Remote Sensing Commons^™

Extreme Development Of Dragon Fruit Agriculture With Nighttime Lighting In Southern Vietnam, Shenyue Jia, Son V. Nghiem, Seung-Hee Kim, Laura Krauser, Andrea E. Gaughan, Forest R. Stevens, Menas Kafatos, Khanh D. Ngo

Institute for ECHO Faculty Books and Book Chapters

Dragon fruit is widely grown in Southeast Asia and other tropical or subtropical regions. As a high-value cash crop ideal for exportation, dragon fruit cultivation has boomed during the past decade in southern Vietnam. Light supplementing during the winter months using artificial lighting sources is a widely adopted cultivation technique to boost productivity in the major dragon fruit planting regions of Vietnam. The application of electric lighting at night leads to a significant increase of nighttime light (NTL) observable by satellite sensors. The strong seasonality signal of NTL in dragon fruit cultivation enables identifying dragon fruit plantations using NTL images. …

Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam

Faculty Publications

Lack of instrument sensitivity to low electron density (N_e) concentration makes it difficult to measure sharp N_e vertical gradients (four orders of magnitude over 30 km) in the D/E-region. A robust algorithm is developed to retrieve global D/E-region N_e from the high-rate GNSS radio occultation (RO) data, to improve spatiotemporal coverage using recent SmallSat/CubeSat constellations. The new algorithm removes F-region contributions in the RO excess phase profile by fitting a linear function to the data below the D-region. The new GNSS-RO observations reveal many interesting features in the diurnal, seasonal, solar-cycle, and magnetic-field-dependent variations in the …

Accuracy Assessment, Comparative Performance, And Enhancement Of Public Domain Digital Elevation Models (Aster 30 M, Srtm 30 M, Cartosat 30 M, Srtm 90 M, Merit 90 M, And Tandem-X 90 M) Using Dgps, Kumari Preety, Anup K. Prasad, Atul K. Varma, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Publicly available Digital Elevation Models (DEM) derived from various space-based platforms (Satellite/Space Shuttle Endeavour) have had a tremendous impact on the quantification of landscape characteristics, and the related processes and products. The accuracy of elevation data from six major public domain satellite-derived Digital Elevation Models (a 30 m grid size—ASTER GDEM version 3 (Ast30), SRTM version 3 (Srt30), CartoDEM version V3R1 (Crt30)—and 90 m grid size—SRTM version 4.1 (Srt90), MERIT (MRT90), and TanDEM-X (TDX90)), as well as the improvement in accuracy achieved by applying a correction (linear fit) using Differential Global Positioning System (DGPS) estimates at Ground Control Points (GCPs) …

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler

School of Computing: Faculty Publications

Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. …

Machine Learning For Modeling Wildfire Susceptibility At The State Level: An Example From Arkansas, Usa, Abdullah Al Saim, Mohamed H. Aly

Geosciences Faculty Publications and Presentations

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 …

High Resolution, Annual Maps Of Field Boundaries For Smallholder-Dominated Croplands At National Scales, Lyndon D. Estes, Su Ye, Lei Song, Boka Luo, J. Ronald Eastman, Zhenhua Meng, Qi Zhang, Dennis Mcritchie, Stephanie R. Debats, Justus Muhando, Angeline H. Amukoa, Brian W. Kaloo, Jackson Makuru, Ben K. Mbatia, Isaac M. Muasa, Julius Mucha, Adelide M. Mugami, Judith M. Mugami, Francis W. Muinde, Fredrick M. Mwawaza, Jeff Ochieng, Charles J. Oduol, Purent Oduor, Thuo Wanjiku, Joseph G. Wanyoike, Ryan B. Avery, Kelly K. Caylor

Geography

Mapping the characteristics of Africa’s smallholder-dominated croplands, including the sizes and numbers of fields, can provide critical insights into food security and a range of other socioeconomic and environmental concerns. However, accurately mapping these systems is difficult because there is 1) a spatial and temporal mismatch between satellite sensors and smallholder fields, and 2) a lack of high-quality labels needed to train and assess machine learning classifiers. We developed an approach designed to address these two problems, and used it to map Ghana’s croplands. To overcome the spatio-temporal mismatch, we converted daily, high resolution imagery into two cloud-free composites (the …

Landslide Detection In The Himalayas Using Machine Learning Algorithms And U-Net, Sansar Raj Meena, Lucas Pedrosa Soares, Carlos H. Grohmann, Cees Van Westen, Kushanav Bhuyan, Ramesh P. Singh, Mario Floris, Filippo Catani

