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

Drought impacts assessment in Brazil - a remote sensing approach

Climate extremes are becoming more frequent in Brazil; studies project an increase in drought occurrences in many regions of the country. In the south, drought events lead to crop yield losses affecting the value chain and, therefore, the local economy. In the northeast, extended periods of drought lead to potential land degradation, affecting the livelihood and hindering local development. In the southern Amazon, an area that experienced intense land use change (LUC) in the last, the impacts are even more complex, ranging from crop yield loss and forest resilience loss, affecting ecosystem health and putting a threat on the native population traditional way of living. In the studies here we analyzed the drought impacts in these regions during the 2000s, which vary in nature and outcomes. We addressed some of the key problems in each of the three regions: i) for the southern agriculture, we tackled the problem of predicting soybean yield based on within-season remote sensing (RS) data, ii) in the northeast we mapped areas presenting trends of land degradation in the wake of an extended drought and, iii) in southern Amazon, we characterized a complex degradation cycle encompassing LUC, fire occurrence, forest resilience loss, carbon balance, and the interconnectedness of these factors impacting the local climate. Advisor: Brian D. Wardlo

Spatial patterning in albedo and biogenic carbon exchange in urban areas

Urbanization alters surface energy and biogenic carbon (C) exchange processes which can exacerbate increases in near-surface temperature and complicate municipal-scale efforts to address the local causes and impacts of climate change. This dissertation integrates field- and remote-sensing datasets to evaluate the magnitude of and spatial patterns in albedo and biogenic C fluxes in the urban landscape, focusing on the region of Greater Boston, Massachusetts. Using surface reflectance measurements from the Landsat and MODIS satellites, we show mean albedo in the Boston metropolitan region was significantly lower in core population centers than nearby rural areas, corresponding to reduced tree cover, greater impervious surface area, and higher surface temperatures. These results establish albedo decline as a gradient in landscape-scale features of urbanization, and offer context for efforts to mitigate extreme urban temperatures through raising the albedo of built surfaces. Pairing field measurements of tree growth with LiDAR-based data on tree biomass and canopy cover, we estimate the distribution of annual woody biomass C uptake in the city of Boston. A substantial portion of tree C uptake occurred in densely developed residential areas dominated by open-grown trees as well as remnant forest fragments. Our results show that estimates based on rural tree growth may under-predict C uptake by up to approximately 50%, and quantifies the scope for policy interventions aimed toward increasing ecosystem services output from the urban forest. Fusing measurements of soil respiration and net vegetation productivity in lawns and trees with high-resolution land surface data, we develop an improved estimate of annual biogenic net carbon fluxes in Boston at a 30 m resolution. We find forested areas of the city may be a modest net sink for C (median 2.7 GgC yr-1), but also estimate substantial C flux from intensively managed landscapes in residential areas. Estimated city-wide biogenic C was relatively small (median 600 MgC yr-1), potentially offsetting less than 1% of estimated annual fossil fuel emissions. Our results imply net biogenic C flux likely will contribute little towards efforts to reduce local net greenhouse gas emissions, but may significantly influence urban atmospheric CO2 concentrations at certain times and places

An analysis of long-term effects of climate change and socioeconomic activities on grassland productivity of inner Mongolia

