Vegetation - Herbivory Dynamics in Rangeland Ecosystems: Geospatial Modeling for Savanna and Wildlife Conservation in California and Namibia

Rangelands cover about half of Earth's land surface, encompass considerable biodiversity, and provide pivotal ecosystem services. However, rangelands across the globe face degradation due to changes in climate, land use, and management. Moreover, since herbivory is fundamental to rangeland ecosystem dynamics, shifts in the distribution of herbivores lead to overgrazing and desertification. To better understand, predict, and prevent changes on rangelands it is important to monitor these landscapes in a timely and efficient manner. Remote sensing can be a viable tool for measuring such change. However, the high spatial and temporal variability of rangeland vegetation, high reflectance from soil background and senesced vegetation during prolonged parts of the year, present challenges to the application of remote sensing in these ecosystems. The goal of my dissertation is to address these challenges and advance the application of remote sensing and geographic information system (GIS) to quantify vegetation and herbivores on rangelands across the world. My dissertation aims to address the connections among three main components of rangelands: the landscape, herbivores and human factors. I first develop a method to characterize the rangeland landscape by measuring and mapping detailed vegetation variables in Etosha National Park, Namibia. Etosha is a 22,270 km2 semiarid savanna, which encompasses great diversity of flora and fauna. I then examine how landscape variables affect the movement patterns of a large mammalian herbivore that is a keystone species in Etosha, the African elephant (Loxodanta africana). Finally, I develop tools to monitor how herbivory affects the productivity of rangelands conservation easement in California. In the first chapter, I outline the importance of rangelands and the threats these ecosystems face. I review the main challenges of measuring change processes on rangelands and describe some of the remote sensing based approaches that have been used to address these challenges. In the second chapter, I show that time series analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices can produce excellent results in predicting detailed field measurement of vegetation in Etosha. Using three innovative approaches I improve the prediction of both woody and herbaceous vegetation on the landscape, providing good measurements of vegetation cover, density, and biomass over large spatial extents. First, I develop field methodology that combines visual estimation of vegetation cover and vegetation type together with accurate field measurements. Second, by integrating time series of remote sensing data over six years and consolidating this information with partial least square regression, I achieve accurate models of vegetation measurements. Third, by using four different MODIS-based vegetation indices: Normalized Difference Vegetation Index (NDVI), Enhanced vegetation index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR), I measure different vegetation forms - grasses, shrubs, and trees, and thereby provide valuable information for monitoring and conserving Etosha's savanna vegetation. An exciting result from this chapter is the ability to transfer the application of these models in space, to other parts of the reserve, and in time, to other seasons and years. This emphasizes the validity of the models I have developed for predicting vegetation measurements and the ability to use these models in other locations. In the third chapter, I use the detailed vegetation maps I have created for Etosha National Park to understand resource selection of African elephants. I show how landscape variables affect both the speed and the direction of elephants' movements. Elephants prefer to move into areas with higher grass and shrub biomass, but lower tree biomass. Moreover, elephants prefer to be closer to water sources and, interestingly, to roads. Elephants' resource selection is influenced by the sex and the age of the individual. Importantly, temporal variation significantly influences the movement in response to the landscape: elephants choose different resources at different times of the day, which illustrates the behavior underpinnings of their resource selection. Moreover, they respond differently to resources at different times of the year, which highlights the ecological importance of these resources to the elephants. This chapter provides valuable information on how to manage resources in a manner that will promote the conservation of these magnificent keynote animals. In the fourth chapter, I use MODIS satellite data to monitor the effects of grazing on rangeland conservation easements in California, using as a study case the Simon Newman Ranch, a conservation property own by The Nature Conservancy. I use time series information of three vegetation indices to measure Residual Dry Matter (RDM), which is the dry grass matter left on the ground in the fall, at the end of the grazing season. RDM is a measure of grazing pressure; moderate levels of RDM are correlated with the health of rangeland ecosystem, soil stability, water retention and biodiversity of native plants and wildlife. Therefore RDM levels are used by The Nature Conservancy and other land managers as a conservation easement compliance measure. I develop a rapid, easy to use, efficient and robust methodology to predict RDM in the fall using spring maximum and annual sum of vegetation index values. My results show that MODIS-based Leaf Area Index (LAI) is the best measure of dry grass biomass. Most importantly, I demonstrate that MODIS data can be efficiently used by range managers and conservationists to estimate RDM easement compliance. In summary, in this dissertation I develop the use of quantitative spatial tools to measure both vegetation and herbivores on rangelands and to characterize landscapes on large spatial scales. I conduct interdisciplinary research connecting landscape ecology, remote sensing science and wildlife ecology. I demonstrate how freely available MODIS satellite imagery and open source software can be used by conservation managers to understand vegetation patterns and wildlife distribution in relatively easy, cost efficient, rapid and robust ways. The tools I develop in this dissertation identify and quantify change in rangelands. My results inform targeted management and conservation practices, and contribute to improve monitoring and to the understanding of these imperiled ecosystems

