# Presentation: 644745

## Use of Remote Sensing to Assess Aquatic Systems 

**Robert K. Hall ****1****, Russell L. Watkins****2,**** Alfonso Blanco****3****, **
**David A. Cacchione****4****, and Daniel T. Heggem****5**
- 1USEPA Region IX, WTR2, 75 Hawthorne St., San Francisco, CA 94105
- 2Spectrum Mapping, LLC.., 3907 SW 5th Place, Gainesville, FL 32607
- 3USEPA1200 Pennsylvania Avenue, NW, MC 4204 Washington, D.C. 20460
- 4CME, 2945 Granite Pointe Drive, Reno, NV, 89511
- 5USEPA ORD Environmental Science Division, Landscape Ecology Branch, Las Vegas, NV 89119

## Introduction

- Stream Physical Habitat Analysis using 1m hyperspectral and LIDAR imagery
- Proposals
- Advanced Monitoring Initiative (AMI) – develop 3-D model of stream physical habitat parameters
- Satellite imagery and lake water quality – development of a near real time monitoring system

## Characterizing Stream Physical Habitat Parameters Using Hyperspectral and LIDAR Imagery

- Structural physical habitat characterization includes measurements of:
- stream size,
- gradient,
- channel substrate type and size,
- riparian vegetation cover,
- structure and complexity,
- and anthropogenic alterations.
- These physical characteristics strongly influence water quality and the capacity of a stream to support a diverse biological community.

## Study Area

**Study Area**

**High-resolution and –spectral Remotely Sensed Imagery**

## Slide 5

## Habitat Complexity

- Habitat complexity is the distribution of various types of features providing fish concealment. For instance, large woody debris, undercut banks, overhanging vegetation, boulders, and residual pools.
- Hyperspectral imagery can be used to detect and map the distribution of large woody debris (> 15 cm diameter) and overhanging vegetation.
- LIDAR is used to measure large boulders greater than 1 meter and and residual pool depth, size distribution and frequency.
- Fusing the two data files provides a means to estimate residual pool volume,  exposed mid-channel gravel and/or sand bars, reach-scale indices of slackwater volume, channel complexity.
- Cross-sections - LIDAR data can quantitatively measure wetted width, bank slope, incised height greater than 15 cm, bankfull height and width at bankfull stage.

## Feature extraction: wetted width, water and vegetation spatial characteristics

- Unsupervised
- Classification:
- 10 classes

- Image segmentation and density slice

- Hyperspectral
- Image:
- 58 10nm bands

- Spectral signatures

- vegetation

- water

**Notes:**
This slide shows a portion of a color infrared 3-band image. 3-bands used for visual presentation. There are 58 10 nm bands. Covers 580 contiguous bands of information for each pixel; an 11 class unsupervised classification (10 land cover categories and 1 unclassified) derived from computer analysis of natural groupings of spectral signatures (isodata routine). The power of hyperspectral is that you can have large number of very narrow contiguous spectral bands of data – finger print; looks for natural groupings; and 2 examples of gross categories extracted from the classification.  This can show spatial extent, allow for quantitative summary and analysis of features... This is an automated way to measure wetted width, and determine the number of hectares of riparian vegetation. Supervised classification from field work. I know what this spectrum is, and then tell the computer what to do.

## Using near-infrared to determine wetted width

**Using near-infrared to determine wetted width**
**Impacted by:**
** ****Stream bed substrate composition and size **
**  ****(Carl Legleiter, et al., 2004)**
** ****Riparian vegetation**
** ****Shadows **

## Stream Dimensions:Stream Reach: 500 meters

**Stream Dimensions:****Stream Reach: 500 meters**

**A**

**B**

**C**

**D**

**E**

**F**

**G**

**H**

**I**

**J**

**K**

- Wetted width at transect F = 14.7 m
- Mean wetted width = 12.4 m
- Std Dev wetted width = 6.1 m
- Mean bank full width  = 63.1 m
- Mean bank full height = 1.3 m
- Mean incised height = 23.8 cm

**Notes:**
3-band CIR image derived from a 58-band hyperspectral data cube showing the longitudinal and transverse transect locations over a defined reach @150m in length

## Channel Gradient/Sinuosity

**A**

**B**

**C**

**D**

**E**

**F**

**G**

**H**

**I**

**J**

**K**

- 0.54%
- 91o

- 0.1%
- 94o

- 0.52%
- 78o

- 0.24%
- 76o

- 0.82%
- 90o

- 0.1%
- 95o

- 0.1%
- 45o

- 0.66%
- 50o

- 0.26%
- 27o

- 0.14%
- 35o

## Transect cross-sections

**Transect cross-sections**

## Transect cross-sections with DEM derived slope

**Transect cross-sections with DEM derived**** slope**

## Slide 13

## Stream Transect 

**Notes:**
3-band CIR image derived from a 58-band hyperspectral data cube showing the longitudinal and transverse transect locations over a defined reach @150m in length

## Stream Transect

**Notes:**
3-band CIR image derived from a 58-band hyperspectral data cube showing the longitudinal and transverse transect locations over a defined reach @150m in length

## Riparian Vegetation

- Canopy cover is essential for moderating stream temperatures, providing habitat and an indicator of potential aquatic community present.
- Kaufmann, et al., 1999,  note that the field measurements "...tell little about the type or structure of this vegetation."
- To determine riparian vegetation structure, EMAP protocols:
- 10 square meter block centered on the cross-section each side of the stream
- The riparian vegetation is identified into three layers 1. Canopy Cover (> 5 meters), 2. Understory (0.5 - 5.0 meters) and 3. Ground Cover (< 0.5 meters).
- Hyperspectral imagery integrated with LIDAR data can determine vegetation type, structure, height and distribution.
- Invasive species

## Riparian Land Cover

- CIR image

- 10-class unsupervised classification

**Notes:**
3-band colored infra-red (CIR) image derived from a 58-band hyperspectral data cube and 10-class unsupervised classification using isodata algorithm. Basically, substituting near infra-red (750 nm) for the true color red. It makes the green vegetation look red.

