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Visual Interpretation
This document describes a land use and land cover classification system for mapping using satellite data. It includes 14 categories organized in a three-level hierarchy with associated codes. The categories include built-up land, agricultural land, forest, wastelands, water bodies, and others. Factors like tone, size, shape, texture, and pattern are used to classify different land types captured by satellites at various scales from 1:2,50,000 to 1:4,000. Ground truthing is used to verify and modify preliminary interpretations of satellite imagery.
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Visual Interpretation
- 1. Dr. V. RGhodake Department of Civil Engg. Sinhgad College of Engineering, Vadgaon, Pune. India
- 18. Category Tone SizeShape Texture Pattern Location Association Season Built – up Dark bluish green and bluish Small to big Irregular discontinuous Coarse and mottled Clustered to scattered Plains By agriculture lands, rivers, road and rail Oct to March Crop Land Bright red to red Varying in size Regular to irregular Medium to smooth Contiguous Plains, hills, slopes, valleys Amidst irrigated June – Sept Oct – Dec Feb – May Evergreen Forest Bright red to dark red Varying in size Irregular size Smooth to medium Contiguous to non contiguous High relief High relief Jan – December Feb – May Gullied Land Light yellow to bluish green Varying in size Irregular broken Very coarse Dendrite to sub dendritc Stream Plain lands Dec to March
- 29. Landuse/ Landcover Satellite Data ProductScale of Mapping I Landsat MSS; IRS LISS I 1:2,50,000 & smaller II Landsat TM, IRS LISS II, LISS III, SPOT MSS 1:50,000 & smaller III SPOT PAN, IRS 1C/1D PAN 1:50,000 to 1:25,000 IV IRS 1C/1D LISS III + PAN 5.8m 1:25,000 to 1:12,500 IV IKONOS MS 4m 1:10,000 IV IKONOS PAN 1m, QuickBird 0.61m 1:4000-1:3000 Landuse/ Landcover Satellite Data Product Scale of Mapping I Landsat MSS; IRS LISS I 1:2,50,000 & smaller II Landsat TM, IRS LISS II, LISS III, SPOT MSS 1:50,000 & smaller III SPOT PAN, IRS 1C/1D PAN 1:50,000 to 1:25,000 IV IRS 1C/1D LISS III + PAN 5.8m 1:25,000 to 1:12,500 IV IKONOS MS 4m 1:10,000 IV IKONOS PAN 1m, QuickBird 0.61m 1:4000-1:3000 View Image View Image View Image View Image View Image View Image
- 30. SOI Toposheets 3 Season satellite imagery Collateral data
- 31. Final QC Satellite data- IRS 1C/1D LISS III + PAN / LISS Geocoded 3 Season Geocoded Data Collateral data SOI toposheet Forest Map Base Map Image Interpretation Key Image analysis of FCC on 1:50,000 scale Classification System Preliminary Interpreted Map Post field correction/ Modification Final Map Ground check
- 32. 1.Forest 2.Cropland 3.Cultivable wasteland 4.Uncultivable wasteland 5.Grassland 6.Tree& Groves 7.Current Fallow 8.Net Sown Area 9.Others
