets

In this article we specify a model of millet prices in the three West African countries of Burkina Faso, Mali, and Niger. Using data obtained from USAID’s Famine Early Warning Systems Network (FEWS NET) we present a unique regional millet price forecasting model that takes advantage of the panel nature of our data and accounts for the distance of rural markets from capital cities. Another novel aspect of our analysis is our use of the Normalized Difference Vegetation Index (NDVI) to detect and control for variation in conditions for productivity. We find that including NDVI information significantly improves price forecasts.

Identification of land surface temperature and albedo trends in AVHRR Pathfinder data from 1982 to 2005 for northern Siberia

The arctic regions are highly vulnerable to climate change. Climate models predict an increase in global mean temperatures for the upcoming century. The arctic environment is subject to significant changes of the land surface. Especially the changes of vegetation pattern and the phenological cycle in the taiga–tundra transition area are of high importance in climate change research. This study focuses on time series and trend analysis of land surface temperature, albedo, snow water equivalent, and normalized difference vegetation index information in the time period of 1982–2005 for northern Siberia. The findings show strong dependencies between these parameters and their inter-annual dynamics, which indicate changes in vegetation growing period. We found a strong negative correlation between land surface temperature and albedo conditions for the beginning (60–90%) of the growing season for selected hot spot trend regions in northern Siberia.

A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications

An algorithm is presented to fuse the Normalized Difference Vegetation Index (NDVI) with Light Detection and Ranging (LiDAR) elevation data to produce a map potentially useful for site-specific management practices in cotton. A bi-variate Gaussian probability density distribution is modified to predict an improper probability distribution that also incorporates categorical variables associated with quadrant direction from the population means for the NDVI and elevation data layers. Water availability, influenced by slope and relative changes in elevation (as captured by the elevation data layer), affects crop phenology (as captured by the NDVI data layer). Thus, this fusion procedure results in a map potentially describing the joint effects of NDVI and elevation on cotton growth in a spatial and temporal way.

Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed

Grasslands form the dominant land cover in the upper reaches of the Yellow River and provide a reliable indicator by being strongly correlated with regional terrestrial ecological status. Remote sensing can provide information useful for vegetation quality assessments, but no single sensor can meet the needs for the high temporal–spatial resolution required for such assessments on a watershed scale. To observe long-term grassland dynamics in the Longliu Watershed located in the upper reaches of the Yellow River, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images were integrated to obtain Normalized Difference Vegetation Index (NDVI) data. The MODIS images were used to identify patterns of monthly variation. With the temporal dynamics of NDVI provided by the MODIS images, an exponential regression model was obtained that described the relationship between Julian day and NDVI. Four time-series data sets from multi-spectral sensors were constructed to obtain regional grassland NDVI information from 1977 to 2006 in the Longliu Watershed. Using the daily NDVI correlation coefficient, NDVI values for different days were obtained from Landsat series images, standardised to the same day and integrated into TM format by using NDVI coefficients between the four different sensors. Thus, the NDVI data obtained from multi-sensors on different days were integrated into a comparable format. A regression analysis correlating the NDVI data from two sensors with fresh grass biomass showed that the integration procedure was reliable.

Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, coefficient of determination (r2), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89–93%. Similarly, the PVI pool model provided coefficient of determination (r2) value of 0.45 as compared to 0.31–0.37 for other index models. Log10 data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool)) was developed using the log10 transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r2 value of 0.72 and standard error of prediction of 0.05 t/ha.

Assessment of Potato Phenological Characteristics Using MODIS-Derived NDVI and LAI Information

Remote sensing techniques are capable of identifying a particular crop as well as monitoring its growing stages, crop vigor, and biomass. Due to the increasing demand for food staples, potato cultivation in Bangladesh has increased substantially over the last decade. A study was carried out in the Munshiganj area, the main potato-producing district in Bangladesh, to assess the growth of potatoes by modeling its important life metrics. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) products were extracted from MODIS Surface Reflectance Eight-Day L3 Global 500 m data from November 25, 2005 to March 6, 2006. NDVI and LAI were extracted for 50 selected fields in the study area and used to construct potato phenological curves. Twenty-two life metrics were derived for potato from the phenological curves. The first 12 metrics are the basic life metrics of potato and the others are supplementary. Results showed a significant amplitude and distinct response period of these vegetation indices. Based on the phenological curves, the spatial distribution of potato growth was estimated for the study area for both NDVI and LAI. The effect of temperature on crop phenology was examined during the potato growing season. It was found that significant growth occurred when the temperature was relatively low. This study demonstrates that remote sensing data can be effectively used to study potato growth in Bangladesh.

