Scholarly Works, Biological Systems Engineering

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Research articles, presentations, and other scholarship


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  • Impact Assessment of Nematode Infestation on Soybean Crop Production Using Aerial Multispectral Imagery and Machine Learning
    Jjagwe, Pius; Chandel, Abhilash K.; Langston, David B. (MDPI, 2024-06-24)
    Accurate and prompt estimation of geospatial soybean yield (SY) is critical for the producers to determine key factors influencing crop growth for improved precision management decisions. This study aims to quantify the impacts of soybean cyst nematode (SCN) infestation on soybean production and the yield of susceptible and resistant seed varieties. Susceptible varieties showed lower yield and crop vigor recovery, and high SCN population (20 to 1080) compared to resistant varieties (SCN populations: 0 to 340). High-resolution (1.3 cm/pixel) aerial multispectral imagery showed the blue band reflectance (r = 0.58) and Green Normalized Difference Vegetation Index (GNDVI, r = −0.6) have the best correlation with the SCN populations. While GDNVI, Green Chlorophyll Index (GCI), and Normalized Difference Red Edge Index (NDRE) were the best differentiators of plant vigor and had the highest correlation with SY (r = 0.59–0.75). Reflectance (REF) and VIs were then used for SY estimation using two statistical and four machine learning (ML) models at 10 different train–test data split ratios (50:50–95:5). The ML models and train–test data split ratio had significant impacts on SY estimation accuracy. Random forest (RF) was the best and consistently performing model (r: 0.84–0.97, rRMSE: 8.72–20%), while a higher train–test split ratio lowered the performances of the ML models. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for modeling over smaller or medium-sized datasets. Such insights derived using high spatial resolution data can be utilized to implement precision crop protective operations for enhanced soybean yield and productivity.
  • Thermal-RGB Imagery and Computer Vision for Water Stress Identification of Okra (Abelmoschus esculentus L.)
    Rajwade, Yogesh A.; Chandel, Narendra S.; Chandel, Abhilash K.; Singh, Satish Kumar; Dubey, Kumkum; Subeesh, A.; Chaudhary, V. P.; Ramanna Rao, K. V.; Manjhi, Monika (MDPI, 2024-06-27)
    Crop canopy temperature has proven beneficial for qualitative and quantitative assessment of plants' biotic and abiotic stresses. In this two-year study, water stress identification in okra crops was evaluated using thermal-RGB imaging and AI approaches. Experimental trials were developed for two irrigation types, sprinkler and flood, and four deficit treatment levels (100, 50, 75, and 25% crop evapotranspiration), replicated thrice. A total of 3200 thermal and RGB images acquired from different crop stages were processed using convolutional neural network architecture-based deep learning models (1) ResNet-50 and (2) MobileNetV2. On evaluation, the accuracy of water stress identification was higher with thermal imagery inputs (87.9% and 84.3%) compared to RGB imagery (78.6% and 74.1%) with ResNet-50 and MobileNetV2 models, respectively. In addition, irrigation treatment and levels had significant impact on yield and crop water use efficiency; the maximum yield of 10,666 kg ha−1 and crop water use efficiency of 1.16 kg m−3 was recorded for flood irrigation, while 9876 kg ha−1 and 1.24 kg m−3 were observed for sprinkler irrigation at 100% irrigation level. Developments and observations from this study not only suggest applications of thermal-RGB imagery with AI for water stress quantification but also developing and deploying automated irrigation systems for higher crop water use efficiency.
