lai apar exercise

8/12/2019 LAI APAR Exercise

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2008-04-18 Lars Eklundh

Exercise: Calculation of Leaf Area Index / FAPARusing LANDSAT data and ArcGIS

Start here

Read this entire document before staring the exercise.

Introduction

The exercise aims at demonstrating how a GIS program, ArcGIS, can be used for setting up a

spatial model, and how important biophysical and radiation parameters can be extracted from

satellite sensor data.

The topic of this exercise is to calculate leaf area index (LAI) and fractional absorbed

photosynthetically active radiation (FAPAR) for an area around Lake Ringsjn in Southern

Sweden using LANDSAT data.

Data

An overview of available data for the exercise is given in Table 1. The satellite data consists ofLANDSAT 7 ETM, 6 channels multispectral data recorded on August 8 1999

(ETM channels 1-5, 7). More info on LANDSAT-7 is available on http://landsat.gsfc.nasa.gov/

Table 1. Available data.

Vector data and some aerial photographs are also added for the curious student, originating from

the Swedish National Survey. All data are in the same reference system (Swedish National grid,RT90 2.5, gon V).

Study areaThe study area is in southern Sweden, around Lake Ringsjn. Major types of land cover/land use

are agriculture grazing areas, coniferous forest, deciduous forest and peat bogs. Some pictures

from the area are provided in the folder Skane_images (The Scania province.html)

A landcover classification is provided (landcov). The classes are defined as follows:

1 = water2 = broadleaf forest

3 = cloud

4 = open soil surfaces and artificial surfaces7 = coniferous forest

8 = open peat quarry

9 = planted agricultural field or grazing land

10 = planted agricultural field or grazing land

Content Type Filname Format

Satellite data Raster Ringsjon.img ERDAS Imagine

Landcover classification Raster landcov ArcGRID

Regional Admin. Polygon Scania.shp Shape

Lakes Polygons Lakes.shp Shape

Aerial photos Raster *.tif. *.tfw, (3 photos) Tiff


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Preparation

Use the How to... in ArcGIS for basic tasks. Check out chapter 10 about working withimages in ArcGIS.

Load all data into ArcMap and get aquainted with all vectors and image data layers.Layers 1 to 5 correspond to ETM channels 1-5. Layer 6 contains ETM channel 7.

Make a nice FCC from the Landsat image. (ringsjon.img/Properties/Symbology).o Select appropriate bands for the FCC with the red channel displaying the NIR

ETM band, green channel the red ETM band and blue channel the green ETM

band. Which Layers should you use?

o Stretch the image data by modifying the standard deviations or by manipulatingthe histograms.

Remember to Use the Help functionto learn more about all the commands as you gothrough the exercise.

Leaf area index calculation

You will be creating a layer of leaf area index based on the satellite data. First read through thistext and then go on to Working procedure to carry out the analysis.

Satellite measured reflectance is related to LAI since the leaves absorb radiation in the reflectedwavelength range. Several studies have shown that satellite-estimated reflectances are related to

canopy LAI. We will use some already established empirical relationships between LAI and

satellite reflectance. These relationships are usually specific to the data and cannot always betransferred to other data. So be cautious about using them. For this exercises we will assume that

the relationships can be used without modification.

Agriculture: relationship fromBoegh et al. 2002, Airborne multispectral ... Remote Sensing ofEnvironment 81, 179-193.The relationship is based on the Enhanced Vegetation Index (EVI):

press Help!


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LAI = 3.618 * EVI 0.118 (eq. 1)

For forest vegetation we will use regression relationships fromEklundh, Hall, Eriksson, Ard and

Pilesj, 2003, Investigating the use of Landsat thematic mapper data for estimation of forest LAI

in southern Sweden", Can. J. Rem. Sens. 29,349-362.

The formulation for deciduous forest is:

LAI = 2.8 - 102 * TM3 + 28 * TM4 - 41 * TM5 (eq. 2)And for coniferous forest it is:

LAI = 5.5 + 47.1 * TM4 - 92.5 * TM5 (eq. 3)

For any other vegetated land we will use the same as for agriculture, and for all other areas wewill set LAI to zero. No negative LAI is allowed.

The equations above assume that we use atmospherically corrected reflectances. Our satellite data

are stored as digital numbers, so we need to convert them to reflectance. We do this by applyingregression equations that were previously defined for the ETM scene using an atmospheric

radiative transfer model named 6S. The equations are specific to these data and cannot be used

for other data.Regressions for scaling of raw data to atmospherically corrected reflectance are

given in Table 2.

Table 2. Regression equations converting the ETM scene from raw DN values (ETM1 etc.) to

atmospherically corrected top-of-canopy reflectance (ETM1_R etc.).

Working procedure

You will compute LAI using the relationships above by defining a model in the ArcMap model

builder. The models can work directly from the Erdas Imagine files, but computations are much

slower than if you first convert them to Arcinfo GRID format. Therefore we suggest that you1) Define a working folder with a very short the path (no folders more than 8 characters long, no

blank spaces). Example c:\gem\Lund\LAI\ . The GRID format does not work well with long filenames.

