Low Normalized Difference Vegetation Index Values Research Articles

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics.

Impact assessment of watershed management programmes on land use/land cover dynamics using remote sensing and GIS

The semi-arid study area, which is the part of Khan-Kali watershed and Anas River from Gujarat, India, was intensely targeted for land reclamation by various government and non-government organizations since last decade. The present study aims at impact assessment of watershed management measure and determines the dynamics of land use/land cover (LU/LC) and vegetation cover in the agricultural area using multi-temporal Remote Sensing (RS) data and Geographical Information System (GIS). The entire study is divided into two parts. The first part deals with LU/LC classification, post classification analysis and change detection over 2001–2011. The ancillary data of both Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were applied at during supervised classification stage and agricultural masks were applied at post supervised classification stage to improve the classification accuracy. In the second part of the study the RS data of the years 2001 and 2012 were used to calculate NDVI, and subsequently the NDVI differencing to detect the change of vegetation cover in agricultural land. Both the changes were estimated at watershed and mini-watershed levels. Specifically, the mini-watershed level changes were evaluated with respect to treated area. The results indicated that the integration of ancillary data with supervised classification has improved the classification accuracy of LU/LC maps by 91.8% and 95.5% for 2001 and 2011, respectively. The key LU/LC changes at watershed level were increased in both the water body and scrub forest areas and decrease in the forest/dense vegetation, barren land and riverine sand areas. At mini-watershed level, for the mini-watersheds treated- (1) less than 25% indicated decrease in forest/dense vegetation and increase in scrub forest area (2) between 25–50% increase in water body and decrease in scrub forest area; and (3) more than 50% indicated increase in water body and decrease in forest/dense vegetation area. To mitigate the observed localised deforestation, the immediate natural vegetation conservation is required. In spite of aridity remain present over the years 2011–2012, the area under agricultural crops showed an increase in high NDVI and decrease in low NDVI values at both watershed and mini-watershed levels. The ground water levels were also increased from 2001 to 2011. The study inferred that the watershed management programmes showed the overall positive impact on the study area.

Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation

Global navigation satellite systems-reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant.

Dynamics of a fringe mangrove forest detected by Landsat images in the Mekong River Delta, Vietnam

AbstractMangrove forests dominate many tropical coastlines and are one of the most bio‐diverse and productive environments on Earth. However, little is known of the large‐scale dynamics of mangrove canopies and how they colonize intertidal areas. Here we focus on a fringe mangrove forest located in the Mekong River Delta, Vietnam; a fast prograding shoreline where mangroves are encroaching tidal flats. The spatial and temporal evolution of the mangrove canopy is studied using a time series of Landsat images spanning two decades as well as Shuttle Radar Topography Mission (SRTM) elevation data. Our results show that fast mangrove expansion is followed by an increase in Normalized Difference Vegetation Index (NDVI) in the newly established canopy. We observe three different dynamics of the mangrove fringe: in the southwest part of the fringe, near a deltaic distributary where the fringe boundary is linear, the canopy expands uniformly on the tidal flats with a high colonization rate and high NDVI values. In the northeast part of the fringe, near another distributary, the canopy expands at a much lower rate with low NDVI values. In the fringe center, far from the river mouths, the fringe boundary is highly irregular and mangroves expansion in characterized by sparse vegetated patches displaying low NDVI values. We ascribe these different dynamics to wave action and southwest longshore transport triggered by energetic northeasterly monsoons during winter. We further link the large‐scale dynamics of the fringe to small‐scale physical disturbances (waves, erosion and deposition) that might prevent the establishment of mangrove seedlings. Based on these results, we include mangrove encroachment in an already published conceptual model of progradation of the Mekong River Delta. We conclude that high NDVI values and a constantly linear vegetation–water interface are indicative of stable mangrove canopies undergoing fast expansion, probably triggered by sediment availability at the shore. Our results can be applied more generally to mangrove forests growing in minerogenic and high tidal range environments with high sediment inputs. Copyright © 2016 John Wiley & Sons, Ltd.

