Thursday, 28 February 2013

Landuse Landcover

Landuse Landcover classification

Landuse Landcover classification of Remote Sensing Data

Landuse Landcover classification of Satellite Images

Baselayer classification of Satellite Images

Settlements classification of Satellite Images

Built-up layer classification of Satellite Images



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Ortho Rectification

Ortho Rectification of Remote Sensing Images

Ortho Rectification of Satellite Images

Landsat images Ortho Rectification

SPOT images Ortho Rectification

Aerial Photos Ortho Rectification


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Crop Health Monitoring

Remote Sensing based Crop Health Monitorin

NDVI based Crop Health Monitoring



Remote Sensing Based Crop Health Monitoring
Introduction:
Remote sensing techniques can play quite an important role in land cover survey and as a source of information relating to land resource condition. Besides, remote sensing techniques of the satellite imageries are also useful whenever there are rapid changes of landscape due to introduction of large scale development specially in the field of agriculture. Remote sensing data are capable of capturing changes in plant phenology (growth) throughout the growing season, whether relating to changes in chlorophyll content or structural changes. Satellite and airborne images are used as mapping tools to classify crops, examine their health and viability, and monitor farming practices. Identifying and mapping crop is important for a number of reasons. The main objective is to prepare an inventory of what was grown in certain areas and when. Key activities include identifying the crop types and delineating their extent (often measured in acres). Traditional methods of obtaining this information are census and ground surveying. In order to standardize measurements however, particularly for multinational agencies and consortiums, remote sensing can provide common data collection and information extraction strategies.

Spectral Reflectance, Vegetation and Normalized Difference Vegetation Index (NDVI):
Spectral vegetation index measurement derived from remotely sensed observations shows great promise as a means to improve knowledge of vegetation pattern. Different band ratios are possible given the number of spectral bands of the satellite image. Various mathematical combinations of satellite bands have been found to be sensitive indicators of the presence and condition of green vegetation. These band combinations are thus referred to as vegetation indices. The dominant method for vegetation area identification and change detection using remotely sensed data is through vegetation indices. Vegetation indices are algorithms aimed at simplifying data from multiple reflectance bands to a single value correlating to physical vegetation parameters (such as biomass, productivity, leaf area index, or percent vegetation ground cover). These vegetation indices are based on the well-documented unique spectral characteristics of healthy green vegetation over the visible to infrared wavelengths.
A green healthy leaf has typical spectral features, which differ in function of the three main optical spectral domains. In the visible bands (400-700 nm), light absorption by pigments dominates the reflectance spectrum of the leaf and leads to generally lower reflectances (15% maximum). There are two main absorption bands, in blue (450nm) and in red (670nm), due to the absorption of the two main leaf pigments: the chlorophyll a and b, which account for 65% of the total leaf pigments of superior plants. These strong absorption bands induce a reflectance peck in the yellow-green (550 nm) band. For this reason, chlorophyll is called the green pigment. Other leaf pigments also have an important effect of the visible spectrum. For example, the yellow to orange-red pigment, the carotene, has a strong absorption in the 350-500 nm range and is responsible for the color of some flowers and fruits as well as of leaves without chlorophyll. The red and blue pigment, the xanthophylls, has a strong absorption in the 350-500 nm range and is responsible for the leaf color in fall. In the near infrared spectrum domain (700-1300 nm), leaf structure explains the optical properties. Leaf pigment and cellulose are transparent to near-infrared wavelengths and therefore leaf absorption is very small (10% maximum), but not the leaf reflectance and transmittance, which can reach 50%. In this region, there is typically a reflectance plateau in the leaf spectrum. The level of this plateau is dependent on the internal leaf structure as well as on the space amount in the mesophyll that determines interfaces with different reflection indices (air or water- cells). Leaf reflectance increases for more heterogeneous cell shapes and contents as well as with increasing number of cell layers, number of inter cell spaces and cell size. This reflectance is therefore depending on the relative thickness of the mesophyll. In order to minimize the effect, on the canopy radiometric response of factors like optical properties of the soil background, illumination and view geometric as well as meteorological factors (wind, cloud), single band reflectances are combined into a vegetation index. An ideal vegetation index must be sensitive to the plant canopy (the green part) and not to the soil. Most of the ratio-based vegetation indices use, as spectral band, the red one, which is related to the chlorophyll light absorption and the near infrared one, which is related to the green vegetation density, because this band contain more than 90% of the information on a plant canopy. So, Photosynthetically active plant components, primarily leaves, produce a stepped reflectance pattern with low reflectance in the visible and high reflectance in the near infrared. This green vegetation spectral reflectance pattern results from strong absorption of visible light by chlorophylls and related pigments and scattering, because of leaf structural properties, but minimal absorption of light in the near infrared. A number of spectral vegetation indices premised on the contrasts in spectral reflectance between green vegetation and background materials. The normalized difference vegetation index (NDVI) is representative of the various spectral vegetation indices. NDVI is the traditional vegetation index used by researchers for extracting vegetation abundance from remotely sensed data. It divides the difference between reflectance values in the visible red and near-infrared wavelengths by the overall reflectance in those wavelengths to give an estimate of green vegetation abundance. In essence, the algorithm isolates the dramatic increase in reflectance over the visible red to near infrared wavelengths, and normalizes it by dividing by the overall brightness of each pixel in those wavelengths. 1t is computed;
NDVI = (NIR-RED)/(NIR + RED)
where NIR = reflectance in the near infrared band
RED = reflectance in the red (visible) band
In theory NDVI measurements range between -1.0 and +1.0. However, in practice the measurements generally range between -0.1 and +0.7. Clouds, water, snow and ice give negative NDVI values. Bare soils and other background materials produce NDVI values between -0.1 and + 0.1. Larger NDVI values occur as the amount of green vegetation in the observed area increases.

