In hydrology remote sensing is used to measure spatial, spectral, and temporal information and providing data on the state of the earth surface. Remote sensing is used for observation changes in the hydrological states, which is vary over time and space that can be to measure hydrological condition and changes. Today many satellite data are available for hydrology applications, such Landsat data, SPOT5, IKONOS, Geo-Eye, World View, radar etc. Remote sensing techniques indirectly measures hydrological variables, so the electromagnetic variables measured by remote sensing have to be related to hydrological variables with empirically or with transfer of functions.
In hydrology, for tropical area remote sensing is used for run off computation, evapotranspiration over land surface Evaluation of soil moisture content, water quality modelling, groundwater identification and estimation, and hydrology modelling.
GIS play fundamental role in the application of spatial distributed data to hydrological model. The integration of GIS, database management system and hydrological models speed up the use of remote sensing data in hydrological applications.
IKONOS data Over Natural Forest
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UAV Data over Forest Plantation
LiDAR remote sensing is suitable for resource inventory for forest conservation and management. LiDAR sensor has a capability the it provides three-dimensional data. The various advantages of LiDAR technology are higher accuracy, weather independence, capability of canopy penetration, lesser time needed for the acquisition and processing, minimum user interference. One more thing is that laser-derived images help in terrain visualization.
Tree height is measured by using vertical component (z-axis) measurement is the backbone of LiDAR technology. This characteristic is exploited in a very straight forward way for tree height estimation. The canopy height is obtained by subtracting the elevations of the first and the last returns. LiDAR data provides input for estimation of above ground biomass with high accuracy. The combination of LiDAR and satellite remote sensing data could be very useful for describing biodiversity and monitoring changes in biodiversity.
Timber volume and the total growing stock are key information required for the forest planning and management. Remote sensing data facilitates in the stratification of forests, which in-tern reduces the sampling error and allowing stock assessment with fewer samples. The satellite image-based forest stratification can be correlated to the actual on-ground timber volume/ growing stock or biomass using two-stage inventory design (Kohl and Kushawaha, 1994). For large areas,multi-phase sampling technique is used. In a multi-phase design, visually or digital classified imagery makes the first stage followed by large scale image interpretation and ground measurements. High resolution satellite data and aerial photograph from uav or fixed wing can be for this purpose.
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