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Special Issue "Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011)"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2013)

Special Issue Editors

Guest Editor
Prof. Ranga B. Myneni

Department of Geography & Environment, Boston University, Boston, MA 20115, USA
Website | E-Mail
Interests: remote sensing of vegetation; satellite data analysis; radiative transfer in vegetative media; algorithms for biophysical variables from satellite data; climate/vegetation interactions; terrestrial carbon cycle
Guest Editor
Dr. Jorge E. Pinzón

Science Systems and Applications, Inc., Code 618, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
E-Mail
Fax: +301 614 6695
Interests: feature extraction from large geophysical temporal, multi- and hyper-spectral data; quality assurance and calibration of geophysical observations; image analysis, image compression, image classification; remote sensing applications for monitoring eco-climatic conditions associated with emerging infectious diseases

Special Issue Information

Dear Colleagues,

Vegetation indices are radiometric measures of photosynthetically active radiation absorbed by chlorophyll in the green leaves of vegetation canopies and are therefore good surrogate measures of the physiologically functioning surface greenness level of a region. In a series of articles during the early 1980s, Compton J. Tucker, demonstrated how the Normalized Difference Vegetation Index (NDVI) generated from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data can be used to map land cover and monitor vegetation changes and desertification at continental and global scales. These papers opened a whole new avenue of investigations regarding monitoring vegetation changes at a host of spatial resolutions and time scales. A simple search on the Web of Science reveals over 5000 articles containing NDVI either in the title or in the abstract. Compton J. Tucker continued to generate the NDVI time series over the past 30 years, in the framework of the Global Inventory Monitoring and Modeling System (GIMMS) project, carefully assembling it from different AVHRR sensors and accounting for various deleterious effects, such as calibration loss, orbital drift, volcanic eruptions, etc. The latest version of the GIMMS NDVI data set spans the period July 1981 to December 2011 and is termed NDVI3g (third generation GIMMS NDVI from AVHRR sensors). The goal of this special issue is to understand variability, long-term trends and changes in vegetation on our planet at a host of spatial scales over the past 30 years using this new, improved data set. Although the NDVI3g data set has not yet been released, scientists interested in contributing to this special issue are encouraged to contact the guest editors with a tentative title and two-line abstract to obtain access to the data set. The following is a tentative list of papers to appear in this special issue.

Prof. Ranga B. Myneni
Dr. Jorge E. Pinzón
Guest Editors

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs).

Keywords

  • NDVI
  • AVHRR
  • remote sensing
  • vegetation trends
  • phenology
  • climate change
  • drought
  • arctic vegetation
  • sahelian vegetation
  • land degradation
  • desertification
  • carbon cycle
  • dynamics vegetation models

Published Papers (34 papers)

