Environmental Health and Protection Commons^™

Broadening The Community Of Nudibranch Enthusiasts Through Multilingual Educational Programming To Increase Climate Advocacy Engagement, Richelle Li Tanner, Cintya Felix Mendivil, Ashley Lam, Lorena Muñoz, Micah Kim, Elena G. Morales Poot, Gabrielle Keeler-May

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Environmental literacy and advocacy for environmental protections are essential components of responsible stewardship. For coastal communities, stewardship requires knowledge of ocean processes and the biological communities living within these ecosystems. While many studies highlight difficulties in engaging American audiences in environmental concern and stewardship due to hyper-individualistic societal values, tidepooling is one recreational pathway to coastal community engagement that strengthens sense of place, and therefore, responsibility to protect natural resources. We sought to broaden the tidepooling community to include more diverse voices through a participatory science program with inland city-dwelling, multilingual, and non-English speaking adults. Using nudibranchs as an environmental …

Deep Learning-Based Burned Area Mapping Of California Wildfires Using Sentinel-2 And Landsat-8 Imagery Enhanced With Super-Resolution Techniques, Youngmin Seo, Seung Hee Kim, Menas Kafatos, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

The increasing frequency of wildfires under a changing climate has led to extensive ecosystem destruction, highlighting the need for reliable burned area assessment using satellite imagery. Single-satellite data are constrained by observation gaps and interference from smoke and clouds, whereas multi-satellite data fusion can mitigate these limitations. Nonetheless, the fusion techniques still encounter challenges such as spatial information loss from resolution differences and cross-satellite domain mismatch. This study presents a burned area mapping framework that integrates super-resolution (SR) with transfer learning to address spatial and domain gaps in multi-satellite data. Specifically, Landsat-8 imagery is super-resolved to 7.5 m resolution, and …

Ai-Driven Multispectral Drone Monitoring For Afforestation In Arid Environments, Hesham Morgan, Ali Elgendy, Brandon Tran, Tamer Ismail, Mohamed M. Moursy, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Mubarak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Monitoring large-scale afforestation projects in arid and semi-arid environments requires accurate, high-resolution, and repeatable methods to assess tree survival and growth. In this study, we integrated unmanned aerial vehicle (UAV) multispectral imaging with an advanced object detection framework to evaluate vegetation establishment in the Shuayb Al-Budai afforestation site, part of the Imam Turki bin Abdullah Royal Natural Reserve, Kingdom of Saudi Arabia (KSA). Multispectral datasets were acquired using a MicaSense Altum-PT sensor and processed through a masked Region-based Convolutional Neural Network (RCNN) with two backbone architectures: ResNet-101 and VGG19-BN. The Mask R-CNN–ResNet-101 model achieved superior performance, with an overall accuracy …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

A Probabilistic Deep Learning Framework For Retrieving Chlorophyll-A From Hyperspectral Imagery: Integrating Channel Attention And Mixture Density Networks, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Junde Chen, Hesham Morgan, Michael J. Garay, Olga V. Kalashnikova, Shahryar Fazli, Charles Ichoku, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Accurate monitoring of Chlorophyll-a (Chla) is critical for assessing aquatic ecosystem health, yet ecological complexity often leads to ambiguous spectral signatures in satellite data. Traditional deterministic models assume a one-to-one mapping between spectra and pigments, often failing to capture these high-dimensional analytical challenges. In this study, we propose a novel deep learning architecture, the Channel Attention-Mixture Density Network (CA-MDN), to retrieve Chla from the National Aeronautics and Space Administration (NASA) Earth Surface Mineral Dust Source Investigation (EMIT) hyperspectral mission. The CA-MDN integrates an attention mechanism to dynamically select ecologically relevant spectral bands and employs a probabilistic output layer to quantify …

A Robust Deep Learning Ensemble Framework For Waterbody Detection Using High-Resolution X-Band Sar Under Data-Constrained Conditions, Soyeon Choi, Seung Hee Kim, Son V. Nghiem, Menas Kafatos, Minha Choi, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, …

High Spatiotemporal Resolution Monitoring Of Crop Water Stress Across The Contiguous United States Using Harmonized Landsat And Sentinel-2 Data, Na Chen, Yanlei Feng, Na Wang, Jevan Yu, Mohammad Reza Alizadeh, Yifeng Cui, Ning Ye, Wenzhe Jiao, Joshua B. Fisher, César Terrer

