This document defines photo interpretation and lists factors that can be assessed to identify features in aerial photographs, such as shape, size, tone, pattern, shadow, texture, and association. It provides examples of how these factors help with identification. It also lists applications of photo interpretation in agriculture like crop classification and mapping, and in forestry like assessing forest cover, type, and changes over time.
To meet the various information requirements in forest management, different data sources like field survey, aerial photography, and satellite imagery is used, depending on the level of detail required and the extension of the area under study.
Remote Sensing: Normalized Difference Vegetation Index (NDVI)Kamlesh Kumar
The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared (NIR) bands of the electromagnetic spectrum to analyze whether the target (image) being observed contains green vegetation or not. Healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light compared to other wavelengths. But it absorbs more red and blue light. This is why our eyes see vegetation as the colour green. If we could see near-infrared, then it would be strong for vegetation too.
It is basically measured through the use of Intensity, Hue and saturation of an image and through pixels as well.
The density of vegetation (NDVI) at a certain point on the image is equal to the difference in the intensities of reflected light in the red and infrared range divided by the sum of these intensities.
푁퐷푉퐼=((푁퐼푅−푅퐸퐷))/((푁퐼푅+푅퐸퐷))
The result of this formula generates a value between -1 and +1. If you have low reflectance (low values) in the red band and high reflectance in the NIR, this will yield a high NDVI value. And vice versa.
To meet the various information requirements in forest management, different data sources like field survey, aerial photography, and satellite imagery is used, depending on the level of detail required and the extension of the area under study.
Remote Sensing: Normalized Difference Vegetation Index (NDVI)Kamlesh Kumar
The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared (NIR) bands of the electromagnetic spectrum to analyze whether the target (image) being observed contains green vegetation or not. Healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light compared to other wavelengths. But it absorbs more red and blue light. This is why our eyes see vegetation as the colour green. If we could see near-infrared, then it would be strong for vegetation too.
It is basically measured through the use of Intensity, Hue and saturation of an image and through pixels as well.
The density of vegetation (NDVI) at a certain point on the image is equal to the difference in the intensities of reflected light in the red and infrared range divided by the sum of these intensities.
푁퐷푉퐼=((푁퐼푅−푅퐸퐷))/((푁퐼푅+푅퐸퐷))
The result of this formula generates a value between -1 and +1. If you have low reflectance (low values) in the red band and high reflectance in the NIR, this will yield a high NDVI value. And vice versa.
IMAGE INTERPRETATION TECHNIQUES of surveyKaran Patel
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
Remote sensing and aerial photography study notes. Including concept and history of RS, visual image interpretation, digital image interpretation, application of RS, digital imaging, application of remote sensing etc.
RS & GIS, Image Interpretation, Methods of Image interpretation, Types of interpretation, Factors governing image interpretation, Activities to interpret image, Sensors, Role of sensors in Image derivation, Aerial Photography, LISS-3, Image characteristics, Special characteristics, Shadow, Texture, Pattern, associated features in images
IMAGE INTERPRETATION TECHNIQUES of surveyKaran Patel
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
Remote sensing and aerial photography study notes. Including concept and history of RS, visual image interpretation, digital image interpretation, application of RS, digital imaging, application of remote sensing etc.
RS & GIS, Image Interpretation, Methods of Image interpretation, Types of interpretation, Factors governing image interpretation, Activities to interpret image, Sensors, Role of sensors in Image derivation, Aerial Photography, LISS-3, Image characteristics, Special characteristics, Shadow, Texture, Pattern, associated features in images
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Session Overview
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2. DEFINITION
• Identifying and recognizing objects in the aerial
photograph and then judging their significance in the
photograph.
• Oblique photographs are normally easy to interpret.
3. Factors to assess to identify a feature :
Shape
Theform of an object on an air photohelps to identify theobject. Regularuniform shapes often indicate a
human involvement.
Size
A measure of theobject's surface area (e.g. single-lane vs. multi-lane highways).
Tone/Colour
The colourcharacteristics of an object, relative tootherobjects in the photo, are used toidentify the feature
(e.g. sand has a bright tone, while waterusually has a dark tone; tree species can be determined by the colour
of their leaves at certain times of the year).
4.
5. Pattern
Similar to shape, the spatial arrangement of objects (e.g. row crops vs. pasture) is also useful to identify an
object and its
Shadow
A shadow provides information about the object's height, shape, and orientation (e.g. tree species).
Texture
Texture is the frequency of the change in tone in the photographicimage. As the photoscale is
reduced, the texture of the given object becomes progressivelyfinerand eventually dissapears.
6. Association/Site
Associating the presence of one object with another,or relatingit to its environment,can help
identify the object (e.g. industrial buildings often have accessto railwaysidings; nuclear power
plantsare often locatedbeside large bodies of water).
7. Applications:
Agriculture
•crop type classification
•Crop condition classification
•Crop yieldestimation
•Mapping of soil characteristics
•Mapping of soil managementpractices
Forestry
•Forestcover
•Type of forest
•Vegetation density
•Deforestation
•Forest fires