GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
Subject Geology
Paper No and Title Remote Sensing and GIS
Module No and Title Environmental Applications of Remote Sensing Module Tag RS & GIS XXI
Principal Investigator Co-Principal Investigator Co-Principal Investigator Prof. Talat Ahmad
Vice-Chancellor Jamia Millia Islamia Delhi
Prof. Devesh K Sinha Department of Geology University of Delhi Delhi
Prof. P. P. Chakraborty Department of Geology University of Delhi Delhi
Paper Coordinator Content Writer Reviewer Dr. Atiqur Rahman
Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia
Delhi
Dr. Atiqur Rahman Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia
Delhi
Dr. Pervez Ahmed Department of Geography University of Kashmir Srinagar
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
Table of Content
1. Introduction
2. Biodiversity and Wetlands Assessment
2.1 Habitat loss and Fragmentation2.2 Habitat Suitability Mapping
2.3 Wetland Mapping and Monitoring
2.4 Identifying and Monitoring of Threats on Vegetation 2.5 Predicting Biodiversity Richness
3. Monitoring of Forest Resources
3.1 Forest Cover Assessment and Monitoring 3.2 Forest Fire Monitoring
4. Carbon Stocks and Sinks
4.1 Carbon Sequestration Monitoring
5. Marine Applications
5.1 Coastal Ecosystem Management 5.2 Oil Spill Verification
6. Monitoring of Desertification processes 7. Other Application
7.1 Air pollution
7.2 Soil, Water and Drought Monitoring 7.3 Enviromenta1 Impacts Assessment
8. Summary
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
1. Introduction
The word ‘environment’ means surroundings, in which organisms live. In other words, environment is the sum total of conditions that surrounds us at a given point of time and space. It is comprised of the interacting systems of physical, biological and cultural elements, which are interlinked both individually and collectively. It influences the growth and development of living forms.
With the ever-increasing human population, nature has been over exploited to meet the basic amenities, like food, fuel and fibre. The senseless exploitation of nature has not only depleted the finite resources but also degraded their quality.
Therefore, environmental considerations have become a major concern for the world. The focus now is preserving the overall balance and value of the natural capital stock. Accurate baseline information and methods to evaluate the quantity and the quality of each resource is the basic requirement for further planning.
Remote sensing has been recognized now as a valuable tool for viewing, analyzing, characterizing and making decisions about our environment (Fig. 1).
The remote sensing has great advantage over traditional methods, as it is capable of providing synoptic view with wall-to-wall coverage of study area. Besides this, it can also address regions of widely varying scales and does not breach national sovereignty. However, it is a costly affair, but with the time, its costs are coming down; both for imagery and software.
Fig. 1 Techniques of remote sensing for various features.
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
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 (Table 1).
Table 1: Applications related to natural resources management and spectral ranges required/employed.
Theme Application Spectral Range required
Agriculture, Forestry and Land use/cover
Crop identification & acreage estimation
Crop condition assessment and yield estimation
Soil moisture
Drought monitoring Land use/cover mapping Forest fire detection
VIS,NIR,MIR,MW
VIS,NIR,TIR
TIR and Microwave (L & C bands)
VIS,NIR,MIR VIS,NIR,
3-4 Micrometer, TIR Water resources Mapping surface water bodies
Water quality monitoring Snow mapping
-aerial extent –depth (water equivalent)
Flood mapping
VIS,NIR
Narrow spectral bands in VIS,NIR Thermal VIS, NIR, MIR Microwave VIS, NIR Marine resources and coastal
studies
Phytoplankton estimation Fluorescence studies for Chlorophyll-A estimation Sea surface temperature Wetland mapping Oil slicks
Narrow spectral bands
~10nm in the VIS, NIR at 685 nm with 5 nm resolution + NIR
TIR, Microwaves
TIR, NIR, MIR and Microwaves (19.1 and 31GHz) Geology/Mineral Resources Structural geology
Rock type identification
VIS, NIR and Microwaves Narrow spectral bands in VIS NIR MIR & TIR
VIS: - 0.4 to 0.9 micrometer, NIR: 0.7 to 1.1 micrometer, MIR: 1.55 to 1.75 micrometer and 2.08 to 2.3micrometer, TIR: 8-14 micrometer and Microwaves: L, C and X bands (Source:
Joseph and Navalgund, 1999).