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and timely monitoring of crop water stress is essential for efficient agricultural water management, ultimately maintaining and improving crop productivity. While Landsat has been used for this purpose, its temporal resolution hampers timely detection of crop water stress. The recently released Harmonized Landsat and Sentinel-2 Version 2.0 dataset, which enables a higher-frequency time series of satellite observations (2–3 days, 30 m), offers a promising solution to this challenge. However, its potential for crop stress monitoring remained unexplored. In this study, we utilized 923 HLS satellite tiles to assess crop water stress across the contiguous United States (CONUS). Crop water …

Climate Change Has Increased Global Evaporative Demand Except In South Asia, Saeed Karimzadeh, Arman Ahmadi, Dennis Baldocchi, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Climate change alters how strongly the atmosphere draws water from the land, yet a consistent global assessment of this evaporative demand has been lacking. Here, we analyze 45 years of climate data and global models to quantify trends in the key drivers—air temperature, humidity, radiation, wind speed, and cloud cover—that determine the atmosphere’s drying power. We find that evaporative demand has increased worldwide, indicating a stronger atmospheric thirst, except in South Asia, where it has declined. There, widespread irrigation has increased soil and air moisture, enhanced cloud formation, and reduced sunlight reaching the surface, counteracting the global signal. These contrasting …

Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Arid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 …

Detecting Burned Vegetation Areas By Merging Spectral And Texture Features In A Resnet Deep Learning Architecture, Jiahui Fan, Yunjun Yao, Yajie Li, Xueyi Zhang, Jiquan Chen, Joshua B. Fisher, Xiaotong Zhang, Bo Jiang, Lu Liu, Zijing Xie, Luna Zhang, Fei Qiu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Timely and accurate detection of burned areas is crucial for assessing fire damage and contributing to ecosystem recovery efforts. In this study, we propose a framework for detecting fire-affected vegetation anomalies on the basis of a ResNet deep learning (DL) algorithm by merging spectral and textural features (ResNet-IST) and the vegetation abnormal spectral texture index (VASTI). To train the ResNet-IST, a vegetation anomaly dataset was constructed on high-resolution 30 m fire-affected remote sensing images selected from the Global Fire Atlas (GFA) to extract the spectral and textural features. We tested the model to detect fire-affected vegetation in ten study areas …

Dramatic Biases In Terrestrial Nitrogen Fixation In Earth System Models Revealed By Natural Isotope Signatures, Maoyuan Fang, Shushi Peng, Philippe Ciais, Daniel S. Goll, Benjamin Z. Houlton, Ying-Ping Wang, Yilong Wang, Pan Liu, Joshua B. Fisher, Pierre Regnier

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Biological nitrogen fixation (BNF) is the primary input of new reactive nitrogen to natural terrestrial ecosystems. However, this flux is poorly constrained due to its unclear drivers and associated control mechanisms. Here, we extend the existing theory of nitrogen (N) isotope mass balance to estimate BNF rates and then use a Bayesian approach to constrain the BNF rates in natural terrestrial ecosystems by using measurements of natural N-isotope ratios (δ¹⁵N) in plants (δ_P) and soil (δ_S). Together with pairwise δ_P and δ_S measurements from 18 forest sites covering diverse climates and thousands …

Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km² in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R² = 0.82), rice (R² = 0.71), and walnut (R² = 0.68), …

Warming Induces Unexpectedly High Soil Respiration In A Wet Tropical Forest, Tana E. Wood, Colin Tucker, Aura M. Alonso-Rodríguez, M. Isabel Loza, Iana F. Grullón-Penkova, Molly A. Cavaleri, Christine S. O'Connell, Sasha C. Reed

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Tropical forests are a dominant regulator of the global carbon cycle, exchanging more carbon dioxide with the atmosphere than any other terrestrial biome. Climate models predict unprecedented climatic warming in tropical regions in the coming decades; however, in situ field warming studies are severely lacking in tropical forests. Here we present results from an in situ warming experiment in Puerto Rico, where soil respiration responses to ⁺4 ^oC warming were assessed half-hourly for a year. Soil respiration rates were 42-204% higher in warmed relative to ambient plots, representing some of the highest soil respiration rates reported for any …