## Watershed Morphology and Invasive Species Classification: Humboldt River, NV

**Watershed Morphology and Invasive Species Classification: Humboldt River, NV**

- Area shown is part of the Special Recreation Management Area Near Elko, NV
- Source: BLM Nevada State Office and BAE Systems Advanced Technologies Inc.

**Notes:**
This is intended to show and example of remote sensing applied to watershed modeling – highlights watershed geomorphology, use of hyperspectral remote sensing to classify features.  It is an RGB (natural color, 3-band image derived from 58-band hyperspectral data cube) overlain on a lidar-derived DTM with 1-meter resolution for both.

## Advanced Monitoring Initiative (AMI)

- The objectives of this study is to develop a 3-D image of aquatic and riparian habitat
- Method
- Spectral Imagery LIDAR Composite (SILC) technology to integrate hyperspectral digital imagery elements with LIDAR surface data.
- The SILC process fuses hyperspectral pixels photogrammetrically with individual X,Y,Z values.
- Each laser return is mathematically projected through collinearity equations onto its proper position on the frame array

**Notes:**
Spectral Imagery LIDAR Composite (SILC) technology to integrate digital imagery elements with LIDAR surface data. The SILC process fuses hyperspectral pixels photogrammetrically with individual X, Y, Z values. The imagery is not ‘draped’ on the surface, rather each laser return is mathematically projected through collinearity equations onto its proper position on the frame array taking into consideration the camera model using photogrammetric techniques. The associating hyperspectral pixels with individual X, Y, Z values allows for the true discrimination and automatic classification of roads, buildings, vegetation, water and other features. 

Because image pixels outside the near-nadir area cannot be accurately correlated with the laser points, traditional film cameras are impractical for the collection of imagery for this application. Spectrum Mapping’s medium format digital camera system is ideal for this innovative treatment of LIDAR data. The longer focal length and smaller field of view of the digital camera virtually eliminates building “lean” and matches the swath width of the laser system allowing the proper alignment of laser data and pixels. 
	By using imagery acquired simultaneously with the surface data, each surface point possesses an accurate spectral signature assigned to its location, allowing accurate classification of features using conventional remote sensing techniques. The SILC data allows urban terrain, forested terrain, agricultural lands, mountains, cliffs, ravines, and wetlands to be subjectively masked and filtered, providing vastly improved bare-earth surfaces and feature extraction. LIDAR points are attributed with RGB values and displayed much like a 3-D real-time orthophotograph which can be edited. Trees, buildings and roads are obvious. Structures, cars, paint stripes, parking lots, walkways and park benches are easily identified (Spectrum Mapping, n.d.). 

## Data Fusion - LIDAR & Hyperspectral

- Hyperspectral Imagery

- LIDAR Returns

- Fused Data

**Spectrum Mapping, LLC**

## LIDAR-derived Buildings: Luxor, Las Vegas Strip

- 2D Building Footprint

- FFT Filter Extract Building
-  Point-Cloud

- 3D Display of Building Point-Cloud

- 3D Perspective View
- Extruded Point Cloud Model

- 1st Iteration Solid Model

## Fusing Water Quality, Physical Dynamics and Remote Sensing

**Notes:**
Water Quality/Clarity:
The objective of this study is to couple multi-spectral and hyperspectral remote sensing data to measurable water quality parameters with the lakes physical dynamics to develop a time series (i.e., seasonal) model of lake water quality/clarity. Methodology includes calibrating the satellite imagery with current water quality parameters (total nitrogen, phosphorus, Chl_a,b,c, turbidity, etc.). Develop a time series model of seasonal fluxes of each of the parameters. Ultimately, develop a 3-dimensional model of the lake by combining high-resolution multi-beam acoustic bathymetry and backscatter data.
Habitat Analysis:
In-lake habitat identification is done using multi-beam acoustics. Shorezone habitats are identified from remote sensing using high-resolution LIDAR and hyperspectral imagery. Terrestrial habitats are identified with LIDAR and satellite imagery using a landscape approach.
Contaminates of concern:
Using terrestrial and water body flow dynamics to determine the fate and transport of constituents of concern.

## Conclusions

- Quantitative structural and biological stream data acquired over a given spatial area and temporal period is a fundamental information need to determine:
- Reference conditions
- Stream stability
- Changing conditions of the stream channel and riparian zones over time
- The need for most state and tribal bioassessment programs is to maximize the return on investment of acquiring this data.
- There are multiple advantages to hyperspectral and LIDAR data. Major advantage is the cost to benefit relative to high density ground sampling assessment to cover the same land area:
- increases resolution accuracy of more qualitative sampling programs
- increases spatial interpolation and extrapolation of point-based ground measurements.

## Conclusions cont’d

*Conclusions cont’d*
- Modeling:
-  Sediment transport
-  Changes in stream morphology
-  Effects of anthropogenic activities
-  USLE, RUSLE, HSPF, ANSWERS,
-  AGNPS, WEPP, IFIM, etc.
-  Environmental Sensitivity Index (ESI)

## Slide 25