Remotely-sensed Normalized Difference Vegetation Index Research Articles

Vulnerability of Wheat Crops to Flooding Outweighs Benefits from Precision Farming and Agroecology Practices: A Case Study in Central Italy

This study aimed at testing whether the integration of precision farming (PF) and agroecological practices could influence wheat yield in the short term on soils exposed to varying degrees of risk from flooding. The study embraced two years (2018–2019 and 2020–2021) of wheat cultivation in Central Italy. A two-way factorial grid with agronomic practice (two levels: agroecology vs. conventional on-farm management) and soil vulnerability to flooding (three levels: extreme, mild, non-vulnerable) as factors was set up. The agroecology level included a number of agroecology practices (rotation, use of nitrogen-fixing crops, mulching, and reduction in chemical fertilization). Crop phenology and photosynthetic activity of wheat was monitored by remotely-sensed Normalized Difference Vegetation Index (NDVI). Grain yield was estimated at twenty sampling points at the end of year 2. A flooding event occurred during year 2, which led to significantly lower photosynthetic activity compared to year 1 in extremely vulnerable plots regardless of agronomic practices. Grain yield measurements confirmed that vulnerability was the sole factor significantly affecting yield. The study concludes that food security on vulnerable land can be guaranteed only when precision farming and agroecological practices are coupled with water management techniques that strengthen the resilience of vulnerable soils to floods.

Remote sensing assessment of changes of surface parameters in response to prolonged drought in the arid zone of central Iran (Gavkhoni playa)

Remotely-sensed normalized difference vegetation index (NDVI) and land surface temperature (LST) are significant indicators for evaluating the environmental consequences of climate changes in arid regions. Understanding of the relationship between these changes and land surface properties of the arid regions is important in land planning. The Gavkhoni playa is a closed basin in the arid zone of Iran that has recently undergone significant environmental changes due to decreasing precipitations. The objective of this study was to explore the spatiotemporal changes of its LST and NDVI before and after the drought event of 2014–2018. Through the application of an integrated method based on cellular fractal model and continuous wavelet transform (CWT), we investigated the relationship between variation of surface fractal dimension and distribution of LST and NDVI changes before and after the drought. The results indicated substantial changes in LST and NDVI values associated with the onset of drought conditions. Changes in the spatial distribution of LST and NDVI were particularly high in the central playa area, the western and southern sides of the playa, including the salt lake, the wetlands, and the clay flat. These areas correspond to the zone 4 of the fractal dimension map where extreme topographic anomaly and complexity. Wavelet analysis confirmed the relationship between the surface fractal dimension pattern and anomalies in LST and NDVI variations. The investigation highlighted the ability of the integrated application of the cellular fractal model and wavelet analysis to quantify the relationship between land surface properties and the spatial variation of LST and NDVI in changing environmental conditions.

Are Vegetation Dynamics Impacted from a Nuclear Disaster? The Case of Chernobyl Using Remotely Sensed NDVI and Land Cover Data

There is a growing interest for scientists and society to acquire deep knowledge on the impacts from environmental disasters. The present work deals with the investigation of vegetation dynamics in the Chernobyl area, a place widely known for the devastating nuclear disaster on the 26th of April 1986. To unveil any possible long-term radiation effects on vegetation phenology, the remotely sensed normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) was analyzed within the 30 km Exclusion Zone, where all human activities were ceased at that time and public access and inhabitation have been prohibited ever since. The analysis comprised applications of seasonal trend analysis using two techniques, i.e., pixel-wise NDVI time series and spatially averaged NDVI time series. Both techniques were applied in each one of the individual land cover types. To assess the existence of abnormal vegetation dynamics, the same analyses were conducted in two broader zones, i.e., from 30 to 60 km and from 60 to 90 km, away from Chernobyl area, where human activities were not substantially altered. Results of both analyses indicated that vegetation dynamics in the 30 km Exclusion Zone correspond to increasing plant productivity at a rate considerably higher than that of the other two examined zones. The outcome of the analyses presented herein attributes greening trends in the 30 km and the 30 to 60 km zones to a combination of climate, minimized human impact and a consequent prevalence of land cover types which seem to be well adapted to increased radioactivity. The vegetation greening trends observed in the third zone, i.e., the 90 km zone, are indicative of the combination of climate and increasing human activities. Results indicate the positive impact from the absence of human activities on vegetation dynamics as far as vegetation productivity and phenology are concerned in the 30 km Exclusion Zone, and to a lower extent in the 60 km zone. Furthermore, there is evidence that land cover changes evolve into the prevalence of woody vegetation in an area with increased levels of radioactivity.

