Spatiotemporal Monitoring Research Articles

Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models.

Dissolved phosphorous through dry-wet-dry transitions in a small-dammed river basin: integrated understanding on transport patterns, export controls, and fate.

An integrated understanding of dissolved phosphorous (DP) export mechanism and controls on export over dry and wet periods is crucial for riverine ecological restorations in dammed river basins considering its high bioavailability and retention rates at dams. Riverine DP transport patterns (composition, sources, and transport pathways), export controls, and fate were investigated over the 2020 wet season (5 events) and dry seasons before and after it (2 events: dry(before) and dry(after)) in a semi-arid, small-dammed watershed to comprehend the links between terrestrial DP sources and aquatic DP sinks. Close spatiotemporal monitoring of the full range of phosphorous and total suspended solids (TSSs) and subsequent analyses (hysteresis, hierarchical partitioning, and coefficient of variation) provided the basis for the study. Total-DP (TDP) shared 13-39% (25%) of total-P (TP) through storms, dissolved organic-P (DOP) shared 6-21% (12%), and phosphate-P shared (PO4-P) 7-22% (13%). DP forms displayed strong connections with discharge trends across the wet season, and marked changes in the shares were reported over dry-wet and wet-dry transitions. The DOP fraction of TDP increased from 4% in dry(before) baseflow to 64% at the end of the wet season. The DOP flux increment in stormflow was 20 folds compared to dry(before) baseflow, while that of PO4-P was 2 folds. DOP displayed the least spatial source heterogeneity with minimum anthropogenic pressure on inherent fluxes. DOP originated from overland and near-stream soil sources and was transported via surface runoff and soil water runoff, respectively. Across the wet season, the attrition of overland DOP sources and activation of near-stream soil DOP sources through strengthened hydrological connectivity governed the seasonal DOP trends. Surface and groundwater runoff pathways were important for PO4-P delivery during stormflow; nonetheless, wastewater treatment plant (WWTP) effluent was the main PO4-P source under both baseflow and stormflow regimes, followed by near-stream traditional agriculture lands. The interaction patterns of small dam systems with DP inputs through dry-wet periods were explained. The riverine PO4-P fluxes were profoundly impacted by in-stream biogeochemical and physical processes and small dam systems, while riverine DOP fluxes were relatively less influenced.

Identifying the Primary Producer of High Concentrations of 2-Methylisoborneol and Assessing the Impact of Weather Conditions at the Haizuka Reservoir, Japan

2-methylisoborneol (2-MIB), produced by cyanobacteria in water systems, has a musty odor and causes discomfort in humans. Extra costs are incurred when removing 2-MIB from water. The growth of 2-MIB-producing cyanobacteria, as well as the occurrence of 2-MIB, are affected by many environmental factors, including water temperature and nutrient concentrations. These factors are influenced by weather conditions. 2-MIB-producing cyanobacteria and 2-MIB production in various water systems have been investigated using several methods. However, the combined spatiotemporal monitoring of various indicators, especially weather conditions, is limited. In May 2023, a high concentration of 2-MIB was detected in the Haizuka Reservoir, Japan. This study aims to clarify the transition of 2-MIB-producing cyanobacteria and the effects of environmental factors, including weather meteorological conditions, on 2-MIB production. To achieve this, combined spatiotemporal monitoring of various indicators was conducted, including 2-MIB concentration, the cell count of the producers, the 2-MIB biosynthesis gene, water quality, and weather conditions. Our analysis revealed that a high concentration of 2-MIB was produced by the proliferated Pseudanabaena sp. in the surface water in May 2023. The proliferation of 2-MIB-producing Pseudanabaena sp. might be related to the higher-than-average rainfall in April and May and consistently higher-than-average temperature from January to May. Although this study was unable to clarify how these weather conditions led to the proliferation of 2-MIB-producing Pseudanabaena sp., these weather conditions can be used to warn of the occurrence of high concentrations of 2-MIB in the Haizuka Reservoir. In conclusion, it is effective to incorporate weather conditions into the monitoring of 2-MIB production in water sources.