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Research

Open AccessArticle 1982–2010 Trends of Light Use Efficiency and Inherent Water Use Efficiency in African vegetation: Sensitivity to Climate and Atmospheric CO2 Concentrations
Remote Sens. 2014, 6(9), 8923-8944; doi:10.3390/rs6098923
Received: 29 April 2014 / Revised: 19 July 2014 / Accepted: 25 August 2014 / Published: 22 September 2014
Cited by 6 | PDF Full-text (2700 KB) | HTML Full-text | XML Full-text
Abstract
Light and water use by vegetation at the ecosystem level, are key components for understanding the carbon and water cycles particularly in regions with high climate variability and dry climates such as Africa. The objective of this study is to examine recent trends
[...] Read more.
Light and water use by vegetation at the ecosystem level, are key components for understanding the carbon and water cycles particularly in regions with high climate variability and dry climates such as Africa. The objective of this study is to examine recent trends over the last 30 years in Light Use Efficiency (LUE) and inherent Water Use Efficiency (iWUE*) for the major biomes of Africa, including their sensitivities to climate and CO2. LUE and iWUE* trends are analyzed using a combination of NOAA-AVHRR NDVI3g and fAPAR3g, and a data-driven model of monthly evapotranspiration and Gross Primary Productivity (based on flux tower measurements and remote sensing fAPAR, yet with no flux tower data in Africa) and the ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms) process-based land surface model driven by variable CO2 and two different gridded climate fields. The iWUE* data product increases by 10%–20% per decade during the 1982–2010 period over the northern savannas (due to positive trend of vegetation productivity) and the central African forest (due to positive trend of vapor pressure deficit). In contrast to the iWUE*, the LUE trends are not statistically significant. The process-based model simulations only show a positive linear trend in iWUE* and LUE over the central African forest. Additionally, factorial model simulations were conducted to attribute trends in iWUE and LUE to climate change and rising CO2 concentrations. We found that the increase of atmospheric CO2 by 52.8 ppm during the period of study explains 30%–50% of the increase in iWUE* and >90% of the LUE trend over the central African forest. The modeled iWUE* trend exhibits a high sensitivity to the climate forcing and environmental conditions, whereas the LUE trend has a smaller sensitivity to the selected climate forcing. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle A Non-Stationary 1981–2012 AVHRR NDVI3g Time Series
Remote Sens. 2014, 6(8), 6929-6960; doi:10.3390/rs6086929
Received: 13 December 2013 / Revised: 12 June 2014 / Accepted: 4 July 2014 / Published: 25 July 2014
Cited by 96 | PDF Full-text (2362 KB) | HTML Full-text | XML Full-text
Abstract
The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting
[...] Read more.
The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of ± 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity
Remote Sens. 2014, 6(6), 5717-5731; doi:10.3390/rs6065717
Received: 31 December 2013 / Revised: 4 May 2014 / Accepted: 13 May 2014 / Published: 18 June 2014
Cited by 9 | PDF Full-text (2731 KB) | HTML Full-text | XML Full-text
Abstract
Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of
[...] Read more.
Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth’s human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Global Ecosystem Response Types Derived from the Standardized Precipitation Evapotranspiration Index and FPAR3g Series
Remote Sens. 2014, 6(5), 4266-4288; doi:10.3390/rs6054266
Received: 16 January 2014 / Revised: 14 April 2014 / Accepted: 21 April 2014 / Published: 8 May 2014
Cited by 2 | PDF Full-text (1661 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Observing trends in global ecosystem dynamics is an important first step, but attributing these trends to climate variability represents a further step in understanding Earth system changes. In the present study, we classified global Ecosystem Response Types (ERTs) based on common spatio-temporal patterns
[...] Read more.
Observing trends in global ecosystem dynamics is an important first step, but attributing these trends to climate variability represents a further step in understanding Earth system changes. In the present study, we classified global Ecosystem Response Types (ERTs) based on common spatio-temporal patterns in time-series of Standardized Precipitation Evapotranspiration Index (SPEI) and FPAR3g anomalies (1982–2011) by using an extended Principal Component Analysis. The ERTs represent region specific spatio-temporal patterns of ecosystems responding to drought or ecosystems with decreasing severity in drought events as well as ecosystems where drought was not a dominant factor in a 30-year period. Highest explanatory values in the SPEI12-FPAR3g anomalies and strongest SPEI12-FPAR3g correlations were seen in the ERTs of Australia and South America whereas lowest explanatory value and lowest correlations were observed in Asia and North America. These ERTs complement traditional pixel based methods by enabling the combined assessment of the location, timing, duration, frequency and severity of climatic and vegetation anomalies with the joint assessment of wetting and drying climatic conditions. The ERTs produced here thus have potential in supporting global change studies by mapping reference conditions of long term ecosystem changes. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Rain-Use-Efficiency: What it Tells us about the Conflicting Sahel Greening and Sahelian Paradox
Remote Sens. 2014, 6(4), 3446-3474; doi:10.3390/rs6043446
Received: 16 January 2014 / Revised: 3 April 2014 / Accepted: 8 April 2014 / Published: 22 April 2014
Cited by 13 | PDF Full-text (3016 KB) | HTML Full-text | XML Full-text
Abstract
Rain Use Efficiency (RUE), defined as Aboveground Net Primary Production (ANPP) divided by rainfall, is increasingly used to diagnose land degradation. Yet, the outcome of RUE monitoring has been much debated since opposite results were found about land degradation in the Sahel region.
[...] Read more.
Rain Use Efficiency (RUE), defined as Aboveground Net Primary Production (ANPP) divided by rainfall, is increasingly used to diagnose land degradation. Yet, the outcome of RUE monitoring has been much debated since opposite results were found about land degradation in the Sahel region. The debate is fueled by methodological issues, especially when using satellite remote sensing data to estimate ANPP, and by differences in the ecological interpretation. An alternative method which solves part of these issues relies on the residuals of ANPP regressed against rainfall (“ANPP residuals”). In this paper, we use long-term field observations of herbaceous vegetation mass collected in the Gourma region in Mali together with remote sensing data (GIMMS-3g Normalized Difference Vegetation Index) to estimate ANPP, RUE, and the ANPP residuals, over the period 1984–2010. The residuals as well as RUE do not reveal any trend over time over the Gourma region, implying that vegetation is resilient over that period, when data are aggregated at the Gourma scale. We find no conflict between field-derived and satellite-derived results in terms of trends. The nature (linearity) of the ANPP/rainfall relationship is investigated and is found to have no impact on the RUE and residuals interpretation. However, at odds with a stable RUE, an increased run-off coefficient has been observed in the area over the same period, pointing towards land degradation. The divergence of these two indicators of ecosystem resilience (stable RUE) and land degradation (increasing run-off coefficient) is referred to as the “second Sahelian paradox”. When shallow soils and deep soils are examined separately, high resilience is diagnosed on the deep soil sites. However, some of the shallow soils show signs of degradation, being characterized by decreasing vegetation cover and increasing run-off coefficient. Such results show that contrasted changes may co-exist within a region where a strong overall re-greening pattern is observed, highlighting that both the scale of observations and the scale of the processes have to be considered when performing assessments of vegetation changes and land degradation. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Thirty-two Years of Sahelian Zone Growing Season Non-Stationary NDVI3g Patterns and Trends
Remote Sens. 2014, 6(4), 3101-3122; doi:10.3390/rs6043101
Received: 16 January 2014 / Revised: 22 March 2014 / Accepted: 26 March 2014 / Published: 4 April 2014
Cited by 17 | PDF Full-text (8667 KB) | HTML Full-text | XML Full-text
Abstract
We update the Global Inventory Modeling and Mapping Studies (GIMMS) analysis of Sahelian vegetation dynamics and trends using the normalized difference vegetation index (NDVI; version 3g) 1981 to 2012 data set. We compare the annual NDIV3g and July to October growing season averages
[...] Read more.
