Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites:TL-LUE Parameterization and Validation

Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (εmsh) was 2.63 to 4.59 times that of sunlit leaves (εmsu). Generally, the relationships of εmsh and εmsu with εmax were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR

Satellite remote sensing priorities for better assimilation in crop growth models : winter wheat LAI and grassland mowing dates case studies

In a context of markets globalization, early, reliable and timely estimations of crop yields at regional to global scale are clearly needed for managing large agricultural lands, determining food pricing and trading policies but also for early warning of harvest shortfalls. Crop growth models are often used to estimate yields within the growing season. The uncertainties associated with these models contribute to the uncertainty of crop yield estimations and forecasts. Satellite remote sensing, through its ability to provide synoptic information on growth conditions over large geographic extents and in near real-time, is a key data source used to reduce uncertainties in biophysical models through data assimilation methods. This thesis aims at assessing possible improvements for the assimilation of remotely-sensed biophysical variables in crop growth models and to estimate their related errors reduction on modelled yield estimates. Assimilated observations are winter wheat leaf area index (LAI) and grassland mowing dates derived respectively from optical (MODIS) and microwave (ERS-2) data. These observations have been assimilated in WOFOST and LINGRA growth models. Observing System Simulation Experiments (OSSE) allowed assessing errors reduction on yield estimates after assimilation for different situations of accuracy and frequency of remotely-sensed estimates and for different assimilation strategies, indicating expected improvements with the currently available and forthcoming sensors; it supports also guidelines for future satellite missions. A main finding is the fact that yield estimate improvements are only possible for assimilation strategies able to correct the possible phenological discrepancies between the remotely-sensed and the simulated data. These phenological shifts arise mainly from uncertainties on the parameters and initial states driving the phenological stages in the models but are also due, in some situations, to lack of pixel purity because of the medium resolution of sensors such as MODIS. This thesis also identifies some of the main sources of uncertainties and assesses their impact on assimilation performances. The impact of water content and biomass on SAR backscattering of grasslands is specifically assessed. The backscattering of grasslands increases with the increases of water content and decreases with the biomass in dry conditions. Based on these results, methodologies to classify grasslands according to land use (mowing or grazing) and to detect mowings are designed and demonstrated. A good classification accuracy is observed (overall accuracy around 80%). Results for mowings detection are a bit lower as half of the mowings are correctly identified but the methodology can be considered as promising in particular in the perspective of very dense SAR time series as currently acquired operationally by Sentinel-1.(AGRO - Sciences agronomiques et ingénierie biologique) -- UCL, 201

Scaling net ecosystem production and net biome production over a heterogeneous region in the western United States

Bottom-up scaling of net ecosystem production (NEP) and net biome production (NBP) was used to generate a carbon budget for a large heterogeneous region (the state of Oregon, 2.5×10<sup>5</sup> km<sup>2</sup>) in the western United States. Landsat resolution (30 m) remote sensing provided the basis for mapping land cover and disturbance history, thus allowing us to account for all major fire and logging events over the last 30 years. For NEP, a 23-year record (1980–2002) of distributed meteorology (1 km resolution) at the daily time step was used to drive a process-based carbon cycle model (Biome-BGC). For NBP, fire emissions were computed from remote sensing based estimates of area burned and our mapped biomass estimates. Our estimates for the contribution of logging and crop harvest removals to NBP were from the model simulations and were checked against public records of forest and crop harvesting. The predominately forested ecoregions within our study region had the highest NEP sinks, with ecoregion averages up to 197 gC m<sup>−2</sup> yr<sup>−1</sup>. Agricultural ecoregions were also NEP sinks, reflecting the imbalance of NPP and decomposition of crop residues. For the period 1996–2000, mean NEP for the study area was 17.0 TgC yr<sup>−1</sup>, with strong interannual variation (SD of 10.6). The sum of forest harvest removals, crop removals, and direct fire emissions amounted to 63% of NEP, leaving a mean NBP of 6.1 TgC yr<sup>−1</sup>. Carbon sequestration was predominantly on public forestland, where the harvest rate has fallen dramatically in the recent years. Comparison of simulation results with estimates of carbon stocks, and changes in carbon stocks, based on forest inventory data showed generally good agreement. The carbon sequestered as NBP, plus accumulation of forest products in slow turnover pools, offset 51% of the annual emissions of fossil fuel CO<sub>2</sub> for the state. State-level NBP dropped below zero in 2002 because of the combination of a dry climate year and a large (200 000 ha) fire. These results highlight the strong influence of land management and interannual variation in climate on the terrestrial carbon flux in the temperate zone