Quantitative assessment of 2014–2015 land-cover changes in Azerbaijan using object-based classification of LANDSAT-8 timeseries

The main goals of this study are the object-based land-cover classification of LANDSAT-8 satellite imagery of 2014 and 2015, the quantitative assessment of gross and net changes of agricultural land, built-up areas, forest, bare soil and forest between 2014 and 2015, the quantification of the Normalized Difference Vegetation Index (NDVI) rates within these land-cover classes, and the change detection analysis between the NDVIs. The achieved overall accuracies of object-based classification for the 2014 and the 2015 land-cover maps were 82 and 87 %, respectively. Therefore, the achieved accuracies were considered to be acceptable for quantified change detection analyses. For the gross areas of agricultural land, forest and built-up areas an increase was observed. The agricultural gross area was 30,911 km2 in 2014 and 31,999 km2 in 2015. The gross area of the built-up land increased from 12,550 to 13,548 km2. The gross area of forest land changed from 8211 to 9175 km2. A decrease was observed in the gross area of grassland from 28,229 to 24,925 km2. This was primarily related to the land-cover shifts driven by agricultural activities. The gross areas of bare soil and water bodies did not change significantly. The net change analysis, however, revealed significant differences in comparison to gross change areas for both gains and losses of the land-cover classes. The net change analysis revealed positive net changes of 7229, 5576, 1337, 399, 951 km2 for agricultural land, forest, built-up areas, bare soil and water bodies, correspondingly. A negative net change of 2198 km2 was observed for grassland. This allows to conclude that the negative net change of grassland was related with the significant changes of grassland into agricultural land. No significant net changes were observed for the bare soil land-cover class. The classification of NDVIs derived from 2014 to 2015 LANDSAT-8 OLI satellite images showed that the vegetation cover of agricultural and built-up land-cover increased for the low (0.1–0.2) and medium (0.2–0.3) and decreased for the high NDVI values (0.3–1). The area of high (0.3–1) NDVIs in the forest land-cover was observed to be higher in 2015 than in 2014. A reduction in the low (0.1–0.2), medium (0.2–0.3) and high NDVI values (0.3–1) was observed for the grasslands land-cover. The reductions of the high NDVI rates (0.3–1) observed for agricultural, build-up and grasslands land-cover types may be related to agricultural and industrial activities and also to climate change impacts. For the entire coverage of Azerbaijan, positive and negative NDVI changes of 3170 and 3859 km2 respectively were observed.

Vegetation response to intensive commercial horticulture and environmental changes within watersheds in central highlands, Kenya, using AVHRR NDVI data

Expansion and heterogeneous clustering of commercial horticulture within the central highlands of Kenya after the mid-1990s impact watersheds and the sustainable resource management. This is distressing since climate conditions for world horticultural regions are projected to change, making such farming extremely difficult and costly to the environment. To understand the scope of impact on vegetation, the study evaluated (1) interannual variability in averaged normalized difference vegetation index (NDVI); (2) trends in average annual NDVI before and after 1990 – the presumed onset of rapid horticulture; and (3) relationship between the average annual NDVI and large-scale commercial farms, population density, and mean annual rainfall in subwatersheds. Time-series analysis of long-term Global Inventory Modeling and Mapping Studies NDVI data were analyzed as indicator of vegetation condition. NDVI trends before 1990s (1982–1989) and after 1990s (1990–2006) were evaluated to determine the slope (sign), and the Spearman’s correlation coefficient (strength). Overall, results show considerable variations in vegetation condition due largely to mixed factors including intensive farming activities, drought, and rainfall variation. Statistical analysis shows significant differences in slopes before 1990 and after 1990 (p < 0.05 and p < 0.1 respectively). Negative (decline) trends were common after 1990, linked to increased commercial horticulture and related anthropogenic disturbances on land cover. There was decline in vegetation over densely populated subwatersheds, though low NDVI values in 1984 and 2000 were the effect of severe droughts. Understanding the linkage between vegetation responses to the effects of human-induced pressure at the subwatershed scale can help natural resource managers approach conservation measures more effectively.

