http://hydrology1.nmsu.edu/teaching_Material/SOIL350/bar13.jpg

HORT 500 CRN# 31275

AGRO 500 CRN# 31638

SOIL 500 CRN# 31639

Special Topics

Remote Sensing Use in Agriculture and Forestry

New Mexico State University

http://hydrology1.nmsu.edu/teaching_Material/SOIL350/bar13.jpg

 


Instructor: Dr. Junming Wang (jwang@nmsu.edu)

Phone 646-3239

Office: Skeen Hall N337

 Office Hr: Wednesday 2:30-3:30

Spring 2009

Classroom: Skeen Hall W130

Tuesday, 6:10-7:00pm

1 credit

Pre-requisite: None.

 

http://hydrology1.nmsu.edu/Teaching_Material/Agro500/Agro500.htm

 

 

Course Description

This course will overview up-to-date remote sensing application in agriculture and forestry. This course provides basic principles and applications of remote sensing evapotranspiration (ET), remote sensing stresses of water and pests, remote sensing vegetation characteristics in crops, grasses, and forests using satellite data, and remote sensing dust emission and dispersion using satellite and lidar (laser radar).  This course gives demonstrations in the interdisciplinary areas of Agronomy, Horticulture, Biology, Pest Management, Geography, Micrometeorology, and Environment Sciences.

 

Goals

At the end of this course, you will:

 

1. Be familiar with the up-to-date technique application in agriculture and forestry;

 

2. Understand the basic principles of sensing ET, plant response to stresses of water and pests, sensing vegetation characteristics in crops, grasses and forests using satellite data, and sensing dust using satellite and lidar (laser radar).

 

Topics for the Class

 

  1. Class introduction

 

Why the subject is important

Introduction to traditional and satellite methods in estimating stress: water and pests, and in estimating vegetation characteristics.

 

Reference paper: Review paper of Remote sensing use in forestry

 

Reference paper: Special Issue on Global Land Product Validation.. IEEE Transactions on Geosciences and Remote Sensing, July 2006, volume 44, number 7  (will be provided by the instructor).

 

Reference papers: Proceedings of the Tenth Forest Service Remote Sensing Applications Conference Salt Lake City, Utah ~ April 5-9 2004 (will be provided by the instructor).

 

  1. Satellite data introduction: electromagnetic radiation

 

Reference book: Chapter 2 (Electromagnetic Radiation) in Introduction to Remote Sensing, Fourth Edition by James B. Campbell. I will distribute the book materials to you in the class.

 

  1. Download and view satellite data

 

Homework:

 Please download one data set collected by any satellite, and show it in HDFView or other software. Please print the image shown in the software and hand in next Tuesday.

 

  1. Vegetation index: NDVI

 

Reference paper: Evaluation of the Consistency of Long-Term NDVI Time Series Derived From   AVHRR, SPOT-Vegetation, SeaWiFS, MODIS,and Landsat ETM+ Sensors

Molly E. Brown, Jorge E. Pinzón, Kamel Didan, Jeffrey T. Morisette, and Compton J. Tucker, 2006

 

Homework:

1. At what landscapes, are the NDVI values of the four satellites different? (AVHRR, SPOT, SeaWiFS, and MODIS)

 

2. Why did the authors use the Landsat data to evaluate other data sets from other satellites?

 

Due at next class

 

5.      Enhanced vegetation index

 

Reference paper: Evaluating MODIS, MERIS, and VEGETATION Vegetation Indices Using In Situ Measurements in a Semiarid Environment

Rasmus Fensholt, Inge Sandholt, and Simon Stisen, 2006

 

Homework:

Which satellite sensor among MODIS, EVISAT, and VEGETATION gives accurate atmospheric corrections of NIR, blue and red data? Why?

 

Due at next class

 

  1. Biomass estimation

 

Reference paper:

Lu, D. 2006.The potential and challenge of remote sensing-based biomass estimation. Journal of Remote Sensing. Vol. 27. No. 7: 10 April 2006, 1297-1328

 

Homework:

         What are the major remote-sensing methods for estimating above-ground biomass?

         What is the accuracy of these methods?

 

Due at the next class.

You can email me your answers.