Modeling of soil salinity in Rheris Oasis (Southeastern of Morocco) using satellite spectral indices

The Rheris oases in Southeastern Morocco are an essential ecosystem. It presents enormous ecological and natural values. Water scarcity coupled with agricultural intensification results in soil salinization and its degradation. This work aims to propose a spatiotemporal monitoring method of soil salinization in the Rheris oasis using spectral indices derived from Thematic Mapper (TM) and Operational Land Imager (OLI) data. The most used indices in the literature were (14 indices) tested and correlated with the results obtained from 50 samples taken from the first soil horizon at a constant depth of 0.20 m from the November 2022 campaign. The findings confirm that this method is highly effective and reliable for the modeling and spatial mapping of soil salinity in this region. The state of the hydroclimate is an aspect that influences soil salinization. An increase in salinized surfaces is observed during the periods of 1990–1996, 2000–2005, and 2017-2022. The spatio-temporal distribution of saline soils in Rheris Oasis is very variable. The monthly variations are more important than the annual ones.

Data Indo InaFire: Spatial Visualization of Peatland Fire Impact and Ecosystem Restoration Monitoring in PHU Jambi using Earth Engine Apps and Sentinel-2 MSI Imagery

Peatlands formed from long-term accumulation of partially decomposed organic matter in wetland areas. This particular ecosystem is not only capable of sequestering significant quantities of carbon but also vulnerable to forest and land fires (karhutla). Peatland produces considerable CO₂ emissions during fire occurrences, which consequently requires spatiotemporal monitoring to sustain its ecological roles and functions. This study aims to map the severity of fires in peatland ecosystems, estimate the success of post-fire restoration, and develop an Earth Engine Apps-based monitoring platform for peatland fire monitoring. Fire severity assessment and post-fire restoration success estimation were conducted in Jambi's Peat Hydrological Unit (PHU) in 2019 using the Normalized Burn Ratio (NBR) index derived from Sentinel-2 MSI satellite imagery. Most of Jambi PHU's fire severity and restoration levels are high. The area of PHU Jambi with high fire severity was 7,822.91 hectares, while the area with high restoration success was 23,744.69 hectares. NBR monitoring in PHU Jambi can be used to detect fire severity and restore success. The visualization of forest and land fire severity was successfully displayed on the Data Indo InaFire webGIS platform, an Earth Engine Apps-based monitoring platform.

The role of rehabilitating and restoration the green dam in managing the impact of invasive forest pests in Algeria

The rehabilitation of Algeria’s "Green Dam" project, a large-scale reforestation initiative, represents a key strategy for mitigating climate change, especially in semi-arid regions. This study, conducted between 2021 to 2024 in the semi-arid region of Djelfa (Algeria), evaluates the impact of defoliators in three different stands. Our findings reveal that infestation levels were significantly lower in mixed-species stands compared to monoculture stands, with monoculture plantations showing the highest infestation rates. Seasonal analysis revealed consistent stability in rehabilitated zones, with lower infestation indices throughout the year. The study highlights the role of species diversity in enhancing forest resilience to pest outbreaks, as mixed-species plantations naturally disrupt pest population dynamics and support healthier ecosystems. These results highlight the effectiveness of Green Dam’s rehabilitation in enhancing ecosystem health, increasing biodiversity, and reducing vulnerability to climate-induced disturbances, positioning it as a critical tool for ecological restoration and climate mitigation in northern Algeria. Furthermore, the project exemplifies a scientifically driven approach to combating desertification and climate change in semi-arid ecosystems. By integrating tritrophic analyses examining forest species, pest insects, and their antagonists—along with spatiotemporal monitoring, the study provides valuable insights into ecological interactions under changing climatic conditions. As expanding defoliators and xylophages pose increasing challenges, adaptive management strategies tailored to semi-arid regions are necessary. The rehabilitation of the Green Dam project emerges as a model for large-scale ecological restoration, combining reforestation efforts with scientific innovation to bolster ecosystem resilience, enhance sustainable land management, and address global issues such as biodiversity loss, desertification, and climate instability. Continued monitoring and adaptive management are essential to ensure the long-term success of reforested landscapes.

HDRSA-Net: Hybrid dynamic residual self-attention network for SAR-assisted optical image cloud and shadow removal

HDRSA-Net: Hybrid dynamic residual self-attention network for SAR-assisted optical image cloud and shadow removal

Seabed Status, Mapping and Assessing Posidonia oceanica in the Edough Mountains MPA, Annaba (Northeastern Algeria): A Baseline for Spatio-Temporal Monitoring

Seabed Status, Mapping and Assessing Posidonia oceanica in the Edough Mountains MPA, Annaba (Northeastern Algeria): A Baseline for Spatio-Temporal Monitoring

Activity-Based Dicyanoisophorone Derivatives: Fluorogenic Toolbox Enables Direct Visualization and Monitoring of Esterase Activity in Tumor Models.

