Journal of Earth Observation and Geospatial Applications
JEOGA is a scholarly publication dedicated to advancing knowledge and practices in the fields of remote sensing, earth observation, and geospatial applications. Designed for early career students and professionals in the fields, JEOGA provides a platform for sharing research, case studies, best practices, and practical applications.
Evaluating Heavy Metal and Physicochemical Pollution in the Karnaphuli River Estuary through Remote Sensing and Machine Learning Approaches
The Karnaphuli River has critical impacts on the environmental and socio-economic situation of the surrounding catchment region, and it is crucial to analyze and monitor pollution for managing river water quality. The objective of the research was to evaluate and establish correlations between physicochemical and heavy metal pollution in water and a combination of laboratory testing, Sentinel-2A satellite imagery analysis, and machine learning for the Karnaphuli River estuary. High concentrations of heavy metals such as 12.8 mg/L Aluminum (Al), 15.5 mg/L Iron (Fe) and other pollutants have been found in the water samples of Karnaphuli. Continuous water pollution values were visualized using GIS interpolation maps, which showed a higher level of pollution in shipwrecking regions near the coast. Multiple linear regression (MLR) and principal component regression (PCR) were identified as the most effective models for predicting the variability of water quality parameters for this study based on remote sensing reflectance data. The satellite reflectance of Sentinel-2A bands produced significant results and predicted 58% (highest) of the variability of total suspended solids (TSS) and 43% (lowest) of the variability of pH in the Karnaphuli River. The interpolation maps aim to represent pollution levels more accurately than the raster calculation maps, as they incorporate spatial autocorrelation techniques based on observed values throughout the modeling process. This analysis provides insights into the dynamics of pollution, aids in prioritizing attention to reducing industrial discharges, encourages necessary infrastructural investments for sewage treatment, and further study and digitization of water quality measurement using remote sensing and machine learning.
Copyright (c) 2026 Journal of Earth Observation and Geospatial Applications
Evaluating Satellite Land Cover Classification Accuracy Using Participatory Remote Sensing
Satellite remote sensing has become a foundational tool for monitoring land cover change and urban development. However, classification accuracy remains limited in complex urban environments where vegetation, built infrastructure, and water coexist within small spatial scales. This study evaluated the accuracy of multiple satellite land cover datasets within a 3 km × 3 km area of interest in Hato Rey, Puerto Rico, using the National Aeronautics and Space Administration (NASA) Adopt-a-Pixel participatory remote sensing framework. Ground observations were collected at 23 sampling locations using the Global Learning and Observations to Benefit the Environment (GLOBE) Observer application and compared with land cover classifications derived from European Space Agency (ESA) WorldCover, Dynamic World, ESRI Land Cover, Meta Tree Canopy, and Landsat imagery. Results showed that several satellite datasets frequently overgeneralized land cover as entirely urban, failing to detect localized vegetation and water features observed on the ground. Dynamic World and ESRI Land Cover achieved higher overall accuracy (91.3%) due to consistent classification of urban areas, while Meta Tree Canopy and ESA WorldCover demonstrated improved detection of vegetation in heterogeneous environments, despite slightly lower overall accuracy (87.0%). Landsat time series analysis revealed mixed vegetation trends over time, reflecting both urban expansion and persistent vegetation. These findings demonstrate the importance of integrating participatory ground observations with satellite data to improve land cover classification accuracy and enhance environmental monitoring in rapidly developing urban environments.