We update the Global Inventory Modeling and Mapping Studies (GIMMS) analysis of Sahelian vegetation dynamics and trends using the normalized difference vegetation index (NDVI; version 3g) 1981 to 2012 data set. We compare the annual NDIV3g and July to October growing season averages with the three rainfall data sets: the Africa Rainfall Climatology from 1983 to 2012, the Variability Analyses of Surface Climate Observations Version-1.1 from 1951 to 2000, and the Nicholson ground-station precipitation rainfall data from 1981 to 1994. We use the Nicholson ground-station annual precipitation data to determine the reliability of the two continental precipitation data sets for specific locations and specific times, extrapolate these confirmed relationships over the Sahelian Zone from 1983 to 2012 with the Africa Rainfall Climatology, and then place these zonal findings within the 1951 to 2000 record of the Variability Analyses of Surface Climate Observations Version-1.1 precipitation data set. We confirm the extreme nature of the 1984–1985 Sahelian drought, a signature event that marked the minima during the 1980s desiccation period followed within ten years by near-maxima rainfall event in 1994 and positive departures is NDVI, marking beginning of predominantly wetter conditions that have persisted to 2012. We also show the NDVI3g data capture “effective” rainfall, the rainfall that is utilized by plants to grow, as compared to rainfall that evaporates or is runoff. Using our effective rainfall concept, we estimate average effective rainfall for the entire Sahelian Zone for the 1984 extreme drought was 223 mm/yr as compared to 406 mm/yr in during the 1994 wet period. We conclude that NDVI3g data can used as a proxy for analyzing and interpreting decadal-scale land surface variability and trends over semi arid-lands. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia
Remote Sens. 2014, 6(3), 2108-2133; doi:10.3390/rs6032108
Received: 14 June 2013 / Revised: 4 February 2014 / Accepted: 19 February 2014 / Published: 7 March 2014
Cited by 10 | PDF Full-text (898 KB) | HTML Full-text | XML Full-text
Abstract
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
[...] Read more.
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. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Validating and Linking the GIMMS Leaf Area Index (LAI3g) with Environmental Controls in Tropical Africa
Remote Sens. 2014, 6(3), 1973-1990; doi:10.3390/rs6031973
Received: 31 October 2013 / Revised: 24 February 2014 / Accepted: 25 February 2014 / Published: 4 March 2014
Cited by 12 | PDF Full-text (1492 KB) | HTML Full-text | XML Full-text
Abstract
The recent Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g product provides a 30-year global times-series of remotely sensed leaf area index (LAI), an essential variable in models of ecosystem process and productivity. In this study, we use a new dataset of field-based
[...] Read more.
The recent Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g product provides a 30-year global times-series of remotely sensed leaf area index (LAI), an essential variable in models of ecosystem process and productivity. In this study, we use a new dataset of field-based LAITrue to indirectly validate the GIMMS LAI3g product, LAIavhrr, in East Africa, comparing the distribution properties of LAIavhrr across biomes and environmental gradients with those properties derived for LAITrue. We show that the increase in LAI with vegetation height in natural biomes is captured by both LAIavhrr and LAITrue, but that LAIavhrr overestimates LAI for all biomes except shrubland and cropland. Non-linear responses of LAI to precipitation and moisture indices, whereby leaf area peaks at intermediate values and declines thereafter, are apparent in both LAITrue and LAIavhrr, although LAITrue reaches its maximum at lower values of the respective environmental driver. Socio-economic variables such as governance (protected areas) and population affect both LAI responses, although cause and effect are not always obvious: a positive relationship with human population pressure was detected, but shown to be an artefact of both LAI and human settlement covarying with precipitation. Despite these complexities, targeted field measurements, stratified according to both environmental and socio-economic gradients, could provide crucial data for improving satellite-derived LAI estimates, especially in the human-modified landscapes of tropical Africa. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Temperature and Snow-Mediated Moisture Controls of Summer Photosynthetic Activity in Northern Terrestrial Ecosystems between 1982 and 2011
Remote Sens. 2014, 6(2), 1390-1431; doi:10.3390/rs6021390
Received: 28 December 2013 / Revised: 22 January 2014 / Accepted: 26 January 2014 / Published: 14 February 2014
Cited by 21 | PDF Full-text (25409 KB) | HTML Full-text | XML Full-text
Abstract
Recent warming has stimulated the productivity of boreal and Arctic vegetation by reducing temperature limitations. However, several studies have hypothesized that warming may have also increased moisture limitations because of intensified summer drought severity. Establishing the connections between warming and drought stress has
[...] Read more.
Recent warming has stimulated the productivity of boreal and Arctic vegetation by reducing temperature limitations. However, several studies have hypothesized that warming may have also increased moisture limitations because of intensified summer drought severity. Establishing the connections between warming and drought stress has been difficult because soil moisture observations are scarce. Here we use recently developed gridded datasets of moisture variability to investigate the links between warming and changes in available soil moisture and summer vegetation photosynthetic activity at northern latitudes (>45°N) based on the Normalized Difference Vegetation Index (NDVI) since 1982. Moisture and temperature exert a significant influence on the interannual variability of summer NDVI over about 29% (mean r2 = 0.29 ± 0.16) and 43% (mean r2 = 0.25 ±  0.12) of the northern vegetated land, respectively. Rapid summer warming since the late 1980s (~0.7 °C) has increased evapotranspiration demand and consequently summer drought severity, but contrary to earlier suggestions it has not changed the dominant climate controls of NDVI over time. Furthermore, changes in snow dynamics (accumulation and melting) appear to be more important than increased evaporative demand in controlling changes in summer soil moisture availability and NDVI in moisture-sensitive regions of the boreal forest. In boreal North America, forest NDVI declines are more consistent with reduced snowpack rather than with temperature-induced increases in evaporative demand as suggested in earlier studies. Moreover, summer NDVI variability over about 28% of the northern vegetated land is not significantly associated with moisture or temperature variability, yet most of this land shows increasing NDVI trends. These results suggest that changes in snow accumulation and melt, together with other possibly non-climatic factors are likely to play a significant role in modulating regional ecosystem responses to the projected warming and increase in evapotranspiration demand during the coming decades. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Intercomparison of Seven NDVI Products over the United States and Mexico
Remote Sens. 2014, 6(2), 1057-1084; doi:10.3390/rs6021057
Received: 4 December 2013 / Revised: 15 January 2014 / Accepted: 16 January 2014 / Published: 27 January 2014
Cited by 12 | PDF Full-text (1833 KB) | HTML Full-text | XML Full-text
Abstract
Satellites have provided large-scale monitoring of vegetation for over three decades, and several satellite-based Normalized Difference Vegetation Index (NDVI) datasets have been produced. Here we intercompare four long-term NDVI datasets based largely on the AVHRR sensor (NDVIg, NDVI3g, STAR, VIP) and three datasets
[...] Read more.
Satellites have provided large-scale monitoring of vegetation for over three decades, and several satellite-based Normalized Difference Vegetation Index (NDVI) datasets have been produced. Here we intercompare four long-term NDVI datasets based largely on the AVHRR sensor (NDVIg, NDVI3g, STAR, VIP) and three datasets based on newer sensors (SPOT, Terra, Aqua) and evaluate the effectiveness of homogenizing the datasets using the green vegetation fraction (GVF) and the impact it has on phenology trends. Results show that all NDVI datasets are highly correlated with each other. However, there are significant differences in the regression slopes that vary spatially and temporally. There is a general trend towards higher maximum annual NDVI over much of the temperate forests of the US and a longer greening period due mostly to a delayed end of the season. These trends are less well-defined over rainfall dependent ecosystems in Mexico and the southwest US Compared with the NDVI datasets, the derived GVF datasets show more one-to-one relationships, have reduced interannual variation, preserve their relationships better over the entire time period and are characterized by weaker trends. Finally, weak agreement between the trends in the datasets stresses the importance of using multiple datasets to evaluate changes in vegetation and its phenology. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series
Remote Sens. 2014, 6(1), 257-284; doi:10.3390/rs6010257