Responses and adaptation strategies of terrestrial ecosystems to climate change

Terrestrial ecosystems are likely to be affected by climate change, as climate change-induced shift of water and heat stresses patterns will have significant impacts on species composition, habitat distribution, and ecosystem functions, and thereby weaken the terrestrial carbon (C) sink and threaten global food security and biofuel production. This thesis investigates the responses of terrestrial ecosystems to climate change and is structured in four main chapters.;The first chapter of the thesis is directed towards the impacts of snow variation on ecosystem phenology. Variations in seasonal snowfall regulate regional and global climatic systems and vegetation growth by changing energy budgets of the lower atmosphere and land surface. We investigated the effects of snow on the start of growing season (SGS) of temperate vegetation in China. Across the entire temperate region in China, the winter snow depth increased at a rate of 0.15 cm•yr-1 (p=0.07) during the period 1982-1998, and decreased at a rate of 0.36 cm•yr-1 (p=0.09) during the period 1998-2005. Correspondingly, the SGS advanced at a rate of 0.68 d•yr-1 (p\u3c0.01) during 1982 to 1998, and delayed at a rate of 2.13 d•yr-1 (p=0.07) during 1998 to 2005, against a warming trend throughout the entire study period of 1982-2005. Spring air temperature strongly regulated the SGS of both deciduous broad-leaf and coniferous forests; whilst the winter snow had a greater impact on the SGS of grassland and shrubs. Snow depth variation combined with air temperature contributed to the variability in the SGS of grassland and shrubs, as snow acted as an insulator and modulated the underground thermal conditions. Additionally, differences were seen between the impacts of winter snow depth and spring snow depth on the SGS; as snow depths increased, the effect associated went from delaying SGS to advancing SGS. The observed thresholds for these effects were snow depths of 6.8 cm (winter) and 4.0 cm (spring). The results of this study suggest that the response of the vegetation\u27s SGS to seasonal snow change may be attributed to the coupling effects of air temperature and snow depth associated with the soil thermal conditions.;The second chapter further addresses snow impacts on terrestrial ecosystem with focus on regional carbon exchange between atmosphere and biosphere. Winter snow has been suggested to regulate terrestrial carbon (C) cycling by modifying micro-climate, but the impacts of snow cover change on the annual C budget at the large scale are poorly understood. Our aim is to quantify the C balance under changing snow depth. Here, we used site-based eddy covariance flux data to investigate the relationship between snow cover depth and ecosystem respiration (Reco) during winter. We then used the Biome-BGC model to estimate the effect of reductions in winter snow cover on C balance of Northern forests in non-permafrost region. According to site observations, winter net ecosystem C exchange (NEE) ranged from 0.028-1.53 gC•m-2•day-1, accounting for 44 +/- 123% of the annual C budget. Model simulation showed that over the past 30 years, snow driven change in winter C fluxes reduced non-growing season CO2 emissions, enhancing the annual C sink of northern forests. Over the entire study area, simulated winter ecosystem respiration (Reco) significantly decreased by 0.33 gC•m-2•day -1•yr-1 in response to decreasing snow cover depth, which accounts for approximately 25% of the simulated annual C sink trend from 1982 to 2009. Soil temperature was primarily controlled by snow cover rather than by air temperature as snow served as an insulator to prevent chilling impacts. A shallow snow cover has less insulation potential, causing colder soil temperatures and potentially lower respiration rates. Both eddy covariance analysis and model-simulated results showed that both Reco and NEE were significantly and positively correlated with variation in soil temperature controlled by variation in snow depth. Overall, our results highlight that a decrease in winter snow cover restrains global warming through emitting less C to the atmosphere.;The third chapter focused on assessing drought\u27s