Global Inventory Modeling And Mapping Studies Research Articles

Precipitation Drives the NDVI Distribution on the Tibetan Plateau While High Warming Rates May Intensify Its Ecological Droughts

Climate change has significantly affected the ecosystem of the Tibetan Plateau. There, temperature rises and altered precipitation patterns have led to notable changes in its vegetation growth processes and vegetation cover features. Yet current research still pays relatively little attention to the regional climatic determinants and response patterns of such vegetation dynamics. In this study, spatial patterns in the response of the normalized difference vegetation index (NDVI) to climate change and its dynamic characteristics during the growing season were examined for the Tibetan Plateau, by using a pixel-scale-based geographically weighted regression (GWR) based on the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data, as well as data for temperature and moisture indices collected at meteorological stations, for the period 1982–2015. The results show the following. Spatial nonstationary relationships, primarily positive, were found between the NDVI and climatic factors in the Tibetan Plateau. However, warming adversely affected vegetation growth and cover in some arid and semiarid regions of the northeast and west Tibetan Plateau. Additionally, precipitation played a dominant role in the NDVI of the Tibetan Plateau in the largest area (accounting for 39.7% of total area). This suggests that increased moisture conditions considerably facilitated vegetation growth and cover in these regions during the study period. Temperature mainly played a dominant role in the NDVI in some parts of the plateau sub-cold zone and some southeastern regions of the Tibetan Plateau. In particular, the minimum temperature was the dominant driver of NDVI over a larger area than any of the other temperature indices. Furthermore, spatial regressions between NDVI dynamics and climatic variability revealed that a faster warming rate in the arid and semiarid regions impeded vegetation growth through mechanisms such as drought intensification. Moisture variability was found to act as a key factor regulating the extent of vegetation cover on the south Tibetan Plateau.

Monitoring Vegetation Greenness in Response to Climate Variation along the Elevation Gradient in the Three-River Source Region of China

The Three-River Source Region (TRSR) is vital to the ecological security of China. However, the impact of global warming on the dynamics of vegetation along the elevation gradient in the TRSR remains unclear. Accordingly, we used multi-source remote sensing vegetation indices (VIs) (GIMMS (Global Inventory Modeling and Mapping Studies) LAI (Leaf Area Index), GIMMS NDVI (Normalized Difference Vegetation Index), GLOBMAP (Global Mapping) LAI, MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index), MODIS NDVI, and MODIS NIRv (near-infrared reflectance of vegetation)) and digital elevation model data to study the changes of VGEG (Vegetation Greenness along the Elevation Gradient) in the TRSR from 2001 to 2016. Results showed that the areas with a positive correlation of vegetation greenness and elevation accounted for 36.34 ± 5.82% of the study areas. The interannual variations of VGEG showed that the significantly changed regions were mainly observed in the elevation gradient of 4–5 km. The VGEG was strongest in the elevation gradient of 4–5 km and weakest in the elevation gradient of >5 km. Correlation analysis showed that the mean annual temperature was positively correlated with VIs, and the effect of the mean annual precipitation on VIs was more obvious at low altitude than in high altitude. This study contributes to our understanding of the VGEG variation in the TRSR under global climate variation and also helps in the prediction of future carbon cycle patterns.

Different response of alpine meadow and alpine steppe to climatic and anthropogenic disturbance on the Qinghai-Tibetan Plateau