Effect of dehydration on spectral reflectance and photosynthetic efficiency in Umbilicaria arctica and U. hyperborea

In many polar and alpine ecosystems, lichens of genus Umbilicaria represent dominant species forming community structure. Photosynthetic and spectral properties of the lichens may change rapidly according to an actual hydration status of their thalli. In this study, we investigated responses of photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), effective quantum yield of photosynthetic efficiency of photosystem (PS) II (ΦPSII), and several photosynthetic parameters derived from fast induction kinetics of chlorophyll fluorescence (OJIP) to controlled dehydration. We used U. arctica and U. hyperborea collected close to Nuuk, Greenland. In both the species, PRI showed a curvilinear increase with dehydration, i.e., a decreasing water potential (Ψw). The increase was apparent within Ψw range of 0 to -10 MPa. The PRI increase was less pronounced in U. arctica than in U. hyperborea. NDVI decreased with a progressive thallus dehydration in both the species, however, throughout Ψw range of 0 to -30 MPa, U. hyperborea had lower NDVI values than U. arctica. The relationship between ΦPSII and Ψw resulted in a typical S curve. A critical Ψw at which photosynthetic processes were fully inhibited was -30 MPa in both the species, however, species-specific differences in the S curve shape were found. Analyses of photosynthetic parameters derived from OJIPs revealed that the absorption of radiation energy and a trapping rate increased with dehydration in active reaction centres of PS II, the number of which decreased with a more pronounced lichen thallus dehydration. It is concluded that U. arctica and U. hyperborea possess effective physiological mechanisms to maintain an effective photosynthesis when partly dehydrated (the Ψw range of 0 to -15 MPa). In spite of similar ecological niches that these two lichens occupy in nature, their spectral and photosynthetic properties differred.

Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations

Vegetation indices are important remotely sensed metrics for ecosystem monitoring and land surface process assessment, among which Normalized Difference Vegetation Index (NDVI) has been most widely used. The newly launched Landsat 8 Operational Land Imager (OLI) sensor, together with its predecessor Landsat 7 Enhanced Thematic Mapper Plus (ETM+), provides continuous earth observations with an 8-day interval. The design improvements of the new sensor, including narrower near-infrared waveband, higher signal-to-noise ratio (SNR), and greater radiometric sensitivity highlight the need for investigating the land surface observation properties, especially its consistency with data from its predecessors and other satellite sensors. This study aims to evaluate the characteristics of Landsat 8 OLI-derived NDVI against Landsat 7 ETM+ by cross-comparison and by comparing with Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI)-derived NDVIs as well as in-situ NDVI measurements. Simulations of Top of Atmosphere (TOA) reflectance and surface reflectance of broadleaf trees and water were conducted for Landsat 8 OLI, Landsat 7 ETM+, and MODIS in order to evaluate the impact of band pass difference on NDVI calculation. Four consecutive pairs of Landsat 8 OLI and Landsat 7 ETM+ data over China and Korea were examined, and NDVIs derived from TOA reflectance and surface reflectance by three atmospheric correction methods were evaluated. Both simulations and comparisons showed that NDVIs derived from atmospherically-corrected surface reflectance had good consistency, while the simulation showed that the agreement varied with atmospheric characteristics. The four pairs of Landsat 8 OLI and Landsat 7 ETM+ NDVI had a mean bias error within ±0.05, and R2 from 0.84 to 0.98. Vegetated land cover types were found to have better NDVI agreement than non-vegetated land cover types. Especially, Landsat 8 OLI consistently generated lower NDVI values in water area than Landsat 7 ETM+, which resulted from higher aerosol optical thickness in atmosphere. Landsat 8 OLI-derived NDVI showed better agreement with MODIS and GOCI NDVI than Landsat 7 ETM+, mainly on vegetated surfaces. Both Landsat 8 OLI and Landsat 7 ETM+ surface reflectance-derived NDVI agreed well with in-situ light emitting diode (LED) NDVI measurements at a homogeneous deciduous forest site. Landsat 8 OLI was also found to produce higher spatial variability of NDVIs than Landsat 7 ETM+ at vegetated and urban areas, but lower variability on water area. The overall good agreement between Landsat 8 OLI NDVI and Landsat 7 ETM+, MODIS and GOCI NDVIs as well as in-situ measurements ensures that it is reliable to integrate the new sensor observations with those from the multiple satellite sensors, given that the same atmospheric correction methods are applied. Furthermore, the greater NDVI contrast between vegetated areas and water areas, and the higher spatial variability of Landsat 8 OLI NDVI indicated that the new sensor has better capability in land surface process monitoring, such as land cover mapping, spatiotemporal dynamics of vegetation growth, and drought assessment.