impact on global terrestrial ecosystems. Drought can affect the structure, composition and function of terrestrial ecosystems, yet the drought impacts and post-drought recovery potential of different land cover types have not been extensively studied at a global scale. Here, we evaluated drought impacts on gross primary productivity (GPP), evapotranspiration (ET), and water use efficiency (WUE) of different global terrestrial ecosystems, as well as the drought-resilience of each ecosystem type during the period of 2000 to 2011. We found the rainfall and soil moisture during drought period were dramatically lower than these in non-drought period, while air temperatures were higher than normal during drought period with amplitudes varied by land cover types. The length of recovery days (LRD) presented an evident gradient of high (\u3e 60 days) in mid- latitude region and low (\u3c 60 days) in low (tropical area) and high (boreal area) latitude regions. As average GPP increased, the LRD showed a significantly decreasing trend among different land covers (R 2=0.53, p\u3c0.0001). Moreover, the most dramatic reduction of the drought-induced GPP was found in the mid-latitude region of north Hemisphere (48% reduction), followed by the low-latitude region of south Hemisphere (13% reduction). In contrast, a slightly enhanced GPP (10%) was showed in the tropical region under drought impact. Additionally, the highest drought-induced reduction of ET was found in the Mediterranean area, followed by Africa. The water use efficiency, however, showed a pattern of decreasing in the north Hemisphere and increasing in the south Hemisphere.;The last chapter compared the differences of performance in trading water for carbon in planted forest and natural forest, with specific focus on China. Planted forests have been widely established in China as an essential approach to improving the ecological environment and mitigating climate change. Large-scale forest planting programs, however, are rarely examined in the context of tradeoffs between carbon sequestration and water yield between planted and natural forests. We reconstructed evapotranspiration (ET) and gross primary production (GPP) data based on remote-sensing and ground observational data, and investigated the differences between natural and planted forests, in order to evaluate the suitability of tree-planting activity in different climate regions where the afforestation and reforestation programs have been extensively implemented during the past three decades in China. While the differences changed with latitude (and region), we found that, on average, planted forests consumed 5.79% (29.13mm) more water but sequestered 1.05% (-12.02 gC m-2 yr -1) less carbon than naturally generated forests, while the amplitudes of discrepancies varied with latitude. It is suggested that the most suitable lands in China for afforestation should be located in the moist south subtropical region (SSTP), followed by the mid-subtropical region (MSTP), to attain a high carbon sequestration potential while maintain a relatively low impact on regional water balance. The high hydrological impact zone, including the north subtropical region (NSTP), warm temperate region (WTEM), and temperate region (TEM) should be cautiously evaluated for future afforestation due to water yield reductions associated with plantations

Estimating the leaf area index of Eucalyptus dunnii in the Midlands area, KwaZulu-Natal province over two seasons using vegetation indices and image texture measures derived from Worldview-3 imagery.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

The use of earth observation multi-sensor systems to monitor and model Pastures: a case of Savannah Grasslands in Hluvukani Village, Bushbuckridge Local Municipality, Mpumalanga Province, South Africa

Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability.Thesis (MSc) -- Faculty of Science and Agriculture, 202

The use of earth observation multi-sensor systems to monitor and model Pastures: a case of Savannah Grasslands in Hluvukani Village, Bushbuckridge Local Municipality, Mpumalanga Province, South Africa

Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability.Thesis (MSc) -- Faculty of Science and Agriculture, 202