Climate change and anthropogenic disturbance are two main drivers for vegetation dynamics on the Qinghai-Tibetan Plateau (QTP). Alpine meadow and alpine steppe are the primary rangeland ecosystem types on the QTP. However, the vegetation trends of the two land cover types and the underlying mechanisms behind their variation remain under debate. In this study, we used Global Inventory Modeling and Mapping Studies (GIMMS) 3g Normalized Difference Vegetation Index (NDVI) (i.e., GIMMS NDVI3g) by coupling the Breaks for Additive Season and Trend (BFAST) model and the Boosted Regression Tree (BRT) model to analyze alpine meadow and alpine steppe vegetation trends on the QTP between 1982 and 2015. We also assessed vegetation variation response to climatic and anthropogenic indicators in conjunction with climatic and human footprint datasets. Results show that growing season NDVI (GSNDVI) values increased overall for both alpine meadow (0.0001 year−1, p = 0.33) and alpine steppe (0.0002 year−1, p < 0.05) throughout 1982–2015. Significant greening trends in both alpine meadow (0.0007 year−1; p < 0.05) and alpine steppe (0.0005 year−1; p < 0.05) ecosystems were obtained before 1998 and 2001, respectively. However, browning trends ascertained by GSNDVI (−0.0006 year−1; p = 0.12) in alpine meadows were observed throughout 1998–2015, while greening trends ascertained by GSNDVI (0.0002 year−1; p = 0.12) in alpine steppes were observed throughout during 2001–2015. Opposing trends in precipitation, solar radiation, and the Standardized Precipitation Evapotranspiration Index (SPEI) occurred before and after breakpoints in both ecosystems. For the alpine meadow ecosystem, adverse precipitation trends caused browning before 1998 followed by greening after 1998 in the Three-River-Source National Park (TNP). Conversely, opposing changes in precipitation, solar radiation, and SPEI resulted in greening before 1998 followed by browning after 1998 in southern Tibet and the southeastern QTP. Alpine meadow vegetation trends were generally dominated by solar radiation before 1998 and jointly by precipitation and solar radiation after 1998. Prior to 2001 variation in alpine steppe greenness was controlled by precipitation, while after 2001 solar radiation dominated. Along with an increase in human footprint pressure (HFP) gradients, greenness trends gradually increased before 1998 but reversed after 1998 in the alpine meadow ecosystem. Additionally, greenness trends gradually decreased before 2001 but remained unchanged after 2001 for the alpine steppe ecosystem. These results highlight the different effects that climate change and anthropogenic disturbances have had on alpine meadow and alpine steppe ecosystems on the QTP over different time frames.

Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.

Vegetation Dynamic Changes and Their Response to Ecological Engineering in the Sanjiangyuan Region of China

In recent decades, the vegetation of the Sanjiangyuan region has undergone a series of changes under the influence of climate change, and ecological restoration projects have been implemented. In this paper, we analyze the spatiotemporal dynamics of vegetation in this region using the satellite-retrieved normalized difference vegetation index (NDVI) from the global inventory modeling and mapping studies (GIMMS) and moderate resolution imaging and spectroradiometer (MODIS) datasets during the past 34 years. Specifically, the characteristics of vegetation changes were analyzed according to the stage of implementation of different ecological engineering programs. The results are as follows. (1) The vegetation in 65.6% of the study area exhibited an upward trend, and in 53.0% of the area, it displayed a large increase, which was mainly distributed in the eastern part of the study area. (2) The vegetation NDVI increased to differing degrees during stages of ecological engineering. (3) The NDVI in the western part of the Sanjiangyuan region is mainly affected by temperature, while in the northeastern part, the NDVI is affected more by precipitation. In the southern part, however, vegetation growth is affected neither by temperature nor by precipitation. On the whole region, vegetation growing is more affected by temperature than by precipitation. (4) The impacts of human activities on vegetation change are both positive and negative. In recent years, ecological engineering projects have had a positive impact on vegetation growth. This study can help us to correctly understand the impact of climate change on vegetation growth, so as to provide a scientific basis for the evaluation of regional ecological engineering effectiveness and the formulation of ecological protection policies.

Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness

Analysing changes in vegetation seasonality of terrestrial ecosystems is important to understand ecological responses to global change. Based on over three decades of observations by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors, the Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset has been widely used for monitoring vegetation trends. However, it is not well known how robust long-term trends in vegetation seasonality derived from GIMMS NDVI are, given inevitable influences from sensor and processing artefacts. Here we analyse long-term seasonality trends in the GIMMS third generation (NDVI3g) record (1982–2013). Changes in vegetation seasonality are decomposed into changes in duration (related to growing season length) and timing (related to peak growing season). We compare seasonality trends from the previous version (NDVI3g v0) with those in the subsequently released version (NDVI3g v1) and, for their common period, with those derived from MODerate Resolution Imaging Spectroradiometer (MODIS) collection 6 NDVI. We find that NDVI3g v0 shows marked seasonality trends for 1982–2013 over more than one-third of the global vegetated area. Long-term trends based on v1 are generally consistent with v0, but v1 shows a strong trend towards earlier timing across the Arctic regions that is absent in v0. NDVI3g v0, v1, and MODIS all point towards an increased duration across the tundra of North Asia and later timing across North Africa. However, several discrepancies are also found between the NDVI datasets. For example, for the North-American tundra, MODIS shows earlier and v0 later timing, while MODIS shows an increased duration and v1 a reduced duration. For North Africa, v0 and v1 exhibit a reduced duration that is absent in MODIS. We conclude that both the primary observations and the subsequent processing can have a marked influence on inferred seasonality trends, and propose that the robustness of trends should be examined and corroborated using alternative data sources wherever possible.

