Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models

Drought events and their effects on vegetation productivity in China

Many parts of the world have experienced frequent and severe droughts during the last few decades. Most previous studies examined the effects of specific drought events on vegetation productivity. In this study, we characterized the drought events in China from 1982 to 2012 and assessed their effects on vegetation productivity inferred from satellite data. We first assessed the occurrence, spatial extent, frequency, and severity of drought using the Palmer Drought Severity Index (PDSI). We then examined the impacts of droughts on China\u27s terrestrial ecosystems using the Normalized Difference Vegetation Index (NDVI). During the period 1982–2012, China\u27s land area (%) experiencing drought showed an insignificant trend. However, the drought conditions had been more severe over most regions in northern parts of China since the end of the 1990s, indicating that droughts hit these regions more frequently due to the drier climate. The severe droughts substantially reduced annual and seasonal NDVI. The magnitude and direction of the detrended NDVI under drought stress varied with season and vegetation type. The inconsistency between the regional means of PDSI and detrended NDVI could be attributed to different responses of vegetation to drought and the timing, duration, severity, and lag effects of droughts. The negative effects of droughts on vegetation productivity were partly offset by the enhancement of plant growth resulting from factors such as lower cloudiness, warming climate, and human activities (e.g., afforestation, improved agricultural management practices)

Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022

Global products of remote sensing Normalized Difference Vegetation Index (NDVI) are critical to assessing the vegetation dynamic and its impacts and feedbacks on climate change from local to global scales. The previous versions of the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI product derived from the Advanced Very High Resolution Radiometer (AVHRR) provide global biweekly NDVI data starting from the 1980s, being a reliable long-term NDVI time series that has been widely applied in Earth and environmental sciences. However, the GIMMS NDVI products have several limitations (e.g., orbital drift and sensor degradation) and cannot provide continuous data for the future. In this study, we presented a machine learning model that employed massive high-quality global Landsat NDVI samples and a data consolidation method to generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI (1982–2022), based on AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples that were well spread across the globe were extracted for vegetation biomes in all seasons. The PKU GIMMS NDVI exhibits higher accuracy than its predecessor (GIMMS NDVI3g) in terms of R2 (0.97 over 0.94), root mean squared error (RMSE: 0.05 over 0.09), mean absolute error (MAE: 0.03 over 0.07), and mean absolute percentage error (MAPE: 9 % over 20 %). Notably, PKU GIMMS NDVI effectively eliminates the evident orbital drift and sensor degradation effects in tropical areas. The consolidated PKU GIMMS NDVI has a high consistency with MODIS NDVI in terms of pixel value (R2 = 0.956, RMSE = 0.048, MAE = 0.034, and MAPE = 6.0 %) and global vegetation trend (0.9×10-3 yr−1). The PKU GIMMS NDVI product can potentially provide a more solid data basis for global change studies. The theoretical framework that employs Landsat data samples can facilitate the generation of remote sensing products for other land surface parameters. The PKU GIMMS NDVI product is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8253971 (Li et al., 2023).</p

Changing Sensitivity of Diverse Tropical Biomes to Precipitation Consistent with the Expected Carbon Dioxide Fertilization Effect

Publisher Copyright: © 2022 Tenaw Geremew et al., published by Sciendo.Global environmental changes have implications for the terrestrial ecosystem functioning, but disentangling individual effects remains elusive. The impact of vegetation responses to increasing atmospheric CO2 concentrations is particularly poorly understood. As the atmospheric CO2 concentration increases, the CO2 acts as a fertilizer for plant growth. An increase in atmospheric CO2 reduces the amount of water needed to produce an equivalent amount of biomass due to closing or a narrowing of the stomata that reduces the amount of water that is transpired by plants. To study the impacts of climate change and CO2 fertilization on plant growth, we analyzed the growing season sensitivity of plant growth to climatic forcing from alpine to semi-desert eco-climatic zones of Ethiopia for various plant functional types over the period of 1982-2011. Growing season 3rd generation Normalized Difference Vegetation Index of Global Inventory Modeling and Mapping Studies (NDVI) was used as a proxy of plant growth, while mean growing season precipitation (prec), temperature (temp), and solar radiation (sr) as the climate forcing. The sensitivities of plant growth are calculated as a partial correlation, and a derivative of NDVI with respect to prec, temp and sr for earliest and recent 15-year periods of the satellite records, and using a moving window of 15-year. Our results show increasing trends of plant growth that are not explained by any climate variables. We also find that an equivalent increase in prec leads to a larger increase in NDVI since the 1980s. This result implies a given amount of prec has sustained greater amounts of plant foliage materials over time due to decreasing transpiration with increasing CO2 concentration as expected from the CO2 fertilization effect on water use efficiency and plant growth. Increasing trends of growth in shallow-rooted vegetation tend to be associated with woody vegetation encroachment.Peer reviewe

Stochastic spatio-temporal models for analysing NDVI distribution of GIMMS NDVI3g images

The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions.This research was supported by the Spanish Ministry of Economy and Competitiveness (Project MTM2014-51992-R), the Government of Navarre (Project PI015, 2016), and by the Fundación Caja Navarra-UNED Pamplona (2016)

A Global Assessment of Long-Term Greening and Browning Trends in Pasture Lands Using the GIMMS LAI3g Dataset