Remote sensing is widely used to assess and monitor environment in the following field:
a) Biodiversity and Wetlands Assessment b) Monitoring of Forest Resources
c) Carbon stocks and sinks
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
d) Marine Applications e) Desertification f) Other Application
g) Enviromenta1 Impacts Assessment
2. Biodiversity and Wetlands Assessment
2.1 Habitat Loss and Fragmentation
It is important to quantify forest loss and its fragmentation, as it is habitats for various organisms. Satellite remote sensing may play key role in generating information about forest cover, vegetation type and land use changes (Fig. 2).
Fig. 2 Multi-temporal satellite data have been used to quantify deforestation and habitat fragmentation in the spatial context. Changes in shape and size of forest fragments can be assessed using satellite data in a GIS environment.
The Land use land cover map generated from remotely sensed satellite data can be used as an input for landscape analysis to derive habitat fragmentation indices such as Patch cohesion, Contagion index, Proximity of patches, Aggregation index and Patch size density. For this, FRAGSTATS computer software is used in remote sensing domain.
2.2 Habitat Suitability Mapping
Habitat evaluation is the first step towards meaningful wildlife conservation.
Geospatial technology including remote sensing, geographic information system (GIS) and global positioning system (GPS) along with a habitat
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
suitability index (HSI) model provides, an efficient and low-cost method for determining habitat quality. A suitability index provides the likelihood of how much area is suitable for a particular species. The higher the value, there will be better chance that a particular location is suitable for the occurrence of that species. While developing the H.S.I. GPS locations of wild animals’
presence/absence is integrated with variables like slope, aspects and distance from roads, settlements, LULC, forest crown density and aspects to produce habitat use-environmental variables matrix. Animal sightings are taken as
“Boolean” (presence/absence) and “binomial multiple logistic regression (BMLR) is run. The coefficients derived from BMLR area used to integrate all layers to arrive at the probability/suitability maps. The estimated log-odds image was then log it transformed to produce the intended probability map.
The output map is sliced to “not suitable” at value lower than 0.5 and
“suitable” at values higher than that (Fig. 3).
Fig. 3 Habitat Suitability Index
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
In India, the use of geospatial technology for analyzing the “habitat suitability index” started during the late 1980s. Indian researchers used data from ‘LANDSAT’ for habitat evaluation of Indian one-horned rhinoceros.
This technology was widely used by Kushwaha and his colleagues for rhinoceros in Kazhiranga National Park, mountain goat in Rajaji National Park. Recently, Imam et al. (2009) used remote sensing and GIS for the habitat evaluation of tiger in Chandoli tiger reserve (Maharashtra).
2.3 Wetland Mapping and Monitoring
Wetlands are valuable natural resources, because they are associated with biological diversity, important ecosystem functions and processes, and useful economically viable products. Wetlands provide wildlife habitat, groundwater recharge, flood control, sediment filtration, nutrient retention, pollutant removal and wetland products. Remote sensing has been a powerful tool for wetland identification, classification, mapping, biomass measurement and change detection. Ministry of Environment and Forests has developed national-level inventory and assessment of wetlands using RESOURCESAT-1 LISS-III data of 2006–7 at 1:50,000 scale. The extent of wetlands has been estimated to be 15.26 m ha. Inland wetlands account for 69.22% (10.564 m ha), whereas the coastal wetlands account for 27.13%
(4.14 m ha). The high-altitude wetlands (situated > 3000 m asl) in the Himalayan states were also mapped, comprising 126,249 ha of areal extent.
Fig. 5 Landsat satellite images of Chilika Lake in 2012 and 2013.
