AndreaBozzo/ceres-open-data-index huggingface dataset:
Structural Diversity as a Reliable and Novel Predictor for Ecosystem Productivity

The physical structure of vegetation is thought to be closely related to ecosystem function, but little is known of its pertinence across geographic regions. Here, we used data from over three million trees in continental North America to evaluate structural diversity – the volumetric capacity and physical arrangement of biotic components in ecosystems – as a predictor of productivity. We show that structural diversity is a robust predictor of forest productivity and consistently outperforms the traditional measure – species diversity – across climate conditions in North America. Moreover, structural diversity appears to be a better surrogate of niche occupancy because it captures variation in size that can be used to measure realized niche space. Structural diversity offers an easily measured metric to direct restoration and management decision making to maximize ecosystem productivity and carbon sequestration.

Organization: IndianaView
Fly High 4 Geo

Through National Geographic Funding, AmericaView helped facilitate geography education and understanding in the Four Corners region of the United States. The FlyHigh4Geo program was designed to engage students through interactive and inexpensive learning tools that focus on the use of remote sensing and Earth observation to gain an appreciation for local geography and culturally important areas; develop map reading/making skills; and establish a foundation for future geographic learning.

Organization: AmericaView
Landsat Memory Game

Remote Sensing Memory is an online version of memory, a card game designed for one or more people. Image file cards are laid out in a grid face down with the earth logo on top. 

On a shared screen, take turns flipping pairs of cards over. On each turn, the player will first turn one card over by clicking onto the earth logo, then a second by doing the same. If the two cards match, the player scores one point, the two cards stay image side turned up on the game board and the player continuous. If the images do not match, the cards are turned back over and it is the other player's turn. 

In a solo game, or if each player has their own screen, the objective is to match all pairs in the shortest amount of time. The player with the shortest amount of time needed after all the cards have been matched wins. When cards are turned over, the challenge is to remember where they are located for when the matching card is turned up later in the game.

Organization: WisconsinView
Forests from Above and Below: 2020 EOD Poster Lesson Plan

A Lesson Plan about forests based on the Earth Observation Day 2020 Factory Earth Poster developed for Middle School teachers. The lesson plan is divided into 4 parts: an Overview, an Introduction, Forests from Below, and Forests from Above.  In addition, there is a video and slides providing an introduction to remote sensing that accompanies the Forests from Above lesson.  This lesson was developed by Dr. Russ Congalton, New HampshireView Director. 

Organization: New HampshireView
Introduction to Chesapeake Bay Mapper and Landsat Data

ChesapeakeView is a collaborative project of the AmericaView program. AmericaView (AV) is a nationwide program that focuses on satellite remote sensing data and technologies in support of applied research, K-16 education, workforce development, and technology transfer.
ChesapeakeView is about free access to data about the Bay Region. It is the first regional AmericaView initiative. The purpose of the ChesapeakeView project is to facilitate access to remotely sensed data as well as habitat, land use, biodiversity, and other types of data related to the Chesapeake Bay region. This effort seeks to highlight the data resources of the AmericaView partners in the Chesapeake Bay region including Maryland, New York, Pennsylvania, Virginia, and West Virginia as well as other organizations that create data related to the bay.

Organization: Pennsylvania View
2022 Earth Observation Day Poster

The 2022 Earth Observation Day poster focuses on the sustainability of the Landsat mission and showcases unique changing landscapes around the world. Since 1972, Landsat satellites have consistently gathered data about our planet for the benefit of the U.S. and the world. The Landsat data archive is the longest continuous remotely sensed global record of Earth’s surface, with all the data free and available to the public. The Landsat satellite missions, jointly managed by NASA and the U.S. Geological Survey, are a central pillar of our national remote sensing capability and established the U.S. as a leader in land imaging.

Organization: AmericaView
Climate and Management Practices Jointly Control Vegetation Phenology in Native and Introduced Prairie Pastures

Climate, human disturbances, and management practices jointly control the spatial and temporal patterns of land surface phenology. However, most studies solely focus on analyzing the climatic controls on the inter-annual variability and trends in vegetation phenology. Investigating the main and interacting effects of management practices and climate might be crucial in determining vegetation phenology and productivity. This study examined the impacts of climate and management practices on vegetation phenology and productivity in adjacent native and introduced prairie pastures, which have detailed long-term management records, by combining climate, management, and satellite remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat). Modeled gross primary production (GPP) using vegetation photosynthesis model (VPM) was also included to investigate the dynamics of productivity. When comparing the impacts of the same management practices on different pastures, we used paired comparison, namely, comparing the native and introduced prairies side by side in the same year. The interactions of management practices and climate were investigated through comparing years with similar management but different climate (e.g., years with rainfall or not following baling events) in the same pasture. Results showed that air temperature (Ta) was an important factor in determining the start of the season (SOS) and the length of the season (LOS). Total rainfall (RF) during the annual growing season (AGS, derived from vegetation indices (VIs)) had the largest explanatory power (R2 = 0.53) in explaining the variations in the seasonal sums of VIs. The variations in GPP were better explained by RF (R2 = 0.43) than Ta (R2 = 0.14). Using the thermal growing season (March–October) or AGS climate factors did not show large differences in determining the relationships between phenology, GPP, and climate factors. Drought shortened the LOS and decreased GPP. In terms of management practices, grazing generally reduced the VIs and burning induced early greening-up and enhanced vegetation growth. Drought plus other management practices (e.g., grazing or baling) greatly affected vegetation phenology and suppressed GPP. The negative impacts (i.e., removal of biomass) of grazing on vegetation was compensated by enhanced vegetation growth after good RF. This study demonstrated that the interactions of climate and management practices could be positive (burning plus baling in a good RF year) or negative (grazing/baling plus drought), and can significantly affect vegetation phenology and production.