Multi-Crop Systems And Crop-Switching Strategies To Enhance Water Use Efficiency And Climate Resilience In Arid Agricultural Regions, Shahryar Fazli, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Agriculture in the Lower Colorado River (LCR) region faces mounting challenges from climate change, arid conditions, and water scarcity. This study evaluates water use efficiency (WUEc) and crop-switching strategies under SSP2-4.5 and SSP5-8.5 scenarios for 2025–2049, 2050–2074, and 2075–2099. Using historical data, climatic drivers such as temperature and precipitation were analyzed for their influence on key crops, including durum wheat, winter wheat, and corn. Results show SSP2-4.5 supports water use reductions up to 16%, stable profits (80–90%), and modest calorie increases (up to 15%), while SSP5-8.5 poses severe challenges, with water use reductions of 2–5%, profits dropping to around 20%, …

Integrating Belowground Recovery Into Tropical Forest Restoration Design And Monitoring, Lauren Toro, Leland K. Werden, Shalom D. Addo-Danso, Kelly M. Andersen, Sarah Batterman, Matilde M. Bragadini, Pooja Choksi, Rebecca J. Cole, Liza S. Comita, Daniela Cusack, Daisy H. Dent, Lee H. Dietterich, Joshua B. Fisher, Katrin Fleischer, Lucia Fuchslueger, Nohemi Huanca-Nunez, Janey R. Lienau, Lindsay A. Mcculloch, Ember M. Morrissey, Jennifer S. Powers, Mareli Sánchez-Juliá, Oscar Valverde-Barrantes, Anita Weissflog, Michelle Y. Wong

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

There is growing recognition that tropical forest restoration is key for sequestering carbon and enhancing ecosystem resilience. Soils, roots, and soil biota are central to ecosystem function and services, but belowground recovery is largely overlooked in restoration monitoring frameworks. Here, we outline current understanding of the links between above- and belowground recovery in tropical forests by examining how belowground properties before and after intervention influence recovery; by evaluating whether aboveground recovery can serve as a proxy for belowground dynamics; and by proposing a blueprint for monitoring dynamic soil physical (bulk density, aggregate stability), chemical (organic matter or carbon, pH), and …

Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfire prediction models that can be applied across diverse regions at fine scales (<  100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km² from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R² = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) …

Evaluation Of Ecostress Collection 2 Evapotranspiration Products: Strengths And Uncertainties For Evapotranspiration Modeling, Zoe Amie Pierrat, Adam J. Purdy, Gregory Halverson, Joshua B. Fisher, Kanishka Mallick, Madeleine Pascolini-Campbell, Youngryel Rye, Martha C. Anderson, Claire Villanueva-Weeks, Margaret C. Johnson, Brenna Hatch, Evan Davis, Yun Yang, Kerry Cawse-Nicholson

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) collects thermal observations from the International Space Station to support evapotranspiration (ET) research at fine spatial resolutions (70 m × 70 m). Initial ET from ECOSTRESS Collection 1 was used in scientific research and applications, though subsequent analyses identified areas for improvement. This study outlines updates to ECOSTRESS Collection 2 ET and presents an accuracy assessment of ET and auxiliary variables validated against in situ data from AmeriFlux. Key updates in Collection 2 include use of four independent model estimates of instantaneous latent energy (LE) and improved auxiliary forcing data. …

Monitoring Mangrove Dynamics And Evaluating Future Afforestation Potential In The Egyptian Red Sea, Rasha M. Abou Samra, Mansour Almazroui, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Mangrove forests are vital for ecosystem services and coastal management but face stressors from anthropogenic activities and climate change. This study estimates the extent and afforestation potential of mangroves along the Egyptian Red Sea coast from 1984 to 2022 using NDVI derived from Landsat-5 and Sentinel-2 data in Google Earth Engine. Aboveground biomass (AGB), belowground biomass (BGB), carbon (C) stock, and CO₂ sequestration potential were evaluated using Sentinel-2 and elevation data. Future afforestation suitability (2020–2050) under SSP2-4.5 and SSP5-8.5 scenarios was assessed with the MaxEnt model. Mangrove area increased from 0.95 km² in 1984 to 1.46 km^{2 …}

Development And Application Of Self-Supervised Machine Learning For Smoke Plume And Active Fire Identification From The Fire Influence On Regional To Global Environments And Air Quality Datasets, Nicholas Lahaye, Anastasija Easley, Kyongsik Yun, Hugo Lee, Erik Linstead, Michael J. Garay, Olga V. Kalashnikova

