Chen Zhang

Chen Zhang
George Mason University | GMU · Center for Spatial Information Science and Systems

Ph.D. in Earth Systems and Geoinformation Sciences

About

46
Publications
23,359
Reads
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437
Citations
Introduction
Ph.D. in Earth Systems and Geoinformation Sciences, M.S./B.S. in Computer Science. My research interests include: Geographic Information Science and Systems, Remote Sensing, Earth System Science, Agro-Geoinformatics, Land Use and Land Cover Change, Environmental Modeling, Geospatial Information Interoperability and Standards, Artificial Intelligence and Machine Learning, Image Processing and Analysis. For more information, please visit http://chenzhang.org.
Additional affiliations
September 2015 - present
George Mason University
Position
  • Research Assistant
Education
September 2015 - May 2020
George Mason University
Field of study
  • Earth Systems and Geoinformation Sciences

Publications

Publications (46)
Article
Full-text available
As an important tool for air quality simulation, the Community Multiscale Air Quality (CMAQ) model is widely used in the environmental modeling community. However, setting up and running the CMAQ model could be challenging for many scientists, especially when they have limited computing resources and little experience in handling large-scale input...
Article
Full-text available
An accurate crop planting map can provide essential information for decision support in agriculture. The method of post-season and in-season crop mapping has been widely studied in the land use and land cover community. However, it remains a challenge to predict the spatial distribution of crop planting before the growing season. This paper is the...
Article
Full-text available
Google Earth Engine (GEE) is an ideal platform for large-scale geospatial agricultural and environmental modeling based on its diverse geospatial datasets, easy-to-use APIs, rich reusable library, and high-performance computational capacity. However, using GEE to prepare agricultural land use data for geospatial agricultural and environment modelin...
Article
Full-text available
A timely and detailed crop-specific land cover map can support many agricultural applications and decision makings. However, in-season crop mapping over a large area is still challenging due to the insufficiency of ground truth in the early stage of a growing season. To address this issue, this paper presents an efficient machine-learning workflow...
Article
Full-text available
Lagos, Nigeria, is considered a rapidly growing urban hub. This study focuses on an urban development characterization with remote sensing-based variables for Lagos as well as understanding spatio-temporal precipitation responses to the changing intensity of urban development. Initially, a harmonic analysis showed an increase in yearly precipitatio...
Article
Full-text available
Currently Lyme disease (LD) is the most common vector-borne disease in the United States. Understanding the potential effects of urban expansion on LD risk is an emerging global health concern. The U.S. Northeastern corridor has experienced a spatio-temporal increase in Lyme disease (LD) and rapid urban expansion over the past decades. The effects...
Preprint
This study aims to develop a general method and evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. Combined daytime and nighttime daily MODIS products were used to fill data biases from satellite-observed data and applied to tropical regions during dry season fro...
Article
Full-text available
Space-based crop identification and acreage estimation have played a significant role in agricultural studies in recent years, due to the development of Remote Sensing technology. The Cropland Data Layer (CDL), which was developed by the U.S. Department of Agriculture (USDA), has been widely used in agricultural studies and achieved massive success...
Article
Full-text available
In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social...
Conference Paper
Full-text available
Cyberinformatics tools have been extensively applied to aid decision support in agriculture. This paper presents an overview of cyberinformatics tools to support decision making for the National Agricultural Statistics Service (NASS) of the U.S. Department of Agriculture (USDA). We review three web-based applications: CropScape, VegScape, and Crop-...
Chapter
Full-text available
Land parcel is the finest unit to describe the location, boundary, and ownership in land management. Land survey is the most popular way to identify land parcel in the history of land management. However, land parcel survey come with huge financial cost while the accuracy of the survey is not acceptable for many applications such as agricultural ma...
Chapter
Image processing is an essential part of the agricultural observation system. This chapter is the first attempt to provide an overview of the image processing methods, technologies, and tools from the perspective of agro-geoinformatics. First, we introduce the origins, definitions, and basic steps of digital image processing. Along with the traditi...
Article
Training samples is fundamental for crop mapping from remotely sensed images, but difficult to acquire in many regions through ground survey, causing significant challenge for crop mapping in these regions. In this paper, a transfer learning (TL) workflow is proposed to use the classification model trained in contiguous U.S.A. (CONUS) to identify c...
Article
Full-text available
As the most widely used crop-specific land use data, the Cropland Data Layer (CDL) product covers the entire Contiguous United States (CONUS) at 30-meter spatial resolution with very high accuracy up to 95% for major crop types (i.e., Corn, Soybean) in major crop area. However, the quality of early-year CDL products were not as good as the recent o...
Conference Paper
Full-text available
Cropland Data Layer (CDL) is an annual crop-specific land use map produced by the U.S. Department of Agricultural (USDA) National Agricultural Statistics Service (NASS). The CDL products are officially hosted on CropScape website which provides capabilities of geospatial data visualization, retrieval, processing, and statistics based on the open ge...
Conference Paper
Full-text available
It is still a challenge to generate the timely crop cover map at large geographic area due to the lack of reliable ground truths at early growing season. This paper introduces an efficient method to extract “trusted pixels” from the historical Cropland Data Layer (CDL) data using crop rotation patterns, which can be used to replace the actual groun...
Article
Full-text available
The remote-sensing based Flood Crop Loss Assessment Service System (RF-CLASS) is a web service based system developed and managed by the Center for Spatial Information Science and Systems (CSISS). The system uses Moderate Resolution Imaging Spectroradiometer (MODIS)-based flood data, which was implemented by the Dartmouth Flood Observatory (DFO), t...
Article
Full-text available
Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal...
Conference Paper
Full-text available
Bangladesh is one of the most vulnerable countries to sea level rise due to the climate change. Soil salinity is one of the potential threat to the coastal ecosystem and agriculture which might hinder the country’s future food security. Conventional field-based soil salinity monitoring over vast region may not be cost and time efficient. Satellite...
Conference Paper
Full-text available
Pollution is one of the main negative outcomes for rapid economic growth without sustainable development in China. Different types of pollutions are harming people's health and the impacts of pollution on environment and people's health could last for decades. Fine particulate matter(PM2.5), which is one of most common types of air pollutions in Ch...
Article
Full-text available
Feeding multisource Earth observation (EO) data into Earth science models (ESM) remains a daunting challenge. This paper presents a service-oriented approach as an alternative solution. It uses geospatial web services to process the EO data and geoprocessing workflow for automation. Different from existing approaches, it takes advantage of virtual...
Conference Paper
Full-text available
Statistics show the volume of Earth Observation (EO) data increases in the exponential level during the past decade. As the new generation computing platform to meet the big data challenge, cloud computing significantly facilitates the large-scale EO data processing depending on its powerful computing capability. In this paper, we propose a Cloud W...
Conference Paper
Full-text available
Abstract: Agriculture is one of the most affected sectors by the flood. Spaceborn remote sensing is widely used for flood mapping and monitoring in recent decades. Some applications such as flood crop loss assessment require data with fine temporal resolution to monitor short-lived flood. MODIS is providing remote sensing data with 1-2 days tempora...
Article
It is still a great challenge to efficiently deliver dynamic and heterogeneous Earth observation (EO) information to users based on their real time locations. However, the rapidly evolving techniques create a chance to meet the challenge. This paper proposes a framework to realize a one-stop and location based service (LBS) for geospatial informati...
Conference Paper
The Pub/Sub, short for Publish-Subscribe, is a flexible mechanism perferred by many users who'd like to passively know the changes of situation. Once a new message is published by a provider, all the subscribers to the specific kind of messages will receive the message and make corresponding responses. In agricultural crop monitoring, such mechanis...
Conference Paper
The timely retrieval of remote sensing imagery by farmers and decision makers is very important for current agricultural activities. Through the various kinds of imageries of agricultural fields, people can conclude the status of the fields and figure out what kind of crops are suitable and how to cultivate and irrigate the fields. This paper demon...
Conference Paper
Full-text available
Flood is defined as water that temporarily submerges land. In the United States, flood is being considered as an event only if it last over 72 hours, but the time frame is much longer in the Europe. The information of water bodies is an important source when examining floods. However, this data is not always available or accurate by traditional sur...
Conference Paper
Full-text available
GeoPackage, an open format for geospatial information, provides a gateway to bridge agricultural geographic information and mobile devices such as smartphones and tablets. In this paper, we present a Cordova framework based GeoPackage mobile application to support field operations in agriculture. By implementing GeoPackage SDK on mobile application...
Conference Paper
Full-text available
Characterized by features of standards-based, platform-independent, portable, self-describing, and compact, GeoPackage, a new open format for geospatial information container, makes it much easier to manipulate geospatial data on mobile devices such as smartphones and tablets. In this paper, we present a GeoPackage based mobile application implemen...