Received: 22 July 2013 / Revised: 21 November 2013 / Accepted: 25 November 2013 / Published: 27 December 2013
Cited by 23 | PDF Full-text (11713 KB) | HTML Full-text | XML Full-text
Abstract
Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with
[...] Read more.
Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with 1/12° spatial and bi-monthly temporal resolution. It covers the time period from 1981 to 2011. However, it is unclear if vegetation density and phenology derived from GIMMS are comparable to those obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI with 250 m ground resolution. To check the consistency between GIMMS and MODIS data sets, a comparative analysis was performed. For a large European window (40 × 40°), data distribution, spatial and temporal agreement were analyzed, as well as the timing of important phenological events. Overall, only a moderately good agreement of NDVI values was found. Large differences occurred during winter. Large discrepancies were also observed for phenological metrics, in particular the start of season. Information regarding the maximum of season was more consistent. Hence, both data sets should be well inter-calibrated before being used concurrently. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna
Remote Sens. 2013, 5(12), 6513-6538; doi:10.3390/rs5126513
Received: 15 September 2013 / Revised: 10 October 2013 / Accepted: 28 October 2013 / Published: 3 December 2013
Cited by 16 | PDF Full-text (2080 KB) | HTML Full-text | XML Full-text
Abstract
Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds
[...] Read more.
Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds in the southern Africa savanna. Dynamic Factor Analysis (DFA), a multivariate time-series dimension reduction technique, was used to identify the most important physical drivers of regional vegetation change. We first evaluated the Advanced Very High Resolution Radiometer (AVHRR)- vs. the Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) datasets across their overlapping period (2001–2010). NDVI follows a general pattern of cyclic seasonal variation, with distinct spatio-temporal patterns across physio-geographic regions. Both NDVI products produced similar DFA models, although MODIS was simulated better. Soil moisture and precipitation controlled NDVI for mean annual precipitation (MAP) < 750 mm, and above this, evaporation and mean temperature dominated. A second DFA with the full AVHRR (1982–2010) data found that for MAP < 750 mm, soil moisture and actual evapotranspiration control NDVI dynamics, followed by mean and maximum temperatures. Above 950 mm, actual evapotranspiration and precipitation dominate. The quantification of the combined spatio-temporal environmental drivers of NDVI expands our ability to understand landscape level changes in vegetation evaluated through remote sensing and improves the basis for the management of vulnerable regions, like the southern Africa savannas. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Recent Changes in Terrestrial Gross Primary Productivity in Asia from 1982 to 2011
Remote Sens. 2013, 5(11), 6043-6062; doi:10.3390/rs5116043
Received: 30 September 2013 / Revised: 25 October 2013 / Accepted: 11 November 2013 / Published: 15 November 2013
Cited by 9 | PDF Full-text (1064 KB) | HTML Full-text | XML Full-text
Abstract
Past changes in gross primary productivity (GPP) were assessed using historical satellite observations based on the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series and four terrestrial biosphere
[...] Read more.
Past changes in gross primary productivity (GPP) were assessed using historical satellite observations based on the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series and four terrestrial biosphere models to identify the trends and driving mechanisms related to GPP and NDVI in Asia. A satellite-based time-series data analysis showed that approximately 40% of the area has experienced a significant increase in the NDVI, while only a few areas have experienced a significant decreasing trend over the last 30 years. The increases in the NDVI are dominant in the sub-continental regions of Siberia, East Asia, and India. Simulations using the terrestrial biosphere models also showed significant increases in GPP, similar to the results for the NDVI, in boreal and temperate regions. A modeled sensitivity analysis showed that the increases in GPP are explained by increased temperature and precipitation in Siberia. Precipitation, solar radiation and CO2 fertilization are important factors in the tropical regions. However, the relative contributions of each factor to GPP changes are different among the models. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs
Remote Sens. 2013, 5(10), 4819-4838; doi:10.3390/rs5104819
Received: 15 August 2013 / Revised: 9 September 2013 / Accepted: 17 September 2013 / Published: 8 October 2013
Cited by 16 | PDF Full-text (2133 KB) | HTML Full-text | XML Full-text
Abstract
Leaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a
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Leaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN) from the latest version (third generation) of GIMMS AVHRR NDVI data over the period 1986–2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Global Trends in Seasonality of Normalized Difference Vegetation Index (NDVI), 1982–2011
Remote Sens. 2013, 5(10), 4799-4818; doi:10.3390/rs5104799
Received: 8 July 2013 / Revised: 20 September 2013 / Accepted: 23 September 2013 / Published: 30 September 2013
Cited by 42 | PDF Full-text (1446 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over half
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A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over half (56.30%) of land surfaces were found to exhibit significant trends. Almost half (46.10%) of the significant trends belonged to three classes of seasonal trends (or changes). Class 1 consisted of areas that experienced a uniform increase in NDVI throughout the year, and was primarily associated with forested areas, particularly broadleaf forests. Class 2 consisted of areas experiencing an increase in the amplitude of the annual seasonal signal whereby increases in NDVI in the green season were balanced by decreases in the brown season. These areas were found primarily in grassland and shrubland regions. Class 3 was found primarily in the Taiga and Tundra biomes and exhibited increases in the annual summer peak in NDVI. While no single attribution of cause could be determined for each of these classes, it was evident that they are primarily found in natural areas (as opposed to anthropogenic land cover conversions) and that they are consistent with climate-related ameliorations of growing conditions during the study period. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Trends in the Start of the Growing Season in Fennoscandia 1982–2011
Remote Sens. 2013, 5(9), 4304-4318; doi:10.3390/rs5094304
Received: 20 July 2013 / Revised: 2 September 2013 / Accepted: 3 September 2013 / Published: 6 September 2013
Cited by 21 | PDF Full-text (1764 KB) | HTML Full-text | XML Full-text
Abstract
Global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. In Fennoscandia, as well as Northern Europe, the advances of phenological events in spring have been recorded in recent decades. In this study, we analyzed the
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Global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. In Fennoscandia, as well as Northern Europe, the advances of phenological events in spring have been recorded in recent decades. In this study, we analyzed the start of the growing season within five different vegetation regions in Fennoscandia using the 30-year Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset. We applied a previously developed pixel-specific Normalized Difference Vegetation Index (NDVI) threshold method, adjusted it to the NDVI3g data and analyzed trends within the different regions. Results show a warming trend with an earlier start of the growing season of 11.8 ± 2.0 days (p < 0.01) for the whole area. However, there are large regional differences, and the warming/trend towards an earlier start of the growing season is most significant in the southern regions (19.3 ± 4.7 days, p < 0.01 in the southern oceanic region), while the start was stable or modest earlier (two to four days; not significant) in the northern regions. To look for temporal variations in the trends, we divided the 30-year period into three separate decadal time periods. Results show significantly more change/trend towards an earlier start of the growing season in the first period compared to the two last. In the second and third period, the trend towards an earlier start of the growing season slowed down, and in two of the regions, the trend towards an earlier start of the growing season was even reversed during the last decade. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Recent Declines in Warming and Vegetation Greening Trends over Pan-Arctic Tundra
Remote Sens. 2013, 5(9), 4229-4254; doi:10.3390/rs5094229
Received: 15 June 2013 / Revised: 17 July 2013 / Accepted: 19 August 2013 / Published: 29 August 2013
