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Maximizing Geospatial Baseline Data for Optimizing Landsat
Imageries: Assessing Climate Change Impacts
B. Acharya, PhD, PLS Earth Mapping International
B. Gyawali, PhD Kentucky State University
D. Zourarakis, PhD Kentucky Division of Geographic Information
T. Shrestha, PhD Kentucky State University
A. Acharya Earth Mapping International
ASPRS 2017 Annual Conference Baltimore, Maryland ◊ March 11-17, 2017 1
Landsat imageries have been the main source of impact analysis for regional and or global climate change studies. With the perpetual advent of new sensors and platforms, it is possible to assess microclimate variations in the local settings. This paper is the product of the USDA-funded research and extension project. The objectives of the research are: to examine land cover change between 1986 and 2016 and evaluate patterns of surface mining and reclamation in the study region; (2) to assess the effects of surface mining and reclamation on human livelihood and microclimatic conditions and develop extension program to address these effects; and (3) to strengthen the research and extension capacity of KSU faculty, field extension staff, and students. This paper will present research performed to accomplish the objective one (1) of the research. The study area covers seven contiguous counties in the Eastern Kentucky; the economy of the area depends on natural resources such as the high record of surface mining. The Mountain Top Removal is the prevalent type of mining out of approximately 7,727 (active and inactive) mines in the study area. The other types of mining such as Area, Open Pit, Contour, Auger, and Underground are also used frequently in combination. The majority of spatially accurate and temporally consistent geospatial/remote sensing data is essential to extract information required to study microclimate variations in the local region. Baseline geospatial: existing LiDAR 1-meter GSD, topographic and MSS 60-cm GSD ortho-imageries are used to classify baseline land use and cover data covering the study area. Two field trips were made, one in September 2016 to tangibly comprehend the situation on the ground by interviewing mining companies, landowners, farmers and mining workers, and the other one in February 2016 to capture 4-band strip images and videos using unmanned airborne system (UAS) to use as ground-truthing data. The UAS captured images covering various mining activities such as reclaimed areas, tailings ponds, water bodies, vegetation, exposed soils, and rocks. More than 600 gigabits of geospatial data were extracted from various portals such as USGS, NASA, USDA, and Kentucky Geoportal. The USDA NAIP 60-cm resolution multispectral ortho-imageries captured in summer 2016, Kentucky LiDAR data of 2009, and NASA SRTM data are used as geospatial baseline data to optimize the Landsat imageries. Thirty years of Landsat time series thermal and MSS imagery data captured during the summer seasons: 1986, 1991, 2001, 2011, 2016 are used to forecast temporal and spatial changes and hotspots in the region. The geospatial baseline data is used to enhance, control, and improve the quality and accuracy of the research. The data is processed to produce normalized difference vegetation index (NDVI) and 20 land cover and land use (LCLU) data using both unsupervised and supervised classifications. The classification baseline data will be used as accuracy check and to enhance classifications of 10 different LCLU classes using Landsat imageries; the LCLU statistical time series data will be used to create various forecasting models.
Landsat Imageries
Airborne Imageries
Seven Contiguous Counties in the Eastern
KY
Surface Mining/Mountain Top Removal
LiDAR 1-m GSD
MSS Orthoimageries 60-cm GSD
Soil, Geology, Mining, Meteorology, Industrial Waste
30-year Time Series, Landsat MSS & Thermal Imageries
GPM and Other Weather Satellites
Airborne Multispectral Orthoimageries, UAS Videos, and Imageries
Enhanced Accuracy for Optimal Landsat Data Usage Improved Quality of Research
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KENTUCKY
(120 Counties, 104,659 km2)
County Area
(km2)
Population Mines
(Active + Inactive)
Floyd 1,023 39,451 1,273
Martin 914 16,346 160
Magoffin 800 13,333 102
Pike 2,041 20,377 3,012
Knott 886 28,712 838
Perry 596 12,929 669
Letcher 878 24,519 1,223
Total 7,138 155,667 7,277
(Data Sources: EMI + KyOMSL) 3/16/2017 ASPRS Conference 3
PRIMARY
EXPANDING CAPACITY FOR RESEARCH & EXTENSION PROGRAMS 1. To examine land cover change between 1982 and 2014 and evaluate patterns of surface
mining and reclamation in the study region;
2. To assess the effects of surface mining and reclamation on human livelihood and microclimatic conditions and develop extension program to address these effects; and
3. To strengthen the research and extension capacity of KSU faculty, field extension staff, and students by creating research and extension skills enrichment and experiential learning opportunities.
RELEVANT TO THIS PRESENTATION To examine land cover change between 1986 and 2016 and evaluate patterns of surface mining and reclamation in the study region. Creating and utilizing optimal geospatial baseline data for maximizing the usage of Landsat archived imageries for analyzing impacts of coal mining on local environments.
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“Plans are nothing, planning is everything” – Dwight D Eisenhower Planning for climate change studies compels comprehensive knowledge, thinking both inside and outside the box, short and long term visions, deployment and optimization of all available tools, and mindful of the reality of time towards creating workable and optimal plans. Planning has to be a non-stop and contemporary process.
The above quote from the former President of the USA is relevant to our research objectives and in accomplishing the anticipated outcomes. Planning for creating the Baseline Geospatial Database (BGDB) comprises a comprehensive knowledge-base not only in the field of geospatial technology but it also requires an integration of information base from all the STEM areas. The steps in planning towards creating and selecting appropriate criteria for optimizing existing and newly collected data are discussed as shown in the Table below.
