Remote Sensing and Modeling (RSM) Lab

Current research highlights 

Integrate Remote Sensing and Water Infiltration Data to Identify Karst Subsurface Fractures

This project at Natural Bridge Caverns, a vast commercial cave system in New Braunfels, TX, aims to study the heterogeneous karst formations and estimate recharge using data gathered by the Light Detection and Ranging (LiDAR) and cave drip loggers. The cavern is developed by an underground ‘river’ moving slowly through cracks and pores within the Cretaceous limestone, resulting in large chambers and break-out domes. Whereas these features, along with the dimensional mass of the caves, provide an ideal setting for robust data collection, given there is a paucity of data. The site provides easy access to the strata and allows an opportunity to gather both LiDAR images and water infiltration data within the subsurface from inside the caverns. We aim to integrate remote sensing and subsurface flow signatures to characterize Cretaceous formations and gain insight into subsurface behavior and possible water infiltration pathways. This study will lay a foundation for calculating groundwater recharge, understanding infiltration water movement, and discovering areas of cavern development.

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Remote Sensing for Improved Forest Biomass Monitoring

Current forestry practices use highly empirical allometric relationships to transform tree diameter into tree biomass. These relationships are based on limited destructive sampling on the ground and can lead to substantial errors when applied to estimate biomass or volume growth. Recent developments in terrestrial Light Detection And Ranging (LiDAR) remote sensing technology allow us for non-destructive three-dimensional (3D) reconstruction of forest aboveground structures at sub-centimeter resolution. LiDAR data provides unprecedented accurate information (centimeter to millimeter) on the dimensions of individual trees. This project evaluates the accuracy of high-resolution LiDAR data and the state-of-the-art digital tree segmentation algorithms to quantify aboveground tree volume, hence accurate biomass estimation of a mature forest. This project aims to advance our understanding of the potential of LiDAR measurements, particularly asking how much detail on tree aboveground structure we can extract from state-of-the-art portable LiDAR equipment. More importantly, we utilize the unprecedented details from LiDAR measurements to generate useful tools for forest structure and growth data. Coupled with extension activities, our results can have real-world impacts on local stakeholders by informing them of the potential of LiDAR technology and data-based practical recommendations for sustainable forestry management strategies.​

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Spatio-temporal analysis of deforestation and its environmental impacts 

Deforestation is a pressing environmental issue that significantly impacts ecosystems and the environment. This project aims to explore the extent of deforestation and its environmental consequences. The primary objective of this study is to track alterations in forest cover within various regions and to map the forest cover disturbances. The study also aims to achieve two specific goals: first, to utilize spatial data technology in detecting and analyzing both temporal and spatial vegetation changes, and second, to comprehend the patterns of forest cover dynamics. Finally, the project targets to identify the correlation between deforestation and various environmental factors using long-term local climate data. The findings of this research contribute to a better understanding of the complex relationship between deforestation and its environmental impacts. The results provide valuable insights for policymakers, conservation organizations, and local communities in developing effective strategies to address deforestation and promote sustainable land management practices.

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Impacts of wildfire smoke aerosols on surface air quality

​Globally, wildfire-driven air pollution is often cited as one of the significant reasons for adverse impacts on public health. In recent times, ambient concentrations of particulate matter with diameters less than 2.5 μm (PM2.5) in the US have experienced a remarkable degradation due to wildfire smoke, which jeopardized the country’s efforts for the last few decades to improve air quality. This study aims to predict the impact of wildfire smoke aerosols on surface air quality and associated health hazards using both ground-based PM2.5 measurements and satellite data products such as Landsat and MODIS.

 

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Previous research highlights

Image quilting (IQ) for conditional simulation of geological textures

I have applied several analytical techniques to better understand different intricate processes within extremely diversified geological features during my post-graduate research. To start with, I have developed a recent multiple point statistics (MPS) method named image quilting (IQ) for various hydrogeological applications that lead to novel and important scientific outcomes [1]. The numerical tool allows the creation of multiple realizations of a study domain, with a spatial continuity based on a TI that contains the variability, connectivity, and structural properties deemed realistic. This is valid for a range of modeling applications including representation of geological structures, Earth surface features, and spatiotemporal applications. I have also established a general methodology to integrate different measurement scales during my PhD second project, allowing a better characterization of aquifers by making the best possible use of hydrogeological data [2]. I have presented a multiscale workflow to deal with hydraulic conductivity data, which is one of the most critical and at the same time one of the most uncertain parameters in many groundwater models. To achieve this particular goal, I have used IQ as a multivariate MPS algorithm, and simplified renormalization as an upscaling technique, but the workflow is general. The method is tested on a series of synthetic examples with different connectivity properties that acts as proof-of-concept case studies.

