Learn to use the related functions of ENVI software to extract the remote sensing information of surface vegetation from TM multi-band remote sensing image data-normalized vegetation index NDVI, specific vegetation index RV I and enhanced vegetation index EVI, and use vegetation index calculator to deepen the understanding of quantitative remote sensing vegetation information types and acquisition methods.
Second, the experimental content
① NDVI extraction of normalized vegetation index; ② ratio vegetation index RVI extraction; ③ enhanced vegetation index EVI extraction; ④ Operation of vegetation index calculator.
Third, the experimental requirements
① Master the concept and significance of vegetation index; (2) Understand the calculation formulas and significance of normalized vegetation index (NDVI), ratio vegetation index (RVI) and enhanced vegetation index (EVI); ③ Using vegetation index calculator to calculate the vegetation index of Guilin Landsat-5 TM remote sensing image. Write an experimental report.
Fourth, the technical conditions
① Microcomputer: ② Lands ta-5 TM remote sensing image of Guilin; (3) ③ENVI software; ④ACDSee software (version 4.0 or above).
Verb (abbreviation of verb) experimental steps
Vegetation index (VI) is a digital parameter that can quantitatively reflect the comprehensive information distribution of ground vegetation. It is composed of multi-band remote sensing data. According to the different mathematical and physical models, there are many kinds of vegetation index algorithms, so there are different vegetation indexes. Each has its own application focus. In this experiment, only three commonly used vegetation indexes are used, namely normalized vegetation index NDVI, specific vegetation index RVI and enhanced vegetation index EVI. The formulas of these three vegetation indices have been mentioned in the course of Remote Sensing Geology, so I won't repeat them here. Learning and using vegetation index must have some basic understanding.
(1) The reflection of healthy green vegetation in NIR and R is quite different (Figure 23- 1), because R is strongly absorbed by green plants, while NIR is highly reflective and highly transmissive;
Figure 23- 1 Vegetation Spectral Characteristics
(2) The purpose of establishing vegetation index is to effectively synthesize relevant spectral signals, enhance vegetation information and reduce non-vegetation information;
(3) Vegetation index has obvious regional and timeliness, which is influenced by vegetation itself, environment, atmosphere and other conditions.
All vegetation indices need to be calculated from high-precision multi-spectral or hyperspectral reflectance data, and radiation brightness without atmospheric correction or one-dimensional DN value data are not suitable for calculating vegetation indices. In this experiment, the Landsat-5 TM remote sensing image of Guilin City is selected to calculate several commonly used vegetation indices, and the specific operation steps are as follows.
1. Radiation correction
The remote sensing image of Landsat-5 TM in Guilin is corrected by radiation. The radiation correction method refers to the book "Experiment 19 Remote Sensing Image Radiation Correction".
2. Normalized Vegetation Index (NDVI)
The normalized difference vegetation index (NDVI) can increase the difference between the scattering of green leaves in the near infrared band and the absorption of chlorophyll in the red band, and the sensitivity will decrease when the vegetation is dense. Commonly used to detect vegetation growth and vegetation coverage, the calculation formula is as follows
NDVI= (near infrared -R)/ (near infrared +R)(23- 1)
Where: NIR is the reflectivity of near infrared band; R is the reflectivity of the red band. The range of NDVI value is-1 ~ 1, and a negative value indicates that the ground is covered by clouds, water and snow. Which has high reflectivity for visible light; 0 means there are rocks or bare soil, etc. , and NIR and r are approximately equal; A positive value indicates that there is vegetation coverage, which increases with the increase of coverage. Generally, the area of green vegetation ranges from 0.2 to 0.8.
For Landsat-5 TM remote sensing images, TM30.62 ~ 0.69 microns is in the red band, and TM40.76 ~ 0.96 microns is in the near infrared band.
3. Vegetation index ratio (RVI)
The ratio vegetation index (RVI), that is, the ratio of the scattering of green leaves in the near infrared band to the absorption of chlorophyll in the red band, will reduce the sensitivity when the vegetation is dense, and its calculation formula is as follows
RVI = near infrared spectrum/reflection spectrum (23-2)
The range of RVl value is 0 ~ 30, and the RVI of the area covered by green and healthy vegetation is much greater than 1, while the RVI of the ground without vegetation (bare soil, artificial buildings, water bodies, dead vegetation or serious pests) is around 1. Generally, the area of green vegetation varies from 2 to 8.
For Landsat-5 TM remote sensing image TM30, 62 ~ 0,69 microns are in the red band, and TM40.76 ~ 0.96 microns are in the near infrared band.
4. Enhanced Vegetation Index (EVI)
EVI(enhanced Vegetation Index) is to enhance vegetation signals by adding blue bands to solve the influence of soil background and atmospheric aerosol scattering on dense vegetation, which is often used in densely populated areas. The calculation formula is as follows
Remote sensing geology experiment course
The EVI value ranges from-1 ~ 1, and the area of general green plants ranges from 0.2 to 0.8.
For Landsat-5 TM remote sensing image, Tm30.62 ~ 0.69μ m is red band, Tm40.76 ~ 0.96μ m is near infrared band, and TM1.45 ~ 0.52 μ m is blue band.
5. Vegetation index calculator
ENVI provides vegetation index calculator, which can select the vegetation index that can be calculated according to the band of the input image, and provides biophysical cross-checking function, which can improve the calculation accuracy of vegetation index.
The input image must contain central wavelength information and must be corrected by radiation. Because there is not enough light energy in the shadow area, the vegetation index in the shadow area is often inaccurate. For the Landsat-5 TM remote sensing data of Guilin city corrected by FLAASH atmosphere, the operation of vegetation index calculator is as follows:
(1) select "spectrum >; Vegetation analysis > vegetation index calculation ",in the data input dialog box, select Guilin Landsat-5 TM remote sensing data corrected by FLAASH atmosphere, and click OK to open the" Vegetation Index Parameters "dialog box (Figure 23-2).
Figure 23-2 Vegetation Index Parameter Settings Dialog Box
(2) In the "Vegetation Index Parameters" dialog box, the "Select Vegetation Index" list shows all the vegetation indexes that can be calculated from this data, and you can choose according to your actual needs.
(3) biophysical cross-checking function: The default value is ON, and if the calculated vegetation index is to be used as a vegetation analysis tool, select OFF.
(4) Select the output path and file name, and click OK to calculate the vegetation index.
Step 6 record the results
The vegetation index of Landsat-5 TM remote sensing image in Guilin was calculated by vegetation index calculator, and the effects of several vegetation index models on vegetation information extraction were compared. Recorded in W ORD document, named "Comparison of Vegetation Extraction Effects of Different Vegetation Index Models", and saved in your own working folder.
An experimental report on intransitive verbs
(1) Briefly describe the experimental process.
(2) Answer the question: ① What is the role of radiation correction in the calculation of vegetation index? Can vegetation index be calculated without radiation correction? Why? ② According to the mathematical models of normalized vegetation index NDVI, ratio vegetation index RVI and enhanced vegetation index EVI, combined with their images, the differences and characteristics of three kinds of remote sensing vegetation information were analyzed.
See Appendix 1 for the format of the experimental report.