Engineering Faculty Articles and Research

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% …

Innovative Soil Classification Approach For Achieving Global Biodiversity Framework Utilizing Integrated Data Fusion Of Emit And Multispectral Satellite Observations: Case Study Of Imam Turki Bin Abdullah Royal Reserve, Kingdom Of Saudi Arabia, Hesham Morgan, Ali Elgendy, Surendra Maharjan, Wenzhao Li, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Soil classification is essential for sustainable land management, ecological conservation, and combating desertification, particularly in arid and semi-arid regions. This study integrates hyperspectral data from the Earth Surface Mineral Dust Source Investigation (EMIT) and multispectral imagery from Sentinel-2 to achieve accurate soil classification for the Imam Turki bin Abdullah Royal Reserve (ITBA) in Saudi Arabia. Using advanced Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), the study highlights the power of data fusion in addressing the limitations of standalone remote sensing methods. The integration of hyperspectral and multispectral data combines the spectral richness of hyperspectral imaging with the spatial …

Transfer Learning In Junction With A Light Use Efficiency Model For Estimating Grassland Gross Primary Production, Ruiyang Yu, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Changliang Shao, Joshua B. Fisher, Jiquan Chen, Xiaotong Zhang, Yufu Li, Jia Xu, Lu Liu, Zijing Xie, Jing Ning, Jiahui Fan, Luna Zhang

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

It is significant to simulate grassland gross primary production (GPP) to understand the terrestrial carbon budget over Inner Mongolia (IMG), China. Nevertheless, there is not sufficient in situ GPP data over this region. In this study, we proposed a novel model-based transfer learning (MTL) approach with generative adversarial networks-long short-term memory (GAN-LSTM) and light use efficiency (LUE) models to derive grassland GPP over IMG, China. We first used 25 grassland eddy covariance sites over the conterminous United States to establish the GAN-LSTM model and then fine-tuned it with six sites over IMG to estimate water constraints that were embedded into …

Glacial Lakes Outburst Susceptibility And Risk In The Eastern Himalayas Using Analytical Hierarchy Process And Backpropagation Neural Network Models, Sandeep Kumar Mondal, Jyotindra Narayan, Chitesh Sharma, Rishikesh Bharti, Santosha Kumar Dwivedy, Pinaki Roy Chowdhury

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Himalayan cryosphere is dynamic, and changing climate conditions threaten breach of glacial lakes. A number of glacial lake outburst floods (GLOFs) occurred in the Himalayas in the recent past, affecting people and infrastructures. Assessment of high-altitude glacial lakes is required to avoid associated hazards and mitigate the impacts. In this study, we have made an inventory of naturally formed lakes within the Sikkim Himalayas, including Nepal, Bhutan, and China, and discussed the GLOF susceptibility. A total of 399 lakes have been identified, out of which 281 lakes have an areal coverage greater than 0.01 Km2. Monitoring temporal changes shows …

Multi-Sensor Data Fusion And Gis-Drastic Integration For Groundwater Vulnerability Assessment With Rainfall Consideration, Wu Jiazhe, Dai Xinrui, Su Yangcheng, Zheng Xiangtian, Bushra Ghaffar, Rabiya Nasir, Ahsan Jamil, Zeeshan Zafar, Mohammad Suhail Meer, M. Abdullah-Al-Wadud, Rahila Naseer, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

In many areas of the world, particularly in arid and semi-arid regions, groundwater is the primary source of fresh water, and it supplies around one-third of the world's fresh water. Agriculture is the primary economic sector on the coast in the southern district (Nowshera). More food productivity is required due to the expanding population and diminishing agricultural lands, which increases the use of chemical pesticides and fertilizers in farming. The current study was conducted in northwestern parts of Pakistan to evaluate the impacts of the frequent use of pesticides and fertilizers in agricultural fields. Nine hydrogeological parameters were considered, and …

Extraordinary 21st Century Drought In The Po River Basin (Italy), Abel Andrés Ramírez Molina, Glenn Tootle, Giuseppe Formetta, Thomas Piechota, Jiaqi Gong