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Stochastic modeling of infiltration water heterogeneity using Lidar data

In my PhD third project, I have presented an exhaustive characterization of Golgotha Cave, South-West Western Australia, based on an extensive remote sensing measurement campaign to better manage groundwater resources in carbonate areas and improve our understanding of speleothem archives [3]. I have offered the first quantitative analysis of the morphology and spatial distribution of stalactites covering the cave ceiling surface. By performing statistical and morphological analysis of karstic features based on remote sensing images at three large chambers in the same cave system, I have been able to identify and categorize different types of possible flow patterns in karstified limestone such as matrix flow and fracture flow. Moreover, I have demonstrated the nature of the relationship between flow types classified by the morphological analysis of karstic features and drip time series characteristics [4]. A spatial survey of automated cave drip monitoring in two large chambers of Golgotha Cave has been utilized to achieve this goal. I have quantified recharge into the cave based on the drip data, Lidar measurements and flow classification. Finally, I have been able to estimate the water balance to develop a simple model describing the ground surface extent from which flow is focused on the monitored cave ceiling area and the associated lateral flow within the Tamala limestone formation. Finally in my fifth paper from PhD, I have demonstrated a complete methodology for automatic cave drip water hydrology measurements [5], which will help better characterize karst drip water hydrogeology and understand the relationship between drip hydrology and surface climate at any cave site. I have showed that the analysis of the time series produced by cave drip loggers generates useful hydrogeological information that can be applied generally. The time series behavior integrates a variety of characteristics that combine the properties of epikarst (storage), fracture configuration, and recharge.

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Quantifying and constraining the terrestrial carbon cycle via DA

My current work is focusing on reducing uncertainty in site, regional and global scale vegetation dynamics and carbon budget simulations. As atmospheric CO2 concentration data show, the ocean and land surface absorb approximately 50% of anthropogenic CO2 emissions; therefore, the land and ocean act as a global carbon sink. However, beyond the global sink magnitude, uncertainty remains about the sink location, inter-annual variability and future trajectory. Uncertainty in TBM vegetation biomass and carbon sink projections is high; some models even predict the land may switch from a sink to a source by the end of the century. There is an urgent need to address this issue – without accurate sink estimates, we cannot quantify the level of carbon emissions that will keep us within a 2°C rise in temperature. In addition, a poor representation of vegetation dynamics leads to inaccurate predictions of land-atmospheric feedbacks. To achieve the goal of reducing TBM uncertainty, I use Bayesian DA methods to optimize uncertain TBM parameters. TBM parameters are often poorly constrained, either because they are not physically based and therefore cannot be measured, or because they are based on limited experimental data covering only a few species which are then grouped into the broad PFTs used in TBMs. Parameter values are therefore a key source of uncertainty in TBM simulations. My work involves optimizing vegetation and carbon cycle related model parameters using a wide range of data streams. These include: i) satellite-derived measures of vegetation activity (NDVI/FAPAR/LAI); ii) digital camera derived imagery (GCC), iii) biomass observations; iv) solar-induced chlorophyll fluorescence (SIF) data; v) biometeorological measurements (net CO2 and latent heat fluxes); vi) chamber-based CH4 fluxes; and vii) atmospheric CO2 concentration data. In a recent project, I combine in situ carbon flux data with a Bayesian DA framework to optimize carbon cycle related model parameters in ORCHIDEE TBM which alleviates model underestimates in both mean annual net C flux and its inter annual variability for dryland ecosystem [6]. This project illustrates the importance of field observations to improve the prediction of TBMs for future climate change.

 

Model-Data assimilation to quantify the effects of climate change on plant physiological processes

In my former role as a plant ecology modeler, I have implemented a DA framework using a simple tree carbon balance model to an experiment where below ground sink strength was manipulated by restricting root volume [7]. The project aimed to infer which processes were affected under sink limitation, and to attribute the overall reduction in growth observed in the experiment, to the effects on component processes. The R project containing the source code to perform all the data processing and analysis to reproduce the figures and tables of the paper is freely available as a Git repository (https://github.com/kashifmahmud/BG-2018-DA_Sink_limited_experiment). While working in this project, I have learnt R markdown script writing to weave together narrative text and code to produce elegantly formatted output. I have also applied the same DA technique to other manipulative experiments, to infer the effects of temperature warming and drought on plant carbon balance processes.

 

Ecosystem carbon budget for mature forest under carbon dioxide enrichment

I have worked in collaboration with other colleagues from Western Sydney University (WSU) where I use data from the Eucalyptus Free Air CO2 Enrichment (EucFACE) experiment, with the aim to construct an ecosystem carbon budget to provide a comprehensive field-based overview of how a mature forest responds to elevated CO2 [8]. We compiled measurements on all major carbon pools and fluxes collected over four years of experimental treatment (2013-2016), including individual and aggregated biomass and associated fluxes measured or inferred from plants, litter, soil, microbes, and insects, and constructed an ecosystem carbon budget under both ambient (aCO2) and eCO2 conditions (+150 ppm). In this entire carbon budget framework, I have contributed by estimating the total stem volume and surface area, which was directly inferred from the Terrestrial Laser Scanning (TLS) data through quantitative structure models (QSMs). I have run the simulation through several Matlab and C++ scripts, to accomplish the entire workflow by optimizing the model parameters for EucFACE site. The data has also been used where we have evaluated alternative stomatal modeling approaches at a woodland site where Vapour pressure deficit (D) reaches high levels every summer [9]. First, we have tested leaf-scale models against in situ observations which showed a reduction in stomatal conductance (gs) and photosynthesis (A) with increasing D. We have then implemented the best model into a canopy scale model against whole-tree-scale sap flow data that showed a decrease in transpiration at high D. We have aimed to quantify how well the alternative gas exchange models captured the high D responses of both gs and A.