  • Valorization of Seafood Waste for Food Packaging Development
    Zhan, Zhijing; Feng, Yiming; Zhao, Jikai; Qiao, Mingyu; Jin, Qing (MDPI, 2024-07-03)
    Packaging plays a crucial role in protecting food by providing excellent mechanical properties as well as effectively blocking water vapor, oxygen, oil, and other contaminants. The low degradation of widely used petroleum-based plastics leads to environmental pollution and poses health risks. This has drawn interest in renewable biopolymers as sustainable alternatives. The seafood industry generates significant waste that is rich in bioactive substances like chitin, chitosan, gelatins, and alginate, which can replace synthetic polymers in food packaging. Although biopolymers offer biodegradability, biocompatibility, and non-toxicity, their films often lack mechanical and barrier properties compared with synthetic polymer films. This comprehensive review discusses the chemical structure, characteristics, and extraction methods of biopolymers derived from seafood waste and their usage in the packaging area as reinforcement or base materials to guide researchers toward successful plastics replacement and commercialization. Our review highlights recent advancements in improving the thermal durability, mechanical strength, and barrier properties of seafood waste-derived packaging, explores the mechanisms behind these improvements, and briefly mentions the antimicrobial activities and mechanisms gained from these biopolymers. In addition, the remaining challenges and future directions for using seafood waste-derived biopolymers for packaging are discussed. This review aims to guide ongoing efforts to develop seafood waste-derived biopolymer films that can ultimately replace traditional plastic packaging.
  • Assessing the Efficacy of Stream Restoration and SCM Retrofitting for Channel Stability in Urbanized Catchments
    Towsif Khan, Sami; Thompson, Theresa M.; Sample, David J. (2024-05-29)
    The hydrological benefits of catchment-scale implementation of stormwater control measures (SCMs) in mitigating the adverse effects of urbanization are well established. Nevertheless, recent studies indicate that Maryland's stormwater regulations, mandating the combined use of distributed and end-of-pipe SCMs, fall short in maintaining channel stability, despite their effectiveness in reducing runoff from impervious surfaces. The study objective was to evaluate the incremental impact of SCM retrofits and stream restoration on channel stability in a small, urbanized catchment (0.9 sq. km) in Montgomery County, Maryland, USA. This study employed a refined, well-calibrated, coupled hierarchical modeling approach, integrating a watershed-scale Storm Water Management Model (SWMM) with the Hydrologic Engineering Centers River Analysis System (HEC-RAS). A comprehensive methodology was developed using the calibrated SWMM and HEC-RAS models. The modeling results revealed that only retrofitting SCMs with multi-stage outlet structures designed to maintain the pre-development mobility of bed particles may not effectively reduce channel degradation. Conversely, stream restoration practices, including the removal of legacy sediments from the floodplain, significantly mitigated channel instability. Notably, the combination of SCM retrofitting, aimed at matching the sediment transport capacity of the predevelopment state, and stream restoration practices did not yield better results compared to stream restoration alone. This finding suggests that for streams impacted by legacy sediments, floodplain restoration alone might suffice to achieve channel stability, eliminating the need to retrofit SCMs designed under existing regulations.
  • Cost Comparison for Emerging Technologies to Haul Round Bales for the Biorefinery Industry
    Cundiff, John S.; Grisso, Robert D.; Webb, Erin G. (MDPI, 2024-05-30)
    Between 20 and 30% of the feedstock delivered cost is the highway hauling. In order to achieve maximum truck productivity, and thus minimize hauling cost, the hauling technology needs to provide for rapid loading and unloading. Three prototype technologies have been proposed to address the hauling issue. The first was developed by Stinger to secure a load of large rectangular bales, and it is identified as the Advanced Load Securing System (ALSS). For this study, the ALSS technology is applied on two trailers hooked in tandem (ALSS-2) loaded with 20 bales each. The second technology (Cable), is a cable system for securing a load of bales (round or rectangular) on a standard flatbed trailer. With the third technology (Rack), bales are loaded into a 20-bale rack at an SSL, and this rack is unloaded as a unit at the biorefinery. Bales remain in the rack until processed, thus avoiding single-bale handling at the receiving facility. A cost comparison, which begins with bales in single-layer ambient storage in SSLs and ends with bales in single file on a conveyor into the biorefinery, was done for the three hauling technologies paired with three load-out technologies. Cost for the nine options ranged from 48.56 USD/Mg (11 load-outs, Cable hauling) to 34.90 USD/Mg (8 loads-outs, ALSS-2 hauling). The most significant cost issue was the reduction in truck cost; 25.54 USD/Mg (20 trucks, Cable) and 15.15 USD/Mg (10 trucks, Rack).