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover, detailed inventories are crucial for the succeeding phases of landslide risk studies like susceptibility and hazard assessment. The openly available inventories differ in the quality and completeness levels. Event-based landslide inventories are created based on manual interpretation, and there can be significant differences in the mapping preferences among interpreters. To address this issue, we used two different datasets to analyze the potential of U-Net and machine learning approaches for automated landslide detection in the Himalayas. Dataset-1 is composed …

Spatial Downscaling Of Goes-R Land Surface Temperature Over Urban Regions: A Case Study For New York City, Abdou Bah, Hamidreza Norouzi, Satya Prakash, Reginald Blake, Reza Khanbilvardi, Cynthia Rosenzweig

Publications and Research

The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min …

Developing Metrics For Nasa Earth Science Interdisciplinary Data Products And Services, Zhong Liu, Chung-Lin Shie, Anthony J. Ritrivi, Guang-Dih Lei, Gary T. Alcott, Mary Greene, James Acker, Jennifer C. Wei, David J. Meyer, Angela Li, Atheer F. Al-Jazrawi

Copyright, Fair Use, Scholarly Communication, etc.

Metrics are measures that are able to produce quantifiable information. There are many applications of metrics in Earth science data and services; for example, metrics are frequently used to track service performance and progress. In short, developing, collecting and analyzing metrics are essential activities to better support Earth science research, applications, and education.

As one of the largest repositories of Earth science data in the world, NASA’s Earth Science Data and Information System (ESDIS) Project supports twelve Distributed Active Archive Centers (DAACs). Standard metrics have been developed by the ESDIS Metrics System (EMS). These metrics are collected and analyzed routinely …

Snow Cover Variability And Trend Over The Hindu Kush Himalayan Region Using Modis And Srtm Data, Nirasindhu Desinayak, Anup K. Prasad, Hesham El-Askary, Menas Kafatos, Ghassem R. Asrar

Mathematics, Physics, and Computer Science Faculty Articles and Research

Snow cover changes have a direct bearing on the regional and global energy and water cycles and the change in the Earth's climate conditions. We studied the relatively long-term (2000–2017) altitudinal spatiotemporal changes in the coverage of snow and glaciers in one of the world's largest mountainous regions, the Hindu Kush Himalayan (HKH) region, including Tibet, using remote sensing data (5 km grid resolution) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. This dataset provided a unique opportunity to study zonal and hypsographic changes in the intra-annual (accumulating season and melting season) and interannual variations in …

A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons

Faculty Publications

Sporadic-E (E_s) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting E_s formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate E_s occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site …

Editorial: Geospace Observation Of Natural Hazards, Dimitar Ouzounov, Jann-Yenq Liu, Patrick T. Taylor, Katsumi Hattori

Mathematics, Physics, and Computer Science Faculty Articles and Research

"This collection of technical papers aims to bring recent data from many sources into the study of natural hazards. They represent a multi-instrumental approach using both ground observations: Global Navigation Satellite System (GNSS); and Low Earth Orbiting Electromagnetic (LEO EM) satellites missions together with Earth Observations (EO), which could reveal new information. Results from latest satellite missions, [(NPP/NASA/NOAA(US), CENTINEL, Swarm/ESA (EU), HIMAWARI (JMA, Japan), FORMOSAT-5 (Taiwan, August 2017), CSES1 (China/Italy, Feb 2018), and FORMOSAT-7/COSMIC-2 (Taiwan/United States, May 2019)], are represented in this volume."

Catastrophic Ice-Debris Flow In The Rishiganga River, Chamoli, Uttarakhand (India), Vijendra Kumar Pandey, Rajesh Kumar, Rupendra Singh, Rajesh Kumar, Suresh Chand Rai, Ramesh P. Singh, Arun Kumar Tripathi, Vijay Kumar Soni, S. Nawaz Ali, Dakshina Tamang, Syed Umer Latief

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

A catastrophic flood occurred on 7 February 2021 around 10:30 AM (local time) in the Rishiganga River, which has been attributed to a rockslide in the upper reach of the Raunthi River. The Resourcesat 2 LISS IV (8 February 2021) and CNES Airbus satellite imagery (9 February 2021) clearly show the location of displaced materials. The solar radiation observed was higher than normal by 10% and 25% on 6 and 7 February 2021, respectively, however, the temperature shows up to 34% changes. These conditions are responsible for the sudden change in instability in glacier blocks causing deadly rock-ice slides that …

