Significant Normalized Difference Vegetation Index Research Articles

Variations over 20 Years in Vegetation Dynamics and Its Coupled Responses to Individual and Compound Meteorological Drivers in Sichuan Province, China

This study presents an innovative investigation into the spatiotemporal dynamics of vegetation growth and its response to both individual and composite climatic factors. The Normalized Difference Vegetation Index (NDVI), derived from SPOT satellite remote sensing data, was employed as a proxy for vegetation growth. Multiple analytical methods, including the coefficient of variation, Mann–Kendall trend analysis, and Hurst index, were applied to characterize the spatiotemporal patterns of the NDVI in Sichuan Province from 2000 to 2020. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated using monthly precipitation and temperature data from 45 meteorological stations to examine the influence of composite climatic factors on vegetation growth, while the time lag effects between the NDVI and various climatic variables were also explored. Our findings unveil three key insights: (1) Vegetation coverage in Sichuan Province exhibited an overall increasing trend, with the highest NDVI values in summer and the lowest in winter. Significant NDVI fluctuations were observed in spring in the western Sichuan plateau and in winter in northern, eastern, and southern Sichuan. (2) A significant upward trend in the NDVI was detected across Sichuan Province, except for Chengdu Plain, where a downward trend prevailed outside the summer season. (3) On shorter time scales, the NDVI was positively correlated with precipitation, temperature, and the SPEI, with a one-month lag. The response of the NDVI to sunlight duration showed a two-month lag, with the weakest correlation and a five-month lag in western Sichuan. This research advances our understanding of the complex interactions between vegetation dynamics and climatic factors in Sichuan Province and provides valuable insights for predicting future vegetation growth trends.

Multivariate analysis of land surface dynamics in Central Asia: patterns of trends and drivers under a changing climate

ABSTRACT With temperatures in Central Asia (CA) increasing more than the global average, this region is one of the global hotspots affected by climate change. CA is mostly characterized by arid climate, which is why available water resources are of paramount importance for the societies, economies, and the environment. In this regard, quantifying changes on the land surface and controlling factors that influence land surface dynamics are of great interest to improve our understanding of climate change impacts in this region. Hence, this study analyzes multivariate time series covering climatic, hydrological and Earth observation (EO)-based land surface variables. The used EO time series characterize the land surface and include data on the normalized difference vegetation index (NDVI), surface water area (SWA), and snow cover area (SCA) between December 2002 to November 2021. To analyze these time series, we employ trend analyses and a causal discovery algorithm. Both analyses were carried out at multiple spatial and temporal scales. The results show that NDVI trends were mostly significantly negative in the Northwest and positive in the Northeast of CA in summer. In summer and autumn, the percentage of significant negative NDVI trends outweighed the positive trends. For SWA, the detected trends were mostly significant negative throughout all scales. Significant negative trends were retrieved for SCA across all seasons, except for autumn regionally. Particularly the Tian Shan and Pamir mountains show significant declines of SCA in winter and spring. The causal analyses revealed that the NDVI is mostly controlled by water availability in summer. In spring and autumn, temperature is the leading driver on the NDVI. Likewise, temperature is found to largely control SWA in spring and autumn. SCA is mostly negatively coupled to temperature during spring and autumn. A positive coupling between SCA and precipitation is identified in winter.

Examining thermal inequities: Land surface temperature, social vulnerability, and historical redlining in San Antonio, TX

Rising urban temperatures significantly impact human health, with their effects varying across socio-economic groups. With a focus on San Antonio, Texas, during its notably intense summer heat in 2023, this research explores the intricate relationship between urban heat and social vulnerability. Through the analysis of Land Surface Temperature (LST) data from Landsat 8 data, captured during three exceptionally hot days, a positive correlation between LST and the Social Vulnerability Index (SVI) was revealed. This relationship shows that areas with higher SVI, indicative of greater vulnerability, consistently experience higher LSTs. The study further investigated two distinct census tracts: one in a previously redlined area with a high SVI and another in a ‘A-best’ HOLC area with a low SVI. The comparative study between these two tracts revealed significant Normalized Difference Vegetation Index (NDVI) and subsequent thermal variances. It was found that the tract in the historically redlined area consistently experiences higher minimum and mean LST, indicating how past housing policies continue to impact present-day environmental and social conditions. Findings reveal the critical connection between urban heat and social vulnerability, contributing to a broader understanding of the interplay between historical and social factors in shaping thermal inequities in a historically redlined city.

