Normalized Difference Vegetation Index Metrics Research Articles

Drivers of heterogeneity in tundra vegetation productivity on the Yamal Peninsula, Siberia, Russia

The direction and magnitude of tundra vegetation productivity trends inferred from the normalized difference vegetation index (NDVI) have exhibited spatiotemporal heterogeneity over recent decades. This study examined the spatial and temporal drivers of Moderate Resolution Imaging Spectroradiometer Max NDVI (a proxy for peak growing season aboveground biomass) and time-integrated (TI)-NDVI (a proxy for total growing season productivity) on the Yamal Peninsula, Siberia, Russia between 2001 and 2018. A suite of remotely-sensed environmental drivers and machine learning methods were employed to analyze this region with varying climatological conditions, landscapes, and vegetation communities to provide insight into the heterogeneity observed across the Arctic. Summer warmth index, the timing of snowmelt, and physiognomic vegetation unit best explained the spatial distribution of Max and TI-NDVI on the Yamal Peninsula, with the highest mean Max and TI-NDVI occurring where summer temperatures were higher, snowmelt occurred earlier, and erect shrub and wetland vegetation communities were dominant. Max and TI-NDVI temporal trends were positive across the majority of the Peninsula (57.4% [5.0% significant] and 97.6% [13.9% significant], respectively) between 2001 and 2018. Max and TI-NDVI trends had variable relationships with environmental drivers and were primarily influenced by coastal-inland gradients in summer warmth and soil moisture. Both Max and TI-NDVI were negatively impacted by human modification, highlighting how human disturbances are becoming an increasingly important driver of Arctic vegetation dynamics. These findings provide insight into the potential future of Arctic regions experiencing warming, moisture regime shifts, and human modification, and demonstrate the usefulness of considering multiple NDVI metrics to disentangle the effects of individual drivers across heterogeneous landscapes. Further, the spatial heterogeneity in the direction and magnitude of interannual covariation between Max NDVI, TI-NDVI, and climatic drivers highlights the difficulty in generalizing the effects of individual drivers on Arctic vegetation productivity across large regions.

A new change detection method for wetlands based on Bi-Temporal Semantic Reasoning UNet++ in Dongting Lake, China

The utility of semantic change detection in myriad change scenarios has garnered considerable attention in contemporary research; however, its applicability in monitoring alterations in wetland ecosystems remains incompletely elucidated. To surmount the constraints associated with binary change detection methodologies—chiefly their insufficiency in the extraction of bi-temporal attributes—we introduced the Bi-Temporal Semantic Reasoning UNet++ (Bi-SRUNet++) algorithm. This algorithm leverages the architectural strengths of UNet++ as its foundational network to precisely delineate features pertinent to multi-class change detection. As a preliminary step, the study focused on the Dongting Lake wetland in China and conducted an analysis of feature trends predicated upon the monthly Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), as derived from Landsat 8 data spanning 2021–2022. Subsequently, the optimal temporal phases for change detection were ascertained through differential analyses between NDWI and NDVI metrics. Implementing the Bi-SRUNet++ algorithm on a pair of Sentinel-2 images, captured during the optimal phases, yielded augmented change information. Comparative evaluations reveal that the Bi-SRUNet++ algorithm, conceptualized on the framework of Bi-Temporal Semantic Reasoning Network (Bi-SRNet), surpasses the performance indices of its counterpart Semantic Segmentation and Change Detection Late Fusion (SSCD-l). Furthermore, the incorporation of the UNet++ backbone network amplifies the algorithm's capacity for semantic feature extraction, thereby enhancing the efficacy of Bi-SRUNet++ in wetland change detection. The analysis divulges that the total altered area of Dongting Lake during the 2021–2022 period amounts to 1187.97 km2, comprising a water loss of 1186.16 km2, a 715.34 km2 transformation into vegetation, and a conversion of 469.96 km2 into mudflats. The codes and partial dataset in this paper are available at: https://github.com/vivianmiumiu/Bi-SRUNetplusplus-for-SCD.

