A5025989584- Remote Sensing in Agriculture
- Service-Oriented Architecture and Web Services
- Geographic Information Systems Studies
- Remote Sensing and LiDAR Applications
- Data Management and Algorithms
- Advanced Computational Techniques and Applications
- Semantic Web and Ontologies
- Remote Sensing and Land Use
- Scientific Computing and Data Management
- Distributed and Parallel Computing Systems
- Soil Moisture and Remote Sensing
- Advanced Database Systems and Queries
- Soil Geostatistics and Mapping
- Smart Agriculture and AI
- Plant Water Relations and Carbon Dynamics
- Flood Risk Assessment and Management
- Land Use and Ecosystem Services
- Big Data Technologies and Applications
- Climate change impacts on agriculture
- Leaf Properties and Growth Measurement
- Precipitation Measurement and Analysis
- Remote-Sensing Image Classification
- Environmental Monitoring and Data Management
- Environmental and Agricultural Sciences
- Hydrology and Watershed Management Studies
George Mason University
2015-2024
University of Arizona
2016
Rutgers, The State University of New Jersey
2011
Wuhan University
2006-2008
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
2006-2008
Binghamton University
2006
Indiana State University
2004
Hong Kong University of Science and Technology
2003
University of Hong Kong
2003
State Key Laboratory of Remote Sensing Science
1998
Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. damages directly related to yield change, which requires accurate assessment quantify the damages. Various remote sensing products indices have been used in past for this purpose. This paper utilizes moderate resolution imaging spectroradiometer (MODIS) weekly normalized difference vegetation index (NDVI) product detect further corn...
Mapping nationwide in-season crop-type data is a significant and challenging task in agriculture remote sensing. The existing product for U.S. planting, such as the Cropland Data Layer (CDL), falls short facilitating near-real-time applications. This paper designed workflow aimed at automating generation of CDL-like products We methodically extracted trusted pixels land cover labels from historical CDL datasets, employing Sentinel-2, Landsat 8, Landsat-9 sources spectrum data, using random...
The remote-sensing based Flood Crop Loss Assessment Service System (RF-CLASS) is a web service system developed and managed by the Center for Spatial Information Science Systems (CSISS). uses Moderate Resolution Imaging Spectroradiometer (MODIS)-based flood data, which was implemented Dartmouth Observatory (DFO), to provide an estimation of crop loss from floods. However, due spectral similarity between water shadow, noticeable amount false classification shadow can be found in DFO products....
Abstract Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research to estimate the that occurs by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating DVDI along with information on crop types inundation extents, assessed three case-study events: Iowa Severe Storms Flooding (DR 4386), Nebraska 4387), Texas 4272). Crop qualitative scale reported at...
Research in different agricultural sectors, including crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used the mapping monitoring of floods. However, inability optical to cloud penetration scarcity fine temporal resolution SAR data hinder application many cases. Soil Moisture Active Passive (SMAP) level 4 products, which are model-driven soil moisture derived from SMAP observations available at...
Floods often cause significant crop loss in the United States. Timely and objective information on flood-related loss, such as flooded acreage degree of damage, is very important for monitoring risk management agricultural disaster-related decision-making at many concerned agencies. Currently agencies mostly rely field surveys to obtain compensate farmers' claim. Such methods are expensive, labor intensive, time consumptive, especially a large flood that affects geographic area. The results...
Land use and land cover (LULC) classification using satellite images is an important approach to monitor changes on earth. To produce LULC maps, supervised methods are often used. For many algorithms, independence of features implied assumption. However, this assumption rarely tested. classification, all bands as input models the default approach. some may be highly correlated, which cause model performances unstable. In research, correlations multicollinearity among multi-spectral analyzed...
In a Web service‐based distributed environment, individual services must be chained together dynamically to solve complex real world problem. The Semantic Service has shown promise for automatic chaining of services. This paper addresses semi‐automatic geospatial service through Services‐based process planning. Process planning includes three phases: modeling, model instantiation and workflow execution. Ontologies Artificial Intelligence (AI) methods are employed in help user create an...
NDVI maps have been proven valuable in providing a spatially complete view of crop's vegetation condition, which manifests disastrous events such as massive flood and drought. It is virtually impossible to obtain from ground survey data. This paper uses NASA MODIS 250m resolution, daily surface reflectance data for crop condition monitoring. The provides an absolute metrics condition. However, relative measurement the current against reference critical understanding, interpreting quantifying...
Understanding the event of flood and its impacts, especially towards agriculture, is an extremely significant component; however exceedingly complicated process at same time. That said research on identifying damages in agricultural sector not getting as much attention it should be. Flood are directly depends impact exert crops needs accurate a prediction possible to quantify these damages. Various remote sensing techniques productions have been used past for this purpose. This paper will...
Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM) based method combining multisource features has been presented. The include mean Normalized Difference Vegetation Index (NDVI), fractal dimension, Accumulated Growing Degree Days (AGDDs). our case, these are global variable, measured in state-level. Moreover, feature each Day Year (DOY) would be impacted by multiple stages. Therefore, mixture...
Normalized Difference Vegetation Index (NDVI)-precipitation correlation has long been studied. In previous studies, the was usually based on global regression model, which assumed such be constant across space. However, NDVI-precipitation is spatially dependent and affected by local factors (e.g., soil background). this paper, geographically weighted model utilized to analyze three land use types (i.e., 1) grassland, 2) fallow/idle land, 3) winter wheat land) within U.S. central great plain...
Agriculture is one of the most affected sectors by flood. Spaceborn remote sensing widely used for flood mapping and monitoring in recent decades. Some applications such as crop loss assessment require data with fine temporal resolution to monitor short-lived MODIS providing 1-2 days which has frequently been a large area. However, incapability penetrate through cloud hindered application optical many cases. Thus, radar especially synthetic aperture (SAR) already shows capability condition....
Bangladesh is one of the most vulnerable countries to sea level rise due climate change. Soil salinity potential threat coastal ecosystem and agriculture which might hinder country's future food security. Conventional field-based soil monitoring over vast region may not be cost time efficient. Satellite remote sensing offering an efficient way monitor via different indices. This study monitors in five years interval from 1990 2015 using a regression equation developed tested same...
A new, simple, remotely-sensed index, the disaster vegetation damage index (DVDI), has been proposed to measure vegetation/crop due natural disasters. DVDI is based on measuring difference of condition immediately before and after a disaster. Preliminary application two recent flood events indicated that an effective degree