Dataset Information

Improving the Prediction of African Savanna Vegetation Variables Using Time Series of MODIS Products.

ABSTRACT: African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 =0.79, relative Root Mean Square Error, rRMSE=1.9%) and tree cover (R2 =0.78, rRMSE=0.3%). EVI provided the best model for shrub density (R2 =0.82) and shrub cover (R2 =0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 =0.76), shrubs (R2 =0.83), and grass (R2 =0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

SUBMITTER: Tsalyuk M

PROVIDER: S-EPMC6368348 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Improving the Prediction of African Savanna Vegetation Variables Using Time Series of MODIS Products.

Tsalyuk Miriam M Kelly Maggi M Getz Wayne M WM

ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) 20170803

African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we ...[more]

PMID: 30739997

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Project description:Shrub encroachment has far-reaching ecological and economic consequences in many ecosystems worldwide. Yet, compositional changes associated with shrub encroachment are often overlooked despite having important effects on ecosystem functioning.We document the compositional change and potential drivers for a northern Namibian Combretum woodland transitioning into a Terminalia shrubland. We use a multiproxy record (pollen, sedimentary ancient DNA, biomarkers, compound-specific carbon (?13C) and deuterium (?D) isotopes, bulk carbon isotopes (?13Corg), grain size, geochemical properties) from Lake Otjikoto at high taxonomical and temporal resolution.We provide evidence that state changes in semiarid environments may occur on a scale of one century and that transitions between stable states can span around 80 years and are characterized by a unique vegetation composition. We demonstrate that the current grass/woody ratio is exceptional for the last 170 years, as supported by n-alkane distributions and the ?13C and ?13Corg records. Comparing vegetation records to environmental proxy data and census data, we infer a complex network of global and local drivers of vegetation change. While our ?D record suggests physiological adaptations of woody species to higher atmospheric pCO2 concentration and drought, our vegetation records reflect the impact of broad-scale logging for the mining industry, and the macrocharcoal record suggests a decrease in fire activity associated with the intensification of farming. Impact of selective grazing is reflected by changes in abundance and taxonomical composition of grasses and by an increase of nonpalatable and trampling-resistant taxa. In addition, grain-size and spore records suggest changes in the erodibility of soils because of reduced grass cover. Synthesis. We conclude that transitions to an encroached savanna state are supported by gradual environmental changes induced by management strategies, which affected the resilience of savanna ecosystems. In addition, feedback mechanisms that reflect the interplay between management legacies and climate change maintain the encroached state.

Project description:BackgroundThe past decade witnessed an increment in the incidence of hand foot mouth disease (HFMD) in the Pacific Asian region; specifically, in Guangzhou China. This emphasized the requirement of an early warning system designed to allow the medical community to better prepare for outbreaks and thus minimize the number of fatalities.MethodsSamples from 1,556 inpatients (hospitalized) and 11,004 outpatients (non-admitted) diagnosed with HFMD were collected in this study from January 2009 to October 2013. Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish high predictive model for inpatients and outpatient as well as three viral serotypes (EV71, Pan-EV and CA16). To integrate climate variables in the data analyses, data from eight climate variables were simultaneously obtained during this period. Significant climate variable identified by correlation analyses was executed to improve time series modeling as external repressors.ResultsAmong inpatients with HFMD, 248 (15.9%) were affected by EV71, 137 (8.8%) were affected by Pan-EV+, and 436 (28.0%) were affected by CA16. Optimal Univariate SARIMA model was identified: (2,0,3)(1,0,0)52 for inpatients, (0,1,0)(0,0,2)52 for outpatients as well as three serotypes (EV71, (1,0,1)(0,0,1)52; CA16, (1,0,1)(0,0,0)52; Pan-EV, (1,0,1)(0,0,0)52). Using climate as our independent variable, precipitation (PP) was first identified to be associated with inpatients (r = 0.211, P = 0.001), CA16-serotype (r = 0.171, P = 0.007) and outpatients (r = 0.214, P = 0.01) in partial correlation analyses, and was then shown a significant lag in cross-autocorrelation analyses. However, inclusion of PP [lag -3 week] as external repressor showed a moderate impact on the predictive performance of the SARIMA model described here-in.ConclusionClimate patterns and HFMD incidences have been shown to be strongly correlated. The SARIMA model developed here can be a helpful tool in developing an early warning system for HFMD.

Dataset Information

Improving the Prediction of African Savanna Vegetation Variables Using Time Series of MODIS Products.

Publications

Improving the Prediction of African Savanna Vegetation Variables Using Time Series of MODIS Products.

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