Cited by 32 | PDF Full-text (1822 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation productivity trends for the Arctic tundra are updated for the 1982–2011 period and examined in the context of land surface temperatures and coastal sea ice. Understanding mechanistic links between vegetation and climate parameters contributes to model advancements that are necessary for improving
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Vegetation productivity trends for the Arctic tundra are updated for the 1982–2011 period and examined in the context of land surface temperatures and coastal sea ice. Understanding mechanistic links between vegetation and climate parameters contributes to model advancements that are necessary for improving climate projections. This study employs remote sensing data: Global Inventory Modeling and Mapping Studies (GIMMS) Maximum Normalized Difference Vegetation Index (MaxNDVI), Special Sensor Microwave Imager (SSM/I) sea-ice concentrations, and Advanced Very High Resolution Radiometer (AVHRR) radiometric surface temperatures. Spring sea ice is declining everywhere except in the Bering Sea, while summer open water area is increasing throughout the Arctic. Summer Warmth Index (SWI—sum of degree months above freezing) trends from 1982 to 2011 are positive around Beringia but are negative over Eurasia from the Barents to the Laptev Seas and in parts of northern Canada. Eastern North America continues to show increased summer warmth and a corresponding steady increase in MaxNDVI. Positive MaxNDVI trends from 1982 to 2011 are generally weaker compared to trends from 1982–2008. So to better understand the changing trends, break points in the time series were quantified using the Breakfit algorithm. The most notable break points identify declines in SWI since 2003 in Eurasia and 1998 in Western North America. The Time Integrated NDVI (TI-NDVI, sum of the biweekly growing season values of MaxNDVI) has declined since 2005 in Eurasia, consistent with SWI declines. Summer (June–August) sea level pressure (slp) averages from 1999–2011 were compared to those from 1982–1998 to reveal higher slp over Greenland and the western Arctic and generally lower pressure over the continental Arctic in the recent period. This suggests that the large-scale circulation is likely a key contributor to the cooler temperatures over Eurasia through increased summer cloud cover and warming in Eastern North America from more cloud-free skies. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle A Comparative Analysis between GIMSS NDVIg and NDVI3g for Monitoring Vegetation Activity Change in the Northern Hemisphere during 1982–2008
Remote Sens. 2013, 5(8), 4031-4044; doi:10.3390/rs5084031
Received: 9 June 2013 / Revised: 18 July 2013 / Accepted: 6 August 2013 / Published: 12 August 2013
Cited by 20 | PDF Full-text (1074 KB) | HTML Full-text | XML Full-text
Abstract
The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies
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The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982–2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales
Remote Sens. 2013, 5(8), 3918-3950; doi:10.3390/rs5083918
Received: 24 May 2013 / Revised: 23 July 2013 / Accepted: 23 July 2013 / Published: 8 August 2013
Cited by 34 | PDF Full-text (4564 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land surface fluxes or for benchmarking prognostic vegetation models.
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Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land surface fluxes or for benchmarking prognostic vegetation models. Here we evaluated the interannual variability 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). Pearson’s correlation and multiple linear stepwise regression analyses were applied to quantify the NDVI interannual variability driven by climate anomalies, and to evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVI signal. 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 systems where in some regions and seasons > 40% of the NDVI variance could be explained by precipitation 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 wet season 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. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Disentangling the Relationships between Net Primary Production and Precipitation in Southern Africa Savannas Using Satellite Observations from 1982 to 2010
Remote Sens. 2013, 5(8), 3803-3825; doi:10.3390/rs5083803
Received: 18 June 2013 / Revised: 29 July 2013 / Accepted: 29 July 2013 / Published: 2 August 2013
Cited by 16 | PDF Full-text (1391 KB) | HTML Full-text | XML Full-text
Abstract
To obtain a better understanding of the variability in net primary production (NPP) in savannas is important for the study of the global carbon cycle and the management of this particular ecosystem. Using satellite and precipitation data sets, we investigated the variations in
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To obtain a better understanding of the variability in net primary production (NPP) in savannas is important for the study of the global carbon cycle and the management of this particular ecosystem. Using satellite and precipitation data sets, we investigated the variations in NPP in southern African savannas from 1982 to 2010, and disentangled the relationships between NPP and precipitation by land cover classes and mean annual precipitation (MAP) gradients. Specifically, we evaluate the utility of the third generation Global Inventory Monitoring and Modeling System (GIMMS3g) normalized difference vegetation index (NDVI) dataset, in comparison with Moderate-resolution Imaging Spectroradiometer (MODIS) derived NPP products, and find strong relationships between the overlapping data periods (2000–2010), such that we can apply our model to derive NPP estimates to the full 29-year NDVI time-series. Generally, the northern portion of the study area is characterized by high NPP and low variability, whereas the southern portion is characteristic of low NPP and high variability. During the period 1982 through 2010, NPP has reduced at a rate of −2.13 g∙C∙m−2∙yr−1 (p < 0.1), corresponding to a decrease of 6.7% over 29 years, and about half of bush and grassland savanna has experienced a decrease in NPP. There is a significant positive relationship between mean annual NPP and MAP in bush and grassland savannas, but no significant relationship is observed in tree savannas. The relationship between mean annual NPP and MAP varies with increases in MAP, characterized as a linear relationship that breaks down when MAP exceeding around 850–900 mm. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Evaluation of Land Surface Models in Reproducing Satellite Derived Leaf Area Index over the High-Latitude Northern Hemisphere. Part II: Earth System Models
Remote Sens. 2013, 5(8), 3637-3661; doi:10.3390/rs5083637
Received: 4 June 2013 / Revised: 17 July 2013 / Accepted: 17 July 2013 / Published: 25 July 2013
Cited by 18 | PDF Full-text (4209 KB) | HTML Full-text | XML Full-text
Abstract
Leaf Area Index (LAI) is a key parameter in the Earth System Models (ESMs) since it strongly affects land-surface boundary conditions and the exchange of matter and energy with the atmosphere. Observations and data products derived from satellite remote sensing are important for
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Leaf Area Index (LAI) is a key parameter in the Earth System Models (ESMs) since it strongly affects land-surface boundary conditions and the exchange of matter and energy with the atmosphere. Observations and data products derived from satellite remote sensing are important for the validation and evaluation of ESMs from regional to global scales. Several decades’ worth of satellite data products are now available at global scale which represents a unique opportunity to contrast observations against model results. The objective of this study is to assess whether ESMs correctly reproduce the spatial variability of LAI when compared with satellite data and to compare the length of the growing season in the different models with the satellite data. To achieve this goal we analyse outputs from 11 coupled carbon-climate models that are based on the set of new global model simulations planned in support of the IPCC Fifth Assessment Report. We focus on the average LAI and the length of the growing season on Northern Hemisphere over the period 1986–2005. Additionally we compare the results with previous analyses (Part I) of uncoupled land surface models (LSMs) to assess the relative contribution of vegetation and climatic drivers on the correct representation of LAI. Our results show that models tend to overestimate the average values of LAI and have a longer growing season due to the later dormancy. The similarities with the uncoupled models suggest that representing the correct vegetation fraction with the associated parameterizations; is more important in controlling the distribution and value of LAI than the climatic variables. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Global Biogeographical Pattern of Ecosystem Functional Types Derived From Earth Observation Data
Remote Sens. 2013, 5(7), 3305-3330; doi:10.3390/rs5073305
Received: 22 May 2013 / Revised: 27 June 2013 / Accepted: 1 July 2013 / Published: 10 July 2013