Fire Emissions In The Tropical Indonesia: Improved Estimation And Driving Forces Investigation, Xiaoman Lu

Electronic Theses and Dissertations

Indonesia has experienced frequent fires since the 1970s due to large-scale peatland conversion and extensive drainage for agricultural development. Fire emissions released from these fires have led to Indonesia being the world’s 3rd largest emitter of greenhouse gases in certain years. Given that fire emissions severely affect climate, weather, and the human environment, numerous approaches have been developed to estimate fire emissions. However, existing emission estimates differ largely by a factor of four in this tropical country because of frequent cloud interferences and low-temperature smoldering fires. Therefore, this dissertation aims to improve the quantification of Indonesian fire emissions through enhanced …

Forging Ahead And Adapting To Change: A Review Of The Initiatives Of The Isprs Student Consortium, Sheryl Rose C. Reyes, Charmaine A. Cruz

SOSE Affiliate: Manila Observatory

The International Society for Photogrammetry and Remote Sensing Student Consortium (ISPRS SC) is an international organization that represents a constituency of the students and the young professionals with common interests and goals within ISPRS in the areas of photogrammetry, remote sensing and spatial information science. The ISPRS SC Board of Directors strengthened the organization’s foundations and increased its engagement in the Society from 2016 to 2022. Given the current global health crisis, selected members of the Board of Directors continued to serve in the ISPRS SC for a two-year extension and developed creative strategies in navigating the new normal. Building …

Shadow-Based Automatic Building Height Estimation From High Spatial Resolution Satellite Imagery, Lonnie Lee Byrnside Iii

Graduate Theses/Dissertations

Three-dimensional city (3D) models are very useful in supporting natural disaster preparation and response. LiDAR surveying is currently the main method by which 3D city models are created; however, LiDAR data on a local scale is hard to obtain for developing countries. This project sought to test whether or not urban feature height data obtained using the photogrammetric sun-angle shadow method is a viable alternative to LiDAR-derived 3D city models. A core element of this work was the development of a toolset to be shared freely to the public to promote crowdsourcing of 3D building data. Prior works were reviewed …

Relative Radiometric Correction Of Pushbroom Satellites Using The Yaw Maneuver, Christopher Begeman

Electronic Theses and Dissertations

Earth imaging satellites commonly acquire multispectral imagery using linear array detectors formatted as a pushbroom scanner. Landsat 8, a well-known example, uses pushbroom scanning and thus has 73,000 individual detectors. These 73,000 detectors are split among 14 different focal plane modules (FPM), and each detector and FPM exhibit unique behavior when monitoring a uniform radiance value. To correct for each detectors differences in sensor measurement a novel technique of relative gain estimation that employs an optimized modified Signal-to-Noise Ratio through a 90˚ yaw maneuver, also known as side slither, is presented that allows for both FPM and detector level relative …

Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li

Electrical & Computer Engineering Faculty Publications

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t₁ together with low-resolution images at a …

Arctic Greening: Characterizing Tundra Vegetation From In-Situ And Remotely Sensed Observations, Shira Ann Ellenson

Graduate Student Theses, Dissertations, & Professional Papers

As the Arctic has warmed at twice the rate of the global average, vegetation productivity has also been increasing. While satellite remote sensing is useful for summarizing Arctic-wide trends, changes in tundra species heights, densities, composition, and distribution can be missed at coarse resolution. Smaller, plot-scale studies are necessary to better understand vegetation dynamics at fine scales occurring on the ground.

In 1995, high-resolution traditional aerial photographs and in-situ measurements of vegetation characteristics were taken at a series of plots established on the Alaskan North Slope. Repeat field surveys in 2021 revealed increases in plant cover for deciduous shrubs and …

Probabilistic Tracking Of Annual Cropland Changes Over Large, Complex Agricultural Landscapes Using Google Earth Engine, Sitian Xiong, Priscilla Baltezar, Morgan A. Crowley, Michael Cecil, Stefano C. Crema, Eli Baldwin, Jeffrey A. Cardille, Lyndon Estes

Geography

Cropland expansion is expected to increase across sub-Saharan African (SSA) countries in the next thirty years to meet growing food needs across the continent. These land transformations will have cascading social and ecological impacts that can be monitored using novel Earth observation techniques that produce datasets complementary to national cropland surveys. In this study, we present a flexible Bayesian data synthesis workflow on Google Earth Engine (GEE) that can be used to fuse optical and synthetic aperture radar data and demonstrate its ability to track agricultural change at national scales. We adapted the previously developed Bayesian Updating of Land Cover …