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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

3/16/2017 ASPRS Conference 2

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.


“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.


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


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



VIDEO

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



(Weather Satellites)

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


“Determine that the thing can and shall be done, and then we shall find the way”- Abraham Lincoln


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


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



VIDEO

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.



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.

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