  • Do Maryland's Stormwater Management Regulations Protect Channel Stability?
    Thompson, Theresa M.; Sample, David J.; Al-Samdi, Mohammad; Towsif Khan, Sami; Shahed Behrouz, Mina; Miller, Andrew; Butcher, Jon (2024-06-20)
    Webinar for the Maryland Stream Restoration Association. 84 participants
  • Effectiveness of stormwater management practices in protecting stream channel stability
    Thompson, Theresa M.; Sample, David J.; Al-Smadi, Mohammad; Towsif Khan, Sami; Shahed Behrouz, Mina; Miller, Andrew (2024-06-11)
    Presentation made as part of the Stream Restoration Webinar Series: Finding Common Ground. Webinar had 284 participants.
  • Cost-Effective Methods for Reducing Sediment Loads in the Lick Run Watershed
    Thompson, Theresa M.; Sample, David J.; Stephenson, Stephen Kurt; Towsif Khan, Sami; Macdonald, Kiara (2024-05-15)
  • Potential for juvenile freshwater mussels to settle onto riverbeds from field investigation
    Sumaiya, Sumaiya; Czuba, Jonathan A.; Russ, William T.; Hoch, Rachael (Taylor & Francis, 2024-05-02)
    Freshwater mussel populations have been declining at an alarming rate around the world. Herein, we investigate whether changing flow conditions, as they affect juvenile freshwater mussel settling, could be a potential causative factor for this decline in the Dan River, North Carolina, USA. We deployed two uplooking velocity sensors on the riverbed between May and November 2019: one where mussels reside and another where they do not. From this data, we calculated shear velocity, which is a measure of the turbulence that acts to lift particles into suspension and acts against particle settling. We determined that a shear velocity less than 0.66 cm/s would be required to settle relatively large and dense juvenile mussels onto the riverbed; however, the lowest shear velocity we measured was 0.9 cm/s. Additionally, we estimated that juvenile freshwater mussels as large as 280-510 µm could always be suspended and not be able to settle onto the riverbed at these two locations. Therefore, the flow during May-November 2019 was high enough to prohibit recruitment of juvenile freshwater mussels at the sensor locations. Furthermore, we have identified that the magnitude of the lowest flows has increased since 2000, which may be exacerbating the decline in freshwater mussels.
  • Channel Morphology Change after Restoration: Drone Laser Scanning versus Traditional Surveying Techniques
    Resop, Jonathan P.; Hendrix, Coral; Wynn-Thompson, Theresa; Hession, W. Cully (MDPI, 2024-04-10)
    Accurate and precise measures of channel morphology are important when monitoring a stream post-restoration to determine changes in stability, water quality, and aquatic habitat availability. Practitioners often rely on traditional surveying methods such as a total station for measuring channel metrics (e.g., cross-sectional area, width, depth, and slope). However, these methods have limitations in terms of coarse sampling densities and time-intensive field efforts. Drone-based lidar or drone laser scanning (DLS) provides much higher resolution point clouds and has the potential to improve post-restoration monitoring efforts. For this study, a 1.3-km reach of Stroubles Creek (Blacksburg, VA, USA), which underwent a restoration in 2010, was surveyed twice with a total station (2010 and 2021) and twice with DLS (2017 and 2021). The initial restoration was divided into three treatment reaches: T1 (livestock exclusion), T2 (livestock exclusion and bank treatment), and T3 (livestock exclusion, bank treatment, and inset floodplain). Cross-sectional channel morphology metrics were extracted from the 2021 DLS scan and compared to metrics calculated from the 2021 total station survey. DLS produced 6.5 times the number of cross sections over the study reach and 8.8 times the number of points per cross section compared to the total station. There was good agreement between the metrics derived from both surveying methods, such as channel width (R2 = 0.672) and cross-sectional area (R2 = 0.597). As a proof of concept to demonstrate the advantage of DLS over traditional surveying, 0.1 m digital terrain models (DTMs) were generated from the DLS data. Based on the drone lidar data, from 2017 to 2021, treatment reach T3 showed the most stability, in terms of the least change and variability in cross-sectional metrics as well as the least erosion area and volume per length of reach.
