A 33-year NPP monitoring study in southwest China by the fusion of multi-source remote sensing and station data

Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a 33-year regional NPP study in southwest China, at a 1-km scale. A multi-sensor fusion framework was applied to obtain a new normalized difference vegetation index (NDVI) time series from 1982 to 2014, combining the respective advantages of the different remote sensing datasets. As another key parameter for NPP modeling, the total solar radiation was calculated by the improved Yang hybrid model (YHM), using meteorological station data. The verification described in this paper proved the feasibility of all the applied data processes, and a greatly improved accuracy was obtained for the NPP calculated with the final processed NDVI. The spatio-temporal analysis results indicated that 68.07% of the study area showed an increasing NPP trend over the past three decades. Significant heterogeneity was found in the correlation between NPP and precipitation at a monthly scale, specifically, the negative correlation in the growing season and the positive correlation in the dry season. The lagged positive correlation in the growing season and no lag in the dry season indicated the important impact of precipitation on NPP.Comment: 20 pages, 11 figure

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Changes in water and carbon in Australian vegetation in response to climate change

Australia has experienced pronounced climate change since 1950, especially in forested areas where a reducing trend in annual precipitation has occurred. However, the interaction between forests and water at multiple scales, in different geographical locations, under different management regimes and in different forest types with diverse species is not fully understood. Therefore, some interactions between forests and hydrological variables, and in particular whether the changes are mediated by management or climate, remain controversial. This thesis investigates the responses of Australia’s terrestrial ecosystems to both historical and projected climate change using remote sensing data and ecohydrological models. The thesis is structured in seven chapters, and contains five research chapters. Vegetation dynamics and sensitivity to precipitation change on the Australian continent for the past long drought period (2002-2010) are explored in Chapter 2 using multi-resource vegetation indices (VIs; normalized difference vegetation index (NDVI) and leaf area index (LAI)) and gridded climate data. During drought, precipitation and VIs declined across 90% and 80% of the whole continent, respectively, compared to the baseline period of 2000-2001. The most dramatic declines in VIs occurred in open shrublands near the centre of Australia and in southwestern Australia coinciding with significant reductions in precipitation and soil moisture. Overall, a strong relationship between water (precipitation and soil moisture) and VIs was detected in places where the decline in precipitation was severe. For five major vegetation types, cropland showed the highest sensitivity to water change, followed by grassland and woody savanna. Open shrublands showed moderate sensitivity to water change, while evergreen broadleaf forests only showed a slight sensitivity to soil moisture change. Although there was no consistent significant relationship between precipitation and VIs of evergreen broadleaf forests, forests in southeastern Australia, where precipitation had declined since 1997, appear to have become more sensitive to precipitation change than in southwestern Australia. The attribution of impacts from climate change and vegetation on streamflow change at the catchment scale for southwestern Australia are described in Chapter 3. This region is characterized by intensive warming and drying since 1970. Along with these significant climate changes, dramatic declines in streamflow have occurred across the region. Here, 79 catchments were analyzed using the Mann-Kendall trend test, Pettitt’s change point test, and the theoretical framework of the Budyko curve to study changes in the rainfall-runoff relationship, and effects of climate and vegetation change on streamflow. A declining trend and relatively consistent change point (2000) of streamflow were found in most catchments, with over 40 catchments showing significant declines (p < 0.05, -20% to -80%) between the two periods of 1982-2000 and 2001-2011. Most of the catchments have been shifting towards a more water-limited climate condition since 2000. Although streamflow is strongly related to precipitation for the period of 1982 to 2011, change of vegetation (land cover/use change and growth of vegetation) dominated the decrease in streamflow in about two-thirds of catchments. The contributions of precipitation, temperature and vegetation to streamflow change for each catchment varied with different catchment characters and climate conditions. In Chapter 4, the magnitude and trend of water use efficiency (WUE) of forest ecosystems in Australia, and their response to drought from 1982 to 2014, were analyzed using a modified version of the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model in the BIOS2 modelling environment. Instead of solely relying on the ratio of gross primary productivity (GPP) to evapotranspiration (ET) as WUE (GPP/ET), the ratio of net primary productivity (NPP) to Transpiration (ETr) (NPP/ETr) was also adopted to more comprehensively understand the response of vegetation to