2) Create GRID datafiles from the Erdas .img file containg the ETM channels. Right-click

ringsjon.img, SelectData,Export Data, setLocation to your working directory (only single-click

on the folder name!), setName to "Ring",Format: GRID, Press Save. Do not change any othersettings. Check the new files with ArcCatalog.

ETM1_R = 0.001485 * ETM1 0.04785

ETM2_R = 0.002125 * ETM2 0.04673

ETM3_R = 0.001739 * ETM3 0.02922

ETM4_R = 0.002992 * ETM4 + 0.00123

ETM5_R = 0.002691 * ETM5 0.00526

ETM7_R = 0.002630 * ETM7 0.01548


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Step 1: Create a new model.

Open ArcToolbox in ArcMap Right-click on the top row of the opened

ArcToolbox window

Select New Toolbox. A new toolbox will beadded to ArcToolbox. Give the new Toolbox a name that separates it

from the other ones.

Right-click on the new Toolbox and select New Model. Later on you can rename thenew model to LAI_model or something similar.

If you exit the model and want to edit it again you select Edit to open it again (do notdouble-click or select Open).

Step 2: Compute atmospherically corrected reflectance for ETM1

Use the Add Data button to add the first ETM channel (ETM1) to the model. If you areusing the Erdas imagine file it is called Layer_1, if you are using the ArcGRID file it iscalled Ringc1. Rename the channel to ETM1 in the model.

Open the Spatial Analyst Tools and drag the layer Single output Map Algebra (under MapAlgebra) and drop it into the Model.

Connect the ETM1 ellipse with the new box using the button Add Connection. Renamethe new box to Compute Reflectance, and the Output raster to ETM1_R.

Now open Compute Reflectance by double-clicking it. Insert the algebraic expressionETM1 * 0.001485 - 0.04785 . This corresponds to the first row in Table 2. Note: alwaysput a space between the mathematical operators * , / , + , - . Press OK.

Right-click on ETM1_R and make sure that Intermediate is ticked. This ensures that theoutput is only saved as a temporary layer. Select Add to Display.

Run the model by selecting Run Entire Model under Model. Check the output result usingthe Identify button in ArcMap. You have now converted the raw ETM data toatmospherically corrected reflectances.

Step 3: Now, repeat the steps above and add new boxesbelow the ones you first created to compute atmospherically

corrected reflectance for all the ETM channels, using the

calibration data in Table 2. Note that ETM7 is stored inLayer_6.

etc.


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Step 4: Add, to the right of the output data a new tool for computing the NDVI from the

atmospherically corrected reflectance values. What is the equation for NDVI?

Step 5: Now add another tool for computing the EVI. For EVI use the equation

EVI = 2.5 * (TM4 TM3) / (1 + (TM4 + 6 * TM3 7.5 * TM 1)) (eq. 4)

Step 6: Add the land cover layer to the model and create a complete model that computes LAI for

the whole area.

Use conditional statements to compute LAI for each of the land cover classes, usingequations 1-3. A separate Map Algebra tool will be necessary for each land covercategory, and the classes that do not meet the condition should be given value 0. Non-

vegetation classes should of course be given LAI of 0. Note that, for example, there

are two codes for the Agriculture class.Here is how a condional statement works: con(logical statement, true, false).

logical statementis e.g. landcov > 8, or, landcov = = 2 (note the two equal signs), or

something similar. True is the value or expression that should be computed if the

logical statement is true.False is the value that should be given otherwise. Checkmore in the Help.

In the last step, the LAI for all classes are added together.Check the output from the model now and then by running it, and do not forget to save regularly

so that nothing is lost if the program crashes (known to occur...). You can run a few selected

boxes (Run) or the whole model (Run entire model).

Step 7: Add a control for negative LAI values to the model. Since we use regression models for

estimating reflectance and LAI any outliers or noisy data may create unrealistic LAI values, e.g.negative values. Set any negative pixels to zero, and if you think appropriate, values above 10 to

10. LAI rarely reaches above 10 in Swedish forests. The final LAI output should be saved to disk.

Step 8: Compute and present as a table the average LAI for each land use zone (Zonal Statistics

as Table), outside the model builder.

Computation of FAPAR

FAPAR is the fraction of photosynthetically absorbed radiation, and is an important step in the

computation of Net Primary Production.


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Create a new model that computes NDVI from the atmospherically corrected ETM red and NIR

channels. Then apply the following scaling to obtain FAPAR:

FAPAR = NDVI * 1.25 0.025.You can copy tools from the LAI model.

Run the model and check that the output makes sense. If not check your model and, if necessaryyou will need to set values outside the 0-1 range to 0 or 1.

Report

Each student should write a ca. 2-3 page short report that includes the results from the LAI andFAPAR models. Include pictures of your models, a table of LAI per land use class and a small

gray-scale map of FAPAR. What is FAPAR, and how is it used to model NPP? What would you

need to obtain a full modeling of NPP for the study area? Also include a brief discussion ofpossible errors with the types of approaches used for estimating LAI and FAPAR here. Pass or

Fail are the only grades for the report.

lai apar exercise

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