AndreaBozzo/ceres-open-data-index huggingface dataset:
Missing Pixel Reconstruction on Landsat 8 Analysis Ready Data Land Surface Temperature Image Patches Using Source-Augmented Partial Convolution

Missing pixels is a common issue in satellite images. Taking Landsat 8 Analysis Ready Data (ARD) Land Surface Temperature (LST) image as an example, the Source-Augmented Partial Convolution v2 model (SAPC2) is developed to reconstruct missing pixels in the target LST image with the assistance of a collocated complete source image. SAPC2 utilizes the partial convolution enabled U-Net as its framework and accommodates the source into the framework by: (1) performing the shared partial convolution on both the source and the target in encoders; and (2) merging the source and the target by using the partial merge layer to create complete skip connection images for the corresponding decoders. The optimized SAPC2 shows superior performance to four baseline models (i.e., SAPC1, SAPC2-OPC, SAPC2-SC, and STS-CNN) in terms of nine validation metrics. For example, the masked MSE of SAPC2 is 7%, 20%, 44%, and 59% lower than that of the four baseline models. On the six scrutinized cases, the repaired target images generated by SAPC2 have the fewest artifacts near the mask boundary and the best recovery of color scales and fine textures compared with the four baseline models.

Organization: ColoradoView
Modeling Evapotranspiration for c4 and c3 Crops in the Western Lake Erie Basin Using Remote Sensing Data

Growing monoculture impacts not just soil properties and biodiversity but also local hydrology including evapotranspiration (ET). The Midwest region of the U.S. is known for its monoculture trend by growing and producing corn, which commonly replaces other crop types. In addition to large areas covered with corn, the photosynthetic adaptations of corn, being the C4 crop, affects ET differently than other C3 crops such as soybean, wheat, and alfalfa. This study aims to model and compare ET for C3 and C4 crops using remote sensing (Sentinel-2 data) and the Boreal Ecosystem Productivity Simulator (BEPS) model, modified to consider C3 and C4 crops. The study explores the ET rate trend for corn and soybean in an agriculture area situated in the Western Lake Erie Basin, where the balance between evapotranspiration, groundwater level, and surface runoff may play a role in agricultural runoff and Lake Erie's algal blooms caused by runoff pollution. The results suggest that the monthly average ET rates for both soybean (C3) and corn (C4) reach its maximum at the mid-to-late growing season. However, the ET rate for corn is higher than for soybean in the early season (June) (ET = 121 mm month-1 for corn; ET = 105 mm month-1 for soybean), while the ET rate for soybean becomes higher than for corn soon after (July) and becomes considerably higher in August (ET = 181 mm month-1 for corn; ET = 218 mm month-1 for soybean). It is surmised that the higher ET rate for corn in the early growing season is due to nitrogen-based fertilizer commonly applied to corn parcels at that time, whereas soybean growth is based on biological nitrogen fixation.

Organization: OhioView
Mapping Invasive Phragmites australis in the Old Woman Creek Using Remote Sensing

The application of remote sensing techniques in mapping, classifying and monitoring
land cover, land use and vegetation are popular among the researchers and scientists for several
decades. It became more productive and economical in recent years with the advancement of
information technology in a sophisticated and revolutionary manner. Currently, remote sensing is
a widely used effective technique that provides spatial and temporal information about vegetation
and invasive species in wetlands. The first objective of this study was to assess the effectiveness
of the data obtained via Unmanned Ariel Vehicles (UAV) to identify invasive Phragmites
australis in the Old Woman Creek (OWC) in Ohio. Secondly, the study aimed to determine the
most suitable algorithm to distinguish between Phragmites australis and other vegetation types
using pixel based and object based classification methods and a combination of feature layers
derived from the UAV images.
Pixel based classification found to be performing better than object based classification.
Pixel based Neural Network (NN) was identified as the best classifier to map Phragmites in
OWC with the least error of omission of 1.59% and the overall accuracy of 94.80% based on the
Sequoia image acquired in August that was stacked with Canopy Height Model (CHM) from
August and NDVI, which was derived using UAV data acquired in October (NDVIOct). The study
emphasizes the necessity of a suitable sampling method and the use of optimum parameters of
non-parametric classifiers. The study provides future directions for data acquisition to map
Phrgamites at early and mid-summer to find data to eradicate Phragmites effectively in OWC
estuary. 