Variations in vegetation dynamics and its cause in national key ecological function zones in China.

Continued long-term monitoring of vegetation activity in national key ecological function zones (NKEFZs) has implications for national ecological security and sustainability in China. We used Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) dataset to map and analyze the spatiotemporal patterns of change in vegetation growth and their linkage with climate change and human activities in NKEFZs during 1982-2013. Statistically significant increases of growing season, spring, and autumn NDVI were observed during all or most periods while 25 NKEFZs are taken as a whole. Non-significant decreases of NDVI were found in 7 NKEFZs during a few periods, and obvious increases were observed during fifteen periods in all other NKEFZs. Vegetation growth in NKEFZs was mainly regulated by a thermal factor, and the dominant climatic drivers varied across different regions and seasons. The influence of temperature was stronger on vegetation activity in spring and autumn for those NKEFZs located in high latitudes and high elevations, while precipitation was the main climatic control factor for NKEFZs in the arid and semi-arid regions. The effects of human activity on the NDVI of NKEFZs were not ignored; a significant decrease of NDVI in the Sanjiang Plain may be related to the rapid change in land use from wetland into farmland.

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Annual land use land cover (LULC) change information at medium spatial resolution (i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change studies. Annual LULC information, however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities. Here we integrate high temporal resolution and coarse spatial resolution satellite images (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)) with high spatial resolution datasets (China’s Land-Use/cover Datasets (CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015. We also test the performance of a statistical based change detection algorithm (Breaks for Additive Seasonal and Trend), originally designed for tracking forest change, in classifying all-type LULC change. As a result, a nominal 30 m annual land use/land cover datasets (CLUD-A) from 1980 to 2015 was developed for the whole China. The mapping results were assessed with a change sample dataset, a regional annual validation sample set and a three-year China sample set. Of the detected change years, 75.61% matched the exact time of conversion within ±1 year. Annual mapping results provided a detail process of urbanization, deforestation, afforestation, water and cropland dynamics over the past 36 years. The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.

Vegetation dynamics based on NDVI in Yangtze River Basin (China) during 1982-2015

Knowledge of vegetation dynamics is important for the sustainability of natural resources and understanding the changes in ecosystems and its impact to earth’s environment. We have carried out analysis of GIMMS (Global inventory modelling and mapping studies) NDVI3g (third generation normalized difference vegetation data for the period 1982-2015. Based on the Linear Regression (LR) analysis, the Mann-Kendall (MK) test with Sen’s slope estimator and Kriging interpolation method, we have investigated the spatiotemporal variations of vegetation NDVI in the Yangtze River Basin (YRB). The results show pronounced increase in the annual mean NDVI at the rate of 0.01/10yr during 1982-2015, with significant turning point (TP) around 1994. The spatial distribution of the annual mean NDVI reasonably increased in the northern, eastern and south-western YRB, while decreased in the Yangtze River Delta (YRD) and parts of the southern YRB during 1982-2015. Pronounced change in NDVI trend is found in different seasons, for example, the increasing trend during spring (0.02/10yr) and in autumn (0.02/10yr) was higher compared to winter (0.006/10yr) and summer (0.002/10yr) seasons.