- 33. LEVEL - 1LEVEL - 2 LEVEL - 3 SYMBOL 1 Built – up land 1.1 Towns/Cities & Industries 01 &1a 1.2 Villages 02 2 Agricultural Land 2.1 Crop land 2.1.1 Kharif 03 2.1.2 Rabi 04 2.1.3 Summer 4a 2.1.4 Kharif + Rabi (Doublecropped) 05 2.2 Fallow 06 2.3 Plantation 07 3 Forest 3.1 Evergreen/ Semi-evergreen 3.1.1 Dense 08 3.2.2 Open 09 3.2 Deciduous (Moist & Dry) 3.2.1 Dense 10 3.2.2 Open 11 3.3 Scrub Forest 12 3.4 Degraded Forest 13 3.5 Forest Plantations 14 3.6 Mangroves 15 LEVEL - 1 LEVEL - 2 LEVEL - 3 SYMBOL 1 Built – up land 1.1 Towns/Cities & Industries 01 &1a 1.2 Villages 02 2 Agricultural Land 2.1 Crop land 2.1.1 Kharif 03 2.1.2 Rabi 04 2.1.3 Summer 4a 2.1.4 Kharif + Rabi (Doublecropped) 05 2.2 Fallow 06 2.3 Plantation 07 3 Forest 3.1 Evergreen/ Semi-evergreen 3.1.1 Dense 08 3.2.2 Open 09 3.2 Deciduous (Moist & Dry) 3.2.1 Dense 10 3.2.2 Open 11 3.3 Scrub Forest 12 3.4 Degraded Forest 13 3.5 Forest Plantations 14 3.6 Mangroves 15
- 34. 4 Wastelands 4.1Salt Affected Land 16 4.2 Waterlogged Land 17 4.3 Marshy / Swampy Land 18 4.4 Gullied / Ravinous Land 19 4.5 Land with scrub 20 4.6 Land without scrub 21 4.7 Sandy area (Coastal & Desertic) 22 4.8 Mining/ Industrial Wasteland 23 4.9 Barren Rocky / Stony Waste/ Sheet Rock Area 24 4 Wastelands 4.1 Salt Affected Land 16 4.2 Waterlogged Land 17 4.3 Marshy / Swampy Land 18 4.4 Gullied / Ravinous Land 19 4.5 Land with scrub 20 4.6 Land without scrub 21 4.7 Sandy area (Coastal & Desertic) 22 4.8 Mining/ Industrial Wasteland 23 4.9 Barren Rocky / Stony Waste/ Sheet Rock Area 24
- 35. 6 Others 6.1Shifting Cultivation 26 6.2 Grassland/ Grazing land 6.2.1 Dense 27 6.2.2 Degraded 28 6.3 Salt Pans 29 6.4 Snow covered / Glacial Area 30 6.5 Prosophys Juliflora 31 6.6 Aquaculture Pond 32 6.7 Habitation With Vegetation 33 6.8 Mixed Vegetation 34 6.9 Tree Groves 35 5 Water Bodies 5.1 River / Stream - 5.2 Canals - 5.3 Lake / Reservoirs / Tanks 25 6 Others 6.1 Shifting Cultivation 26 6.2 Grassland/ Grazing land 6.2.1 Dense 27 6.2.2 Degraded 28 6.3 Salt Pans 29 6.4 Snow covered / Glacial Area 30 6.5 Prosophys Juliflora 31 6.6 Aquaculture Pond 32 6.7 Habitation With Vegetation 33 6.8 Mixed Vegetation 34 6.9 Tree Groves 35 5 Water Bodies 5.1 River / Stream - 5.2 Canals - 5.3 Lake / Reservoirs / Tanks 25
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Editor's Notes
- #5 Shape.The external form, outline, or configuration of the object. Size.Scale dependent on the photo. Pattern.Spatial arrangement of objects into distinctive, recurring forms. Tone/Colour Also color. Relates to the spectral reflectance properties of objects. On B/W tones vary white to black. On color emulsions, hue (color), intensity (brightness), and saturation are examined. Texture.Roughness or smoothness. Caused by differences in illumination and shadowing. Frequency of tonal change. Shadows.Provide profile view of objects. Site. Topographic or geographic location. Association.Occurrence of features in relation to others.