Homogenization in forest performance across an environmental gradient – The interplay between rainfall and topographic aspect

This study aimed to investigate the interaction between local and regional environmental factors that affect forest performance during drought periods. In previous studies, contradictory results regarding the effect of aspect on forests performance, under different settings, were reported. However, each study focused on a different forest ecosystem at a different time frame, making synthesis inadequate. Monoculture planted Pinus halepensis forests in Israel, covering a broad climatic gradient (200–850mm annual rainfall), form a suitable study system to address this question.We used remote sensing and GIS methods to observe a large number of afforested stands over a wide area at high resolution. Normalized Difference Vegetation Index (NDVI), obtained from Landsat satellite images for 14years between 1994 and 2011, served as an inclusive measure of forest performance. Data on the examined environmental factors were obtained from spatially interpolated annual rainfall maps and a topographic aspects map. The effects of aspect on NDVI were evaluated separately for three regions along the rainfall gradient: arid (200–350mm), intermediate (350–500mm), and humid (500–850mm).During the studied period, NDVI declined in the arid region but remained constant in the intermediate and humid regions. NDVI was positively related to annual rainfall in all three regions. The effect of aspect on NDVI was positively associated with rainfall in the arid region, but not in the intermediate and humid regions. In other words, forest performance homogenization across local habitats occurred in the arid region under drought stress. Relatively wet years were characterized by high NDVI values (∼0.4), with large differences (∼0.025) between northern and southern aspects, whereas dry years were characterized by low NDVI values (∼0.3) and small differences (∼0.01).The present study supports the concept that under severe drought stress forest performance becomes more homogeneous across local habitats, both temporally (in drought years) and spatially (towards the arid forest boundary). Performance homogenization may occur when low soil water levels are reached, and climatic conditions become the dominant limiting factor. When water availability is high enough, differential performance responses among local habitats are maintained. We evaluated the trends and relations among local and regional environmental factors on performance, and assessed their relative effect sizes. Such an evaluation is essential to link local and global studies aimed at predicting the fate of forests facing global climate change.

Vegetation and its relationship with geomorphologic units in the Parana River floodplain, Argentina