Soil moisture outweighs temperature for triggering the green-up date in temperate grasslands

Investigating grassland phenology and its relationship with climatic factors is crucial for understanding ecosystem responses to climate change. However, there have been few studies on the impact of soil moisture on grassland phenology. In this study, we extracted the green-up date in the Inner Mongolia grasslands, China, using the normalized difference vegetation index (NDVI) data from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset (1982–2015) and the Moderate Resolution Imaging Spectroradiometer (2001–2015). We investigated the spatiotemporal pattern of the green-up date and its relationship with four climatic factors, including growing degree days, chilling days, soil moisture, and solar radiation. Most areas (67.1%) exhibited a trend towards earlier green-up date, but the significant trends (p < 0.05) were only found in 13.4% of the areas. For both datasets, the pixels with a negative regression coefficient of soil moisture accounted for more than 87% of the total area (more than 21% significantly, p < 0.05). Regarding the other factors, the sign of regression coefficients was not consistent among pixels. Thus, soil moisture correlated more significantly with the green-up date in temperate grasslands compared with the other climatic factors. This study provides the scientific basis for better understanding the mechanism of phenological response to climate change in temperate grasslands.

Evidence for satellite observed changes in the relative influence of climate indicators on autumn phenology over the Northern Hemisphere

Abstract Recent climate changes have elicited diverse phenological shifts in the ecosystems of the Northern Hemisphere (NH), with changes in the end growth of season (EOS) varying considerably with time. Due to the anthropogenic forcing (e.g. anthropogenic radiative forcing, et al.), both the climate conditions and EOS greatly changed since 2000, but the relationship between the two remains unclear. To better understand the responses of EOS to climate change with time, we used Global Inventory Modeling and Mapping Studies (GIMMS) derived normalized difference vegetation index (NDVI) and temperature, precipitation, and cloud cover during 1982–2000 and 2001–2015 separately, aiming to identify and quantify the spatiotemporal distributions of drivers of EOS across different biomes over the NH (>30°). Considering separately these two distinct periods, we found that beginning in the 21st century both the proportion of positive significant pixels (increased by 9.2%) as well as the sensitivity (1.5 ± 4.3 day °C−1 vs. 2.7 ± 7.9 day °C−1) of the temperature vs. EOS relationship had increased. Precipitation affected EOS over a slightly increase area during these two periods, but the sensitivity decreased. Conversely, we found that the sensitivity of EOS to light availability increased for most of biomes, whereas the areas influenced increased by 7.9%. With respect to the sign of relationships between EOS and its drivers, we found that for high latitude regions (i.e., boreal forests and tundra), precipitation advanced EOS during 2001–2015 but delayed EOS during 1982–2000. Although precipitation has significant correlations for a larger area than temperature, its sensitivity was lower. Our results imply that the relative magnitude and sign of drivers of EOS has changed substantially over time and space. Improved representation of these dynamic changes will advance understanding of future climate change on plant phenology by ecosystem models.

한반도 식생의 녹색화에 대한 시계열적 분석

The vegetation is feedback on environmental change due to global warming. Also, the growth status of vegetation and the coverage area of vegetation are greatly affected by the environmental changes. The quantitative change of vegetation growth status is the primary task of vegetation response to environmental changes. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) based Normalized Difference Vegetation Index (NDVI) and CRU climate data are used to analyze the spatio‐temporal characteristics of vegetation greening evolution and its response to climate change from 1982 to 2015 in the Korean Peninsula by applying the partial correlation and trend analysis. The results show that the average NDVI value of the Korean Peninsula in the period 1982‐2015 was 0.68, among which the average NDVI in North Korea and South Korea was 0.69 and 0.67, respectively. The NDVI of the Korean Peninsula between 1982 and 2015 increased by 0.6x10SUP‐3/SUP yearSUP‐1/SUP. The increasing trend prior to and after 1998 was 2.5×10SUP‐3/SUP yearSUP‐1/SUP and 0.9×10SUP‐3/SUP yearSUP‐1/SUP, respectively. During the 1982‐2015 years, the NDVI of DPRK and South Korea have grown mainly with the trend of 0.2×10SUP‐3/SUP yearSUP‐1/SUP and 1.1×10SUP‐3/SUP yearSUP‐1/SUP, respectively. According to the analysis of NDVI and climatic factors, the distribution of NDVI in the three‐time series of Korean Peninsula is consistent in spatial distribution. According to the results of partial correlation analysis of climate factor and NDVI distribution in Korean Peninsula, the region has significant partial correlation with temperature change. The climate factor of temperature is the main driver of NDVI change, which plays a key role in controlling NDVI change accumulation.