Identifying the Effects of Vegetation on Urban Surface Temperatures Based on Urban–Rural Local Climate Zones in a Subtropical Metropolis

Many studies have observed the crucial role of vegetated local climate zone (LCZ) types in mitigating the surface urban heat island (SUHI) effect. However, research analyzing the spatial variations in land surface temperature (LST) in a metropolis based on an urban–rural LCZ scheme and exploring the cooling effects of different vegetation types is still lacking. Here, our study focuses on the Guangzhou–Foshan metropolis and aims to elucidate the spatial variations in LST in subtropical cities and the regulating effect of vegetation on LST changes. We used a normalized difference vegetation index (NDVI) and LST data from space-borne MODIS products for the years 2000, 2009, and 2019, as well as LCZ maps, urban–rural gradient data, and land use and land cover (LULC) maps. Urban–rural, seasonal, daytime, nighttime, and diurnal comparative analyses were conducted using logarithmic regression, Pearson partial correlation, and comparison analysis. The results showed that LST values for built LCZ types were generally higher than those of land cover LCZ types, showing a positive correlation with building density and height. The LST decreased logarithmically across the urban–rural gradients, with a rapid decrease initially in the near-gradient urban area, followed by a flattening trend in farther-gradient suburban and rural areas. Regarding vegetated LCZ types, the NDVI metrics showed a significant negative correlation with the LST during the daytime but a positive correlation during the nighttime. The cooling effect of vegetated LCZ types was evident, with an average cooling amplitude of 1.92 °C over the three investigated years. In conclusion, urban LST changes are closely associated with LCZ types, urban–rural gradients, NDVI values, and vegetation types. The cooling ability of vegetation exhibited seasonal and diurnal variations, with a special emphasis on the cooling effect of dense evergreen broadleaf forests. Our findings offer valuable insights and can guide urban ecological construction and management by comprehensively assessing the impact of vegetation on urban surface temperatures.

Identifying Edaphic Factors and Normalized Difference Vegetation Index Metrics Driving Wildlife Mortality From Anthrax in Kenya’s Wildlife Areas

Anthrax, an acute disease of homeotherms caused by soil-borne Bacillus anthracis is implicated in dramatic declines in wildlife mainly in sub-Saharan Africa. Anthrax outbreaks are often localized in space and time. Therefore, understanding predictors of the spatial and temporal occurrence of anthrax in wildlife areas is useful in supporting early warning and improved response and targeting measures to reduce the impact of epizootic risk on populations. Spatial localization of anthrax is hypothesized to be driven by edaphic factors, while the temporal outbreaks are thought to be driven by extreme weather events including temperature, humidity, rainfall, and drought. Here, we test the role of select edaphic factors and normalized difference vegetation index (NDVI) metrics driven by vegetation structure and climate variability on the spatial and temporal patterns of wildlife mortality from anthrax in key wildlife areas in Kenya over a 20-year period, from 2000 to 2019. There was a positive association between the number of anthrax outbreaks and the total number of months anthrax was reported during the study period and the nitrogen and organic carbon content of the soil in each wildlife area. The monthly occurrence (timing) of anthrax in Lake Nakuru (with the most intense outbreaks) was positively related to the previous month’s spatial heterogeneity in NDVI and monthly NDVI deviation from 20-year monthly means. Generalized linear models revealed that the number of months anthrax was reported in a year (intensity) was positively related to spatial heterogeneity in NDVI, total organic carbon and cation exchange capacity of the soil. These results, examined in the light of experimental studies on anthrax persistence and amplification in the soil enlighten on mechanisms by which these factors are driving anthrax outbreaks and spatial localization.