The Parana River is one of the most important fluvial systems of South America and its floodplain includes the most diverse subtropical ecosystem on the continent. However, the relationship between basic aspects, such as the vegetation and geomorphology of the river floodplain, has scarcely been investigated. In this paper, the annual dynamics of vegetation in relation to the geomorphologic and hydrological characteristics of a river floodplain around 31° 30′ S, are analyzed. The annual dynamics of vegetation was investigated using values of the Normalized Difference Vegetation Index (NDVI) obtained from satellite images at two scales of spatial analysis: the first, at the geomorphologic unit level, through several transects crossing the total width of each unit and, the second, through some transects selected from each unit. Our analysis considered variables of different temporal stability (such as geomorphology, hydrology, vegetation, precipitation, and ground temperature), using scenes corresponding to two hydrological cycles of the system (2009 and 2010), which represented relatively “dry” and “humid” years. Five main geomorphologic units were identified in the floodplain of this anabranching system, which were named considering the predominant landforms and the most important (or typical) water course of each area: Bars and Islands of the Main Channel of the Parana River (BI-MCH), Scroll Bars of the Colastine Branch (SB-C), Scroll Bars of the San Javier River Channel (SB-SJ), Crevasse Splays and Levees of the Malo–Mendieta minor channels (CSL-MM), and Crevasse Splays and Levees of the Santa Fe–Coronda river channels (CSL-SFC). These major units are assembled at different general levels and with variable slopes, which partially control the permanence and other characteristics of the flood flow. The crevasse splays and river levees units were predominantly characterized by herbaceous–bushy marshy vegetation, with low mean NDVI values, while SB-C and BI-MCH units showed two types of forests. The latter showed the highest NDVI values. Unit SB-SJ showed mixed characteristics between the two above-mentioned groups. Mean NDVI values were different among the varying geomorphologic units and during the studied years, showing extreme values of 0.25 and 0.80 after both hydrological cycles. According to our results, the main variations in the seasonal dynamics of vegetation show a higher dependence on the dynamics of the hydrological cycle than on surface temperature or precipitation. The hydrological dynamics of the Parana River floodplain are in turn regulated by its geomorphological architecture, so the annual dynamics of vegetation activity in this system are finally influenced by the geomorphologic unit's level, rather than by the short duration modeling processes (i.e., hydrological cycles or drought–flood pulses).

Comparison of cloud-reconstruction methods for time series of composite NDVI data

Land cover change can be assessed from ground measurements or remotely sensed data. As regards remotely sensed data, such as NDVI (Normalized Difference Vegetation Index) parameter, the presence of atmospherically contaminated data in the time series introduces some noise that may blur the change analysis. Several methods have already been developed to reconstruct NDVI time series, although most methods have been dedicated to reconstruction of acquired time series, while publicly available databases are usually composited over time. This paper presents the IDR (iterative Interpolation for Data Reconstruction) method, a new method designed to approximate the upper envelope of the NDVI time series while conserving as much as possible of the original data. This method is compared quantitatively to two previously applied methods to NDVI time series over different land cover classes. The IDR method provides the best profile reconstruction in most cases. Nevertheless, the IDR method tends to overestimate low NDVI values when high rates of change are present, although this effect can be lowered with shorter compositing periods. This method could also be applied to data before compositing, as well as to reconstruct time series for other biophysical parameters such as land surface temperature, as long as atmospheric contamination affects these parameters negatively.

Assessing the recent grassland greening trend in a long-term context based on tree-ring analysis: A case study in North China

Records of the Normalized Difference Vegetation Index (NDVI) and of the productivity of grassland are not long enough to reliably capture natural variation of the grassland dynamics. Herein, we compared a 20-year long growing-season NDVI time series of a typical steppe in North China, and the first principal component (PC1) of four tree-ring width chronologies (based on 69 trees from 3 Pinus tabulaeformis sites and 1 Picea meyeri site) with climate data. Both variables are significantly correlated to each other and to April–July rainfall as well as relative air humidity. This is why PC1 was taken as an indicator of greenness of vegetation in a typical steppe since 1850. The recent greening trend may have happened several times in the past as part of the moisture-driven natural variability. Periods of low growing-season NDVI values derived from tree rings were validated by historical documents. In spite of the ecological complexity, tree-ring analysis provides a simple and efficient method to mirror the key characteristics of the underlying ecological process that was primarily controlled by moisture availability. Thus, tree-ring width time series, clearly documenting the signals of environmental influences, conveys invaluable information about the steppe greenness dynamics and enables us to put the recent greening trend in a long-term context.