Tempo-Spatial Variation of Vegetation Coverage and Influencing Factors of Large-Scale Mining Areas in Eastern Inner Mongolia, China.

Vegetation in eastern Inner Mongolia grasslands plays an important role in preventing desertification, but mineral exploration has negative effects on the vegetation of these regions. In this study, the changing trend types of vegetation in eastern Inner Mongolia were analyzed using the normalized difference vegetation index (NDVI) time series from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI 3g dataset from 1982 to 2015. Meanwhile, changing trend and influencing factors of 25 large-scale mining areas before and after mining were explored with the methods of trend line, residual calculation, and correlation analysis. The vegetation coverage towards increasing in eastern Inner Mongolia decreased in the order of Tongliao > Hinggan League > Chifeng > Hulunbuir > Xilingol over the past 34 years. Vegetation showed a decreasing tendency in 40% mining areas, but an increasing tendency in 60% mining areas after mining. Vegetation change in Shengli No. 1 had a significant correlation with precipitation and human activities after mining. Except Shengli No. 1, an obvious correlation was found between vegetation change and precipitation in 45.83% mining areas after mining. Human activities had significant positive effects on vegetation growth in 25% mining areas. Significant negative effects of human activities were found in 8.34% mining areas, causing the vegetation degradation. However, there were 20.83% mining areas with vegetation changes not affected by precipitation and human activities.

Monitoring Phenology in the Temperate Grasslands of China from 1982 to 2015 and Its Relation to Net Primary Productivity

Both vegetation phenology and net primary productivity (NPP) are crucial topics under the background of global change, but the relationships between them are far from clear. In this study, we quantified the spatial-temporal vegetation start (SOS), end (EOS), and length (LOS) of the growing season and NPP for the temperate grasslands of China based on a 34-year time-series (1982–2015) normalized difference vegetation index (NDVI) derived from global inventory modeling and mapping studies (GIMMS) and meteorological data. Then, we demonstrated the relationships between NPP and phenology dynamics. The results showed that more than half of the grasslands experienced significant changes in their phenology and NPP. The rates of their changes exhibited spatial heterogeneity, but their phenological changes could be roughly divided into three different clustered trend regions, while NPP presented a polarized pattern that increased in the south and decreased in the north. Different trend zones’ analyses revealed that phenology trends accelerated after 1997, which was a turning point. Prolonged LOS did not necessarily increase the current year’s NPP. SOS correlated with the NPP most closely during the same year compared to EOS and LOS. Delayed SOS contributed to increasing the summer NPP, and vice versa. Thus, SOS could be a predictor for current year grass growth. In view of this result, we suggest that future studies should further explore the mechanisms of SOS and plant growth.

Water and heat availability are drivers of the aboveground plant carbon accumulation rate in alpine grasslands on the Tibetan Plateau

AbstractAimClimate change is expected to have important effects on plant phenology and carbon storage, with further shifts predicted in the future. Therefore, we proposed the community carbon accumulation rate (CAR) from the start of the growing season (SOS) to the peak of the growing season (POS) to fill the gap that the dynamic interactions between plant phenology and plant carbon research.LocationTibetan Plateau.Major taxaAlpine grassland plants.Time period2015.MethodsWe conducted a transect survey across grasslands to measure community aboveground net primary production and carbon concentration. Additionally, phenology indicator data (SOS and POS) were extracted from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index version 3 database. Next, we used ‘changepoint’ analysis to detect the patterns of CARs, and performed linear regression and one‐way ANOVA to explore the variability of CARs in response to the environmental factors. Ultimately, the total effects of environmental factors on CARs were illustrated by a structural equation model.ResultsOur results indicated that three CAR patterns were detected, which are low‐CAR (0.15 g/m2/day), medium‐CAR (0.31 g/m2/day) and high‐CAR (0.84 g/m2/day) patterns. We found that the availabilities of water and heat mediated CARs by regulating soil nutrition variability, and that drought climate and insufficient soil resources co‐constrained the community CAR at long time‐scales. In contrast, high CAR could be explained by more water and heat availability via either direct or indirect effects on soil moisture and soil nutrients.Main conclusionsOur findings highlight that water and heat availability are critical driving factors in ecological carbon accumulation processes undergoing climate change. Meanwhile, the vegetative phenology also has important effect on carbon accumulation. Consequently, we propose incorporating the dynamic interactions between plant phenology and plant carbon into the ecological carbon cycle model to improve our understanding of resource utilization and survival strategies of plants under environmental change.

