ABSTRACT:

The Kearney Outdoor Learning Area (KOLA) is located adjacent to Kearney High School, along the North Channel Platte River (known locally as Turkey Creek), in south-central Nebraska. KOLA provides a place for students to learn about ecosystems, regional biodiversity, and water resources, as well as a space for community-based stewardship projects. Partnerships, including with regional conservation organizations and the University of Nebraska, support ecological monitoring and other activities at KOLA. The area also offers open space to experience the beauty, sights, and sounds of nature within city limits. (Source: https://outdoorclassne.com/)

ABSTRACT:

Water level is one of the most fundamental measurements for monitoring, using and managing water resources. Lake, stream and canal water levels are important for wildlife habitat, recreation, agricultural production and municipal and industrial water use. This study advances the use of trail (game) cameras for water level monitoring by integrating an octagon calibration target with open-source GRIME2 software. This integrated system, including a specialized target background (“stop sign”) installed in the water, enhances reliability and ease of use, delivering consistent and accurate measurements over many months. Maintenance issues, including cleaning the sign to remove biofouling, detecting the presence of ice or snow, and identifying when shadows or other lighting issues impact measurements, are key to successful monitoring. After accounting for these issues, 90% of GRIME2 measurements were within 3 cm (0.1 feet) of traditional pressure transducer measurements over a 26-month period (52,570 images). Importantly, the new system automatically adapts to inevitable shifts in the field of view that occur with ground-based time-lapse cameras.

ABSTRACT:

Water level is one of the most fundamental measurements for monitoring, using and managing water resources. Lake, stream and canal water levels are important for wildlife habitat, recreation, agricultural production and municipal and industrial water use. This study advances the use of trail (game) cameras for water level monitoring by integrating an octagon calibration target with open-source GRIME2 software. This integrated system, including a specialized target background (“stop sign”) installed in the water, enhances reliability and ease of use, delivering consistent and accurate measurements over many months. Maintenance issues, including cleaning the sign to remove biofouling, detecting the presence of ice or snow, and identifying when shadows or other lighting issues impact measurements, are key to successful monitoring. After accounting for these issues, 90% of GRIME2 measurements were within 3 cm (0.1 feet) of traditional pressure transducer measurements over a 26-month period (52,570 images). Importantly, the new system automatically adapts to inevitable shifts in the field of view that occur with ground-based time-lapse cameras.

ABSTRACT:

Water level is one of the most fundamental measurements for monitoring, using and managing water resources. Lake, stream and canal water levels are important for wildlife habitat, recreation, agricultural production and municipal and industrial water use. This study advances the use of trail (game) cameras for water level monitoring by integrating an octagon calibration target with open-source GRIME2 software. This integrated system, including a specialized target background (“stop sign”) installed in the water, enhances reliability and ease of use, delivering consistent and accurate measurements over many months. Maintenance issues, including cleaning the sign to remove biofouling, detecting the presence of ice or snow, and identifying when shadows or other lighting issues impact measurements, are key to successful monitoring. After accounting for these issues, 90% of GRIME2 measurements were within 3 cm (0.1 feet) of traditional pressure transducer measurements over a 26-month period (52,570 images). Importantly, the new system automatically adapts to inevitable shifts in the field of view that occur with ground-based time-lapse cameras.

ABSTRACT:

Water level is one of the most fundamental measurements for monitoring, using and managing water resources. Lake, stream and canal water levels are important for wildlife habitat, recreation, agricultural production and municipal and industrial water use. This study advances the use of trail (game) cameras for water level monitoring by integrating an octagon calibration target with open-source GRIME2 software. This integrated system, including a specialized target background (“stop sign”) installed in the water, enhances reliability and ease of use, delivering consistent and accurate measurements over many months. Maintenance issues, including cleaning the sign to remove biofouling, detecting the presence of ice or snow, and identifying when shadows or other lighting issues impact measurements, are key to successful monitoring. After accounting for these issues, 90% of GRIME2 measurements were within 3 cm (0.1 feet) of traditional pressure transducer measurements over a 26-month period (52,570 images). Importantly, the new system automatically adapts to inevitable shifts in the field of view that occur with ground-based time-lapse cameras.

ABSTRACT:

This collection holds sample datasets and work products from groups participating in the PEP2026 Workshop. Pixels to Enviro Patterns 2026 (PEP2026), is a two-day workshop at the University of Nebraska–Lincoln (Nebraska), where cameras, AI, and storytelling converge for scientific discovery. Held March 7–8 at Hardin Hall on East Campus, PEP 2026 invites researchers, educators, and creatives to explore open imagery and dive into hands-on learning with GRIME AI software. Collaborate on mini-projects to turn pixels into environmental patterns that drive scientific inquiry and narratives. Whether you’re passionate about water, phenology, artificial intelligence, or communication, this is your chance to connect, create, and be inspired.

Datasets are curated for use with GRIME AI software, which is available as a Conda package (https://anaconda.org/channels/GRIMELab/packages/grime-ai/overview), with source code and Wiki on GitHub (https://github.com/JohnStranzl/GRIME-AI/wiki). GRIME AI is free, open-source software (Apache 2.0). GRIME AI leverages Meta's Segment Anything 2 (SAM2) model for image segmentation and also facilitates the entire data science workflow, from data retrieval to model deployment.