Seasonality of cryptosporidiosis: A meta-analysis approach

ObjectivesWe developed methodology for and conducted a meta-analysis to examine how seasonal patterns of cryptosporidiosis, a primarily waterborne diarrheal illness, relate to precipitation and temperature fluctuations worldwide. MethodsMonthly cryptosporidiosis data were abstracted from 61 published epidemiological studies that cover various climate regions based on the Köppen Climate Classification. Outcome data were supplemented with monthly aggregated ambient temperature and precipitation for each study location. We applied a linear mixed-effect model to relate the monthly normalized cryptosporidiosis incidence with normalized location-specific temperature and precipitation data. We also conducted a sub-analysis of associations between the Normalized Difference Vegetation Index (NDVI), a remote sensing measure for the combined effect of temperature and precipitation on vegetation, and cryptosporidiosis in Sub-Saharan Africa. ResultsOverall, and after adjusting for distance from the equator, increases in temperature and precipitation predict an increase in cryptosporidiosis; the strengths of relationship vary by climate subcategory. In moist tropical locations, precipitation is a strong seasonal driver for cryptosporidiosis whereas temperature is in mid-latitude and temperate climates. When assessing lagged relationships, temperature and precipitation remain strong predictors. In Sub-Saharan Africa, after adjusting for distance from the equator, low NDVI values are predictive of an increase in cryptosporidiosis in the following month. DiscussionIn this study we propose novel methodology to assess relationships between disease outcomes and meteorological data on a global scale. Our findings demonstrate that while climatic conditions typically define a pathogen habitat area, meteorological factors affect timing and intensity of seasonal outbreaks. Therefore, meteorological forecasts can be utilized to develop focused prevention programs for waterborne cryptosporidiosis.

Assessing Mongolian snow disaster risk using livestock and satellite data

In Mongolia, several record-breaking disastrous dzuds (mass livestock loss directly induced by harsh winter conditions but often influenced by drought in the previous summer) occurred from 1999 to 2003. To understand the mechanism of this climatic disaster, we conducted a tree regression analysis. The predictor variables included two indices developed from remote sensing data—the Normalized Difference Vegetation Index (NDVI) and the Snow Water Equivalent (SWE)—as well as the previous year's livestock numbers and mortality rates. According to the model, serious livestock mortality was associated with low NDVI values (i.e., poor vegetation) in August of the previous year, high SWE values (i.e., significant snow accumulation) in December of the previous year, a high previous year's mortality, and high previous year's livestock population. This result suggests that for dzud risk assessment, we need to monitor snowfall in winter, the vegetation condition in the previous summer, and the density and health condition of the livestock. The tree-based model developed in this study is effective only for a white dzud (deep snow), the most common type of dzud. The large cross-validation error indicates that more data are needed before using the model in order to make predictions.

Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI

Arctic vegetation distribution is largely controlled by climate, particularly summer temperatures. Summer temperatures have been increasing in the Arctic and this trend is expected to continue. Arctic vegetation has been shown to change in response to increases in summer temperatures, which in turn affects arctic fauna, human communities and industries. An understanding of the relationship of existing plant communities to temperature is important in order to monitor change effectively. In addition, variation along existing climate gradients can help predict where and how vegetation changes may occur as climate warming continues. In this study we described the spatial relationship between satellite-derived land surface temperature (LST), circumpolar arctic vegetation, and normalized difference vegetation index (NDVI). LST, mapped as summer warmth index (SWI), accurately portrayed temperature gradients due to latitude, elevation and distance from the coast. The SWI maps also reflected NDVI patterns, though NDVI patterns were more complex due to the effects of lakes, different substrates and different-aged glacial surfaces. We found that for the whole Arctic, a 5 °C increase in SWI along the climate gradient corresponded to an increase in NDVI of approximately 0.07. This result supports and is of similar magnitude as temporal studies showing increases of arctic NDVI corresponding to increases in growing season temperatures over the length of the satellite record. The strongest positive relationship between NDVI and SWI occurred in partially vegetated and graminoid vegetation types. Recently deglaciated areas, areas with many water bodies, carbonate soil areas, and high mountains had lower NDVI values than predicted by SWI. Plant growth in these areas was limited by substrate factors as well as temperature, and thus is likely to respond less to climate warming than other areas.

