207 research outputs found

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

    Get PDF
    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

    Moisture availability mediates the relationship between terrestrial gross primary production and solar‐induced chlorophyll fluorescence: Insights from global‐scale variations

    Get PDF
    Effective use of solar‐induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global‐scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold‐and‐wet climates to hot‐and‐dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales

    식생유형이 알라스카 총 1차 생산성의 연간변화에 미치는 상대적인 기여

    Get PDF
    학위논문 (석사)-- 서울대학교 대학원 : 농업생명과학대학 협동과정농림기상학, 2018. 2. 류영렬.Vegetation in the high latitude ecosystem is most responsive to climate variables, leading to high year to year variability of gross primary productivity (GPP). Therefore, understanding the spatiotemporal patterns of GPP and how climate variables drive its interannual variability (IAV) is important to account for their present and future status. In this study, we examine the spatiotemporal patterns of Alaskan GPP and further investigate how their relation to climate drivers. We use GPP derived from four different approaches, a process-based approach (Breathing Earth System Simulator), a semi-empirical approach (Moderate Resolution Spectroradiometer 17A2) and the machine-learning approaches (Support Vector Regression and FLUXCOM). Model evaluation with eddy covariance data from 17 sites showed that the models explained 65% to 85% of the monthly variation with relative bias ranging from -22% to 33%. Model performance was better in the boreal forest compared to tundra and fire disturbed ecosystems. The spatial and temporal variation of GPP in the models displayed a consistent pattern, where the deciduous broadleaf forest showed the highest variability of GPP IAV by 14%, followed by fire and evergreen forest (13%) and then tundra (10%). Tundra accounted for the largest fraction of IAV of GPP with 55%, exceeding evergreen needleleaf forest (38%), deciduous broadleaf forest (7%) and areas that had been disturbed by fire (0.8%). GPP in tundra has the smallest variation among the PFTs. 68% of Alaska is tundra which led to the largest contribution to the IAV of GPP. The IAV of GPP from 2001 to 2011 had a similar pattern to the IAV of both air temperature and radiation, where warmer years had a larger GPP anomaly compared to the colder years. Therefore, warming and cooling as a result of climate change could significantly impact the IAV of land-atmosphere interaction of carbon dioxide.1. Introduction 1 2. Material and Method 5 2.1 Study Region 5 2.2 Flux Tower Data 6 2.3 Satellite-based GPP Datasets 9 2.4 Dataset of climate variables 14 2.5 Landcover map 15 2.6 Evaluation and analysis of GPP 16 3. Results 20 3.1 Evaluation of Models against flux tower data 20 3.2 IAV of GPP 23 3.3 Relationship between IAV of GPP and Climate Variables 31 4. Discussion 37 4.1 Model Performance across different PFTs 37 4.2 IAV of GPP 39 4.3 Controlling factors in IAV of GPP 41 5. Conclusion 43 References 44 Abstract in Korean 53Maste

    Methods to Evaluate Land-Atmosphere Exchanges in Amazonia Based on Satellite Imagery and Ground Measurements

    Get PDF
    During the last three decades, intensive campaigns and experiments have been conducted for acquiring micrometeorological data in the Amazonian ecosystems, which has increased our understanding of the variation, especially seasonally, of the total energy available for the atmospheric heating process by the surface, evapotranspiration and carbon exchanges. However, the measurements obtained by such experiments generally cover small areas and are not representative of the spatial variability of these processes. This chapter aims to discuss several algorithms developed to estimate surface energy and carbon fluxes combining satellite data and micrometeorological observations, highlighting the potentialities and limitations of such models for applications in the Amazon region. We show that the use of these models presents an important role in understanding the spatial and temporal patterns of biophysical surface parameters in a region where most of the information is local. Data generated may be used as inputs in earth system surface models allowing the evaluation of the impact, both in regional as well as global scales, caused by land-use and land-cover changes