- #6 Tone: Refers to the colour or relative brightness of an object. The tonal variation is due to the reflection, emittance, transmission or absorption character of an objects. This may vary from one object to another and also changes with reference to different bands. In General smooth surface tends to have high reflectance, rougher surface less reflectance. This phenomenon can be easily explained through Infrared and Radar imagery . Tone can be defined as each distinguishable variation from white to black. Color may be defined as each distinguishable variation on an image produced by a multitude of combinations of hue, value and chroma. Many factors influence the tone or color of objects or features recorded on photographic emulsions. But, if there is not sufficient contrast between an object and its background to permit at least detection, there can be no identification. While a human interpreter may only be able to distinguish between ten and twenty shades of grey; interpreters can distinguish many more colors. Some authors state that interpreters can distinguish at least 100 times more variations of color on color photography than shades of gray on black and white photography
- #8 Texture: The frequency of tonal change. It creaks a visual impression of surface roughness or smoothness of objects. This property depends upon the size, shape, pattern and shadow The frequency of change and arrangement of tones. This is a micro image characteristic. The visual impression of smoothness or roughness of an area can often be a valuable clue in image interpretation. Water bodies are typically fine textured, while grass is medium, and brush is rough, although there are always exceptions
- #9 Shape: The external form, outline or configuration of the object. This includes natural features (Example:River) and Man Made feature (Example : Chinnaswamy Stadium, Bangalore). The shape of objects/features can provide diagnostic clues that aid identification. The Pentagon building in Washington is a diagnostic shape. Man-made features have straight edges that natural features tend not to. Roads can have right angle turns, railroads do not.
- #10 Size : This property depends on the scale and resolution of the image/photo. Smaller feature will be easily indented in large scale image/photo. The high spatial resolutions imagery/photographs is useful in identifying small objects. The size of objects can be important in discrimination of objects and features (cars vs. trucks or buses; single family vs. multi-family residences, brush vs. trees, etc.). In the use of size as a diagnostic characteristic both the relative and absolute sizes of objects can be important. Size can also be used in judging the significance of objects and features (size of trees related to board feet which may be cut; size of agricultural fields related to water use in arid areas, or amount of fertilizers used; size of runways gives an indication of the types of aircraft that can be accomodated).
- #11 Shadow: Indicates the outline of an object and its length which is useful is measuring the height of an object. The shadow effect in Radar images is due to look angle and slope of the terrain. Taller features cast larger shadows than shorter features. Geologists like low sun angle photography because shadow patterns can help identify objects. Steeples and smoke stacks can cast shadows that can facilitate interpretations. Tree identification can be aided by an examination of the shadows thrown. Shadows can also inhibit interpretation
- #12 Pattern is the spatial arrangement of objects. Pattern can be either man-made or natural. Pattern is a macro image characteristic. It is the regular arrangement of objects that can be diagnostic of features on the landscape. An orchard has a particular pattern. Likewise, the network or grid of streets in a subdivision or urban area can aid identification and aid in problem solving such as the growth patterns of a city. Pattern can also be very important in geologic or geomorphologic analysis. Drainage pattern can tell the trained observer a great deal about the lithology and structural patterns in an area. Dendritic drainage patterns develop on flat bedded sediments; radial on/over domes; linear or trellis in areas with faults or other structural controls It must be noted here that pattern is scale dependent. An orchard on a low altitude aerial photo may be an area of rough texture on a high altitude air photo or an area of medium or rough texture on a satellite image. This pattern may be thought of as being scale dependent
- #13 Association: Occurrence of features in relation to others. The relationship of feature to the surrounding features provides clues to words its identity. Example: certain tree species words associated with high altitude areas Some objects are so commonly associated with one another that identification of one tends to indicate or confirm the existence of another. Smoke stacks, step buildings, cooling ponds, transformer yards, coal piles, railroad tracks = coal fired power plant. Arid terrain, basin bottom location, highly reflective surface, sparse vegetation = playa. Association is one of the most helpful clues in identifying man made installations. Aluminum manufacture requires large amounts of electrical energy. Absence of a power supply may rule out this industry. Cement plants have rotary kilns. Schools at different levels typically have characteristic playing fields, parking lots, and clusters of buildings in urban areas. Large farm silos typically indicate the presence of livestock. The relative importance of each of these factors will vary with local conditions, but all are important. Just as some vegetation grows in swamps others grow on sandy ridges. Agricultural crops may like certain conditions. Man made features may also be found on rivers (e.g. power plant) or on a hill top (observatory or radar facility).