The visualization and spatiotemporal monitoring of endogenous esterase activity are crucial for clinical diagnostics and treatment of liver diseases. Our research adopts a novel substrate hydrolysis-enzymatic activity (SHEA) approach using dicyanoisophorone-based fluorogenic ester substrates DCIP-R (R = R1-R6) to evaluate esterase preferences on diverse substrate libraries. Esterase-mediated hydrolysis yielded fluorescent DCIP-OH with a nanomolar detection limit in vitro. These probes effectively monitor ester hydrolysis kinetics with a turnover number of 4.73 s-1 and catalytic efficiency (kcat/Km) of 106 M-1 s-1 (DCIP-R1). Comparative studies utilizing two-photon imaging have indicated that substrates containing alkyl groups (DCIP-R1) as recognition elements exhibit enhanced enzymatic cleavage compared to those containing phenyl substitution on alkyl chains (DCIP-R4). Time-dependent variations in endogenous esterase levels were tracked in healthy and liver tumor models, especially in diethylnitrosamine (DEN)-induced tumors and HepG2-transplanted liver tumors. Overall, fluorescence signal quantifications demonstrated the excellent proficiency of DCIP-R1 in detecting esterase activity both in vitro and in vivo, showing promising potential for biomedical applications.

Satellite remote sensing reveals the footprint of biodiversity on multiple ecosystem functions across the NEON eddy covariance network

Abstract Biodiversity relates to ecosystem functioning by modulating biogeochemical cycles of carbon, water, energy, and nutrients within and between multiple biotic and abiotic components of the ecosystems. However, large-scale, systematic measurements of plant biodiversity are still lacking, and the effects of biodiversity on measured biogeochemical processes are understudied. Here, we combined alpha (α) and beta (β) taxonomic measurements, spectral diversity from satellite observations, structural properties of the vegetation, and climatic drivers to assess the effect of biodiversity on ecosystem functional properties. Ecosystem functional properties were computed from eddy-covariance fluxes at 44 sites of the National Ecological Observatory Network. Based on the spectral variation hypothesis, we used the near-infrared reflectance of vegetation (NIRv) derived from Sentinel-2 satellite imagery to compute Rao’s quadratic entropy (Rao Q), a distance metric related to spatial heterogeneity. Using an automatic model averaging technique, we found that biodiversity proxies hold substantial explanatory power when predicting several ecosystem functions related to carbon and water exchange. In particular, NIRv-based Rao Q (RaoQNIRv) reflected positive biodiversity effects on productivity, as expected from the literature. In contrast, traditional taxonomic α-diversity indices were generally not selected as relevant predictors of the ecosystem functional properties. Yet, β-diversity strongly contributed to the prediction of carbon use efficiency, surface conductance, and water use efficiency. We also found that the RaoQNIRv is less affected by issues of saturation and bare soil contribution compared to RaoQNDVI. We show that spectral heterogeneity based on remotely sensed NIRv holds the potential for globally characterizing the biodiversity-ecosystem functioning relationship (BEF). While systematic measurements of taxonomic diversity co-located at biogeochemical measurement stations could reduce the uncertainty surrounding the BEF relationship at whole-ecosystem scale, remotely- sensed metrics characterizing important functional and structural diversity aspects of the landscape will be crucial for continuous spatiotemporal monitoring of biodiversity with relevant implications for ecosystem services to humankind.

Spatiotemporal monitoring of groundwater supply and active energy for irrigation practice in semi-arid regions of Tunisia with machine learning

ABSTRACT Semiarid regions are facing overexploitation of groundwater resources to meet irrigation needs. Monitoring the water-energy nexus allows for optimal management of extracted water volumes and consumed energy. The Nabeul region of Tunisia was selected where 14 farmers, whose wells were equipped with smart electricity and water meters (SWEMs), for instant monitoring of pumped water volumes and the electrical energy required for irrigation. Monthly data over a period of eight months were used to study the variations in water volumes and active energy. The analysis of variance classified farmers into four groups based on water volumes and five groups based on active energy. Spatial variability analysis using kriging showed that the northeast zone is the most solicited in terms of water pumping and energy consumption with water volume exceeding 4,000 m3/month and active energy reaching 2,500 kWh/month. The prediction of energy based on water volume using machine learning techniques such as random forest and support vector machine was successfully conducted. The tools generated by the methodology were applied to a chosen case in the region to estimate active energy and validate the results obtained. The implemented framework allows for better management of groundwater resources for irrigation.

