AndreaBozzo/ceres-open-data-index huggingface dataset:
Biophysical Effects of Paddy Rice Expansion on Land Surface Temperature in Northeastern Asia

The pending extensive rice expansion in northeastern Asia, especially in northeast China, affects regional climate by altering both biogeochemical and biophysical processes. While the biogeochemical effects (e.g., CO2, CH4) of rice expansion have attracted plenty of attention, its biophysical effects have not been well documented, especially its influences on diurnal and seasonal land surface temperature (LST). In this study, we used a pair-wise comparison approach to examine biophysical effects of paddy rice expansion at different temporal scales (diurnal and seasonal) in northeast China, based on satellite-derived biophysical proxies and a high-resolution crop map in 2017. We found that the daily mean LST of rice paddies was 0.5 °C lower than that of corn and soybean fields during the growing season (from May to September), as a result of daytime cooling (-1.8 and -2.0 °C) and nighttime warming (0.8 and 1.1 °C), which subsequently led to a narrower diurnal LST range (-2.6 and -3.0 °C) than in upland crops (i.e. corn and soybean). The cooling effects were stronger in the early period of the growing season (May and June) than in the late season (July to September). Using a temperature response model, we found that the nonradiative processes (i.e., evapotranspiration and sensible heat) dominated the LST response in paddy rice, while the radiative process (i.e., albedo) played a secondary role. The daytime cooling and nighttime warming implies that we need to consider the unsymmetrical diurnal LST dynamics when evaluating the short-term effects of paddy rice expansion. Stronger cooling effects in the early growing season has to be accounted for when modeling its biophysical impacts at seasonal scale. This study explained the local climate effects of rice expansion through the biophysical mechanism with both radiative and nonradiative controls on the surface energy balance, which can contribute to improved modeling of biophysical effects of land use change.

Organization: OklahomaView
Indoor GIS Solution for Space Use Assessment

As GIS and computing technologies advanced rapidly, many indoor space studies began to adopt GIS technology, data models, and analysis methods. However, even with a considerable amount of research on indoor GIS and various indoor systems developed for different applications, there has not been much attention devoted to adopting indoor GIS for the evaluation space usage. Applying indoor GIS for space usage assessment can not only provide a map-based interface for data collection, but also brings spatial analysis and reporting capabilities for this purpose. This study aims to explore best practice of using an indoor GIS platform to assess space usage and design a complete indoor GIS solution to facilitate and streamline the data collection, a management and reporting workflow. The design has a user-friendly interface for data collectors and an automated mechanism to aggregate and visualize the space usage statistics. A case study was carried out at the Purdue University Libraries to assess study space usage. The system is efficient and effective in collecting student counts and activities and generating reports to interested parties in a timely manner. The analysis results of the collected data provide insights into the user preferences in terms of space usage. This study demonstrates the advantages of applying an indoor GIS solution to evaluate space usage as well as providing a framework to design and implement such a system. The system can be easily extended and applied to other buildings for space usage assessment purposes with minimal development efforts.

Organization: IndianaView
Assessing the Ability to Quantify Bathymetric Change over Time Using Solely Satellite-Based Measurements

Coastal regions are undergoing rapid change, due to natural and anthropogenic forcings. A current constraint in understanding and modeling these changes is the lack of multi-temporal bathymetric data, or recursive observations. Often, it is difficult to obtain the repeat observations needed to quantify bathymetric change over time or events. However, the recent availability of ICESat-2 bathymetric lidar creates the option to map coastal bathymetry from solely space-based measurements via satellite-derived bathymetry with multispectral imagery (IS-2/SDB). This compositional space-based bathymetric mapping technique can assess temporal change along the coasts without other remote sensing or in situ data. However, questions exist as to the accuracy of the technique relative to both quantitative uncertainties and the ability to resolve the spatial patterns of erosion and deposition in the nearshore environment, indicative of geomorphologic change. This paper addresses the concept using data from the Florida panhandle (Northern Gulf of Mexico) collected by Sentinel-2 and ICESat-2 at two epochs to assess the feasibility of using IS-2/SDB for bathymetric change detection at scientifically relevant scales, spatial resolutions and accuracies. The comparison of the satellite-only result is compared to airborne data collected at similar epochs to reveal both quantitatively and qualitatively the utility of this technique.