drought. For the study period, national average annual forest WUE was 1.39 ± 0.80 g C kg−1 H2O for GPP/ET and 1.48 ± 0.28 g C kg−1 H2O for NPP/ETr. The WUE increased in the entire study area during this period (with a rate of 0.003 g C kg−1 H2O yr-1 for GPP/ET; p < 0.005 and a rate of 0.0035 g C kg−1 H2O yr-1 for NPP/ETr; p < 0.01), whereas different trends were detected in different biomes. A significantly increasing trend of annual WUE was only found in woodland areas due to higher magnitudes of increases in GPP and NPP than ET and ETr. The exception was in eucalyptus open forest area where ET and ETr decreased more than reductions in GPP and NPP. The response of WUE to drought was further analyzed using 1-48 month scales standardised precipitation-evapotranspiration index (SPEI). More severe (SPEI < -1) and frequent droughts (over ca. 8 years) occurred in the north than in the southwest and southeast of Australia since 1982. The response of WUE to drought varied significantly regionally and across forest types. The response of WUE to drought varied significantly regionally and across forest types, due to the different responses of carbon sequestration and water consumption to drought. The cumulative lagged effect of drought on monthly WUE derived from NPP/ETr was consistent and relatively short and stable between biomes (< 4 months), but notably varied for WUE based on GPP/ET, with a long time lag (mean of 16 months). As Chapters 2-4 confirmed that climate change has been playing an important role in the water yield and vegetation dynamics in Australia, the response of water yield and carbon sequestration to projected future climate change scenarios were integrated using the Water Supply Stress Index and Carbon model (WaSSI-C) ecohydrology model in Chapter 5. This model was calibrated with the latest water and carbon observations from the OzFlux network. The performance of the WaSSI-C model was assessed with measures of Q from 222 Hydrologic Reference Stations (HRSs) in Australia. Across the 222 HRSs, the WaSSI-C model generally captured the spatial variability of mean annual and monthly Q as evaluated by the Correlation Coefficient (R2 = 0.1-1.0), Nash-Sutcliffe Efficiency (NSE = -0.4-0.97), and normalized Root Mean Squared Error by Q (RMSE/Q = 0.01-2.2). Then 19 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5), across all Representative Concentration Pathways (RCPs) (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), were used to investigate the potential impacts of climate change on water and carbon fluxes. Compared with the baseline period of 1995-2015 across the 222 HRSs, the temperature was projected to rise by an average of 0.56 to 2.49 ˚C by 2080, while annual precipitation was projected to vary significantly. All RCPs demonstrated a similar spatial pattern of change of projected Q and GPP by 2080, however, the magnitude varied widely among the 19 GCMs. Overall, future climate change may result in a significant reduction in Q but may be accompanied by an increase in ecosystem productivity. Mean annual Q was projected to decrease by 5 - 211 mm yr-1 (34% - 99%) by 2080, with over 90% of the watersheds declining. On the contrary, GPP was projected to increase by 17 - 255 g C m-2 yr-1 (2% - 17%) by 2080 in comparison with 1995-2015 in southeastern Australia. A significant limitation of WaSSI-C model is that it only runs serially. High resolution simulations at the continental scale are therefore not only computationally expensive but also present a run-time memory burden. In Chapter 6, using distributed (Message Passing Interface, MPI) and shared (Open Multi-Processing, OpenMP) memory parallelism techniques, the model was parallelized (and renamed as dWaSSI-C), and this approach was very effective in reducing the computing run-time and memory use. By using the parallelized model, several experiments were carried out to simulate water and carbon fluxes over the Australian continent to test the sensitivity of the model to input data-sets of different resolutions, as well as the sensitivity of the model to its WUE parameter for different vegetation types. These simulations were completed within minutes using dWaSSI-C, and this would not have been possible with the serial version. Results show that the model is able to simulate the seasonal cycle of GPP reasonably well when compared to observations at 4 eddy flux sites in Australia. The sensitivity analysis showed that simulated GPP was more sensitive to WUE during the Australian summer as compared to winter, and woody savannas and grasslands showed higher sensitivity than evergreen broadleaf forests and shrublands. With the parallelized dWaSSI-C model, it will now be much easier and faster to conduct continental scale analyses of the impacts of climate change and land cover change on water and carbon. Overall, vegetation and water of Australian ecosystems have become very sensitive to climate change after a considerable decline in streamflow. Australian ecosystems, especially in temperate Australia, are projected to experience warmer and drier climate conditions with increasing drought risk. However, the prediction from different models varied significantly due to the uncertainty of each climate model. The impacts of different forest management scenarios should be studied to find the best land use pattern under the changing climate. Forest management methods, such as thinning and reforestation, may be conducted to mitigate the impacts of drought on water yield and carbon sequestration in the future