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
2.4 Identifying and monitoring of threats on vegetation
Remote sensing satellite data is capable of identifying and monitoring threats imposed on vegetation due to various environmental stresses. Multiple plant stresses affect the health, aesthetic condition, and timber harvest value of forests. Remote sensing may provide timely, accurate, and cost-effective information that is needed to monitor spatial and temporal dynamic forest stress conditions. Imagery of Rapid Eye satellite provides spectral information in five broad bands, including the red-edge region (690-730 nm) of the electromagnetic spectrum. It is reported that broadband, red-edge satellite information improves early detection of stress in a woodland ecosystem relative to the more commonly used band combinations of red, green, blue, and near infrared band reflectance spectra. The Normalized Difference Red-Edge index (NDRE) allowed stress to be detected 13 days after girdling, which is between 12 to 16 days earlier than traditionally used broadband spectral indices such as the Normalized Difference Vegetation Index (NDVI) and the Green NDVI. Using this technique, a conceptual framework can also be developed that could be used as a decision support tool for optimizing approaches to detect early onset of plant stress.
2.5 Predicting biodiversity richness
One of the main goals in nature conservation and land use planning is to identify areas important for biodiversity. One possible cost-effective substitute for deriving appropriate estimates of spatial patterns of species richness is provided by predictive modeling combining with remote sensing and topographic data. Luoto (2004) used species richness data from a spatial grid system (105 squares of 0.25 km2 within an area of 26.25 km2), and tested the usefulness of Landsat TM satellite-based remote sensing and topographic data in bird species richness modeling in a boreal agricultural- forest mosaic in southwestern Finland. For this, he built generalized linear models for the bird species richness and validated the accuracy of the models with an independent test area of 50 grid squares (12.5 km2). He evaluated
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
separately the Modeling performance of habitat structure, habitat composition, topographical-moisture variables. In this technique only about 5% of the land in the study area was considered for modelling and finally it was extrapolated to the whole study area of 600 km2 and produced bird species richness probability maps. It is considered that spatial grid system with several environmental variables derived from remote sensing data produces the most reliable data sets, which can be used in predicting species richness in other landscapes.
3. Monitoring of Forest Resources
3.1 Forest cover assessment and monitoring
In recent decades, intensive and rapid deforestation has led to increased worldwide attention for sustainable management of forest resources. Remote sensing is established as state of the art tool for forest monitoring systems capable of evaluating deforestation patterns. Map of forest cover change derived from remote sensing data is helpful in identification of deforestation hotspots. The temporal evaluation of forest change based on satellite imagery is becoming a valuable set of technique for assessing the degree of threat to ecosystems. Digital archive of remotely sensed data provides an excellent opportunity to study historical forest cover changes and relate to spatiotemporal pattern of such changes to other environmental and human factors. In ERDAS software, satellite data is exported in image format and classified using supervised classification or unsupervised classification to produce land use land cover map where as NDVI can be used to produce forest crown density map (Fig. 6). Temporal maps of LULC and forest crown density may be used to provide change detection to provide information on forest cover and changes in it in due course of time.
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
Fig. 6 Forest cover mapping 3.2 Forest fire monitoring
Remote sensing plays important role in fire detection, its observation and its magnitude on ground. NASA’s Fire Information for Resource Management System (FIRMS/GFIMS) was originally developed to get MODIS (MODerate resolution Imaging Spectroradiometer)-derived active fire/hotspot information. FIRMS provides fire locations in formats that can easily be ingested in to a Geographic Information System (GIS) or Google Earth-type application, web mapping tools to view, query and download fire locations, and an email alert service, which notifies users of fires in or around their area of interest.
National Polar-orbiting Partnership satellite (NPP's) Visible Infrared Imaging Radiometer Suite (VIIRS) instrument is capable of capturing fire and smoke generated by the fire. Suomi NPP is a joint mission of NASA, NOAA and the Department of Defense. The Suomi National Polar-orbiting Partnership satellite carries an instrument so sensitive to low light levels that it can detect
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
wildfires in the middle of the night as well as during the daytime and captured day and night images.
For example, Suomi NPP's Visible Infrared Imaging Radiometer Suite (VIIRS) instrument captured a look at the fire and the smoke generated by the fire during the daytime on May 5, 2016 at 3:45 p.m. EDT (19:45 UTC). In the image, the hotspots indicate the location of the fires. The smoke was blowing to the south-southeast of the fires in Fort McMurray (Fig. 7). On May 6, 2016 at 5:56 a.m. EDT (0956 UTC), the VIIRS instrument on Suomi NPP acquired a night time image of the Fort McMurray wildfire by using its "day-night band" to sense the fire in the visible portion of the spectrum. In the image, the brightest parts of the fire appear white while smoke appears light gray.
