Remote Sensing according to Campbell (1987) is the science of deriving information about the earth's land water areas form images acquired at a distance. It relies upon measurement of electromagnetic energy reflected or emitted from the features of interest. Lillesand and Keifer (1979) defined Remote Sensing as the science or art of obtaining information about an object, area or phenomenon through the analysis of the data acquired by a device that is not in contact with the object area or phenomenon under investigation. Regardless of the orientation of the various definitions of Remote Sensing, the acquisition of images of earth surface features, using sensors, through the electromagnetic spectrum, the synoptic view advantage and Remote Sensing's ability to provide data for scientific technological and sustainable management and monitoring of the environment offer a convergence. The environment virtually encompasses everything in the world around us. It includes natural, physical, biotic and abiotic as well as human socio-economic features. Geographers have long claimed interest not only in the unity that exists in the biosphere, but also in the intrinsic quality of individual places. Environmental Impact Assessment (EIA) has been defined by Munn (1979) as an activity designed to identify and predict the impact on the biogeochemical environment and on man's health and well being of legislative proposals, policies, programs, projects and operational procedures and to interpret and communicate information about the impact. Hence, EIA is a planning tool, a formal study problem used to predict the environmental consequences of a proposed major development project.
Remote Sensing in Environmental Sustainability
Conservation authorities are concerned with monitoring the quantity, health and diversity of the earth's forests. The remote sensing data are using for the following applications- Deforestation (rainforest, mangrove colonies); Species inventory; Watershed protection (riparian strips); Coastal protection (mangrove forests); Forest health and energy.
Geology involves the study of landforms, structures and the subsurface to understand physical processes creating and modifying the earth's crust. It is most commonly understood as the exploration and exploitation of mineral and hydrocarbon resources, generally to improve the condition and standard of living in society. It is also the study of potential hazards such as volcanoes, landslides, and earth quakes. Remote sensing is used as a tool to extract information about the land structure, composition or subsurface. The use of remote sensing technology in geological resource management are given below - Surficial deposit/ bedrock mapping; Litho logical mapping; Structural mapping; Sand and gravel exploration; Mineral exploration; Hydrocarbon exploration; Geo-botany; Baseline infrastructure; Sedimentation mapping and monitoring; Event mapping and monitoring; Geo-hazard mapping and Planetary mapping.
The use of remotely-sensed data in natural resources mapping and as source of input data for environmental processes modeling have been popular in recent years. With the availability of remotely sensed data from different sensors of various platforms with a wide range of spatiotemporal, radiometric and spectral resolutions has made remote sensing as, perhaps, the best source of data for large scale applications and study. In this review, the most commonly used applications of the technique in environmental resources mapping and modeling. Applications of remote sensing in hydrological modeling, watershed mapping, energy and water flux estimation, fractional vegetation cover, impervious surface area mapping, urban modeling and drought predictions based on soil water index derived from remotely-sensed data is reported. The review also summarizes the different eras of sensors development and remote sensing and future directions of the remote sensing applications. Many applications in environmental monitoring require frequent Coverage of the same area. This can be maximized by using data from multiple sensors However, since data from these sensors are acquired in multiple resolutions (spatial, spectral, radiometric), multiple bandwidth, and in varying conditions, they need to be harmonized and Synthesized before being used. This will help normalize for sensor Characteristics such as pixel sizes, radiometry, spectral domain, and time of acquisitions, as well as for scales.
The majority of remote sensing work has been focused on natural environments over the past decades. Applying remote sensing technology to urban areas is relatively new. With the advent of high resolution imagery and more capable techniques, urban remote sensing is rapidly gaining interest in the remote sensing community. Driven by technology advances and societal needs, remote sensing of urban areas has increasing become a new arena of geospatial technology and has applications in all socioeconomic sectors. Urban landscapes are typically a complex combination of buildings, roads, parking lots, sidewalks, garden, cemetery, soil, water, and so on. Each of the urban component surface exhibits a unique radiative, thermal, moisture, and aerodynamic properties, and relates to their surrounding site environment to create the spatial complexity of ecological systems. To understand the dynamics of patterns and processes and their interactions in heterogeneous landscapes such as urban areas, one must be able to quantify accurately the spatial pattern of the landscape and its temporal changes.