  • Sediment Pollution in Sinking Creek from MVP activities
    Czuba, Jonathan A.; Pitt, Donna; Nelson, Amy; Malbon, Elizabeth S. (New River Symposium, 2024-04-12)
    For over 10 days, sediment from a highly turbid spring, affected by activities for the Mountain Valley Pipeline (MVP), entered into Sinking Creek, a tributary of the New River. This presentation will describe what is known about the incident, to what extent the impact on Sinking Creek can be assessed with available information, and what is unknown that limits a full impact assessment. This presentation will mostly focus on quantifying the transport and fate of sediment delivered to Sinking Creek between January 27th and February 6th prior to sediment control efforts. This presentation will also highlight what is not known and what limits a full impact assessment.
  • Assessment of Recycled and Manufactured Adsorptive Materials for Phosphate Removal from Municipal Wastewater
    Drummond, Deja; Brink, Shannon; Bell, Natasha (UCOWR, 2024)
    Elevated concentrations of phosphorus (P) and other nutrients common in wastewater treatment plant (WWTP) effluent have been shown to contribute to the proliferation of harmful algal blooms, which may lead to fish kills related to aquatic hypoxia. Increased understanding of the negative effects associated with elevated P concentrations have prompted more strict regulation of WWTP effluent in recent years. The use of low-cost and potentially regenerative adsorptive phosphate filters has the potential to decrease P concentrations in WWTP effluent released to natural waters. This research focuses on assessing the capacities of recycled concrete aggregate (RCA), expanded slate, and expanded clay to remove phosphate from P-amended WWTP effluent. Results from a flow-through column study indicate that RCA consistently removed an average of 97% of phosphate over 20 weeks of continuous flow at an 8-hour hydraulic retention time (HRT). Expanded clay removed an average of 63% of introduced phosphate but decreased in removal capacity from 91 to 42% over the 20-week duration. Sorption data from batch studies were fitted to Langmuir models and RCA was shown to have the highest maximum sorption capacity (6.16 mg P/g), followed by expanded clay (3.65 mg P/g). RCA and expanded clay are promising options for use in passive filters for further reduction of phosphate from WWTP effluent.
  • Incidence of Per-And Polyfluoroalkyl Substances (PFAS) in Private Drinking Water Supplies in Southwest Virginia, USA
    Hohweiler, Kathleen; Krometis, Leigh-Anne H.; Ling, Erin; Xia, Kang (2024)
    Per- and polyfluoroalkyl substances (PFAS) are a class of man-made contaminants of increasing human health concern due to their resistance to degradation, widespread environmental occurrence, bioaccumulation in organ tissue, and potential negative health impacts. Private drinking water supplies may be uniquely vulnerable to PFAS contamination, as these systems are not subject to federal regulations and often include limited treatment prior to use. The goal of this study was to determine the incidence of PFAS contamination in private drinking water supplies in two counties in Southwest Virginia, USA (Floyd and Roanoke), and to examine the potential for reliance on citizen-science based strategies for sample collection in subsequent broader efforts. Samples for inorganic ions, bacteria, and PFAS analysis were collected on separate occasions by participants and experts at the home drinking water point of use (POU) for comparison. Experts also collected outside tap samples for PFAS analysis. At least one PFAS was detectable in 88% of POU samples collected (n=60), with an average total PFAS concentration of 23.5±30.8 ppt. PFOA and PFOS, two PFAS compounds which presently have EPA health advisories, were detectable in 13% and 22% of POU samples, respectively. Of the 31 compounds targeted, 15 were detectable in at least one sample. On average, each POU sample contained approximately 3.3 PFAS compounds, and one sample contained as many as 8 compounds, indicating that exposure to a mixture of PFAS in drinking water may be occurring. Although there were significant differences in total PFAS concentrations between expert and participant collected samples (Wilcoxon, alpha = 0.05), collector bias was inconsistent, and may be due to the time of day of sampling (i.e. morning, afternoon) or specific attributes of a given home. Future studies reliant on participant collection of samples appear possible given proper training, coordination, and instruction.