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition

Spatial-temporal responses of Louisiana forests to climate change and hurricane disturbance

This dissertation research focused on three questions: (1) what is the current carbon stock in Louisiana’s forest ecosystems? (2) how will the biomass carbon stock respond to future climate change? and (3) how vulnerable are the coastal forest resources to natural disturbances, such as hurricanes? The research utilized a geographic information system, remote sensing techniques, ecosystem modeling, and statistical approaches with existing data and in-situ measurements. Future climate changes were adapted from predictions by the Community Climate System Model on the basis of low (B1), moderate (A1B), and high (A2) greenhouse gas emission scenarios. The study on forest carbon assessment found that Louisiana’s forests currently store 219.2 Tg of biomass carbon, 90% of which is stored in wetland and evergreen forests. Spatial variation of the carbon storage was mainly affected by forest biomass distribution. No correlation was identified between carbon storage in watersheds with the average watershed slope and drainage density. The modeling study on growth response to future climate found that forest net primary productivity (NPP) would decline from 2000 to 2050 under scenario B1, but may increase under scenarios A1B and A2 due primarily to minimum temperature and precipitation changes. Uncertainties of the NPP prediction were apparent, owing to spatial resolution of the climate variables. The remote sensing study on hurricane disturbance to coastal forests found that increases in the intensity of severe weather in the future would likely increase the turn-over rate of coastal forest carbon stock. Forest attributes and site conditions had a variety of effects on the vulnerability of forests to hurricane disturbance and thereby, spatial patterns of disturbed landscape. Soil groups and stand factors, including forest types, forest coverage, and stand density contributed to 85% of accuracy in the modeling probability of Hurricane Katrina disturbance to forests. In conclusion, this research demonstrated that quantification of forest biomass carbon, using geo-referenced datasets and GIS techniques, provides a credible approach to increase accuracy and constrain the uncertainty of large-scale carbon assessment. A combination of ecosystem modeling and GIS/Remote Sensing techniques can provide insight into future climate change effects on forest carbon change at the landscape scale

Utilizing Satellite Fusion Methods to Assess Vegetation Phenology in a Semi-Arid Ecosystem

Dryland ecosystems cover over 40% of the Earth’s surface, and are highly heterogeneous systems dependent upon rainfall and temperature. Climate change and anthropogenic activities have caused considerable shifts in vegetation and fire regimes, leading to desertification, habitat loss, and the spread of invasive species. Modern public satellite imagery is unable to detect fine temporal and spatial changes that occur in drylands. These ecosystems can have rapid phenological changes, and the heterogeneity of the ground cover is unable to be identified at course pixel sizes (e.g. 250 m). We develop a system that uses data from multiple satellites to model finer data to detect phenology in a semi-arid ecosystem, a dryland ecosystem type. The first study in this thesis uses recent developments in readily available satellite imagery, coupled with new systems for large-scale data analysis. Google Earth Engine is used with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to create high resolution imagery from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). The 250 m daily MODIS data are downscaled using the 16-day, 30 m Landsat imagery resulting in daily, 30 m data. The downscaled images are used to observe vegetation phenology over the semi-arid region of the Morley Nelson Snake River Birds of Prey National Conservation Area in Southwestern Idaho, USA. We found the fused satellite imagery has a high accuracy, with R2 ranging from 0.73 to 0.99, when comparing fusion products to the true Landsat imagery. From these data, we observed the phenology of native and invasive vegetation, which can help scientists develop models and classifications of this ecosystem. The second study in this thesis builds upon the fused satellite imagery to understand pre-and post-fire vegetation response in the same ecosystem. We investigate the phenology of five areas that burned in 2012 by using the fusion imagery (daily) to derive the normalized difference vegetation index (NDVI, a measure of vegetation greenness) in areas dominated by grass (n=4) and shrub (n=1). The five areas also had a range of historical burns before 2012, and overall we investigated the phenology of these areas over a decade. This proof of concept resulted in observations of the relationship between the timing of fire and the vegetation greenness recovery. For example, we found that early and late season fires take the longest amount of time for vegetation greenness to recover, and that the number of historical fires has little impact in the vegetation greenness response if it has already burned once, and is a grass-dominated region. The greenness dynamics of the shrub-dominated study site provides insight into the potential to monitor post-fire invasion by nonnative grasses. Ultimately the systems developed in this thesis can be used to monitor semi-arid ecosystems over long-time periods at high spatial and temporal resolution

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

A review of carbon monitoring in wet carbon systems using remote sensing

Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry