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Samsts Satellite Time Series Gap Filling Source Codes - Landsat, Lin Yan, David P. Roy 2020 South Dakota State University

Samsts Satellite Time Series Gap Filling Source Codes - Landsat, Lin Yan, David P. Roy

SAMSTS Satellite Time Series Gap Filling Source Codes

  • SAMSTS is open-source software (coded in C) for gap filling of Landsat multispectral time series developed by Drs. Lin Yan and David Roy.
  • SAMSTS 1.1.1 release includes source codes, user manual and test data.
  • The test data are year-2013 time series of Landsat 7 ETM+ and Landsat 8 OLI ARD surface reflectance products (http://landsat.usgs.gov/ard) over Kansas and Iowa. Each ARD tile is composed of 5000 x 5000 30m pixels with six bands (blue, red, green, NIR, SWIR 1, SWIR 2).
  • Major Software functionality:
    • Automatic gap filling of Landsat multispectral time series caused by clouds ...


Changes In Land Use Land Cover (Lulc), Surface Water Quality And Modelling Surface Discharge In Beaver Creek Watershed, Northeast Tennessee And Southwest Virginia, Tosin James 2020 East Tennessee State University

Changes In Land Use Land Cover (Lulc), Surface Water Quality And Modelling Surface Discharge In Beaver Creek Watershed, Northeast Tennessee And Southwest Virginia, Tosin James

Electronic Theses and Dissertations

Beaver Creek is an impaired streams that is not supporting its designated use for recreation due to Escherichia coli (E.coli), and sediment. To address this problem, this thesis was divided into two studies.

The first study explored changes in Land Use Land Cover (LULC), and its impact on surface water quality. Changes in E.coli load between 1997-2001 and 2014-2018 were analyzed. Also, Landsat data of 2001, and 2018 were examined in Terrset 18.31. Mann-Whitney test only showed a significant reduction in E.coli for one site. Negative correlation was established between E.coli load, and Developed LULC ...


Learning About Learning With Deep Learning: Satellite Estimates Of School Test Scores, Heather M. Baier 2020 William & Mary

Learning About Learning With Deep Learning: Satellite Estimates Of School Test Scores, Heather M. Baier

Undergraduate Honors Theses

Convolutional neural networks are deep-learning models commonly applied when analyzing imagery. Convolutional neural networks and satellite imagery have shown potential for the global estimation of key factors driving socioeconomic ability to adapt to global change. Unlike more traditional approaches to data collection, such as surveys, approaches based on satellite data are low cost, timely, and allow replication by a wide range of parties. We illustrate the potential of this approach with a case study estimating school test scores based solely on publicly available imagery in both the Philippines (2010, 2014) and Brazil (2016), with predictive accuracy across years and regions ...


Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing, Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola 2020 Resonant Sciences

Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing, Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola

Faculty Publications

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed ...


A View From Above: Alternative Perspectives On Smallholder Livelihoods And Agrobiodiversity Conservation In Northern Ecuador, Chris Hair 2020 The University of Southern Mississippi

A View From Above: Alternative Perspectives On Smallholder Livelihoods And Agrobiodiversity Conservation In Northern Ecuador, Chris Hair

Dissertations

Food security and deintensification of agriculture are serious concerns in Latin America. Agriculture, especially at small-scale subsistence levels, is hard work, and comes with some economic and physical risk. Transitions from traditional multi-cropping to mono-cropping systems introduce two particular risks that are new to most smallholders: (1) the loss of agricultural diversity and (2) the potential for widespread failure when focusing on the cultivation of a single crop. This research explores how Small Unmanned Aerial Systems (sUAS), or drones, can be used for rapid inventories of crop diversity and to enhance crop management techniques on small-scale farms. In the community ...


Unveiling Shadows: How To Optimize Shadow Detection In Hsi Through Combination Of Lidar And Histogram Thresholding, Maritza Salinas 2020 University of Northern Iowa

Unveiling Shadows: How To Optimize Shadow Detection In Hsi Through Combination Of Lidar And Histogram Thresholding, Maritza Salinas

Research in the Capitol

From “multi-” to “hyper-” spectral, remote sensing capacities have improved tremendously in how we measure Earth’s unique signatures. Unfortunately, shadow detection and correction remain an issue in most images, especially those with high spatial resolution. Shadows result when direct sun light is obstructed and the spectral reflectance values for pixels in those regions decrease. Many successful approaches exist to correct this blue skew to shorter wavelengths, but it can be daunting to truly assess which approach to employ since each require different levels of priori knowledge. This research attempts to generate and cross-validate shadow masks using popular GIS software ...


Changes In Atmospheric, Meteorological, And Ocean Parameters Associated With The 12 January 2020 Taal Volcanic Eruption, Feng Jing, Akshansa Chauhan, Ramesh P. Singh, Prasanjit Dash 2020 China Earthquake Administration

Changes In Atmospheric, Meteorological, And Ocean Parameters Associated With The 12 January 2020 Taal Volcanic Eruption, Feng Jing, Akshansa Chauhan, Ramesh P. Singh, Prasanjit Dash

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The Taal volcano erupted on 12 January 2020, the first time since 1977. About 35 mild earthquakes (magnitude greater than 4.0) were observed on 12 January 2020 induced from the eruption. In the present paper, we analyzed optical properties of volcanic aerosols, volcanic gas emission, ocean parameters using multi-satellite sensors, namely, MODIS (Moderate Resolution Imaging Spectroradiometer), AIRS (Atmospheric Infrared Sounder), OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) and ground observations, namely, Argo, and AERONET (AErosol RObotic NETwork) data. Our detailed analysis shows pronounced changes in all the parameters, which mainly occurred in the western and south-western regions because ...


Microwave Brightness Temperature Characteristics Of Three Strong Earthquakes In Sichuan Province, China, Feng Jing, Ramesh P. Singh, Yueju Cui, Ke Sun 2020 China Earthquake Administration

Microwave Brightness Temperature Characteristics Of Three Strong Earthquakes In Sichuan Province, China, Feng Jing, Ramesh P. Singh, Yueju Cui, Ke Sun

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Passive microwave remote sensing technology is an effective means to identify the thermal anomalies associated with earthquakes due to its penetrating capability through clouds compared with infrared sensors. However, observed microwave brightness temperature is strongly influenced by soil moisture and other surface parameters. In the present article, the segmented threshold method has been proposed to detect anomalous microwave brightness temperature associated with the strong earthquakes occurred in Sichuan province, China, an earthquake-prone area with high soil moisture. The index of microwave radiation anomaly (IMRA) computed by the proposed method is found to enhance prior to the three strong earthquakes, 2008 ...


Regional Forest Volume Estimation By Expanding Lidar Samples Using Multi-Sensor Satellite Data, Bo Xie, Chunxiang Cao, Min Xu, Barjeece Bashir, Ramesh P. Singh, Zhibin Huang, Xiaojuan Lin 2020 Chinese Academy of Sciences

Regional Forest Volume Estimation By Expanding Lidar Samples Using Multi-Sensor Satellite Data, Bo Xie, Chunxiang Cao, Min Xu, Barjeece Bashir, Ramesh P. Singh, Zhibin Huang, Xiaojuan Lin

Mathematics, Physics, and Computer Science Faculty Articles and Research

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini ...


Remote Sensing Monitoring Of Vegetation Dynamic Changes After Fire In The Greater Hinggan Mountain Area: The Algorithm And Application For Eliminating Phenological Impacts, Zhibin Huang, Chunxiang Cao, Wei Chen, Min Xu, Yongfeng Dang, Ramesh P. Singh, Barjeece Bashir, Bo Xie, Xiaojuan Lin 2020 Chinese Academy of Sciences

Remote Sensing Monitoring Of Vegetation Dynamic Changes After Fire In The Greater Hinggan Mountain Area: The Algorithm And Application For Eliminating Phenological Impacts, Zhibin Huang, Chunxiang Cao, Wei Chen, Min Xu, Yongfeng Dang, Ramesh P. Singh, Barjeece Bashir, Bo Xie, Xiaojuan Lin

Mathematics, Physics, and Computer Science Faculty Articles and Research

Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent ...


Using Unmanned Aerial Systems (Drones) With A Thermal Sensor To Map And Count Deer Population, Maxwell C. Ott 2020 The University of Akron

Using Unmanned Aerial Systems (Drones) With A Thermal Sensor To Map And Count Deer Population, Maxwell C. Ott

Williams Honors College, Honors Research Projects

The number of deer in an area is an important statistic for land managers to know, as overabundance has many negative effects. There are many methods that have been used to count deer in the past, such as using manned helicopters and airplanes, walking on foot, and conducting controlled hunts. UAS (unmanned aerial systems) is a growing field that provides many benefits over traditional methods of counting deer, such as lower cost and missions being less time consuming. Using a thermal sensor attached to a UAS makes it simple to spot any deer during a flight. Two main methods of ...


Spectrally Derived Values Of Community Leaf Dry Matter Content Link Shifts In Grassland Composition With Change In Biomass Production, H. Wayne Polley, Chenghai Yang, Brian J. Wilsey, Philip A. Fay 2020 U.S. Department of Agriculture

Spectrally Derived Values Of Community Leaf Dry Matter Content Link Shifts In Grassland Composition With Change In Biomass Production, H. Wayne Polley, Chenghai Yang, Brian J. Wilsey, Philip A. Fay

Ecology, Evolution and Organismal Biology Publications

Leaf traits link environmental effects on plant species abundances to changes in ecosystem processes but are a challenge to measure regularly and over large areas. We used measurements of canopy reflectance from grassland communities to derive a regression model for one leaf trait, leaf dry matter content (LDMC). Partial least squares regression (PLSR) analysis was used to model community‐weighted (species abundance‐weighted) values of LDMC as a function of canopy reflectance in visible and near‐infrared (NIR) wavebands. The PLSR model then was applied to airborne measurements of canopy reflectance to determine how community LDMC interacts with inter‐annual ...


Use Of Gis Spatial Analysis, Remote Sensing, And Unmanned Aerial Systems In Determining The Susceptibility To Wildfires In Barber County, Kansas, Kara Sill 2020 Fort Hays State University

Use Of Gis Spatial Analysis, Remote Sensing, And Unmanned Aerial Systems In Determining The Susceptibility To Wildfires In Barber County, Kansas, Kara Sill

Master's Theses

Wildfires are becoming more frequent each year not only in the United States, but throughout the world. Barber County, Kansas experienced a devastating wildfire in March 2016, and continues to be at risk of wildfires during the fire season months. This study involved creating a functional GIS Database with layers corresponding to communication, transportation, and infrastructure throughout the county. This will allow responders and officials to have one unified reference space, which will facilitate communication and navigation. Within the ArcGIS environment, route maps can be created to show potential routes and quickest drive time to the scene of the emergency ...


A Karst Feature Predictability Model Within Barber County, Kansas, Gary M. Kelner 2020 Fort Hays State University

A Karst Feature Predictability Model Within Barber County, Kansas, Gary M. Kelner

Master's Theses

This research consisted of two topics: 1) geographic predictive models of karst features and 2), a petrographic study examining the lithology of the study area. The study area is a privately owned ranch in the Gypsum Hills of Barber County, Kansas and is known to have karst features. Two predictive models for karst features were utilized. Previously identified features, Light Detection and Ranging (LiDAR), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery aided in the creation of these predictive models. These predictability models also used the ESRI ArcMap software platform. The data for these models consists of slope ...


Characterizing Burn Severity Of Beetle-Killed Forest Stands Leveraging Google Earth Engine-Derived Normalized Burn Ratios, Sofronio Catalino Propios III 2020 University of Montana

Characterizing Burn Severity Of Beetle-Killed Forest Stands Leveraging Google Earth Engine-Derived Normalized Burn Ratios, Sofronio Catalino Propios Iii

Graduate Student Theses, Dissertations, & Professional Papers

Following numerous studies, a general consensus on burn severity in forests affected by bark beetle outbreaks has not yet been achieved. The purpose of this study is to characterize burn severities in forest stands affected by mountain pine beetle (MPB) outbreaks, especially in relation to “time since outbreak”, vegetation cover, and topographic factors. This study focuses on wildfires that occurred in the northern Rocky Mountains of Idaho and Montana during the 2012 fire season within forested areas that had previously experienced prior MPB outbreaks. Remote sensing techniques were used to quantify and compare the burn severities of MPB-outbreak stands with ...


