AndreaBozzo/ceres-open-data-index huggingface dataset:
Unmanned Aircraft System (UAS) Photogrammetry for Tracking Streambank Erosion and Geomorphic Change along a Protected River Corridor

Excessive erosion and bank failure within river corridors pose a significant risk to infrastructure and natural systems and reliable characterization of bank erosion is a critical component when designing mitigation strategies and conducting post-restoration monitoring. Advances in unmanned aircraft system (UAS) and digital photogrammetry techniques provide a powerful new tool for capturing high resolution topographic data at less cost. This case study focuses on a 1-km section of the New Haven River in central Vermont, located in the northeastern United States. The New Haven River reach has exhibited instability due to its sensitive landscape position and history of watershed and channel disturbances. A fixed-wing UAS was deployed repeatedly over a two-year period (2016–2018). Digital elevation models (DEMs) and DEMs of difference quantified the volumetric change along the survey area where notable erosion and deposition occurred. Results showed the UAS to be capable of producing high quality topographic data at fine resolutions and an effective method for monitoring continued bank erosion.

Organization: VermontView
Burning with Potential: Understanding the Relationship Between Biochar and Agriculture of the Northern Glaciated Plains Ecoregion

Achieving global sustainable agriculture is one of the most incredible challenges of this century, yet many continue to try to solve this problem through the development of precision technologies. Biotechnologies, such as biochar, can perform like a precision technology while protecting agricultural land from soil erosion and fertility loss. The Northern Glaciated Plains ecoregion of the United States is little researched in the benefits from the use of biochar through improved soil nutrient capture and water retention, crop health improvements, and yield increases. The study plot has four sections of corn stover biochar and eight sections of control sections. This project assessed soil chemical properties by testing topsoil samples, resulting in increased soil pH and electrical conductivity in biochar-amended soils. Remotely sensed normalized difference vegetation index images created from a spectral camera measured soybean phenology through reproductive growth stages and showed the positive effect biochar has on health and associated greenness of soybean plants. Destructive, dry weight soybean biomass measurements taken at soybean maturity showed increased soybean biomass in biochar amended plot sections. The goal was to determine how biochar reacts with a haploboroll soil in Brookings County, South Dakota and if biochar application is an appropriate management strategy for this soil and soils of the greater Northern Glaciated Plains ecoregion of the United States. In this study, results conclude that biochar application may not have the significant productivity increases necessary to make biochar a highly recommended amendment for this region through this study’s soil and soybean reactions to biochar, but biochar has the potential to reduce soil productivity loss through other aspects of soil fertility improvement.

Organization: South DakotaView
An Efficient Workflow and Accuracy Assessment for ICESat-2 and Multispectral Imagery Fusion for Bathymetric Mapping

	
Interest in mapping bathymetry from multispectral satellite imagery has grown steadily since the 1970s. Notwithstanding the significant advancements made during this time, one limitation is that the group of algorithms collectively referred to as Satellite Derived Bathymetry (SDB) generally require “seed depths,” hindering their use for areas lacking reliable reference data. Recently, NASA’s ICESat-2 ATLAS, a satellite-based, photon counting green lidar, has been shown capable of bathymetric mapping. Although ICESat-2 bathymetry is constrained to discrete tracklines, synergistic fusion of ICESat-2 and multispectral, passive imagery can generate contiguous bathymetry over large spatial extents. Despite these significant recent results and the possibilities they open for filling shallow nearshore data voids worldwide, significant challenges remain, including: 1) ICESat 2/multispectral imagery fusion techniques are still in the early research stage and not efficient enough for widespread operational use for the average user, and 2) detailed, empirical accuracy assessments of fusion-based bathymetric products generated through operationally-efficient workflows have not been conducted. This research addresses these gaps. A highly-efficient workflow is developed and tested for generating bathymetric DEMs for large areas through largely-automated retrieval and fusion of ICESat-2 photon clouds and multispectral imagery, with the outputs disseminated via a webGIS. Empirical accuracy assessments are performed in two locations, selected to encompass vastly differing environmental conditions. One is a freshwater, inland water site where airborne imagery was used, while the other is a tropical, coastal saltwater site, covered by Landsat 8 and Sentinel-2 data. The workflow is shown to be operationally efficient and capable of producing bathymetry of sufficient accuracy for many coastal science applications, with RMSEs for the two sites being 0.95 m for Lake Tahoe (σ = 0.89 m, μ = 0.34 m) and 0.80 m (Sentinel-2; σ = 0.77 m, μ = -0.19 m) and 1.42 m (Landsat 8; σ = 1.19 m, μ = 0.77 m) for St. Croix.

