1. Remote Sensing
- Vegetation Applications
안영성 김민

2. Vegetation To know vegetation application Remote sensing Vegetation 1.
Introduction
conclusion
Vegetation
Research Topics 1.
To know vegetation application
Research Purpose 2.

3. Contents 1. Vegetation 2. Vegetation application 3. Application partsㅐ
1-1. About Vegetation
1-2. General Principles
For Recognizing vegetation
1-3. Remote sensing
2. Vegetation application
2-1. Vegetation damage assessment
2-2. Management in Urban environments
2-3. Vegetation separability
3. Application parts
3-1. Agriculture
3-2. Forestry
3-3. Geology
4. Quiz

4. 1-2. General Principles For Recognizing Vegetation 1-3. Remote sensing
1-1. About vegetation
1-2. General Principles For
Recognizing Vegetation
1-3. Remote sensing

5. <Introduction>
1. Vegetation
1. About Vegetation
2. Principles
Vegetation behavior depends on ‘the nature of the vegetation’ itself.
<Introduction>
Vegetation can be distinguished using remote sensing data from most other (mainly inorganic) materials by virtue of its notable absorption in the red and blue segments of the visible spectrum, its higher green reflectance and, especially, its very strong reflectance in the near-IR. Different types of vegetation show often distinctive variability from one another owing to such parameters as leaf shape and size, overall plant shape, water content, and associated background (e.g., soil types and spacing of the plants (density of vegetative cover within the scene)).,Even marine/lake vegetation can be detected. Use of remote sensing to monitor crops, in terms of their identity, stage of growth, predicted yields (productivity) and health is a major endeavor. This is an excellent example of the value of multitemporal observations, as several looks during the growing season allows better crop type determination and estimates of output. Vegetation distribution and characteristics in forests and grasslands also are readily determinable.
내용 편집…대충 집어넣었습니다.

6. <Principle> 2. General Principles For Recognizing Vegetation
Vegetation in remote-sensing images depends on several plant characteristics
<Principle>
Absorption centered at about 0.65 µm (visible red) by chlorophyll pigment in green-leaf chloroplasts that reside in the outer or Palisade leaf, and to a similar extent in the blue, removes these colors from white light, leaving the predominant but diminished reflectance for visible wavelengths concentrated in the green. Thus, most vegetation has a green-leafy color. There is also strong reflectance between 0.7 and 1.0 µm (near IR) in the spongy mesophyll cells located in the interior or back of a leaf, within which light reflects mainly at cell wall/air space interfaces, much of which emerges as strong reflection rays. The intensity of this reflectance is commonly greater (higher percentage) than from most inorganic materials, so vegetation appears bright in the near-IR wavelengths.
내용 편집…대충 집어넣었습니다.

7. 3. Remote Sensing Use the Remote Sensing Landsat TM SPOT HRV
Topographic variables obtained through fieldwork
DEMs(digital elevation model)
Traditional methods are still demanded and used, and include airphoto
interpretation, fieldwork, literature reviews, map interpretation, and
collateral and ancillary data. With improvements in remote sensing, there
are certain advantages in its' use.
For example, preparing and upgrading vegetation inventories for large regions in a short period of time, and monitoring and detecting changes in vegetation will be more easily obtainable with satellite sensing and thematic
mapper techniques.
내용 편집…대충 집어넣었습니다.

8. 2-1. Vegetation damage assessment
2. Vegetation application
2-1. Vegetation damage assessment
2-2. Management in Urban environments
2-3. Vegetation separability
발표자 :

9. <Introduction>
1. Application
2. Application
1. Vegetation damage assessment
3. Application
Many natural and technological hazards entail the potential to significantly adversely affect natural and agricultural ecosystems, the human built an environment and etc.
<Introduction>
For natural or anthropogenic disasters, rapid assessment is critical for an appropriate and effective emergency response. Remote sensing has served a vital function in disaster damage-assessment activities. This includes disaster-mapping of natural and agricultural ecosystems and human settlements, which may involve assessments of structural damage, contamination, and affected populations. Single- and multi-date (change detection) analyses can be employed, and a need to exploit both spectral and spatial information in order to delineate damage regions from remote sensor imagery is identified. Specific attention is given to remote sensing-based detection of vegetation damage and soil contamination, including a discussion of the remote-sensing implications of artificial radionuclide contamination, as well as damage to urbanized areas and other human settlements.
내용 편집…대충 집어넣었습니다.