Cited by 6 | PDF Full-text (1729 KB) | HTML Full-text | XML Full-text
Abstract
The present study classified global Ecosystem Functional Types (EFTs) derived from seasonal vegetation dynamics of the GIMMS3g NDVI time-series. Rotated Principal Component Analysis (PCA) was run on the derived phenological and productivity variables, which selected the Standing Biomass (approximation of Net Primary Productivity),
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The present study classified global Ecosystem Functional Types (EFTs) derived from seasonal vegetation dynamics of the GIMMS3g NDVI time-series. Rotated Principal Component Analysis (PCA) was run on the derived phenological and productivity variables, which selected the Standing Biomass (approximation of Net Primary Productivity), the Cyclic Fraction (seasonal vegetation productivity), the Permanent Fraction (permanent surface vegetation), the Maximum Day (day of maximum vegetation development) and the Season Length (length of vegetation growing season) variables, describing 98% of the variation in global ecosystems. EFTs were created based on Isodata classification of the spatial patterns of the Principal Components and were interpreted via gradient analysis using the selected remote sensing variables and climatic constraints (radiation, temperature, and water) of vegetation growth. The association of the EFTs with existing climate and land cover classifications was demonstrated via Detrended Correspondence Analysis (DCA). The ordination indicated good description of the global environmental gradient by the EFTs, supporting the understanding of phenological and productivity dynamics of global ecosystems. Climatic constraints of vegetation growth explained 50% of variation in the phenological data along the EFTs showing that part of the variation in the global phenological gradient is not climate related but is unique to the Earth Observation derived variables. DCA demonstrated good correspondence of the EFTs to global climate and also to land use classification. The results show the great potential of Earth Observation derived parameters for the quantification of ecosystem functional dynamics and for providing reference status information for future assessments of ecosystem changes. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets
Remote Sens. 2013, 5(6), 2857-2882; doi:10.3390/rs5062857
Received: 14 March 2013 / Revised: 3 May 2013 / Accepted: 31 May 2013 / Published: 5 June 2013
Cited by 8 | PDF Full-text (1978 KB) | HTML Full-text | XML Full-text
Abstract
This paper examines a land surface solar radiation partitioning scheme, i.e., that of the Community Land Model version 4 (CLM4) with coupled carbon and nitrogen cycles. Taking advantage of a unique 30-year fraction of absorbed photosynthetically active radiation (FPAR) dataset, derived from
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This paper examines a land surface solar radiation partitioning scheme, i.e., that of the Community Land Model version 4 (CLM4) with coupled carbon and nitrogen cycles. Taking advantage of a unique 30-year fraction of absorbed photosynthetically active radiation (FPAR) dataset, derived from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) data set, multiple other remote sensing datasets, and site level observations, we evaluated the CLM4 FPAR’s seasonal cycle, diurnal cycle, long-term trends, and spatial patterns. Our findings show that the model generally agrees with observations in the seasonal cycle, long-term trends, and spatial patterns, but does not reproduce the diurnal cycle. Discrepancies also exist in seasonality magnitudes, peak value months, and spatial heterogeneity. We identify the discrepancy in the diurnal cycle as, due to, the absence of dependence on sun angle in the model. Implementation of sun angle dependence in a one-dimensional (1-D) model is proposed. The need for better relating of vegetation to climate in the model, indicated by long-term trends, is also noted. Evaluation of the CLM4 land surface solar radiation partitioning scheme using remote sensing and site level FPAR datasets provides targets for future development in its representation of this naturally complicated process. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle A Global Assessment of Long-Term Greening and Browning Trends in Pasture Lands Using the GIMMS LAI3g Dataset
Remote Sens. 2013, 5(5), 2492-2512; doi:10.3390/rs5052492
Received: 25 March 2013 / Revised: 2 May 2013 / Accepted: 7 May 2013 / Published: 17 May 2013
Cited by 10 | PDF Full-text (6434 KB) | HTML Full-text | XML Full-text
Abstract
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
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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. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology
Remote Sens. 2013, 5(5), 2113-2144; doi:10.3390/rs5052113
Received: 27 February 2013 / Revised: 17 April 2013 / Accepted: 25 April 2013 / Published: 3 May 2013
Cited by 58 | PDF Full-text (3235 KB) | HTML Full-text | XML Full-text
Abstract
Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here
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Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Divergent Arctic-Boreal Vegetation Changes between North America and Eurasia over the Past 30 Years
Remote Sens. 2013, 5(5), 2093-2112; doi:10.3390/rs5052093
Received: 1 March 2013 / Revised: 24 April 2013 / Accepted: 24 April 2013 / Published: 2 May 2013
Cited by 24 | PDF Full-text (2833 KB) | HTML Full-text | XML Full-text
Abstract
Arctic-Boreal region—mainly consisting of tundra, shrub lands, and boreal forests—has been experiencing an amplified warming over the past 30 years. As the main driving force of vegetation growth in the north, temperature exhibits tight coupling with the Normalized Difference Vegetation Index (NDVI)—a proxy
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Arctic-Boreal region—mainly consisting of tundra, shrub lands, and boreal forests—has been experiencing an amplified warming over the past 30 years. As the main driving force of vegetation growth in the north, temperature exhibits tight coupling with the Normalized Difference Vegetation Index (NDVI)—a proxy to photosynthetic activity. However, the comparison between North America (NA) and northern Eurasia (EA) shows a weakened spatial dependency of vegetation growth on temperature changes in NA during the past decade. If this relationship holds over time, it suggests a 2/3 decrease in vegetation growth under the same rate of warming in NA, while the vegetation response in EA stays the same. This divergence accompanies a circumpolar widespread greening trend, but 20 times more browning in the Boreal NA compared to EA, and comparative greening and browning trends in the Arctic. These observed spatial patterns of NDVI are consistent with the temperature record, except in the Arctic NA, where vegetation exhibits a similar long-term trend of greening to EA under less warming. This unusual growth pattern in Arctic NA could be due to a lack of precipitation velocity compared to the temperature velocity, when taking velocity as a measure of northward migration of climatic conditions. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Global Latitudinal-Asymmetric Vegetation Growth Trends and Their Driving Mechanisms: 1982–2009
Remote Sens. 2013, 5(3), 1484-1497; doi:10.3390/rs5031484
Received: 1 February 2013 / Revised: 13 March 2013 / Accepted: 18 March 2013 / Published: 21 March 2013
Cited by 36 | PDF Full-text (4309 KB) | HTML Full-text | XML Full-text
Abstract
Using a recent Leaf Area Index (LAI) dataset and the Community Land Model version 4 (CLM4), we investigated percent changes and controlling factors of global vegetation growth for the period 1982 to 2009. Over that 28-year period, both the remote-sensing estimate and model
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Using a recent Leaf Area Index (LAI) dataset and the Community Land Model version 4 (CLM4), we investigated percent changes and controlling factors of global vegetation growth for the period 1982 to 2009. Over that 28-year period, both the remote-sensing estimate and model simulation show a significant increasing trend in annual vegetation growth. Latitudinal asymmetry appeared in both products, with small increases in the Southern Hemisphere (SH) and larger increases at high latitudes in the Northern Hemisphere (NH). The south-to-north asymmetric land surface warming was assessed to be the principal driver of this latitudinal asymmetry of LAI trend. Heterogeneous precipitation functioned to decrease this latitudinal LAI gradient, and considerably regulated the local LAI change. A series of factorial experiments were specially-designed to isolate and quantify contributions to LAI trend from different external forcings such as climate variation, CO2, nitrogen deposition and land use and land cover change. The climate-only simulation confirms that climate change, particularly the asymmetry of land temperature variation, can explain the latitudinal pattern of LAI change. CO2 fertilization during the last three decades was simulated to be the dominant cause for the enhanced vegetation growth. Our study, though limited by observational and modeling uncertainties, adds further insight into vegetation growth trends and environmental correlations. These validation exercises also provide new quantitative and objective metrics for evaluation of land ecosystem process models at multiple spatio-temporal scales. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Structural Uncertainty in Model-Simulated Trends of Global Gross Primary Production