 

 

7.Forest damage mapping (beetle infestation)

NDVI and SAVI variation with different soil and vegetation backgrounds

 

Reference paper:

Wulder et al., 2005. Remote sensing in the survey of mountain pine beetle impacts:

Review and recommendations. Information Report BC-X-401. Natural Resources Canada,

Canadian Forest Service, and Pacific Forestry Centre.

 

Spring break:

 

  1. Leaf area index (LAI)

Yang, Wenze, Dong Huang, Bin Tan, Julienne C. Stroeve, Nikolay V. Shabanov, Yuri Knyazikhin, Ramakrishna R. Nemani, and Ranga B. Myneni. 2006. Analysis of Leaf Area Index and Fraction of PAR Absorbed by Vegetation Products From the Terra MODIS Sensor: 2000–2005. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 7, JULY 2006. 1829-1842.

 

Homework:

1. Why is the new version of the LAI and FPAR algorithm (collection 4) better than the older version (collection 3)? 

 

Due at the next class.

You can email me your answers.

 

 

  1. Estimating Gross Primary Production (GPP)-CO2 Assimilation and Biomass Production

 

 

Homework: Please download one GPP data set at:

      http://redhook.gsfc.nasa.gov/~imswww/pub/imswelcome/

 

Tutorial video:

GPPDownload.wmv

GPPDownloadSummerData.wmv

 

 

 

Turner, David P. , William David Ritts, Maosheng Zhao, Shirley A. Kurc, Allison L. Dunn, Steven C. Wofsy, Eric E. Small, and Steven W. Running. 2006. Assessing Interannual Variation in MODIS-Based Estimates of Gross Primary Production. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 7, JULY 2006. 1899-1907.

 

 

  1. Fire danger monitoring

Reference paper: Leblon, B. 2005. Monitoring forest fire danger with remote sensing. Natural Hazards. 35:343-359.

 

Reference paper: Validation of Active Fire Detection From Moderate-Resolution Satellite Sensors: The MODIS Example in Northern Eurasia Ivan A. Csiszar, Jeffrey T. Morisette, and Louis Giglio, 2006

 

 

  1. Estimating ET

 

Reference papers:

http://ams.confex.com/ams/pdfpapers/92016.pdf

 

Thomas J. Schmugge, William P. Kustas, Jerry C. Ritchie, Thomas J. Jackson,

Al Rango. 2002. Remote sensing in hydrology. Advances in Water Resources 25 (2002) 1367–1385.

 

90 m resolution Excel excise sheets and tutorial materials (ASTER data) and the software

 

 

1 km resolution ET calculation (MODIS data)

 

12.  Remote sensing dust dispersion

Lidar principles, data analysis, and other education materials

 

Course structure:

Part of the course is a set of lectures.  The lecture material will typically be posted on the course Web site.  It will be developed from both the text (see blow) and my own synthesis of the literature and my own research (lecture topics are listed in the above section). A second part of the course will be methodology literature search by students (homework). The search will be for classic methodologies mentioned in the lectures and cited by the textbooks (see below). A methodology summary (one-page) will be required for each search. The summary should mention: what satellites were used in the methodology, what were the equations, and what was the accuracy.

We will make selected summaries on the Web site public, serving as a resource for learning about remote sensing use in agriculture and forestry. 

 

Textbooks:

The following textbooks will be provided by the instructor.

1.

IEEE Transactions on Geosciences and Remote Sensing, July 2006, volume 44, number 7. A Special Issue on Global Land Product Validation. (will be provided by the instructor )

 

2.

Proceedings of the Tenth Forest Service Remote Sensing Applications Conference
Salt Lake City, Utah ~ April 5-9 2004. (will be provided by the instructor )

 

3.

I have books for our class:

 

Introduction to Remote Sensing, Fourth Edition by James B. Campbell

 

In lecture 2 (Satellite data introduction: electromagnetic radiation), I use some materials from this book (Chapter 2: Electromagnetic radiation).

 

If you want to know more basics about remote sensing in addition to the lectures, you can read this book (I will distribute the book materials to you in the class).

 

 

Grade:

Lively discussion is very desirable, and 40% of the grade will be based on class participation. The 60% of the grade will be based on the summaries. There are no exams for this class.