Organization: OregonView
Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS’s Bathymetric Mapping Performance

NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and Kd(490) in the range of 0.05–0.12 m−1. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

Organization: OregonView
Thematic Classification Accuracy Assessment with Inherently Uncertain Boundaries: An Argument for Center-Weighted Accuracy Assessment Metrics

Accuracy assessment is one of the most important components of both applied and research-oriented remote sensing projects. For mapped classes that have sharp and easily identified boundaries, a broad array of accuracy assessment methods has been developed. However, accuracy assessment is in many cases complicated by classes that have fuzzy, indeterminate, or gradational boundaries, a condition which is common in real landscapes; for example, the boundaries of wetlands, many soil map units, and tree crowns. In such circumstances, the conventional approach of treating all reference pixels as equally important, whether located on the map close to the boundary of a class, or in the class center, can lead to misleading results. We therefore propose an accuracy assessment approach that relies on center-weighting map segment area to calculate a variety of common classification metrics including overall accuracy, class user’s and producer’s accuracy, precision, recall, specificity, and the F1 score. This method offers an augmentation of traditional assessment methods, can be used for both binary and multiclass assessment, allows for the calculation of count- and area-based measures, and permits the user to define the impact of distance from map segment edges based on a distance weighting exponent and a saturation threshold distance, after which the weighting ceases to grow. The method is demonstrated using synthetic and real examples, highlighting its use when the accuracy of maps with inherently uncertain class boundaries is evaluated.

Organization: West Virginia View
Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data

Modern elevation-determining remote sensing technologies such as light-detection and ranging (LiDAR) produce a wealth of topographic information that is increasingly being used in a wide range of disciplines, including archaeology and geomorphology. However, automated methods for mapping topographic features have remained a significant challenge. Deep learning (DL) mask regional-convolutional neural networks (Mask R-CNN), which provides context-based instance mapping, offers the potential to overcome many of the difficulties of previous approaches to topographic mapping. We therefore explore the application of Mask R-CNN to extract valley fill faces (VFFs), which are a product of mountaintop removal (MTR) coal mining in the Appalachian region of the eastern United States. LiDAR-derived slopeshades are provided as the only predictor variable in the model. Model generalization is evaluated by mapping multiple study sites outside the training data region. A range of assessment methods, including precision, recall, and F1 score, all based on VFF counts, as well as area- and a fuzzy area-based user’s and producer’s accuracy, indicate that the model was successful in mapping VFFs in new geographic regions, using elevation data derived from different LiDAR sensors. Precision, recall, and F1-score values were above 0.85 using VFF counts while user’s and producer’s accuracy were above 0.75 and 0.85 when using the area- and fuzzy area-based methods, respectively, when averaged across all study areas characterized with LiDAR data. Due to the limited availability of LiDAR data until relatively recently, we also assessed how well the model generalizes to terrain data created using photogrammetric methods that characterize past terrain conditions. Unfortunately, the model was not sufficiently general to allow successful mapping of VFFs using photogrammetrically-derived slopeshades, as all assessment metrics were lower than 0.60; however, this may partially be attributed to the quality of the photogrammetric data. The overall results suggest that the combination of Mask R-CNN and LiDAR has great potential for mapping anthropogenic and natural landscape features. To realize this vision, however, research on the mapping of other topographic features is needed, as well as the development of large topographic training datasets including a variety of features for calibrating and testing new methods.

Organization: West Virginia View
Relating leaf spectral reflectance to its color: an inquiry-based activity to enhance understanding of electromagnetic radiation

Understanding the concept of electromagnetic radiation (EMR) and its interaction with matter (absorption, reflection, and transmission) can be difficult for students in seventh and eighth grade physical science classes. This inquiry-based activity (IBA) is aimed at improving their understanding of these concepts by exploring the interaction of EMR with leaves of different plant species or health conditions. Incorporating at least two different types of leaves from the local environment and measuring their interaction with EMR peaks student interest in this activity. After completing this IBA, students will see how objects interact in different regions of the electromagnetic spectrum and gain a better understanding of phenomena such as reflection and absorption. Further, this IBA can help them understand how human eyes perceive different colors. This activity can be expanded by a biology teacher to include a discussion about leaf pigments and how their interaction with EMR determines the colors of leaves. Concepts covered in this IBA can be related to remote sensing science and how sensors on board satellites collect Earth Observation data.

Organization: WyomingView
No-cost LANDSAT Data Redefines Student Research Projects In Applied Remote Sensing Classes at UW

Students enrolled in an applied remote sensing class at University of Wyoming are required to complete a term research project. They have to apply the concepts learned throughout the semester to address a real-world problem in a natural resource management topic that uses remotely sensed imagery data. Prior to the availability of no-cost Landsat data, students in this class had to restrict the scope of their projects to images available in the smaller archives maintained by academic and research institutions. Since the US Geological Survey (USGS) made the entire Landsat archive available at no-cost, opportunities have increased for students to use those data in class projects. Now, students can download as many Landsat images as is needed to address the scope of their project. Using examples from previous years, this paper describes how no-cost Landsat imagery has allowed students enrolled in an applied remote sensing class to add depth and breadth to their projects.