Establishing forest resilience indicators in the hilly red soil region of southern China from vegetation greenness and landscape metrics using dense Landsat time series

Resilience is the capacity of an ecosystem to respond and recover from damage or stress. It can inherently exhibit the cycle and feedback of the disturbed ecosystem recovery process for a specified period. The current availability of dense and consistent time series of satellite images holds the promise of monitoring forest resilience. The aim of this study is to establish forest resilience indicators using dense Landsat time series and assess forest resilience in response to the maximum disturbance magnitude in the hilly red soil region, Hengyang Basin, Southern China. To achieve this, Landsat images of the study area from 1987 to 2017 were collected. Normalized Difference Vegetation Index (NDVI), i.e., proxy of forest green characteristics, number of patch (NP), average patch size (PS), patch perimeter-area ratio (PPAR) and aggregation index (AI), i.e., proxies of forest landscape metrics were calculated from Landsat images. And then elasticity (i.e., the time and rate of recovery), malleability (i.e., degree of deviation from an initial state) and trend (i.e., the pattern of change) as the indicators of forest resilience were constructed. The local space–time Moran’s I (STI) based on NDVI residual space–time Moran’s I (STI) was employed to characterize the forest disturbance and recovery process. The results revealed the following. Firstly, the STI calculated from dense time series NDVI residuals are successful at monitoring the forest disturbance recovery process, regardless of whether changes were dramatic or subtle. Secondly, the most forest disturbances (i.e., > 75%) occurred in the late 1980s and early 1990s. NDVI and landscape metrics also differed in their response to disturbances; NDVI, PPAR and AI are more malleable to disturbance than PS and NP are. Finally, approximately 40% of the disturbed forest had the strong elasticity with a short recovery time (i.e., a year). We conclude that measuring forest resilience via vegetation greenness and landscape metrics using dense time series satellite images is practical as an operational tool for policy makers, landowners, and national park managers. Moreover, insights into ecological dynamics are emerging from capturing the process showing the difference both before and after the change.

Changes in Growing Season Phenology Following Wildfires in Alaska

Trends and geographic patterns of change in vegetation phenology metrics and snowmelt timing from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed across the state of Alaska for all wildfires that burned during the years 2004 and 2005. Phenology metric patterns (over the period 2000 to 2018) derived from the normalized difference vegetation index (NDVI) time-series at 250-m resolution tracked changes in the growing season length and integrated greenness cover over the past two decades. NDVI metrics showed that end of the growing season timing (EOST) and integrated greenness increased significantly in the majority of severely burned areas in Interior Alaska over the past decade particularly in low elevation zones below 500 m. In years with relatively early snowmelt dates (3 to 10 days earlier than the long-term mean), lower plant growth was observed over the ensuing growing season, potentially due to lower snow water inputs that can maintain available soil moisture levels for plant growth into the mid- and late-summer months. Statewide trends in MODIS phenology metrics indicate that the predominant vegetation cover type in most regrowing burned areas of Interior Alaska a decade post-fire is still deciduous shrub and young tree cover. Several large fires of special interest were identified to monitor with remote sensing and continue to track long-term recovery patterns and rates.

Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992–2012

Changes in vegetation phenology are among the most sensitive biological responses to global change. While land surface phenological changes in the Northern Hemisphere have been extensively studied from the widely used long-term AVHRR (Advanced Very High Resolution Radiometer) data, current knowledge on land surface phenological trends and the associated drivers remains uncertain for the tropics. This uncertainty is partly due to the well-known challenges of applying satellite-derived vegetation indices from the optical domain in areas prone to frequent cloud cover. The long-term vegetation optical depth (VOD) product from satellite passive microwaves features less sensitivity to atmospheric perturbations and measures different vegetation traits and functioning as compared to optical sensors. VOD thereby provides an independent and complementary data source for studying land surface phenology and here we performed a combined analysis of the VOD and AVHRR NDVI (Normalized Difference Vegetation Index) datasets for the dry tropics (25°N to 25°S) during 1992–2012. We find a general delay in the VOD derived start of season (SOS) and end of season (EOS) as compared to NDVI derived metrics, however with clear differences among land cover and continents. Pixels characterized by significant phenological trends (P < 0.05) account for up to 20% of the study area for each phenological metric of NDVI and VOD, with large spatial difference between the two sensor systems. About 50% of the pixels studied show significant phenological changes in either VOD or NDVI metrics. Drivers of phenological changes were assessed for pixels of high agreement between VOD and NDVI phenological metrics (serving as a means of reducing noise-related uncertainty). We find rainfall variability and woody vegetation change to be the main forcing variables of phenological trends for most of the dry tropical biomes, while fire events and land cover change are recognized as second-order drivers. Taken together, our study provides new insights on land surface phenological changes and the associated drivers in the dry tropics, as based on the complementary long-term data sources of VOD and NDVI, sensitive to changes in vegetation water content and greenness, respectively.