Organization: OklahomaView
Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments

Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.

Organization: MontanaView
Multiple UAV Flights across the Growing Season Can Characterize Fine Scale Phenological Heterogeneity within and among Vegetation Functional Groups

Grasslands and shrublands exhibit pronounced spatial and temporal variability in structure and function with differences in phenology that can be difficult to observe. Unpiloted aerial vehicles (UAVs) can measure vegetation spectral patterns relatively cheaply and repeatably at fine spatial resolution. We tested the ability of UAVs to measure phenological variability within vegetation functional groups and to improve classification accuracy at two sites in Montana, U.S.A. We tested four flight frequencies during the growing season. Classification accuracy based on reference data increased by 5–10% between a single flight and scenarios including all conducted flights. Accuracy increased from 50.6% to 61.4% at the drier site, while at the more mesic/densely vegetated site, we found an increase of 59.0% to 64.4% between a single and multiple flights over the growing season. Peak green-up varied by 2–4 weeks within the scenes, and sparse vegetation classes had only a short detectable window of active phtosynthesis; therefore, a single flight could not capture all vegetation that was active across the growing season. The multi-temporal analyses identified differences in the seasonal timing of green-up and senescence within herbaceous and sagebrush classes. Multiple UAV measurements can identify the fine-scale phenological variability in complex mixed grass/shrub vegetation.

Organization: MontanaView
Change in Doctoral Dissertation Topics in Forest Resources from U.S. Universities over Four Decades

Changes in forest resources expertise from 1978 to 2017, as measured by annual number of doctoral dissertations published on twenty topics, were examined. Using the ProQuest Dissertations and Theses Global database, titles and abstracts from 11,530 dissertations produced by fifty-six universities in the United States were selected. Automated content analysis and latent Dirichlet allocation were used to identify the optimal number of topic groupings among 7,742 dissertations that met selection criteria. Substantial differences were found in the pattern of publication among the twenty topics over time. The number of dissertations related to forest growth and silviculture; tree seedling propagation, physiology, and regeneration; and forest soil nutrients, ecology, and management declined over the past two decades. Dissertations related to forest wildlife management, including terrestrial wildlife ecology and management; wildlife food and nutrition; and fish ecology and management also declined during the same period. The number of dissertations in the fields of forest policy, politics, and social science; forest modeling, biometrics, and statistics; wood science; forest vegetation ecology; and avian ecology increased during the four decades. Dissertations published in the fields of forest economics, and forest entomology and pathology, remained relatively stable.

Study Implications: We found decreasing production of doctoral dissertations focused on applied forest and wildlife management topics in recent decades. Declining doctoral-level expertise in applied fields after the early 2000s suggest that there may be reduced capacity to address practical problems facing both forest and wildlife managers. This decline also suggests that finding university instructors qualified to teach more applied forest and wildlife courses may have been more difficult over the past decade and possibly into the future. Our analysis indicated that the increased number of dissertations in adjacent sciences supporting forest resources has substantially increased capacity in these areas.

Organization: IndianaView
Vine Water Status Mapping with Multispectral UAV imagery and Machine Learning

Optimizing water management has become one of the biggest challenges for grapevine growers in California, especially during drought conditions. Monitoring grapevine water status and stress level across the whole vineyard is an essential step for precision irrigation management of vineyards to conserve water. We developed a unified machine learning model to map leaf water potential (ψleaf), by combining high-resolution multispectral remote sensing imagery and weather data. We conducted six unmanned aerial vehicle (UAV) flights with a five-band multispectral camera from 2018 to 2020 over three commercial vineyards, concurrently with ground measurements of sampled vines. Using vegetation indices from the orthomosaiced UAV imagery and weather data as predictors, the random forest (RF) full model captured 77% of ψleaf variance, with a root mean square error (RMSE) of 0.123 MPa, and a mean absolute error (MAE) of 0.100 MPa, based on the validation datasets. Air temperature, vapor pressure deficit, and red edge indices such as the normalized difference red edge index (NDRE) were found as the most important variables in estimating ψleaf across space and time. The reduced RF models excluding weather and red edge indices explained 52–48% of ψleaf variance, respectively. Maps of the estimated ψleaf from the RF full model captured well the patterns of both within- and cross-field spatial variability and the temporal change of vine water status, consistent with irrigation management and patterns observed from the ground sampling. Our results demonstrated the utility of UAV-based aerial multispectral imaging for supplementing and scaling up the traditional point-based ground sampling of ψleaf. The pre-trained machine learning model, driven by UAV imagery and weather data, provides a cost-effective and scalable tool to facilitate data-driven precision irrigation management at individual vine levels in vineyards.