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Projects

Projects (3)
Project
PRINCIPAL INVESTIGATOR (PI): Prof. Liping Di Irrigation significantly increases crop yields but consumes approximately 80% of consumptive water and 23-48% of all energy used in crop production in U.S. Therefore, developing low-cost efficient irrigation technologies, applicable at the state or even national level, will have significant impact in enhancing agricultural profitability and competitivity and environmental and economical health. Data-driven irrigation scheduling approaches, based on timely and reliable measures of crop, soil, and weather conditions, have demonstrated significant water and energy savings and economic and environmental benefits, but require farmers to have knowledge of the approaches and timely and reliable measures, which they don’t normally possess. The project will strengthen food and energy security and environmental health of our nation, bring immense economic benefits to farmers, and help the US Department of Agriculture reach its 2030 sustainable agriculture goals. The goals are to save ~10% of overall irrigation water and energy usage, maximize irrigation returns on food and energy, and reduce farmer’s required time and investment in irrigation management. WaterSmart, if successfully demonstrated in Nebraska, can be scaled to national level, resulting in a potential direct saving of 8.85 million acre-feet of water and $270 million in irrigation energy cost per year. WaterSmart will be built through maturing and systematically integrating recent advances in data-driven irrigation management, remote sensing, agro-geoinformatics, ontology-driven decision-making, modeling, Web service, and CI technologies. This project will automate data-driven approaches by developing a cyberinfrastructure (CI)-based integrated agro-geoinformatic decision-support Web service system, WaterSmart, and demonstrating its real-time operational capability for Nebraska at virtually no cost to farmers.
Project
for Supporting USDA Crop Statistics and Insurance Decision Making