Organization: OregonView
Aerial Imagery-Based Building Footprint Detection with an Integrated Deep Learning Framework: Applications for Fine Scale Wildland–Urban Interface Mapping

Human encroachment into wildlands has resulted in a rapid increase in wildland–urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland–urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction.

Organization: CaliforniaView
Machine Learning for Modeling Wildfire Susceptibility at the State Level: An Example from Arkansas, USA

Fire susceptibility modeling is crucial for sustaining and managing forests among many other valuable land resources. With 56% of its area covered by forests, Arkansas is known as the “natural state”. About 1000 wildfires occurred and burned more than 10,000 acres each year during 1981–2018. In this paper, we use remote-sensing-based machine learning methods to address the natural and anthropogenic factors influencing wildfires and model fire susceptibility in Arkansas. Among the 15 explored variables, potential evapotranspiration, soil moisture, Palmer drought severity index, and dry season precipitation were recognized as the most significant factors contributing to the fire density. The obtained R-squared values are significant, with 0.99 for training the model and 0.92 for the validation. The results show that the Ouachita National Forest and the Ozark Forest, in west-central and west Arkansas, respectively, have the highest susceptibility to wildfires. The southern part of Arkansas has low-to-moderate fire susceptibility, while the eastern part of the state has the lowest fire susceptibility. These new results for Arkansas demonstrate the potency of remote-sensing-based random forest in predicting fire susceptibility at the state level that can be adapted to study fires in other states and help with fire preparedness to reduce loss and save the precious environment.

Organization: ArkansasView
Science of Where: An Introduction to How to Think Spatially

This tutorial has 7 lectures and 3 exercises that introduce the viewer how to think spatially. The material is appropriate for life long learners high school and beyond. Spatial thinking is critical in all aspects of living and especially in the STEM disciplines. Becoming aware of how much we think spatially is the first step to improving our ability to do so. There are three exercises that go along with the lectures to give the viewer hands-on experience with the science of where.

Organization: New HampshireView
Forest Type Differentiation Using GLAD Phenology Metrics, Land Surface Parameters, and Machine Learning

This study investigates the mapping of forest community types for the entire state of West Virginia, United States, using Global Land Analysis and Discovery (GLAD) Phenology Metrics, Analysis Ready Data (ARD) derived from Landsat time series data, and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study was to explore the use of globally consistent ARD for operational forest type mapping over a large spatial extent. Mean overall accuracy calculated from 50 model replicates for differentiating seven forest community types using only variables selected from the 188 GLAD Phenology Metrics used in the study resulted in an overall accuracy (OA) of 54.3% (map-level image classification efficacy (MICE) = 0.433). Accuracy increased to a mean OA of 64.8% (MICE = 0.496) when the Oak/Hickory and Oak/Pine classes were combined into an Oak Dominant class. Once selected terrain variables were added to the model, the mean OA for differentiating the seven forest types increased to 65.3% (MICE = 0.570), while the accuracy for differentiating six classes increased to 76.2% (MICE = 0.660). Our results highlight the benefits of combining spectral data and terrain variables and also the enhancement of the product’s usefulness when probabilistic predictions are provided alongside a hard classification. The GLAD Phenology Metrics did not provide an accuracy comparable to those obtained using harmonic regression coefficients; however, they generally outperformed models trained using only summer or fall seasonal medians and performed comparably to those trained using spring medians. We suggest further exploration of the GLAD Phenology Metrics as input for other spatial predictive mapping and modeling tasks.

Organization: West Virginia View
Informing Conservation Decisions to Target Private Lands of Highest Ecological Value and Risk of Loss

Natural habitats on private lands are potentially important components of national biodiversity conservation strategies, yet they are being rapidly lost to development. Conservation easements and other means of protecting these habitats have expanded in use and will be most effective if they target private lands of highest biodiversity value and risk of loss. We developed a Biodiversity Conservation Priority Index (BCPI) based on ecological value and risk of habitat loss for remaining areas of natural vegetation cover (NVC) in the northwestern United States and addressed two questions: (1) Which remaining NVC on private lands is the highest priority for biodiversity conservation based on ecological value and risk of development? And (2) are conservation easements in NVC placed preferentially in locations of high biodiversity conservation priority? Drawing on the concept of ecological integrity, we integrated five metrics of ecological structure, function, and composition to quantify ecological value of NVC. These included net primary productivity, species richness, ecosystem type representation, imperiled species range rarity, and connectivity among “Greater Wildland Ecosystems.” Risk of habitat loss was derived from analysis of biophysical and sociodemographic predictors of NVC loss. Ecological value and risk of loss were combined into the BCPI. We then analyzed spatial patterns of BCPI to identify the NVC highest in biodiversity conservation priority and examined the relationship between BCPI and conservation easement status. We found that BCPI varied spatially across the study area and was highest in western and southern portions of the study area. High BCPI was associated with suburban and rural development, roads, urban proximity, valley bottom landforms, and low intensity of current development. Existing conservation easements were distributed more towards lower BCPI values than unprotected NVC at both the study area and region scales. The BCPI can be used to better inform land use decision making at local, regional, and potentially national scales in order to better achieve biodiversity goals.