Catchment characteristics control boreal mire nutrient regime and vegetation patterns over ~5000 years of landscape development

Vegetation holds the key to many properties that make natural mires unique, such as surface microtopography, high biodiversity values, effective carbon sequestration and regulation of water and nutrient fluxes across the landscape. Despite this, landscape controls behind mire vegetation patterns have previously been poorly described at large spatial scales, which limits the understanding of basic drivers underpinning mire ecosystem services. We studied catchment controls on mire nutrient regimes and vegetation patterns using a geographically constrained natural mire chronosequence along the isostatically rising coastline in Northern Sweden. By comparing mires of different ages, we can partition vegetation patterns caused by long-term mire succession (<5000 years) and present-day vegetation responses to catchment eco-hydrological settings. We used the remote sensing based normalized difference vegetation index (NDVI) to describe mire vegetation and combined peat physicochemical measures with catchment properties to identify the most important factors that determine mire NDVI. We found strong evidence that mire NDVI depends on nutrient inputs from the catchment area or underlying mineral soil, especially concerning phosphorus and potassium concentrations. Steep mire and catchment slopes, dry conditions and large catchment areas relative to mire areas were associated with higher NDVI. We also found long-term successional patterns, with lower NDVI in older mires. Importantly, the NDVI should be used to describe mire vegetation patterns in open mires if the focus is on surface vegetation, since the canopy cover in tree-covered mires completely dominated the NDVI signal. With our study approach, we can quantitatively describe the connection between landscape properties and mire nutrient regime. Our results confirm that mire vegetation responds to the upslope catchment area, but importantly, also suggest that mire and catchment aging can override the role of catchment influence. This effect was clear across mires of all ages, but was strongest in younger mires.

MAPPING AREAS IMPACTED BY VOLCANIC FLOWS DURING AN ERUPTION USING SYNTHETIC APERTURE RADAR AND OPTICAL IMAGERY

Abstract. During volcanic disasters, the remoteness of the terrains combined with potentially incapacitated lifelines (e.g., disturbed transportation network) prevent ground-based surveys for timely assessment of damage extents. To that effect, we worked to combine satellite optical and Synthetic Aperture Radar (SAR) data to rapidly delineate the areas impacted by fast-moving volcanic flows during an eruption (e.g., Pyroclastic Density Currents (PDCs), lahars), which can in turn be used to target and organize the response efforts. We used the 2015 eruptions of Volcán de Colima (Mexico) and Volcán Calbuco (Chile) volcanoes to calibrate detection thresholds of different types of volcanic flows, from optical and SAR imagery. Optical imagery is used to calculate temporal changes of Normalized Difference Vegetation Index (NDVI) associated with the presence of erupted materials on the surface. SAR amplitude images are used to detect changes in surface roughness (sigma0) attributed to the emplacement of new volcanic flows. Classification of the respective NDVI and SAR amplitude signal changes for different types of volcanic flows is done using very-high-resolution imagery and ground-based data obtained during field work. Linear rescaling of minimal and maximal threshold signals is used to create probability maps of volcanic flow deposits extent, and then combined into a joint probability map to maximize the accuracy of the deposit extents. We tested our ability to generate volcanic flow extent maps during the April 2021 eruption of Soufrière St Vincent, using this detection method and the calibrated threshold values for PDCs and lahars.