WOODY PLANT RICHNESS AND NDVI RESPONSE TO DROUGHT EVENTS IN CATALONIAN (NORTHEASTERN SPAIN) FORESTS

The role of species diversity on ecosystem resistance in the face of strong environmental fluctuations has been addressed from both theoretical and experimental viewpoints to reveal a variety of positive and negative relationships. Here we explore empirically the relationship between the richness of forest woody species and canopy resistance to extreme drought episodes. We compare richness data from an extensive forest inventory to a temporal series of satellite imagery that estimated drought impact on forest canopy as NDVI (normalized difference vegetation index) anomalies of the dry summer in 2003 in relation to records of previous years. We considered five different types of forests that are representative of the main climatic and altitudinal gradients of the region, ranging from lowland Mediterranean to mountain boreal-temperate climates. The observed relationship differed among forest types and interacted with the climate, summarised by the Thorntwaite index. In Mediterranean Pinus halepensis forests, NDVI decreased during the drought. This decrease was stronger in forests with lower richness. In Mediterranean evergreen forests of Quercus ilex, drought did not result in an overall NDVI loss, but lower NDVI values were observed in drier localities with lower richness, and in more moist localities with higher number of species. In mountain Pinus sylvestris forests NDVI decreased, mostly due to the drought impact on drier localities, while no relation to species richness was observed. In moist Fagus sylvatica forests, NDVI only decreased in plots with high richness. No effect of drought was observed in the high mountain Pinus uncinata forests. Our results show that a shift on the diversity-stability relationship appears across the regional, climatic gradient. A positive relationship appears in drier localities, supporting a null model where the probability of finding a species able to cope with drier conditions increases with the number of species. However, in more moist localities we hypothesize that the proportion of drought-sensitive species would increase in richer localities, due to a higher likelihood of co-occurrence of species that share moist climatic requirements. The study points to the convenience of considering the causes of disturbance in relation to current environmental gradients and historical environmental constraints on the community.

Spatial characteristics of AVHRR‐NDVI along latitudinal transects in northern Alaska

Abstract. Two‐weekly AVHRR images were used to examine spatial patterns of the normalized difference vegetation index (NDVI) and their relationships with environmental variables for moist acidic tundra (MAT) and moist non‐acidic tundra (MNT) along two latitudinal transects in northern Alaska. The NDVI database was derived from a 5‐yr time series (1995–1999) of two‐weekly AVHRR composites for Alaska. A digital climate map, digital elevation map and vegetation map were processed and overlain with the NDVI grid. Homogeneous vegetation patches for both MAT and MNT were defined as sample sites using infrared aerial photos, MSS images and the vegetation map along the transects. Linear and non‐linear regression modeling were performed between NDVI indices and environmental variables, total summer warmth (TSW) and elevation. It was demonstrated that along both western and eastern transects, there were obvious latitudinal trends of peak NDVI (AP‐NDVI), average growing season NDVI (GS‐NDVI), and early June NDVI (EJ‐NDVI). In most cases, MNT had lower NDVI values than MAT throughout the year. There were significant (p&lt;0.01) relations between NDVI (AP‐NDVI, GS‐NDVI and EJ‐NDVI) and total summer warmth (TSW) and elevation in the region. EJ‐NDVI showed the strongest correlation with TSW or elevation, making it the most sensitive NDVI indicator along environmental gradients in northern Alaska. NDVI was likely controlled by TSW and elevation, with the former being dominant.

Spatial characteristics of AVHRR-NDVI along latitudinal transects in northern Alaska

Two-weekly AVHRR images were used to exam- ine spatial patterns of the normalized difference vegetation index (NDVI) and their relationships with environmental vari- ables for moist acidic tundra (MAT) and moist non-acidic tundra (MNT) along two latitudinal transects in northern Alaska. The NDVI database was derived from a 5-yr time series (1995-1999) of two-weekly AVHRR composites for Alaska. A digital climate map, digital elevation map and vegetation map were processed and overlain with the NDVI grid. Homo- geneous vegetation patches for both MAT and MNT were defined as sample sites using infrared aerial photos, MSS images and the vegetation map along the transects. Linear and non-linear regression modeling were performed between NDVI indices and environmental variables, total summer warmth (TSW) and elevation. It was demonstrated that along both western and eastern transects, there were obvious latitudinal trends of peak NDVI (AP-NDVI), average growing season NDVI (GS-NDVI), and early June NDVI (EJ-NDVI). In most cases, MNT had lower NDVI values than MAT throughout the year. There were significant (p < 0.01) relations between NDVI (AP-NDVI, GS-NDVI and EJ-NDVI) and total summer warmth (TSW) and elevation in the region. EJ-NDVI showed the strongest correlation with TSW or elevation, making it the most sensitive NDVI indicator along environmental gradients in northern Alaska. NDVI was likely controlled by TSW and elevation, with the former being dominant.