In recent years, researchers have recognized the complexity of the interactions between the ecological system and the economic development of human society. However, the complicated relationships overwhelm traditional statistical procedures and require an innovative approach to investigate their dynamics. We proposed this study to provide a unique perspective in analyzing the long-term causal relationships between the grassland productivity, climate change, and socioeconomic development of Inner Mongolia Autonomous Region (IMAR) of China. Our attempt began with acquiring remotely sensed satellite imagery, climatic variations, and aggregated annual reports of the socio-economy of the IMAR in vegetation growing seasons for 15 years. The spatial and temporal dissimilarities of the raw observations prevented us from exploiting the potential of this valuable dataset; thus, we interpolated and extrapolated the data to generate a panel dataset with consistent spatial and temporal resolutions. Then, we took another step to preprocess the panel data by applying a signal filter to isolate the long-term trend of change from the inter- and intra-annual cyclic patterns and used the trends as the input for a panel data model. The results from our statistical analysis indicated that the independent variables explained the variations in the dependent variable extremely well, while the polynomial terms of climatic variables were significant with limited marginal effect and most of the climatic variables showed negative linear impact on the grassland productivity. In the meantime, we found not all socioeconomic variables we attempted to include into the model significantly affected grassland productivity, especially the variables describing the financial status of the IMAR residents

A unified vegetation index for quantifying the terrestrial biosphere

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change

Understanding Striga occurrence and risk under changing climatic conditions across different agroecological farming systems at local and regional scales122

Philosophiae Doctor - PhDThe invasion by Striga in most cereal crop fields in Africa has posed an acute threat to food security and socioeconomic integrity. Consequently, numerous technological and research developments have been made to minimize and even control the Striga impacts on crop production. So far, efforts to control Striga have primarily focused on the manipulation of the genetics of the host crops, as well as understanding the phenological and physiological traits, along with the chemical composition of the weed

Constraints on ecosystem carbon and water flux : estimates in a temperate Australian evergreen forest

Land-atmosphere carbon dioxide (CO2) exchange is the least constrained component of the global carbon cycle, yet it is driving most of its inter-annual variability. Seasonal and interannual variations in weather conditions affect biological activity and resulting CO2 exchanges, but the relative effects of phenology and climate on carbon cycling are not well understood. I used four years of eddy covariance data from a eucalypt woodland located near Sydney, South-East Australia, to better constrain carbon and water fluxes from this forest type. At our site, I observed a seasonal pattern of net ecosystem exchange (NEE) that contrasted with other flux tower sites in eucalypt forests. While similar Australian sites acted as a sink of carbon all year, especially in summer, our site behaved as a net sink of carbon in winter and a net source of carbon in summer. This pattern was caused by ecosystem respiration (Reco) driving the seasonal course of NEE, as the seasonal variability in Reco was bigger than that of gross primary production (GPP). GPP was limited by stomatal closure at high vapour pressure deficit in summer, but remained high in winter, while Reco was high in summer, and lower in winter. Leaf area index (LAI) varied seasonally, increasing rapidly mid-summer to reach a maximum in late summer, then decreased until the next year. LAI was a good predictor of canopy photosynthetic capacity (PC). The Community Atmosphere Biosphere Land Exchange (CABLE) land surface model was able to reproduce the seasonal variation in forest NEE but did not entirely capture canopy PC variability. Leaf demography, which is not accounted for in the model, may partly explain the mismatch between observed and simulated PC and should be further investigated. Our estimate of allocation of net primary productivity (NPP) to leaf growth was dynamic seasonally, which contrasts with the CABLE model assumption of a constant allocation factor in the evergreen broadleaf forest biome. Improved representation of dynamic allocation may further improve carbon cycle predictions in evergreen broadleaf forests. A semi-mechanistic model of heterotrophic respiration, the Dual Arrhenius Michaelis Menten model (DAMM), reproduced seasonal variations of Rsoil and Reco as a function of temperature and soil moisture. Daily to seasonal patterns of soil CO2 efflux were similar to those of Reco, but hourly dynamics were different, as Rsoil remained nearly constant overnight while Reco decreased. While decreasing air temperatures overnight may explain decreasing above-ground respiration, advection could also play a role, leading to a systematic data bias. Additional continuous, high frequency measurements of Reco components such as leaf respiration, stem respiration and soil respiration would improve mechanistic understanding of nighttime and daytime Reco. While weather variation was the major control of fluxes, the canopy phenology (leaf area index variations and leaf demography) also played an important role and needs to be incorporated in land surface models