Expanding vegetated areas by human activities and strengthening vegetation growth concurrently explain the greening of Seoul

Many studies have reported that urbanization leads to a decrease in the normalized difference vegetation index (NDVI) due to the expansion of impervious cover. Some studies, however, have reported positive NDVI trends in urban areas due to warming and CO2 fertilization effects, as well as the creation of green space. Thus, we examined spatial and temporal variations of the growing season maximum NDVI in a megacity, Seoul, which has rapidly urbanized over the past decades, by analyzing a 32-year time series (1987 – 2018) of Landsat satellite images in Google Earth Engine. Continuous change detection and classification and random forest algorithms were integrated to classify Seoul land cover types annually. We found an overall increasing NDVI trend at the city scale (0.002 yr−1). Significant NDVI trends were found for approximately 46 % of Seoul, with greening and browning trends accounting for 39 % and 7 %, respectively. Greening pixels appeared mainly on impervious (23 % with a significant NDVI trend), deciduous (10 %), and evergreen (3 %) land cover as of 2018. Stable impervious, deciduous, and evergreen land cover pixels showed a greening trend over the 32 years (0.002 yr−1), which stemmed from the planting of trees in areas with impervious cover, such as streets and residential areas, and vegetation growth in forest areas. Disturbed area pixels showed fluctuating NDVI values, but there were more greening pixels (20 %) than browning pixels (5 %). Our findings indicate that a detailed knowledge of land use and land cover changes is required to understand NDVI trends in urban areas.

Vegetation Dynamics and its Response to Climate Change in the Yellow River Basin, China

As an important ecological corridor, the Yellow River basin (YRB) is crucial for the eco-environmental security and sustainable socio-economic development of China. Systematic studies on the spatiotemporal evolution of vegetation cover and the response of vegetation dynamics to climate change in the YRB at different timescales are lacking. Utilizing a long-term remotely sensed Normalized Difference Vegetation Index (NDVI) and gridded climate dataset, we examined the spatiotemporal variability of vegetation cover and its response to climate variables in the Yellow River Basin (YRB) at multiple timescales by using the Mann-Kendall test, rescaled range analysis, and partial correlation analysis. Results indicated that the annual NDVI in the YRB decreased spatially from southeast to northwest, and peaked in August. From 1982 to 2015, the YRB experienced greening during the annual, growing season and spring, with statistically significant NDVI increases (p &amp;lt; 0.05) recorded in over 55% of the vegetated areas. NDVI trends should be expected to persist in the future, as evidenced by the Hurst index exceeding 0.5 in over 85% areas of the YRB. Temperature and precipitation determined the spatiotemporal pattern of vegetation cover in the YRB, and vegetation dynamics response to climatic variations varied among seasons and climatic zones. In contrast to other seasons, spring NDVI was significantly correlated with temperature, whereas winter vegetation was more vulnerable to suppression by increased precipitation. Vegetation growth was more susceptible to precipitation than to temperature in the arid and semiarid zones, while temperature dominated vegetation dynamics in the semi-humid zone, and the sunshine duration was essential for vegetation growth in high-altitude regions. The study contributes to a deeper understanding of the interrelationship between vegetation dynamics and climate change in the YRB and provides useful suggestions for the regional ecological conservation in the context of global warming.

Research on the spatiotemporal coupling relationships between land use/land cover compositions or patterns and the surface urban heat island effect.