Methodology for Crop Health Monitoring:
The timing and amount of rainfall is very critical for crop yield. Factors like temperature, bright sunshine hours, wind speed; sources of seed, timely use and quantity of fertilizers are the other important factors affecting the crop vigor and ultimately the yield of the crop. It is possible to assess the crop vigor using appropriate sets of satellite images during the critical stage of crop growth. Each crop type has its own growth cycle and hence has a standard trend of crop vigor variation. Crop vigor in the form of NDVI is calculated for the crop at different stages of plant growth and is compared with the standard trend of NDVI values of a particular crop. The NDVI values are also compared with corresponding period of NDVI values of a crop during a Normal year. Any major reduction in the value of NDVI from standard values, pin points the areas of concern, and is verified at field. This data, when analyzed in conjunction with administrative boundaries in a GIS environment, can help assessing the real ground conditions prevailing in the area concerned with a relatively good accuracy. This study will be done by using low resolution satellite imageries because its repetivity  like NOAA or MODIS having repetition is once or twice in a day and also the drawback is its resolution and the resolution for NOAA is around   1Km and for MODIS resolution is 250M.  Whereas medium resolution images are best for crop health monitoring like AWiFS and the repetivity is 5 days of 80% area. The AWiFS satellite images ground resolution is 56M and overage of area is 370 km x 370 km.



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Time Series Classification - - Remote Sensing

Time Series Images Classification

Remote Sensing Time Series

Best Examples of Time Series images - Remote Sensing


Floods Monitoring using Remote Sensing

Floods Monitoring using MODIS images

Remote Sensing Technology based Floods Monitoring



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Tuesday, 26 February 2013

Remote Sensing Agriculture Complete Process

Remote Sensing based Agricultural Crops Acreage Estimation

Remote Sensing based Agricultural Crops Stressed Areas Identifications

Remote Sensing based Agricultural Crops Yield Estimation

Hybrid Classification using Remote Sensing Techniques.

Overall flow chart is given below:




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