Organization: MontanaView
An Introduction to Precision Agriculture: An Educator's Guide to Agricultural Earth Observation

Small unmanned aircraft systems (sUAS) are being increasingly utilized to support an array of applications including agriculture, natural resource management, facility/infrastructure inspections, and package delivery. Currently, precision agriculture is the fastest growing market for sUAS. One of the major barriers to the adoption of transformative innovation is education.

Currently, educators lack classroom ready resources that can be utilized to introduce precision agriculture. This 120 page publication was developed to directly address this need. This publication will support the needs of agricultural educators across Virginia, by providing an overview of precision agricultural principles, coupled with activities and learning lessons. All of the developed curriculum modules incorporate various STEM related concepts. The information, resources, and activities in this workbook are provided as an 'educational buffet'. These materials do not need to be consumed all at once. Educators can sample, take, modify, and deliver just the topics / activities / resources in this workbook that are needed.

Organization: VirginiaView
Observing Our Natural World with Drones

Small uncrewed aircraft systems (sUAS, aka ‘drones’) are being increasingly employed to support an expanding array of applications and business needs. Natural resource professionals are increasingly incorporating drones as a data collection tool to support management decisions. This educational guide is based off of the sUAS Operations Technician DACUM, and was developed to directly respond to the needs of educators. The information and activities included in this workbook are provided as an ‘educational buffet’. Educators can use the entire resource, or they can select and extract specific activities to target their needs. This resource can be used as a follow-up to the publication “sUAS Manual Flight Exercises”. 

Organization: VirginiaView
Spatiotemporal Distribution Change of Online Reference During the Time of COVID-19

The goal of this project was to identify the impact of the COVID-19 pandemic on the spatiotemporal distribution of the library’s online patrons, so that we could assess if the scheduled library reference hours are meeting the needs of the academic community. We collected each online reference patron’s location information via their IP address, as well as the timestamp of each online reference instance. The spatiotemporal distribution patterns were analyzed and compared before and after in-person instruction was suspended due to COVID-19 distance protocols and a closing of the campus in the 2020 spring semester. The results show that the geographic origins of reference questions redistributed after COVID-19 protocols were initially implemented and the university community underwent a temporary geographical redistribution. Reference question origins tended to move away from campus to other areas of the state, other states, and internationally. This population redistribution suggested that the library could adjust the online reference schedule to provide better access and service to patrons.

Organization: IndianaView
Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties

The capacity of terrestrial ecosystems to sequester carbon dioxide (CO2) from the atmosphere is expected to be altered by climate change and CO2 fertilization, but this projection is limited by our understanding of how the soil system interacts with plants. Understanding the soil–vegetation interactions is essential to assess the magnitude and response of terrestrial ecosystems to the changing climate. Here, we used soil profile and satellite data to explore the role that soil properties play in regulating water and carbon use by plants. Data obtained for 19 terrestrial ecosystem sites in a warm temperate and humid climate were used to investigate the relationship between remotely sensed data and soil physical and chemical properties. Classification and regression tree results showed that in situ soil carbon isotope (δ13C), and soil order were significant predictors (r2 = 0.39, mean absolute error (MAE) = 0 of 0.175 gC/KgH2O) of remotely sensed water use efficiency (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS). Soil extractable calcium (Ca), and land cover type were significant predictors of remotely sensed carbon use efficiency (CUE) based on MODIS and Landsat data-(r2 = 0.64–0.78, MAE = 0.04–0.06). We used gross primary productivity (GPP) derived from solar-induced fluorescence (SIF) data, based on the Orbiting Carbon Observatory-2 (OCO-2), to calculate WUE and CUE (referred to as WUESIF and CUESIF, respectively) for our study sites. The regression tree analysis revealed that soil organic matter and soil extractable magnesium (Mg), δ13C, and soil silt content were the important predictors of both WUESIF (r2 = 0.19, MAE = 0.64 gC/KgH2O) and CUESIF (r2 = 0.45, MAE = 0.1), respectively. Our results revealed the importance of soil extractable Ca, soil carbon (S13C is a facet of soil carbon content), and soil organic matter predicting CUE and WUE. Insights gained from this study highlighted the importance of biotic and abiotic factors regulating plant and soil interactions. These types of data are timely and critical for accurate predictions of how terrestrial ecosystems respond to climate change.

