Remote Sensing
in Natural Resources Mapping
NRS 516, Spring 2016
Overview of
Remote Sensing in Natural Resources Mapping
• What is remote sensing? • Why remote sensing?
• Examples of remote sensing in natural resources and environmental mapping
A remote sensing instrument
collects information about an object or phenomenon within the
instantaneous-field-of-view (IFOV) of the sensor system without being in direct physical contact with it.
What is Remote Sensing …
Remote Sensing
is the observation of the Earth from satellites or aircrafts. Sensors mounted on these platforms capture images of the Earth that reveal features may or may not apparent to the naked eye. The end users or image analysts interpret the data, extract information, and use it to answer real world questions.Why Remote Sensing …
T
o discover and identify universal truths, we need to observe and make measurements about:• the physical world (e.g., the atmosphere, water, soil, rock) • its living inhabitants (e.g., Homo sapiens, flora, fauna) • the processes at work (e.g., erosion, deforestation, urban
sprawl, effects of climate and environmental change … )
In situ data vs. Remote Sensing Observation
Scientists formulate hypotheses and then attempt to accept or reject them in a systematic, unbiased fashion.
The data necessary to accept or reject a hypothesis may be collected directly in the field, often referred to as in situ or
in-place data collection. This can be a time-consuming, expensive,
In situ data vs. Remote Sensing
Observation
Possible problems …
• Sampling design does not capture the spatial variability of the phenomena under investigation (i.e., some phenomena or geographic areas are oversampled while others are undersampled);
• Improper operation of in situ measurement instruments; or • Uncalibrated in situ measurement
instruments.
In situ data vs. Remote Sensing Observation
• Remote sensing is unobtrusive if the sensor passively records the EMR reflected or emitted by the object of interest.
• Remote sensing devices may be programmed to collect data systematically. This systematic data collection can remove the sampling bias introduced in some in situ investigations.
In situ data vs. Remote Sensing Observation
• Remote sensing–derived information is critical to the successful modeling of numerous natural (e.g., water-supply estimation; eutrophication studies; nonpoint source pollution, climate change…) and cultural (e.g., land-use conversion at the urban fringe; water-demand estimation; population estimation) processes.
• Collect data in different spatial scales and time frequencies • Prediction, forecasting, decision support …
• … …
The Remote Sensing Process
• The hypothesis to be tested is defined using a specific type of logic (e.g., inductive, deductive) and an appropriate processing model (e.g., deterministic, stochastic).
• In situ and collateral data necessary to calibrate the remote
sensor data and/or judge its geometric, radiometric, and thematic characteristics are collected.
• Remote sensor data are collected passively or actively using analog or digital remote sensing instruments, ideally at the same time as the in situ data.
The Remote Sensing Process
• In situ and remotely sensed data are processed using a) analog image processing,
b) digital image processing, c) modeling, and
d) n-dimensional visualization.
• Metadata, processing lineage, and the accuracy of the information are provided and the results communicated using images, graphs, statistical tables, GIS databases, Spatial Decision Support Systems (SDSS), etc.
The Remote Sensing Process
• Multispectral • Multitemporal • Multisensor • Multisources • … …
QuickBird Data Acquisition Multiple date and path imaging
May 4, 2004 May 6, 2004 April 8, 2004 April 16, 2004 April 8, 2004 Long Island
Multitemporal
After Geomatic Rectification and Mosaicking
Before Geometric Rectification
May 4, 2004 May 6, 2004
Ground Control Points for Image to Image Rectification
Orthophoto QuickBird Satellite Image
After Geometric Rectification and Mosaicking
Before Geometric Rectification
39.50 m
?
April 16, 2004
Acquired June 3, 2002 NOAA Coastal Service Center 0.5 m resolution, orthorectified A Mosaic of Digital True Color Aerial
Photos
Geometric Rectification Subsetting
Multispectral Image 2.5 m Spatial Resolution Panchromatic Image 0.6 m Spatial Resolution
Resolution Merge?
Enhanced Multispectral Image (0.6 m Spatial Resolution) Enhanced Multispectral Image (0.6 m Spatial Resolution) Enhanced Multispectral Image (0.6 m Spatial Resolution)Mapping is about
Information
Extraction
Impervious
Surface Areas
True-color
Orthophoto
Salt Marsh
Mixed
Thematic information extraction?
1985 1999
1972
Rhode Island Land-Cover Maps and Change Detection
Earth Resources Analysis Perspective
This class focuses on the art and science of applying remote sensing digital image processing for the extraction of thematic information resource in natural resources and environmental mapping.
Earth resource information is defined as any information concerning terrestrial vegetation, soils, minerals, rocks, water, certain atmospheric characteristics, and urban infrastructure.
Remote Sensing in Natural Resources Mapping
Create critical information for resource management and scientific research (including but not limited to):
• Phenology and vegetation dynamics
• Habitat condition, suitability, conservation planning • Ecosystem functions and services
• Biology and biochemistry of ecosystems • Water and energy cycle
• Climate variability and prediction • Ecological security
Class Goals
• Develop an understanding of spectral reflectance properties of various earth surface materials on digital remote sensing data
• Become familiar with the concepts in computer-assisted data analysis
• Examine and apply geometric and radiometric transformations to remote sensing data
• Apply various strategies for thematic information extraction (via classifications) on remote sensing data • Evaluate the utility of multitemporal remote sensing data
for change detection and analysis
• Gain experience in the use of state-of-the-art software systems for digital image processing and natural resource mapping and management.