Predictive Modeling of Flash Floods: Investigating Hydrology and Land Cover Dynamics through Remote Sensing Data
Accounting for more than 75% of Federal Disaster Declarations, floods in the United States of America have increased both in intensity and frequency, outpacing the development of reliable predictive models and warning systems. This issue has only been exacerbated by a lack of large-scale public participation and access to predictive models. This paper focuses on classifying risk factors and building a prototype flood-prediction tool to clearly communicate results within a defined Area of Interest (AOI). The investigative strategy involves gathering and analyzing several geospatial indicators, which could result in high-risk flood conditions, such as relevant soil properties, elevation/slope, Curve Number (CN) values, and land cover types within an AOI. To ensure dataset reliability and scientific validity, the considered geological and atmospheric variables were sourced from a range of open-source databases such as Earth Map, GLOBE Citizen Science, FEMA, and Collect Earth Online. The second portion of this study concentrates on developing a classification-based prototype machine learning model that outputs flood risk in a given area into five levels. This model facilitates citizen science efforts through an interactive Colab project and subsequent GitHub repository, which allows for user input. Findings were that Land cover and slope/elevation are key factors in determining runoff potential, while soil properties do not contribute to Curve Number differences in the AOI due to a lack of detailed data. The S.H.I.E.L.D. model can easily be ported into a cross-platform application, further integrating citizen science observations based on the findings of this paper.
The Implications of Unreported Tidal Fluctuations in Satellite Imagery
Remote sensing data and satellite imagery can be challenging to accurately produce, especially when tidal ranges affect the variation of land cover types daily, which are not represented in any Earth Map layer. Inconsistencies among maps create confusion and daily tidal fluctuations have effects on not only the land type and change over time, but many coastal species and their behavior like the American herring gull. Three grids were established on Fire Island, NY to understand if there had been a land change over time, if there is a difference among one forestry and three land cover Earth Map layers, their accuracy to ground cover photos collected through the Global Learning and Observations to Benefit the Environment (GLOBE) app, tidal height differences, and difference in activity of American herring gulls between high and low tide. This was done by utilizing the GLOBE Program to establish grids, collect ground cover photos using the GLOBE observer app, a ground count at 10-minute intervals for American herring gull data, Landsat time-series for land change over time, and Collect Earth Online for land cover type categorization. The results showed that the layers presented inconsistencies among one another and when compared to ground cover photos. There was also a significant difference in daily tidal fluctuations between high tide and low tide, a significant difference in American herring gull activity between high tide and low tide, and the land had changed over time from the year of 1984 to present. These findings suggest the value of daily tidal representations in satellite imagery, especially land cover layers like those presented in Earth Map, as well as the implementation of ground cover photos in the production of satellite imagery to produce the most accurate and usable maps possible.
tides, satellite imagery, ground photos, land cover, American herring gull
Qualitative Analysis of Application Remote Sensing towards the Understanding of a Rural Community
Remote sensing and human observation provide the resources necessary to view changes in land cover and land use (LULC) in an area of interest (AOI). Remote sensing tools have limitations and fail to accurately summarize areas individually. However, tools can be used in collaboration with human observation in order to better analyze an area and overcome their limitations. To accomplish this, numerous datasets such as the 1984–2024 Multi-Resolution Land Characteristics (MRLC), National Land Cover Database (NLCD) Viewer, various layers available in Earth Map digital software from the baseline year 2020, 1984-2025 Landsat satellite imagery, Collect Earth Online (CEO), and ground observations from the Global Learning and Observations to Benefit the Environment (GLOBE) Observer application were synthesized and analyzed. In order to collect data from the same places across all datasets, an AOI measuring 3 km by 3 km was constructed. This AOI contained 37 kernels, each measuring 10,000 m². The kernels were constructed into a 6 × 6 matrix, and the final kernel was placed in the center, totaling 37 kernels. In analyzing these datasets holistically, inconsistencies were found between remote and ground observations. Each provided important information but was sometimes inconsistent. The synthesis of remote and ground datasets, however, was able to shed light on the LULC of the study area and provide an understanding of how it has changed over time. It was found that the study area had a relatively harmonious relationship between urban/built-up elements and vegetation, but that multiple tools were needed to draw this conclusion.