Spatial–temporal changes in vegetation coverage in the global coastal zone based on GIMMS NDVI3g data

ABSTRACTIn this paper, we used the Global Inventory Modelling and Mapping Studies (GIMMS) third-generation Normalized Difference Vegetation Index (NDVI) (GIMMS NDVI3g) dataset. Based on GIMMS NDVI3g data over the global coastal zone from 1982 to 2014, the spatial–temporal characteristics of vegetation coverage were analysed by plotting the spatial pattern and monthly calendar of NDVI; furthermore, historical trends and future evolutions of vegetation coverage change at the pixel scale were studied by performing the Mann-Kendall trend test and calculating the trend slope (β) and Hurst index (H) of NDVI. The main findings are as follows: 1) Vegetation density exhibits dramatic differences in the global coastal zone. Specifically, desert belts mostly have perennial non-vegetation or low vegetation coverage, and tundra belts principally have moderate or high vegetation coverage; additionally, forest belts mainly have dense vegetation coverage. 2) In the global coastal zone, intra-annual variations in vegetation coverage show a ‘∩’-shaped curve with an obvious peak from June to September (maximum in July or August), while inter-annual variations show a fluctuating but generally slowly increasing trend over the entire study period; accordingly, variations in different subregions show significant differences. 3) At monthly, seasonal and annual scales, the overall vegetation coverage increases in the global coastal zone, while there are relatively few areas with decreasing vegetation coverage; furthermore, change trends of vegetation coverage in most areas will demonstrate relatively strong positive persistence in the future. 4) The increasing trend in high-latitude coastal tundra is extremely significant in the growing season because vegetation in the tundra belts is highly sensitive to climate change. 5) Areas with a decreasing trend of vegetation coverage exhibit spatial patterns of aggregation in the ‘circum urban agglomeration’ and ‘nearby desert belt’ regions, that is, the decreasing trend of vegetation coverage is relatively high in coastal urban agglomeration areas and desert belt peripheries. This paper is expected to provide knowledge to support vegetation conservation, ecosystem management, integrated coastal zone management and climate change adaptation in coastal areas.

Associations between large-scale climate oscillations and land surface phenology in Iran

Variations in land surface phenology arising from large-scale and regional climatic oscillations are important determinants for environmental and ecological conditions. Spearman’s correlation coefficient was employed to delineate, within the geographical territory of Iran, the relationship between phenological metrics [cumulative NDVI (cumNDVI), start of growing season (SOS), and end of growing season (EOS)], which were obtained using the Normalized Difference Vegetation Index (NDVI) derived from the AVHRR Global Inventory Modeling and Mapping Studies (GIMMS), and six large-scale climate oscillations [the Arctic Oscillation (AO), the Dipole Mode Index (DMI), the Multivariate El Niño Southern Oscillation Index (MEI), the North Atlantic Oscillation (NAO), the North Sea-Caspian Pattern (NCP), and the Pacific Decadal Oscillation (PDO)]. Results showed that the NCP and NAO had the greatest association, while the AO and MEI had the least association, with the studied phenological metrics for the different types of vegetation in Iran. The NCP was significantly correlated with 25–41% of the pixels for the different vegetation types. The climate oscillations that dominated the relation to phenological metrics differed depending on whether relations with current or previous year climatic oscillation indices were used. The results have implications for environmental and ecological forecasting and analysis, especially for vegetation trends in Iran’s arid to semi-arid climate.