Urbanization leads to changes in landscape configuration and land use/land cover (LULC) patterns, and these changes are important factors affecting the surface urban heat island (SUHI) effect. However, from the perspective of spatiotemporal changes, quantitative analytical results regarding the impacts of the LULC composition, configuration, and pattern in inland plateau lakeside cities on the SUHI effect, and the responsive relationships among these factors remain unclear. By combining satellite remote sensing data with analytical methods, such as urban-rural gradients, spatial statistics, and landscape pattern indices, the impacts of LULC changes on the SUHI effect in Kunming, China, are revealed. The results show the following. (1) The explosive growth in impervious surfaces (ISs) caused by urbanization, leading to changes in the LULC composition, configuration and pattern, is the main reason for the deterioration of the SUHI effect. Over the past 30years, Kunming's ISs have increased by 304.58 km2, SUHI has expanded by 764.26 km2, and the regional average land surface temperature (LST) has increased by 1°C. (2) This study also found that a large area of bare ground is another important reason for the sharp rise in LST, explaining why bare land (BL) has the highest average LST (28.72°C). (3) The pattern of LULC can well explain the spatial distribution characteristics of SUHIs. The normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), and LST have the same change curve along the urban-rural gradient, while the normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and LST have opposite trends. (4) ISs and water body (WB) are the main types of warming and cooling, respectively, but the warming effect of ISs is greater than the cooling effect of WB. From the average value of the correlation coefficient with LST, NDBI (0.84) > MNDWI (-0.63). (5) Kunming's remote sensing index values do not have simple linear relationships with the LST. NDBaI, NDBI, and LST show significant exponential relationships, and NDVI, MNDWI, and LST show significant quadratic polynomial relationships. (6) The dominant landscape type determines the correlation between the landscape shape index (LSI) and the LST of green spaces (GSs). (7) Adopting a simple and regular landscape layout can effectively reduce the SUHI effect. These research results could provide a scientific decision-making basis for the spatial urban planning and ecological construction of Kunming and could have practical significance for guiding the green, healthy, and sustainable development of the city.

Inundation Impact on Croplands of 2020 Flood Event in Three Provinces of China

Floods are threatening agricultural production and causing enormous crop losses globally. In June–July 2020, the Yangtze and Huai River basins were hit again by exceptionally heavy rainfall and flooding, followed by massive inundation of croplands. However, quantitative studies on the impact of flooding on crops are still lacking. Therefore, quick and accurate mapping of flood and quantitative assessment of inundation impact on crops is vital. The main objectives of this article are to map the flooding areas and analyze the inundation impact on crops. The result of flood mapping with SAR images achieves an overall accuracy of 0.9023, which facilitates the analyses of phenological temporal change and Normalized Difference Vegetation Index (NDVI) losses of crops. It is found that the inundation resulted in a phenological lag in crops grown in the 2020 summer. The phenological lag resulted in a significant NDVI loss, especially in the earlier stage. With the extension of phenology latency, crops suffered even greater losses. Conversely, in the winter of 2020, the crops show obvious phenological advances, and compared with 2019, there was no obvious NDVI loss.

Canopy spectral characteristics of typical invasive and native plants in the coastal wetland of Yancheng City, China

Spartina alterniflora (S. alterniflora) is a dominant invasive alien species that occurs in Yancheng Wetland National Nature Reserve, the largest coastal wetland in China. It expands rapidly and exerts great threats to local ecosystem. The main native species there are Phragmites australis (P. australis) and Suaeda salsa (S. salsa), respectively. In order to monitor their dynamics, it is of great significance to analyze their spectral discrimination. Canopy spectra of these typical species were measured in July and October in order to compare species differences, as well as the seasonal variation. The Normalized Difference Vegetation Index (NDVI) was calculated based on canopy spectra. The significances of differences in spectral characteristic among species were tested using the one-way analysis of variance (ANOVA) (P < 0.05). The results showed that the visible (VI) bands were the optimum wavelengths for species discrimination in both seasons. S. alterniflora always had the greatest green peak height and red absorption depth. S. salsa had no obvious green peak, but an obvious red reflection peak. P. australis generally had intermediate values. Spectra in the near-infrared (NIR) bands were not appropriate for delineating S. alterniflora and P. australis in summer, as they showed similar values in these wavelengths. But NIR bands could be used to distinguish S. salsa in summer, as it had significantly lower reflectance in NIR bands than the others. Meanwhile, the reflectance in short-wavelength infrared (SWIR) bands became another suitable approach for distinguishing species in autumn. S. salsa had significantly higher values than the others in SWIR regions. Significant NDVI differences between different species proved that it could improve the species discrimination. S. alterniflora always had the highest NDVI values, while S. salsa had the lowest values. The seasonal trend of canopy spectra was also revealed. With plant maturity, the reflectance values in green bands and NIR bands decreased significantly, but increased significantly around the yellow-red wavelengths. The green peak heights, red absorption depths, and NDVI values of the species decreased remarkably. Furthermore, S. alterniflora and P. australis illustrated obvious ‘blue shift’ of the red edge with senescence. These results pointed out the the potential bands and appropriate spectral parameters appropriate for species discrimination, and highlighted the influence of seasonality on spectral information. They provided an important basis for salt marshes identification and dynamic monitoring on a large scale.