Organization: CaliforniaView
The Spatial Variability of NDVI Within a Wheat Field: Information Content and Implications for Yield and Grain Protein Monitoring

Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.

Organization: MontanaView
Effects of Increasing C4-Crop Cover and Stomatal Conductance on Evapotranspiration: Simulations for a Lake Erie Watershed

Accurate quantification of evapotranspiration (ET) is crucial for surface water resources and best agricultural management practices in watersheds. The aim of this study was to better understand ET changes caused by the rapid expansion of C4 (corn) cover and rapid changes in stomatal conductance, which may be amplified in the future due to environmental and human-contributing factors, such as climate change and agricultural practices. Linking the enlargement of agricultural land with the physiological properties of crops, such as photosynthetic adaptations and stomatal conductance, is necessary to explore the magnitude of these impacts. This study examined the effects of increased C4 (corn) crop cover and stomatal conductance on evapotranspiration (ET) rates in the Lower Maumee River Watershed, Ohio, USA, during the 2018 growing season. Simulation results using a modified-for-crops version of the Boreal Ecosystem Productivity Simulator (BEPS) showed that a hypothetical increase of corn cover by as much as 100% would not significantly impact the watershed ET rate, with a 5.05% overall increase in ET in July and a 3.96% increase in August. Changes in the stomatal conductance of crops, however, impacted ET more. The results showed a significant increase in the ET rate (up to 24.04% for corn and 5.10% for soybean) for the modeling scenario that integrated high stomatal conductance, which agreed with the thermal-based ECOSTRESS ET product derived over the study area (+/−0.9 mm day−1) for the same period. We suggest that the alteration of the crop stomata mechanism, caused largely by rapid climate change and intensive farming practices, should be carefully quantified, and its impact on hydrology at the ecosystem level further explored.

Organization: OhioView
Virtualizing Soil Science Field Trips

In response to the coronavirus pandemic in the fall of 2020, we created hybrid–virtual or fully virtual field trips to replace in-person field trips in two courses. For an introductory soil science course with 178 students enrolled, we used a narrative format supported by 360° ground-level and drone photos, newspaper articles, videos, websites, and landscape diagrams. After reading the narratives, the students took a quiz and then visited the sites (public parks near campus) on their own. For the advanced pedology course with seven students enrolled, a virtual trip replaced an all-day field trip that travels a 100-mile route from West Lafayette to Michigan City, IN. The virtual trip was created within the Soil Explorer platform and consists of >50 points of interest (POIs) overlayed on detailed soil maps. Each POI includes relevant information about the soils, agriculture, geology, geomorphology, or history at that point. At two POIs students examined four or five soils along two transects by filling in worksheets using the official series descriptions. Learning was facilitated by a study guide. Effectiveness was assessed by anonymous surveys. Students found the hybrid–virtual or fully virtual field trips informative, interesting, enjoyable, and appropriate given the pandemic. For the introductory class, 42% preferred a virtual field trip format, 39% preferred an in-person format, and 19% were neutral. In the advanced class there was with a clear preference for in-person field trips.

Organization: IndianaView
Enhancing Reproducibility and Replicability in Remote Sensing Deep Learning Research and Practice

Many issues can reduce the reproducibility and replicability of deep learning (DL) research and application in remote sensing, including the complexity and customizability of architectures, variable model training and assessment processes and practice, inability to fully control random components of the modeling workflow, data leakage, computational demands, and the inherent nature of the process, which is complex, difficult to perform systematically, and challenging to fully document. This communication discusses key issues associated with convolutional neural network (CNN)-based DL in remote sensing for undertaking semantic segmentation, object detection, and instance segmentation tasks and offers suggestions for best practices for enhancing reproducibility and replicability and the subsequent utility of research results, proposed workflows, and generated data. We also highlight lingering issues and challenges facing researchers as they attempt to improve the reproducibility and replicability of their experiments.

Organization: West Virginia View
Cooling Effects of Increased Green Fodder Area on Native Grassland in the Northeastern Tibetan Plateau

With increasing livestock production due to high demand for consumption, the planted area of green fodder, an essential livestock supplement, has grown rapidly and will continue to grow in China. However, the climate feedback of this rapid land cover conversion is still unclear. Using multisource data (e.g. remote sensing observation and meteorological data), we compared the land surface temperature of green fodder plantation areas and native grassland in the northeastern Tibetan Plateau. The green fodder area was detected to be cooler than the native grassland by −0.54 ± 0.98 °C in the daytime throughout the growing season. The highest magnitude (−1.20 ± 1.68 °C) of cooling was observed in August. A nonradiative process, indicated by the energy redistribution factor, dominated the cooling effects compared to the radiative process altered by albedo variation. The results indicate the potential cooling effects of increasing green fodder area on native grassland, highlighting the necessity of investigating climate feedback from anthropogenic land use change, including green fodder expansion.

Organization: OklahomaView