Extracting Agronomic Information From Smos Vegetation Optical Depth In The Us Corn Belt Using A Nonlinear Hierarchical Model, Colin Lewis-Beck, Victoria A. Walker, Jarad Niemi, Petrutza Caragea, Brian K. Hornbuckle 2020 Iowa State University

Extracting Agronomic Information From Smos Vegetation Optical Depth In The Us Corn Belt Using A Nonlinear Hierarchical Model, Colin Lewis-Beck, Victoria A. Walker, Jarad Niemi, Petrutza Caragea, Brian K. Hornbuckle

Statistics Publications

Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of ...


Gendered Access To Wetland Gardens (Dimba) In Northern Malawi, Rhoda Nyirenda 2020 WVU

Gendered Access To Wetland Gardens (Dimba) In Northern Malawi, Rhoda Nyirenda

Graduate Theses, Dissertations, and Problem Reports

Due to increasing degradation of upland fields and in the face of erratic rains and increasing occurrence of droughts and floods and increasing food prices, smallholder farmers in many places across sub Saharan Africa engage in wetland cultivation for livelihoods security (Mutambikwa et al., 2000). A cultivatable wetland area is considered prime land, and a desired opportunity that every rural family need for the purpose of food production. Studies have indicated that wetland cultivation significantly contributes to household’s income and food security. Wetland agriculture, however, in Malawi and most of the sub-Saharan African countries is marred with issues of ...


Reconstructing Past Forest Composition And Abundance By Using Archived Landsat And National Forest Inventory Data, Bina Thapa, Peter T. Wolter, Brian R. Sturtevant, Philip A. Townsend 2020 Iowa State University

Reconstructing Past Forest Composition And Abundance By Using Archived Landsat And National Forest Inventory Data, Bina Thapa, Peter T. Wolter, Brian R. Sturtevant, Philip A. Townsend

Natural Resource Ecology and Management Publications

Effective modelling of forest susceptibility to defoliating insect outbreaks requires a better understanding of outbreak dynamics, which includes detailed knowledge of the pre- and post-outbreak forest status as well as subsequent feedback mechanisms. In this paper, we strive to fill the forest status need by combining archived Landsat sensor data (pre- and post-outbreak) with different formats and dates of the U.S. Forest Service’s Forest Inventory and Analysis (FIA) data (periodic [1970s, 1990s] and annual [2003–2006]). Specifically, we explore the utility of these FIA ground data for calibrating models of forest species and type abundance for mapping past ...


Remote Sensing Approaches To Predict Forest Characteristics In Northwest Montana, Ryan P. Rock 2020 University of Montana

Remote Sensing Approaches To Predict Forest Characteristics In Northwest Montana, Ryan P. Rock

Graduate Student Theses, Dissertations, & Professional Papers

Remote sensing can be utilized by land management organizations to save money and time. Mapping vegetation using either aerial photographs or satellite imagery and the applications for forest management are of particular interest to the Montana Department of Natural Resources. In 2018, the organization began a pilot program to test the incorporation of raster analysis of remotely sensed data into their inventory program and had limited success. This analysis identified two areas of improvement: the selection method of inventory plots and the imagery used for classification and metrics. This study found that selecting inventory plots using a generalized random tessellation ...


Using Unmanned Aircraft Systems To Identify Invasive Species, Tithe Ahmed 2020 Western Kentucky University

Using Unmanned Aircraft Systems To Identify Invasive Species, Tithe Ahmed

Mahurin Honors College Capstone Experience/Thesis Projects

Invasive species serve as a threat to native biodiversity and ecosystem sustainability. Combatting the spread of invasive species requires long-term physical and monetary commitments. In Balule Nature Reserve of Greater Kruger National Park, South Africa, Opuntia ficus-inidica (the common prickly pear) has been a relentless invader, displacing the local flora and fauna. The goal of this project is to battle invasive species such as prickly pear using efficient and inexpensive technology: unmanned aerial vehicles (UAVs or drones) and multispectral sensors.

Using a 4-bandwidth Parrot Sequoia multispectral sensor in tandem with the DJI Phantom Pro 3TM UAV, images of land ...


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