The trend of vegetation greening and its drivers in the Agro-pastoral ecotone of northern China, 2000–2020

Revegetation practices have continued for nearly 20 years in the agro-pastoral ecotone of northern China (AENC), but it still remains unknown how human-induced efforts have contributed to vegetation restoration. This study analyzed the spatiotemporal dynamics of vegetation coverage in the AENC during 2000–2020 in terms of the normalized difference vegetation index (NDVI) using MODIS NDVI datasets, projected the future trend of vegetation variations, and was first to quantify the relative contributions of climatic and anthropogenic impacts on the vegetation variations in the AENC by residual analysis. The results showed that the vegetation in the AENC was greening in recent 20 years due to pronounced increases in NDVI at a rate of 0.00623 year−1, but the trend of vegetation greening was unsustainable as a whole, especially in the central AENC, where large areas of vegetation will degrade in the future. Specifically, anthropogenic and climatic impacts jointly determined vegetation greening in almost 89% of the AENC. Human activities, especially large-scale afforestation, was the most important driver that contributed to vegetation greening in the AENC as a result of the relative role of approximately 66% of NDVI variations (34% from climate change). Despite the significant expansion in forest coverage, excessive afforestation had complex and unpredictable impacts on vegetation growth, and hence the eco-restoraiton is better to make vegetation recover in its natural form. This study revealed a more significant NDVI uptrend in the AENC than previously understood, highlighted the decisive role of human activities in vegetation greening over the last 20 years, and was expected to offer a better understanding and information for local ecological restoration.

Vegetation dynamics and its linkage with climatic and anthropogenic factors in the Dawen River Watershed of China from 1999 through 2018.

The Dawen River Watershed (DRW), an important sub-basin of the Yellow River, has been experiencing substantial climatic and anthropogenic stresses. Identifying how stressors relate to shifts in vegetation growth is critical for maintaining the health and stability of its regional ecosystems. To address this, we constructed a 20-year dataset (1999-2018) reflecting changes in satellite-based normalized difference vegetation index (NDVI), climate variables, and land use in the DRW. We then used time series, principal component, and partial correlation analyses to detect spatial and temporal patterns in vegetation dynamics over time, as well as linkages with temperature, precipitation, and anthropogenic activities. Over 20 years, the DRW exhibited a warming-greening trend and experienced four regime shifts in its climate-vegetation system, roughly centered on 2001, 2006, 2013, and 2016. Both the average and maximum NDVI increased in all seasons, likely due to favorable changes in seasonal climatic conditions. Temperature was the dominant factor promoting vegetative growth in spring, autumn, and throughout the growing season. Precipitation had a considerable positive effect on the average NDVI during the summer. Spatial analyses indicated that 67.94% of the study area exhibited significant increase in NDVI values over time, mainly locating in the mountains and in Dongping County; Significant NDVI decrease was generally located in the urban expansion areas around cities and counties. Land cover types and annual growth cycles appeared to govern spatial patterns and the extent of variation in vegetation growth, followed by land use-related drivers and climate anomalies. These findings offer an insight on appropriate ecological management and climatic adaptation within the Dawen River Watershed.