TRENDS AND VULNERABILITY ASSESSMENT OF METEOROLOGICAL AND AGRICULTURAL DROUGHT CONDITIONS OVER INDIAN REGION USING TIME-SERIES (1982–2015) SATELLITE DATA

Abstract. The vulnerability and trends of meteorological as well as agricultural drought conditions over Indian region was studied using long-term (1982–2015) gridded precipitation and time-series normalized difference vegetation index (NDVI) data. The Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS) precipitation data (~5 km) was used to compute Standardized precipitation index (SPI) at 3-month time scale for Indian summer monsoon season (June-September). Subsequently, the long-term Global Inventory Modelling and Mapping Studies (GIMMS) time-series NDVI data (~8 km) was interpolated at daily scale and smoothened using Savitzky and Golay filtering method. Further, the time-series NDVI data was transformed into several phenological parameters using threshold and derivative approach. As integrated NDVI, i.e. the area under seasonal NDVI curve, is able to represent the anomalies in seasonal agricultural production, it was transformed into standardized vegetation index (SVI) using empirical distribution. Several drought parameters, e.g. magnitude and extent, were computed at district level based on the SPI and SVI values, where values with SPI or SVI less than minus one was considered as meteorological and agricultural drought year, respectively. The trends of drought magnitude and extent for both the meteorological and agricultural drought were estimated using Sen’s slope. The direction of trends and magnitude were found to be varying spatially across different parts of Indian region. Further, the mean SPI/SVI values along with drought frequency were utilized to categorize entire Indian agricultural area into different vulnerable zones during three decades separately. The overall drought vulnerability was found to be decreasing over time.

Spatial-Temporal Pattern of Human-Induced Land Degradation in Northern China in the Past 3 Decades-RESTREND Approach.

Land degradation is one of the world’s most serious environmental issues. Human activities play an important role in it. Therefore, human-induced land degradation monitoring is of crucial scientific significance in revealing the evolution of land degradation and guiding its governance. Based on the residual trend (RESTREND) approach and using Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) 3g and monthly precipitation as data sources, a quantitative evaluation is conducted on the conditions of human-induced land degradation during 1982–2012 in northern China. The results indicate that (1) the “optimal cumulative precipitation-NDVImax” regression model constructed herein can improve the capability of recognizing human-induced land degradation of arid and semiarid areas in the RESTREND approach. Moreover, long time-series NDVI and precipitation data may reduce the uncertainty of quantifying human-induced land degradation. (2) In the past 3 decades, northern China has experienced three stages of human-induced land degradation, i.e., rapid development, overall reversal with local development, and continuous reversion. Human-induced land degradation in the agro-pastoral ecotone of northern China has shown a rapid restoration trend since the 1990s. (3) It is believed that the dominant factor of land degradation has a significant spatial-temporal scale effect and spatial heterogeneity. Therefore, concrete issues should be specifically analyzed to improve our understanding of land degradation development and reversal, the spatial-temporal pattern and the driving forces of land degradation in the past 3 decades in northern China. Climate change may be the main driving force of land degradation. However, the influence of human activities on the development and reversal of land degradation in small areas and in a short time is more remarkable.

Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations

The representation of vegetation in land surface models (LSM) is crucial for modeling atmospheric processes in regional climate models (RCMs). Vegetation is characterized by the green fractional vegetation cover (FVC) and/or the leaf area index (LAI) that are obtained from nearest difference vegetation index (NDVI) data. Most regional climate models use a constant FVC for each month and grid cell. In this work, three FVC datasets have been constructed using three methods: ZENG, WETZEL and GUTMAN. These datasets have been implemented in a RCM to explore, through sensitivity experiments over the Iberian Peninsula (IP), the effects of the differences among the FVC data-sets on the near surface temperature (T2m). Firstly, we noted that the selection of the NDVI database is of crucial importance, because there are important bias in mean and variability among them. The comparison between the three methods extracted from the same NDVI database, the global inventory modeling and mapping studies (GIMMS), reveals important differences reaching up to 12% in spatial average and and 35% locally. Such differences depend on the FVC magnitude and type of biome. The methods that use the frequency distribution of NDVI (ZENG and GUTMAN) are more similar, and the differences mainly depends on the land type. The comparison of the RCM experiments exhibits a not negligible effect of the FVC uncertainty on the monthly T2m values. Differences of 30% in FVC can produce bias of 1 ∘ C in monthly T2m, although they depend on the time of the year. Therefore, the selection of a certain FVC dataset will introduce bias in T2m and will affect the annual cycle. On the other hand, fixing a FVC database, the use of synchronized FVC instead of climatological values produces differences up to 1 ∘ C, that will modify the T2m interannual variability.