10. The assessment of vegetation damage has various characteristics
1. Application
2. Application
1. Vegetation damage assessment
3. Application
The assessment of vegetation damage by remote sensing has reached a fairly sophisticated level.
Characteristics
The use of many parts of
electromagnetic spectrum
Visual observation techniques
(sketch mapping and strip recording)
The saving of time, money, and
manpower
Color and color infrared (CIR)
photography (both large and very
small scale) when properly matched
with damage symptom 
The use of successive remote sensing
surveys to follow damage trends.
Multistage sampling
The assessment of vegetation damage has various characteristics

11. <Introduction>
1. Application
2. Application
2. Management in Urban environments
3. Application
Vegetation has been identified as an essential component of healthy urban environments.
<Introduction>
Vegetation has been identified as a vital component of healthy urban environments and the benefits of urban vegetation range widely, influencing both the physical conditions of the city as well as the social well-being of urban residents. These links form the foundation for studies examining social-ecological systems (SES), which suggest that many human and ecological systems are tightly and inextricably linked (Alessa et al., 2008). In a review of societal needs in urban areas, Matsuka and Kaplan (2008) suggest that human actions and attitudes are directly connected to the physical features of the environmental setting, of which trees are a major component.
내용 편집…대충 집어넣었습니다.

12. <Introduction>
1. Application
2. Application
3. Vegetation separability
3. Application
<Introduction>
Mapping the location and spatial extent of trees, vegetated ground cover, and high level vegetation detail provides a valuable addition to vegetation land cover mapping using high spatial resolution imagery. Image classification techniques developed to date extract a basic vegetation class which encompasses a broad range of features whose structural and spectral diversity have a variety of impacts on urban processes. Small and Lu (2006) explain that high spatial resolution image vegetation fractions provide more informative vegetation estimates than moderate resolution imagery due to the reduction of possible distinct mixtures and add that Quickbird pixels can resolve many of the individual components representing vegetation.
내용 편집…대충 집어넣었습니다.

13. 3. Application parts
1. Agriculture
2. Forestry
3. Geology

14. 1. Agriculture
1. Agriculture
Crop-type identification is base on : Spectral characteristics, Image texture, Knowledge of crop development over time
Crop conditions : Health and vigor of the crop, Detection of drought, pests, flooding, and disease
Most common remote sensing tools are : Normalized Difference Vegetation Index(NDVI)

15. Weed control : Weeds are not always randomly distributed across
1. Agriculture
1. Agriculture
Weed control
: Weeds are not always
randomly distributed across
a filed, but are often found
in patches. We can use
weed maps to observe
these patches and note
how they change over time.

16. 2. Forestry
2. Forestry
The most important forest information obtained from remotely sensed data is : Detailed forest, Broad area monitoring of forest health, Assessment of forest structure in support of sustainable forest management

17. 2. Forestry
2. Forestry
Detailed Forest Inventory : Stand boundaries, Update harvest information, Polygon decomposition, Individual tree-crown recognition

18. Forest health 2. Forestry A. Insect disturbance
Damage affects morphological/physiological chars
Defoliation
B. Fire
Real-time
Firefighter GPS
AVHRR, SPOT & MODIS – hot spot information
b. Post-processing
Change detection
Prediction for future hot spots
C. Other natural disasters

19. 2. Forestry
2. Forestry
MODIS-detected real-time fire hot-spot image

20. Landscape ecology, habitat & biodiversity :
2. Forestry
2. Forestry
Landscape ecology, habitat & biodiversity :
Spatial Patterns, Habitat, Ecological Processes

21. Geological Structures
3. Geology
3. Geology
Geological Structures
Exposed structures
Fault lines
Identifying rocks
Response to weathering & erosion
Texture, pattern & tone
Hyperspectral
Obscured structures
Indirect effects
Subsurface instrumentation
Detection of hydrocarbons
Oil & Gas

22. Normalized magnitude of 0.75 Hz electric fields at the seafloor
3. Geology
3. Geology
Normalized magnitude of 0.75
Hz electric fields at the seafloor
for a range bin of m,
plotted as a function of source
-receiver midpoint across the survey
area. Plotted for reference
is the approximate outline of
the known hydrocarbon resevoir.

23. 4. Quiz
Quiz1
Quiz2
Quiz3
Quiz4

24. Quiz1 The nature of the vegetation <Answer>
What is Vegetation behavior ?
<Answer>
약 854억 원
The nature of the vegetation
약 3,888억 원

25. Quiz2 Landsat TM , SPOT HRV, DEMs <Answer>
What kinds of remote sensing does Vegetation application have ?
<Answer>
약 854억 원
Landsat TM , SPOT HRV, DEMs
약 3,888억 원

26. Quiz3 Agriculture, Forestry, Geology <Answer>
Which parts does Vegetation application have?
<Answer>
약 854억 원
Agriculture, Forestry, Geology
약 3,888억 원

27. Quiz4
4. Quiz4
What are characters of assessment of vegetation damage ?
<Answer>
The use of many parts of electromagnetic spectrum
약 854억 원
The saving of time, money, and manpower
약 3,888억 원
The use of successive remote sensing surveys to follow damage trends.
Visual observation techniques (sketch mapping and strip recording)
Color and color infrared (CIR) photography (both large and very small scale)
when properly matched with damage symptom 
Multistage sampling