Since 1972 (launch of the first Earth Resources Technology Satellite, ERTS-1), scientist have used remotely-sensed data from different sensors to characterize, map, analyze and model the state of the land surface and surface processes. With the help of new algorithms, new hydrological information were extracted from remotely-sensed data and used in hydrological and environmental modeling. These new information and hydrological parameters have increased our understanding of the different hydrological processes by helping in quantifying the rate and amount of water and energy fluxes in the environment. The ability of these sensors in providing various spatiotemporal scales data has also increased our capability in looking into one of the challenges of environmental modeling, mismatch between scales of environmental process and available data. The role of remote sensing in understanding hydrological processes and fluxes across different spatial and temporal scales can be tremendous, if appropriate spatial and temporal resolution remotely sensed data are available under ranges of bands. The application of remote sensing to water resources has been increasing in recent years. The application of remote sensing in water resources research and management mainly lies in one of the three categories: mapping of watersheds and features, indirect hydrological parameter estimation and direct estimation of hydrological variables.
Water Pollution control
Since water is an excellent neutral blackbody radiation, TIR radiometric data can be coupled with fluid dynamics and heat transfer theories to evaluate and refine existing mathematical and physical hydrodynamic models. Aerial TIR data can be effectively used for such a purpose. Many researchers investigated the use of remotely sensed MSS data for water quality mapping in inland and estuarine systems. The high resolution data of TM, SPOT, and IRS permit more accurate of water quality mapping. Remote sensed data for mapping chlorophyll and other water quality parameters has also been reported. Developed regression models to represents best relationships between salinity, turbidity, total suspended solids and chlorophyll concentrations and the corresponding mean radiance values from LANDSAT MSS data for San Fransisco Bay and Delta. The CIR, TIR data are used for salt water intrusion. The UV, TIR, MW sensors are used to detect oil spills. The V, C, CIR sensors are used for pollution control related to agriculture, forestry, mining, and land development activities.
Soil and Drought Monitoring
With the advent of grid-based remotely-sensed rainfall data, the application of crop water balance models for crop monitoring and yield forecasting has gained increased acceptance by various international, national and local organizations around the world. Soil water is a key state variable in hydrological modeling and determines the partitioning of rainfall into runoff and deep percolation, and also controls the rate of evapo-transpiration. Although the estimation of actual evapotranspiration is the ultimate goal of many researchers for hydrological and agronomical applications, it is often difficult to quantify and requires expensive instrumentation. For monitoring large areas using remotely sensed data, the water balance approach provides an operational advantage in terms of data availability. While the energy balance models are mainly driven by the thermal data, the water balance models are driven by rainfall. The most widely used water balance technique for operational use is the FAO water balance algorithm that produces the crop water requirement satisfaction index (WRSI), which is also known as the crop specific drought index (CSDI). The WRSI shows the relative relationship (ratio/percent).All these combine to make Remote Sensing a veritable tool for obtaining baseline information for establishing baseline conditions of an area at the preproject analysis stage, as well as monitoring changes in the environmental conditions of such area after the project has been dc-commissioned.
Coastal and Mangroves Ecosystem Management
Based on remote sensing a variety of data pertaining to the coastal zone like, identification of plant community, biomass estimation, shoreline changes, delineation of coastal landforms and tidal boundary, qualitative estimation of suspended sediment concentration, chlorophyll mapping, bathymetry of shallow waters, etc. can be collected and all these data will help in effective coastal ecosystem management. Satellite remote sensing has been found to be a very valuable application tool in forest management including mangroves, not only in monitoring, but also carrying out relevant observations, which can bring out the impact of deforestation on global climate. Remote sensing of change detection is a process of determining and evaluating differences in a variety of surface phenomena over time. For detection of land cover change, multi temporal data of Landsat TM were found to be more suitable for identification of deforestation areas, mapping the regeneration/ re growth of forest area and tracing major changes in land cover.
We should note that the whole life support system of air, water, soil, faunal, floral all biophysical and ecological constituents of nature are useful to man. The actual use of resource during the transformation of neutral stuff into commodity or service to serve human needs and aspiration called resource development should be met with environmental impact assessment. Remote sensing is the only technique that can provide holistic approach to learn of total environment.
The Writer is an Environmental Analyst and Associate Member, Bangladesh Economic Association.
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