    CARBON-WATER COUPLING OF TERRESTRIAL ECOSYSTEMS IN RESPONSE TO CLIMATE CHANGE AND CLIMATE VARIABILITY

    Get PDF
    Carbon and water cycles are two fundamental biophysical processes in terrestrial ecosystems. Rain use efficiency (RUE), defined as the ratio of ecosystem productivity to precipitation (PPT), and water use efficiency (WUE), defined as the ratio of ecosystem productivity to evapotranspiration (ET), are critical metrics of ecosystem function linking ecosystem carbon and water cycles. Under the context of global climate change and climate variability, much attention has been paid to the variation in RUE or WUE across biomes or species, and its responses to drought, elevated atmospheric CO2 concentration, and other environmental changes. However, due to differences in research method, study areas, and complexity in definitions, there is a lack of consensus on the coupling of carbon and water fluxes across different ecosystems and it’s responses to climate change and climate variability. Chapter 1 reviews the current status of carbon-water coupling studies and raises the major scientific questions that will be addressed in the dissertation. Chapter 2 examined the spatiotemporal variations in coupling of gross primary production (GPP) to PPT, and water fluxes (ET and T (transpiration)) at site and global scales. In-situ climate, and carbon and water fluxes datasets from 111 FLUXNET sites, global climate data, and remote sensing based GPP and ET data were combined to explore the relationships of GPP to PPT, ET, and T across different ecosystems and under different hydroclimatic conditions. Generally, GPP had a saturating relationship with PPT, and was linearly coupled with ET and T. This strong carbon-water flux coupling could be further improved by the incorporation of vapor pressure deficit (VPD) at site level. The sensitivity of GPP to PPT increased in severe drought years and decreased in pluvial years. There was no obvious change in the sensitivity of GPP to ET or T under altered climate conditions. Chapter 3 identified extreme drought events globally based on rain-use efficiency [RUE; GPP/PPT]. Ecosystem RUE is expected to increase with decreasing precipitation to a maximum (RUEmax) during moderate drought and will likely decline when water shortage is beyond the tolerance of vegetation, leading to a loss of ecosystem function. In this chapter, the PPT at the RUEmax was identified as a threshold of extreme drought condition, and the deviation of the RUE in drought condition from the norms in non-drought condition was further tested to determine if it exceeds the normal variability. Well-known extreme drought events were detected, e.g. 2003 drought in Europe, 2002 and 2011 drought in the U.S., and 2010 drought in Russia. Moreover, the reduced carbon uptake caused by extreme droughts (0.14±0.03 PgC/yr) could explain >70% of the GPP anomaly in drought-affected areas. Chapter 4 investigated the responses of WUE to environmental change in forests and grasslands in Northern Hemisphere. On the basis of Chapter 1, underlying water use efficiency (UWUE; GPP×VPD0.5/ET) incorporated the VPD effects on carbon assimilation and transpiration and hence provided an optimal indicator of carbon-water coupling in flux tower dataset. In this chapter, the interannual trend in UWUE and its responses to environmental factors were analyzed across 11 evergreen needleleaf forest (ENF) sites, 7 deciduous broadleaf forest (DBF) sites, and 9 grassland (GRA) sites. Results showed that, there was an obvious increase in UWUE in forests which was triggered by CO2 fertilization, increasing VPD, as well as the decreasing soil moisture in DBF. In GRA, the positive effect of CO2 fertilization on UWUE was offset by the negative effect of increasing soil moisture on UWUE, leading to no obvious trend in UWUE. Chapter 5 estimated the global ET at 8-day, 0.05° resolution from 2003 to 2015 based on GPP from Vegetation Photosynthesis Model (VPM), VPD estimated from Atmospheric Infrared Sounder (AIRS), and biome-level UWUE parameters. Biome-level UWUE was derived from the FLUXNET2015 dataset at 8-day timescale. The ET was calibrated and validated at the biome level against flux tower ET. The interannual trends in ET, GPP, and VPD were also analyzed at the global scale. There was an increasing trend in global ET over the study period (1.47 mm/yr). This ET product on the basis of carbon-water coupling showed better performance than a traditional approach, i.e. Penman-Monteith equation. Chapter 6 briefly summarizes the conclusions and perspectives from this dissertation

    VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing

    Get PDF
    This is the final version. Available on open access from Copernicus Publications via the DOI in this recordData availability: The VODCA2GPP data can be accessed (CC BY-NC-SA 4.0) at TU Data Repository under https://doi.org/10.48436/1k7aj-bdz35 (Wild et al., 2021).Long-term global monitoring of terrestrial gross primary production (GPP) is crucial for assessing ecosystem responses to global climate change. In recent decades, great advances have been made in estimating GPP and many global GPP datasets have been published. These datasets are based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. Although these approaches are well established within the scientific community, datasets nevertheless differ significantly. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of vegetation optical depth (VOD) to estimate GPP at the global scale for the period 1988–2020. VODCA2GPP applies a previously developed carbon-sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Moesinger et al., 2020; Zotta et al., 2022​​​​​​​), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and compared against largely independent state-of-the-art GPP datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS), FLUXCOM, and the TRENDY-v7 process-based model ensemble. The site-level evaluation with FLUXNET GPP indicates an overall robust performance of VODCA2GPP with only a small bias and good temporal agreement. The comparisons with MODIS, FLUXCOM, and TRENDY-v7 show that VODCA2GPP exhibits very similar spatial patterns across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 (Pearson's r 0.53 and 0.61) but less well with FLUXCOM (Pearson's r 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP at the global scale but rather suggests regionally different long-term changes in GPP. For the shorter overlapping observation period (2003–2015) of VODCA2GPP, MODIS, and the TRENDY-v7 ensemble, significant increases in global GPP were found. VODCA2GPP can complement existing GPP products and is a valuable dataset for the assessment of large-scale and long-term changes in GPP for global vegetation and carbon cycle studies. The VODCA2GPP dataset is available at the TU Data Repository of TU Wien (https://doi.org/10.48436/1k7aj-bdz35, Wild et al., 2021).Technische Universität Wie
    corecore