Fig. 7 Forest fire mapping assessment
4. Carbon stocks and sinks
4.1 Carbon sequestration monitoring
Climate change is one of the greatest challenges of our time. According to the National Oceanic and Atmospheric Administration of the USA, concentration of CO2 in the atmosphere has steadily increased from 280 ppm in 1800 to 385 ppm in 2008. This increase has apparently triggered global temperature rise, causing a great deal of discomfort to the world population.
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
Carbon Sequestration (CS) is the process of transfer and secure storage of atmospheric CO2 into other long-lived carbon pools (Lal, 2007). Terrestrial carbon sinks include forests, wetlands and the soil biome. Quantification and management of CS in a regional scale would involve the deployment of cutting edge geo-spatial technologies. These technologies can be potentially used in varying levels of integration for monitoring and management of CS.
One such approach that is gaining credence in ecological and environmental applications is the integration of remote sensing and geographical information system.
The primary scaling tool in this approach is the Biome-BGC carbon-cycle process model. The model has a daily time step and is run over multiple years to simulate primary and secondary succession. Simulated processes include photosynthesis, plant respiration, heterotrophic respiration, plant carbon allocation, and plant mortality.
The BIOME-BGC (BioGeochemical Cycles) model is a multi-biome generalization of FOREST-BGC, a model originally developed to simulate a forest stand development through a life cycle. The model requires daily climate data and vegetation, and site conditions to estimate fluxes of carbon, nitrogen, and water through ecosystems. Allometric relationships are used to initialize plant and soil carbon (C) and nitrogen (N) pools based on the leaf pools of these elements. Components of BIOME-BGC have previously undergone testing and validation, including the carbon dynamics and the hydrology.
5. Marine Applications
5.1 Coastal 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
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
shallow waters, etc. can be collected and all these data will help in effective coastal ecosystem management.
5.2 Oil Spill Verification
A satellite image of the area where oil spill occurred contains suspended substances, such as hydrocarbons, sediment, foam or dye. In a remote sensing domain, these can be identified and measured in advance to curb and make effective assessment.
Oil spill detection can be done by RADAR and thermal imagine. Miros offers a radar based OSD solution, which is thoroughly tested in oil-on-water exercises since 2004. The radar-based system has fully automated detection, giving oil spill position, tracking and measurement of drift. Miros OSD can operate in nearly all visibility conditions on a 24-hour basis, and has become an essential tool for navigating the recovery vessel and boom efficiently towards the oil slick (Fig. 8).
Fig. 8
Using thermal (IR) imaging, identification of the thickest part of the oil slick becomes available. This contributes when estimating the magnitude of the spill and enables targeting the response effort to the part of the slick where the majority of oil is found.
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
6. Monitoring of desertification processes
India has an arid zone, comprising both hot and cold deserts, covering approximately 390,000 km2. Indian scientists have been using satellite imagery to understand desertification processes and evolution of deserts. Earth observation satellites provide significant contributions to desertification assessment and monitoring, particularly by providing the spatial information needed for regional- scale analyses of the relationships between climate change, land degradation and desertification processes (Fig. 9). In most cases, optical remote sensing data observing the earth in wavelength regions between 400 and 2500 nm are employed, i.e. data covering the visible to the shortwave-infrared domain. The studies conducted by various scientists have shown that MODIS and ASTER imagery has potential for desertification mapping at small and medium scales.
Fig. 9
For evaluating and monitoring the desertification, use of multispectral and temporal satellite images are used along with regression model based on the NDVI–a
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
relationship. The Desertification degree index (DDI) offered a panorama of the spatio-temporal changes in terms of vegetation cover, soil, climate, and water availability, by means of calculating the albedo. The NDVI–BSI (Normalized Difference Vegetation Index-Bare-Soil Index) relationship facilitated the characterization of desertification and helped to identify zones of degradation or re- growth over time by qualitatively classifying the state of desertification and its direction and rate of change.
7. Other Application
7.1 Air Pollution
Remote sensing is capable of detecting accidental release of toxic chemicals and chemical tank fire through analyzing satellite imageries. Remote sensing is also helpful in measuring the air dispersion of pollutants and taking precautions against the impact of the source region.