Pasture ecosystems may be particularly vulnerable to land degradation due to the high risk of human disturbance (e.g., overgrazing, burning, etc.), especially when compared with natural ecosystems (non-pasture, non-cultivated) where direct human impacts are minimal. Using maximum annual leaf area index (LAImax) as a proxy for standing biomass and peak annual aboveground productivity, we analyze greening and browning trends in pasture areas from 1982-2008. Inter-annual variability in pasture productivity is strongly controlled by precipitation (positive correlation) and, to a lesser extent, temperature (negative correlation). Linear temporal trends are significant in 23% of pasture cells, with the vast majority of these areas showing positive LAImax trends. Spatially extensive productivity declines are only found in a few regions, most notably central Asia, southwest North America, and southeast Australia. Statistically removing the influence of precipitation reduces LAImax trends by only 13%, suggesting that precipitation trends are only a minor contributor to long-term greening and browning of pasture lands. No significant global relationship was found between LAImax and pasture intensity, although the magnitude of trends did vary between cells classified as natural versus pasture. In the tropics and Southern Hemisphere, the median rate of greening in pasture cells is significantly higher than for cells dominated by natural vegetation. In the Northern Hemisphere extra-tropics, conversely, greening of natural areas is 2-4 times the magnitude of greening in pasture areas. This analysis presents one of the first global assessments of greening and browning trends in global pasture lands, including a comparison with vegetation trends in regions dominated by natural ecosystems. Our results suggest that degradation of pasture lands is not a globally widespread phenomenon and, consistent with much of the terrestrial biosphere, there have been widespread increases in pasture productivity over the last 30 years

Automated temporal NDVI analysis over the Middle East for the period 1982 - 2010

The NDVI time-series consist of trend, season and noise. Changes in the season component are related to climate factors and they happen gradually over long period of time. The changes in the trend component are often due to human activities, fires and etc. This paper implements two algorithms (PolyTrend and DBEST) in R language, in order to examine the vegetation changes in the Middle East and to give more possibilities in the hands of the remote sensing communities. DBEST can analyse the gradual and the abrupt changes by decomposing the data, while PolyTrend classifies the inter-annual change between the picks of the green season. PolyTrend and DBEST were adapted for R language environment. Two additional algorithms were developed to apply both algorithms over NDVI3g data set of the Middle East. A third algorithm discovered the affected land-cover through an overlay analysis by the use of the UMD land-cover classification data set. PolyTrend showed linear (4%), quadratic (2%) and cubic (3%) trends. The different trend types were often found to be grouped in clusters. The largest clusters of trends were found near the south-eastern corner of the Arabian Peninsula and in the central regions of Saudi Arabia. More than 10% of all mixed forests were affected by these trends, most of which were in negative direction. DBEST showed that 1% of the vegetation experienced a higher magnitude of change. Clusters of these changes were mainly located in the south-eastern and the western part of Turkey, the northern regions of Iraq and Syria, as well as along the coastlines of the Black Sea and the Caspian Sea. The changes were mainly related to the cropland and the grassland and were more in positive directions. The study demonstrated the potential of PolyTrend and DBEST in R language for the remote sensing. It concludes that probably climatic factors affected the forests in Turkey and Iran. The high magnitude of changes of the cropland and grassland indicates that in some regions the agriculture expanded, while in others it declined.The constant Earth observations from space allow monitoring of the vegetation changes on a regional scale. The changes in vegetation can be long and gradual (e.g. due to climatic factors) or more sudden and abrupt (e.g. due to fires, diseases and etc.). In order to estimate the changes of the vegetation, researchers use algorithms that decompose the observed data to seasonal, trend and remainder (e.g. noise). The algorithms that can distinguish these changes are of limited number, often hard to be accessed and most of the existing ones could be applied only to specific situations. This paper implements two such algorithms (PolyTrend and DBEST) in R language, in order to give more possibilities in the hands of the remote sensing communities, and both are used to examine the vegetation changes in the Middle East. DBEST can analyse the gradual and the abrupt changes by decomposing the data, while PolyTrend classifies the inter-annual change between the picks of the green season. PolyTrend and DBEST were re-coded and adapted for R language environment. Two other algorithms were developed to apply both algorithms over imagery data of the Middle East for the period between 1982 and 2010. A third algorithm related the results to a specific class of vegetation by comparing the results from the last two and a land-cover data set. PolyTrend showed linear (4%), quadratic (2%) and cubic (3%) trends. The different trend types were often found to be grouped in clusters. The largest clusters of trends were found near the south-eastern corner of the Arabian Peninsula and in the central regions of Saudi Arabia. More than 10% of all mixed forests were affected by these trends, most of which were in negative direction. DBEST showed that 1% of the vegetation experienced a higher magnitude of change. Clusters of these changes were mainly located in the south-eastern and the western part of Turkey, the northern regions of Iraq and Syria, as well as along the coastlines of the Black Sea and the Caspian Sea. The changes were mainly related to the cropland and the grassland and were more in positive directions. The study demonstrated the potential of PolyTrend and DBEST in R language for the remote sensing. The obtained results showed that long gradual inter-annual changes affected the forests in Turkey and Iran. The reasons for these changes should be further investigated, but are probably related to climatic factors. The land-cover associated with high magnitude of more sudden changes was related to grassland and cropland. This leads to the suggestion that in some regions the agriculture expanded, while in others it declined