 

Different treatment strategies for highly polluted landfill leachate

The aim of my MSc research was to determine appropriate treatment technique for effective treatment of heavily polluted landfill leachate. We accomplished several treatment experiments: (i) aerobic biological treatment, (ii) chemical coagulation, (iii) advanced oxidation process (AOP) and (iv) several combined treatment strategies [10]. Efficiency of these treatment procedures were monitored by analysing Chemical Oxygen Demand (COD) and colour removal. Leachate used for this study was taken from Matuail landfill site at Dhaka city. Fenton treatment which is an advanced oxidation process was the most successful between these three separate treatment procedures. Among the combined treatment options performed, we found extended aeration followed by Fenton method was the most suitable one.

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References

1. Mahmud, K., Mariethoz, G., Tahmasebi, P., Caers, J. and Baker A. (2014) “Simulation of Earth Textures by Conditional Image Quilting” Water Resources Research, Vol. 50 (4), pp. 3088–3107, DOI: 10.1002/2013WR015069.

2. Mahmud, K., Mariethoz, Baker A. and Sharma A. (2015) “Integrating Multiple Scales of Hydraulic Conductivity Measurements in Training Image-Based Stochastic Models” Water Resources Research, Vol. 51(1), pp. 465–480, DOI: 10.1002/2014WR016150.

3. Mahmud K., Mariethoz G., Pauline C.T., Baker A. (2015). “Terrestrial LiDAR Survey and Morphological Analysis to Identify Infiltration Properties in the Tamala Limestone, Western Australia, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2015.2451088.

4. Mahmud K., Mariethoz G., Baker A., Treble P.C., Markowska M. and McGuire L. (2016). “Estimation of deep infiltration in unsaturated limestone environments using cave LiDAR and drip count data” Hydrol. Earth Syst. Sci., 20, 359-373, https://doi.org/10.5194/hess-20-359-2016.

5. Mahmud, K., Mariethoz G., Baker A. and Pauline C.T. (2018) “Hydrological characterization of cave drip waters in a porous limestone: Golgotha Cave, Western Australia” Hydrol. Earth Syst. Sci., 22, 977-988, https://doi.org/10.5194/hess-22-977-2018.

6. Mahmud K., Scott R. L., Biederman J., Litvak M., Kolb T., Meyers T. P., Krishnan P., Bastrikov V., MacBean N. (2021) “Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter-Annual Variability” Accepted in Journal of Geophysical Research Biogeosciences.

7. Mahmud, K., Medlyn B., Duursma R.A., Campany C. and De Kauwe M. (2018) “Inferring the effects of sink strength on plant carbon balance from experimental measurements” Biogeosciences, 15, 4003-4018, https://doi.org/10.5194/bg-15-4003-2018.

8. Mingkai J., Medlyn B., Duursma R.A., Drake J.E., Anderson I., Barton C., Boer M., Carrillo Y., Collins L., Crous K.Y., De Kauwe M., Facey S.L., Gherlenda A., Gimeno T.E., Gomez L.C., Hasegawa S., MacDonald C., Mahmud K., Moore B., Moreno R.L.S., Nazaries L., Nevado J.P., Noh N.J., Pathare V., Pendall E., Powell J., Power S., Reich P., Renchon A., Riegler M., Rymer P., Tjoelker M., Wujeska-Klause A., Yang J., Zaehle S., and Ellsworth D.S. (2020) “The fate of carbon in a mature forest under carbon dioxide enrichment” Accepted in Nature.

9. Yang J., Duursma R.A., De Kauwe M.G., Kumarathunge D., Mingkai J., Mahmud K., Gimeno T.E., Crous K.Y., Ellsworth D.S., Peters J., Choat B., Eamus D., Medlyn B. (2019) “Incorporating non-stomatal limitation improves the performance of leaf and canopy models at high vapour pressure deficit” Tree Physiology, Volume 39. Issue 12, pp. 1961-1974, https://doi.org/10.1093/treephys/tpz103.

10. Mahmud, K., Hossain, M.D. and Shams, S. (2012) “Different Treatment Strategies for Highly Polluted Landfill Leachate in Developing Countries”, Waste Management, Volume 32, Issue 11, pp. 2096–2105, DOI:10.1016/j.wasman.2011.10.026.