Use of NDVI and Landscape Metrics to Assess Effects of Riverine Inputs on Wetland Productivity and Stability

Alterations to Louisiana’s river systems and local hydrology have resulted in reduced freshwater, sediment, and nutrient inputs to wetland landscapes, causing significant negative impacts on marsh productivity and stability. This study set out to assess regional- and basin-scale impacts of river connectivity and sediment availability on wetland productivity. Satellite data were used in conjunction with river discharge, river sediment concentration, and wetland accretion data to evaluate correlations between river connectivity and wetland productivity and stability. Significant correlations were observed between river connectivity and Normalized Difference Vegetation Index (NDVI) and Aggregation Index (AI) values across a 10 year period of analysis. Moderate correlations (r2 = 0.51) between mean NDVI and AI values were observed for all wetland vegetation in coastal Louisiana. Middle Coast wetlands had the highest river connectivity and significantly higher aboveground productivity, spatial integrity, and wetland area. The Chenier Plain, with moderate sediment and nutrient inputs, consisted primarily of moderate productivity and integrity. The majority of the inactive Deltaic Plain, which is largely sediment deprived, consists of landscapes with the lowest wetland productivity and spatial integrity. This study linked wetland area, configuration, and productivity with river connectivity to provide an enhanced understanding of river and sediment importance for wetland stability and restoration.

Spatiotemporal Contextual Uncertainties in Green Space Exposure Measures: Exploring a Time Series of the Normalized Difference Vegetation Indices.

Environmental health studies on green space may be affected by contextual uncertainties originating from the temporality of environmental exposures and by how the spatial context is delimitated. The Normalized Difference Vegetation Index (NDVI) is frequently used as an outdoor green space metric capturing the chlorophyll content in the vegetation canopy. This study assessed (1) whether residential NDVI exposures vary over time, and (2) how these time series of NDVI scores vary across spatial context delimitations. Multi-temporal NDVI data for the period 2006–2017 for the Netherlands were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform. Annual NDVI exposures were determined across multiple buffer sizes (i.e., 300, 600, and 1000 m) centered on a random sample of 10,000 Dutch residential addresses. Besides the descriptive statistics, pairwise Wilcoxon tests and Fligner–Killeen tests were used to determine mean and variance differences in annual NDVI scores across buffer widths. Heat maps visualized the correlation matrices. Significance levels were adjusted for multiple hypotheses testing. The results indicated that annual NDVI metrics were significantly correlated but their magnitude varied notably between 0.60 to 0.97. Numerous mean and variance differences in annual NDVI exposures were significant. It seems that the disparate buffers (i.e., 300 and 1000 m) were less strongly correlated, possibly because variance heterogeneity is reduced in larger buffers. These results have been largely consistent over the years and have passed Monte Carlo-based sensitivity tests. In conclusion, besides assessing green space exposures along different buffer sizes, our findings suggest that green space–health studies should employ NDVI data that are well-aligned with epidemiological data. Even an annual temporal incompatibility may obscure or distort green space–health associations. Both strategies may diminish contextual uncertainties in environmental exposure assessments.