Fig. 10 An early photo taken in 1973 from the NASA Skylab space station. It shows a thick layer of smog in the Los Angeles Basin (circled). Photo credit:
NASA.
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
Remotely sensed MSS data has been used by various investigators 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. 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.
7.2 Soil, Water 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 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 evapotranspiration (ET). 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). All these combine to make Remote Sensing a veritable tool for obtaining baseline information for establishing baseline conditions of an area at the pre project analysis stage, as well as monitoring changes in the environmental conditions of such area after the project has been commissioned.
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
7.3 Environmental Impacts Assessment
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 used to predict the environmental consequences of a proposed major development project.
With the use of geo-spatial techniques like remote sensing, Geographical Information Systems (GIS), and Global Positioning Systems (GPS), EIA has enhanced substantial viewing, movement, query, and even map-making capabilities. Ministry of Forest and Climate Change, Government of India has made EIA compulsory before implementing a project. Without submission of EIA, project will not be cleared.
For example, India plans a large expansion of nuclear power. The Indian Department of Atomic Energy (DAE) projects that hundreds of nuclear reactors will be constructed over the next few decades. There is widespread concern about the potential environmental impact of these projects. Before implementing all these projects, Environment Impact Assessment is needed to predict impacts of these activities on environment and society; hence, DAE issues a notice for EIA.
8. Summary
The word ‘environment’ means surroundings, in which organisms live. It is comprised of the interacting systems of physical, biological and cultural elements.
With the increase in human population, nature has been over exploited to meet the basic amenities, like food, fuel and fibre. The senseless exploitation of nature has not only depleted the finite resources but also degraded their quality. Therefore, environmental considerations have become a major concern for the world. The focus now is preserving the overall balance and value of the natural capital stock. Accurate
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
baseline information and methods to evaluate the quantity and the quality of each resource is the basic requirement for further planning. Remote sensing has been recognized now as a valuable tool for viewing, analyzing, characterizing and making decisions about our environment. The remote sensing has great advantage over traditional methods, as it is capable of providing synoptic view with wall-to-wall coverage of study area. 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 best source of data for large-scale applications and study. Remote Sensing has been widely used in Enviromenta1 Impacts Assessment, Biodiversity & Wetlands Assessment, Monitoring of Forest Resources, Assessment of Carbon stocks & sinks, Marine Applications and monitoring of Desertification. Remote sensing provided fast results on these aspects, which may help in taking timely decision.
Frequently Asked Questions-
Q1. Describe the role of Remote Sensing in forest fire monitoring?
Ans: Remote sensing plays important role in fire detection. NASA’s MODIS sensor, onboard the Terra and Aqua Earth Observing System satellites have been providing global fire observations. On another hand National Polar-orbiting Partnership satellite (NPP's) Visible Infrared Imaging Radiometer Suite (VIIRS) instrument is capable of capturing fire and smoke generated by the fire. It carries an instrument so sensitive to low light levels that it can detect wildfires in the middle of the night as well as during the daytime and captured day and night images.
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
Q2. How Habitat suitability Index modelling is developed using Remote Sensing and GIS?
Ans: Remote Sensing along with geographic information system (GIS) and global positioning system (GPS) may help in developing habitat suitability index (HSI) model. A suitability index provides the likelihood of how much area is suitable for a particular species. The higher the value, there will be better chance that a particular location is suitable for the occurrence of that species. While developing the H.S.I.
GPS locations of wild animals’ presence/absence is integrated with variables like slope, aspects and distance from roads, settlements, LULC, forest crown density and aspects to produce habitat use-environmental variables matrix. Animal sightings are taken as “Boolean” (presence/absence) and “binomial multiple logistic regression (BMLR) is run. The coefficients derived from BMLR area used to integrate all layers to arrive at the probability/suitability maps. The estimated log-odds image was then logit transformed to produce the intended probability map. The output map is sliced to
“not suitable” at value lower than 0.5 and “suitable” at values higher than that.
Q3. What are the spectral ranges required for application in Agriculture, Forestry and Land use / cover?
Ans: The spectral ranges required for application in Agriculture, Forestry and Land use/land cover is given below.