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Recent research identified 2022 as being the year of lowest seasonal April–May–June–July (AMJJ) observed streamflow for the Po River Basin (PRB) in the past two centuries. Expanding upon this research, we applied filters (2-year to 30-year filters) to the AMJJ observed streamflow and identified the late 20th and 21st century as displaying extreme drought. In this study, we introduce PALEO-RECON, an automated reconstruction tool developed to leverage tree ring-based proxies and streamline regression processes. Using PALEO-RECON, we reconstructed the AMJJ streamflow, applying traditional regression techniques and using a nested approach in which 30-, 40-, and 50-year windows within the ~200-year …

Invasion Stress Mitigates Climate Stress In A Brackish Marsh Amphipod, Lorna E. Haworth, Sarah Nancollas, Susie N. Landa, Anne E. Todgham, Richelle L. Tanner

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

1. Organisms in coastal brackish ecosystems face not only highly variable environmental conditions, but the intensity and stochasticity of these environmental conditions are anticipated to increase due to climate change. While environmental changes are often presented unilaterally as stressors, there are some anthropogenic environmental transformations, such as the introduction of habitat forming plant species, that may mitigate physiological stress imposed by warming. In Suisun Marsh in the California Delta, native plant canopies allow light and heat to penetrate to the understory yet the canopies of the introduced Phragmites australis reed block out sunlight and heat for organisms living below.
2. We set …

Identifying Opportunities For Nature-Based Solutions With Geospatialized Life Cycle Assessments And Fine-Scale Socioecological Data, Gabriela Shirkey, Annick Anctil, Ranjeet John, Venkatesh Kolluru, Leah Mungai, Herve Kashongwe, Lauren T. Cooper, Ilke Celik, Joshua B. Fisher, Jiquan Chen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

As we increasingly understand the impact that land management intensification has on local and global climate, the call for nature-based solutions (NbS) in agroecosystems has expanded. Moreover, the pressing need to determine when and where NbS should be used raises challenges to socioecological data integration as we overcome spatiotemporal resolutions. Natural and working lands is an effort promoting NbS, particularly emissions reduction and carbon stock maintenance in forests. To overcome the spatiotemporal limitation, we integrated life cycle assessments (LCA), an ecological carbon stock model, and a land cover land use change model to synthesize rates of global warming potential (GWP) …

Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …

Multi-Temporal Analysis Of Urbanization-Driven Slope And Ecological Impact Using Machine-Learning And Remote Sensing Techniques, Zhang Hao, Muhammad Haseeb, Zheng Xiangtian, Zainab Tahir, Syed Amer Mahmood, Aqil Tariq, Rana Waqar Aslam, M. Abdullah-Al-Wadud, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Rapid urbanization in Lahore, Pakistan, has led to significant ecological and thermal challenges, particularly the intensification of Urban Heat Island (UHI) effects and increased thermal stress as measured by the Urban Thermal Field Variance Index (UTFVI). This study employs a multi-temporal evaluation of Landsat satellite imagery and GIS-based analysis to investigate the Spatio-temporal trends in land-use and land-cover (LULC) changes from 1994 to 2024. We detected substantial changes in urban growth, vegetation cover, and barren areas using supervised classification (Random Forest) methods and remote sensing indices such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), NDBI (Normalized …

Spatial Gap-Filling Of Himawari-8 Hourly Aod Products Using Machine Learning With Model-Based Aod And Meteorological Data: A Focus On The Korean Peninsula, Youjeong Youn, Seoyeon Kim, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, …

Changing Characteristics Of Land Cover, Landscape Pattern And Ecosystem Services In The Bohai Rim Region Of China, Jiaqi Liu, Wei Chen, Hu Ding, Zhanhang Liu, Min Xu, Ramesh P. Singh, Congqiang Liu

Mathematics, Physics, and Computer Science Faculty Articles and Research

Since the Anthropocene, ecosystems have been continuously deteriorating due to global climate change and human intervention. Exploring the changing characteristics of land use/land cover (LULC), landscape pattern and ecosystem service (ES) and their drivers is crucial for regional ecosystem management and sustainable development. Taking the Bohai Rim region of China as an example, we used the land use transfer matrix, landscape pattern index and InVEST model to analyze the changing characteristics of LULC, landscape pattern and six key ESs [crop production (CP), water yield (WY), carbon storage (CS), soil conservation (SC), habitat quality (HQ), landscape aesthetics (LA)] during 2000–2020. Detailed …