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Reviews Data Exploration & Mining (600+ Gigabytes of Data)
Methodology
Deployment of Appropriate Tools Data Standardization Integration and Fusion
Ground Verifications Satellite + Airborne Classification Techniques
Accuracy Assessments UAS Data Capture Time Series Analysis
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VIDEO
Product Resolution (GSD)
(Ground Sampling
Distance)
Agency Acquisition Time
UAS Data 1-cm, 2.5-cm, 5-cm KSU-EMI 2017
NAIP Orthoimageries 60-cm, 1-m, 4-m USDA-KY 2016, 2012, 2006
Landsat Imageries
Landsat 8, 7, 5
15-m, 30-m, 100-m
USGS 2016, 2011, 2006,
2001, 1996, 1991,
1986
NASA SRTM DEM Data 1 arc second (~30-m) NASA 2006
Kentucky LiDAR Data 1-m GSD State of Kentucky 2009
Archived Single Photos NASA, USGS 1958
MODIS NASA
GPM 10-km x 10-km NASA 2015
Hydrometeorological Data NOAA
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VIDEO
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Historical Landsat Scenes
Geographic Location 37.5˚N 83.5 ˚W
Ranges of Sun’s
Average Vertical
Angle
24˚-32˚ 38˚-54˚ 57˚-65˚
Months November-December
January-February
September-October
March-April
May – June
July-August
Seasons Fall -Winter Fall - Spring Spring-Summer
Ground condition Low reflective energy,
largest shadows,
ground covered with
snow
Medium to high
reflective energy
High reflective energy,
smallest shadows,
maximum cloud and rain
Applications Geology, Cryosphere,
Soil, etc.
Regular mapping ,
remote sensing
Crop health and growth
hydrology, soil moisture,
cultivation assessments
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(Weather Satellites)
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Primary Geospatial Data
Geodesy Topography Remote Sensing Photogrammetry GIS
GNSS Geodetic
Gravity
Conventional-Surveys
OPUS
Meteorological-
Observations
Quality Control
Accuracy -Assurance
Airborne LiDAR
Terrestrial LiDAR
Stereo-
photogrammetry
Ground Surveys
Contours
DSM/DTM/Volume
Accuracy
Landsat MSS/TM
Hyperspectral
RADAR
High Resolution
Classifications
LULCC
Impervious Surface
Soil Moisture
Airborne Stereo
Multispectral
Hyperspectral
Impervious
Surface
Planimetric
Vegetation
Hydrology
Hydrographic
Comprehensive
Layers
Metadata
Accuracy &
Standards
Modeling
Weather & Socio-economic Data
Weather/Climate Socio-economic Point and Survey Hydrometeorology Other
Applications
Accuracy
Assessment
Ground Water
3-D Models Soil, Water, Air
Geology/Geophysics
Engineering
Planning
Surface Hydrology
Various
Modeling for
Climate Research
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“Determine that the thing can and shall be done, and then we shall find the way”- Abraham Lincoln
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The above quote from the former President of the USA is an inspirational and positive step
towards finding simple solutions to the complex problems. A six-step methodology is developed as follows:
Manmade Vegetation Water Geophysical
Transportation
Networks
1. Roads
2. Rail Roads
3. Airports
4. Parking
1. Deciduous forest 1. Rivers
1. Bare Soils
Built-ups
1. Commercial
2. Residential
1. Coniferous
Evergreen forest
1. Creeks 1. Bare Rocks
Mines
1. Mountaintop
Removal Mining
Subsurface Mining
1. Bushes/Shrubs/M
ixed
1. Lakes/Ponds/
Reservoirs
1. Agricultural
Land
Utilities
1. Powerlines
1. Grasslands 1. Swamps/
Wetlands
1. Horticultural
Land
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Manmade Vegetation Water Geophysical
Transportation
networks
Deciduous
Coniferous forest
Rivers
Bare Soils
Rocks
Built-ups
Parking areas
Dams
Canals
Bushes
Shrubs
Mixed
Grassland
Lakes
Ponds
Reservoirs
Agricultural
Land
Horticultural
Land
Surface Mines
Wetlands
Swamps
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VIDEO
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VIDEO
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Search, collection, and processing of more than 600 Gigabytes of raster/vector/attribute data were cumbersome and many times it was confusing to extract data from various sources. Therefore, a methodology needs to be developed to optimize data searching process which is beyond the scope of this paper. A small step towards this giant scope has been started in collaboration with Dr. Krishnamurti’s Weather Modeling Lab at FSU. The 30-year span Landsat series data extraction and processing were challenging task; however, we will overcome the problems such as SLC and cloud covers by integrating airborne and existing multiple time series data. The Landsat 8 program has uniqueness due to the Bands 9-10-11 which are not yet really explored and exploited fully. Our further research goal is to integrate existing airborne imageries and UAS data with Landsat 8 imageries to optimize its usage to full potentials for climate change studies.
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The research is funded by USDA/NIFA (Award Number 2014-38821-22398, Kentucky State University, PI, Dr. Buddhi R. Gyawali). The research team would like to acknowledge the support which made this research possible. The team would recognize the work of Jeremy Sanders, Kentucky State University, during the UAV data collection and Laura and Ralph Rogers for arranging field visits for conducting interviews and interactions with mining companies, local businesses, mining workers, and farmers to understand the environmental and economic ground realities related to the coal mining in the research study area. The principal author is thankful to Dr. T. N. Krishnamurti and his research team at Florida State University for their support in processing the GPM/IMERGE data for the Southeast region covering our research study area. He is also thankful to Kentucky Division of Geographic Information for high-resolution MSS and LiDAR Data, the EROS Data Center/USGS for their assistance in getting Landsat 8 data and NASA for SRTM data. The research team acknowledges that it used Google Earth maps during the first field work planning and ground truth data gathering. The team is also thankful to the ASPRS 2017 Convention Committee members and moderator for their assistance and guidance during the preparation of this presentation.