  • When does a stream become a river?
    Czuba, Jonathan A.; Allen, George H. (Wiley, 2023-07-13)
    The distinction between a “stream” and “river” is imprecise and vague despite the popular usage of the terms across disciplines for describing flowing waterbodies. Based on an analysis of named flowing waterbodies in the continental United States, we suggest a bank-to-bank channel width of 15 m as a working threshold in defining smaller “streams” from larger “rivers.”.
  • Load-Out and Hauling Cost Increase with Increasing Feedstock Production Area
    Cundiff, John S.; Grisso, Robert D.; Resop, Jonathan P.; Ignosh, John (MDPI, 2023-09-29)
    The impact of average delivered feedstock cost on the overall financial viability of biorefineries is the focus of this study, and it is explored by modeling the efficient delivery of round bales of herbaceous biomass to a hypothetical biorefinery in the Piedmont, a physiographic region across five states in the Southeastern USA. The complete database (nominal 150,000 Mg/y biorefinery capacity) had 199 satellite storage locations (SSLs) within a 50-km radius of Gretna, a town in South Central Virginia USA, chosen as the biorefinery location. Two additional databases, nominal 50,000 Mg/y (29.1-km radius, 71 SSLs) and nominal 100,000 Mg/y (40-km radius, 133 SSLs) were created, and delivery was simulated for a 24/7 operation, 48 wk/y. The biorefinery capacities were 15.5, 31.1, and 47.3 bales/h for the 50,000, 100,000, and 150,000 Mg/y databases, respectively. Three load-outs operated simultaneously to supply the 15.5 bale/h biorefinery, six for the 31.1 bale/h biorefinery, and nine for the 47.3 bale/h biorefinery. The required truck fleet was three, six, and nine trucks, respectively. The cost for load-out and delivery was 11.63 USD/Mg for the 50,000 Mg/y biorefinery. It increased to 12.46 and 12.99 USD/Mg as the biorefinery capacity doubled to 100,000 Mg/y and tripled to 150,000 Mg/y. Most of the cost increase was due to an increase in truck cost as haul distance increased with the radius of the feedstock supply area. There was a small increase in load-out cost due to an increased cost for travel to support the load-out operations. The less-than-expected increase in average hauling cost for the increase in feedstock production area highlights the influence of efficient scheduling achieved with central control of the truck fleet.
  • Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques
    Jjagwe, Pius; Chandel, Abhilash K.; Langston, David B. (MDPI, 2023-12-18)
    Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%). However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers.
  • Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding
    Nourali, Zahra; Shortridge, Julie E.; Bukvic, Anamaria; Shao, Yang; Irish, Jennifer L. (MDPI, 2024-01-11)
    Human migration triggered by flooding will create sociodemographic, economic, and cultural challenges in coastal communities, and adaptation to these challenges will primarily occur at the municipal level. However, existing migration models at larger spatial scales do not necessarily capture relevant social responses to flooding at the local and municipal levels. Furthermore, projecting migration dynamics into the future becomes difficult due to uncertainties in human–environment interactions, particularly when historic observations are used for model calibration. This study proposes a stochastic agent-based model (ABM) designed for the long-term projection of municipal-scale migration due to repeated flood events. A baseline model is demonstrated initially, capable of using stochastic bottom-up decision rules to replicate county-level population. This approach is then combined with physical flood-exposure data to simulate how population projections diverge under different flooding assumptions. The methodology is applied to a study area comprising 16 counties in coastal Virginia and Maryland, U.S., and include rural areas which are often overlooked in adaptation research. The results show that incorporating flood impacts results in divergent population growth patterns in both urban and rural locations, demonstrating potential municipal-level migration response to coastal flooding.