Enhancing Fire Scar Anomalies in AVHRR NDVI Time‐Series Data

Fire mapping science can benefit from standard techniques for analyzing time‐series data. The Z transform produces the z‐score, termed a standardized variable because the units are dimensionless standard deviations. The Z transform represents a simple, older way to characterize non‐image data, but is presented here as a new way to enhance fire scar anomalies in image time‐series data. The transform is invertible, and can be applied to any continuous, gridded data. Time series data from the Advanced Very High Resolution Radiometer (AVHRR) are featured. When applied in the x, y (i.e., spatial) plane of AVHRR Normalized Difference Vegetation Index (NDVI) data, the Z transform places each NDVI value in the statistical context at the time of image formation, producing Z‐standardized NDVI (ZNDVI) values. Invoking the Z transform across the z (time series) plane of ZNDVI images produces a multitemporal Z (MTZ) score for each pixel in a ZNDVI target image for a selected time step. The MTZ image thus depicts the deviation or departure of a given pixel, for a specific step in the series, relative to the mean for that pixel across the time series. A case study demonstrates an MTZ enhancement of AVHRR NDVI data that enhances in the Rincon Mountains east of Tucson, Arizona unusually low NDVI values associated with a 1994 wildfire scar. Enhancements are confirmed using higher‐resolution remote sensor data from the Landsat Thematic Mapper (TM).

Transplanted Lichens in and around the Mount Carmel National Park and the Haifa Bay Industrial Region in Israel: Physiological and Chemical Responses

This study investigated the impact of air pollution on the spectral reflectance of the epiphytic lichen Ramalina lacera, indicated by the normalized difference vegetation index (NDVI), on the integrity of chlorophyll, indicated by the ratio OD435 nm/OD415 nm, and on the integrity of cell membranes, indicated by electric conductivity. Data relating to physiological parameters of injury were integrated with data concerning the detrimental deposition of mineral elements. The transplanted lichen, originating in a relatively unpolluted site in Israel, was placed in 17 sites on and around the Carmel Mountain and in 2 sites in an industrial region in the Haifa Bay, northwest Israel, for a period of 10 months. The accumulated amounts of Ca, Ti, Cu, Mg, Fe, Si, Ni, Zn, V, Cr, Mn, Cl−, K, F−, Na, Ba, Sr, B, S, P, Al, PO3−4, SO2−4, and NO−3 were related to alterations in spectral reflectance and injury caused to chlorophyll and cell membranes. At the end of the period of exposure, the retrieved transplants from the Haifa Bay exhibited low NDVI values and low OD435 nm/OD415 nm ratios, indicating chlorophyll degradation, and high electric conductivity values, indicating damaged cell membranes. NDVI values correlated positively with OD435 nm/OD415 nm ratios and negatively with accumulated amounts of Ba, Cu, Ni, S, SO2−4, V, and Zn. OD435 nm/OD415 nm ratios correlated negatively with amounts of Ba, Cu, Ni, NO−3, SO2−4, and V. Values obtained for electric conductivity correlated positively with amounts of B, Ba, Cl−, Cr, Cu, Na, Ni, NO−3, S, and SO2−4. Both elemental and ion content and the physiological status of the R. lacera transplants indicated that the greater part of the biomonitoring sites on and around the Carmel Mountain were slightly polluted or unpolluted, whereas the Haifa Bay region was rather polluted. The greater part of the Haifa Bay pollution derives from the combustion of heavy fuel oil.