Vegetation dynamics and rainfall sensitivity of the Amazon

Hilker, Thomas; Lyapustin, A.; Tucker, Compton J.; Hall, Forrest G.; Myneni, Ranga B.; Wang, Yujie; Bi, Jian; Moura, Yhasmin Mendes de; Sellers, P. J.

doi:10.1073/pnas.1404870111

Cited by 283 publications

(255 citation statements)

References 67 publications

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“…Ten years of observations from MODIS data are insufficient to establish long-term patterns or thresholds after which die-off might occur (Hilker et al, 2014). However, our results show a clear trend toward warmer and drier conditions in August and September across a variety of ecoregions and FTGs.…”

Section: Discussionmentioning

confidence: 54%

A forest vulnerability index based on drought and high temperatures

Mildrexler

Yang

Cohen

et al. 2016

Remote Sensing of Environment

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Increasing forest stress and tree mortality has been directly linked to combinations of drought and high temperatures. The climatic changes expected during the next decades -large increases in mean temperature, increased heat waves, and significant long-term regional drying in the western USA -will likely increase chronic forest stress and mortality. The aim of this research is to develop and apply a new forest vulnerability index (FVI) associated with drought and high temperatures across the Pacific Northwest region (PNW; Oregon and Washington) of the USA during the MODIS Aqua era (since 2003). Our technique incorporates the alterations to canopy water and energy exchange processes caused by drought and high temperatures with spatially continuous MODIS land surface temperature (LST) and evapotranspiration (ET), and with Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation (P) data. With P and ET, we calculate a monthly water balance variable for each individual pixel normalized by forest type group (FTG), and then difference the water balance with the corresponding normalized monthly mean LST to calculate a monthly forest stress index (FSI). We then extract the pixel-specific (800-m resolution) statistically significant temporal trends of the FSI from 2003 to 2012 by month (April to October). The FVI is the slope of the monthly FSI across years, such that there is a FVI for each month. Statistically significant positive slopes indicate interannual increases in stress leading to expected forest vulnerability (positive FVI) for a given month. Positive FVI values were concentrated in the months of August and September, with peak vulnerability occurring at different times for different FTGs. Overall, increased vulnerability rates were the highest in drier FTGs such as Ponderosa Pine, Juniper, and Lodgepole Pine. Western Larch and Fir/Spruce/Mountain Hemlock groups occupy moister sites but also had relatively high proportion of positive FVI values. The Douglas-fir group had the second largest total area of increased vulnerability due to its large areal extent in the study area. Based on an analysis using imagery viewed in Google Earth, we confirm that areas with increased vulnerability are associated with greater amounts of stress and mortality. The FVI is a new way to conceptualize and monitor forest vulnerability based on first-order principles and has the potential to be generalized to other geographical areas.

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Section: Discussionmentioning

confidence: 54%

A forest vulnerability index based on drought and high temperatures

Mildrexler

Yang

Cohen

et al. 2016

Remote Sensing of Environment

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“…The effects of natural factors on the distribution and growth of vegetation cover have been analyzed in previous studies. The timescales of these studies varied from monthly to inter-annual scales, and the spatial scales varied from pixel to regional scales (Sun et al, 2010;Chuai et al, 2013;Hilker et al, 2014;. Mu et al (2012), amongst others, analyzed the response of the vegetation dynamic changes to climate change on different timescales, including monthly and annual scales, analyzed at both regional and overall spatial scales, dividing the land area into ecological forest areas, grassland ecological areas and desert ecological areas.…”

Section: Introductionmentioning

confidence: 99%

Effects of rural–urban migration on vegetation greenness in fragile areas: A case study of Inner Mongolia in China

Sun

Tan

et al. 2016

J. Geogr. Sci.

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Different government departments and researchers have paid considerable attention at various levels to improving the eco-environment in ecologically fragile areas. Over the past decade, large numbers of people have emigrated from rural areas as a result of the rapid urbanization in Chinese society. The question then remains: to what extent does this migration affect the regional vegetation greenness in the areas that people have moved from? Based on normalized difference vegetation index (NDVI) data with a resolution of 1 km, as well as meteorological data and socio-economic data from 2000 to 2010 in Inner Mongolia, the spatio-temporal variation of vegetation greenness in the study area was analyzed via trend analysis and significance test methods. The contributions of human activities and natural factors to the variation of vegetation conditions during this period were also quantitatively tested and verified, using a multi-regression analysis method. We found that: (1) the vegetation greenness of the study area increased by 10.1% during 2000-2010. More than 28% of the vegetation greenness increased significantly, and only about 2% decreased evidently during the study period. (2) The area with significant degradation showed a banded distribution at the northern edge of the agro-pastoral ecotone in central Inner Mongolia. This indicates that the eco-environment is still fragile in this area, which should be paid close attention. The area where vegetation greenness significantly improved showed a concentrated distribution in the southeast and west of Inner Mongolia. (3) The effect of agricultural labor on vegetation greenness exceeded those due to natural factors (i.e. precipitation and temperature). The emigration of agricultural labor improved the regional vegetation greenness significantly.

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“…As an alternative to conventional MODIS at-surface products, the Multi-Angle Implementation of Atmospheric Correction (MAIAC) is a novel cloud-screening technique that enhances land surface reflectance based on MODIS C6 Level 1B data [21]. MAIAC's cloud detection technique yields a noise reduction by a factor of 3-10 compared to standard MODIS surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4, and MYD13A2-Vegetation Index) without the conventional assumption of Lambertian land surface behavior [19,20].…”

Section: Maiac Datamentioning

confidence: 99%

Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes

2017

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Abstract:The spatial distribution of disturbances in Andean tropical forests and protected areas has commonly been calculated using bi or tri-temporal analysis because of persistent cloud cover and complex topography. Long-term trends of vegetative decline (browning) or improvement (greening) have thus not been evaluated despite their importance for assessing conservation strategy implementation in regions where field-based monitoring by environmental authorities is limited. Using Colombia's Cordillera de los Picachos National Natural Park as a case study, we provide a temporally rigorous assessment of regional vegetation change from 2001-2015 with two remote sensing-based approaches using the Breaks For Additive Season and Trend (BFAST) algorithm. First, we measured long-term vegetation trends using a Moderate Resolution Imaging Spectroradiometer (MODIS)-based Multi-Angle Implementation of Atmospheric Correction (MAIAC) time series, and, second, we mapped short-term disturbances using all available Landsat images. MAIAC-derived trends indicate a net greening in 6% of the park, but in the surrounding 10 km area outside of the park, a net browning trend prevails at 2.5%. We also identified a 12,500 ha area within Picachos (4% of the park's total area) that has shown at least 13 years of consecutive browning, a result that was corroborated with our Landsat-based approach that recorded a 12,642 ha (±1440 ha) area of disturbed forest within the park. Landsat vegetation disturbance results had user's and producer's accuracies of 0.95 ± 0.02 and 0.83 ± 0.18, respectively, and 75% of Landsat-detected dates of disturbance events were accurate within ±6 months. This study provides new insights into the contribution of short-term disturbance to long-term trends of vegetation change, and offers an unprecedented perspective on the distribution of small-scale disturbances over a 15-year period in one of the most inaccessible national parks in the Andes.

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Vegetation dynamics and rainfall sensitivity of the Amazon

Cited by 283 publications

References 67 publications

A forest vulnerability index based on drought and high temperatures

A forest vulnerability index based on drought and high temperatures

Effects of rural–urban migration on vegetation greenness in fragile areas: A case study of Inner Mongolia in China

Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes

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