Vegetation - Herbivory Dynamics in Rangeland Ecosystems: Geospatial Modeling for Savanna and Wildlife Conservation in California and Namibia

Rangelands cover about half of Earth's land surface, encompass considerable biodiversity, and provide pivotal ecosystem services. However, rangelands across the globe face degradation due to changes in climate, land use, and management. Moreover, since herbivory is fundamental to rangeland ecosystem dynamics, shifts in the distribution of herbivores lead to overgrazing and desertification. To better understand, predict, and prevent changes on rangelands it is important to monitor these landscapes in a timely and efficient manner. Remote sensing can be a viable tool for measuring such change. However, the high spatial and temporal variability of rangeland vegetation, high reflectance from soil background and senesced vegetation during prolonged parts of the year, present challenges to the application of remote sensing in these ecosystems. The goal of my dissertation is to address these challenges and advance the application of remote sensing and geographic information system (GIS) to quantify vegetation and herbivores on rangelands across the world. My dissertation aims to address the connections among three main components of rangelands: the landscape, herbivores and human factors. I first develop a method to characterize the rangeland landscape by measuring and mapping detailed vegetation variables in Etosha National Park, Namibia. Etosha is a 22,270 km2 semiarid savanna, which encompasses great diversity of flora and fauna. I then examine how landscape variables affect the movement patterns of a large mammalian herbivore that is a keystone species in Etosha, the African elephant (Loxodanta africana). Finally, I develop tools to monitor how herbivory affects the productivity of rangelands conservation easement in California. In the first chapter, I outline the importance of rangelands and the threats these ecosystems face. I review the main challenges of measuring change processes on rangelands and describe some of the remote sensing based approaches that have been used to address these challenges. In the second chapter, I show that time series analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices can produce excellent results in predicting detailed field measurement of vegetation in Etosha. Using three innovative approaches I improve the prediction of both woody and herbaceous vegetation on the landscape, providing good measurements of vegetation cover, density, and biomass over large spatial extents. First, I develop field methodology that combines visual estimation of vegetation cover and vegetation type together with accurate field measurements. Second, by integrating time series of remote sensing data over six years and consolidating this information with partial least square regression, I achieve accurate models of vegetation measurements. Third, by using four different MODIS-based vegetation indices: Normalized Difference Vegetation Index (NDVI), Enhanced vegetation index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR), I measure different vegetation forms - grasses, shrubs, and trees, and thereby provide valuable information for monitoring and conserving Etosha's savanna vegetation. An exciting result from this chapter is the ability to transfer the application of these models in space, to other parts of the reserve, and in time, to other seasons and years. This emphasizes the validity of the models I have developed for predicting vegetation measurements and the ability to use these models in other locations. In the third chapter, I use the detailed vegetation maps I have created for Etosha National Park to understand resource selection of African elephants. I show how landscape variables affect both the speed and the direction of elephants' movements. Elephants prefer to move into areas with higher grass and shrub biomass, but lower tree biomass. Moreover, elephants prefer to be closer to water sources and, interestingly, to roads. Elephants' resource selection is influenced by the sex and the age of the individual. Importantly, temporal variation significantly influences the movement in response to the landscape: elephants choose different resources at different times of the day, which illustrates the behavior underpinnings of their resource selection. Moreover, they respond differently to resources at different times of the year, which highlights the ecological importance of these resources to the elephants. This chapter provides valuable information on how to manage resources in a manner that will promote the conservation of these magnificent keynote animals. In the fourth chapter, I use MODIS satellite data to monitor the effects of grazing on rangeland conservation easements in California, using as a study case the Simon Newman Ranch, a conservation property own by The Nature Conservancy. I use time series information of three vegetation indices to measure Residual Dry Matter (RDM), which is the dry grass matter left on the ground in the fall, at the end of the grazing season. RDM is a measure of grazing pressure; moderate levels of RDM are correlated with the health of rangeland ecosystem, soil stability, water retention and biodiversity of native plants and wildlife. Therefore RDM levels are used by The Nature Conservancy and other land managers as a conservation easement compliance measure. I develop a rapid, easy to use, efficient and robust methodology to predict RDM in the fall using spring maximum and annual sum of vegetation index values. My results show that MODIS-based Leaf Area Index (LAI) is the best measure of dry grass biomass. Most importantly, I demonstrate that MODIS data can be efficiently used by range managers and conservationists to estimate RDM easement compliance. In summary, in this dissertation I develop the use of quantitative spatial tools to measure both vegetation and herbivores on rangelands and to characterize landscapes on large spatial scales. I conduct interdisciplinary research connecting landscape ecology, remote sensing science and wildlife ecology. I demonstrate how freely available MODIS satellite imagery and open source software can be used by conservation managers to understand vegetation patterns and wildlife distribution in relatively easy, cost efficient, rapid and robust ways. The tools I develop in this dissertation identify and quantify change in rangelands. My results inform targeted management and conservation practices, and contribute to improve monitoring and to the understanding of these imperiled ecosystems