Analysis and assessment of urbanization dynamics: actors and forms of encroachment on the agricultural land of Constantine in eastern Algeria (1985-2020)

The environmental concern has increasingly become a focus in recent research on town planning and regional development. This paper discusses the renewed issue of urbanization affecting agricultural land and natural environments, with a new approach combining spatio-temporal monitoring of the evolution of the urban area of the province of Constantine, in relation to the agronomic quality of the land. In this context, the present study is based on the agronomic classification of land in the province of Constantine carried out by BNEDER study office in 1987. In addition, it uses maps extracted from Landsat satellite images for the years 1985, 2000, and 2020, and the annual land use data from the agriculture department (DSA), recorded between the years 2000 and 2020. The results obtained from this approach highlight the negative impact of urbanization over the last two decades in the Constantine agglomeration, as well as the strong vulnerability of natural environments and agricultural soil expected in the coming years. The accelerated pace of land artificialization has led to a significant reduction in agricultural space in the Constantine province. The urbanization of the city of Constantine alone consumed more land in 13 years (between 1987 and 2000) than it had in the twenty-seven years following independence, and more than it had consumed in a century before independence. In the event that public authorities do not take corrective and environmental protection actions, the situation regarding this problem will become more difficult to resolve.

Spatio-Temporal Monitoring and Assessment of Air Quality and its Impact on Public Health from Geospatial perspective over Haryana, India

Abstract. This study aims to evaluate the air quality of the Haryana state, India by utilizing Sentinel-5P satellite data. This study aims to track, quantify, and analyze the level of air pollutants in several regions of Haryana. By integrating and analyzing diverse satellite datasets, the study provides a comprehensive understanding of air quality conditions in the region and supports informed decision-making for pollution mitigation and environmental sustainability. This also examines the public health and environmental impacts of air pollution, identifies pollution sources, reviews existing policies, and also provides recommendations for prevention of air pollution. The outcomes of this study will improve the understanding of air quality dynamics in Haryana and provide preventive measures towards sustainable environmental practices. This paper presents a detailed assessment of air quality in Haryana, India, covering the period from 2019 to 2023 using satellite datasets. This evaluates the concentrations and spatial distributions of air pollutants across Haryana. The experimental results provide significant variations in pollution levels over the four years, with higher concentrations in 2019 and 2021, and lower levels in 2020 and 2023. Moreover, spatial distribution maps illustrate pollutant patterns, emphasizing the temporal and spatial changes in pollution levels. The results also highlight the ongoing challenge of increasing pollution in certain areas of Haryana and the need for continuous monitoring to improve air quality.

Network of artificial olfactory receptors for spatiotemporal monitoring of toxic gas.

Excessive human exposure to toxic gases can lead to chronic lung and cardiovascular diseases. Thus, precise in situ monitoring of toxic gases in the atmosphere is crucial. Here, we present an artificial olfactory system for spatiotemporal recognition of NO2 gas flow by integrating a network of chemical receptors with a near-sensor computing. The artificial olfactory receptor features nano-islands of metal-based catalysts that cover the graphene surface on the heterostructure of an AlGaN/GaN two-dimensional electron gas (2DEG) channel. Catalytically dissociated NO2 molecules bind to graphene, thereby modulating the conductivity of the 2DEG channel. For the energy/resource-efficient gas flow monitoring, trust-region Bayesian optimization algorithm allocates many sensors optimally in a complex space. Integrated artificial neural networks on a compact microprocessor with a network of sensors provide in situ gas flow predictions. This system enhances protective measures against toxic environments through spatiotemporal monitoring of toxic gases.

How to evaluate the reduction effect of the park on PM2.5? Exploratory application of the maximum and cumulative perspective

How to evaluate the reduction effect of the park on PM2.5? Exploratory application of the maximum and cumulative perspective

Monitoring the Impact of Artificial Structure on Hydrogeological Environment: A Case Study of Hydraulic Tunnel at Pirot Hydropower Plant.

