Monitoring vegetation dynamics from lunar orbiting satellites

PresentationFUNCTION STATEMENT: Our analyses are based on data from Terra MISR/MODIS, Aqua MODIS, DSCIVR EPIC and EO-1 Hyperion sensors. Science questions as identified in NASA’s Science Plan: Detect and predict changes in Earth’s ecological and chemical cycles, including land cover, biodiversity, and the global carbon cycle.First author draf

Toward a better understanding of changes in Northern vegetation using long-term remote sensing data

Cascading consequences of recent changes in the physical environment of northern lands associated with rapid warming have affected a broad range of ecosystem processes, particularly, changes in structure, composition, and functioning of vegetation. Incomplete understanding of underlying processes driving such changes is the primary motivation for this research. We report here the results of three studies that use long-term remote sensing data to advance our knowledge of spatiotemporal changes in growing season, greenness and productivity of northern vegetation. First, we improve the remote sensing-based detection of growing season by fusing vegetation greenness, snow and soil freeze/thaw condition. The satellite record reveals extensive lengthening trends of growing season and enhanced annual total greenness during the last three decades. Regionally varying seasonal responses are linked to local climate constraints and their relaxation. Second, we incorporate available land surface histories including disturbances and human land management practices to understand changes in remotely sensed vegetation greenness. This investigation indicates that multiple drivers including natural (wildfire) and anthropogenic (harvesting) disturbances, changing climate and agricultural activities govern the large-scale greening trends in northern lands. The timing and type of disturbances are important to fully comprehend spatially uneven vegetation changes in the boreal and temperate regions. In the final part, we question how photosynthetic seasonality evolved into its current state, and what role climatic constraints and their variability played in this process and ultimately in the carbon cycle. We take the ‘laws of minimum’ as a basis and introduce a new framework where the timing of peak photosynthetic activity (DOYPmax) acts as a proxy for plants adaptive state to climatic constraints on their growth. The result shows a widespread warming-induced advance in DOYPmax with an increase of total gross primary productivity across northern lands, which leads to an earlier phase shift in land-atmosphere carbon fluxes and an increase in their amplitude. The research presented in this dissertation suggests that understanding past, present and likely future changes in northern vegetation requires a multitude of approaches that consider linked climatic, social and ecological drivers and processes

Characterizing spatial burn severity patterns of 2016 Chimney Tops 2 fire using multi-temporal Landsat and NEON LiDAR data

The Chimney Tops 2 wildfire (CT2) in 2016 at Great Smoky Mountains National Park (GSMNP) was recorded as the largest fire in GSMNP history. Understanding spatial patterns of burn severity and its underlying controlling factors is essential for managing the forests affected and reducing future fire risks; however, this has not been well understood. Here, we formulated two research questions: 1) What were the most important factors characterizing the patterns of burn severity in the CT2 fire? 2) Were burn severity measures from passive and active optical remote sensing sensors providing consistent views of fire damage? To address these questions, we used multitemporal Landsat- and lidar-based burn severity measures, i.e., relativized differenced Normalized Burn Ratio (RdNBR) and relativized differenced Mean Tree Height (RdMTH). A random forest approach was used to identify key drivers in characterizing spatial variability of burn severity, and the partial dependence of each explanatory variable was further evaluated. We found that pre-fire vegetation structure and topography both play significant roles in characterizing heterogeneous mixed burn severity patterns in the CT2 fire. Mean tree height, elevation, and topographic position emerged as key factors in explaining burn severity variation. We observed generally consistent spatial patterns from Landsat- and lidar-based burn severity measures. However, vegetation type and structure-dependent relations between RdNBR and RdMTH caused locally inconsistent burn severity patterns, particularly in high RdNBR regions. Our study highlights the important roles of pre-fire vegetation structure and topography in understanding burn severity patterns and urges to integrate both spectral and structural changes to fully map and understand fire impacts on forest ecosystems

Earth system data record from DSCOVR EPIC observations: product description and analyses

The NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR) mission was launched on February 11, 2015 to the Sun-Earth Lagrangian L1 point where it began to collect radiance data of the entire sunlit Earth every 65 to 110 min in June 2015. It provides imageries in near backscattering directions at ten ultraviolet to near infrared narrow spectral bands. The DSCOVR EPIC science product suite includes vegetation Earth system data record (VESDR) that provides leaf area index (LAI) and diurnal courses of normalized difference vegetation index (NDVI), sunlit LAI (SLAI), fraction of incident photosynthetically active radiation (FPAR) absorbed by the vegetation and Directional Area Scattering Function (DASF). The parameters at 10-km sinusoidal grid and 65-110 min temporal frequency are generated from the upstream EPIC MAIAC surface reflectance product. The DSCOVR EPIC science team also provides two ancillary science data products derived from 500m MODIS land cover type 3 product: 10 km Land Cover Type and Distribution of Land Cover Types within 10 km EPIC pixel. All products were released on June-7-2018 and publicly available from the NASA Langley Atmospheric Science Data Center (https://eosweb.larc.nasa.gov/project/dscovr/dscovr_epic_l2_vesdr_01). This poster presents an overview of the EPIC VESDR research, which includes descriptions of the algorithm and product, initial assessment of its quality and obtaining new information on vegetation properties from the VESDR product.Accepted manuscrip