Naomi Beth Schwartz

Assistant Professor

Research Classification

Research Interests

community ecology
Ecosystem Services and Conservation Policy
Environmental Change
GIS
remote sensing
Tropical forest landscapes

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Graduate Student Supervision

Master's Student Supervision (2010 - 2021)
A multi-scaled approach to understanding the environmental drivers of Southeast Asian forest-savanna mosaics (2021)

Seasonally dry tropical forest in Southeast Asia is a complex mosaic of dry evergreen forest and deciduous dipterocarp “forest” (which are structurally and functionally savanna). These patchy savannas are threatened by fragmentation and forest-centric management practices. Understanding the ecological processes that govern the boundaries between forest and savanna is essential for their proper conservation. I analyzed patterns of remotely sensed tree cover and identified the key determinants of both tree cover and landscape mosaics across SE Asia. First, I examined distributions of tree cover for bimodality—evidence that forests and savanna exist as alternate stable states. I found no bimodality in tree cover regionally; however, bimodality was present within landscapes. Second, I analyzed the relative importance of environmental factors in shaping patterns of tree cover and landscape mosaics, using regression models. I found that fire was the strongest predictor of tree cover at the regional scale and that the prevalence of landscape mosaics is dependent on climate (rainfall and seasonality). These results reveal that the same environmental factors important in delineating the distribution of savannas globally drive patterns of tree cover and landscape mosaics across SE Asia. More broadly, this work shows a useful approach for parsing out which environmental factors maintain and drive forest-savanna mosaic formation. While global vegetation products (GVPs), such as those I employed in my first analysis, are powerful and easy to use, their applicability in highly heterogeneous landscapes such as savanna-forest mosaics is poorly understood. I therefore explored the limitations of global vegetation structure products, including those used in my primary analysis using Airborne Laser Scanning. I found that no GVP was a clear winner. MODIS VCF significantly underestimates tree cover in SE Asian savannas, relative to forests, and the Global Forest Change (GFC) product’s ability to accurately map tree cover within forest-savanna mosaics is limited. Overall, I argue that while datasets such as GFC allow ecologists to test theories at large spatial scales (such as alternate stable states), researchers should be mindful of where and what type of vegetation was used to validate the GVP before conducting research at regional and global scales.

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A remote-sensing based investigation on the impacts of canopy height differences on microclimate in a burned forest (2021)

Forested canopies buffer seedlings from extreme climate conditions, but whether burned forests maintain this buffering capacity is not well understood. Previously, modeling the impact of a forest canopy on microclimate conditions was difficult because microclimate dynamics occur over fine-spatial scales. Inputs for microclimate modeling thus require high-resolution data. New technology like remotely piloted aircraft (RPAs) and low-cost microclimate sensors allow for a rapid expansion in microclimate modeling. My research capitalized on technological advancements to produce accurate and high spatial resolution descriptions of forest canopies to explain microclimate variation in a sub-boreal forest impacted by variable fire severity. To address a need for standardized microclimate modeling methods, I compare correlations of microclimate metrics to canopy height summarized at different scales of spatial buffers. Results demonstrate that the optimum scale for summarizing canopy height is dependent on the variable of interest – soil moisture is better explained by smaller buffers and temperature by moderately sized spatial buffers. I use these buffers to model the relationship between canopy height and microclimate. I found that growing season near-surface, surface, and soil temperatures increased linearly with decreasing canopy height and cover. Of near-ground temperatures, soil temperature showed the strongest correlation with canopy height, where a reduction of 10 m in canopy height was associated with a 1.5 °C increase in mean growing-season soil temperature. There was a weak negative relationship between canopy height and soil moisture, which I attribute to confounding effects of high evaporation in burned canopies and high transpiration in unburned canopies. My findings underline the importance of including canopy in post-disturbance microclimate models, as differences in soil temperature can impact the distribution of seedlings and other species. Supplementary materials available at: http://hdl.handle.net/2429/79092

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Detailed land cover mapping in a seasonally dry tropical forest landscape using multiple sensor types (2021)

Detailed mapping of land cover is essential for supporting science-based sustainable landscapemanagement. Despite the importance of land cover mapping in monitoring landscapes dynamics,land cover data are not always available. Even when the land cover data are available, they oftenlack detailed discrimination between forest types and plantations. This issue was found in a seasonally dry tropical forest landscape in Siem Reap and Preah Vihear, Cambodia. In this thesis,I explored the potential of (1) the fusion of optical and radar data in developing detailed land covermaps and revealing the driver of landscape change, and (2) vertical vegetation structure acquiredby the Global Ecosystem Dynamics Investigation (GEDI) mission—a new mission that harnessesa space-borne waveform lidar sensor installed on the International Space Station—to improve thevegetation mapping in the studied landscape. The fusion between radar (Sentinel-1) and optical(Sentinel-2) satellite data slightly improved the land cover classification accuracy (1.6% overallaccuracy increase) relative to Sentinel-2-only classification. Between 2015 and 2019, I detected a247,781.04 Ha dry deciduous forest loss; most were due to logging (147,314 Ha). Landdesignations, such as the protected areas and the economic land concessions (ELCs), significantlydetermine land cover change. The classification of vegetation types using GEDI data had 81.9%overall accuracy despite the limited spatial coverage of GEDI data. The GEDI-only classificationresults could identify the seasonally inundated forests with better accuracy than the land cover map derived from the fusion of optical-radar data. These results demonstrate the potential of structural information acquired by Sentinel-1 and GEDI to improve our ability to identify vegetation types in complex, heterogeneous landscapes.

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Member of G+PS
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