Novel sensor-based method (quick test) for the in-season rapid evaluation of herbicide efficacy under real field conditions in durum wheat

AbstractOptimum herbicide use is a key factor affecting the success of any integrated weed management strategy. The main objective of the current study was to implement a method based on spectrometer measurements for the in situ evaluation of herbicide efficacy and the detection of potentially herbicide-resistant weeds. Field trials were conducted in Greece between 2018 and 2020 in several durum wheat fields (Triticum durum Desf.). In all trials, the overall effect of herbicide application on the recorded Normalized Difference Vegetation Index (NDVI) values (at 1 and 2 wk after treatment [WAT]) was significant (P ≤ 0.001). For the majority of the surveyed fields, low NDVI values were recorded after 2,4-D application and a mixture of clopyralid + florasulam from 1 WAT, suggesting their increased efficacy. In several cases, the application of pyroxsulam + florasulam resulted in significant NDVI reductions at 2 WAT. As observed at the end of the growing seasons, the herbicides that reduced NDVI resulted in lower weed biomass. Strong correlations were observed between weed aboveground biomass and NDVI (2 WAT). In particular, R2 values were 0.8234 to 0.8649, 0.8453, 0.8595, 0.8149, and 0.8925 for the Aliartos, Thiva, Domokos, Larissa, and Orestiada fields, respectively. The overall effects of herbicide application on wheat grain yield were also significant (P ≤ 0.001). Pot experiments confirmed that the high NDVI values in some cases could be attributed to the presence of herbicide-resistant weeds. For instance, the resistance indices of two accessions of catchweed bedstraw (Galium aparine L.) to mesosulfuron-methyl + iodosulfuron-methyl-sodium ranged between 9.7 and 13.2, whereas one sterile oat [Avena sterilis L. ssp. ludoviciana (Durieu) Gillet &amp; Magne] accession was 8.8 times more resistant to fenoxaprop-p-ethyl than a susceptible one. The present study is targeted at making a significant contribution toward establishing cause–effect relationships and presenting a useful tool for developing more effective weed management practices in more arable crops and under different soil and climatic conditions.

Effects of Climate Variability on Normalized Difference Vegetation Index (NDVI) in the Gojeb River Catchment, Omo-Gibe Basin, Ethiopia

Vegetation dynamics have been visibly influenced by climate variability. The Normalized Difference Vegetation Index (NDVI) has been the most commonly used index in vegetation dynamics. The study was conducted to examine the effects of climatic variability (rainfall) on NDVI for the periods 1982–2015 in the Gojeb River Catchment (GRC), Omo-Gibe Basin, Ethiopia. The spatiotemporal trend in NDVI and rainfall time series was assessed using a Theil–Sen (Sen) slope and Mann–Kendall (MK) statistical significance test at a 95% confidence interval. Moreover, the residual trend analysis (RESTREND) method was used to investigate the effect of rainfall and human induction on vegetation degradation. The Sen’s slope trend analysis and MK significant test indicated that the magnitude of annual NDVI and rainfall showed significant decrement and/or increment in various portions of the GRC. The concurrent decrement and/or increment of annual NDVI and rainfall distributions both spatially and temporarily could be attributed to the significant positive correlation of the monthly (RNDVI-RF = 0.189, P≤0.001) and annual (RNDVI-RF = 0.637, P≤0.001) NDVI with rainfall in almost all portions of the catchment. In the GRC, a strongly negative decrement and strong positive increment of NDVI could be derived by human-induced and rainfall variability, respectively. Accordingly, the significant NDVI decrement in the downstream portion and significant increment in the northern portion of the catchment could be attributed to human-induced vegetation degradation and the variability of rainfall, respectively. The dominance of a decreasing trend in the residuals at the pixel level for the NDVI from 1982, 1984, 2000, 2008 to 2012 indicates vegetation degradation. The strong upward trend in the residuals evident from 1983, 1991, 1998 to 2007 was indicative of vegetation improvements. In the GRC, the residuals may be derived from climatic variations (mainly rainfall) and human activities. The time lag between NDVI and climate factors (rainfall) varied mainly from two to three months. In the study catchment, since vegetation degradations are mainly caused by human induction and rainfall variability, integrated and sustainable landscape management and climate-smart agricultural practices could have paramount importance in reversing the degradation processes.