Woody encroachment and related soil properties in different tenure-based management systems of semiarid rangelands

Woody encroachment is increasingly threatening savanna ecosystems, but it remains unclear how this is driven by different land tenures and management systems. In South Africa, communal land is mainly managed under continuous grazing, while commercial land is under rotational grazing. We hypothesize that woody encroachment has increased since the end of the Apartheid era in 1994, when rotational grazing systems in communal land switched to continuous grazing. To test this hypothesis, we sampled six subsites in each of three replicates of the two tenure systems in the savannah biome, South Africa, and monitored the degree of woody encroachment and effects on soil using remotely-sensed normalized difference vegetation index (NDVI), dendro-ecology, and grid-based soil analyses. The results confirmed that there has been a positive greening trend over the past 25 years in communal areas due to an increase in woody cover, particularly for tree in the 50–150 cm height class. These trees corresponded to Senegalia tree ages between 10 and 25 years. Greater woody cover in communal areas was accompanied by height-dependent elevated nutrient and organic matter concentrations that increased in the topsoils by a factor of up to 1.5, relative to the freehold systems. Isotopic analyses identified debris of C3 bushes and trees as main carbon input into the soil of communal areas, where the δ13C value of −20.1‰ was significantly lower than it was at private farms (−19.2‰). Isotopic values also indicated that this carbon input took place beyond the edges of the bush canopy. We conclude that the tenure-based management system in the communal areas has promoted woody encroachment, with the associated changes in soil properties homogenizing the savanna system.

Phenology-adjusted dynamic curve number for improved hydrologic modeling

The Soil Conservation Service Curve Number (SCS-CN, or CN) is a widely used method to estimate runoff from rainfall events. It has been adapted to many parts of the world with different land uses, land cover types, and climatic conditions and successfully applied to situations ranging from simple runoff calculations and land use change assessment to comprehensive hydrologic/water quality simulations. However, the CN method lacks the ability to incorporate seasonal variations in vegetated surface conditions, and unnoticed landuse/landcover (LULC) change that shape infiltration and storm runoff. Plant phenology is a main determinant of changes in hydrologic processes and water balances across seasons through its influence on surface roughness and evapotranspiration. This study used regression analysis to develop a dynamic CN (CNNDVI) based on seasonal variations in the remotely-sensed Normalized Difference Vegetation Index (NDVI) to monitor intra-annual plant phenological development. A time series of 16-day MODIS NDVI (MOD13Q1 Collection 5) images were used to monitor vegetation development and provide NDVI data necessary for CNNDVI model calibration and validation. Twelve years of rainfall and runoff data (2001–2012) from four small watersheds located in the Konza Prairie Biological Station, Kansas were used to develop, calibrate, and validate the method. Results showed CNNDVI performed significantly better in predicting runoff with calibrated CNNDVI runoff increasing by approximately 0.74 for every unit increase in observed runoff compared to 0.46 for SCS-CN runoff and was more highly correlated to observed runoff (r = 0.78 vs. r = 0.38). In addition, CNNDVI runoff had better NSE (0.53) and PBIAS (4.22) compared to the SCS-CN runoff (−0.87 and −94.86 respectively). In the validated model, CNNDVI runoff increased by approximately 0.96 for every unit of observed runoff, while SCS-CN runoff increased by 0.49. Validated runoff was also better correlated to observed runoff than SCS-CN runoff (r = 0.52 vs. r = 0.33). These findings suggest that the CNNDVI can yield improved estimates of surface runoff from precipitation events, leading to more informed water and land management decisions.

Simple models to predict grassland ecosystem C exchange and actual evapotranspiration using NDVI and environmental variables

Semiarid grasslands contribute significantly to net terrestrial carbon flux as plant productivity and heterotrophic respiration in these moisture-limited systems are correlated with metrics related to water availability (e.g., precipitation, Actual EvapoTranspiration or AET). These variables are also correlated with remotely sensed metrics such as the Normalized Difference Vegetation Index (NDVI). We used measurements of growing season net ecosystem exchange of carbon (NEE), NDVI from eMODIS and AVHRR, precipitation, and volumetric soil water content (VSWC) from grazed pastures in the semiarid, shortgrass steppe to quantify the correlation of NEE with these driving variables. eMODIS NDVI explained 60 and 40% of the variability in daytime and nighttime NEE, respectively, on non-rain days; these correlations were reduced to 41 and 15%, respectively, on rain days. Daytime NEE was almost always negative (sink) on non-rain days but positive on most rain days. In contrast, nighttime NEE was always positive (source), across rain and non-rain days. A model based on eMODIS NDVI, VSWC, daytime vs. nighttime, and rain vs. non-rain days explained 48% of observed variability in NEE at a daily scale; this increased to 62% and 77%, respectively, at the weekly and monthly scales. eMODIS NDVI explained 50–52% of the variability in AET regardless of rain or non-rain days. A model based on eMODIS NDVI, VSWC, Potential EvapoTranspiration (or PET), and rain vs. non-rain days explained 70% of the observed variability in AET at a daily scale; this increased to 90 and 96%, respectively, at weekly and monthly scales. Models based on AVHRR NDVI showed similar patterns as those using eMODIS, but correlations with observations were lower. We conclude that remotely-sensed NDVI is a robust tool, when combined with VSWC and knowledge of rain events, for predicting NEE and AET across multiple temporal scales (day to season) in semiarid grasslands.