Organization: OhioView
Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

Unmanned aerial vehicles (UAV) are increasingly used for spatiotemporal monitoring of invasive plants in coastal wetlands. Early identification of invasive species is necessary in planning, restoring, and managing wetlands. This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek (OWC) estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN). The ML algorithms were compared with the parametric maximum likelihood classifier (MLC) using pixel- and object-based methods. Pixel-based NN was identified as the best classifier with an overall accuracy of 94.80% and the lowest error of omission of 1.59%, the outcome desirable for effective eradication of Phragmites. The results were reached combining Sequoia multispectral imagery (green, red, red edge, and near-infrared bands) combined with the canopy height model (CHM) acquired in the mid-growing season and normalized difference vegetation index (NDVI) acquired later in the season. The sensitivity analysis, using various vegetation indices, image texture, CHM, and principal components (PC), demonstrated the impact of various feature layers on the classifiers. The study emphasizes the necessity of a suitable sampling and cross-validation methods, as well as the importance of optimum classification parameters.

Organization: OhioView
Seasonal home ranges and habitat selection of three elk (Cervus elaphus) herds in North Dakota

Changes in land use have resulted in range shifts of many wildlife species, including those entering novel environments, resulting in the critical need to understand their spatial ecology to inform ecosystem effects and management decisions. Dispersing elk (Cervus elaphus) were colonizing areas of suitable habitat in the Northern Great Plains, USA, resulting in crop depredation complaints in these areas. Although state resource managers had little information on these elk herds, limited evidence suggested temporal movements into Canada. We collected and analyzed essential information on home range and habitat selection for 3 elk herds residing in North Dakota. We captured 5 adult female elk in each study area, affixed global positioning system collars, and monitored them for 1 year (2016–2017). We estimated diel period, seasonal, and hunting season home ranges using Brownian Bridge Movement Models for each individual. We analyzed habitat selection using multinomial logit models to test for differences in use of land classes, and for departures from proportionate use based on random sampling; our predictor variables included individual elk, diel period, and season. Home ranges differed between the 3 herds, seasons, and diel period; gun and winter season home ranges were both larger than in summer, as was night when compared with day. Female elk generally restricted themselves to cover during the day and entered open areas at night and during winter months. Our results also suggest that elk in our study areas tended to seek more cover, and in the case of our Turtle Mountain study area, some cross into Canada during gun season. Our study provides a better understanding of the spatial ecology of elk in the Northern Great Plains while highlighting the need for enhanced international cooperative management efforts.

Organization: North DakotaView
A comparison of drone imagery and groundbased methods for estimating the extent of habitat destruction by lesser snow geese (Anser caerulescens caerulescens) in La Pe´rouse Bay

Lesser snow goose (Anser caerulescens caerulescens) populations have dramatically altered vegetation communities through increased foraging pressure. In remote regions, regular habitat assessments are logistically challenging and time consuming. Drones are increasingly being used by ecologists to conduct habitat assessments, but reliance on georeferenced data as ground truth may not always be feasible. We estimated goose habitat degradation using photointerpretation of drone imagery and compared estimates to those made with ground-based linear transects. In July 2016, we surveyed five study plots in La Pérouse Bay, Manitoba, to evaluate the effectiveness of a fixed-wing drone with simple Red Green Blue (RGB) imagery for evaluating habitat degradation by snow geese. Ground-based land cover data was collected and grouped into barren, shrub, or non-shrub categories. We compared estimates between ground-based transects and those made from unsupervised classification of drone imagery collected at altitudes of 75, 100, and 120 m above ground level (ground sampling distances of 2.4, 3.2, and 3.8 cm respectively). We found large time savings during the data collection step of drone surveys, but these savings were ultimately lost during imagery processing. Based on photointerpretation, overall accuracy of drone imagery was generally high (88.8% to 92.0%) and Kappa coefficients were similar to previously published habitat assessments from drone imagery. Mixed model estimates indicated 75m drone imagery overestimated barren (F2,182 = 100.03, P < 0.0001) and shrub classes (F2,182 = 160.16, P < 0.0001) compared to ground estimates. Inconspicuous graminoid and forb species (non-shrubs) were difficult to detect from drone imagery and were underestimated compared to ground-based transects (F2,182 = 843.77, P < 0.0001). Our findings corroborate previous findings, and that simple RGB imagery is useful for evaluating broad scale goose damage, and may play an important role in measuring habitat destruction by geese and other agents of environmental change.

Organization: North DakotaView
Fundamentals of Image Analysis in Google Earth Engine

Cloud-based image processing platforms like the Google Earth Engine (GEE) bring unprecedented possibilities for education, research, and outreach. This workshop will focus on an interactive exploration of GEE capabilities, the repository of all of publicly available aerial and satellite data, and user upload of imagery for analysis. The workshop will begin with a presentation of examples of GEE projects with a focus on education, undergraduate research, and outreach followed by hands-activities.

Organization: New YorkView
Automated Fractional Snow Cover Monitoring From Near-Surface Remote Sensing In Grassland

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology.