Remote Sens. 2013, 5(3), 1258-1273; doi:10.3390/rs5031258
Received: 19 January 2013 / Revised: 28 January 2013 / Accepted: 5 March 2013 / Published: 12 March 2013
Cited by 11 | PDF Full-text (851 KB) | HTML Full-text | XML Full-text
Abstract
Projected changes in the frequency and severity of droughts as a result of increase in greenhouse gases have a significant impact on the role of vegetation in regulating the global carbon cycle. Drought effect on vegetation Gross Primary Production (GPP) is usually modeled
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Projected changes in the frequency and severity of droughts as a result of increase in greenhouse gases have a significant impact on the role of vegetation in regulating the global carbon cycle. Drought effect on vegetation Gross Primary Production (GPP) is usually modeled as a function of Vapor Pressure Deficit (VPD) and/or soil moisture. Climate projections suggest a strong likelihood of increasing trend in VPD, while regional changes in precipitation are less certain. This difference in projections between VPD and precipitation can cause considerable discrepancies in the predictions of vegetation behavior depending on how ecosystem models represent the drought effect. In this study, we scrutinized the model responses to drought using the 30-year record of Global Inventory Modeling and Mapping Studies (GIMMS) 3g Normalized Difference Vegetation Index (NDVI) dataset. A diagnostic ecosystem model, Terrestrial Observation and Prediction System (TOPS), was used to estimate global GPP from 1982 to 2009 under nine different experimental simulations. The control run of global GPP increased until 2000, but stayed constant after 2000. Among the simulations with single climate constraint (temperature, VPD, rainfall and solar radiation), only the VPD-driven simulation showed a decrease in 2000s, while the other scenarios simulated an increase in GPP. The diverging responses in 2000s can be attributed to the difference in the representation of the impact of water stress on vegetation in models, i.e., using VPD and/or precipitation. Spatial map of trend in simulated GPP using GIMMS 3g data is consistent with the GPP driven by soil moisture than the GPP driven by VPD, confirming the need for a soil moisture constraint in modeling global GPP. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Trends and ENSO/AAO Driven Variability in NDVI Derived Productivity and Phenology alongside the Andes Mountains
Remote Sens. 2013, 5(3), 1177-1203; doi:10.3390/rs5031177
Received: 31 December 2012 / Revised: 27 February 2013 / Accepted: 27 February 2013 / Published: 6 March 2013
Cited by 14 | PDF Full-text (4919 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Increasing water use and droughts, along with climate variability and land use change, have seriously altered vegetation growth patterns and ecosystem response in several regions alongside the Andes Mountains. Thirty years of the new generation biweekly normalized difference vegetation index (NDVI3g) time series
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Increasing water use and droughts, along with climate variability and land use change, have seriously altered vegetation growth patterns and ecosystem response in several regions alongside the Andes Mountains. Thirty years of the new generation biweekly normalized difference vegetation index (NDVI3g) time series data show significant land cover specific trends and variability in annual productivity and land surface phenological response. Productivity is represented by the growing season mean NDVI values (July to June). Arid and semi-arid and sub humid vegetation types (Atacama desert, Chaco and Patagonia) across Argentina, northern Chile, northwest Uruguay and southeast Bolivia show negative trends in productivity, while some temperate forest and agricultural areas in Chile and sub humid and humid areas in Brazil, Bolivia and Peru show positive trends in productivity. The start (SOS) and length (LOS) of the growing season results show large variability and regional hot spots where later SOS often coincides with reduced productivity. A longer growing season is generally found for some locations in the south of Chile (sub-antarctic forest) and Argentina (Patagonia steppe), while central Argentina (Pampa-mixed grasslands and agriculture) has a shorter LOS. Some of the areas have significant shifts in SOS and LOS of one to several months. The seasonal Multivariate ENSO Indicator (MEI) and the Antarctic Oscillation (AAO) index have a significant impact on vegetation productivity and phenology in southeastern and northeastern Argentina (Patagonia and Pampa), central and southern Chile (mixed shrubland, temperate and sub-antarctic forest), and Paraguay (Chaco). Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Shifts in Global Vegetation Activity Trends
Remote Sens. 2013, 5(3), 1117-1133; doi:10.3390/rs5031117
Received: 20 December 2012 / Revised: 14 February 2013 / Accepted: 21 February 2013 / Published: 1 March 2013
Cited by 61 | PDF Full-text (17198 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation belongs to the components of the Earth surface, which are most extensively studied using historic and present satellite records. Recently, these records exceeded a 30-year time span composed of preprocessed fortnightly observations (1981–2011). The existence of monotonic changes and trend shifts present
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Vegetation belongs to the components of the Earth surface, which are most extensively studied using historic and present satellite records. Recently, these records exceeded a 30-year time span composed of preprocessed fortnightly observations (1981–2011). The existence of monotonic changes and trend shifts present in such records has previously been demonstrated. However, information on timing and type of such trend shifts was lacking at global scale. In this work, we detected major shifts in vegetation activity trends and their associated type (either interruptions or reversals) and timing. It appeared that the biospheric trend shifts have, over time, increased in frequency, confirming recent findings of increased turnover rates in vegetated areas. Signs of greening-to-browning reversals around the millennium transition were found in many regions (Patagonia, the Sahel, northern Kazakhstan, among others), as well as negative interruptions—“setbacks”—in greening trends (southern Africa, India, Asia Minor, among others). A minority (26%) of all significant trends appeared monotonic. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011
Remote Sens. 2013, 5(2), 927-948; doi:10.3390/rs5020927
Received: 28 December 2012 / Revised: 7 February 2013 / Accepted: 16 February 2013 / Published: 22 February 2013
Cited by 136 | PDF Full-text (1479 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary
[...] Read more.
Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series
Remote Sens. 2013, 5(2), 982-1000; doi:10.3390/rs5020982
Received: 20 December 2012 / Revised: 19 February 2013 / Accepted: 19 February 2013 / Published: 22 February 2013
Cited by 38 | PDF Full-text (2487 KB) | HTML Full-text | XML Full-text
Abstract
The spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data—scarce in large parts
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The spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data—scarce in large parts of Africa—or coarse-resolution rainfall estimates derived from weather satellites. In this study, we analyzed LGP and its variability based on the 1981–2011 GIMMS NDVI3g dataset. We applied a variable threshold method in combination with a searching algorithm to determine start- and end-of-season. We obtained reliable LGP estimates for arid, semi-arid and sub-humid climates that are consistent in space and time. This approach effectively mapped bimodality for clearly separated wet seasons in the Horn of Africa. Due to cloud contamination, the identified bimodality along the Guinea coast was judged to be less certain. High LGP variability is dominant in arid and semi-arid areas, and is indicative of crop failure risk. Significant negative trends in LGP were found for the northern part of the Sahel, for parts of Tanzania and northern Mozambique, and for the short rains of eastern Kenya. Positive trends occurred across western Africa, in southern Africa, and in eastern Kenya for the long rains. Our LGP analysis provides useful information for the mapping of farming systems, and to study the effects of climate variability and other drivers of change on vegetation and crop suitability. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle Assessing Performance of NDVI and NDVI3g in Monitoring Leaf Unfolding Dates of the Deciduous Broadleaf Forest in Northern China
Remote Sens. 2013, 5(2), 845-861; doi:10.3390/rs5020845
Received: 4 December 2012 / Revised: 4 February 2013 / Accepted: 5 February 2013 / Published: 18 February 2013