Artificial objects, particularly tunnels used for water transport under pressure, impact the geological and hydrogeological environment to a greater or lesser extent, and it is vital to assess their contributions to groundwater quality. Although tunnels are typically lined with concrete, their interaction with the hydrogeological environment intensifies over time. In this study, the detailed spatiotemporal monitoring of all hydrogeological features within the potential influence zone of the hydraulic tunnel of the Pirot Hydropower Plant has been conducted in order to determine the degree of interaction between the artificial object and the natural environment in real time, and to assess the correlation between monitored parameters. Natural conditions of the environment were defined, as well as potential changes through the observing groundwater regimes. The monitoring network included observations of groundwater regimes at seven springs located in close proximity to the hydraulic tunnel, within the tunnel, at three piezometers, and along the river, while methods employed were hydrological monitoring, physicochemical monitoring, and groundwater piezometer sensing. Cross-correlation analysis has been applied for assessing the impact of precipitation dynamics on the spring discharge regime. The results indicate a direct influence of the tunnel on the hydrogeological environment, proving the consistency and high correlation between the monitored parameters.

Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs

Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs

Data gap or biodiversity gap? Evaluating apparent spatial biases in community science observations of Odonata in the east-central United States

Odonates (dragonflies and damselflies) have become popular study organisms for insect-based climate studies, due to the taxon’s strong sensitivity to environmental conditions, and an enthusiastic following by community scientists due to their charismatic appearance and size. Where formal records of this taxon can be limited, public efforts have provided nearly 1,500,000 open-sourced odonate records through online databases, making real-time spatio-temporal monitoring more feasible. While these databases can be extensive, concerns regarding these public endeavors have arisen from a variety of sources: records may be biased by human factors (ex: density, technological access) which may cause erroneous interpretations. Indeed, records of odonates in the east-central US documented in the popular database iNaturalist bear striking patterns corresponding to political boundaries and other human activities. We conducted a ‘ground-truthing’ study using a structured sampling method to examine these patterns in an area where community science reports indicated variable abundance, richness, and diversity which appeared to be linked to observation biases. Our observations were largely consistent with patterns recorded by community scientists, suggesting these databases were indeed capturing representative biological trends and raising further questions about environmental drivers in the observed data gaps.

Neuromodulator and neuropeptide sensors and probes for precise circuit interrogation in vivo.

To determine how neuronal circuits encode and drive behavior, it is often necessary to measure and manipulate different aspects of neurochemical signaling in awake animals. Optogenetics and calcium sensors have paved the way for these types of studies, allowing for the perturbation and readout of spiking activity within genetically defined cell types. However, these methods lack the ability to further disentangle the roles of individual neuromodulator and neuropeptides on circuits and behavior. We review recent advances in chemical biology tools that enable precise spatiotemporal monitoring and control over individual neuroeffectors and their receptors in vivo. We also highlight discoveries enabled by such tools, revealing how these molecules signal across different timescales to drive learning, orchestrate behavioral changes, and modulate circuit activity.

Spatio-Temporal Monitoring and Risk Mapping of Glacial Lake Outburst Flood in Hunza Valley, Pakistan

Glacial lake outburst flood (GLOF) disasters are serious and potentially increase huge risks to livelihoods and infrastructure in the mountain regions of the world. The northern highland regions of Pakistan are home to some of the biggest alpine glaciers. In this investigation, the Hunza Valley of Pakistan has undergone remote sensing-based risk assessment for Glacial Lake Outburst Floods. Borith and Passu Lakes were chosen to identify flood risk in the downstream areas. For such, Landsat images were used from 1990-2020. Different spectral indices such as the Normalized Difference Snow Index (NDSI), Normalized Difference Glacier Index (NDGI) and Normalized Difference Water Index (NDWI) were applied to evaluate snow cover changes. Furthermore, these Lakes were digitized to evaluate and check the total increase in Lake Areas. Also, built-up areas close to lakes were digitized to identify total risk. The Land Surface Temperature (LST), NDSI, NDGI, NDWI, Digital Elevation Model (DEM) and Slope variables were given weights using the Analytic Hierarchy Process (AHP) method. In the analysis of flood risk mapping, maximum weight was assigned to Land Surface Temperature, and minimum weight was assigned to the slope. The result revealed that the settlements located in the Ghulkin, Gulmit, Husseini, Passu, Zarabad, and Khorramabad are at moderate risk while settlements located near Hunza River such as Karimabad, Khanna Abad, and Aliabad are at high risk. The outcome also showed that Borith Lake's area expanded, going from 0.059 km2 in 1985 to 0.074 km2 in 2020 and Passu Lake's area also grew, going from 0.074 km2 in 2005 to 0.077 km2 in 2020. In last, Buffer analysis was performed to identify areas that are likely to be affected by the flood. The result of the study can help carry out a downstream risk assessment and better preparedness for future flood hazards.