Evaluating Satellite Land Cover Accuracy in a Suburban Environment Using Citizen Science: New Hyde Park, NY
Accurate land cover classification is essential for environmental monitoring, urban planning, and climate research; however, suburban landscapes remain difficult to characterize due to heterogeneous land cover. This case study evaluates the agreement between satellite-derived land cover tools and ground-based citizen science observations in New Hyde Park, New York. Using the Adopt-a-Pixel 3 km methodology, thirty-seven primary sampling units were established within a standardized area of interest. Field observations were collected using National Aeronautics and Space Administration (NASA)’s Global Learning and Observations to Benefit the Environment (GLOBE) Observer application and supplemented with high-resolution reference classifications generated through Collect Earth Online. These datasets were compared with satellite observation, specifically from European Space Agency (ESA) WorldCover, Dynamic World, ESRI Land Cover, Landsat time-series, and Meta/WRI Global Canopy Height datasets. Results indicate frequent over-generalization of developed land cover and underrepresentation of tree canopy, with the strongest agreement observed where surface water is present. Qualitative field documentation and community accounts revealed that storm damage, aging trees, and housing management practices contributed to long-term greenery loss, helping to explain some discrepancies between ground and satellite observations. Findings demonstrate that integrating citizen science, community knowledge, and reference data will continue to improve land cover assessment in suburban regions and support more inclusive and reliable environmental monitoring.
Comparison of Land Cover Classifications between Ground Observations and Remote Sensing Data in San Diego, California
Land cover and its changes are intertwined with human and natural activities. Accurate land cover data is invaluable for land use decisions as well as understanding local and global land cover change. This study explored the local land cover trends of an area of interest in San Diego, California, with the goal of evaluating consistency between ground observations, manual land cover classifications, and land cover maps from remote sensing sources. Ground observations were taken using the Global Learning and Observations to Benefit the Environment (GLOBE) Observer application. Remote sensing sources were accessed through Earth Map, and included the World Cover, Dynamic World, ESRI, and Meta/WRI Global Canopy Height maps of land cover and tree canopy based on satellite imagery. While there was general agreement between ground observations and remote sensing sources over the area as a whole, small inconsistencies were found at nearly all sampling points, and blatant differences were found at three of thirty-seven sampling points between the chosen remote sensing sources as well as between ground observations and remote sensing maps. These inconsistencies were most obvious with the Dynamic World and ESRI datasets, which tended to overgeneralize urban land cover compared to the World Cover dataset. The discrepancies between sources describing the land cover of the same area show the need for caution when examining remote sensing sources and the challenges of remote sensing data, which are increased with differing classification measures.
Pixels and Pupils: A Comparative Analysis of Remote Sensing and Citizen Science Land Cover Data in Los Osos, CA
Human impact is constantly reshaping land cover dynamics. Remote sensing technology is revolutionary in its ability to provide imagery and land cover data of local and global regions. However, the accuracy of localized remote sensing data remains uncertain. This validation study uses citizen science data to assess the accuracy of remote sensing datasets at a local scale. This evaluation was performed in Los Osos, California, an environmentally sensitive region currently assessing human impact and the health of habitats and endangered species. The null hypothesis was that there would be no differences between remote sensing and citizen science data sets. Research was conducted through the two citizen science platforms: the GLOBE Observer App and Collect Earth Online. These were compared to open-source remote sensing data sets obtained through the Earth Map platform. Visual analysis of data collected at each of the 37 sample units rejected the null hypothesis because of conflicting classification patterns. Citizen science data revealed that remote sensing disagreements were often associated with the incorrect consolidation of land cover elements or confusion between similar features. While remote sensing is generally more efficient, citizen science field observations were critical to the improved understanding of the land cover of local regions. These findings demonstrate citizen science data’s ability to validate remote sensing data sets and identify misclassifications. This emphasizes the need for the inclusion of field observations to improve local land cover maps. .