Theme Application Spectral ranges required
Agriculture, Forestry and Land use / cover
Crop identification & acreage estimation
VIS,NIR,MIR,MW
Crop condition assessment and yield estimation
VIS,NIR,TIR
Soil moisture TIR and Microwave (L & C bands)
Drought monitoring VIS,NIR,MIR Land use/cover mapping VIS,NIR,
Forest fire detection 3-4 Micrometer, TIR
GEOLOGY
Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
Q4. How Remote Sensing helps in monitoring of desertification?
Ans: For evaluating and monitoring the desertification, use of multispectral and temporal satellite images are used along with regression model based on the NDVI–
a relationship. The Desertification degree index (DDI) offered a panorama of the spatio-temporal changes in terms of vegetation cover, soil, climate, and water availability, by means of calculating the albedo. The NDVI–BSI (Normalized Difference Vegetation Index-Bare-Soil Index) relationship facilitated the characterization of desertification and helped to identify zones of degradation or re- growth over time by qualitatively classifying the state of desertification and its direction and rate of change.
Q5. How oil spill is verified using remote Sensing?
Ans: Oil spill on satellite image of the area where it occurred can be easily identified as it contains suspended substances, such as hydrocarbons, sediment, foam or dye.
In a remote sensing domain, these can be identified and measured in advance to curb and make effective assessment. Oil spill detection can be done by RADAR and thermal imagine. The radar-based system has fully automated detection, giving oil spill position, tracking and measurement of drift. Miros OSD can operate in nearly all visibility conditions on a 24-hour basis, and has become an essential tool for navigating the recovery vessel and boom efficiently towards the oil slick. To estimate the magnitude of the spill, thermal (IR) imaging can be is used. It may be useful in identification of the thickest part of the oil slick.
Multiple Choice Questions-
1. FRAGSTATS software is used to analyze (a) Air Pollution
(b) Water Pollution (c) Forest fire
(d) Habitat Fragmentation
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
2. Desertification degree index (DDI) offered a panorama of the spatio-temporal changes in terms of
(a) Vegetation Cover (b) Soil
(c) Water Availability (d) All of the above
3. Crop condition assessment and yield estimation can be done by using following spectral ranges
(a) VIS (b) NIR (c) TIR
(d) All of the above 4. MODIS stands for
(a) Moderate resolution Imaging Spectroradiometer (b) Modern resolution Imaging Spectrophotometer (c) Moderate resolution Imaging Spatiotemporal (d) None of the Above
5. For desertification mapping following are potential (a) MODIS
(b) ASTER
(c) NDVI–BSI (Normalized Difference Vegetation Index-Bare-Soil Index) (d) All of the above
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Paper: Hydrogeology and Engineering Geology Module: Environmental Applications of Remote Sensing
Suggested Readings:
1. Jha, C.S., Dutt, C.B.S. & Bawa, K.S. (2000). Deforestation and land use changes in Western Ghats, India. Current Science, 79, 231–238.
2. NRSA (2007). Biodiversity Characterisation at Landscape Level in Eastern Ghats and East Coast using remote sensing and Geographic Information System. National Remote Sensing Agency, Hyderabad. ISBN-978-81-7525- 878-5.
3. Jeyanny, V., Balasundram, S.K. and Husni, M.H.A. (2011). Geo-Spatial Technologies for Carbon Sequestration Monitoring and Management.
American Journal of Environmental Sciences, 7 (5): 456-462.
4. Ray, S. S., Dadhwal, V. K. and Navalgund, R. R. (2002) Performance evaluation of an irrigation command area using remote sensing: A Case Study of Mahi Command, Gujarat, India, Agrl. Water Management, 56(2):
81-91.
5. Skidmore, A. K., Oindo, B. O., & Said, M. Y. (2003). Biodiversity assessment by remote sensing. In Proceedings of the 30th International symposium on remote sensing of the environment: information for risk management and sustainable development (p. 4).
6. Cao, C., X. Xiong, A. Wu, and X. Wu. (2008). Assessing the consistency of AVHRR and MODIS L1B reflectance for generating fundamental climate data records. Journal of Geophysical Research: Atmospheres 113: DOI:
10.1029/2007jd009363.
7. Anonymous (2016) Remote Sensing Applications.
http://cn.cgwic.com/VRSS1/english/user.html.