This research explores the utility of the PhenoCam network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve satellite-based determination of phenological metrics.

The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. I developed a mostly semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research, which make use of RGB images only, the use of the monochrome RGB + NIR (near-infrared) channel reduced pixel misclassification and increased accuracy. The results have an average RMSE of 7.67 compared to visual estimates. This is a promising outcome, although not every PhenoCam system has NIR capability.

Organization: North DakotaView
Mapping And Quantifying The Spatial Characteristics Of Agricultural Drainage Systems In Red River Valley Of The North

The purpose of the study is to map agricultural drainage systems (ADS) at the watershed scale using remote sensing and GIS techniques and examine the effect of ADS. For achieving the purpose of the study, this study selected the Red River Valley (RRV) of the North in which agriculture is a primary industry at the region. Excessive nutrients, sediment, and pesticide from this agricultural area flow into the Red River throughout subsurface drainages. The ADS aims to remove excessed water from agricultural fields, and this ADS is divided into two systems - uncontrolled drainage system (UCDS) and controlled drainage system (CDS). While UCDS allows water flows to the stream or river through using pipes without controlling water table, CDS regulates water table by an equipped structure that controls the volume of water flows in the agricultural fields.

For mapping artificially drained tiles between UCDS and CDS fields in the RRV, this study used DEM to digitize linear, map slope, and to calculate surface area. This study digitized linear maps with eight UCDS and twenty CDS fields and the map contains a digitization of tile drainage locations, drainage system patterns, ADS types, and artificially drained surface areas. In the analysis of the two different ADS systems – i.e., UCDS and CDS, this study obtained the indexes by using NDVI, NDWI, and MSAVI2 provided by PlanetScope imagery. In testing the group difference between UCDS and CDS in the three different indexes is examined by computing Analysis of Variance Analysis (ANOVA). Also, this study postulated CDS is more effective to the healthiness of crops than UCDS does in the ADS system.

The results of ANOVA indicated that there is no difference in the spectral indexes analyzed by ADS type (i.e., NDVI, NDWI, and MSAVI2). This result implies that the healthiness of crops is not affected ADS type, at least for the year studied here. The causes of this result, as a limitation, is derived from missing information, which is that agricultural research should consider region-specific crop calendars that involve significant idiosyncratic information, such as crop types and cycles, and regional climates. Nonetheless, natural factors in the RRV in 2019 – e.g., weather – are, of course, things outside of the control of researchers and agricultural producers.

Organization: North DakotaView
An Evaluation Of Remote Sensing Methods For Ecological Management In Theodore Roosevelt National Park

This thesis is comprised of four chapters that examine various aspects of remote sensing for ecological management in Theodore Roosevelt National Park. Chapter one reviews the use of remote sensing as a management tool and discusses its applications in TRNP. Chapter two describes the application of multiple remote sensing methods and how well they can detect and map invasive vegetation, particularly leafy spurge. Chapter three provides a study on defining and mapping vegetation communities in TRNP at varying spatial scales. Chapter four explores the use of remote sensing techniques to detect prairie dog mounds and map colonies.

Organization: North DakotaView
Use Of Landsat Satellite Imagery To Identify The Salinization Of Soil Due To Brine Spills In Northwestern North Dakota

Oil and gas development in northwestern North Dakota, has resulted in the drilling of more than 32,000 wells and produced large volumes of salt water (brine). Brine spills can have negative impacts on plant and water resources. This research evaluated the detection of brine spills in Bottineau County, North Dakota using Landsat imagery and the Canopy Response Salinity Index (CRSI). The CRSI uses ratios of near-infrared and infrared bands from Landsat imagery. Spill data for Bottineau County from 1975 to 2017 were collected from the North Dakota Department of Health for analysis of spill volume, number of spills, and timing of the spills. A total of 24 Landsat TM, ETM+ and OLI sensor images were collected and analyzed for the month of June from 1982 through 2017 using ENVI 5.3 and Arc GIS 10.6. Pre and post spill CRSI values at 197 known spill locations were evaluated using least squares linear regression analysis and the non-parametric Mann-Kendall and Kendall’s Tau tests. Regression analysis showed significant (p<0.05) decreases in CRSI values at 53.9% spill locations, while 29% showed no change and about 17.1% showed an increasing trend. The non-parametric Mann-Kendall and Kendall’s Tau analysis showed decreasing trends at 60% of the spill locations, while 30 % showed no change and 10 % showed an increasing trend. The results show that decreased CRSI values can be used to identify brine spill locations. Further study will require analysis of soil sampled from the identified local areas to confirm the chemical constituents of the soil and the source of salinity.