Evaluating the Relationships of Inter-Annual Farmland Vegetation Dynamics with Biodiversity Using Multi-Spatial and Multi-Temporal Remote Sensing Data

Agricultural landscapes are highly dynamic ecosystems, but the effects of temporal farmland vegetation dynamics on species diversity have not been widely studied. In 93 sample farm landscapes in eastern Ontario, Canada, biodiversity data for seven taxa were collected in 2011 and 2012, prior to the initiation of this study. The goal of this study was to determine if trends and variability in vegetation productivity detected in these sample landscapes using long-term archived moderate and coarse resolution remote sensing time series data are related to the measured biodiversity. Mid-summer Moderate Resolution Imaging Spectroradiometer (MODIS) (2000–2011) and Landsat 5 (1985–2011) Normalized Difference Vegetation Index (NDVI) data were used with the Thiel–Sen slope and Contextual Mann–Kendall trend analysis to identify pixels showing significant trends. NDVI temporal metrics included 1) the percentage of pixels in each landscape with a significant negative or positive trend, and 2) the temporal coefficient of variation (CV) of both the mean and spatial CV of landscape NDVI. Larger areas of significant positive NDVI trends were found in the sample landscapes than negative trends, the former being associated with agricultural intensification or crop changes and the latter with smaller areas of natural vegetation removal. Landsat better-detected changes in individual fields or small areas of natural vegetation due to its much smaller pixel size. In addition, the longer Landsat time series showed a change in the NDVI trend from positive (1985–2000) to negative or a leveling off (2000–2011) for many pixels. In biodiversity modeling, the Landsat temporal CV of NDVI was negatively correlated with 2011–2012 plant and beetle diversity, while plant biodiversity was positively correlated with the percentage of pixels in a sample landscape showing a significantly positive NDVI trend. No significant relationships were found using the MODIS data. This study shows that temporal trends and variability in farmland vegetation density derived from Landsat data are related to biodiversity for certain taxa and that such relationships should be considered along with the more commonly studied spatial landscape attributes in evaluating landscape-level impacts of farming on biodiversity.

Forest browning trends in response to drought in a highly threatened mediterranean landscape of South America

• Effect on forest NDVI of recent 7 years “mega-drought” was assessed. • One-third of Mediterranean forest showed a significant NDVI decrease during drought. • Local wet conditions provide drought refuges. • Browning occurs in communities with species showing different drought resistance. • Potential resistance loss is suggested by high browning in the driest zones. Deforestation is widely studied throughout the world. However, a less evident issue is the effect of climate change and drought on remnants of native forests. The objective of this work was to understand the geographic variations in resistance to drought of the Mediterranean sclerophyllous forests of central Chile. These forests have been historically reduced and fragmented and in recent years were subjected to the most prolonged drought occurred between 2010 and 2017. Using data from the MODIS satellite sensor, temporal trends in the NDVI (Normalized Difference Vegetation Index) were quantified. We related these trends with different environmental variables to understand the effects of geographical variation and forest type as indicators of resistance to drought. We observed a significant direct effect of drought, attributable to the reduced precipitation in central Chile, and a significantly reduced NDVI in near one-third of the region forests (browning). However, NDVI and therefore forest productivity were more stable in some mesic sites such as ravine bottoms, but not on south-facing slopes. This suggests that under a regime of reduced precipitations, a greater available soil humidity would be a more important factor than the fact of receiving less solar radiation. Finally, the highest degree of browning was observed in semi-arid sclerophyllous forest dominated by species tolerant to drought. Our findings emphasize the need to consider topographic site conditions to adequately assess forest productivity and vulnerability where local wet conditions could provide drought refuges. This recent drought may be analogous to forecasted warmer and drier climate conditions with more frequent and severe droughts, so our results may serve as a general framework for climate-smart decisions in highly threatened forest restoration and conservation.