Cited by 14 | PDF Full-text (757 KB) | HTML Full-text | XML Full-text
Abstract
Using estimated leaf unfolding data and two types of Normalized Difference Vegetation Index (NDVI and NDVI3g) data generated from the Advanced Very High Resolution Radiometer (AVHRR) in the deciduous broadleaf forest of northern China during 1986 to 2006, we analyzed spatial, temporal and
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Using estimated leaf unfolding data and two types of Normalized Difference Vegetation Index (NDVI and NDVI3g) data generated from the Advanced Very High Resolution Radiometer (AVHRR) in the deciduous broadleaf forest of northern China during 1986 to 2006, we analyzed spatial, temporal and spatiotemporal relationships and differences between ground-based growing season beginning (BGS) and NDVI (NDVI3g)-retrieved start of season (SOS and SOS3g), and compared effectiveness of NDVI and NDVI3g in monitoring BGS. Results show that the spatial series of SOS (SOS3g) correlates positively with the spatial series of BGS at all pixels in each year (P < 0.001). Meanwhile, the time series of SOS (SOS3g) correlates positively with the time series of BGS at more than 65% of all pixels during the study period (P < 0.05). Furthermore, when pooling SOS (SOS3g) time series and BGS time series from all pixels, a significant positive correlation (P < 0.001) was also detectable between the spatiotemporal series of SOS (SOS3g) and BGS. In addition, the spatial, temporal and spatiotemporal differences between SOS (SOS3g) and BGS are at acceptable levels overall. Generally speaking, SOS3g is more consistent and accurate than SOS in capturing BGS, which suggests that NDVI3g data might be more sensitive than NDVI data in monitoring vegetation leaf unfolding. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Assessing Land Degradation/Recovery in the African Sahel from Long-Term Earth Observation Based Primary Productivity and Precipitation Relationships
Remote Sens. 2013, 5(2), 664-686; doi:10.3390/rs5020664
Received: 10 December 2012 / Revised: 24 January 2013 / Accepted: 28 January 2013 / Published: 4 February 2013
Cited by 64 | PDF Full-text (1887 KB) | HTML Full-text | XML Full-text
Abstract
The ‘rain use efficiency’ (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to
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The ‘rain use efficiency’ (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to non-precipitation related land degradation. Consequently, RUE may be regarded as means of normalizing ANPP for the impact of annual precipitation, and as an indicator of non-precipitation related land degradation. Large scale and long term identification and monitoring of land degradation in drylands, such as the Sahel, can only be achieved by use of Earth Observation (EO) data. This paper demonstrates that the use of the standard EO-based proxy for ANPP, summed normalized difference vegetation index (NDVI) (National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g)) over the year (ΣNDVI), and the blended EO/rain gauge based data-set for annual precipitation (Climate Prediction Center Merged Analysis of Precipitation, CMAP) results in RUE-estimates which are highly correlated with precipitation, rendering RUE useless as a means of normalizing for the impact of annual precipitation on ANPP. By replacing ΣNDVI by a ‘small NDVI integral’, covering only the rainy season and counting only the increase of NDVI relative to some reference level, this problem is solved. Using this approach, RUE is calculated for the period 1982–2010. The result is that positive RUE-trends dominate in most of the Sahel, indicating that non-precipitation related land degradation is not a widespread phenomenon. Furthermore, it is argued that two preconditions need to be fulfilled in order to obtain meaningful results from the RUE temporal trend analysis: First, there must be a significant positive linear correlation between annual precipitation and the ANPP proxy applied. Second, there must be a near-zero correlation between RUE and annual precipitation. Thirty-seven percent of the pixels in Sahel satisfy these requirements and the paper points to a range of different reasons why this may be the case. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1      Anav et al. Evaluation of DGVMs in reproducing satellite derived LAI, Part 1 Coupled ESMs
2      Anyamba et al. 30 years of growing season NDVI patterns and trends over the Sahel
3      Alessandri et al.    
4      Atzberger Phenological metrics derived over the European continent from NDVI3g data and MODIS time series
5      Barichivich et al. Drought and growing season photosynthetic activity in northern terrestrial ecosystems
6      Beck et al. Interpretation of cross-sensor remotely sensed boreal vegetation productivity pattens
7      Bhatt et al. Pan-Arctic tundra vegetation change and variability
8      Bi et al. Divergence in trends of vegetation greenness between North America and Eurasia
9      Brown et al. Relating observed trends in nutritional status from 1985-2011 using demographic and health survey data to variations in environmental conditions as measured by the GIMMS NDVI3g dataset
10    Ciais et al. Comparison of phenology and soil moisture simulated by a process based model with 30 years of microwave and visible remote sensing observations over Africa
11    Cook et al. Long–term greening and browning trends in global pasture lands (RS-29129; Jan-09-2013)
12    Cécile Dardel et al. 28 years of rangeland monitoring in Sahel from the sky and on the ground (Gourma, Mali)
13    Dong et al. Climatic warming affecting cropping system and pattern in Northern Temperate regions
14    Dye et al. Seasonality and trends in snow-cover, vegetation index, and temperature in northern Eurasia: 1982 to 2011.
15    Eastman et al. Global trends in seasonality of NDVI, 1982-2011
16    Erasmi et al. Vegetation greenness and dynamics over Northeast Brazil and its relation to ENSO
17    Fensholt et al. Assessment of land degradation/recovery in the African Sahel from long-term Earth Observation based vegetation and precipitation trends: EO-based land degradation assessment in Sahel (RS-27454)
18    Forkel et al. Inter-annual variability controls the reliability of greening and browning trend change estimates in NDVI time series.
18    Guerschman et al. Assimilating observations of FPAR in a hydrological model for Australia
19    Hashimoto et al. Structural uncertainty in model-simulated trends of global Gross Primary Production (RS-27795; Dec-12-2012)
20    Høgda et al Vegetation dynamics in northern Fennoscandia with focus on the start of growing season
21    Ichii et al. Evaluation of temporal variations in modeled terrestrial carbon cycle in Asia using long-term record of satellite based vegetation indices.
22    Ivits et al. Characterization of phenological change varieties of global ecosystems
23    de Jong et al. Shifts in global vegetation-activity trends
24    Van Leeuwen et al. Trends and ENSO/AAO driven variability in productivity and phenology alongside the Andes Mountains (RS-27271; Nov-30-2012)
25    Luo et. al. Validating satellite-derived start of season with estimated leaf unfolding data in the deciduous broadleaf forest of northern China (RS-27421; Dec-04-2012)
26    Mao et al. Global latitudinal-asymmetric vegetation growth trends and their driving mechanisms: 1982-2009
27    Mao et al. Global estimation of CMIP5 Earth System Models in simulating Leaf Area Index against remote-sensing products
28    Murray et al. Evaluation of DGVMs in reproducing satellite derived LAI, Part 2 Uncoupled DGVMs
29    Mathukumalli Long-term variability in vegetation dynamics in the Indo-Bangladesh Sundarban mangrove ecosystem
30    Nan et al. Detecting turning points in seasonal vegetation greenness in the Northern Hemisphere over the last 30 years
31    Pettorelli et al. NDVI in ecology: where and when does it work best?
32    Piao et al. Vegetation greeness changes in SouthEast Asia over the last three decades
33    Pinzon et al. Revisiting error, precision and uncertainty in NDVI AVHRR data: development of a consistent NDVI3g time series
34    Saatchi et al. Coupled impact of climate and land use change on Caspian Sea basin hydrology
35    Scheftic & Zeng et al. Comparison of the interannual variability of Fractional Vegetation Cover and the seasonal variability of Green Vegetation Fraction over the North American Monsoon Region using four different NDVI products
36    Vrieling et al. Length of growing period over Africa: variability and trends from 30 years of NDVI time series (RS-27228; Nov-30-2012)
37    Wang Kai et al. Evaluation of a land surface solar radiation partitioning scheme using remote sensing and FLUXNET FPAR datasets
38    Williams et al.    
39    Xu et al. Analysis of vegetation dynamics and its relationship with climate in China from 1982 to 2011
40    Zhou et al. Interannual variability of vegetation greenness and its linkage with hydroclimate variables over the last 30 years
41    Zhu et al. Global data sets of vegetation LAI3g and FPAR3g derived from GIMMS NDVI3g for the period 1981 to 2011
42    Zhu et al. Trends in global vegetation greenness 1982 to 2011

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