land cover, remote sensing, citizen science
Analyzing Urban Land Cover and Demographic Shifts through Remote Sensing Technologies
This study investigates patterns of urban land cover change in Euless, Texas, using an integrated framework of satellite remote sensing and field-based observation. A 900-hectare area of interest was divided into a 37-point sampling grid, where ground photographs were collected using the GLOBE Observer application to validate and interpret satellite-derived classifications. Multiple remote sensing datasets, including Landsat Time Series Explorer (normalized burn ratio, NBR), ESRI Land Cover (2017/2024), WorldCover 10 m, Dynamic World (2016/2024), Meta/WRI Global Canopy Height, and MRLC NLCD Fractional Impervious Surfaces, were analyzed to quantify changes in built-up area, vegetation cover, canopy height, and surface imperviousness over time. Results indicate a clear increase in built-up land and impervious surfaces across much of the study area, accompanied by an overall reduction in tree canopy; however, localized anomalies in the southwestern portion of the area of interest, especially at sampling points 25 and 26 of the area of interest, demonstrated sustained canopy density and improved vegetation health, with exemplified NBR values increasing over the years. These findings highlight the spatial heterogeneity of urban growth and demonstrate that remote sensing, when validated with field observations, can effectively identify both broad development trends and localized ecological stability within a suburban landscape.
Long-Term Assessment of Cattail (Typha spp.) Dynamics and Wetland Restoration in the Everglades Using Object-Based Landsat Time Series and Markov Modeling
Assessing long-term vegetation response to restoration requires spatially consistent monitoring approaches that capture both observed change and likely future trajectories. In the Florida Everglades, nutrient enrichment contributed to the expansion of cattail (Typha spp.) within Water Conservation Area 3A (WCA-3A), raising concerns about restoration effectiveness. This study integrates a multi-temporal Landsat time series (2004–2024), object-based image analysis (OBIA), support vector machine (SVM) classification, and Markov transition modeling to quantify and project vegetation change in WCA-3A. Object-based segmentation reduced classification fragmentation in heterogeneous marsh landscapes, while SVM improved separation among spectrally similar vegetation classes. Classification accuracies remained consistently high across sensors (overall accuracy 90–93%; kappa 0.80–0.87). Cattail cover peaked at over 11% in the late 2000s but declined to 1.6% by 2024, coinciding with the expansion of native sawgrass and open marsh communities. Transition probabilities derived from object-level change detection were used to project vegetation patterns to 2034 under continued management conditions, indicating sustained suppression of cattail dominance if current controls persist. By directly linking object-based classification with probabilistic forecasting, this study advances beyond static land-cover mapping and provides a transferable framework for evaluating restoration trajectories in large managed wetlands.
Typha, Landsat time series, object-based image analysis, support vector machine, Markov modeling
Mapping Proportions of Eastern Redcedar in Kansas Using Sentinel-2 Imagery and Random Forest Model
Eastern redcedar (Juniperus virginiana L.) encroachment is transforming grasslands across the central United States, with significant ecological and hydrological consequences. Effective management requires maps quantifying proportional cover to detect early invasion and prioritize interventions. We developed a workflow producing annual 10 m fractional cover maps at the University of Kansas Field Station (2016–2023). A 1 m reference dataset was generated by intersecting light detection and ranging (LiDAR)-derived canopy-height stability with leaf-off aerial imagery and aggregated to the Sentinel-2 grid. Random forest models were trained on seasonal Sentinel-2 composites using proportion-aware sampling, with LandTrendr temporal segmentation applied to reduce interannual spectral noise. Classification accuracy reached 0.84 (κ = 0.67), comparable to models incorporating National Ecological Observatory Network (NEON) hyperspectral data (0.85); however, high spatial autocorrelation among training samples suggests these represent upper-bound estimates. Winter normalized difference vegetation index (NDVI) and Simple Ratio Index were the most influential predictors, confirming dormant-season imagery value for evergreen discrimination. Regression models achieved low error (RMSE = 0.04–0.05), though accuracy decreased in stands exceeding 70% cover. LandTrendr-stabilized maps closely matched seasonal composites (R = 0.95–0.99) while producing smoother trajectories. The workflow demonstrates particular sensitivity to low-density invasion (<10% cover), enabling early detection when interventions remain cost-effective. Because independent validation was unavailable after 2016, temporal predictions warrant caution. Operational deployment elsewhere requires local reference data and model recalibration; transferability depends on species composition, phenological contrast, and landscape configuration.