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

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

Monitoring the Impact of Grazing on Rangeland Conservation Easements Using MODIS Vegetation Indices

Monitoring the effects of grazing on rangelands is crucial for ensuring sustainable rangeland ecosystem function and maintaining its conservation values. Residual dry matter (RDM), the dry grass biomass left on the ground at the end of the grazing season, is a commonly used proxy for rangeland condition in Mediterranean climates. Moderate levels of RDM are correlated with soil stability, forage production, wildlife habitat, and diversity of native plants. Therefore RDM is widely monitored on rangeland conservation properties. Current ground-based methods for RDM monitoring are expensive, are labor intensive, and provide information in the fall, after the effects of grazing have already occurred. In this paper we present a cost-effective, rapid, and robust methodology to monitor and predict RDM using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. We performed a time series analysis of three MODIS-based vegetation indices (VIs) measured over the period 2000-2012: Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR). We examined the correlation between the four VIs and fall RDM measured at The Nature Conservancy's Simon Newman Ranch in central California. We found strong and significant correlations between maximum VI values in late spring and RDM in the fall. Among the VIs, LAI values had the most significant correlation with fall RDM. MODIS-based multivariate models predicted up to 63% of fall RDM. Importantly, maximum and sum VIs values were significantly higher in management units with RDM levels in compliance with RDM conservation easement terms compared with units out of compliance. On the basis of these results, we propose a management model that uses time series analysis of MODIS VIs to predict forage quantities, manage stocking rates, and monitor rangeland easement compliance. This model can be used to improve monitoring of rangeland conservation by providing information on range conditions throughout the year