Predicting habitat quality of protected dry grasslands using Landsat NDVI phenology

Dry grasslands are species rich and ecologically valuable habitats that have experienced a massive decline in Switzerland during the last century due to agricultural intensification and land abandonment. Appropriate management is a key factor in maintaining habitat quality of the remaining most valuable sites and should thus be an essential part of monitoring studies. However, information on management is often missing and fine-scale patterns are difficult to assess, especially over large areas and for past decades.The aim of this study was to predict habitat quality of protected dry grasslands in Switzerland. Using a nation-wide in-situ vegetation data set with plot-based species lists, we derived six habitat quality indicators (management tolerance, light availability, nutrient content, moisture content and species richness). We then tested how well satellite-based phenology metrics, in combination with environmental and climate data, can predict these dry grassland habitat quality indicators. We expected that the seasonal pattern of vegetation activity, based on the Normalized Difference Vegetation Index (NDVI), would represent local productivity and management patterns, two crucial indicators of dry grassland habitat quality. Linear regression analysis was conducted to assess the relative importance and ecological relationship of different NDVI metrics and other environmental and climate predictors for habitat quality. Variance partitioning was applied to assess model contributions of the three variable groups which represent different data sources for productivity and management.Accuracies for the habitat quality prediction models ranged between 34% and 57% and significant correlations with multiple NDVI metrics were found. Including NDVI phenology improved all models by 7–12%. Single contributions of NDVI phenology were highest for management tolerance and nutrient content. However, we found high variation of contributions between management types. NDVI metrics were highly informative for the habitat qualities of abandoned sites, but grazing and mowing reduced or even cancelled their predictive power. Moreover, our results demonstrate the limitation of single-date NDVI values in predicting habitat quality of dry grasslands, in particular pastures and meadows. For monitoring applications of dry grasslands, we propose using a combination of NDVI metrics, as our results showed that they greatly improve prediction results of essential habitat qualities. The Landsat legacy dataset facilitates the assessment of habitat changes during past decades and can be complemented in the future with higher resolution data, such as Sentinel-2, to increase the temporal and spatial resolution so analyses are more appropriate for the typically limited size of dry grassland habitat sites in Switzerland.

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

ABSTRACTIn the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

A small, fixed-wing unmanned aircraft system (UAS) was used to survey a replicated small plot field experiment designed to estimate sorghum damage caused by an invasive aphid. Plant stress varied among 40 plots through manipulation of aphid densities. Equipped with a consumer-grade near-infrared camera, the UAS was flown on a recurring basis over the growing season. The raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds. NDVI and plant height metrics were averaged on a per plot basis and evaluated for their ability to identify aphid-induced plant stress. Experimental soil signal filtering was performed on both metrics, and a method filtering low near-infrared values before NDVI calculation was found to be the most effective. UAS NDVI was compared with NDVI from sensors onboard a manned aircraft and a tractor. The correlation results showed dependence on the growth stage. Plot averages of NDVI and canopy height values were compared with per-plot yield at 14% moisture and aphid density. The UAS measures of plant height and NDVI were correlated to plot averages of yield and insect density. Negative correlations between aphid density and NDVI were seen near the end of the season in the most damaged crops.

Estimation and Prediction of Grassland Cover in Western Mongolia Using MODIS-Derived Vegetation Indices

Spectral indices derived from satellite observations, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), are widely used for grassland monitoring and management around the globe. In this study we contrasted performance of NDVI and EVI metrics obtained from Aqua and Terra, the two satellite platforms carrying the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, for estimating grassland cover measured at ground level on ninety-two 1 × 1 km blocks distributed from semidesert to high montane grasslands in the Sailugem Range of western Mongolia, where overgrazing and overstocking of domestic livestock are concerns for pastureland management. We also explored utility of late spring (May) vegetation indices for forecasting vegetation cover at the peak of the growing season (July). Vegetation indices developed using MODIS 1-km monthly data (MOD13A3 and MYD13A3) were strongly related to on-the-ground field estimates of the percentage of vegetation cover in July (74−85% variation explained), with second-order polynomial regressions demonstrating better fit to the data than first-order regressions, Aqua vegetation indices (VIs) explaining slightly more variance than Terra’s VIs, and NDVI performing comparably to EVI for both Aqua and Terra. Both Aqua and Terra VIs for May were highly predictive of July vegetation cover (R2 = 0.80−0.84). We conclude that monthly MODIS NDVI and EVI datasets can be useful for rangeland managers in western Mongolia to monitor and predict summer pasture conditions at the regional level, where science-based guidance on grazing policy and practices is much needed.

Exploring Relationships among Tree-Ring Growth, Climate Variability, and Seasonal Leaf Activity on Varying Timescales and Spatial Resolutions

In the first section of this study, we explored the relationship between ring width index (RWI) and normalized difference vegetation index (NDVI) time series on varying timescales and spatial resolutions, hypothesizing positive associations between RWI and current and previous- year NDVI at 69 forest sites scattered in the Northern Hemisphere. We noted that the relationship between RWI and NDVI varies over space and between tree types (deciduous versus coniferous), bioclimatic zones, cumulative NDVI periods, and spatial resolutions. The high-spatial-resolution NDVI (MODIS) reflected stronger growth patterns than those with coarse-spatial-resolution NDVI (GIMMS3g). In the second section, we explore the link between RWI, climate and NDVI phenological metrics (in place of NDVI) for the same forest sites using random forest models to assess the complicated and nonlinear relationships among them. The results are as following (a) The model using high-spatial-resolution NDVI time series explained a higher proportion of the variance in RWI than that of the model using coarse-spatial-resolution NDVI time series. (b) Amongst all NDVI phenological metrics, summer NDVI sum could best explain RWI followed by the previous year’s summer NDVI sum and the previous year’s spring NDVI sum. (c) We demonstrated the potential of NDVI metrics derived from phenology to improve the existing RWI-climate relationships. However, further research is required to investigate the robustness of the relationship between NDVI and RWI, particularly when more tree-ring data and longer records of the high-spatial-resolution NDVI become available.

Does EO NDVI seasonal metrics capture variations in species composition and biomass due to grazing in semi-arid grassland savannas?

Abstract. Most regional scale studies of vegetation in the Sahel have been based on Earth observation (EO) imagery due to the limited number of sites providing continuous and long term in situ meteorological and vegetation measurements. From a long time series of coarse resolution normalized difference vegetation index (NDVI) data a greening of the Sahel since the 1980s has been identified. However, it is poorly understood how commonly applied remote sensing techniques reflect the influence of extensive grazing (and changes in grazing pressure) on natural rangeland vegetation. This paper analyses the time series of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI metrics by comparing it with data from the Widou Thiengoly test site in northern Senegal. Field data include grazing intensity, end of season standing biomass (ESSB) and species composition from sizeable areas suitable for comparison with moderate – coarse resolution satellite imagery. It is shown that sampling plots excluded from grazing have a different species composition characterized by a longer growth cycle as compared to plots under controlled grazing or communal grazing. Also substantially higher ESSB is observed for grazing exclosures as compared to grazed areas, substantially exceeding the amount of biomass expected to be ingested by livestock for this area. The seasonal integrated NDVI (NDVI small integral; capturing only the signal inherent to the growing season recurrent vegetation), derived using absolute thresholds to estimate start and end of growing seasons, is identified as the metric most strongly related to ESSB for all grazing regimes. However plot-pixel comparisons demonstrate how the NDVI/ESSB relationship changes due to grazing-induced variation in annual plant species composition and the NDVI values for grazed plots are only slightly lower than the values observed for the ungrazed plots. Hence, average ESSB in ungrazed plots since 2000 was 0.93 t ha−1, compared to 0.51 t ha−1 for plots subjected to controlled grazing and 0.49 t ha−1 for communally grazed plots, but the average integrated NDVI values for the same period were 1.56, 1.49, and 1.45 for ungrazed, controlled and communal, respectively, i.e. a much smaller difference. This indicates that a grazing-induced development towards less ESSB and shorter-cycled annual plants with reduced ability to turn additional water in wet years into biomass is not adequately captured by seasonal NDVI metrics.

Global‐scale mapping of changes in ecosystem functioning from earth observation‐based trends in total and recurrent vegetation

AbstractAimTo evaluate trend analysis of earth observation (EO) dense time series as a new way of describing and mapping changes in ecosystem functioning at regional to global scales. Spatio‐temporal patterns of change covering 1982–2011 are discussed in the context of changes in land use and land cover (LULCC).LocationGlobal.MethodsThis study takes advantage of the different phenological cycles of recurrent vegetation (herbaceous vegetation) and persistent vegetation (woody/shrub cover) in combining trend analyses of global‐scale vegetation based on different annual/seasonal normalized difference vegetation index (NDVI) metrics. Spatial patterns of combined vegetation trends derived from the Global Inventory Modeling and Mapping Studies NDVI are analysed using land‐cover information (GLC2000).ResultsThe direction of change in annual and seasonal NDVI metrics is similar for most global terrestrial ecosystems, but areas of diverging trends were also observed for certain regions across the globe. These areas are shown to be dominated by land‐cover classes of deciduous forest in tropical/subtropical areas. Areas of observed change are found in dry deciduous forest in South America and central southern Africa and are in accordance with studies of hotspot LULCC areas conducted at local and regional scales. The results show that dense time series of EO data can be used to map large‐scale changes in ecosystem functional type that are due to forest cover dynamics, including forest degradation, deforestation/reforestation and bush encroachment.Main conclusionsWe show that areas characterized by changes in ecosystem functioning governed by LULCC at regional and global scales can be mapped from dense time series of global EO data. The patterns of diverging NDVI metric trends can be used as a reference in evaluating the impacts of environmental changes related to LULCC and the approach may be used to detect changes in ecosystem functioning over time.

Landscape models of space use by desert bighorn sheep in the Sonoran Desert of southwestern Arizona

The desert bighorn sheep (Ovis canadensis mexicana) is one of the few ungulates native to North America capable of surviving in harsh desert environments and xeric conditions. However, the effects of an increasingly arid climate on this species and its habitat are unknown. We sought to identify important predictors of desert bighorn sheep diurnal space use to provide new insight into management and conservation opportunities. We evaluated home range size and relative intensity of space use for 41 collared individuals monitored between 2007 and 2010 across a 14,217-km2 extent of Arizona's Sonoran Desert, including the Kofa National Wildlife Refuge and the United States Army Yuma Proving Ground. Using linear mixed models and an information-theoretic approach, we developed seasonal models of intensity of space use for the summer (Jun–Aug) and non-summer (Sep–May) seasons. Models included environmental variables representing escape terrain and distance from developed waters, and Normalized Difference Vegetation Index metrics that represented forage and preformed water resources. We found intensity of space use to be greater for females (than for males) in the summer season and smaller home range sizes for both sexes in the summer season. Escape terrain was the dominant predictor of intensity of space use in both seasons, but forage and preformed water resources were strong predictors only in the non-summer season. These results likely reflected a seasonal trade-off between escape terrain (i.e., predator avoidance) and limited forage and water resources, particularly for more risk-averse females. Effective conservation and management strategies for desert bighorn sheep should consider the availability of both quality forage and water resources in areas of escape terrain. Although more research is needed on species-specific vulnerabilities to climate change and drought, our approach to integrating vegetation indices into estimates of space use is highly transferable to analyses of other wide-ranging species occupying dynamic arid environments. © 2014 The Wildlife Society.

Monitoring Vegetation Dynamics Inferred by Satellite Data Using the PhenoSat Tool

PhenoSat is an experimental software tool that produces phenological information from satellite vegetation index time series. The main characteristics and functionalities of the PhenoSat tool are presented, and its performance is compared against observed measures and other available software applications. A multiyear experiment was carried out for different vegetation types: vineyard, low shrublands, and seminatural meadows. Temporal satellite normalized difference vegetation index (NDVI) data provided by MODerate resolution Imaging Spectroradiometer and Satellite Pour l'Observation de la Terre VEGETATION were used to test the ability of the software in extracting vegetation dynamics information. Three important PhenoSat features were analyzed: extraction of the main growing season information, estimation of double growth season parameters, and the advantage of selecting a temporal region of interest. Seven noise reduction filters were applied: cubic smoothing splines, polynomial curve fitting, Fourier series, Gaussian models, piecewise logistic, Savitzky-Golay (SG), and a combination of the last two. The results showed that PhenoSat is a useful tool to extract NDVI metrics related to vegetation dynamics, obtaining high significant correlations between observed and estimated parameters for most of the phenological stages and vegetation types studied. Using the combination of SG and piecewise logistic to fit the NDVI time series, PhenoSat obtained correlations higher than 0.71, except for the seminatural meadow start of season. The selection of a temporal region of interest improved the fitting process, consequently providing more reliable phenological information.

Broad‐scale satellite Normalized Difference Vegetation Index data predict plant biomass and peak date of nitrogen concentration in Arctic tundra vegetation

AbstractQuestionsIs the satellite‐derived Normalized Difference Vegetation Index (NDVI) an adequate proxy for the timing of the peak in plant nitrogen concentration in an Arctic tundra system? Can NDVI be used to reliably assess seasonal changes in aboveground plant biomass?LocationThe south plain of Bylot Island, an Arctic tundra ecosystem north of Baffin Island, Nunavut, Canada (73°08′ N, 80°00′ W).MethodsUsing plant data collected every 2 wk throughout the summer in 1991, 1993–1996 and 2006–2008, we assessed the relationship between four NDVI indices (AVHRR satellite data at 1‐km2 spatial resolution) and the date of peak nitrogen concentration in wetland graminoid plants, which represents seasonal variability in plant quality. We also examined the relationship between NDVI and the seasonal changes in aboveground live plant biomass.ResultsThree out of the four NDVI metrics that we tested were significantly related to date of peak nitrogen concentration. The strongest relationship was found with the date at which NDVI values reached 50% of their annual maximum (r2 = 0.87). We also found a positive exponential relationship between NDVI and aboveground biomass of plants (r2 = 0.58), though this relationship was strongest early in the growing season.ConclusionsNDVI can be used as a proxy to determine date of peak nitrogen concentration in some tundra plants, and can thus be a reliable measure of the yearly changes in the timing of the availability of high quality food for herbivores. To a lesser extent, NDVI can also be used to assess seasonal change in plant biomass. This study provides additional support for the use of broad‐scale satellite‐derived NDVI to assess seasonal changes in habitat quality for herbivores.

Remotely sensed vegetation phenology and productivity along a climatic gradient: on the value of incorporating the dimension of woody plant cover

ABSTRACTAim  Woody plants affect vegetation–environment interactions by modifying microclimate, soil moisture dynamics and carbon cycling. In examining broad‐scale patterns in terrestrial vegetation dynamics, explicit consideration of variation in the amount of woody plant cover could provide additional explanatory power that might not be available when only considering landscape‐scale climate patterns or specific vegetation assemblages. Here we evaluate the interactive influence of woody plant cover on remotely sensed vegetation dynamics across a climatic gradient along a sky island.Location  The Santa Rita Mountains, Arizona, USA.Methods  Using a satellite‐measured normalized difference vegetation index (NDVI) from 2000 to 2008, we conducted time‐series and regression analyses to explain the variation in functional attributes of vegetation (productivity, seasonality and phenology) related to: (1) vegetation community, (2) elevation as a proxy for climate, and (3) woody plant cover, given the effects of the other environmental variables, as an additional ecological dimension that reflects potential vegetation–environment feedbacks at the local scale.Results  NDVI metrics were well explained by interactions among elevation, vegetation community and woody plant cover. After accounting for elevation and vegetation community, woody plant cover explained up to 67% of variation in NDVI metrics and, notably, clarified elevation‐ and community‐specific patterns of vegetation dynamics across the gradient.Main conclusions  In addition to the environmental factors usually considered – climate, reflecting resources and constraints, and vegetation community, reflecting species composition and relative dominance – woody plant cover, a broad‐scale proxy of many vegetation–environment interactions, represents an ecological dimension that provides additional process‐related understanding of landscape‐scale patterns of vegetation function.