  • Internet of Things‐Enabled Food and Plant Sensors to Empower Sustainability
    Ali, Azahar; Ataei Kachouei, Matin; Kaushik, Ajeet (Wiley, 2023-10-10)
    To promote sustainability, this review explores: 1) the utilization of affordable high-performance sensors that can enhance food safety and quality, plant growth, and disease management and 2) the Internet of Things (IoT)-supported sensors to empower farmers, stakeholders, and agro-food industries via rapid testing and predictive analysis based on sensing generated informatics using artificial intelligence (AI). The performance of such sensors is noticeable, but this technology is still not suitable to be used in real applications owing to the lack of focus, scalability, well-coordinated research, and regulations. To cover this gap, this review carefully and critically analyzes state-of-the-art sensing technologies developed for food quality assurance (i.e., contaminants, toxins, and packaging testing) and plant growth monitoring (i.e., phenotyping, stresses, volatile organic components, nutrient levels, hormones, and pathogens) along with the possible challenges. The following has been proposed for future research: 1) promoting the optimized combination of green sensing units supported by IoT to perform testing at the location, considering the remote and urban areas as a key focus, and 2) analyzing generated informatics via AI should also be a focus for risk assessment understanding and optimizing necessary safety majors. Finally, the perspectives of IoT-enabled sensors, along with their real-world limitations, are discussed.
  • Future prediction of scenario based land use land cover (LU&LC) using DynaCLUE model for a river basin
    Loukika, Kotapati Narayana; Keesara, Venkata Reddy; Buri, Eswar Sai; Sridhar, Venkataramana (Elsevier, 2023-11)
    Human activities that cause changes to the surface of the Earth lead to alterations in Land Use and Land Cover (LU&LC) which have an impact on biodiversity, ecosystem functioning, and the well-being of humans. In order to comprehend and manage the effects of human activities on the environment, prediction of scenario-based LU&LC in future periods are crucial. Scenario-based predictions of LU&LC provide valuable insights for decision-makers in the sustainable governance of land and water resources. In the present study, the Dynamic Conversion of Land Use and its Effects (DynaCLUE) modelling platform was used to predict future LU&LC for Munneru river basin, India. Using six different user defined scenarios LU&LC change patterns were analyzed in 2030, 2050 and 2080 so as to understand the pressure on the natural resources and to plan sustainable Land Use Planning by preserving the important land use classes. The connection between LU&LC classes and input driving factors was quantified using Binary Logistic Regression (BLR) analysis. The β-coefficient was estimated using LU&LC type as a dependent variable and driving factors as independent variables. The demands of each LU&LC type, spatial policies and constraints, characteristics of each location and land use conversions are used as inputs for prediction of future LU&LC maps. Major conversions in LU&LC observed in this basin from last two decades are the rapid increase in built-up area due to urbanization in the outskirts of cities and towns. The major LU&LC changes projected for the period of 2019–2080 are expansion of built-up area ranging from 42.5% to 88.5%, and a reduction in barren land ranging from 57.3% to 74.5% across all six scenarios in the entire basin. The projected LU&LC maps under different scenarios provide valuable insights that could aid local communities, government agencies, and stakeholders in systematically allocating resources at the local level and in preparing the policies for long-term benefits.
  • Analysis of the causes of extreme precipitation in major cities of Peninsular India using remotely sensed data
    Kotrike, Tharani; Keesara, Venkata Reddy; Sridhar, Venkataramana (Elsevier, 2024-01)