Earlier nesting by generalist predatory bird is associated with human responses to climate change.

Warming temperatures cause temporal changes in growing seasons and prey abundance that drive earlier breeding by birds, especially dietary specialists within homogeneous habitat. Less is known about how generalists respond to climate-associated shifts in growing seasons or prey phenology, which may occur at different rates across land cover types. We studied whether breeding phenology of a generalist predator, the American kestrel (Falco sparverius), was associated with shifts in growing seasons and, presumably, prey abundance, in a mosaic of non-irrigated shrub/grasslands and irrigated crops/pastures. We examined the relationship between remotely-sensed normalized difference vegetation index (NDVI) and abundance of small mammals that, with insects, constitute approximately 93% of kestrel diet biomass. We used NDVI to estimate the start of the growing season (SoGS) in irrigated and non-irrigated lands from 1992 to 2015 and tested whether either estimate of annual SoGS predicted the timing of kestrel nesting. Finally, we examined relationships among irrigated SoGS, weather and crop planting. NDVI was a useful proxy for kestrel prey because it predicted small mammal abundance and past studies showed that NDVI predicts insect abundance. NDVI-estimated SoGS advanced significantly in irrigated lands (β=-1·09±0·30 SE) but not in non-irrigated lands (β=-0·57±0·53). Average date of kestrel nesting advanced 15days in the past 24years and was positively associated with the SoGS in irrigated lands, but not the SoGS in non-irrigated lands. Advanced SoGS in irrigated lands was related to earlier planting of crops after relatively warm winters, which were more common in recent years. Despite different patterns of SoGS change between land cover types, kestrel nesting phenology shifted with earlier prey availability in irrigated lands. Kestrels may preferentially track prey in irrigated lands over non-irrigated lands because of higher quality prey on irrigated lands, or earlier prey abundance may release former constraints on other selective pressures to breed early, such as seasonal declines in fecundity or competition for high-quality mates. This is one of the first examples of an association between human adaptation to climate change and shifts in breeding phenology of wildlife.

The Spatial and Temporal Dynamics of Remotely-sensed Vegetation Phenology in Central Asia in the 1982–2011 Period

An understanding of present and past vegetation dynamics is essential to future reaction strategies to climate change, especially in developing regions. This study seeks to widen such knowledge by utilizing remotely-sensed normalized difference vegetation index (NDVI) data to compute phenology metrics for Central Asia in the 1982–2011 period. Spatial and temporal analysis was performed for nine phenological metrics and for sub- regions with consistent vegetation dynamics. A general increase of biomass was disclosed, except in bare deserts, and the growing season appears to be starting earlier. Despite of the dominant greening trend, deserts show inverse dynamics, possibly a sign of rising aridity.

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Abstract Pastoralist households across East Africa face major livestock losses during drought periods that can cause persistent poverty. For Kenya and southern Ethiopia, an existing index insurance scheme aims to reduce the adverse effects of such losses. The scheme insures individual households through an area-aggregated seasonal forage scarcity index derived from remotely-sensed normalized difference vegetation index (NDVI) time series. Until recently, insurance contracts covered animal losses and indemnity payouts were consequently made late in the season, based on a forage scarcity index incorporating both wet and dry season NDVI data. Season timing and duration were fixed for the whole area (March–September for long rains, October–February for short rains). Due to demand for asset protection insurance (pre-loss intervention) our aim was to identify earlier payout options by shortening the temporal integration period of the index. We used 250 m-resolution 10-day NDVI composites for 2001–2014 from the Moderate Resolution Imaging Spectroradiometer (MODIS). To better describe the period during which forage develops, we first retrieved per-pixel average season start- and end-dates using a phenological model. These dates were averaged per insurance unit to obtain unit-specific growing period definitions. With these definitions a new forage scarcity index was calculated. We then examined if shortening the temporal period further could effectively predict most (> 90%) of the interannual variability of the new index, and assessed the effects of shortening the period on indemnity payouts. Our analysis shows that insurance payouts could be made one to three months earlier as compared to the current index definition, depending on the insurance unit. This would allow pastoralists to use indemnity payments to protect their livestock through purchase of forage, water, or medicines.