Three Decades of Changes in the Urban Heat Island Effect in Denver, Colorado, Revealed by Landsat
Rapid urbanization has imposed negative environmental impacts and contributed to global climate change on multiple scales. The urban heat island (UHI) is directly caused by urban expansion, dramatically increasing land surface temperature (LST). Despite the growing concern about the UHI in Denver, Colorado, few studies among the existing literature have addressed this issue. This study used United States Geological Survey (USGS) Landsat 5, 7, and 8 analysis-ready data (ARD) to characterize land cover changes and UHI in Denver for three decades (e.g., 1990, 2000, 2010, and 2020). Land surface temperature was derived from USGS Landsat Collection 2 ARD for twelve dates across summer, fall, and winter seasons in 1990, 2000, 2010, and 2020, and analyzed on both seasonal and annual scales. Land cover change (LCC) analysis detected a 13% increase in developed land cover and a 12% decrease in cropland and grass/shrubs from 1990 to 2020. The relationship between land cover and LST was modeled using ordinary least squares (OLS) and geographically weighted regression (GWR) analysis between land cover indices—e.g., the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2), urban index (UI), and LST. The results from the regression models showed seasonal variability, spatiotemporal variations, and other underlying factors affecting the bivariate correlations. Finally, the seasonal and annual distribution and variation of UHI intensity were measured, and it was identified that the mean annual UHI intensity in 2020 was 1°C higher than that recorded in 1990, which illustrated a consistent spatial distribution throughout downtown Denver and the central areas of the city, while the intensity of UHI represented a more scattered distribution in the non-urban areas. The methods applied in this study can serve as a model for future research on other cities, and the findings can be used to inform sustainable urban planning and to mitigate the effects of UHI in Denver.
Copyright (c) 2025 Journal of Earth Observations and Geospatial Applications
Spatial and Temporal Assessment of Meteorological Drought Using the Standardized Precipitation Index (SPI) and Its Effect on Crop Yield Over the Corn Belt Region of the United States from 2000 to 2023
Climate change and global warming have contributed to extreme weather events and patterns, including severe flooding, earthquakes, wildfires, and drought. Drought happens to be the deadliest catastrophic event around the world. Drought's impact on ecosystems and local communities has been increasing in many parts of the world, including the United States and, most importantly, the Corn Belt, which also happens to be the country's food basket. Drought prediction and mitigation can be investigated through drought indices. The standardized precipitation index (SPI) is considered the most reliable in this study. The index is the most reliable as it serves as one of the most fundamental. It is used in this study to present the critical identifier for diagnosing the intensity, duration, and frequency of drought severity. Additionally, SPI is a key identifier in extreme precipitation events. It is comparable to various landscapes across a region and is simple to use concerning its calculation process. This study focuses on the Midwest Corn Belt Region of the United States, between the climate record from 2000–2023. This study is designed to understand precipitation regimes and their impact on cropland cover and yield (condition monitoring and crop progress) over the climate record. Results of the study showed drought affecting portions of the region, especially during the first half of the period, with 2012 indicating peak severity plaguing both corn belts. Generally, pre-2012 and post-2012 indicated the wettest conditions, especially in 2010, 2014, and 2016. While cropland cover highlighted the widespread distribution of corn and soybean cover in both Corn Belts, crop condition and progress corresponded and responded well to low yields during the intense drought years and high yields during the wettest years. Our analysis shows that climate change has undoubtedly resulted in a temperature rise, exacerbating the frequency and severity of heat waves and extreme precipitation events impacting parts of the United States, including the Corn Belt.