Enhancement of NDVI Information from Satellite Imagery by Combining with Low-altitude Sensing

Precision farming (PF) was begun in the latter 20th century. In Japan and other countries, there are many researches on remote sensing which use different kinds of sensors and platforms. One of the applications of the remote sensing in agriculture is to obtain crop status images. Recently, field images can be obtained by QuickBird-2, SPOT and other satellites. However, these satellites are affected by the atmospheric conditions, and also the spatial resolution is fairly low. In addition, the images taken by these satellites have large position errors. And generally, a large number of ground truth reference points must be set to make image calibration successful.The objective of this study was to develop a reliable field monitoring system combining helicopter-based and satellite-based remote-sensing. Since an ambient illumination (AI) sensor is installed into the sensing system, the effect of atmospheric condition to the satellite image can be compensated. The normalized difference vegetation index (NDVI) of satellite was transformed to reflectance through the image by the unmanned helicopter. In addition, the images taken by the helicopter were also used for calibration of satellite imagery. Therefore, the helicopter-based system will contribute to enhance the satellite-based remote-sensing. In this study, the R2 value is 0.80 between helicopter-based NDVI and Quickbird2 satellite-based NDVI.

Mapping tropical dry forest habitats integrating Landsat NDVI, Ikonos imagery, and topographic information in the Caribbean Island of Mona

Assessing the status of tropical dry forest habitats using remote sensing technologies is one of the research priorities for Neotropical forests. We developed a simple method for mapping vegetation and habitats in a tropical dry forest reserve, Mona Island, Puerto Rico, by integrating the Normalized Difference Vegetation Index (NDVI) from Landsat, topographic information, and high-resolution Ikonos imagery. The method was practical for identifying vegetation types in areas with a great variety of plant communities and complex relief, and can be adapted to other dry forest habitats of the Caribbean Islands. NDVI was useful for identifying the distribution of forests, woodlands, and shrubland, providing a natural representation of the vegetation patterns on the island. The use of Ikonos imagery allowed increasing the number of land cover classes. As a result, sixteen land-cover types were mapped over the 5500 ha area, with a kappa coefficient of accuracy equal to 79%. This map is a central piece for modeling vertebrate species distribution and biodiversity patterns by the Puerto Rico Gap Analysis Project, and it is of great value for assisting research and management actions in the island.

Ecological covariates of Ascaris lumbricoides infection in schoolchildren from rural KwaZulu‐Natal, South Africa

To identify environmental factors that could serve to predict Ascaris lumbricoides infection patterns and thus guide control efforts in the absence of epidemiological information; to assess whether A. lumbricoides infection is positively associated with the soil clay content. Information on A. lumbricoides infection and re-infection in a cohort of primary schoolchildren and interview data on their socioeconomic background and behaviour were combined with environmental data using a geographical information system (GIS). Multivariate models served to explore the covariation of environmental and infection patterns adjusted for possible confounders. Prevalence maps and spatial statistics revealed considerable spatial clustering of infection in the small study area. Logistic multivariate regression models showed strong positive associations of infection with vegetation density measured as the normalized difference vegetation index (NDVI) at baseline [odds ratio (OR) for a 10% increase: 1.82; 95% confidence interval (95% CI): 1.24-2.68; P=0.002] and after re-infection (OR: 2.22; 95% CI: 1.71-2.87; P<0.001). We also found a strong negative association of re-infection with the sun exposure of the soil surface as estimated from digital elevation models (OR: 0.78; 95% CI: 0.88; P<0.001). The soil clay content was only moderately positively associated with infection and re-infection. Socioeconomic and behavioural variables, although correlated with A. lumbricoides infection, did not appear to confound the above associations in the demographically homogeneous study area. Spatial analysis of the model residuals suggested that as the models accounted for most of the spatial pattern, the model standard errors should not be affected by spatial clustering. NDVI seems to have a high potential for the prediction of A. lumbricoides infection as it was strongly associated with infection patterns in the study area. Further advantages are that NDVI information is easy to use, affordable and available with global coverage.