Organization: North DakotaView
Applications Of An Unmanned Aircraft Vehicle And Remote Cameras For Studying A Sub-Arctic Ecosystem

The midcontinent population of lesser snow geese (Anser caerulescens caerulescens) has increased dramatically since the 1960’s due to changing agricultural practices in their southern wintering areas. The destructive foraging and continued population growth of lesser snow geese has resulted in cascading negative impacts on northern ecosystems. Studying remote sub-Arctic ecosystems is logistically challenging, but the advent of remote sensing technologies (such as drones and remote cameras) may assist ecologists in understanding snow goose ecology. Before these tools can be integrated into snow goose research programs, precursor “proof-of-concept” studies are required to validate tool use. The objectives of this study were to investigate the use of unmanned aircraft systems (hereafter “drones”) and remote cameras for studying various aspects of lesser snow goose ecology within the sub-Arctic ecosystem of the Cape Churchill Peninsula, Manitoba, Canada.

Organization: North DakotaView
A time-series approach for mapping mountain pine beetle infestation extent and severity in the U.S. Central Rocky Mountains

. Severe mountain pine beetle (MPB) epidemics can degrade ecosystem services and
socioeconomic assets. Mapping outbreak progression provides tools to mitigate damages and
analyze MPB attack processes. Current time-series methods for mapping disturbance focus
on extent rather than severity. Infestation severity, defined by within-pixel percentage, is
more robust for answering a variety of ecologic questions. We develop a time-series regression
approach to map infestation severity from 2005 to 2015 in the U.S. Central Rocky Mountains.
Covariates include spectral data from all available dates of Landsat imagery, topographic data,
and US Forest Service aerial detection survey (ADS) polygons. We collect model reference data
by interpreting National Agricultural Imagery Program images. Validation against a randomly
selected subset of the data results in no statistical difference between predicted and observed
severity. The mean absolute deviation is 7.7% with a root-mean-square error of 9.9%.
Average (maximum) severity increased from 9.4% (49.7%) in 2005 to 17.6% (58.8%) in
2015. Our raster maps identify widespread, lower severity infestation absent from the ADS.
Our maps can improve mitigation efforts by allowing managers to: address low-severity
infestations before they intensify, monitor intensifying infestations within previously identified
outbreak extents, and combine infestation severity with other forest metrics.

Organization: MontanaView
Using Landsat 8 data to compare percent impervious surface area and normalized difference vegetation index as indicators of urban heat island effects in Connecticut, USA

This empirical research examines the normalized difference vegetation index (NDVI) and the percent impervious surface area (%ISA) as indicators of urban heat island (UHI) effects, using the relationships between land surface temperature (LST), %ISA, and NDVI. Landsat 8 Operational Land Imager and Thermal Infrared Sensor data were used to estimate the LST in Connecticut at different times. A map of the percent impervious surface was generated using the Impervious Surface Analysis Tool developed by the Center for Land Use Education and Research and distributed through the National Oceanic and Atmospheric Administration. Strong linear relationships between LST and %ISA exist, as stated in previous studies, whereas the relationship between LST and NDVI is evidently affected by the seasons. As the patterns of LST in urban areas are influenced by the UHI, the results demonstrate that %ISA is a more reliable indicator of UHI effects than NDVI. Thus, %ISA could be a promising alternative for use in the quantitative analysis of LST when studying UHI effects.

Organization: Connecticut View
Reconstruct missing pixels of Landsat land surface temperature product using a CNN with partial convolution

U.S. Landsat Analysis Ready Data (ARD) recently included the Land Surface Temperature (LST) product, which contains widespread and irregularly-shaped missing pixels due to cloud contamination or incomplete satellite coverage. Many analyses rely on complete LST images therefore techniques that accurately fill data gaps are needed. Here, the development of a partial-convolution based model with the U-Net like architecture to reconstruct the missing pixels in the ARD LST images is discussed. The original partial convolution layer is modified to consider both the convolution kernel weights and the number of valid pixels in the calculation of the mask correction ratio. In addition, the new partial merge layer is developed to merge feature maps according to their masks. Pixel reconstruction using this model was conducted using Landsat 8 ARD LST images in Colorado between 2014 and 2018. Complete LST patches (64x64) for two identical scenes acquired at different dates (up to 48 days apart) were randomly paired with ARD cloud masks to generate the model inputs. The model was trained for 10 epochs and the validation results show that the average RMSE values for a restored LST image in the unmasked, masked, and whole region are 0.29K, 1.00K, and 0.62K, respectively. In general, the model is capable of capturing the high-level semantics from the inputs and bridging the difference in acquisition dates for gap filling. The transition between the masked and unmasked regions (including the edge area of the image) in restored images is smooth and reflects realistic features (e.g., LST gradients). For large masked areas, the reference provides semantics at both low and high levels.

Organization: ColoradoView