Mapping Fluvial Terraces Using Geospatial Analysis: A Case Study on the Piedmont of Georgia, USA
Mapping of alluvial soils and geomorphic units can be useful for purposes as varied as land management, cultural resource planning, flood risk assessment, and modeling stream system response to environmental change. This research project develops a method that combines USDA-NRCS soil map units with a geospatial workflow processing 1-m LiDAR imagery to delineate stream terraces in the Upper Little Tallapoosa basin of western Georgia (USA). Existing soil surveys distinguish between high and low terrace alluvium by different soil series. “Old alluvium” is represented by the Masada (map unit abbreviations MoB, MpB, MpC2), Augusta (Asl), and Worsham (Wsl) series. These are further distinguished as low vs. high terraces: Masada is typically found in high terraces, and Augusta and Worsham in lower terraces. We use various geospatial tools to de-trend a LiDAR-based DEM in order to optimize for identifying stream terrace surfaces. This approach results in planimetric terrace surface polygons that differ somewhat from the soil survey map units. We then create gradient profiles to identify any correlated terrace surfaces along the stream valley. Hypsometric curves and histograms of select stream reaches are used to visualize elevation distributions and to identify any terrace surfaces. Field checks at several sites, based on backhoe trenches and hand augered cores, indicate that our new integrated approach is an effective method for mapping terraces.
Analysis of Ecological Impacts of a Stream Restoration on Riparian Vegetation in Baltimore County, Maryland
Riparian forests play a vital role in supporting wildlife, managing runoff, and enhancing human well-being, yet they have been degraded by urban development and engineered stormwater systems. Stream restorations have emerged as nature-based solution to water management, but their effect on adjacent riparian ecosystems requires more research. This study evaluates the ecological impact of the Upper Jones Falls Stream Restoration in Baltimore County, Maryland, USA, using satellite and aerial imagery to assess vegetation health and land cover. Vegetation health during the period between 2015 and 2025 was assessed using the normalized difference vegetation index (NDVI), derived from high-resolution multispectral PlanetScope satellite imagery. Results show that although mature forest canopy was substantially lost, vegetation steadily regenerated, with NDVI returning to baseline in less than 10 years. Land cover analysis confirmed some tree plantings closest to the water have been thriving in recent years and also revealed the persistence of exposed water and barren zones. These findings show that NDVI is a useful metric for monitoring vegetation recovery post-restoration, while emphasizing the importance of integrating land cover classification and direct observation. This study highlights the need for more nuanced evaluation of riparian restoration outcomes, including the use of evolving remote sensing technologies and modeling.
Addressing Challenges and Exploring Solutions to Enhance Earth Observation Applications for Emergency Management
Earth observations (EO) and remote sensing technology provide essential data for understanding natural hazards and their effect on the environment and society. Emergency management relies on data-driven decision making to address disruptive and damaging events, increasingly using remote sensing to improve situational awareness. Unfortunately, gaps between the cutting edge of remote sensing science and application in emergency management diminish the value of data before use. Challenges include: the trade-offs in temporal resolution, spatial resolution, and area coverage; geographic nuances affecting asset utility; complexity in the tasking, collection, processing, exploitation, and dissemination process; expectations for analysis and interpretation; data accessibility; and the rapid advancement of remote sensing and space technology. The NASA Disasters Response Coordination System (DRCS) launched in June 2024 to improve the application of NASA science for disaster response. This paper shares challenges the DRCS experienced throughout its first year and discusses best practices to improve the effectiveness of remote sensing support of emergency management activities. To bridge the gap, the DRCS proposes better curation and communication of product catalogues, tailored and varied approaches to capacity building, and enhanced coordination and collaboration.
Urban Heat Island Disparities: A Geospatial Analysis of Household Income and Land Surface Temperatures in Lafayette, Louisiana
The urban heat island (UHI) effect increases heat exposure in cities and disproportionately affects vulnerable groups. This study investigates the relationship between household income, vegetation cover, and land surface temperature (LST) in Lafayette, Louisiana. We used Landsat 8 thermal infrared imagery from July 30, 2023, to measure LST at a 30-m resolution. We then compared these results with 2018 U.S. Census block group income data. We classified land cover into vegetation and non-vegetation classes using 2023 NAIP aerial images, and we validated our classification with an accuracy assessment based on 200 random points. Our results show a negative relationship (r = −0.38, R² = 0.14, p < 0.05) between income and surface temperature and a positive relationship (r = 0.41, R² = 0.17, p < 0.05) between income and vegetation. Block groups with lower incomes also had less vegetation cover. This suggests that the lack of vegetation contributes to higher LST. We did not find a significant relationship between parcel size and income, which means that land ownership density alone does not explain differences in heat. These findings highlight how socioeconomic and environmental factors work together to create uneven heat burdens. Policymakers may consider targeted tree planting and green infrastructure projects in vulnerable communities to mitigate heat-related risks and reduce energy costs.