Dynamics of Vegetation Change and Its Relationship with Nature and Human Activities — A Case Study of Poyang Lake Basin, China

ABSTRACT Vegetation coverage directly affects the quality of a regional ecological environment. Current analyzes of the factors driving vegetation coverage do not focus on human activities. Poyang Lake is an internationally important wetland. This study is based on 814 MODIS normalized difference vegetation index (NDVI) data from 2000 to 2017 in the Poyang Lake Basin. Natural effects (temperature, precipitation, terrain niche index) and human activity intensity are employed to analyze the driving forces of NDVI changes. The results demonstrate the following: NDVI in the Poyang Lake Basin fluctuated and increased over the 18 years examined. Spatially, NDVI significantly decreased around the central city, and NDVI significantly increased on both sides of the river. NDVI changes are less affected by topographic factors and are more affected by climatic factors and human activities. Among the climatic factors, the correlation between NDVI and temperature was greater than that between NDVI and precipitation. Climatic factors had a greater impact on NDVI than human activities throughout the study area. However, in areas with significant NDVI changes, the correlation between climatic factors and NDVI was not obvious, and the correlation between human activities and NDVI was more obvious than in other areas. Therefore, from 2000 to 2017, the vegetation coverage of the Poyang Lake Basin generally showed an increasing trend; climatic factors had a greater impact on the overall vegetation coverage of the basin than human activities, but significant changes in vegetation coverage were caused by human activities.

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

The role of land cover change in Arctic-Boreal greening and browning trends

Many studies have used time series of satellite-derived vegetation indices to identify so-called greening and browning trends across the northern high-latitudes and to suggest that the productivity of Arctic-Boreal ecosystems is changing in response to climate forcing at local and continental scales. However, disturbances that alter land cover are prevalent in Arctic-Boreal ecosystems, and changes in Arctic-Boreal land cover, which complicate interpretation of trends in vegetation indices, have mostly been ignored in previous studies. Here we use a new land cover change dataset derived from Landsat imagery to explore the extent to which land cover and land cover change influence trends in the normalized difference vegetation index (NDVI) over a large (3.76 M km2) area of NASA’s Arctic Boreal Vulnerability Experiment, which spans much of northwestern Canada and Alaska. Between 1984 and 2012, 21.2% of the study domain experienced land cover change and 42.7% had significant NDVI trends. Land cover change occurred in 27.6% of locations with significant NDVI trends during this period and resulted in greening and browning rates 48%–128% higher than in areas of stable land cover. While the majority of land cover change areas experienced significant NDVI trends, more than half of areas with stable land cover did not. Further, the extent and magnitude of browning and greening trends varied substantially as a function of land cover class and land cover change type. Forest disturbance from fire and timber harvest drove over one third of statistically significant NDVI trends and created complex mosaics of recent forest loss (as browning) and post-disturbance recovery (as greening) at both landscape and continental scale. Our results demonstrate the importance of land cover changes in highly disturbed high-latitude ecosystems for interpreting trends of NDVI and productivity across multiple spatial scales.

Evaluation of Near-Surface Air Temperature From Reanalysis Over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

In this paper, we evaluate the near-surface air temperature datasets from the ERA-Interim (ERAI), Japanese 55-Year Reanalysis, Modern-Era Retrospective Analysis for Research and Applications Version 2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well ( r 2 > 0.95) and that more modern reanalysis, such as ERAI, had lower errors [root-mean-square difference (RMSD) ∼0.9 °C than the older, lower resolution datasets, such as NCEP1 (RMSD ∼2.4 °C). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated growing degree day (GDD) during the growing season to estimate the peak normalized difference vegetation index signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season.