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.

Temporal variation in resource selection of African elephants follows long‐term variability in resource availability

“…Therefore, FPAR measures both green and dry biomass, and is strongly correlated with herbaceous vegetation (Tsalyuk et al. ).…”

“…According to our satellite‐based vegetation models, NDVI was the best predictor of tree biomass while FPAR was the best predictor of predictor of grass biomass (Tsalyuk et al. ). Selection of areas with either high NDVI or high FPAR indicates elephants’ mixed utilization of both herbaceous and woody habitats.…”

“…Based on these models, we created detailed maps of the cover, density, and biomass of woody and herbaceous vegetation in Etosha, as described in Tsalyuk et al. (). In short, we measured the density, cover, and biomass of the main vegetation types in Etosha: grasses, shrubs, and trees, in 348 sampling points across Etosha, during the dry and wet season.…”

“…By using multiannual time series of four VIs we were able to create robust prediction models for each vegetation type with high variance explained and low error (Tsalyuk et al. ). For each vegetation variable, we selected the VI‐based model that resulted in the highest percentage of variance explained (equivalent to R 2 ) and the lowest leave‐one‐out cross‐validation root mean squared error of prediction (RMSEP; Mevik and Cederkvist , Darvishzadeh et al.…”

“…Further, resource availability at a given time reflects the cumulative outcomes of the forces (e.g., rainfall, fire, grazing) shaping the state of that resource over previous time frames (days, month, years) (Tsalyuk et al. ). Therefore, in species with higher cognitive abilities and capacity for memory, movement response to the landscape may reflect not only the current condition of the resource, but also the state of the resource integrated over prior periods.…”