Mapping monkeypox transmission risk through time and space in the Congo Basin.

Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox.

Integrated Responses of Grassland Biodiversity and Ecosystem Properties to Hay Management: A Field Experiment

We examined effects of fertilization and haying on grassland biodiversity, primary production and other ecosystem properties in a hay management experiment conducted in northeastern Kansas. We examined relationships between biodiversity and ecosystem productivity across our experimental landscape and evaluated relationships of biodiversity and ecosystem properties to a remotely-sensed Normalized Difference Vegetation Index (NDVI). Fertilization increased production, measured as standing crop biomass and seasonally integrated NDVI and altered a range of ecosystem traits above- and belowground. Shifts at the ecosystem level in response to fertilization were accompanied by declines in plant and arthropod diversity, and negative relationships of both to gradients of standing crop and NDVI, the magnitude of which was altered by haying disturbance in the case of plants. Bacterial diversity showed no response to fertilization, but was increased by haying. In contrast to plants and arthropods, bacterial diversity...

Wheat basal crop coefficients determined by normalized difference vegetation index

Crop coefficient methodologies are widely used to estimate actual crop evapotranspiration (ETc) for determining irrigation scheduling. Generalized crop coefficient curves presented in the literature are limited to providing estimates of ETc for “optimum” crop condition within a field, which often need to be modified for local conditions and cultural practices, as well as adjusted for the variations from normal crop and weather conditions that might occur during a given growing season. Consequently, the uncertainties associated with generalized crop coefficients can result in ETc estimates that are significantly different from actual ETc, which could ultimately contribute to poor irrigation water management. Some important crop properties such as percent cover and leaf area index have been modeled with various vegetation indices (VIs), providing a means to quantify real-time crop variations from remotely-sensed VI observations. Limited research has also shown that VIs can be used to estimate the basal crop coefficient (K cb) for several crops, including corn and cotton. The objective of this research was to develop a model for estimating K cb values from observations of the normalized difference vegetation index (NDVI) for spring wheat. The K cb data were derived from back-calculations of the FAO-56 dual crop coefficient procedures using field data obtained during two wheat experiments conducted during 1993–1994 and 1995–1996 in Maricopa, Arizona. The performance of the K cb model for estimating ETc was evaluated using data from a third wheat experiment in 1996–1997, also in Maricopa, Arizona. The K cb was modeled as a function of a normalized quantity for NDVI, using a third-order polynomial regression relationship (r 2=0.90, n=232). The estimated seasonal ETc for the 1996–1997 season agreed to within −33 mm (−5%) to 18 mm (3%) of measured ETc. However, the mean absolute percent difference between the estimated and measured daily ETc varied from 9% to 10%, which was similar to the 10% variation for K cb that was unexplained by NDVI. The preliminary evaluation suggests that remotely-sensed NDVI observations could provide real-time K cb estimates for determining the actual wheat ETc during the growing season.

Ticks and tick-borne disease systems in space and from space

Analyses within geographical information systems (GISs) indicate that small- and large-scale ranges of hard tick species (Ixodidae) are determined more by climate and vegetation than by host-related factors. Spatial distributions of ticks may therefore be analysed by statistical methods that seek correlations between known tick presence/absence and ground- or remotely-sensed (RS) environmental factors. In this way, local habitats of Amblyomma variegatum in the Caribbean and Ixodes ricinus in Europe have been mapped using Landsat RS imagery, while regional and continental distributions of African and temperate tick species have been predicted using multi-temporal information from the National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) imagery. These studies illustrate ways of maximizing statistical accuracy, whose interpretation is then discussed in a biological framework. Methods such as discriminant analysis are biologically transparent and interpretable, while others, such as logistic regression and tree-based classifications, are less so. Furthermore, the most consistently significant variable for predicting tick distributions, the RS Normalized Difference Vegetation Index (NDVI), has a sound biological basis in that it is related to moisture availability to free-living ticks and correlated with tick mortality rates. The development of biological process-based models for predicting the spatial dynamics of ticks is a top priority, especially as the risk of tick-borne infections is commonly related not simply to the vector's density, but to its seasonal population dynamics. Nevertheless, using statistical pattern-matching, the combination of RS temperature indices and NDVI successfully predicts certain temporal features essential for the transmission of tick-borne encephalitis virus, which translate into a spatial pattern of disease foci on a continental scale.