Organization: WyomingView
Sunbathed Aspen Grow to Show how Sunlight Influences Aspen Leaf Changes in the Autumn Season

Every autumn, leaves of deciduous trees change from green to other colors and eventually drop to the ground. The rate of color change is influenced by a several factors including the amount of sunlight and temperature. As part of an inquiry-based learning activity, University of Wyoming students have been recording leaf color change (% change) and its drop date (%) in Aspen trees (Populus tremuloides) growing in Laramie (WY) using NEON’s (National Ecology Observation Network) Phenology data form. In this study, the data recorded from 2015 through 2018 were analyzed to identify trends in the rate of color change in dry and normal years. Trees that were in an area with a high amount of shade were observed to change leaf color and drop their leaves faster than those in areas that received more sun. This pattern was consistent even in years that experienced winter-like conditions in September. Findings from this multi-year study indicate that future environmental modeling projects must factor in the amount of sunlight received by aspen trees in the growing season into account.

Organization: WyomingView
Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images

Following flooding disasters, satellite images provide valuable information required for generating flood inundation maps. Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds. We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre- and post-flood satellite images. Values of the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) will be higher in the post-flood image for flooded areas compared to the pre-flood image. Based on a threshold value, pixels corresponding to the flooded areas can be separated from non-flooded areas. Inundation maps derived from differencing MNDWI values accurately captured the flooded areas. However the output image will be influenced by the choice of the pre-flood image, hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years. Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features. Advantages of the proposed technique are that flood impacted areas can be identified rapidly, and that the pre-existing water bodies can be excluded from the inundation maps. Using pairs of other satellite data, several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.

Organization: WyomingView
Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

The ability to predict spatially explicit nitrogen uptake (NUP) in maize (Zea mays L.) during the early development stages provides clear value for making in-season nitrogen fertilizer applications that can improve NUP efficiency and reduce the risk of nitrogen loss to the environment. Aerial hyperspectral imaging is an attractive agronomic research tool for its ability to capture spectral data over relatively large areas, enabling its use for predicting NUP at the field scale. The overarching goal of this work was to use supervised learning regression algorithms—Lasso, support vector regression (SVR), random forest, and partial least squares regression (PLSR)—to predict early season (i.e., V6–V14) maize NUP at three experimental sites in Minnesota using high-resolution hyperspectral imagery. In addition to the spectral features offered by hyperspectral imaging, the 10th percentile Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) was made available to the learning models as an auxiliary feature to assess its ability to improve NUP prediction accuracy. The trained models demonstrated robustness by maintaining satisfactory prediction accuracy across locations, pixel sizes, development stages, and a broad range of NUP values (4.8 to 182 kg ha−1). Using the four most informative spectral features in addition to the auxiliary feature, the mean absolute error (MAE) of Lasso, SVR, and PLSR models (9.4, 9.7, and 9.5 kg ha−1, respectively) was lower than that of random forest (11.2 kg ha−1). The relative MAE for the Lasso, SVR, PLSR, and random forest models was 16.5%, 17.0%, 16.6%, and 19.6%, respectively. The inclusion of the auxiliary feature not only improved overall prediction accuracy by 1.6 kg ha−1 (14%) across all models, but it also reduced the number of input features required to reach optimal performance. The variance of predicted NUP increased as the measured NUP increased (MAE of the Lasso model increased from 4.0 to 12.1 kg ha−1 for measured NUP less than 25 kg ha−1 and greater than 100 kg ha−1, respectively). The most influential spectral features were oftentimes adjacent to each other (i.e., within approximately 6 nm), indicating the importance of both spectral precision and derivative spectra around key wavelengths for explaining NUP. Finally, several challenges and opportunities are discussed regarding the use of these results in the context of improving nitrogen fertilizer management.

Organization: MinnesotaView
Ash Presence and Abundance Derived from Composite Landsat and Sentinel-2 Time Series and Lidar Surface Models in Minnesota, USA

Ash trees (Fraxinus spp.) are a prominent species in Minnesota forests, with an estimated 1.1 billion trees in the state, totaling approximately 8% of all trees. Ash trees are threatened by the invasive emerald ash borer (Agrilus planipennis Fairmaire), which typically results in close to 100% tree mortality within one to five years of infestation. A detailed, wall-to-wall map of ash presence is highly desirable for forest management and monitoring applications. We used Google Earth Engine to compile Landsat time series analysis, which provided unique information on phenologic patterns across the landscape to identify ash species. Topographic position information derived from lidar was added to improve spatial maps of ash abundance. These input data were combined to produce a classification map and identify the abundance of ash forests that exist in the state of Minnesota. Overall, 12,524 km2 of forestland was predicted to have greater than 10% probability of ash species present. The overall accuracy of the composite ash presence/absence map was 64% for all ash species and 72% for black ash, and classification accuracy increased with the length of the time series. Average height derived from lidar was the best model predictor for ash basal area (R2 = 0.40), which, on average, was estimated as 16.1 m2 ha−1. Information produced from this map will be useful for natural resource managers and planners in developing forest management strategies which account for the spatial distribution of ash on the landscape. The approach used in this analysis is easily transferable and broadly scalable to other regions threatened with forest health problems such as invasive insects

Organization: MinnesotaView
Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.

Organization: MichiganView