Organization: KentuckyView
Estimating Urban Tree Metrics Using Terrestrial LiDAR Scanning

Trees grown in an urban environment are typically from a selected list of suitable species due to their appearance and other factors. A popular oak species in recent decades has been the Nuttall oak (Quercus texana). A total of seven Nuttall oaks were scanned using a terrestrial LiDAR scanner and modeled for comparison to manual measurements. These trees were then destructively sampled in place to measure their above-ground biomass. The biomass data were compiled and statistically compared against digital models of each tree that were created from the LiDAR scans. This resulted in a Pearson coefficient of .977 and linear regression R2 value of .99 for the LiDAR derived measurements predictive ability in comparison to the manually derived measurements. This indicates an ability of this ground based LiDAR model to predict both the linear dimensions and volumetrics of the standing specimens without the need for such labor intensive and expensive sampling given the sensitivity and value of urban forests.

Organization: AlabamaView
Flash Drought Identification from Satellite-Based Land Surface Water Index

Flash droughts can lead to significant agricultural and ecosystem impacts via rapid land surface desiccation. While gridded weather and climate datasets, land surface models, or widely spaced in situ observations are typically used to quantify flash drought development, coarse spatial data limits the ability to determine fine-scale spatial evolution of flash drought at landscape and ecosystem scales. In this study, a novel approach is introduced to objectively identify flash drought using the land surface water index (LSWI) derived from satellite observations. LSWI is a water-related vegetation index that represents the total water content in vegetation by using the near-infrared and shortwave infrared bands. LSWI was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance (MOD09A1) with a 500 m spatial resolution and an 8-day temporal resolution. When applied to two well-established case studies, LSWI anomalies were able to capture the temporal and spatial evolution of flash drought over Oklahoma during the years of 2011 and 2012. In addition, rapid changes of LSWI during flash drought were comparable across space and time to the reanalysis-based Standardized Evaporative Stress Ratio (SESR), while negative anomalies of LSWI following flash drought corresponded with drought impacts via the United States Drought Monitor. It was found that LSWI was able to identify flash drought with a finer spatial resolution (500 m) and revealed spatial propagation of flash drought events that would not otherwise be seen with coarser meteorological data (e.g., ∼32 km at the lowest latitude for the North American Regional Reanalysis data). Furthermore, LSWI greatly enhanced the ability to detect rapid changes in surface conditions driven by flash drought and was able to provide early warning for drought development when compared with the USDM across Oklahoma. As such, the temporal and spatial evolution of flash drought depicted by LSWI and the presented methodology improves our ability to identify flash drought at high spatial resolution using satellite remote sensing and detect rapid changes in surface conditions. In light of these results, the novel LSWI approach demonstrates that satellite remote sensing applications using an objective technique are advantageous for flash drought detection in near real-time and at fine spatial scales.

Organization: OklahomaView
Modeling Airport Catchment Areas: Using Spatial Analysis Approach

The airport catchment area is the geographic area from which an airport can reasonably expect to draw commercial air service passengers. The purpose of this interdisciplinary research is to estimate airport catchment areas using a spatial analysis method for informed airport management. In order to ensure the comprehensiveness and reliability of the research, we chose to analyze the catchment areas for five airports of different sizes and in different geographic locations in the United States. The Huff model, which is usually used in marketing, economics, and retail research, was adopted in this study. We applied this model in airport catchment analysis for the selected airports, with consideration of ground transportation distance and airport attractiveness. We identified the best distance factors and parameters for modeling purposes. The results provided reliable information for the selected five airports to estimate their catchment areas. In addition, the model can provide a good reference for other airport catchment analysis studies about the effectiveness of the Huff model in prediction.


Organization: IndianaView