Satellite Imagery Analyses of Seasonal Shifts of Vegetation Greenness at the Great Smoky Mountains National Park
This study investigates the temporal shifts in seasonal vegetation coloration, focusing on changes in greenness levels (measured by NDVI) from 1982 to 2023, using multiple satellite sources including AVHRR, Landsat, MODIS, and VIIRS. The primary objectives of this research are twofold: (1) to determine whether greenness levels have significantly changed during spring and fall, and (2) to assess whether there are notable differences in NDVI values among the various imaging sensors used. Given the context of global warming, it is plausible that greenness levels would show an increase on the same date in early spring or late fall across multiple years. Our study area consists of the Great Smoky Mountains National Park, where we analyze the timing of seasonal changes, revealing a trend toward earlier springs and longer falls. Our findings indicate significant increases in NDVI values during spring across all datasets, notably from AVHRR and Landsat, while fall data shows less consistent trends, with MODIS and VIIRS revealing minimal changes. The research highlights the importance of sensor selection in NDVI measurements, with non-parametric test results confirming significant variances among the sensors analyzed. This study contributes to understanding the implications of climate change on vegetation dynamics, underscoring the need for continued monitoring as seasonal patterns evolve.
Analysis of ET and NDVI Correlation in Different Land Cover Types of Colorado
Climate change and water scarcity are pressing public concerns, severely impacting agricultural production and ecological balance. Leveraging advanced remote sensing, this study analyzed 30m-resolution Landsat normalized difference vegetation index (NDVI) and evapotranspiration (ET) data during Colorado's growing seasons (April–August) from 2000 to 2018. Examining correlations across various land cover types, it detailed data processing, relation analysis at different lead times, and correlation classification. Results show a mostly positive correlation in Colorado, aligning with coarser-resolution studies, with some negative exceptions. Uniquely, ET's influence on NDVI weakens as lead time extends, regardless of correlation. Combining with land cover analysis, forests with high water storage maintain stable ET. Additionally, human intervention significantly affects these correlations.
Estimating North Texas Urban Tree Above-Ground Biomass Based on Terrestrial LiDAR and Optimized Quantitative Structure Models
Several studies have revealed that terrestrial light detection and ranging (LiDAR) remote-sensing technology can be an alternative, more accurate approach to estimate tree above-ground biomass (AGB). A set of algorithms has been developed to create a volume reconstruction of tree point clouds, which can be converted to an AGB estimate. These calculate AGB non-destructively and more accurately compared to current forestry practices, which use generic allometric equations. LiDAR scans of trees within Midwestern State University’s campus were collected and analyzed with a tree segmentation/modeling algorithm. The validation method was based on comparing the estimated above-ground attributes to actual field measurements. Optimized PD1 and PD2Max increase as the tree size increases, whereas PD2Min remains relatively the same for different tree sizes. Quantitative structure modeling (QSM) produces accurate diameter at breast height (DBH) estimates, however it fails to calculate it precisely when there are low branches or dense leaves within the canopy. These occlusions commonly occur with certain tree species, such as Pinus echinata and Juniperus virginiana. Our result suggests good agreement of QSM-derived AGB estimates for larger trees but overestimated AGB for smaller trees. This is due to the limitations in LiDAR technology, struggling to accurately scan fine branches and twigs of small trees, leading to errors in point cloud data and subsequent overestimation of their volume in the QSM. While the study provides valuable insights, the small sample size due to the complexity of destructive tree harvesting in urban ecosystems might limit the generalizability of the results.